Application

app

---

Assembly:                Wario
Filename:                app.py
Author:                  Terry D. Eppler
Created:                 01-31-2026

Last Modified By:        Terry D. Eppler
Last Modified On:        01-20-2026

---

       app.py
       Copyright ©  2026  Terry Eppler

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

You can contact me at: terryeppler@gmail.com

app.py

---

load_embedder

load_embedder() -> SentenceTransformer

Load embedder.

Purpose

Retrieves load embedder for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Returns:

Name	Type	Description
SentenceTransformer	SentenceTransformer	Normalized result produced for the active application workflow.

Source code in app.py

@st.cache_resource
def load_embedder( ) -> SentenceTransformer:
	"""Load embedder.

	Purpose:
	    Retrieves load embedder for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Returns:
	    SentenceTransformer: Normalized result produced for the active application workflow.
	"""
	return SentenceTransformer( 'all-MiniLM-L6-v2' )

resolve_gemini_api_key

resolve_gemini_api_key() -> Optional[str]

Resolve gemini api key.

Purpose

Supports the resolve gemini api key application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Returns:

Type	Description
Optional[str]	Optional[str]: Text value produced for the active application workflow.

Source code in app.py

def resolve_gemini_api_key( ) -> Optional[ str ]:
	"""Resolve gemini api key.

	Purpose:
	    Supports the resolve gemini api key application workflow by coordinating validated
	    inputs, Streamlit session state, provider configuration, and local data processing.

	Returns:
	    Optional[str]: Text value produced for the active application workflow.
	"""
	session_key = st.session_state.get( "gemini_api_key" )
	if session_key:
		return session_key

	cfg_key = getattr( cfg, "GOOGLE_API_KEY", None )
	if cfg_key:
		return cfg_key

	return os.environ.get( "GOOGLE_API_KEY" )

extract_response_text

extract_response_text(response: object) -> str

Extract response text.

Purpose

Transforms extract response text inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
response	object	Response value used by the application workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def extract_response_text( response: object ) -> str:
	"""Extract response text.

	Purpose:
	    Transforms extract response text inputs into a normalized representation used by
	    provider calls, document retrieval, data management, or UI rendering.

	Args:
	    response: Response value used by the application workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if response is None:
		return ""

	output = getattr( response, "output", None )
	if not output or not isinstance( output, list ):
		return ""

	text_chunks: list[ str ] = [ ]

	for item in output:
		if not hasattr( item, "type" ):
			continue

		if item.type == "message":
			content = getattr( item, "content", None )
			if not content or not isinstance( content, list ):
				continue

			for part in content:
				if getattr( part, "type", None ) == "output_text":
					text = getattr( part, "text", "" )
					if text:
						text_chunks.append( text )

	return "".join( text_chunks ).strip( )

convert_xml

convert_xml(text: str) -> str

Convert xml.

Purpose

Transforms convert xml inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
text	str	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def convert_xml( text: str ) -> str:
	"""Convert xml.

	Purpose:
	    Transforms convert xml inputs into a normalized representation used by provider calls,
	    document retrieval, data management, or UI rendering.

	Args:
	    text: Text value supplied to the prompt, conversion, retrieval, or provider workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	markdown_blocks: List[ str ] = [ ]
	for match in cfg.XML_BLOCK_PATTERN.finditer( text ):
		raw_tag: str = match.group( "tag" )
		body: str = match.group( "body" ).strip( )

		# Humanize tag name for Markdown heading
		heading: str = raw_tag.replace( "_", " " ).replace( "-", " " ).title( )
		markdown_blocks.append( f"## {heading}" )
		if body:
			markdown_blocks.append( body )
	return "\n\n".join( markdown_blocks )

convert_markdown

convert_markdown(text: Any) -> str

Convert markdown.

Purpose

Transforms convert markdown inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
text	Any	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def convert_markdown( text: Any ) -> str:
	"""Convert markdown.

	Purpose:
	    Transforms convert markdown inputs into a normalized representation used by provider
	    calls, document retrieval, data management, or UI rendering.

	Args:
	    text: Text value supplied to the prompt, conversion, retrieval, or provider workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not isinstance( text, str ) or not text.strip( ):
		return ""

	# Normalize newlines
	src = text.replace( "\r\n", "\n" ).replace( "\r", "\n" )

	htag_pattern = re.compile( r"<h([1-6])>(.*?)</h\1>", flags=re.IGNORECASE | re.DOTALL )
	md_heading_pattern = re.compile( r"^(#{1,6})[ \t]+(.+?)[ \t]*$", flags=re.MULTILINE )

	# ------------------------------------------------------------------
	# Direction detection
	# ------------------------------------------------------------------
	contains_htags = bool( htag_pattern.search( src ) )

	# ------------------------------------------------------------------
	# XML-like heading tags -> Markdown headings
	# ------------------------------------------------------------------
	if contains_htags:
		def _htag_to_md( match: re.Match ) -> str:
			"""Htag to md.

			Purpose:
			    Supports the htag to md application workflow by coordinating validated inputs,
			    Streamlit session state, provider configuration, and local data processing.

			Args:
			    match: Match value used by the application workflow.

			Returns:
			    str: Text value produced for the active application workflow.
			"""
			level = int( match.group( 1 ) )
			content = match.group( 2 ).strip( )

			# Preserve inner newlines safely by collapsing interior whitespace
			# while keeping content readable.
			content = re.sub( r"[ \t]+\n", "\n", content )
			content = re.sub( r"\n[ \t]+", "\n", content )

			return f"{'#' * level} {content}"

		out = htag_pattern.sub( _htag_to_md, src )
		return out.strip( )

	# ------------------------------------------------------------------
	# Markdown headings -> XML-like heading tags
	# ------------------------------------------------------------------
	def _md_to_htag( match: re.Match ) -> str:
		"""Md to htag.

		Purpose:
		    Supports the md to htag application workflow by coordinating validated inputs,
		    Streamlit session state, provider configuration, and local data processing.

		Args:
		    match: Match value used by the application workflow.

		Returns:
		    str: Text value produced for the active application workflow.
		"""
		hashes = match.group( 1 )
		content = match.group( 2 ).strip( )
		level = len( hashes )
		return f"<h{level}>{content}</h{level}>"

	out = md_heading_pattern.sub( _md_to_htag, src )
	return out.strip( )

encode_image_base64

encode_image_base64(path: str) -> str

Encode image base64.

Purpose

Supports the encode image base64 application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
path	str	File, upload, or path value used by the document or storage workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def encode_image_base64( path: str ) -> str:
	"""Encode image base64.

	Purpose:
	    Supports the encode image base64 application workflow by coordinating validated inputs,
	    Streamlit session state, provider configuration, and local data processing.

	Args:
	    path: File, upload, or path value used by the document or storage workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	data = Path( path ).read_bytes( )
	return base64.b64encode( data ).decode( "utf-8" )

normalize_text

normalize_text(text: str) -> str

Normalize text.

Purpose

Transforms normalize text inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
text	str	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def normalize_text( text: str ) -> str:
	"""Normalize text.

	Purpose:
	    Transforms normalize text inputs into a normalized representation used by provider
	    calls, document retrieval, data management, or UI rendering.

	Args:
	    text: Text value supplied to the prompt, conversion, retrieval, or provider workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not text:
		return ""

	# Lowercase
	text = text.lower( )

	# Remove punctuation except . ! ?
	text = re.sub( r"[^\w\s\.\!\?]", "", text )

	# Ensure single space after sentence delimiters
	text = re.sub( r"([.!?])\s*", r"\1 ", text )

	# Normalize whitespace
	text = re.sub( r"\s+", " ", text ).strip( )

	return text

chunk_text

chunk_text(text: str, max_tokens: int = 400) -> list[str]

Chunk text.

Purpose

Supports the chunk text application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
text	str	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required
max_tokens	int	Numeric control value that constrains the operation.	400

Returns:

Type	Description
list[str]	list[str]: List of normalized values used by the application workflow.

Source code in app.py

def chunk_text( text: str, max_tokens: int = 400 ) -> list[ str ]:
	"""Chunk text.

	Purpose:
	    Supports the chunk text application workflow by coordinating validated inputs,
	    Streamlit session state, provider configuration, and local data processing.

	Args:
	    text: Text value supplied to the prompt, conversion, retrieval, or provider workflow.
	    max_tokens: Numeric control value that constrains the operation.

	Returns:
	    list[str]: List of normalized values used by the application workflow.
	"""
	if not text:
		return [ ]

	# Sentence-based segmentation
	sentences = re.split( r"(?<=[.!?])\s+", text )
	sentences = [ s.strip( ) for s in sentences if s.strip( ) ]

	if len( sentences ) > 1:
		return sentences

	# Fallback: token window segmentation
	words = text.split( )
	chunks = [ ]
	current_chunk = [ ]
	token_count = 0

	for word in words:
		current_chunk.append( word )
		token_count += 1

		if token_count >= max_tokens:
			chunks.append( " ".join( current_chunk ) )
			current_chunk = [ ]
			token_count = 0

	if current_chunk:
		chunks.append( " ".join( current_chunk ) )

	return chunks

cosine_sim

cosine_sim(a: ndarray, b: ndarray) -> float

Cosine sim.

Purpose

Supports the cosine sim application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
a	ndarray	A value used by the application workflow.	required
b	ndarray	B value used by the application workflow.	required

Returns:

Name	Type	Description
float	float	Normalized result produced for the active application workflow.

Source code in app.py

def cosine_sim( a: np.ndarray, b: np.ndarray ) -> float:
	"""Cosine sim.

	Purpose:
	    Supports the cosine sim application workflow by coordinating validated inputs,
	    Streamlit session state, provider configuration, and local data processing.

	Args:
	    a: A value used by the application workflow.
	    b: B value used by the application workflow.

	Returns:
	    float: Normalized result produced for the active application workflow.
	"""
	denom = np.linalg.norm( a ) * np.linalg.norm( b )
	return float( np.dot( a, b ) / denom ) if denom else 0.0

sanitize_markdown

sanitize_markdown(text: str) -> str

Sanitize markdown.

Purpose

Transforms sanitize markdown inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
text	str	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def sanitize_markdown( text: str ) -> str:
	"""Sanitize markdown.

	Purpose:
	    Transforms sanitize markdown inputs into a normalized representation used by provider
	    calls, document retrieval, data management, or UI rendering.

	Args:
	    text: Text value supplied to the prompt, conversion, retrieval, or provider workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	# Remove bold markers
	text = re.sub( r"\*\*(.*?)\*\*", r"\1", text )
	# Optional: remove italics
	text = re.sub( r"\*(.*?)\*", r"\1", text )
	return text

inject_response_css

inject_response_css() -> None

Inject response css.

Purpose

Renders inject response css in the Streamlit interface while preserving the active session state and provider workflow context.

Source code in app.py

def inject_response_css( ) -> None:
	"""Inject response css.

	Purpose:
	    Renders inject response css in the Streamlit interface while preserving the active
	    session state and provider workflow context.
	"""
	st.markdown(
		"""
		<style>
		/* Chat message text */
		.stChatMessage p {
			color: rgb(220, 220, 220);
			font-size: 1rem;
			line-height: 1.6;
		}

		/* Headings inside chat responses */
		.stChatMessage h1 {
			color: rgb(0, 120, 252); /* DoD Blue */
			font-size: 1.6rem;
		}

		.stChatMessage h2 {
			color: rgb(0, 120, 252);
			font-size: 1.35rem;
		}

		.stChatMessage h3 {
			color: rgb(0, 120, 252);
			font-size: 1.15rem;
		}

		.stChatMessage a {
			color: rgb(0, 120, 252); /* DoD Blue */
			text-decoration: underline;
		}

		.stChatMessage a:hover {
			color: rgb(80, 160, 255);
		}

		</style>
		""", unsafe_allow_html=True )

style_subheaders

style_subheaders() -> None

Style subheaders.

Purpose

Renders style subheaders in the Streamlit interface while preserving the active session state and provider workflow context.

Source code in app.py

def style_subheaders( ) -> None:
	"""Style subheaders.

	Purpose:
	    Renders style subheaders in the Streamlit interface while preserving the active session
	    state and provider workflow context.
	"""
	st.markdown(
		"""
		<style>
		div[data-testid="stMarkdownContainer"] h2,
		div[data-testid="stMarkdownContainer"] h3,
		div[data-testid="stChatMessage"] div[data-testid="stMarkdownContainer"] h2,
		div[data-testid="stChatMessage"] div[data-testid="stMarkdownContainer"] h3 {
			color: rgb(0, 120, 252) !important;
		}
		</style>
		""",
		unsafe_allow_html=True,
	)

init_state

init_state() -> None

Init state.

Purpose

Maintains application runtime state for init state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def init_state( ) -> None:
	"""Init state.

	Purpose:
	    Maintains application runtime state for init state by initializing, clearing, or
	    restoring the session values used by the active Streamlit workflow.
	"""
	if 'chat_history' not in st.session_state:
		st.session_state.chat_history = [ ]

	if 'chat_messages' not in st.session_state:
		st.session_state.chat_messages = [ ]

	if 'execution_mode' not in st.session_state:
		st.session_state.execution_mode = 'Standard'

	for k in ('audio_system_instructions',
	          'image_system_instructions',
	          'docqna_system_instructions',
	          'text_system_instructions'):
		st.session_state.setdefault( k, "" )

reset_state

reset_state() -> None

Reset state.

Purpose

Maintains application runtime state for reset state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_state( ) -> None:
	"""Reset state.

	Purpose:
	    Maintains application runtime state for reset state by initializing, clearing, or
	    restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state.chat_history = [ ]
	st.session_state.last_answer = ""
	st.session_state.last_sources = [ ]
	st.session_state.last_analysis = {
			'tables': [ ],
			'files': [ ],
			'text': [ ],
	}

normalize

normalize(obj: Any) -> Any

Normalize.

Purpose

Supports the normalize application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
obj	Any	Object or nested value to normalize for safe display, serialization, or provider handling.	required

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def normalize( obj: Any ) -> Any:
	"""Normalize.

	Purpose:
	    Supports the normalize application workflow by coordinating validated inputs, Streamlit
	    session state, provider configuration, and local data processing.

	Args:
	    obj: Object or nested value to normalize for safe display, serialization, or provider handling.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	if obj is None or isinstance( obj, (str, int, float, bool) ):
		return obj

	if isinstance( obj, dict ):
		return { k: normalize( v ) for k, v in obj.items( ) }

	if isinstance( obj, (list, tuple, set) ):
		return [ normalize( v ) for v in obj ]
	if hasattr( obj, "model_dump" ):
		try:
			return obj.model_dump( )
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'normalize'
			exception.method = 'normalize( obj ) -> object'
			Logger( ).write( exception )
			return str( obj )
	return str( obj )

extract_sources

extract_sources(response: Any) -> List[Dict[str, Any]]

Extract sources.

Purpose

Transforms extract sources inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
response	Any	Response value used by the application workflow.	required

Returns:

Type	Description
List[Dict[str, Any]]	List[Dict[str, Any]]: List of normalized values used by the application workflow.

Source code in app.py

def extract_sources( response: Any ) -> List[ Dict[ str, Any ] ]:
	"""Extract sources.

	Purpose:
	    Transforms extract sources inputs into a normalized representation used by provider
	    calls, document retrieval, data management, or UI rendering.

	Args:
	    response: Response value used by the application workflow.

	Returns:
	    List[Dict[str, Any]]: List of normalized values used by the application workflow.
	"""
	sources: List[ Dict[ str, Any ] ] = [ ]

	if response is None:
		return sources

	output = getattr( response, 'output', None )
	if not isinstance( output, list ):
		return sources

	for item in output:
		if item is None:
			continue

		t = getattr( item, 'type', None )

		# ------------------------------------------------
		# Web search
		# ------------------------------------------------
		if t == 'web_search_call':
			action = getattr( item, 'action', None )
			raw = getattr( action, 'sources', None ) if action else None

			if not isinstance( raw, (list, tuple) ):
				continue

			for src in raw:
				s = normalize( src )
				if not isinstance( s, dict ):
					continue

				sources.append( { 'title': s.get( 'title' ), 'snippet': s.get( 'snippet' ),
				                  'url': s.get( 'url' ), 'files_id': None, } )

		# ------------------------------------------------
		# File search (vector store)
		# ------------------------------------------------
		elif t == 'file_search_call':
			raw = getattr( item, 'results', None )

			if not isinstance( raw, (list, tuple) ):
				continue

			for r in raw:
				s = normalize( r )
				if not isinstance( s, dict ):
					continue

				sources.append( { 'title': s.get( 'file_name' ) or s.get( 'title' ),
				                  'snippet': s.get( 'text' ), 'url': None,
				                  'files_id': s.get( 'files_id' ), } )

	return sources

save_temp

save_temp(upload: Any) -> str | None

Save temp.

Purpose

Applies the save temp operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
upload	Any	Streamlit uploaded-file object to persist to a temporary path.	required

Returns:

Type	Description
str | None	str | None: Text value produced for the active application workflow.

Source code in app.py

def save_temp( upload: Any ) -> str | None:
	"""Save temp.

	Purpose:
	    Applies the save temp operation to application-managed data, files, prompts, or
	    provider resources while preserving the surrounding workflow state.

	Args:
	    upload: Streamlit uploaded-file object to persist to a temporary path.

	Returns:
	    str | None: Text value produced for the active application workflow.
	"""
	if upload is None:
		return None

	try:
		_, ext = os.path.splitext( upload.name )
		ext = ext or ""
		with tempfile.NamedTemporaryFile( delete=False, suffix=ext ) as tmp:
			tmp.write( upload.getbuffer( ) )
			tmp_path = tmp.name

		return tmp_path
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'save_temp'
		exception.method = 'save_temp( upload ) -> str | None'
		Logger( ).write( exception )
		return None

update_token_counters

update_token_counters(resp: Any) -> None

Update token counters.

Purpose

Applies the update token counters operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
resp	Any	Resp value used by the application workflow.	required

Source code in app.py

def update_token_counters( resp: Any ) -> None:
	"""Update token counters.

	Purpose:
	    Applies the update token counters operation to application-managed data, files,
	    prompts, or provider resources while preserving the surrounding workflow state.

	Args:
	    resp: Resp value used by the application workflow.
	"""
	usage = _extract_usage_from_response( resp )
	st.session_state.last_call_usage = usage
	st.session_state.token_usage[ "prompt_tokens" ] += usage.get( "prompt_tokens", 0 )
	st.session_state.token_usage[ "completion_tokens" ] += usage.get( "completion_tokens", 0 )
	st.session_state.token_usage[ "total_tokens" ] += usage.get( "total_tokens", 0 )

build_intent_prefix

build_intent_prefix(mode: str) -> str

Build intent prefix.

Purpose

Builds build intent prefix from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
mode	str	Mode value used by the application workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def build_intent_prefix( mode: str ) -> str:
	"""Build intent prefix.

	Purpose:
	    Builds build intent prefix from validated runtime inputs and prepares the resulting
	    object, payload, table, or display structure for later application processing.

	Args:
	    mode: Mode value used by the application workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if mode == 'Guidance Only':
		return (
				'[ANALYST INTENT]\n'
				'Respond using authoritative policy and guidance only. '
				'Do not perform financial computation.\n\n'
		)
	if mode == 'Analysis Only':
		return (
				'[ANALYST INTENT]\n'
				'Respond using financial analysis and computation only. '
				'Minimize policy citation.\n\n'
		)
	return ''

save_message

save_message(role: str, content: str) -> None

Save message.

Purpose

Applies the save message operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
role	str	Role value used by the application workflow.	required
content	str	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required

Source code in app.py

def save_message( role: str, content: str ) -> None:
	"""Save message.

	Purpose:
	    Applies the save message operation to application-managed data, files, prompts, or
	    provider resources while preserving the surrounding workflow state.

	Args:
	    role: Role value used by the application workflow.
	    content: Text value supplied to the prompt, conversion, retrieval, or provider
	        workflow.
	"""
	with sqlite3.connect( cfg.DB_PATH ) as conn:
		conn.execute( "INSERT INTO chat_history (role, content) VALUES (?, ?)", (role, content) )

load_history

load_history() -> List[Tuple[str, str]]

Load history.

Purpose

Retrieves load history for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Returns:

Type	Description
List[Tuple[str, str]]	List[Tuple[str, str]]: List of normalized values used by the application workflow.

Source code in app.py

def load_history( ) -> List[ Tuple[ str, str ] ]:
	"""Load history.

	Purpose:
	    Retrieves load history for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Returns:
	    List[Tuple[str, str]]: List of normalized values used by the application workflow.
	"""
	with sqlite3.connect( cfg.DB_PATH ) as conn:
		return conn.execute( "SELECT role, content FROM chat_history ORDER BY id" ).fetchall( )

clear_history

clear_history() -> None

Clear history.

Purpose

Maintains application runtime state for clear history by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_history( ) -> None:
	"""Clear history.

	Purpose:
	    Maintains application runtime state for clear history by initializing, clearing, or
	    restoring the session values used by the active Streamlit workflow.
	"""
	with sqlite3.connect( cfg.DB_PATH ) as conn:
		conn.execute( "DELETE FROM chat_history" )

format_results

format_results(results: Any) -> str

Format results.

Purpose

Supports the format results application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
results	Any	Provider result collection containing a data sequence with name attributes.	required

Returns:

Name	Type	Description
str	str	HTML list markup containing formatted result names.

Source code in app.py

def format_results( results: Any ) -> str:
	"""Format results.

	Purpose:
	    Supports the format results application workflow by coordinating validated inputs,
	    Streamlit session state, provider configuration, and local data processing.

	Args:
	    results: Provider result collection containing a data sequence with name attributes.

	Returns:
	    str: HTML list markup containing formatted result names.
	"""
	formatted_results = ''
	for result in results.data:
		formatted_result = f"<li> '{result.name}'"
		formatted_results += formatted_result + "</li>"
	return f"<p>{formatted_results}</p>"

count_tokens

count_tokens(text: str) -> int

Count tokens.

Purpose

Transforms count tokens inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
text	str	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required

Returns:

Name	Type	Description
int	int	Normalized result produced for the active application workflow.

Source code in app.py

def count_tokens( text: str ) -> int:
	"""Count tokens.

	Purpose:
	    Transforms count tokens inputs into a normalized representation used by provider calls,
	    document retrieval, data management, or UI rendering.

	Args:
	    text: Text value supplied to the prompt, conversion, retrieval, or provider workflow.

	Returns:
	    int: Normalized result produced for the active application workflow.
	"""
	encoding = tiktoken.get_encoding( 'cl100k_base' )
	num_tokens = len( encoding.encode( text ) )
	return num_tokens

fetch_prompt_names

fetch_prompt_names(db_path: str) -> list[str]

Fetch prompt names.

Purpose

Retrieves fetch prompt names for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
db_path	str	File, upload, or path value used by the document or storage workflow.	required

Returns:

Type	Description
list[str]	list[str]: List of normalized values used by the application workflow.

Source code in app.py

def fetch_prompt_names( db_path: str ) -> list[ str ]:
	"""Fetch prompt names.

	Purpose:
	    Retrieves fetch prompt names for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    db_path: File, upload, or path value used by the document or storage workflow.

	Returns:
	    list[str]: List of normalized values used by the application workflow.
	"""
	try:
		conn = sqlite3.connect( db_path )
		cur = conn.cursor( )
		cur.execute( "SELECT Caption FROM Prompts ORDER BY PromptsId;" )
		rows = cur.fetchall( )
		conn.close( )
		return [ r[ 0 ] for r in rows if r and r[ 0 ] is not None ]
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'fetch_prompt_names'
		exception.method = 'fetch_prompt_names( db_path ) -> list[str]'
		Logger( ).write( exception )
		return [ ]

fetch_prompt_text

fetch_prompt_text(db_path: str, name: str) -> str | None

Fetch prompt text.

Purpose

Retrieves fetch prompt text for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
db_path	str	File, upload, or path value used by the document or storage workflow.	required
name	str	Name value used by the application workflow.	required

Returns:

Type	Description
str | None	str | None: Text value produced for the active application workflow.

Source code in app.py

def fetch_prompt_text( db_path: str, name: str ) -> str | None:
	"""Fetch prompt text.

	Purpose:
	    Retrieves fetch prompt text for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    db_path: File, upload, or path value used by the document or storage workflow.
	    name: Name value used by the application workflow.

	Returns:
	    str | None: Text value produced for the active application workflow.
	"""
	try:
		conn = sqlite3.connect( db_path )
		cur = conn.cursor( )
		cur.execute( "SELECT Text FROM Prompts WHERE Caption = ?;", (name,) )
		row = cur.fetchone( )
		conn.close( )
		return str( row[ 0 ] ) if row and row[ 0 ] is not None else None
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'fetch_prompt_text'
		exception.method = 'fetch_prompt_text( db_path, name ) -> str | None'
		Logger( ).write( exception )
		return None

fetch_prompts_df

fetch_prompts_df() -> pd.DataFrame

Fetch prompts df.

Purpose

Retrieves fetch prompts df for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Returns:

Type	Description
DataFrame	pd.DataFrame: DataFrame produced for application display, profiling, retrieval, or export.

Source code in app.py

def fetch_prompts_df( ) -> pd.DataFrame:
	"""Fetch prompts df.

	Purpose:
	    Retrieves fetch prompts df for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Returns:
	    pd.DataFrame: DataFrame produced for application display, profiling, retrieval, or
	        export.
	"""
	with sqlite3.connect( cfg.DB_PATH ) as conn:
		df = pd.read_sql_query(
			"SELECT PromptsId, Caption,  Name, Version, ID FROM Prompts ORDER BY PromptsId DESC",
			conn )
	df.insert( 0, "Selected", False )
	return df

fetch_prompt_by_id

fetch_prompt_by_id(pid: int) -> Dict[str, Any] | None

Fetch prompt by id.

Purpose

Retrieves fetch prompt by id for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
pid	int	Pid value used by the application workflow.	required

Returns:

Type	Description
Dict[str, Any] | None	Dict[str, Any] | None: Dictionary containing normalized workflow configuration or results.

Source code in app.py

def fetch_prompt_by_id( pid: int ) -> Dict[ str, Any ] | None:
	"""Fetch prompt by id.

	Purpose:
	    Retrieves fetch prompt by id for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    pid: Pid value used by the application workflow.

	Returns:
	    Dict[str, Any] | None: Dictionary containing normalized workflow configuration or
	        results.
	"""
	with sqlite3.connect( cfg.DB_PATH ) as conn:
		cur = conn.execute(
			"SELECT PromptsId, Caption, Name, Text, Version, ID FROM Prompts WHERE PromptsId=?",
			(pid,)
		)
		row = cur.fetchone( )
		return dict( zip( [ c[ 0 ] for c in cur.description ], row ) ) if row else None

fetch_prompt_by_name

fetch_prompt_by_name(name: str) -> Dict[str, Any] | None

Fetch prompt by name.

Purpose

Retrieves fetch prompt by name for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
name	str	Name value used by the application workflow.	required

Returns:

Type	Description
Dict[str, Any] | None	Dict[str, Any] | None: Dictionary containing normalized workflow configuration or results.

Source code in app.py

def fetch_prompt_by_name( name: str ) -> Dict[ str, Any ] | None:
	"""Fetch prompt by name.

	Purpose:
	    Retrieves fetch prompt by name for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    name: Name value used by the application workflow.

	Returns:
	    Dict[str, Any] | None: Dictionary containing normalized workflow configuration or
	        results.
	"""
	with sqlite3.connect( cfg.DB_PATH ) as conn:
		cur = conn.execute(
			"SELECT PromptsId, Caption, Name, Text, Version, ID FROM Prompts WHERE Caption=?",
			(name,)
		)
		row = cur.fetchone( )
		return dict( zip( [ c[ 0 ] for c in cur.description ], row ) ) if row else None

insert_prompt

insert_prompt(data: Dict[str, Any]) -> None

Insert prompt.

Purpose

Applies the insert prompt operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
data	Dict[str, Any]	Data value used by the application workflow.	required

Source code in app.py

def insert_prompt( data: Dict[ str, Any ] ) -> None:
	"""Insert prompt.

	Purpose:
	    Applies the insert prompt operation to application-managed data, files, prompts, or
	    provider resources while preserving the surrounding workflow state.

	Args:
	    data: Data value used by the application workflow.
	"""
	with sqlite3.connect( cfg.DB_PATH ) as conn:
		conn.execute( 'INSERT INTO Prompts (Caption, Name, Text, Version, ID) VALUES (?, ?, ?, ?)',
			(data[ 'Caption' ], data[ 'Name' ], data[ 'Text' ], data[ 'Version' ], data[ 'ID' ]) )

update_prompt

update_prompt(pid: int, data: Dict[str, Any]) -> None

Update prompt.

Purpose

Applies the update prompt operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
pid	int	Pid value used by the application workflow.	required
data	Dict[str, Any]	Data value used by the application workflow.	required

Source code in app.py

def update_prompt( pid: int, data: Dict[ str, Any ] ) -> None:
	"""Update prompt.

	Purpose:
	    Applies the update prompt operation to application-managed data, files, prompts, or
	    provider resources while preserving the surrounding workflow state.

	Args:
	    pid: Pid value used by the application workflow.
	    data: Data value used by the application workflow.
	"""
	with sqlite3.connect( cfg.DB_PATH ) as conn:
		conn.execute(
			"UPDATE Prompts SET Caption=?, Name=?, Text=?, Version=?, ID=? WHERE PromptsId=?",
			(data[ "Caption" ], data[ "Name" ], data[ "Text" ], data[ "Version" ], data[ "ID" ],
			 pid)
		)

delete_prompt

delete_prompt(pid: int) -> None

Delete prompt.

Purpose

Applies the delete prompt operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
pid	int	Pid value used by the application workflow.	required

Source code in app.py

def delete_prompt( pid: int ) -> None:
	"""Delete prompt.

	Purpose:
	    Applies the delete prompt operation to application-managed data, files, prompts, or
	    provider resources while preserving the surrounding workflow state.

	Args:
	    pid: Pid value used by the application workflow.
	"""
	with sqlite3.connect( cfg.DB_PATH ) as conn:
		conn.execute( "DELETE FROM Prompts WHERE PromptsId=?", (pid,) )

build_prompt

build_prompt(user_input: str) -> str

Build prompt.

Purpose

Builds build prompt from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
user_input	str	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def build_prompt( user_input: str ) -> str:
	"""Build prompt.

	Purpose:
	    Builds build prompt from validated runtime inputs and prepares the resulting object,
	    payload, table, or display structure for later application processing.

	Args:
	    user_input: Text value supplied to the prompt, conversion, retrieval, or provider
	        workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	system_instructions = st.session_state.get( 'system_instructions', '' )
	use_semantic = bool( st.session_state.get( 'use_semantic', False ) )
	basic_docs = st.session_state.get( 'basic_docs', [ ] )
	messages = st.session_state.get( 'messages', [ ] )

	top_k_value = int( st.session_state.get( 'top_k', 0 ) )
	if top_k_value <= 0:
		top_k_value = 4

	prompt = f"<|system|>\n{system_instructions}\n</s>\n"

	if use_semantic:
		with sqlite3.connect( cfg.DB_PATH ) as conn:
			rows = conn.execute( "SELECT chunk, vector FROM embeddings" ).fetchall( )

		if rows:
			q = embedder.encode( [ user_input ] )[ 0 ]
			scored = [ (c, cosine_sim( q, np.frombuffer( v ) )) for c, v in rows ]
			for c, _ in sorted( scored, key=lambda x: x[ 1 ], reverse=True )[ :top_k_value ]:
				prompt += f"<|system|>\n{c}\n</s>\n"

	for d in basic_docs[ :6 ]:
		prompt += f"<|system|>\n{d}\n</s>\n"

	if isinstance( messages, list ):
		for msg in messages:
			role = ''
			content = ''

			if isinstance( msg, tuple ) or isinstance( msg, list ):
				if len( msg ) == 2:
					role = str( msg[ 0 ] or '' ).strip( )
					content = str( msg[ 1 ] or '' )
			elif isinstance( msg, dict ):
				role = str( msg.get( 'role', '' ) or '' ).strip( )
				content = str( msg.get( 'content', '' ) or '' )

			if role:
				prompt += f"<|{role}|>\n{content}\n</s>\n"

	prompt += f"<|user|>\n{user_input}\n</s>\n<|assistant|>\n"
	return prompt

initialize_database

initialize_database() -> None

Initialize database.

Purpose

Maintains application runtime state for initialize database by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def initialize_database( ) -> None:
	"""Initialize database.

	Purpose:
	    Maintains application runtime state for initialize database by initializing, clearing,
	    or restoring the session values used by the active Streamlit workflow.
	"""
	Path( "stores/sqlite" ).mkdir( parents=True, exist_ok=True )
	with sqlite3.connect( cfg.DB_PATH ) as conn:
		conn.execute( """
                      CREATE TABLE IF NOT EXISTS chat_history
                      (
                          id
                          INTEGER
                          PRIMARY
                          KEY
                          AUTOINCREMENT,
                          role
                          TEXT,
                          content
                          TEXT
                      )
		              """ )
		conn.execute( """
                      CREATE TABLE IF NOT EXISTS embeddings
                      (
                          id
                          INTEGER
                          PRIMARY
                          KEY
                          AUTOINCREMENT,
                          chunk
                          TEXT,
                          vector
                          BLOB
                      )
		              """ )
		conn.execute( """
                      CREATE TABLE IF NOT EXISTS Prompts
                      (
                          PromptsId
                          INTEGER
                          NOT
                          NULL
                          UNIQUE,
                          Name
                          TEXT
                      (
                          80
                      ),
                          Text TEXT,
                          Version TEXT
                      (
                          80
                      ),
                          ID TEXT
                      (
                          80
                      ),
                          PRIMARY KEY
                      (
                          PromptsId
                          AUTOINCREMENT
                      )
                          )
		              """ )

create_connection

create_connection() -> sqlite3.Connection

Create connection.

Purpose

Builds create connection from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Returns:

Type	Description
Connection	sqlite3.Connection: SQLite connection used by the local data-management workflow.

Source code in app.py

def create_connection( ) -> sqlite3.Connection:
	"""Create connection.

	Purpose:
	    Builds create connection from validated runtime inputs and prepares the resulting
	    object, payload, table, or display structure for later application processing.

	Returns:
	    sqlite3.Connection: SQLite connection used by the local data-management workflow.
	"""
	return sqlite3.connect( DM_DB_PATH )

list_tables

list_tables() -> List[str]

List tables.

Purpose

Retrieves list tables for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def list_tables( ) -> List[ str ]:
	"""List tables.

	Purpose:
	    Retrieves list tables for the Streamlit application workflow and returns the normalized
	    value used by downstream UI, provider, database, or document-processing steps.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	with create_connection( ) as conn:
		_query = "SELECT name FROM sqlite_master WHERE type='table' ORDER BY name;"
		rows = conn.execute( _query ).fetchall( )
		return [ r[ 0 ] for r in rows ]

create_schema

create_schema(table: str) -> List[Tuple]

Create schema.

Purpose

Builds create schema from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
table	str	SQLite table name used by the data-management workflow.	required

Returns:

Type	Description
List[Tuple]	List[Tuple]: List of normalized values used by the application workflow.

Source code in app.py

def create_schema( table: str ) -> List[ Tuple ]:
	"""Create schema.

	Purpose:
	    Builds create schema from validated runtime inputs and prepares the resulting object,
	    payload, table, or display structure for later application processing.

	Args:
	    table: SQLite table name used by the data-management workflow.

	Returns:
	    List[Tuple]: List of normalized values used by the application workflow.
	"""
	with create_connection( ) as conn:
		return conn.execute( f'PRAGMA table_info("{table}");' ).fetchall( )

read_table

read_table(
    table: str, limit: int = None, offset: int = 0
) -> pd.DataFrame

Read table.

Purpose

Retrieves read table for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
table	str	SQLite table name used by the data-management workflow.	required
limit	int	Numeric control value that constrains the operation.	None
offset	int	Numeric control value that constrains the operation.	0

Returns:

Type	Description
DataFrame	pd.DataFrame: DataFrame produced for application display, profiling, retrieval, or export.

Source code in app.py

def read_table( table: str, limit: int = None, offset: int = 0 ) -> pd.DataFrame:
	"""Read table.

	Purpose:
	    Retrieves read table for the Streamlit application workflow and returns the normalized
	    value used by downstream UI, provider, database, or document-processing steps.

	Args:
	    table: SQLite table name used by the data-management workflow.
	    limit: Numeric control value that constrains the operation.
	    offset: Numeric control value that constrains the operation.

	Returns:
	    pd.DataFrame: DataFrame produced for application display, profiling, retrieval, or
	        export.
	"""
	query = f'SELECT rowid, * FROM "{table}"'
	if limit:
		query += f" LIMIT {limit} OFFSET {offset}"
	with create_connection( ) as conn:
		return pd.read_sql_query( query, conn )

drop_table

drop_table(table: str) -> None

Drop table.

Purpose

Applies the drop table operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
table	str	SQLite table name used by the data-management workflow.	required

Source code in app.py

def drop_table( table: str ) -> None:
	"""Drop table.

	Purpose:
	    Applies the drop table operation to application-managed data, files, prompts, or
	    provider resources while preserving the surrounding workflow state.

	Args:
	    table: SQLite table name used by the data-management workflow.
	"""
	if not table:
		return

	with create_connection( ) as conn:
		conn.execute( f'DROP TABLE IF EXISTS "{table}";' )
		conn.commit( )

create_index

create_index(table: str, column: str) -> None

Create index.

Purpose

Builds create index from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
table	str	SQLite table name used by the data-management workflow.	required
column	str	Column name used for filtering, schema, indexing, or profiling operations.	required

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def create_index( table: str, column: str ) -> None:
	"""Create index.

	Purpose:
	    Builds create index from validated runtime inputs and prepares the resulting object,
	    payload, table, or display structure for later application processing.

	Args:
	    table: SQLite table name used by the data-management workflow.
	    column: Column name used for filtering, schema, indexing, or profiling operations.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not table or not column:
		return

	# ------------------------------------------------------------------
	# Validate table exists
	# ------------------------------------------------------------------
	tables = list_tables( )
	if table not in tables:
		raise ValueError( "Invalid table name." )

	# ------------------------------------------------------------------
	# Validate column exists
	# ------------------------------------------------------------------
	schema = create_schema( table )
	valid_columns = [ col[ 1 ] for col in schema ]

	if column not in valid_columns:
		raise ValueError( "Invalid column name." )

	# ------------------------------------------------------------------
	# Sanitize index name (identifier only)
	# ------------------------------------------------------------------
	safe_index_name = re.sub( r"[^0-9a-zA-Z_]+", "_", f"idx_{table}_{column}" )

	# ------------------------------------------------------------------
	# Create index safely (quote identifiers)
	# ------------------------------------------------------------------
	sql = f'CREATE INDEX IF NOT EXISTS "{safe_index_name}" ON "{table}"("{column}");'

	with create_connection( ) as conn:
		conn.execute( sql )
		conn.commit( )

apply_filters

apply_filters(df: DataFrame) -> pd.DataFrame

Apply filters.

Purpose

Supports the apply filters application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
df	DataFrame	DataFrame supplied to the application data-management or display workflow.	required

Returns:

Type	Description
DataFrame	pd.DataFrame: DataFrame produced for application display, profiling, retrieval, or export.

Source code in app.py

def apply_filters( df: pd.DataFrame ) -> pd.DataFrame:
	"""Apply filters.

	Purpose:
	    Supports the apply filters application workflow by coordinating validated inputs,
	    Streamlit session state, provider configuration, and local data processing.

	Args:
	    df: DataFrame supplied to the application data-management or display workflow.

	Returns:
	    pd.DataFrame: DataFrame produced for application display, profiling, retrieval, or
	        export.
	"""
	st.subheader( 'Advanced Filters' )
	conditions = [ ]
	col1, col2, col3 = st.columns( 3 )
	column = col1.selectbox( 'Column', df.columns )
	operator = col2.selectbox( 'Operator', [ '=', '!=', '>', '<', '>=', '<=', 'contains' ] )
	value = col3.text_input( 'Value' )
	if value:
		if operator == '=':
			df = df[ df[ column ] == value ]
		elif operator == '!=':
			df = df[ df[ column ] != value ]
		elif operator == '>':
			df = df[ df[ column ].astype( float ) > float( value ) ]
		elif operator == '<':
			df = df[ df[ column ].astype( float ) < float( value ) ]
		elif operator == '>=':
			df = df[ df[ column ].astype( float ) >= float( value ) ]
		elif operator == '<=':
			df = df[ df[ column ].astype( float ) <= float( value ) ]
		elif operator == 'contains':
			df = df[ df[ column ].astype( str ).str.contains( value ) ]

	return df

create_aggregation

create_aggregation(df: DataFrame)

Create aggregation.

Purpose

Builds create aggregation from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
df	DataFrame	DataFrame supplied to the application data-management or display workflow.	required

Source code in app.py

def create_aggregation( df: pd.DataFrame ):
	"""Create aggregation.

	Purpose:
	    Builds create aggregation from validated runtime inputs and prepares the resulting
	    object, payload, table, or display structure for later application processing.

	Args:
	    df: DataFrame supplied to the application data-management or display workflow.
	"""
	st.subheader( 'Aggregation Engine' )

	numeric_cols = df.select_dtypes( include=[ 'number' ] ).columns.tolist( )

	if not numeric_cols:
		st.info( 'No numeric columns available.' )
		return

	col = st.selectbox( 'Column', numeric_cols )
	agg = st.selectbox( 'Aggregation', [ 'COUNT', 'SUM', 'AVG', 'MIN', 'MAX', 'MEDIAN' ] )

	if agg == 'COUNT':
		result = df[ col ].count( )
	elif agg == 'SUM':
		result = df[ col ].sum( )
	elif agg == 'AVG':
		result = df[ col ].mean( )
	elif agg == 'MIN':
		result = df[ col ].min( )
	elif agg == 'MAX':
		result = df[ col ].max( )
	elif agg == 'MEDIAN':
		result = df[ col ].median( )

	st.metric( 'Result', result )

create_visualization

create_visualization(df: DataFrame)

Create visualization.

Purpose

Builds create visualization from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
df	DataFrame	DataFrame supplied to the application data-management or display workflow.	required

Source code in app.py

def create_visualization( df: pd.DataFrame ):
	"""Create visualization.

	Purpose:
	    Builds create visualization from validated runtime inputs and prepares the resulting
	    object, payload, table, or display structure for later application processing.

	Args:
	    df: DataFrame supplied to the application data-management or display workflow.
	"""
	st.subheader( 'Visualization Engine' )

	numeric_cols = df.select_dtypes( include=[ 'number' ] ).columns.tolist( )
	categorical_cols = df.select_dtypes( include=[ 'object' ] ).columns.tolist( )

	chart = st.selectbox( 'Chart Type', [ 'Histogram', 'Bar', 'Line',
	                                      'Scatter', 'Box', 'Pie', 'Correlation' ] )

	if chart == 'Histogram' and numeric_cols:
		col = st.selectbox( 'Column', numeric_cols )
		fig = px.histogram( df, x=col )
		st.plotly_chart( fig, use_container_width=True )

	elif chart == 'Bar':
		x = st.selectbox( 'X', df.columns )
		y = st.selectbox( 'Y', numeric_cols )
		fig = px.bar( df, x=x, y=y )
		st.plotly_chart( fig, use_container_width=True )

	elif chart == 'Line':
		x = st.selectbox( 'X', df.columns )
		y = st.selectbox( 'Y', numeric_cols )
		fig = px.line( df, x=x, y=y )
		st.plotly_chart( fig, use_container_width=True )

	elif chart == 'Scatter':
		x = st.selectbox( 'X', numeric_cols )
		y = st.selectbox( 'Y', numeric_cols )
		fig = px.scatter( df, x=x, y=y )
		st.plotly_chart( fig, use_container_width=True )

	elif chart == 'Box':
		col = st.selectbox( 'Column', numeric_cols )
		fig = px.box( df, y=col )
		st.plotly_chart( fig, use_container_width=True )

	elif chart == 'Pie':
		col = st.selectbox( 'Category Column', categorical_cols )
		fig = px.pie( df, names=col )
		st.plotly_chart( fig, use_container_width=True )

	elif chart == 'Correlation' and len( numeric_cols ) > 1:
		corr = df[ numeric_cols ].corr( )
		fig = px.imshow( corr, text_auto=True )
		st.plotly_chart( fig, use_container_width=True )

dm_create_table_from_df

dm_create_table_from_df(table_name: str, df: DataFrame)

Dm create table from df.

Purpose

Supports the dm create table from df application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
table_name	str	SQLite table name used by the data-management workflow.	required
df	DataFrame	DataFrame supplied to the application data-management or display workflow.	required

Source code in app.py

def dm_create_table_from_df( table_name: str, df: pd.DataFrame ):
	"""Dm create table from df.

	Purpose:
	    Supports the dm create table from df application workflow by coordinating validated
	    inputs, Streamlit session state, provider configuration, and local data processing.

	Args:
	    table_name: SQLite table name used by the data-management workflow.
	    df: DataFrame supplied to the application data-management or display workflow.
	"""
	columns = [ ]
	for col in df.columns:
		sql_type = get_sqlite_type( df[ col ].dtype )
		safe_col = col.replace( ' ', '_' )
		columns.append( f'{safe_col} {sql_type}' )

	create_stmt = f'CREATE TABLE IF NOT EXISTS {table_name} ({", ".join( columns )});'

	with create_connection( ) as conn:
		conn.execute( create_stmt )
		conn.commit( )

insert_data

insert_data(table_name: str, df: DataFrame)

Insert data.

Purpose

Applies the insert data operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
table_name	str	SQLite table name used by the data-management workflow.	required
df	DataFrame	DataFrame supplied to the application data-management or display workflow.	required

Source code in app.py

def insert_data( table_name: str, df: pd.DataFrame ):
	"""Insert data.

	Purpose:
	    Applies the insert data operation to application-managed data, files, prompts, or
	    provider resources while preserving the surrounding workflow state.

	Args:
	    table_name: SQLite table name used by the data-management workflow.
	    df: DataFrame supplied to the application data-management or display workflow.
	"""
	df = df.copy( )
	df.columns = [ c.replace( ' ', '_' ) for c in df.columns ]

	placeholders = ', '.join( [ '?' ] * len( df.columns ) )
	stmt = f'INSERT INTO {table_name} VALUES ({placeholders});'

	with create_connection( ) as conn:
		conn.executemany( stmt, df.values.tolist( ) )
		conn.commit( )

get_sqlite_type

get_sqlite_type(dtype: Any) -> str

Get sqlite type.

Purpose

Retrieves get sqlite type for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
dtype	Any	Pandas dtype or dtype-like object to map into a SQLite storage type.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_sqlite_type( dtype: Any ) -> str:
	"""Get sqlite type.

	Purpose:
	    Retrieves get sqlite type for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    dtype: Pandas dtype or dtype-like object to map into a SQLite storage type.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	dtype_str = str( dtype ).lower( )

	# ------------------------------------------------------------------
	# Integer Types (including nullable Int64)
	# ------------------------------------------------------------------
	if "int" in dtype_str:
		return "INTEGER"

	# ------------------------------------------------------------------
	# Float Types
	# ------------------------------------------------------------------
	if "float" in dtype_str:
		return "REAL"

	# ------------------------------------------------------------------
	# Boolean
	# ------------------------------------------------------------------
	if "bool" in dtype_str:
		return "INTEGER"

	# ------------------------------------------------------------------
	# Datetime
	# ------------------------------------------------------------------
	if "datetime" in dtype_str:
		return "TEXT"

	# ------------------------------------------------------------------
	# Categorical
	# ------------------------------------------------------------------
	if "category" in dtype_str:
		return "TEXT"

	# ------------------------------------------------------------------
	# Default fallback
	# ------------------------------------------------------------------
	return "TEXT"

create_custom_table

create_custom_table(table_name: str, columns: list) -> None

Create custom table.

Purpose

Builds create custom table from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
table_name	str	SQLite table name used by the data-management workflow.	required
columns	list	Column name used for filtering, schema, indexing, or profiling operations.	required

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def create_custom_table( table_name: str, columns: list ) -> None:
	"""Create custom table.

	Purpose:
	    Builds create custom table from validated runtime inputs and prepares the resulting
	    object, payload, table, or display structure for later application processing.

	Args:
	    table_name: SQLite table name used by the data-management workflow.
	    columns: Column name used for filtering, schema, indexing, or profiling operations.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not table_name:
		raise ValueError( "Table name required." )

	# Validate identifier
	if not re.match( r"^[A-Za-z_][A-Za-z0-9_]*$", table_name ):
		raise ValueError( "Invalid table name." )

	col_defs = [ ]

	for col in columns:
		col_name = col[ "name" ]
		col_type = col[ "type" ].upper( )

		if not re.match( r"^[A-Za-z_][A-Za-z0-9_]*$", col_name ):
			raise ValueError( f"Invalid column name: {col_name}" )

		definition = f'"{col_name}" {col_type}'

		if col[ "primary_key" ]:
			definition += " PRIMARY KEY"
			if col[ "auto_increment" ] and col_type == "INTEGER":
				definition += " AUTOINCREMENT"

		if col[ "not_null" ]:
			definition += " NOT NULL"

		col_defs.append( definition )

	sql = f'CREATE TABLE IF NOT EXISTS "{table_name}" ({", ".join( col_defs )});'

	with create_connection( ) as conn:
		conn.execute( sql )
		conn.commit( )

is_safe_query

is_safe_query(query: str) -> bool

Is safe query.

Purpose

Evaluates whether is safe query is enabled or safe for the current provider, model, table, query, or workflow context.

Parameters:

Name	Type	Description	Default
query	str	Query string used by the database, retrieval, or provider workflow.	required

Returns:

Name	Type	Description
bool	bool	True when the requested condition is satisfied; otherwise, False.

Source code in app.py

def is_safe_query( query: str ) -> bool:
	"""Is safe query.

	Purpose:
	    Evaluates whether is safe query is enabled or safe for the current provider, model,
	    table, query, or workflow context.

	Args:
	    query: Query string used by the database, retrieval, or provider workflow.

	Returns:
	    bool: True when the requested condition is satisfied; otherwise, False.
	"""
	if not query or not isinstance( query, str ):
		return False

	q = query.strip( ).lower( )

	# ------------------------------------------------------------------
	# Block multiple statements
	# ------------------------------------------------------------------
	if ';' in q[ :-1 ]:
		return False

	# ------------------------------------------------------------------
	# Remove SQL comments
	# ------------------------------------------------------------------
	q = re.sub( r"--.*?$", "", q, flags=re.MULTILINE )
	q = re.sub( r"/\*.*?\*/", "", q, flags=re.DOTALL )
	q = q.strip( )

	# ------------------------------------------------------------------
	# Allowed starting keywords
	# ------------------------------------------------------------------
	allowed_starts = ('select', 'with', 'explain', 'pragma')
	if not q.startswith( allowed_starts ):
		return False

	# ------------------------------------------------------------------
	# Block dangerous keywords anywhere
	# ------------------------------------------------------------------
	blocked_keywords = ('insert ', 'update ', 'delete ', 'drop ', 'alter ',
	                    'create ', 'attach ', 'detach ', 'vacuum ', 'replace ', 'trigger ')

	for keyword in blocked_keywords:
		if keyword in q:
			return False

	return True

create_identifier

create_identifier(name: str) -> str

Create identifier.

Purpose

Builds create identifier from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
name	str	Name value used by the application workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def create_identifier( name: str ) -> str:
	"""Create identifier.

	Purpose:
	    Builds create identifier from validated runtime inputs and prepares the resulting
	    object, payload, table, or display structure for later application processing.

	Args:
	    name: Name value used by the application workflow.

	Returns:
	    str: Text value produced for the active application workflow.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not name or not isinstance( name, str ):
		raise ValueError( 'Invalid Identifier.' )

	safe = re.sub( r'[^0-9a-zA-Z_]', '_', name.strip( ) )
	if not re.match( r'^[A-Za-z_]', safe ):
		safe = f'_{safe}'

	if not safe:
		raise ValueError( 'Invalid identifier after sanitization.' )

	return safe

get_indexes

get_indexes(table: str) -> List[Tuple[Any, ...]]

Get indexes.

Purpose

Retrieves get indexes for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
table	str	SQLite table name used by the data-management workflow.	required

Returns:

Type	Description
List[Tuple[Any, ...]]	List[Tuple[Any, ...]]: SQLite index metadata rows returned by PRAGMA index_list.

Source code in app.py

def get_indexes( table: str ) -> List[ Tuple[ Any, ... ] ]:
	"""Get indexes.

	Purpose:
	    Retrieves get indexes for the Streamlit application workflow and returns the normalized
	    value used by downstream UI, provider, database, or document-processing steps.

	Args:
	    table: SQLite table name used by the data-management workflow.

	Returns:
	    List[Tuple[Any, ...]]: SQLite index metadata rows returned by PRAGMA index_list.
	"""
	with create_connection( ) as conn:
		rows = conn.execute( f'PRAGMA index_list("{table}");' ).fetchall( )
		return rows

add_column

add_column(table: str, column: str, col_type: str)

Add column.

Purpose

Applies the add column operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
table	str	SQLite table name used by the data-management workflow.	required
column	str	Column name used for filtering, schema, indexing, or profiling operations.	required
col_type	str	Col Type value used by the application workflow.	required

Source code in app.py

def add_column( table: str, column: str, col_type: str ):
	"""Add column.

	Purpose:
	    Applies the add column operation to application-managed data, files, prompts, or
	    provider resources while preserving the surrounding workflow state.

	Args:
	    table: SQLite table name used by the data-management workflow.
	    column: Column name used for filtering, schema, indexing, or profiling operations.
	    col_type: Col Type value used by the application workflow.
	"""
	column = create_identifier( column )
	col_type = col_type.upper( )

	with create_connection( ) as conn:
		conn.execute(
			f'ALTER TABLE "{table}" ADD COLUMN "{column}" {col_type};' )
		conn.commit( )

create_profile_table

create_profile_table(table: str) -> pd.DataFrame

Create profile table.

Purpose

Builds create profile table from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
table	str	SQLite table name used by the data-management workflow.	required

Returns:

Type	Description
DataFrame	pd.DataFrame: Column-level profile dataframe for the selected SQLite table.

Source code in app.py

def create_profile_table( table: str ) -> pd.DataFrame:
	"""Create profile table.

	Purpose:
	    Builds create profile table from validated runtime inputs and prepares the resulting
	    object, payload, table, or display structure for later application processing.

	Args:
	    table: SQLite table name used by the data-management workflow.

	Returns:
	    pd.DataFrame: Column-level profile dataframe for the selected SQLite table.
	"""
	df = read_table( table )
	profile_rows = [ ]
	total_rows = len( df )
	for col in df.columns:
		series = df[ col ]
		null_count = series.isna( ).sum( )
		distinct_count = series.nunique( dropna=True )
		row = \
			{
					'column': col, 'dtype': str( series.dtype ),
					'null_%': round( (null_count / total_rows) * 100, 2 ) if total_rows else 0,
					'distinct_%': round( (distinct_count / total_rows) * 100,
						2 ) if total_rows else 0,
			}

		if pd.api.types.is_numeric_dtype( series ):
			row[ "min" ] = series.min( )
			row[ "max" ] = series.max( )
			row[ "mean" ] = series.mean( )
		else:
			row[ "min" ] = None
			row[ "max" ] = None
			row[ "mean" ] = None

		profile_rows.append( row )

	return pd.DataFrame( profile_rows )

drop_column

drop_column(table: str, column: str)

Drop column.

Purpose

Applies the drop column operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
table	str	SQLite table name used by the data-management workflow.	required
column	str	Column name used for filtering, schema, indexing, or profiling operations.	required

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def drop_column( table: str, column: str ):
	"""Drop column.

	Purpose:
	    Applies the drop column operation to application-managed data, files, prompts, or
	    provider resources while preserving the surrounding workflow state.

	Args:
	    table: SQLite table name used by the data-management workflow.
	    column: Column name used for filtering, schema, indexing, or profiling operations.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not table or not column:
		raise ValueError( "Table and column required." )

	with create_connection( ) as conn:
		# ------------------------------------------------------------
		# Fetch original CREATE TABLE statement
		# ------------------------------------------------------------
		row = conn.execute(
			"""
            SELECT sql
            FROM sqlite_master
            WHERE type ='table' AND name =?
			""",
			(table,)
		).fetchone( )

		if not row or not row[ 0 ]:
			raise ValueError( "Table definition not found." )

		create_sql = row[ 0 ]

		# ------------------------------------------------------------
		# Extract column definitions
		# ------------------------------------------------------------
		open_paren = create_sql.find( "(" )
		close_paren = create_sql.rfind( ")" )

		if open_paren == -1 or close_paren == -1:
			raise ValueError( "Malformed CREATE TABLE statement." )

		inner = create_sql[ open_paren + 1: close_paren ]

		column_defs = [ c.strip( ) for c in inner.split( "," ) ]

		# Remove target column
		new_defs = [ ]
		for col_def in column_defs:
			col_name = col_def.split( )[ 0 ].strip( '"' )
			if col_name != column:
				new_defs.append( col_def )

		if len( new_defs ) == len( column_defs ):
			raise ValueError( "Column not found." )

		# ------------------------------------------------------------
		# Build new CREATE TABLE statement
		# ------------------------------------------------------------
		temp_table = f"{table}_rebuild_temp"

		new_create_sql = (
				f'CREATE TABLE "{temp_table}" ('
				+ ", ".join( new_defs )
				+ ");"
		)

		# ------------------------------------------------------------
		# Begin transaction
		# ------------------------------------------------------------
		conn.execute( "BEGIN" )

		conn.execute( new_create_sql )

		remaining_cols = [
				c.split( )[ 0 ].strip( '"' )
				for c in new_defs
		]

		col_list = ", ".join( [ f'"{c}"' for c in remaining_cols ] )

		conn.execute(
			f'INSERT INTO "{temp_table}" ({col_list}) '
			f'SELECT {col_list} FROM "{table}";'
		)

		# Preserve indexes
		indexes = conn.execute(
			"""
            SELECT sql
            FROM sqlite_master
            WHERE type ='index' AND tbl_name=? AND sql IS NOT NULL
			""",
			(table,)
		).fetchall( )

		conn.execute( f'DROP TABLE "{table}";' )
		conn.execute(
			f'ALTER TABLE "{temp_table}" RENAME TO "{table}";'
		)

		# Recreate indexes
		for idx in indexes:
			idx_sql = idx[ 0 ]
			if column not in idx_sql:
				conn.execute( idx_sql )

		conn.commit( )

rename_table

rename_table(old_name: str, new_name: str) -> None

Rename table.

Purpose

Applies the rename table operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
old_name	str	Old Name value used by the application workflow.	required
new_name	str	New Name value used by the application workflow.	required

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def rename_table( old_name: str, new_name: str ) -> None:
	"""Rename table.

	Purpose:
	    Applies the rename table operation to application-managed data, files, prompts, or
	    provider resources while preserving the surrounding workflow state.

	Args:
	    old_name: Old Name value used by the application workflow.
	    new_name: New Name value used by the application workflow.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not old_name or not new_name:
		return

	with create_connection( ) as conn:
		try:
			conn.execute( f'ALTER TABLE "{old_name}" RENAME TO "{new_name}";' )
			conn.commit( )
			return
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'rename_table'
			exception.method = 'rename_table( old_name, new_name ) -> None'
			Logger( ).write( exception )
			pass

		row = conn.execute(
			"""
            SELECT sql
            FROM sqlite_master
            WHERE type ='table' AND name =?
			""",
			(old_name,)
		).fetchone( )

		if not row or not row[ 0 ]:
			raise ValueError( "Table definition not found." )

		create_sql = row[ 0 ]

		indexes = conn.execute(
			"""
            SELECT sql
            FROM sqlite_master
            WHERE type ='index' AND tbl_name=? AND sql IS NOT NULL
			""",
			(old_name,)
		).fetchall( )

		open_paren = create_sql.find( "(" )
		if open_paren == -1:
			raise ValueError( "Malformed CREATE TABLE statement." )

		temp_name = f"{new_name}__rebuild_temp"

		conn.execute( "BEGIN" )
		conn.execute( f'CREATE TABLE "{temp_name}" {create_sql[ open_paren: ]}' )

		cols = [ r[ 1 ] for r in conn.execute( f'PRAGMA table_info("{old_name}");' ).fetchall( ) ]
		col_list = ", ".join( [ f'"{c}"' for c in cols ] )

		conn.execute(
			f'INSERT INTO "{temp_name}" ({col_list}) SELECT {col_list} FROM "{old_name}";'
		)

		conn.execute( f'DROP TABLE "{old_name}";' )
		conn.execute( f'ALTER TABLE "{temp_name}" RENAME TO "{new_name}";' )

		for idx in indexes:
			idx_sql = idx[ 0 ]
			if idx_sql:
				idx_sql = idx_sql.replace( f'ON "{old_name}"', f'ON "{new_name}"' )
				conn.execute( idx_sql )

		conn.commit( )

rename_column

rename_column(
    table_name: str, old_name: str, new_name: str
) -> None

Rename column.

Purpose

Applies the rename column operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
table_name	str	SQLite table name used by the data-management workflow.	required
old_name	str	Old Name value used by the application workflow.	required
new_name	str	New Name value used by the application workflow.	required

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def rename_column( table_name: str, old_name: str, new_name: str ) -> None:
	"""Rename column.

	Purpose:
	    Applies the rename column operation to application-managed data, files, prompts, or
	    provider resources while preserving the surrounding workflow state.

	Args:
	    table_name: SQLite table name used by the data-management workflow.
	    old_name: Old Name value used by the application workflow.
	    new_name: New Name value used by the application workflow.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not table_name or not old_name or not new_name:
		return

	with create_connection( ) as conn:
		try:
			conn.execute(
				f'ALTER TABLE "{table_name}" RENAME COLUMN "{old_name}" TO "{new_name}";'
			)
			conn.commit( )
			return
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'rename_column'
			exception.method = 'rename_column( table_name, old_name, new_name ) -> None'
			Logger( ).write( exception )
			pass

		row = conn.execute(
			"""
            SELECT sql
            FROM sqlite_master
            WHERE type ='table' AND name =?
			""",
			(table_name,)
		).fetchone( )

		if not row or not row[ 0 ]:
			raise ValueError( "Table definition not found." )

		create_sql = row[ 0 ]

		indexes = conn.execute(
			"""
            SELECT sql
            FROM sqlite_master
            WHERE type ='index' AND tbl_name=? AND sql IS NOT NULL
			""",
			(table_name,)
		).fetchall( )

		schema = conn.execute( f'PRAGMA table_info("{table_name}");' ).fetchall( )
		cols = [ r[ 1 ] for r in schema ]
		if old_name not in cols:
			raise ValueError( "Column not found." )

		mapped_cols = [ (new_name if c == old_name else c) for c in cols ]

		temp_table = f"{table_name}__rebuild_temp"

		col_defs: List[ str ] = [ ]
		pk_cols = [ r for r in schema if int( r[ 5 ] or 0 ) > 0 ]
		single_pk = len( pk_cols ) == 1

		for row in schema:
			col_name = row[ 1 ]
			col_type = row[ 2 ] or ''
			not_null = int( row[ 3 ] or 0 )
			default_value = row[ 4 ]
			pk = int( row[ 5 ] or 0 )

			out_name = new_name if col_name == old_name else col_name
			col_def = f'"{out_name}" {col_type}'.strip( )

			if not_null:
				col_def += ' NOT NULL'

			if default_value is not None:
				col_def += f' DEFAULT {default_value}'

			if single_pk and pk == 1:
				col_def += ' PRIMARY KEY'

			col_defs.append( col_def )

		new_create_sql = f'CREATE TABLE "{temp_table}" ({", ".join( col_defs )});'

		old_select = ", ".join( [ f'"{c}"' for c in cols ] )
		new_insert = ", ".join( [ f'"{c}"' for c in mapped_cols ] )

		conn.execute( "BEGIN" )
		conn.execute( new_create_sql )
		conn.execute(
			f'INSERT INTO "{temp_table}" ({new_insert}) SELECT {old_select} FROM "{table_name}";'
		)

		conn.execute( f'DROP TABLE "{table_name}";' )
		conn.execute( f'ALTER TABLE "{temp_table}" RENAME TO "{table_name}";' )

		for idx in indexes:
			idx_sql = idx[ 0 ]
			if idx_sql:
				idx_sql = idx_sql.replace( f'"{old_name}"', f'"{new_name}"' )
				conn.execute( idx_sql )

		conn.commit( )

ensure_session_key

ensure_session_key(key: str, default: Any) -> None

Ensure session key.

Purpose

Maintains application runtime state for ensure session key by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Parameters:

Name	Type	Description	Default
key	str	Session-state key or state value used by the Streamlit workflow.	required
default	Any	Default value used by the application workflow.	required

Source code in app.py

def ensure_session_key( key: str, default: Any ) -> None:
	"""Ensure session key.

	Purpose:
	    Maintains application runtime state for ensure session key by initializing, clearing,
	    or restoring the session values used by the active Streamlit workflow.

	Args:
	    key: Session-state key or state value used by the Streamlit workflow.
	    default: Default value used by the application workflow.
	"""
	if key not in st.session_state:
		st.session_state[ key ] = default

get_provider_name

get_provider_name(
    provider_name: Optional[str] = None,
) -> str

Get provider name.

Purpose

Retrieves get provider name for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_provider_name( provider_name: Optional[ str ] = None ) -> str:
	"""Get provider name.

	Purpose:
	    Retrieves get provider name for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	selected = provider_name

	if selected is None:
		selected = st.session_state.get( 'provider', 'GPT' )

	if not isinstance( selected, str ) or selected.strip( ) not in PROVIDER_MODULES:
		return 'GPT'

	return selected.strip( )

get_provider_module

get_provider_module(
    provider_name: Optional[str] = None,
) -> Any

Get provider module.

Purpose

Retrieves get provider module for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def get_provider_module( provider_name: Optional[ str ] = None ) -> Any:
	"""Get provider module.

	Purpose:
	    Retrieves get provider module for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	return PROVIDER_MODULES[ get_provider_name( provider_name ) ]

provider_supports

provider_supports(
    capability_name: str,
    provider_name: Optional[str] = None,
) -> bool

Provider supports.

Purpose

Evaluates whether provider supports is enabled or safe for the current provider, model, table, query, or workflow context.

Parameters:

Name	Type	Description	Default
capability_name	str	Capability Name value used by the application workflow.	required
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
bool	bool	True when the requested condition is satisfied; otherwise, False.

Source code in app.py

def provider_supports( capability_name: str, provider_name: Optional[ str ] = None ) -> bool:
	"""Provider supports.

	Purpose:
	    Evaluates whether provider supports is enabled or safe for the current provider, model,
	    table, query, or workflow context.

	Args:
	    capability_name: Capability Name value used by the application workflow.
	    provider_name: Provider name used to route the operation.

	Returns:
	    bool: True when the requested condition is satisfied; otherwise, False.
	"""
	if not isinstance( capability_name, str ) or not capability_name.strip( ):
		return False

	provider_module = get_provider_module( provider_name )
	return hasattr( provider_module, capability_name.strip( ) )

get_provider_capability

get_provider_capability(
    capability_name: str,
    provider_name: Optional[str] = None,
) -> Any

Get provider capability.

Purpose

Retrieves get provider capability for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
capability_name	str	Capability Name value used by the application workflow.	required
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def get_provider_capability( capability_name: str, provider_name: Optional[ str ] = None ) -> Any:
	"""Get provider capability.

	Purpose:
	    Retrieves get provider capability for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    capability_name: Capability Name value used by the application workflow.
	    provider_name: Provider name used to route the operation.

	Returns:
	    Any: Normalized result produced for the active application workflow.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	provider = get_provider_name( provider_name )
	provider_module = get_provider_module( provider )
	name = str( capability_name or '' ).strip( )

	if not name:
		raise AttributeError( 'Provider capability name cannot be empty.' )

	if not hasattr( provider_module, name ):
		raise AttributeError( f'{provider} provider does not expose {name}.' )

	return getattr( provider_module, name )

create_provider_capability

create_provider_capability(
    capability_name: str,
    provider_name: Optional[str] = None,
) -> Any

Create provider capability.

Purpose

Builds create provider capability from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
capability_name	str	Capability Name value used by the application workflow.	required
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def create_provider_capability( capability_name: str,
		provider_name: Optional[ str ] = None ) -> Any:
	"""Create provider capability.

	Purpose:
	    Builds create provider capability from validated runtime inputs and prepares the
	    resulting object, payload, table, or display structure for later application
	    processing.

	Args:
	    capability_name: Capability Name value used by the application workflow.
	    provider_name: Provider name used to route the operation.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	capability = get_provider_capability( capability_name=capability_name,
		provider_name=provider_name )

	return capability( )

get_chat_module

get_chat_module(provider_name: Optional[str] = None) -> Any

Get chat module.

Purpose

Retrieves get chat module for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def get_chat_module( provider_name: Optional[ str ] = None ) -> Any:
	"""Get chat module.

	Purpose:
	    Retrieves get chat module for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	return create_provider_capability( 'Chat', provider_name )

get_images_module

get_images_module(
    provider_name: Optional[str] = None,
) -> Any

Get images module.

Purpose

Retrieves get images module for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def get_images_module( provider_name: Optional[ str ] = None ) -> Any:
	"""Get images module.

	Purpose:
	    Retrieves get images module for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	return create_provider_capability( 'Images', provider_name )

get_embeddings_module

get_embeddings_module(
    provider_name: Optional[str] = None,
) -> Any

Get embeddings module.

Purpose

Retrieves get embeddings module for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def get_embeddings_module( provider_name: Optional[ str ] = None ) -> Any:
	"""Get embeddings module.

	Purpose:
	    Retrieves get embeddings module for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	return create_provider_capability( 'Embeddings', provider_name )

get_tts_module

get_tts_module(provider_name: Optional[str] = None) -> Any

Get tts module.

Purpose

Retrieves get tts module for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def get_tts_module( provider_name: Optional[ str ] = None ) -> Any:
	"""Get tts module.

	Purpose:
	    Retrieves get tts module for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	return create_provider_capability( 'TTS', provider_name )

get_transcription_module

get_transcription_module(
    provider_name: Optional[str] = None,
) -> Any

Get transcription module.

Purpose

Retrieves get transcription module for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def get_transcription_module( provider_name: Optional[ str ] = None ) -> Any:
	"""Get transcription module.

	Purpose:
	    Retrieves get transcription module for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	return create_provider_capability( 'Transcription', provider_name )

get_translation_module

get_translation_module(
    provider_name: Optional[str] = None,
) -> Any

Get translation module.

Purpose

Retrieves get translation module for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def get_translation_module( provider_name: Optional[ str ] = None ) -> Any:
	"""Get translation module.

	Purpose:
	    Retrieves get translation module for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	return create_provider_capability( 'Translation', provider_name )

get_files_module

get_files_module(
    provider_name: Optional[str] = None,
) -> Any

Get files module.

Purpose

Retrieves get files module for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def get_files_module( provider_name: Optional[ str ] = None ) -> Any:
	"""Get files module.

	Purpose:
	    Retrieves get files module for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	return create_provider_capability( 'Files', provider_name )

get_file_search_module

get_file_search_module(
    provider_name: Optional[str] = None,
) -> Any

Get file search module.

Purpose

Retrieves get file search module for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def get_file_search_module( provider_name: Optional[ str ] = None ) -> Any:
	"""Get file search module.

	Purpose:
	    Retrieves get file search module for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    Any: Normalized result produced for the active application workflow.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	provider = get_provider_name( provider_name )

	if provider != 'Gemini':
		raise AttributeError( f'{provider} does not expose Gemini FileSearch.' )

	return create_provider_capability( 'FileSearch', provider )

get_cloudbuckets_module

get_cloudbuckets_module(
    provider_name: Optional[str] = None,
) -> Any

Get cloudbuckets module.

Purpose

Retrieves get cloudbuckets module for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def get_cloudbuckets_module( provider_name: Optional[ str ] = None ) -> Any:
	"""Get cloudbuckets module.

	Purpose:
	    Retrieves get cloudbuckets module for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    Any: Normalized result produced for the active application workflow.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	provider = get_provider_name( provider_name )

	if provider != 'Gemini':
		raise AttributeError( f'{provider} does not expose Gemini CloudBuckets.' )

	return create_provider_capability( 'CloudBuckets', provider )

get_cloud_buckets_module

get_cloud_buckets_module(
    provider_name: Optional[str] = None,
) -> Any

Get cloud buckets module.

Purpose

Retrieves get cloud buckets module for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def get_cloud_buckets_module( provider_name: Optional[ str ] = None ) -> Any:
	"""Get cloud buckets module.

	Purpose:
	    Retrieves get cloud buckets module for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	return get_cloudbuckets_module( provider_name )

get_gemini_vector_backend

get_gemini_vector_backend() -> str

Get gemini vector backend.

Purpose

Retrieves get gemini vector backend for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_gemini_vector_backend( ) -> str:
	"""Get gemini vector backend.

	Purpose:
	    Retrieves get gemini vector backend for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	backend = st.session_state.get( 'stores_backend', 'File Search Stores' )

	if backend not in [ 'File Search Stores', 'Cloud Buckets' ]:
		return 'File Search Stores'

	return backend

get_vectorstores_module

get_vectorstores_module(
    provider_name: Optional[str] = None,
    backend: Optional[str] = None,
) -> Any

Get vectorstores module.

Purpose

Retrieves get vectorstores module for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None
backend	Optional[str]	Backend value used by the application workflow.	None

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def get_vectorstores_module( provider_name: Optional[ str ] = None,
		backend: Optional[ str ] = None ) -> Any:
	"""Get vectorstores module.

	Purpose:
	    Retrieves get vectorstores module for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.
	    backend: Backend value used by the application workflow.

	Returns:
	    Any: Normalized result produced for the active application workflow.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	provider = get_provider_name( provider_name )

	if provider in [ 'GPT', 'Grok' ]:
		return create_provider_capability( 'VectorStores', provider )

	if provider == 'Gemini':
		selected_backend = backend if backend is not None else get_gemini_vector_backend( )

		if selected_backend == 'Cloud Buckets':
			return get_cloudbuckets_module( provider )

		return get_file_search_module( provider )

	raise AttributeError( f'{provider} does not expose a Vector Stores backend.' )

get_vectorstores_backend_name

get_vectorstores_backend_name(
    provider_name: Optional[str] = None,
    backend: Optional[str] = None,
) -> str

Get vectorstores backend name.

Purpose

Retrieves get vectorstores backend name for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None
backend	Optional[str]	Backend value used by the application workflow.	None

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_vectorstores_backend_name( provider_name: Optional[ str ] = None,
		backend: Optional[ str ] = None ) -> str:
	"""Get vectorstores backend name.

	Purpose:
	    Retrieves get vectorstores backend name for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.

	Args:
	    provider_name: Provider name used to route the operation.
	    backend: Backend value used by the application workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	provider = get_provider_name( provider_name )

	if provider == 'GPT':
		return 'OpenAI Vector Stores'

	if provider == 'Grok':
		return 'xAI Collections'

	if provider == 'Gemini':
		selected_backend = backend if backend is not None else get_gemini_vector_backend( )
		return f'Gemini {selected_backend}'

	return 'Unsupported Storage Backend'

ensure_storage_mode_state

ensure_storage_mode_state() -> None

Ensure storage mode state.

Purpose

Maintains application runtime state for ensure storage mode state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_storage_mode_state( ) -> None:
	"""Ensure storage mode state.

	Purpose:
	    Maintains application runtime state for ensure storage mode state by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	ensure_vectorstores_mode_state( )

normalize_storage_rows

normalize_storage_rows(value: Any) -> List[Dict[str, Any]]

Normalize storage rows.

Purpose

Transforms normalize storage rows inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
value	Any	Value value used by the application workflow.	required

Returns:

Type	Description
List[Dict[str, Any]]	List[Dict[str, Any]]: List of normalized values used by the application workflow.

Source code in app.py

def normalize_storage_rows( value: Any ) -> List[ Dict[ str, Any ] ]:
	"""Normalize storage rows.

	Purpose:
	    Transforms normalize storage rows inputs into a normalized representation used by
	    provider calls, document retrieval, data management, or UI rendering.

	Args:
	    value: Value value used by the application workflow.

	Returns:
	    List[Dict[str, Any]]: List of normalized values used by the application workflow.
	"""
	if value is None:
		return [ ]

	if isinstance( value, list ):
		return [ normalize_storage_object( item ) for item in value ]

	if isinstance( value, tuple ):
		return [ normalize_storage_object( item ) for item in value ]

	if isinstance( value, dict ):
		for key in [ 'data', 'items', 'files', 'buckets', 'collections', 'stores',
		             'file_search_stores', 'vector_stores', 'results' ]:
			items = value.get( key )
			if isinstance( items, list ):
				return [ normalize_storage_object( item ) for item in items ]

		return [ normalize_storage_object( value ) ]

	for attr_name in [ 'data', 'items', 'files', 'buckets', 'collections', 'stores',
	                   'file_search_stores', 'vector_stores', 'results' ]:
		try:
			items = getattr( value, attr_name, None )
			if isinstance( items, list ):
				return [ normalize_storage_object( item ) for item in items ]
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'normalize_storage_rows'
			exception.method = 'normalize_storage_rows( value ) -> List[Dict[str, Any]]'
			Logger( ).write( exception )
			continue

	try:
		return [ normalize_storage_object( item ) for item in value ]
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'normalize_storage_rows'
		exception.method = 'normalize_storage_rows( value ) -> List[Dict[str, Any]]'
		Logger( ).write( exception )
		return [ normalize_storage_object( value ) ]

parse_storage_ids

parse_storage_ids(value: Any) -> List[str]

Parse storage ids.

Purpose

Transforms parse storage ids inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
value	Any	Value value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def parse_storage_ids( value: Any ) -> List[ str ]:
	"""Parse storage ids.

	Purpose:
	    Transforms parse storage ids inputs into a normalized representation used by provider
	    calls, document retrieval, data management, or UI rendering.

	Args:
	    value: Value value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	if value is None:
		return [ ]

	if isinstance( value, (list, tuple, set) ):
		return [
				str( item ).strip( )
				for item in value
				if str( item ).strip( )
		]

	if not isinstance( value, str ):
		return [ str( value ).strip( ) ] if str( value ).strip( ) else [ ]

	text = value.strip( )
	if not text:
		return [ ]

	parts = re.split( r'[,;\n\r\t]+', text )

	return [
			part.strip( )
			for part in parts
			if part.strip( )
	]

parse_storage_json

parse_storage_json(value: Any) -> Dict[str, Any]

Parse storage json.

Purpose

Transforms parse storage json inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
value	Any	Value value used by the application workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def parse_storage_json( value: Any ) -> Dict[ str, Any ]:
	"""Parse storage json.

	Purpose:
	    Transforms parse storage json inputs into a normalized representation used by provider
	    calls, document retrieval, data management, or UI rendering.

	Args:
	    value: Value value used by the application workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if value is None:
		return { }

	if isinstance( value, dict ):
		return value

	if not isinstance( value, str ) or not value.strip( ):
		return { }

	try:
		result = json.loads( value.strip( ) )
		return result if isinstance( result, dict ) else { 'value': result }
	except Exception as exc:
		exception = Error( exc )
		exception.module = 'app'
		exception.cause = 'parse_storage_json'
		exception.method = 'parse_storage_json( value ) -> Dict[str, Any]'
		Logger( ).write( exception )
		raise ValueError( f'Invalid JSON metadata: {exc}' ) from exc

get_storage_identifier

get_storage_identifier(row: Dict[str, Any]) -> str

Get storage identifier.

Purpose

Retrieves get storage identifier for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
row	Dict[str, Any]	Row value used by the application workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_storage_identifier( row: Dict[ str, Any ] ) -> str:
	"""Get storage identifier.

	Purpose:
	    Retrieves get storage identifier for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    row: Row value used by the application workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not isinstance( row, dict ):
		return ''

	for key in [
			'id',
			'name',
			'resource_name',
			'resourceName',
			'uri',
			'file_id',
			'store_id',
			'vector_store_id',
			'collection_id',
			'bucket',
			'bucket_name',
			'bucketName',
	]:
		value = row.get( key )
		if isinstance( value, str ) and value.strip( ):
			return value.strip( )

	return ''

get_storage_display_name

get_storage_display_name(row: Dict[str, Any]) -> str

Get storage display name.

Purpose

Retrieves get storage display name for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
row	Dict[str, Any]	Row value used by the application workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_storage_display_name( row: Dict[ str, Any ] ) -> str:
	"""Get storage display name.

	Purpose:
	    Retrieves get storage display name for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    row: Row value used by the application workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not isinstance( row, dict ):
		return 'resource'

	for key in [
			'display_name',
			'displayName',
			'filename',
			'file_name',
			'name',
			'id',
			'bucket_name',
			'bucketName',
			'collection',
			'title',
	]:
		value = row.get( key )
		if isinstance( value, str ) and value.strip( ):
			return value.strip( )

	return 'resource'

get_selected_store_id

get_selected_store_id(
    manual_key: str = "stores_manual_id",
    selected_key: str = "stores_selected_id",
    fallback_key: str = "stores_id",
) -> str

Get selected store id.

Purpose

Retrieves get selected store id for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
manual_key	str	Session-state key or state value used by the Streamlit workflow.	'stores_manual_id'
selected_key	str	Session-state key or state value used by the Streamlit workflow.	'stores_selected_id'
fallback_key	str	Session-state key or state value used by the Streamlit workflow.	'stores_id'

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_selected_store_id( manual_key: str = 'stores_manual_id',
		selected_key: str = 'stores_selected_id', fallback_key: str = 'stores_id' ) -> str:
	"""Get selected store id.

	Purpose:
	    Retrieves get selected store id for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    manual_key: Session-state key or state value used by the Streamlit workflow.
	    selected_key: Session-state key or state value used by the Streamlit workflow.
	    fallback_key: Session-state key or state value used by the Streamlit workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	for key in [ manual_key, selected_key, fallback_key ]:
		value = st.session_state.get( key, '' )
		if isinstance( value, str ) and value.strip( ):
			return value.strip( )

	return ''

get_vectorstores_selected_id

get_vectorstores_selected_id() -> str

Get vectorstores selected id.

Purpose

Retrieves get vectorstores selected id for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_vectorstores_selected_id( ) -> str:
	"""Get vectorstores selected id.

	Purpose:
	    Retrieves get vectorstores selected id for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	provider_name = get_provider_name( )

	if provider_name == 'Gemini':
		backend = get_gemini_vector_backend( )

		if backend == 'Cloud Buckets':
			return get_selected_store_id(
				manual_key='bucket_name',
				selected_key='bucket_selected_id',
				fallback_key='bucket_name' )

		return get_selected_store_id(
			manual_key='filestore_id',
			selected_key='filestore_selected_id',
			fallback_key='filestore_id' )

	return get_selected_store_id(
		manual_key='stores_manual_id',
		selected_key='stores_selected_id',
		fallback_key='stores_id' )

call_storage_method

call_storage_method(
    target: Any,
    method_names: List[str],
    *args: Any,
    **kwargs: Any
) -> Any

Call storage method.

Purpose

Executes the call storage method workflow using the current provider, document, prompt, and session-state configuration.

Parameters:

Name	Type	Description	Default
target	Any	Target value used by the application workflow.	required
method_names	List[str]	Method Names value used by the application workflow.	required
args	Any	Args value used by the application workflow.	()
kwargs	Any	Kwargs value used by the application workflow.	{}

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def call_storage_method( target: Any, method_names: List[ str ], *args: Any, **kwargs: Any ) -> Any:
	"""Call storage method.

	Purpose:
	    Executes the call storage method workflow using the current provider, document, prompt,
	    and session-state configuration.

	Args:
	    target: Target value used by the application workflow.
	    method_names: Method Names value used by the application workflow.
	    args: Args value used by the application workflow.
	    kwargs: Kwargs value used by the application workflow.

	Returns:
	    Any: Normalized result produced for the active application workflow.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if target is None:
		raise ValueError( 'Storage target cannot be None.' )

	if not isinstance( method_names, list ) or len( method_names ) == 0:
		raise ValueError( 'At least one storage method name is required.' )

	last_error: Optional[ Exception ] = None
	for method_name in method_names:
		if not isinstance( method_name, str ) or not method_name.strip( ):
			continue

		name = method_name.strip( )

		if not hasattr( target, name ):
			continue

		method = getattr( target, name )

		if not callable( method ):
			continue

		try:
			return method( *args, **kwargs )
		except TypeError as exc:
			last_error = exc

			if len( kwargs ) > 0 and len( args ) > 0:
				try:
					return method( *args )
				except TypeError as inner_exc:
					last_error = inner_exc

			if len( kwargs ) > 0:
				try:
					return method( **kwargs )
				except TypeError as inner_exc:
					last_error = inner_exc

			continue
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'call_storage_method'
			exception.method = 'call_storage_method( *args ) -> Any'
			Logger( ).write( exception )
			raise

	if last_error is not None:
		raise AttributeError(
			f'No compatible storage method was callable. Last error: {last_error}' )

	raise AttributeError(
		f'Target does not expose any of these methods: {", ".join( method_names )}' )

save_uploaded_storage_file

save_uploaded_storage_file(uploaded_file: Any) -> str

Save uploaded storage file.

Purpose

Applies the save uploaded storage file operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
uploaded_file	Any	File, upload, or path value used by the document or storage workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def save_uploaded_storage_file( uploaded_file: Any ) -> str:
	"""Save uploaded storage file.

	Purpose:
	    Applies the save uploaded storage file operation to application-managed data, files,
	    prompts, or provider resources while preserving the surrounding workflow state.

	Args:
	    uploaded_file: File, upload, or path value used by the document or storage workflow.

	Returns:
	    str: Text value produced for the active application workflow.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if uploaded_file is None:
		raise ValueError( 'An uploaded file is required.' )

	name = getattr( uploaded_file, 'name', 'uploaded_file' )
	suffix = Path( name ).suffix
	if not suffix:
		suffix = '.bin'

	with tempfile.NamedTemporaryFile( delete=False, suffix=suffix ) as tmp:
		if hasattr( uploaded_file, 'getbuffer' ):
			tmp.write( uploaded_file.getbuffer( ) )
		elif hasattr( uploaded_file, 'read' ):
			tmp.write( uploaded_file.read( ) )
		else:
			raise ValueError( 'Uploaded file object does not expose getbuffer() or read().' )

		return tmp.name

set_storage_rows

set_storage_rows(
    rows: Any, table_key: str = "storage_table_data"
) -> List[Dict[str, Any]]

Set storage rows.

Purpose

Supports the set storage rows application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
rows	Any	Rows value used by the application workflow.	required
table_key	str	SQLite table name used by the data-management workflow.	'storage_table_data'

Returns:

Type	Description
List[Dict[str, Any]]	List[Dict[str, Any]]: List of normalized values used by the application workflow.

Source code in app.py

def set_storage_rows( rows: Any, table_key: str = 'storage_table_data' ) -> List[
	Dict[ str, Any ] ]:
	"""Set storage rows.

	Purpose:
	    Supports the set storage rows application workflow by coordinating validated inputs,
	    Streamlit session state, provider configuration, and local data processing.

	Args:
	    rows: Rows value used by the application workflow.
	    table_key: SQLite table name used by the data-management workflow.

	Returns:
	    List[Dict[str, Any]]: List of normalized values used by the application workflow.
	"""
	normalized_rows = normalize_storage_rows( rows )
	st.session_state[ 'storage_table_data' ] = normalized_rows
	st.session_state[ table_key ] = normalized_rows

	return normalized_rows

set_storage_result

set_storage_result(
    result: Any,
    operation: str,
    result_key: str = "storage_operation_result",
) -> Dict[str, Any]

Set storage result.

Purpose

Supports the set storage result application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
result	Any	Result value used by the application workflow.	required
operation	str	Operation value used by the application workflow.	required
result_key	str	Session-state key or state value used by the Streamlit workflow.	'storage_operation_result'

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Source code in app.py

def set_storage_result( result: Any, operation: str,
		result_key: str = 'storage_operation_result' ) -> Dict[ str, Any ]:
	"""Set storage result.

	Purpose:
	    Supports the set storage result application workflow by coordinating validated inputs,
	    Streamlit session state, provider configuration, and local data processing.

	Args:
	    result: Result value used by the application workflow.
	    operation: Operation value used by the application workflow.
	    result_key: Session-state key or state value used by the Streamlit workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.
	"""
	normalized = normalize_storage_object( result )
	st.session_state[ 'storage_operation_result' ] = normalized
	st.session_state[ result_key ] = normalized
	st.session_state[ 'storage_last_operation' ] = operation
	st.session_state[ 'stores_last_operation' ] = operation

	return normalized

sync_storage_selection

sync_storage_selection(
    selected_option: Optional[str],
    provider_name: Optional[str] = None,
    backend: Optional[str] = None,
) -> str

Sync storage selection.

Purpose

Supports the sync storage selection application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
selected_option	Optional[str]	Selected Option value used by the application workflow.	required
provider_name	Optional[str]	Provider name used to route the operation.	None
backend	Optional[str]	Backend value used by the application workflow.	None

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def sync_storage_selection( selected_option: Optional[ str ], provider_name: Optional[ str ] = None,
		backend: Optional[ str ] = None ) -> str:
	"""Sync storage selection.

	Purpose:
	    Supports the sync storage selection application workflow by coordinating validated
	    inputs, Streamlit session state, provider configuration, and local data processing.

	Args:
	    selected_option: Selected Option value used by the application workflow.
	    provider_name: Provider name used to route the operation.
	    backend: Backend value used by the application workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	selected_id = get_storage_id_from_option( selected_option )

	if not selected_id:
		return ''

	provider = get_provider_name( provider_name )

	if provider == 'Gemini':
		selected_backend = backend if backend is not None else get_gemini_vector_backend( )

		if selected_backend == 'Cloud Buckets':
			st.session_state[ 'bucket_selected_id' ] = selected_id
			st.session_state[ 'bucket_selected_label' ] = selected_option or ''
			st.session_state[ 'bucket_name' ] = selected_id
		else:
			st.session_state[ 'filestore_selected_id' ] = selected_id
			st.session_state[ 'filestore_selected_label' ] = selected_option or ''
			st.session_state[ 'filestore_id' ] = selected_id

		return selected_id

	st.session_state[ 'stores_selected_id' ] = selected_id
	st.session_state[ 'stores_selected_label' ] = selected_option or ''
	st.session_state[ 'stores_id' ] = selected_id

	return selected_id

clear_vectorstore_outputs

clear_vectorstore_outputs() -> None

Clear vectorstore outputs.

Purpose

Maintains application runtime state for clear vectorstore outputs by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_vectorstore_outputs( ) -> None:
	"""Clear vectorstore outputs.

	Purpose:
	    Maintains application runtime state for clear vectorstore outputs by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'stores_table' ] = [ ]
	st.session_state[ 'stores_files_table' ] = [ ]
	st.session_state[ 'stores_store_metadata' ] = { }
	st.session_state[ 'stores_file_metadata' ] = { }
	st.session_state[ 'stores_operation_result' ] = { }
	st.session_state[ 'stores_batch_result' ] = { }
	st.session_state[ 'stores_upload_result' ] = { }
	st.session_state[ 'stores_delete_result' ] = { }
	st.session_state[ 'stores_search_result' ] = { }
	st.session_state[ 'stores_survey_result' ] = { }
	st.session_state[ 'stores_answer' ] = ''
	st.session_state[ 'stores_content' ] = ''
	st.session_state[ 'stores_last_operation' ] = ''

	st.session_state[ 'filestore_metadata' ] = { }
	st.session_state[ 'filestore_table' ] = [ ]
	st.session_state[ 'filestore_upload_result' ] = { }
	st.session_state[ 'filestore_delete_result' ] = { }
	st.session_state[ 'filestore_operation_result' ] = { }

	st.session_state[ 'bucket_metadata' ] = { }
	st.session_state[ 'bucket_table' ] = [ ]
	st.session_state[ 'bucket_upload_result' ] = { }
	st.session_state[ 'bucket_delete_result' ] = { }
	st.session_state[ 'bucket_operation_result' ] = { }
	st.session_state[ 'bucket_results' ] = { }

	st.session_state[ 'storage_operation_result' ] = { }
	st.session_state[ 'storage_table_data' ] = [ ]
	st.session_state[ 'storage_last_operation' ] = ''
	st.session_state[ 'storage_last_answer' ] = ''

reset_vectorstore_controls

reset_vectorstore_controls() -> None

Reset vectorstore controls.

Purpose

Maintains application runtime state for reset vectorstore controls by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_vectorstore_controls( ) -> None:
	"""Reset vectorstore controls.

	Purpose:
	    Maintains application runtime state for reset vectorstore controls by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	for key in [
			'stores_backend',
			'stores_id',
			'stores_name',
			'stores_description',
			'stores_metadata',
			'stores_manual_id',
			'stores_selected_id',
			'stores_selected_label',
			'stores_query',
			'stores_file_id',
			'stores_file_ids',
			'stores_file_ids_text',
			'stores_batch_id',
			'stores_limit',
			'stores_order',
			'filestore_id',
			'filestore_name',
			'filestore_selected_id',
			'filestore_selected_label',
			'bucket_name',
			'bucket_object_name',
			'bucket_selected_id',
			'bucket_selected_label',
			'storage_selected_option',
	]:
		if key in st.session_state:
			del st.session_state[ key ]

reset_vectorstore_all

reset_vectorstore_all() -> None

Reset vectorstore all.

Purpose

Maintains application runtime state for reset vectorstore all by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_vectorstore_all( ) -> None:
	"""Reset vectorstore all.

	Purpose:
	    Maintains application runtime state for reset vectorstore all by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	reset_vectorstore_controls( )
	clear_vectorstore_outputs( )
	ensure_vectorstores_mode_state( )

require_storage_value

require_storage_value(name: str, value: Any) -> str

Require storage value.

Purpose

Supports the require storage value application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
name	str	Name value used by the application workflow.	required
value	Any	Value value used by the application workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def require_storage_value( name: str, value: Any ) -> str:
	"""Require storage value.

	Purpose:
	    Supports the require storage value application workflow by coordinating validated
	    inputs, Streamlit session state, provider configuration, and local data processing.

	Args:
	    name: Name value used by the application workflow.
	    value: Value value used by the application workflow.

	Returns:
	    str: Text value produced for the active application workflow.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if value is None:
		raise ValueError( f'{name} is required.' )

	text = str( value ).strip( )

	if not text:
		raise ValueError( f'{name} is required.' )

	return text

get_grok_collections

get_grok_collections(
    vectorstores: Any,
) -> List[Dict[str, Any]]

Get grok collections.

Purpose

Retrieves get grok collections for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
vectorstores	Any	Vectorstores value used by the application workflow.	required

Returns:

Type	Description
List[Dict[str, Any]]	List[Dict[str, Any]]: List of normalized values used by the application workflow.

Source code in app.py

def get_grok_collections( vectorstores: Any ) -> List[ Dict[ str, Any ] ]:
	"""Get grok collections.

	Purpose:
	    Retrieves get grok collections for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    vectorstores: Vectorstores value used by the application workflow.

	Returns:
	    List[Dict[str, Any]]: List of normalized values used by the application workflow.
	"""
	for method_names in [ [ 'list_collections', 'list_stores', 'list' ],
	                      [ 'survey_collections', 'survey' ], ]:
		try:
			result = call_storage_method( vectorstores, method_names )
			rows = normalize_storage_rows( result )
			if len( rows ) > 0:
				return rows
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'get_grok_collections'
			exception.method = 'get_grok_collections( vectorstores ) -> List[Dict[str, Any]]'
			Logger( ).write( exception )
			continue

	collections = getattr( vectorstores, 'collections', None )
	if isinstance( collections, dict ):
		return [ {
				'name': name,
				'id': collection_id,
				'type': 'xAI Collection',
		} for name, collection_id in collections.items( ) ]

	return [ ]

warn_grok_unsupported_operation

warn_grok_unsupported_operation(
    operation_name: str,
) -> None

Warn grok unsupported operation.

Purpose

Supports the warn grok unsupported operation application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
operation_name	str	Operation Name value used by the application workflow.	required

Source code in app.py

def warn_grok_unsupported_operation( operation_name: str ) -> None:
	"""Warn grok unsupported operation.

	Purpose:
	    Supports the warn grok unsupported operation application workflow by coordinating
	    validated inputs, Streamlit session state, provider configuration, and local data
	    processing.

	Args:
	    operation_name: Operation Name value used by the application workflow.
	"""
	st.warning(
		f'Grok {operation_name} requires xAI collection-management capability. '
		f'This Buddy integration currently supports configured collection list, '
		f'retrieve, search, and survey operations.' )

get_storage_backend_summary

get_storage_backend_summary(
    provider_name: Optional[str] = None,
    backend: Optional[str] = None,
) -> Dict[str, Any]

Get storage backend summary.

Purpose

Retrieves get storage backend summary for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None
backend	Optional[str]	Backend value used by the application workflow.	None

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Source code in app.py

def get_storage_backend_summary( provider_name: Optional[ str ] = None,
		backend: Optional[ str ] = None ) -> Dict[ str, Any ]:
	"""Get storage backend summary.

	Purpose:
	    Retrieves get storage backend summary for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.

	Args:
	    provider_name: Provider name used to route the operation.
	    backend: Backend value used by the application workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.
	"""
	provider = get_provider_name( provider_name )
	backend_name = get_vectorstores_backend_name( provider, backend )
	supports_create = provider == 'GPT' or (
			provider == 'Gemini' and get_gemini_vector_backend( ) in [ 'File Search Stores',
			                                                           'Cloud Buckets', ])

	supports_upload = provider == 'GPT' or provider == 'Gemini'
	supports_delete = provider == 'GPT' or provider == 'Gemini'
	supports_search = provider in [ 'GPT', 'Grok', 'Gemini' ]

	return {
			'provider': provider,
			'backend': backend_name,
			'supports_create': supports_create,
			'supports_upload': supports_upload,
			'supports_delete': supports_delete,
			'supports_search': supports_search,
	}

ensure_key

ensure_key(key: str, default: Any) -> None

Ensure key.

Purpose

Maintains application runtime state for ensure key by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Parameters:

Name	Type	Description	Default
key	str	Session-state key or state value used by the Streamlit workflow.	required
default	Any	Default value used by the application workflow.	required

Source code in app.py

def ensure_key( key: str, default: Any ) -> None:
	"""Ensure key.

	Purpose:
	    Maintains application runtime state for ensure key by initializing, clearing, or
	    restoring the session values used by the active Streamlit workflow.

	Args:
	    key: Session-state key or state value used by the Streamlit workflow.
	    default: Default value used by the application workflow.
	"""
	if key not in st.session_state:
		st.session_state[ key ] = default

ensure_common_mode_state

ensure_common_mode_state() -> None

Ensure common mode state.

Purpose

Maintains application runtime state for ensure common mode state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_common_mode_state( ) -> None:
	"""Ensure common mode state.

	Purpose:
	    Maintains application runtime state for ensure common mode state by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	ensure_key( 'api_keys', { 'GPT': None, 'Grok': None, 'Gemini': None } )
	ensure_key( 'provider', 'GPT' )
	ensure_key( 'mode', 'Chat' )
	ensure_key( 'messages', [ ] )
	ensure_key( 'chat_history', [ ] )
	ensure_key( 'files', [ ] )
	ensure_key( 'uploaded', [ ] )
	ensure_key( 'use_semantic', False )
	ensure_key( 'is_grounded', False )
	ensure_key( 'selected_prompt_id', '' )
	ensure_key( 'pending_system_prompt_name', '' )
	ensure_key( 'last_answer', '' )
	ensure_key( 'last_sources', [ ] )
	ensure_key( 'last_call_usage', {
			'prompt_tokens': 0,
			'completion_tokens': 0,
			'total_tokens': 0,
	} )
	ensure_key( 'token_usage', {
			'prompt_tokens': 0,
			'completion_tokens': 0,
			'total_tokens': 0,
	} )
	ensure_key( 'last_analysis', {
			'tables': [ ],
			'docqna_files': [ ],
			'files': [ ],
			'text': [ ],
	} )

	# ------------------------------------------------------------------
	# API Keys
	# ------------------------------------------------------------------
	ensure_key( 'openai_api_key', '' )
	ensure_key( 'gemini_api_key', '' )
	ensure_key( 'groq_api_key', '' )
	ensure_key( 'xai_api_key', '' )
	ensure_key( 'google_api_key', '' )
	ensure_key( 'google_cse_id', '' )
	ensure_key( 'googlemaps_api_key', '' )
	ensure_key( 'geocoding_api_key', '' )
	ensure_key( 'google_cloud_project_id', '' )
	ensure_key( 'google_cloud_location', '' )

	# ------------------------------------------------------------------
	# Shared Model Keys
	# ------------------------------------------------------------------
	ensure_key( 'chat_model', '' )
	ensure_key( 'text_model', '' )
	ensure_key( 'image_model', '' )
	ensure_key( 'audio_model', '' )
	ensure_key( 'embedding_model', '' )
	ensure_key( 'docqna_model', '' )
	ensure_key( 'files_model', '' )
	ensure_key( 'stores_model', '' )
	ensure_key( 'bucket_model', '' )
	ensure_key( 'tts_model', '' )
	ensure_key( 'transcription_model', '' )
	ensure_key( 'translation_model', '' )

	# ------------------------------------------------------------------
	# Shared Instruction Keys
	# ------------------------------------------------------------------
	ensure_key( 'instructions', '' )
	ensure_key( 'chat_system_instructions', '' )
	ensure_key( 'text_system_instructions', '' )
	ensure_key( 'image_system_instructions', '' )
	ensure_key( 'audio_system_instructions', '' )
	ensure_key( 'docqna_system_instructions', '' )
	ensure_key( 'stores_system_instructions', '' )
	ensure_key( 'bucket_system_instructions', '' )

	# ------------------------------------------------------------------
	# Shared Generation Keys
	# ------------------------------------------------------------------
	ensure_key( 'max_tools', 0 )
	ensure_key( 'max_tokens', 0 )
	ensure_key( 'temperature', 0.0 )
	ensure_key( 'top_percent', 0.0 )
	ensure_key( 'frequency_penalty', 0.0 )
	ensure_key( 'presence_penalty', 0.0 )
	ensure_key( 'presense_penalty', 0.0 )
	ensure_key( 'background', False )
	ensure_key( 'parallel_tools', False )
	ensure_key( 'store', False )
	ensure_key( 'stream', False )
	ensure_key( 'execution_mode', 'Standard' )
	ensure_key( 'response_format', '' )
	ensure_key( 'tool_choice', '' )
	ensure_key( 'reasoning', '' )
	ensure_key( 'stops', [ ] )
	ensure_key( 'include', [ ] )
	ensure_key( 'input', [ ] )
	ensure_key( 'tools', [ ] )

ensure_text_mode_state

ensure_text_mode_state() -> None

Ensure text mode state.

Purpose

Maintains application runtime state for ensure text mode state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_text_mode_state( ) -> None:
	"""Ensure text mode state.

	Purpose:
	    Maintains application runtime state for ensure text mode state by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	ensure_common_mode_state( )

	ensure_key( 'text_number', 0 )
	ensure_key( 'text_max_calls', 0 )
	ensure_key( 'text_top_k', 0 )
	ensure_key( 'text_max_urls', 0 )
	ensure_key( 'text_max_searches', 0 )
	ensure_key( 'text_max_tokens', 0 )
	ensure_key( 'text_temperature', 0.0 )
	ensure_key( 'text_top_percent', 0.0 )
	ensure_key( 'text_frequency_penalty', 0.0 )
	ensure_key( 'text_presence_penalty', 0.0 )
	ensure_key( 'text_presense_penalty', 0.0 )
	ensure_key( 'text_parallel_tools', False )
	ensure_key( 'text_parallel_calls', False )
	ensure_key( 'text_background', False )
	ensure_key( 'text_store', False )
	ensure_key( 'text_stream', False )
	ensure_key( 'text_google_grounding', False )
	ensure_key( 'text_response_format', '' )
	ensure_key( 'text_tool_choice', '' )
	ensure_key( 'text_resolution', '' )
	ensure_key( 'text_media_resolution', '' )
	ensure_key( 'text_reasoning', '' )
	ensure_key( 'text_response_schema', '' )
	ensure_key( 'text_safety_profile', '' )
	ensure_key( 'text_input', '' )
	ensure_key( 'text_content', '' )
	ensure_key( 'text_previous_response_id', '' )
	ensure_key( 'text_conversation_id', '' )
	ensure_key( 'text_include', [ ] )
	ensure_key( 'text_includes', [ ] )
	ensure_key( 'text_domains', [ ] )
	ensure_key( 'text_urls', [ ] )
	ensure_key( 'text_tools', [ ] )
	ensure_key( 'text_stops', [ ] )
	ensure_key( 'text_modalities', [ ] )
	ensure_key( 'text_context', [ ] )
	ensure_key( 'text_messages', [ ] )
	ensure_key( 'text_gemini_history', [ ] )
	ensure_key( 'text_file_search_store_names', [ ] )
	ensure_key( 'selected_filestore_id', '' )
	ensure_key( 'selected_filestore_label', '' )
	ensure_key( 'text_vector_store_ids', '' )
	ensure_key( 'text_json_schema_name', 'structured_response' )
	ensure_key( 'text_json_schema', '' )
	ensure_key( 'text_json_schema_strict', True )

ensure_image_mode_state

ensure_image_mode_state() -> None

Ensure image mode state.

Purpose

Maintains application runtime state for ensure image mode state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_image_mode_state( ) -> None:
	"""Ensure image mode state.

	Purpose:
	    Maintains application runtime state for ensure image mode state by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	ensure_common_mode_state( )

	ensure_key( 'image_mode', '' )
	ensure_key( 'image_analysis_model', '' )
	ensure_key( 'image_analysis_detail', 'auto' )
	ensure_key( 'image_max_tokens', 0 )
	ensure_key( 'image_max_calls', 0 )
	ensure_key( 'image_max_searches', 0 )
	ensure_key( 'image_number', 1 )
	ensure_key( 'image_compression', 0.0 )
	ensure_key( 'image_temperature', 0.0 )
	ensure_key( 'image_top_percent', 0.0 )
	ensure_key( 'image_frequency_penalty', 0.0 )
	ensure_key( 'image_presence_penalty', 0.0 )
	ensure_key( 'image_parallel_calls', False )
	ensure_key( 'image_background', False )
	ensure_key( 'image_store', False )
	ensure_key( 'image_stream', False )
	ensure_key( 'image_tool_choice', '' )
	ensure_key( 'image_reasoning', '' )
	ensure_key( 'image_mime_type', '' )
	ensure_key( 'image_response_format', '' )
	ensure_key( 'image_previous_response_id', '' )
	ensure_key( 'image_input', [ ] )
	ensure_key( 'image_include', [ ] )
	ensure_key( 'image_tools', [ ] )
	ensure_key( 'image_modalities', [ ] )
	ensure_key( 'image_modality', '' )
	ensure_key( 'image_messages', [ ] )
	ensure_key( 'image_context', [ ] )
	ensure_key( 'image_domains', [ ] )
	ensure_key( 'image_content', [ ] )
	ensure_key( 'image_output_bytes', None )
	ensure_key( 'image_aspect_ratio', '' )
	ensure_key( 'image_size', '' )
	ensure_key( 'image_quality', '' )
	ensure_key( 'image_backcolor', '' )
	ensure_key( 'image_detail', '' )
	ensure_key( 'image_grounded', False )
	ensure_key( 'image_image_search', False )

ensure_audio_mode_state

ensure_audio_mode_state() -> None

Ensure audio mode state.

Purpose

Maintains application runtime state for ensure audio mode state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_audio_mode_state( ) -> None:
	"""Ensure audio mode state.

	Purpose:
	    Maintains application runtime state for ensure audio mode state by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	ensure_common_mode_state( )

	ensure_key( 'audio_max_tokens', 0 )
	ensure_key( 'audio_temperature', 0.0 )
	ensure_key( 'audio_top_percent', 0.0 )
	ensure_key( 'audio_frequency_penalty', 0.0 )
	ensure_key( 'audio_presence_penalty', 0.0 )
	ensure_key( 'audio_background', False )
	ensure_key( 'audio_store', False )
	ensure_key( 'audio_stream', False )
	ensure_key( 'audio_tool_choice', '' )
	ensure_key( 'audio_reasoning', '' )
	ensure_key( 'audio_response_format', '' )
	ensure_key( 'audio_format', '' )
	ensure_key( 'audio_input', '' )
	ensure_key( 'audio_mime_type', '' )
	ensure_key( 'audio_media_resolution', '' )
	ensure_key( 'audio_stops', [ ] )
	ensure_key( 'audio_include', [ ] )
	ensure_key( 'audio_includes', [ ] )
	ensure_key( 'audio_tools', [ ] )
	ensure_key( 'audio_context', [ ] )
	ensure_key( 'audio_modalities', [ ] )
	ensure_key( 'audio_messages', [ ] )
	ensure_key( 'audio_output_bytes', None )

	# ------------------------------------------------------------------
	# Audio-Specific Keys
	# ------------------------------------------------------------------
	ensure_key( 'audio_task', '' )
	ensure_key( 'audio_file', '' )
	ensure_key( 'audio_rate', int( cfg.SAMPLE_RATES[ 0 ] ) if hasattr( cfg, 'SAMPLE_RATES' )
	                                                          and cfg.SAMPLE_RATES else 44100 )
	ensure_key( 'audio_language', '' )
	ensure_key( 'audio_voice', '' )
	ensure_key( 'audio_start_time', 0.0 )
	ensure_key( 'audio_end_time', 0.0 )
	ensure_key( 'audio_loop', False )
	ensure_key( 'audio_autoplay', False )
	ensure_key( 'audio_output', '' )

ensure_embeddings_mode_state

ensure_embeddings_mode_state() -> None

Ensure embeddings mode state.

Purpose

Maintains application runtime state for ensure embeddings mode state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_embeddings_mode_state( ) -> None:
	"""Ensure embeddings mode state.

	Purpose:
	    Maintains application runtime state for ensure embeddings mode state by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	ensure_common_mode_state( )

	ensure_key( 'embedding_model', '' )
	ensure_key( 'embeddings_dimensions', 0 )
	ensure_key( 'embeddings_chunk_size', 800 )
	ensure_key( 'embeddings_overlap_amount', 0 )
	ensure_key( 'embeddings_input_text', '' )
	ensure_key( 'embeddings_encoding_format', 'float' )
	ensure_key( 'embeddings_method', '' )
	ensure_key( 'embeddings_user', '' )
	ensure_key( 'embeddings', [ ] )
	ensure_key( 'embeddings_chunks', [ ] )
	ensure_key( 'embeddings_df', pd.DataFrame( ) )
	ensure_key( 'embedding_metrics', { } )
	ensure_key( 'embedding_usage', { } )

ensure_docqna_mode_state

ensure_docqna_mode_state() -> None

Ensure docqna mode state.

Purpose

Maintains application runtime state for ensure docqna mode state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_docqna_mode_state( ) -> None:
	"""Ensure docqna mode state.

	Purpose:
	    Maintains application runtime state for ensure docqna mode state by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	ensure_common_mode_state( )

	# ------------------------------------------------------------------
	# Document Q&A Generation Keys
	# ------------------------------------------------------------------
	ensure_key( 'docqna_max_tools', 0 )
	ensure_key( 'docqna_max_tokens', 0 )
	ensure_key( 'docqna_max_calls', 0 )
	ensure_key( 'docqna_temperature', 0.0 )
	ensure_key( 'docqna_top_percent', 0.0 )
	ensure_key( 'docqna_frequency_penalty', 0.0 )
	ensure_key( 'docqna_presence_penalty', 0.0 )
	ensure_key( 'docqna_number', 0 )
	ensure_key( 'docqna_top_k', 6 )
	ensure_key( 'docqna_max_searches', 0 )
	ensure_key( 'docqna_parallel_tools', False )
	ensure_key( 'docqna_background', False )
	ensure_key( 'docqna_store', False )
	ensure_key( 'docqna_stream', False )
	ensure_key( 'docqna_response_format', '' )
	ensure_key( 'docqna_tool_choice', '' )
	ensure_key( 'docqna_resolution', '' )
	ensure_key( 'docqna_media_resolution', '' )
	ensure_key( 'docqna_reasoning', '' )
	ensure_key( 'docqna_input', '' )
	ensure_key( 'docqna_stops', [ ] )
	ensure_key( 'docqna_modalities', [ ] )
	ensure_key( 'docqna_include', [ ] )
	ensure_key( 'docqna_domains', [ ] )
	ensure_key( 'docqna_tools', [ ] )
	ensure_key( 'docqna_context', '' )
	ensure_key( 'docqna_content', [ ] )

	# ------------------------------------------------------------------
	# Document Q&A Source and Retrieval Keys
	# ------------------------------------------------------------------
	ensure_key( 'docqna_source', 'Local Upload' )
	ensure_key( 'doc_source', 'uploadlocal' )
	ensure_key( 'docqna_uploaded', None )
	ensure_key( 'docqna_files', [ ] )
	ensure_key( 'docqna_active_docs', [ ] )
	ensure_key( 'active_docs', [ ] )
	ensure_key( 'docqna_bytes', None )
	ensure_key( 'doc_bytes', { } )
	ensure_key( 'docqna_texts', { } )
	ensure_key( 'docqna_chunks', [ ] )
	ensure_key( 'docqna_messages', [ ] )
	ensure_key( 'docqna_multi_mode', False )
	ensure_key( 'docqna_vec_ready', False )
	ensure_key( 'docqna_fingerprint', '' )
	ensure_key( 'docqna_chunk_count', 0 )
	ensure_key( 'docqna_fallback_rows', [ ] )
	ensure_key( 'docqna_last_hits', [ ] )
	ensure_key( 'docqna_last_sources', [ ] )
	ensure_key( 'docqna_last_answer', '' )
	ensure_key( 'docqna_index_status', 'Not indexed' )
	ensure_key( 'docqna_backend', 'local' )
	ensure_key( 'docqna_show_diagnostics', True )
	ensure_key( 'docqna_file_id', '' )
	ensure_key( 'docqna_vector_store_id', '' )
	ensure_key( 'docqna_chunk_size', 900 )
	ensure_key( 'docqna_chunk_overlap', 150 )

ensure_files_mode_state

ensure_files_mode_state() -> None

Ensure files mode state.

Purpose

Maintains application runtime state for ensure files mode state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_files_mode_state( ) -> None:
	"""Ensure files mode state.

	Purpose:
	    Maintains application runtime state for ensure files mode state by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	ensure_common_mode_state( )

	ensure_key( 'files_max_tokens', 0 )
	ensure_key( 'files_temperature', 0.0 )
	ensure_key( 'files_top_percent', 0.0 )
	ensure_key( 'files_frequency_penalty', 0.0 )
	ensure_key( 'files_presence_penalty', 0.0 )
	ensure_key( 'files_background', False )
	ensure_key( 'files_store', False )
	ensure_key( 'files_stream', False )
	ensure_key( 'files_tool_choice', '' )
	ensure_key( 'files_reasoning', '' )
	ensure_key( 'files_response_format', '' )
	ensure_key( 'files_input', '' )
	ensure_key( 'files_media_resolution', '' )
	ensure_key( 'files_stops', [ ] )
	ensure_key( 'files_include', [ ] )
	ensure_key( 'files_includes', [ ] )
	ensure_key( 'files_tools', [ ] )
	ensure_key( 'files_context', [ ] )
	ensure_key( 'files_messages', [ ] )

	# ------------------------------------------------------------------
	# Files-Specific Keys
	# ------------------------------------------------------------------
	ensure_key( 'files_purpose', '' )
	ensure_key( 'files_type', '' )
	ensure_key( 'files_id', '' )
	ensure_key( 'files_url', '' )
	ensure_key( 'files_table', '' )
	ensure_key( 'files_uploaded', None )
	ensure_key( 'files_path', '' )
	ensure_key( 'files_operation', '' )

ensure_vectorstores_mode_state

ensure_vectorstores_mode_state() -> None

Ensure vectorstores mode state.

Purpose

Maintains application runtime state for ensure vectorstores mode state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_vectorstores_mode_state( ) -> None:
	"""Ensure vectorstores mode state.

	Purpose:
	    Maintains application runtime state for ensure vectorstores mode state by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	ensure_common_mode_state( )

	ensure_key( 'stores_temperature', 0.0 )
	ensure_key( 'stores_top_percent', 0.0 )
	ensure_key( 'stores_max_tokens', 0 )
	ensure_key( 'stores_frequency_penalty', 0.0 )
	ensure_key( 'stores_presence_penalty', 0.0 )
	ensure_key( 'stores_max_calls', 0 )
	ensure_key( 'stores_tool_choice', '' )
	ensure_key( 'stores_response_format', '' )
	ensure_key( 'stores_reasoning', '' )
	ensure_key( 'stores_resolution', '' )
	ensure_key( 'stores_media_resolution', '' )
	ensure_key( 'stores_parallel_tools', False )
	ensure_key( 'stores_background', False )
	ensure_key( 'stores_store', False )
	ensure_key( 'stores_stream', False )
	ensure_key( 'stores_input', [ ] )
	ensure_key( 'stores_tools', [ ] )
	ensure_key( 'stores_messages', [ ] )
	ensure_key( 'stores_stops', [ ] )
	ensure_key( 'stores_include', [ ] )
	ensure_key( 'stores_includes', [ ] )

	# ------------------------------------------------------------------
	# Vector Store-Specific Keys
	# ------------------------------------------------------------------
	ensure_key( 'stores_id', '' )
	ensure_key( 'stores_name', '' )
	ensure_key( 'stores_file_id', '' )
	ensure_key( 'stores_file_ids', [ ] )
	ensure_key( 'stores_uploaded', None )
	ensure_key( 'stores_path', '' )
	ensure_key( 'stores_operation', '' )
	ensure_key( 'stores_collection', '' )

ensure_file_search_mode_state

ensure_file_search_mode_state() -> None

Ensure file search mode state.

Purpose

Maintains application runtime state for ensure file search mode state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_file_search_mode_state( ) -> None:
	"""Ensure file search mode state.

	Purpose:
	    Maintains application runtime state for ensure file search mode state by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	ensure_common_mode_state( )

	ensure_key( 'filestore_model', '' )
	ensure_key( 'filestore_temperature', 0.0 )
	ensure_key( 'filestore_top_percent', 0.0 )
	ensure_key( 'filestore_max_tokens', 0 )
	ensure_key( 'filestore_frequency_penalty', 0.0 )
	ensure_key( 'filestore_presence_penalty', 0.0 )
	ensure_key( 'filestore_max_calls', 0 )
	ensure_key( 'filestore_tool_choice', '' )
	ensure_key( 'filestore_response_format', '' )
	ensure_key( 'filestore_reasoning', '' )
	ensure_key( 'filestore_parallel_tools', False )
	ensure_key( 'filestore_background', False )
	ensure_key( 'filestore_store', False )
	ensure_key( 'filestore_stream', False )
	ensure_key( 'filestore_input', [ ] )
	ensure_key( 'filestore_tools', [ ] )
	ensure_key( 'filestore_messages', [ ] )
	ensure_key( 'filestore_stops', [ ] )
	ensure_key( 'filestore_include', [ ] )
	ensure_key( 'filestore_id', '' )
	ensure_key( 'filestore_name', '' )
	ensure_key( 'filestore_selected_label', '' )

ensure_cloudbuckets_mode_state

ensure_cloudbuckets_mode_state() -> None

Ensure cloudbuckets mode state.

Purpose

Maintains application runtime state for ensure cloudbuckets mode state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_cloudbuckets_mode_state( ) -> None:
	"""Ensure cloudbuckets mode state.

	Purpose:
	    Maintains application runtime state for ensure cloudbuckets mode state by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	ensure_common_mode_state( )

	ensure_key( 'bucket_model', '' )
	ensure_key( 'bucket_temperature', 0.0 )
	ensure_key( 'bucket_top_percent', 0.0 )
	ensure_key( 'bucket_max_tokens', 0 )
	ensure_key( 'bucket_frequency_penalty', 0.0 )
	ensure_key( 'bucket_presence_penalty', 0.0 )
	ensure_key( 'bucket_number', 0 )
	ensure_key( 'bucket_max_calls', 0 )
	ensure_key( 'bucket_tool_choice', '' )
	ensure_key( 'bucket_response_format', '' )
	ensure_key( 'bucket_reasoning', '' )
	ensure_key( 'bucket_resolution', '' )
	ensure_key( 'bucket_media_resolution', '' )
	ensure_key( 'bucket_parallel_tools', False )
	ensure_key( 'bucket_background', False )
	ensure_key( 'bucket_store', False )
	ensure_key( 'bucket_stream', False )
	ensure_key( 'bucket_input', [ ] )
	ensure_key( 'bucket_tools', [ ] )
	ensure_key( 'bucket_messages', [ ] )
	ensure_key( 'bucket_stops', [ ] )
	ensure_key( 'bucket_include', [ ] )
	ensure_key( 'bucket_includes', [ ] )

	# ------------------------------------------------------------------
	# Cloud Bucket-Specific Keys
	# ------------------------------------------------------------------
	ensure_key( 'bucket_id', '' )
	ensure_key( 'bucket_name', '' )
	ensure_key( 'bucket_object_name', '' )
	ensure_key( 'bucket_path', '' )
	ensure_key( 'bucket_uploaded', None )
	ensure_key( 'bucket_operation', '' )
	ensure_key( 'selected_bucket_id', '' )
	ensure_key( 'selected_bucket_label', '' )

ensure_export_mode_state

ensure_export_mode_state() -> None

Ensure export mode state.

Purpose

Maintains application runtime state for ensure export mode state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_export_mode_state( ) -> None:
	"""Ensure export mode state.

	Purpose:
	    Maintains application runtime state for ensure export mode state by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	ensure_common_mode_state( )

	ensure_key( 'export_format', '' )
	ensure_key( 'export_source', '' )
	ensure_key( 'export_filename', '' )
	ensure_key( 'export_content', '' )

ensure_data_management_mode_state

ensure_data_management_mode_state() -> None

Ensure data management mode state.

Purpose

Maintains application runtime state for ensure data management mode state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_data_management_mode_state( ) -> None:
	"""Ensure data management mode state.

	Purpose:
	    Maintains application runtime state for ensure data management mode state by
	    initializing, clearing, or restoring the session values used by the active Streamlit
	    workflow.
	"""
	ensure_common_mode_state( )

	ensure_key( 'dm_table', '' )
	ensure_key( 'dm_selected_table', '' )
	ensure_key( 'dm_query', '' )
	ensure_key( 'dm_limit', 500 )
	ensure_key( 'dm_offset', 0 )
	ensure_key( 'dm_uploaded', None )
	ensure_key( 'dm_upload_table_name', '' )
	ensure_key( 'dm_chart_type', '' )
	ensure_key( 'dm_filter_column', '' )
	ensure_key( 'dm_filter_operator', '' )
	ensure_key( 'dm_filter_value', '' )
	ensure_key( 'df_current', pd.DataFrame( ) )

ensure_mode_state

ensure_mode_state(mode_name: str | None = None) -> None

Ensure mode state.

Purpose

Maintains application runtime state for ensure mode state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Parameters:

Name	Type	Description	Default
mode_name	str | None	Mode Name value used by the application workflow.	None

Source code in app.py

def ensure_mode_state( mode_name: str | None = None ) -> None:
	"""Ensure mode state.

	Purpose:
	    Maintains application runtime state for ensure mode state by initializing, clearing, or
	    restoring the session values used by the active Streamlit workflow.

	Args:
	    mode_name: Mode Name value used by the application workflow.
	"""
	current_mode = mode_name if isinstance( mode_name, str ) and mode_name.strip( ) else \
		st.session_state.get( 'mode', 'Chat' )

	ensure_common_mode_state( )

	if current_mode in [ 'Chat', 'Text' ]:
		ensure_text_mode_state( )
	elif current_mode in [ 'Images', 'Image' ]:
		ensure_image_mode_state( )
	elif current_mode in [ 'Audio' ]:
		ensure_audio_mode_state( )
	elif current_mode in [ 'Embeddings' ]:
		ensure_embeddings_mode_state( )
	elif current_mode in [ 'Document Q&A', 'Documents' ]:
		ensure_docqna_mode_state( )
	elif current_mode in [ 'Files' ]:
		ensure_files_mode_state( )
	elif current_mode in [ 'Vector Stores', 'VectorStores' ]:
		ensure_vectorstores_mode_state( )
	elif current_mode in [ 'File Search Stores', 'FileSearchStores' ]:
		ensure_file_search_mode_state( )
	elif current_mode in [ 'Google Cloud Buckets', 'Cloud Buckets', 'CloudBuckets' ]:
		ensure_cloudbuckets_mode_state( )
	elif current_mode in [ 'Data Management' ]:
		ensure_data_management_mode_state( )
	elif current_mode in [ 'Export' ]:
		ensure_export_mode_state( )

get_text_avatar

get_text_avatar(provider_name: str) -> str

Get text avatar.

Purpose

Retrieves get text avatar for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	str	Provider name used to route the operation.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_text_avatar( provider_name: str ) -> str:
	"""Get text avatar.

	Purpose:
	    Retrieves get text avatar for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if provider_name == 'GPT':
		return getattr( cfg, 'GPT_AVATAR', getattr( cfg, 'BUDDY', '🧠' ) )

	if provider_name == 'Gemini':
		return getattr( cfg, 'GEMINI_AVATAR', getattr( cfg, 'BUDDY', '🧠' ) )

	if provider_name == 'Grok':
		return getattr( cfg, 'GROK_AVATAR', getattr( cfg, 'BUDDY', '🧠' ) )

	return getattr( cfg, 'BUDDY', '🧠' )

get_text_option_list

get_text_option_list(
    source: Any, attr_name: str, fallback: List[str]
) -> List[str]

Get text option list.

Purpose

Retrieves get text option list for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
source	Any	Source value used by the application workflow.	required
attr_name	str	Attr Name value used by the application workflow.	required
fallback	List[str]	Fallback value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_text_option_list( source: Any, attr_name: str, fallback: List[ str ] ) -> List[ str ]:
	"""Get text option list.

	Purpose:
	    Retrieves get text option list for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    source: Source value used by the application workflow.
	    attr_name: Attr Name value used by the application workflow.
	    fallback: Fallback value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	try:
		options = getattr( source, attr_name, None )
		if isinstance( options, list ) and len( options ) > 0:
			return options
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'get_text_option_list'
		exception.method = 'get_text_option_list( source, attr_name, fallback ) -> List[str]'
		Logger( ).write( exception )
		pass

	return fallback

clear_text_messages

clear_text_messages() -> None

Clear text messages.

Purpose

Maintains application runtime state for clear text messages by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_text_messages( ) -> None:
	"""Clear text messages.

	Purpose:
	    Maintains application runtime state for clear text messages by initializing, clearing,
	    or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'text_messages' ] = [ ]
	st.session_state[ 'text_context' ] = [ ]
	st.session_state[ 'text_gemini_history' ] = [ ]
	st.session_state[ 'last_answer' ] = ''
	st.session_state[ 'last_sources' ] = [ ]

clear_text_instructions

clear_text_instructions() -> None

Clear text instructions.

Purpose

Maintains application runtime state for clear text instructions by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_text_instructions( ) -> None:
	"""Clear text instructions.

	Purpose:
	    Maintains application runtime state for clear text instructions by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'text_system_instructions' ] = ''
	st.session_state[ 'instructions' ] = ''

load_text_instruction_template

load_text_instruction_template() -> None

Load text instruction template.

Purpose

Retrieves load text instruction template for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Source code in app.py

def load_text_instruction_template( ) -> None:
	"""Load text instruction template.

	Purpose:
	    Retrieves load text instruction template for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.
	"""
	name = st.session_state.get( 'instructions' )
	if name and name != 'No Templates Found':
		prompt_text = fetch_prompt_text( cfg.DB_PATH, name )
		if prompt_text is not None:
			st.session_state[ 'text_system_instructions' ] = prompt_text

convert_text_system_instructions

convert_text_system_instructions() -> None

Convert text system instructions.

Purpose

Transforms convert text system instructions inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Source code in app.py

def convert_text_system_instructions( ) -> None:
	"""Convert text system instructions.

	Purpose:
	    Transforms convert text system instructions inputs into a normalized representation
	    used by provider calls, document retrieval, data management, or UI rendering.
	"""
	text_value = st.session_state.get( 'text_system_instructions', '' )
	if not isinstance( text_value, str ) or not text_value.strip( ):
		return

	source = text_value.strip( )
	if cfg.XML_BLOCK_PATTERN.search( source ):
		converted = convert_xml( source )
	else:
		converted = convert_markdown( source )

	st.session_state[ 'text_system_instructions' ] = converted

reset_text_model_settings

reset_text_model_settings() -> None

Reset text model settings.

Purpose

Maintains application runtime state for reset text model settings by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_text_model_settings( ) -> None:
	"""Reset text model settings.

	Purpose:
	    Maintains application runtime state for reset text model settings by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	for key in [ 'text_model', 'text_reasoning', 'text_modalities',
	             'text_media_resolution', 'text_number' ]:
		if key in st.session_state:
			del st.session_state[ key ]

reset_text_inference_settings

reset_text_inference_settings() -> None

Reset text inference settings.

Purpose

Maintains application runtime state for reset text inference settings by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_text_inference_settings( ) -> None:
	"""Reset text inference settings.

	Purpose:
	    Maintains application runtime state for reset text inference settings by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	for key in [ 'text_temperature', 'text_top_percent', 'text_top_k',
	             'text_frequency_penalty', 'text_presence_penalty',
	             'text_presense_penalty' ]:
		if key in st.session_state:
			del st.session_state[ key ]

reset_text_tool_settings

reset_text_tool_settings() -> None

Reset text tool settings.

Purpose

Maintains application runtime state for reset text tool settings by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_text_tool_settings( ) -> None:
	"""Reset text tool settings.

	Purpose:
	    Maintains application runtime state for reset text tool settings by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	for key in [ 'text_max_calls', 'text_tool_choice', 'text_include',
	             'text_includes', 'text_tools', 'text_domains_input',
	             'text_domains', 'text_urls_input', 'text_urls',
	             'text_parallel_tools', 'text_parallel_calls',
	             'text_vector_store_ids', 'text_google_grounding',
	             'text_file_search_store_names', 'selected_filestore_id',
	             'selected_filestore_label' ]:
		if key in st.session_state:
			del st.session_state[ key ]

reset_text_response_settings

reset_text_response_settings() -> None

Reset text response settings.

Purpose

Maintains application runtime state for reset text response settings by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_text_response_settings( ) -> None:
	"""Reset text response settings.

	Purpose:
	    Maintains application runtime state for reset text response settings by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	for key in [ 'text_stream', 'text_store', 'text_max_tokens',
	             'text_background', 'text_response_format', 'text_input',
	             'text_previous_response_id', 'text_conversation_id',
	             'text_json_schema_name', 'text_json_schema',
	             'text_json_schema_strict', 'text_response_schema',
	             'text_stops_input', 'text_stops', 'text_safety_profile' ]:
		if key in st.session_state:
			del st.session_state[ key ]

split_text_values

split_text_values(
    value: Any, delimiter: str = ","
) -> List[str]

Split text values.

Purpose

Transforms split text values inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
value	Any	Value value used by the application workflow.	required
delimiter	str	Numeric control value that constrains the operation.	','

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def split_text_values( value: Any, delimiter: str = ',' ) -> List[ str ]:
	"""Split text values.

	Purpose:
	    Transforms split text values inputs into a normalized representation used by provider
	    calls, document retrieval, data management, or UI rendering.

	Args:
	    value: Value value used by the application workflow.
	    delimiter: Numeric control value that constrains the operation.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	if value is None:
		return [ ]

	if isinstance( value, list ):
		return [ str( item ).strip( ) for item in value if str( item ).strip( ) ]

	if not isinstance( value, str ) or not value.strip( ):
		return [ ]

	return [ item.strip( ) for item in value.split( delimiter ) if item.strip( ) ]

parse_text_vector_store_ids

parse_text_vector_store_ids(
    value: str | List[str] | None,
) -> List[str]

Parse text vector store ids.

Purpose

Transforms parse text vector store ids inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
value	str | List[str] | None	Value value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def parse_text_vector_store_ids( value: str | List[ str ] | None ) -> List[ str ]:
	"""Parse text vector store ids.

	Purpose:
	    Transforms parse text vector store ids inputs into a normalized representation used by
	    provider calls, document retrieval, data management, or UI rendering.

	Args:
	    value: Value value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	return split_text_values( value=value, delimiter=',' )

build_text_response_format

build_text_response_format(
    response_format: str | None,
    schema_name: str | None = None,
    schema_text: str | None = None,
    strict: bool = True,
) -> Dict[str, Any] | None

Build text response format.

Purpose

Builds build text response format from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
response_format	str | None	Response Format value used by the application workflow.	required
schema_name	str | None	Schema Name value used by the application workflow.	None
schema_text	str | None	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	None
strict	bool	Strict value used by the application workflow.	True

Returns:

Type	Description
Dict[str, Any] | None	Dict[str, Any] | None: Dictionary containing normalized workflow configuration or results.

Source code in app.py

def build_text_response_format( response_format: str | None, schema_name: str | None = None,
		schema_text: str | None = None, strict: bool = True ) -> Dict[ str, Any ] | None:
	"""Build text response format.

	Purpose:
	    Builds build text response format from validated runtime inputs and prepares the
	    resulting object, payload, table, or display structure for later application
	    processing.

	Args:
	    response_format: Response Format value used by the application workflow.
	    schema_name: Schema Name value used by the application workflow.
	    schema_text: Text value supplied to the prompt, conversion, retrieval, or provider
	        workflow.
	    strict: Strict value used by the application workflow.

	Returns:
	    Dict[str, Any] | None: Dictionary containing normalized workflow configuration or
	        results.
	"""
	if not isinstance( response_format, str ) or not response_format.strip( ):
		return None

	format_name = response_format.strip( )

	if format_name == 'text':
		return { 'format': { 'type': 'text' } }

	if format_name == 'json_object':
		return { 'format': { 'type': 'json_object' } }

	if format_name == 'json_schema':
		if not isinstance( schema_text, str ) or not schema_text.strip( ):
			st.warning( 'JSON Schema output requires a schema. Falling back to plain text.' )
			return { 'format': { 'type': 'text' } }

		try:
			schema = json.loads( schema_text )
		except Exception as exc:
			exception = Error( exc )
			exception.module = 'app'
			exception.cause = 'build_text_response_format'
			exception.method = 'build_text_response_format( *args ) -> Dict[str, Any] | None'
			Logger( ).write( exception )
			st.warning( f'JSON Schema could not be parsed. Falling back to plain text: {exc}' )
			return { 'format': { 'type': 'text' } }

		name = schema_name if isinstance( schema_name, str ) and schema_name.strip( ) else \
			'structured_response'

		return { 'format': {
				'type': 'json_schema',
				'name': name.strip( ),
				'schema': schema,
				'strict': bool( strict ),
		} }

	return None

build_text_tools

build_text_tools(
    selected_tools: List[str] | None,
    vector_store_ids: List[str] | None = None,
) -> List[Dict[str, Any]]

Build text tools.

Purpose

Builds build text tools from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
selected_tools	List[str] | None	Selected Tools value used by the application workflow.	required
vector_store_ids	List[str] | None	Vector Store Ids value used by the application workflow.	None

Returns:

Type	Description
List[Dict[str, Any]]	List[Dict[str, Any]]: List of normalized values used by the application workflow.

Source code in app.py

def build_text_tools( selected_tools: List[ str ] | None,
		vector_store_ids: List[ str ] | None = None ) -> List[ Dict[ str, Any ] ]:
	"""Build text tools.

	Purpose:
	    Builds build text tools from validated runtime inputs and prepares the resulting
	    object, payload, table, or display structure for later application processing.

	Args:
	    selected_tools: Selected Tools value used by the application workflow.
	    vector_store_ids: Vector Store Ids value used by the application workflow.

	Returns:
	    List[Dict[str, Any]]: List of normalized values used by the application workflow.
	"""
	tools: List[ Dict[ str, Any ] ] = [ ]
	vector_ids = vector_store_ids if isinstance( vector_store_ids, list ) else [ ]

	if not isinstance( selected_tools, list ) or len( selected_tools ) == 0:
		return tools

	for name in selected_tools:
		tool_name = str( name or '' ).strip( )
		if not tool_name:
			continue

		if tool_name in [ 'web_search', 'web_search_preview' ]:
			tools.append( { 'type': 'web_search' } )
			continue

		if tool_name == 'file_search':
			if len( vector_ids ) == 0:
				st.warning( 'File Search was selected, but no vector store IDs were provided.' )
				continue

			tools.append( {
					'type': 'file_search',
					'vector_store_ids': vector_ids,
			} )
			continue

	return tools

build_text_include

build_text_include(
    selected_include: List[str] | None,
    selected_tools: List[Dict[str, Any]] | None = None,
) -> List[str]

Build text include.

Purpose

Builds build text include from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
selected_include	List[str] | None	Selected Include value used by the application workflow.	required
selected_tools	List[Dict[str, Any]] | None	Selected Tools value used by the application workflow.	None

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def build_text_include( selected_include: List[ str ] | None,
		selected_tools: List[ Dict[ str, Any ] ] | None = None ) -> List[ str ]:
	"""Build text include.

	Purpose:
	    Builds build text include from validated runtime inputs and prepares the resulting
	    object, payload, table, or display structure for later application processing.

	Args:
	    selected_include: Selected Include value used by the application workflow.
	    selected_tools: Selected Tools value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	if not isinstance( selected_include, list ) or len( selected_include ) == 0:
		return [ ]

	tool_types: List[ str ] = [ ]
	if isinstance( selected_tools, list ):
		for tool in selected_tools:
			if isinstance( tool, dict ) and tool.get( 'type' ):
				tool_types.append( str( tool.get( 'type' ) ) )

	include_values: List[ str ] = [ ]
	for value in selected_include:
		include_name = str( value or '' ).strip( )
		if not include_name:
			continue

		if include_name in [ 'reasoning.encrypted_content', 'message.output_text.logprobs' ]:
			include_values.append( include_name )
			continue

		if include_name.startswith( 'web_search_call.' ) and 'web_search' in tool_types:
			include_values.append( include_name )
			continue

		if include_name == 'file_search_call.results' and 'file_search' in tool_types:
			include_values.append( include_name )
			continue

	return include_values

build_text_tool_choice

build_text_tool_choice(
    tool_choice: str | None,
    selected_tools: List[Dict[str, Any]] | None = None,
) -> str | None

Build text tool choice.

Purpose

Builds build text tool choice from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
tool_choice	str | None	Tool Choice value used by the application workflow.	required
selected_tools	List[Dict[str, Any]] | None	Selected Tools value used by the application workflow.	None

Returns:

Type	Description
str | None	str | None: Text value produced for the active application workflow.

Source code in app.py

def build_text_tool_choice( tool_choice: str | None,
		selected_tools: List[ Dict[ str, Any ] ] | None = None ) -> str | None:
	"""Build text tool choice.

	Purpose:
	    Builds build text tool choice from validated runtime inputs and prepares the resulting
	    object, payload, table, or display structure for later application processing.

	Args:
	    tool_choice: Tool Choice value used by the application workflow.
	    selected_tools: Selected Tools value used by the application workflow.

	Returns:
	    str | None: Text value produced for the active application workflow.
	"""
	if not isinstance( tool_choice, str ) or not tool_choice.strip( ):
		return None

	choice = tool_choice.strip( )
	if choice not in [ 'auto', 'required', 'none' ]:
		return None

	if choice == 'none':
		return 'none'

	if not isinstance( selected_tools, list ) or len( selected_tools ) == 0:
		return None

	return choice

build_text_context

build_text_context(
    messages: List[Dict[str, Any]] | None,
    include_last_message: bool = False,
) -> List[Dict[str, str]]

Build text context.

Purpose

Builds build text context from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
messages	List[Dict[str, Any]] | None	Messages value used by the application workflow.	required
include_last_message	bool	Include Last Message value used by the application workflow.	False

Returns:

Type	Description
List[Dict[str, str]]	List[Dict[str, str]]: List of normalized values used by the application workflow.

Source code in app.py

def build_text_context( messages: List[ Dict[ str, Any ] ] | None,
		include_last_message: bool = False ) -> List[ Dict[ str, str ] ]:
	"""Build text context.

	Purpose:
	    Builds build text context from validated runtime inputs and prepares the resulting
	    object, payload, table, or display structure for later application processing.

	Args:
	    messages: Messages value used by the application workflow.
	    include_last_message: Include Last Message value used by the application workflow.

	Returns:
	    List[Dict[str, str]]: List of normalized values used by the application workflow.
	"""
	if not isinstance( messages, list ):
		return [ ]

	items = messages if include_last_message else messages[ :-1 ]
	context: List[ Dict[ str, str ] ] = [ ]
	for item in items:
		if not isinstance( item, dict ):
			continue

		role = str( item.get( 'role', '' ) or '' ).strip( )
		content = item.get( 'content', '' )

		if role not in [ 'user', 'assistant', 'system', 'developer' ]:
			continue

		if not isinstance( content, str ) or not content.strip( ):
			continue

		context.append( {
				'role': role,
				'content': content.strip( ),
		} )

	return context

get_text_conversation_id

get_text_conversation_id(
    input_mode: str | None, conversation_id: str | None
) -> str | None

Get text conversation id.

Purpose

Retrieves get text conversation id for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
input_mode	str | None	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required
conversation_id	str | None	Conversation Id value used by the application workflow.	required

Returns:

Type	Description
str | None	str | None: Text value produced for the active application workflow.

Source code in app.py

def get_text_conversation_id( input_mode: str | None, conversation_id: str | None ) -> str | None:
	"""Get text conversation id.

	Purpose:
	    Retrieves get text conversation id for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    input_mode: Text value supplied to the prompt, conversion, retrieval, or provider
	        workflow.
	    conversation_id: Conversation Id value used by the application workflow.

	Returns:
	    str | None: Text value produced for the active application workflow.
	"""
	if input_mode != 'conversation':
		return None

	if not isinstance( conversation_id, str ) or not conversation_id.strip( ):
		return None

	return conversation_id.strip( )

get_text_previous_response_id

get_text_previous_response_id(
    input_mode: str | None, previous_id: str | None
) -> str | None

Get text previous response id.

Purpose

Retrieves get text previous response id for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Parameters:

Name	Type	Description	Default
input_mode	str | None	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required
previous_id	str | None	Previous Id value used by the application workflow.	required

Returns:

Type	Description
str | None	str | None: Text value produced for the active application workflow.

Source code in app.py

def get_text_previous_response_id( input_mode: str | None, previous_id: str | None ) -> str | None:
	"""Get text previous response id.

	Purpose:
	    Retrieves get text previous response id for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.

	Args:
	    input_mode: Text value supplied to the prompt, conversion, retrieval, or provider
	        workflow.
	    previous_id: Previous Id value used by the application workflow.

	Returns:
	    str | None: Text value produced for the active application workflow.
	"""
	if input_mode in [ 'single_turn', 'conversation' ]:
		return None

	if not isinstance( previous_id, str ) or not previous_id.strip( ):
		return None

	return previous_id.strip( )

apply_gemini_runtime_config

apply_gemini_runtime_config() -> None

Apply gemini runtime config.

Purpose

Supports the apply gemini runtime config application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Source code in app.py

def apply_gemini_runtime_config( ) -> None:
	"""Apply gemini runtime config.

	Purpose:
	    Supports the apply gemini runtime config application workflow by coordinating validated
	    inputs, Streamlit session state, provider configuration, and local data processing.
	"""
	key = (st.session_state.get( 'gemini_api_key' )
	       or st.session_state.get( 'google_api_key' )
	       or getattr( cfg, 'GEMINI_API_KEY', None )
	       or getattr( cfg, 'GOOGLE_API_KEY', None )
	       or os.environ.get( 'GEMINI_API_KEY' )
	       or os.environ.get( 'GOOGLE_API_KEY' ))

	if key:
		os.environ[ 'GEMINI_API_KEY' ] = key
		os.environ[ 'GOOGLE_API_KEY' ] = key

	for env_name in [ 'GOOGLE_GENAI_USE_VERTEXAI', 'GOOGLE_CLOUD_PROJECT',
	                  'GOOGLE_CLOUD_PROJECT_ID', 'GOOGLE_CLOUD_LOCATION' ]:
		os.environ.pop( env_name, None )

	for attr_name in [ 'GOOGLE_GENAI_USE_VERTEXAI', 'GOOGLE_CLOUD_PROJECT',
	                   'GOOGLE_CLOUD_PROJECT_ID', 'GOOGLE_CLOUD_LOCATION' ]:
		try:
			setattr( cfg, attr_name, None )
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'apply_gemini_runtime_config'
			exception.method = 'apply_gemini_runtime_config( ) -> None'
			Logger( ).write( exception )
			pass

clear_image_messages

clear_image_messages() -> None

Clear image messages.

Purpose

Maintains application runtime state for clear image messages by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_image_messages( ) -> None:
	"""Clear image messages.

	Purpose:
	    Maintains application runtime state for clear image messages by initializing, clearing,
	    or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'image_input' ] = [ ]
	st.session_state[ 'image_messages' ] = [ ]
	st.session_state[ 'image_context' ] = [ ]
	st.session_state[ 'image_output_bytes' ] = None
	st.session_state[ 'last_answer' ] = ''

clear_image_instructions

clear_image_instructions() -> None

Clear image instructions.

Purpose

Maintains application runtime state for clear image instructions by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_image_instructions( ) -> None:
	"""Clear image instructions.

	Purpose:
	    Maintains application runtime state for clear image instructions by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'image_system_instructions' ] = ''
	st.session_state[ 'instructions' ] = ''

append_image_message

append_image_message(role: str, content: str) -> None

Append image message.

Purpose

Supports the append image message application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
role	str	Role value used by the application workflow.	required
content	str	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required

Source code in app.py

def append_image_message( role: str, content: str ) -> None:
	"""Append image message.

	Purpose:
	    Supports the append image message application workflow by coordinating validated
	    inputs, Streamlit session state, provider configuration, and local data processing.

	Args:
	    role: Role value used by the application workflow.
	    content: Text value supplied to the prompt, conversion, retrieval, or provider
	        workflow.
	"""
	if 'image_input' not in st.session_state or not isinstance(
			st.session_state[ 'image_input' ], list ):
		st.session_state[ 'image_input' ] = [ ]

	if 'image_messages' not in st.session_state or not isinstance(
			st.session_state[ 'image_messages' ], list ):
		st.session_state[ 'image_messages' ] = [ ]

	message = {
			'role': role,
			'content': content,
	}

	st.session_state[ 'image_input' ].append( message )
	st.session_state[ 'image_messages' ].append( message )

load_image_instruction_template

load_image_instruction_template() -> None

Load image instruction template.

Purpose

Retrieves load image instruction template for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Source code in app.py

def load_image_instruction_template( ) -> None:
	"""Load image instruction template.

	Purpose:
	    Retrieves load image instruction template for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.
	"""
	name = st.session_state.get( 'instructions' )
	if name and name != 'No Templates Found':
		prompt_text = fetch_prompt_text( cfg.DB_PATH, name )
		if prompt_text is not None:
			st.session_state[ 'image_system_instructions' ] = prompt_text

convert_image_system_instructions

convert_image_system_instructions() -> None

Convert image system instructions.

Purpose

Transforms convert image system instructions inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Source code in app.py

def convert_image_system_instructions( ) -> None:
	"""Convert image system instructions.

	Purpose:
	    Transforms convert image system instructions inputs into a normalized representation
	    used by provider calls, document retrieval, data management, or UI rendering.
	"""
	text_value = st.session_state.get( 'image_system_instructions', '' )
	if not isinstance( text_value, str ) or not text_value.strip( ):
		return

	source = text_value.strip( )
	if cfg.XML_BLOCK_PATTERN.search( source ):
		converted = convert_xml( source )
	else:
		converted = convert_markdown( source )

	st.session_state[ 'image_system_instructions' ] = converted

reset_image_llm_settings

reset_image_llm_settings() -> None

Reset image llm settings.

Purpose

Maintains application runtime state for reset image llm settings by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_image_llm_settings( ) -> None:
	"""Reset image llm settings.

	Purpose:
	    Maintains application runtime state for reset image llm settings by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	for key in [ 'image_mode', 'image_model', 'image_analysis_model', 'image_number',
	             'image_modality' ]:
		if key in st.session_state:
			del st.session_state[ key ]

reset_image_visual_settings

reset_image_visual_settings() -> None

Reset image visual settings.

Purpose

Maintains application runtime state for reset image visual settings by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_image_visual_settings( ) -> None:
	"""Reset image visual settings.

	Purpose:
	    Maintains application runtime state for reset image visual settings by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	for key in [ 'image_mime_type', 'image_size', 'image_quality', 'image_backcolor',
	             'image_compression', 'image_aspect_ratio', 'image_detail' ]:
		if key in st.session_state:
			del st.session_state[ key ]

reset_image_tool_settings

reset_image_tool_settings() -> None

Reset image tool settings.

Purpose

Maintains application runtime state for reset image tool settings by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_image_tool_settings( ) -> None:
	"""Reset image tool settings.

	Purpose:
	    Maintains application runtime state for reset image tool settings by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	for key in [ 'image_include', 'image_tools', 'image_domains_input', 'image_domains',
	             'image_tool_choice', 'image_grounded', 'image_image_search',
	             'image_max_calls', 'image_max_searches', 'image_parallel_calls' ]:
		if key in st.session_state:
			del st.session_state[ key ]

reset_image_response_settings

reset_image_response_settings() -> None

Reset image response settings.

Purpose

Maintains application runtime state for reset image response settings by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_image_response_settings( ) -> None:
	"""Reset image response settings.

	Purpose:
	    Maintains application runtime state for reset image response settings by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	for key in [ 'image_temperature', 'image_top_percent', 'image_frequency_penalty',
	             'image_presence_penalty', 'image_max_tokens', 'image_store',
	             'image_stream', 'image_background', 'image_response_format',
	             'image_reasoning', 'image_previous_response_id' ]:
		if key in st.session_state:
			del st.session_state[ key ]

get_image_models

get_image_models(image: Any) -> List[str]

Get image models.

Purpose

Retrieves get image models for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
image	Any	Image value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_image_models( image: Any ) -> List[ str ]:
	"""Get image models.

	Purpose:
	    Retrieves get image models for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    image: Image value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	options = getattr( image, 'model_options', None )
	if isinstance( options, list ) and len( options ) > 0:
		return [ '' ] + options

	provider_name = get_provider_name( )

	if provider_name == 'GPT':
		return [ '' ] + list( getattr( cfg, 'GPT_GENERATION',
			[ 'gpt-image-1', 'gpt-image-1-mini' ] ) )

	if provider_name == 'Gemini':
		return [ '' ] + list( getattr( cfg, 'GEMINI_GENERATION',
			[ 'gemini-2.5-flash-image' ] ) )

	if provider_name == 'Grok':
		return [ '' ] + list( getattr( cfg, 'GROK_GENERATION', [ 'grok-2-image' ] ) )

	return [ '' ]

get_image_analysis_models

get_image_analysis_models(image: Any = None) -> List[str]

Get image analysis models.

Purpose

Retrieves get image analysis models for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
image	Any	Image value used by the application workflow.	None

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_image_analysis_models( image: Any = None ) -> List[ str ]:
	"""Get image analysis models.

	Purpose:
	    Retrieves get image analysis models for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    image: Image value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	if image is not None:
		options = getattr( image, 'analysis_model_options', None )
		if isinstance( options, list ) and len( options ) > 0:
			return [ '' ] + options

	provider_name = get_provider_name( )

	if provider_name == 'GPT':
		return [ '' ] + list( getattr( cfg, 'GPT_ANALYSIS',
			[ 'gpt-4o-mini', 'gpt-4o', 'gpt-5-mini', 'gpt-5' ] ) )

	if provider_name == 'Gemini':
		return [ '' ] + list( getattr( cfg, 'GEMINI_ANALYSIS',
			[ 'gemini-2.5-flash', 'gemini-2.5-flash-image' ] ) )

	if provider_name == 'Grok':
		return [ '' ] + list( getattr( cfg, 'GROK_ANALYSIS', [ 'grok-4' ] ) )

	return [ '' ]

get_image_editing_models

get_image_editing_models(image: Any = None) -> List[str]

Get image editing models.

Purpose

Retrieves get image editing models for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
image	Any	Image value used by the application workflow.	None

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_image_editing_models( image: Any = None ) -> List[ str ]:
	"""Get image editing models.

	Purpose:
	    Retrieves get image editing models for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    image: Image value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	provider_name = get_provider_name( )

	if provider_name == 'GPT':
		return [ '' ] + list( getattr( cfg, 'GPT_EDITING',
			[ 'gpt-image-1', 'gpt-image-1-mini' ] ) )

	if provider_name == 'Gemini':
		return [ '' ] + list( getattr( cfg, 'GEMINI_EDITING',
			[ 'gemini-2.5-flash-image' ] ) )

	if image is not None:
		options = getattr( image, 'model_options', None )
		if isinstance( options, list ) and len( options ) > 0:
			return [ '' ] + options

	return [ '' ]

get_image_size_options

get_image_size_options(image: Any) -> List[str]

Get image size options.

Purpose

Retrieves get image size options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
image	Any	Image value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_image_size_options( image: Any ) -> List[ str ]:
	"""Get image size options.

	Purpose:
	    Retrieves get image size options for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    image: Image value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	options = getattr( image, 'size_options', None )
	if isinstance( options, list ) and len( options ) > 0:
		return [ '' ] + options

	return [ '', 'auto', '1024x1024', '1024x1536', '1536x1024' ]

get_image_quality_options

get_image_quality_options(image: Any) -> List[str]

Get image quality options.

Purpose

Retrieves get image quality options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
image	Any	Image value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_image_quality_options( image: Any ) -> List[ str ]:
	"""Get image quality options.

	Purpose:
	    Retrieves get image quality options for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    image: Image value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	options = getattr( image, 'quality_options', None )
	if isinstance( options, list ) and len( options ) > 0:
		return [ '' ] + options

	return [ '', 'auto', 'low', 'medium', 'high' ]

get_image_mime_options

get_image_mime_options(image: Any) -> List[str]

Get image mime options.

Purpose

Retrieves get image mime options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
image	Any	Image value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_image_mime_options( image: Any ) -> List[ str ]:
	"""Get image mime options.

	Purpose:
	    Retrieves get image mime options for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    image: Image value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	options = getattr( image, 'mime_options', None )
	if isinstance( options, list ) and len( options ) > 0:
		return [ '' ] + options

	return [ '', 'png', 'jpeg', 'webp' ]

get_image_background_options

get_image_background_options(image: Any) -> List[str]

Get image background options.

Purpose

Retrieves get image background options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Parameters:

Name	Type	Description	Default
image	Any	Image value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_image_background_options( image: Any ) -> List[ str ]:
	"""Get image background options.

	Purpose:
	    Retrieves get image background options for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.

	Args:
	    image: Image value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	options = getattr( image, 'backcolor_options', None )
	if isinstance( options, list ) and len( options ) > 0:
		return [ '' ] + options

	return [ '', 'auto', 'transparent', 'opaque' ]

get_image_detail_options

get_image_detail_options(image: Any) -> List[str]

Get image detail options.

Purpose

Retrieves get image detail options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
image	Any	Image value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_image_detail_options( image: Any ) -> List[ str ]:
	"""Get image detail options.

	Purpose:
	    Retrieves get image detail options for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    image: Image value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	options = getattr( image, 'detail_options', None )
	if isinstance( options, list ) and len( options ) > 0:
		return [ '' ] + options

	return [ '', 'auto', 'low', 'high', 'original' ]

get_image_aspect_options

get_image_aspect_options(image: Any) -> List[str]

Get image aspect options.

Purpose

Retrieves get image aspect options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
image	Any	Image value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_image_aspect_options( image: Any ) -> List[ str ]:
	"""Get image aspect options.

	Purpose:
	    Retrieves get image aspect options for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    image: Image value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	options = getattr( image, 'aspect_options', None )
	if isinstance( options, list ) and len( options ) > 0:
		return [ '' ] + options

	return [ '', '1:1', '2:3', '3:2', '3:4', '4:3', '4:5', '5:4', '9:16', '16:9' ]

get_image_modality_options

get_image_modality_options(image: Any) -> List[str]

Get image modality options.

Purpose

Retrieves get image modality options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
image	Any	Image value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_image_modality_options( image: Any ) -> List[ str ]:
	"""Get image modality options.

	Purpose:
	    Retrieves get image modality options for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    image: Image value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	options = getattr( image, 'modality_options', None )
	if isinstance( options, list ) and len( options ) > 0:
		return [ '' ] + options

	return [ '', 'text', 'image', 'auto' ]

render_image_output

render_image_output(
    image_result: (
        str | bytes | List[str | bytes] | Any | None
    ),
    caption: str = "Image output",
) -> bool

Render image output.

Purpose

Renders render image output in the Streamlit interface while preserving the active session state and provider workflow context.

Parameters:

Name	Type	Description	Default
image_result	str | bytes | List[str | bytes] | Any | None	Image Result value used by the application workflow.	required
caption	str	Caption value used by the application workflow.	'Image output'

Returns:

Name	Type	Description
bool	bool	True when the requested condition is satisfied; otherwise, False.

Source code in app.py

def render_image_output( image_result: str | bytes | List[ str | bytes ] | Any | None,
		caption: str = 'Image output' ) -> bool:
	"""Render image output.

	Purpose:
	    Renders render image output in the Streamlit interface while preserving the active
	    session state and provider workflow context.

	Args:
	    image_result: Image Result value used by the application workflow.
	    caption: Caption value used by the application workflow.

	Returns:
	    bool: True when the requested condition is satisfied; otherwise, False.
	"""
	if image_result is None:
		return False

	outputs = image_result if isinstance( image_result, list ) else [ image_result ]
	rendered = False

	for index, item in enumerate( outputs, start=1 ):
		if item is None:
			continue

		output_caption = f'{caption} {index}' if len( outputs ) > 1 else caption

		if isinstance( item, bytes ) and len( item ) > 0:
			st.image( item, caption=output_caption, use_column_width=True )
			rendered = True
			continue

		if isinstance( item, str ) and item.strip( ):
			value = item.strip( )
			if value.lower( ).startswith( ('http://', 'https://') ):
				st.image( value, caption=output_caption, use_column_width=True )
			else:
				st.markdown( value )

			rendered = True
			continue

		try:
			st.image( item, caption=output_caption, use_column_width=True )
			rendered = True
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'render_image_output'
			exception.method = 'render_image_output( image_result, caption ) -> bool'
			Logger( ).write( exception )
			st.write( item )
			rendered = True

	return rendered

get_image_prompt

get_image_prompt(prompt: str | None) -> str

Get image prompt.

Purpose

Retrieves get image prompt for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
prompt	str | None	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_image_prompt( prompt: str | None ) -> str:
	"""Get image prompt.

	Purpose:
	    Retrieves get image prompt for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    prompt: Text value supplied to the prompt, conversion, retrieval, or provider workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not isinstance( prompt, str ) or not prompt.strip( ):
		return ''

	return prompt.strip( )

ensure_audio_runtime_state

ensure_audio_runtime_state() -> None

Ensure audio runtime state.

Purpose

Maintains application runtime state for ensure audio runtime state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_audio_runtime_state( ) -> None:
	"""Ensure audio runtime state.

	Purpose:
	    Maintains application runtime state for ensure audio runtime state by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	ensure_audio_mode_state( )
	ensure_key( 'audio_tts_input', '' )
	ensure_key( 'audio_speed', 1.0 )
	ensure_key( 'audio_last_result', { } )
	ensure_key( 'audio_last_usage', { } )

get_audio_task_options

get_audio_task_options() -> List[str]

Get audio task options.

Purpose

Retrieves get audio task options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_audio_task_options( ) -> List[ str ]:
	"""Get audio task options.

	Purpose:
	    Retrieves get audio task options for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	options: List[ str ] = [ ]

	if provider_supports( 'Transcription' ):
		options.append( 'Transcribe' )

	if provider_supports( 'Translation' ):
		options.append( 'Translate' )

	if provider_supports( 'TTS' ):
		options.append( 'Text-to-Speech' )

	return options

get_audio_option_list

get_audio_option_list(
    source: Any, attr_name: str, fallback: List[str]
) -> List[str]

Get audio option list.

Purpose

Retrieves get audio option list for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
source	Any	Source value used by the application workflow.	required
attr_name	str	Attr Name value used by the application workflow.	required
fallback	List[str]	Fallback value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_audio_option_list( source: Any, attr_name: str, fallback: List[ str ] ) -> List[ str ]:
	"""Get audio option list.

	Purpose:
	    Retrieves get audio option list for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    source: Source value used by the application workflow.
	    attr_name: Attr Name value used by the application workflow.
	    fallback: Fallback value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	if source is not None:
		try:
			options = getattr( source, attr_name, None )
			if isinstance( options, list ) and len( options ) > 0:
				return options
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'get_audio_option_list'
			exception.method = 'get_audio_option_list( source, attr_name, fallback ) -> List[str]'
			Logger( ).write( exception )
			pass

	return fallback

get_audio_model_options

get_audio_model_options(
    task: str | None,
    transcriber: Any,
    translator: Any,
    tts: Any,
) -> List[str]

Get audio model options.

Purpose

Retrieves get audio model options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
task	str | None	Task value used by the application workflow.	required
transcriber	Any	Transcriber value used by the application workflow.	required
translator	Any	Translator value used by the application workflow.	required
tts	Any	Tts value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_audio_model_options( task: str | None, transcriber: Any, translator: Any, tts: Any ) -> \
List[ str ]:
	"""Get audio model options.

	Purpose:
	    Retrieves get audio model options for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    task: Task value used by the application workflow.
	    transcriber: Transcriber value used by the application workflow.
	    translator: Translator value used by the application workflow.
	    tts: Tts value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	provider_name = get_provider_name( )

	if task == 'Transcribe':
		return [ '' ] + get_audio_option_list( transcriber, 'model_options',
			[ 'gpt-4o-transcribe', 'gpt-4o-mini-transcribe', 'whisper-1' ]
			if provider_name == 'GPT' else [ 'gemini-3-flash-preview', 'gemini-2.0-flash' ] )

	if task == 'Translate':
		return [ '' ] + get_audio_option_list( translator, 'model_options',
			[ 'whisper-1' ] if provider_name == 'GPT'
			else [ 'gemini-3-flash-preview', 'gemini-2.0-flash' ] )

	if task == 'Text-to-Speech':
		return [ '' ] + get_audio_option_list( tts, 'model_options',
			[ 'gpt-4o-mini-tts', 'tts-1', 'tts-1-hd' ]
			if provider_name == 'GPT' else [ 'gemini-2.5-flash-preview-tts' ] )

	return [ '' ]

get_audio_language_options

get_audio_language_options(
    task: str | None, transcriber: Any, translator: Any
) -> List[str]

Get audio language options.

Purpose

Retrieves get audio language options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
task	str | None	Task value used by the application workflow.	required
transcriber	Any	Transcriber value used by the application workflow.	required
translator	Any	Translator value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_audio_language_options( task: str | None, transcriber: Any, translator: Any ) -> List[
	str ]:
	"""Get audio language options.

	Purpose:
	    Retrieves get audio language options for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    task: Task value used by the application workflow.
	    transcriber: Transcriber value used by the application workflow.
	    translator: Translator value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	if task == 'Transcribe':
		return [ '' ] + get_audio_option_list( transcriber, 'language_options',
			[ 'en', 'es', 'fr', 'de', 'it', 'pt', 'ja', 'ko', 'zh' ] )

	if task == 'Translate':
		return [ '' ] + get_audio_option_list( translator, 'language_options',
			[ 'English', 'Spanish', 'French', 'German', 'Italian', 'Portuguese' ] )

	return [ '' ]

get_audio_voice_options

get_audio_voice_options(tts: Any) -> List[str]

Get audio voice options.

Purpose

Retrieves get audio voice options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
tts	Any	Tts value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_audio_voice_options( tts: Any ) -> List[ str ]:
	"""Get audio voice options.

	Purpose:
	    Retrieves get audio voice options for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    tts: Tts value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	return [ '' ] + get_audio_option_list( tts, 'voice_options',
		[ 'alloy', 'ash', 'ballad', 'coral', 'echo', 'fable', 'nova', 'onyx', 'sage', 'shimmer' ] )

get_audio_response_format_options

get_audio_response_format_options(
    task: str | None,
    model: str | None,
    transcriber: Any,
    translator: Any,
    tts: Any,
) -> List[str]

Get audio response format options.

Purpose

Retrieves get audio response format options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Parameters:

Name	Type	Description	Default
task	str | None	Task value used by the application workflow.	required
model	str | None	Provider model identifier selected for the active workflow.	required
transcriber	Any	Transcriber value used by the application workflow.	required
translator	Any	Translator value used by the application workflow.	required
tts	Any	Tts value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_audio_response_format_options( task: str | None, model: str | None,
		transcriber: Any, translator: Any, tts: Any ) -> List[ str ]:
	"""Get audio response format options.

	Purpose:
	    Retrieves get audio response format options for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.

	Args:
	    task: Task value used by the application workflow.
	    model: Provider model identifier selected for the active workflow.
	    transcriber: Transcriber value used by the application workflow.
	    translator: Translator value used by the application workflow.
	    tts: Tts value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	if task == 'Transcribe':
		return [ '' ] + get_audio_option_list( transcriber, 'format_options',
			[ 'json', 'text', 'srt', 'verbose_json', 'vtt' ] )

	if task == 'Translate':
		return [ '' ] + get_audio_option_list( translator, 'format_options',
			[ 'json', 'text', 'srt', 'verbose_json', 'vtt' ] )

	if task == 'Text-to-Speech':
		return [ '' ] + get_audio_option_list( tts, 'format_options',
			[ 'mp3', 'wav', 'aac', 'flac', 'opus', 'pcm' ] )

	return [ '' ]

get_audio_prompt_value

get_audio_prompt_value(
    task: str | None, prompt: str | None
) -> str | None

Get audio prompt value.

Purpose

Retrieves get audio prompt value for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
task	str | None	Task value used by the application workflow.	required
prompt	str | None	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required

Returns:

Type	Description
str | None	str | None: Text value produced for the active application workflow.

Source code in app.py

def get_audio_prompt_value( task: str | None, prompt: str | None ) -> str | None:
	"""Get audio prompt value.

	Purpose:
	    Retrieves get audio prompt value for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    task: Task value used by the application workflow.
	    prompt: Text value supplied to the prompt, conversion, retrieval, or provider workflow.

	Returns:
	    str | None: Text value produced for the active application workflow.
	"""
	if not isinstance( prompt, str ) or not prompt.strip( ):
		return None

	return prompt.strip( )

get_audio_response_format_value

get_audio_response_format_value(
    task: str | None,
    selected_format: str | None,
    selected_mime_type: str | None = None,
) -> str | None

Get audio response format value.

Purpose

Retrieves get audio response format value for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Parameters:

Name	Type	Description	Default
task	str | None	Task value used by the application workflow.	required
selected_format	str | None	Selected Format value used by the application workflow.	required
selected_mime_type	str | None	Selected Mime Type value used by the application workflow.	None

Returns:

Type	Description
str | None	str | None: Text value produced for the active application workflow.

Source code in app.py

def get_audio_response_format_value( task: str | None, selected_format: str | None,
		selected_mime_type: str | None = None ) -> str | None:
	"""Get audio response format value.

	Purpose:
	    Retrieves get audio response format value for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.

	Args:
	    task: Task value used by the application workflow.
	    selected_format: Selected Format value used by the application workflow.
	    selected_mime_type: Selected Mime Type value used by the application workflow.

	Returns:
	    str | None: Text value produced for the active application workflow.
	"""
	if isinstance( selected_format, str ) and selected_format.strip( ):
		value = selected_format.strip( )
		if value.startswith( 'audio/' ):
			return value.split( '/', 1 )[ 1 ]

		return value

	if isinstance( selected_mime_type, str ) and selected_mime_type.strip( ):
		value = selected_mime_type.strip( )
		if value.startswith( 'audio/' ):
			return value.split( '/', 1 )[ 1 ]

		return value

	return None

extract_audio_usage

extract_audio_usage(response: Any) -> Dict[str, Any]

Extract audio usage.

Purpose

Transforms extract audio usage inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
response	Any	Response value used by the application workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Source code in app.py

def extract_audio_usage( response: Any ) -> Dict[ str, Any ]:
	"""Extract audio usage.

	Purpose:
	    Transforms extract audio usage inputs into a normalized representation used by provider
	    calls, document retrieval, data management, or UI rendering.

	Args:
	    response: Response value used by the application workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.
	"""
	try:
		return _extract_usage_from_response( response )
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'extract_audio_usage'
		exception.method = 'extract_audio_usage( response ) -> Dict[str, Any]'
		Logger( ).write( exception )
		return { }

clear_audio_outputs

clear_audio_outputs() -> None

Clear audio outputs.

Purpose

Maintains application runtime state for clear audio outputs by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_audio_outputs( ) -> None:
	"""Clear audio outputs.

	Purpose:
	    Maintains application runtime state for clear audio outputs by initializing, clearing,
	    or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'audio_output' ] = ''
	st.session_state[ 'audio_output_bytes' ] = None
	st.session_state[ 'audio_last_result' ] = { }
	st.session_state[ 'audio_last_usage' ] = { }

clear_audio_messages

clear_audio_messages() -> None

Clear audio messages.

Purpose

Maintains application runtime state for clear audio messages by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_audio_messages( ) -> None:
	"""Clear audio messages.

	Purpose:
	    Maintains application runtime state for clear audio messages by initializing, clearing,
	    or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'audio_messages' ] = [ ]
	clear_audio_outputs( )

clear_audio_instructions

clear_audio_instructions() -> None

Clear audio instructions.

Purpose

Maintains application runtime state for clear audio instructions by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_audio_instructions( ) -> None:
	"""Clear audio instructions.

	Purpose:
	    Maintains application runtime state for clear audio instructions by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'audio_system_instructions' ] = ''
	st.session_state[ 'instructions' ] = ''

load_audio_instruction_template

load_audio_instruction_template() -> None

Load audio instruction template.

Purpose

Retrieves load audio instruction template for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Source code in app.py

def load_audio_instruction_template( ) -> None:
	"""Load audio instruction template.

	Purpose:
	    Retrieves load audio instruction template for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.
	"""
	name = st.session_state.get( 'instructions' )
	if name and name != 'No Templates Found':
		prompt_text = fetch_prompt_text( cfg.DB_PATH, name )
		if prompt_text is not None:
			st.session_state[ 'audio_system_instructions' ] = prompt_text

convert_audio_system_instructions

convert_audio_system_instructions() -> None

Convert audio system instructions.

Purpose

Transforms convert audio system instructions inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Source code in app.py

def convert_audio_system_instructions( ) -> None:
	"""Convert audio system instructions.

	Purpose:
	    Transforms convert audio system instructions inputs into a normalized representation
	    used by provider calls, document retrieval, data management, or UI rendering.
	"""
	text_value = st.session_state.get( 'audio_system_instructions', '' )
	if not isinstance( text_value, str ) or not text_value.strip( ):
		return

	source = text_value.strip( )
	if cfg.XML_BLOCK_PATTERN.search( source ):
		converted = convert_xml( source )
	else:
		converted = convert_markdown( source )

	st.session_state[ 'audio_system_instructions' ] = converted

reset_audio_llm_settings

reset_audio_llm_settings() -> None

Reset audio llm settings.

Purpose

Maintains application runtime state for reset audio llm settings by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_audio_llm_settings( ) -> None:
	"""Reset audio llm settings.

	Purpose:
	    Maintains application runtime state for reset audio llm settings by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	for key in [ 'audio_task', 'audio_model', 'audio_language', 'audio_voice',
	             'audio_response_format', 'audio_mime_type' ]:
		if key in st.session_state:
			del st.session_state[ key ]

reset_audio_response_settings

reset_audio_response_settings() -> None

Reset audio response settings.

Purpose

Maintains application runtime state for reset audio response settings by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_audio_response_settings( ) -> None:
	"""Reset audio response settings.

	Purpose:
	    Maintains application runtime state for reset audio response settings by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	for key in [ 'audio_temperature', 'audio_top_percent', 'audio_frequency_penalty',
	             'audio_presence_penalty', 'audio_max_tokens', 'audio_speed',
	             'audio_store', 'audio_stream', 'audio_background',
	             'audio_start_time', 'audio_end_time', 'audio_loop',
	             'audio_autoplay' ]:
		if key in st.session_state:
			del st.session_state[ key ]

save_audio_upload

save_audio_upload(upload: Any) -> str | None

Save audio upload.

Purpose

Applies the save audio upload operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
upload	Any	Streamlit uploaded-file object to persist to a temporary path.	required

Returns:

Type	Description
str | None	str | None: Text value produced for the active application workflow.

Source code in app.py

def save_audio_upload( upload: Any ) -> str | None:
	"""Save audio upload.

	Purpose:
	    Applies the save audio upload operation to application-managed data, files, prompts, or
	    provider resources while preserving the surrounding workflow state.

	Args:
	    upload: Streamlit uploaded-file object to persist to a temporary path.

	Returns:
	    str | None: Text value produced for the active application workflow.
	"""
	if upload is None:
		return None

	try:
		name = getattr( upload, 'name', 'audio.wav' )
		_, ext = os.path.splitext( name )
		ext = ext if ext else '.wav'

		with tempfile.NamedTemporaryFile( delete=False, suffix=ext ) as tmp:
			if hasattr( upload, 'getbuffer' ):
				tmp.write( upload.getbuffer( ) )
			elif hasattr( upload, 'read' ):
				tmp.write( upload.read( ) )
			else:
				return None

			return tmp.name
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'save_audio_upload'
		exception.method = 'save_audio_upload( upload ) -> str | None'
		Logger( ).write( exception )
		return None

run_audio_file_task

run_audio_file_task(
    task: str | None,
    file_path: str | None,
    transcriber: Any,
    translator: Any,
) -> str | None

Run audio file task.

Purpose

Executes the run audio file task workflow using the current provider, document, prompt, and session-state configuration.

Parameters:

Name	Type	Description	Default
task	str | None	Task value used by the application workflow.	required
file_path	str | None	File, upload, or path value used by the document or storage workflow.	required
transcriber	Any	Transcriber value used by the application workflow.	required
translator	Any	Translator value used by the application workflow.	required

Returns:

Type	Description
str | None	str | None: Text value produced for the active application workflow.

Source code in app.py

def run_audio_file_task( task: str | None, file_path: str | None,
		transcriber: Any, translator: Any ) -> str | None:
	"""Run audio file task.

	Purpose:
	    Executes the run audio file task workflow using the current provider, document, prompt,
	    and session-state configuration.

	Args:
	    task: Task value used by the application workflow.
	    file_path: File, upload, or path value used by the document or storage workflow.
	    transcriber: Transcriber value used by the application workflow.
	    translator: Translator value used by the application workflow.

	Returns:
	    str | None: Text value produced for the active application workflow.
	"""
	if not isinstance( task, str ) or not task.strip( ):
		st.warning( 'Select an audio task before processing audio.' )
		return None

	if not isinstance( file_path, str ) or not file_path.strip( ):
		st.warning( 'Upload or record audio before processing.' )
		return None

	provider_name = get_provider_name( )
	file_suffix = Path( file_path ).suffix.lower( ).replace( '.', '' )

	if provider_name == 'GPT':
		valid_extensions = [
				'flac',
				'mp3',
				'mp4',
				'mpeg',
				'mpga',
				'm4a',
				'ogg',
				'wav',
				'webm',
		]

		if file_suffix not in valid_extensions:
			st.warning( 'OpenAI audio transcription and translation support flac, mp3, mp4, '
			            'mpeg, mpga, m4a, ogg, wav, and webm files. Convert the file before '
			            'processing it with GPT.' )
			return None

	prompt_value = get_audio_prompt_value(
		task=task,
		prompt=st.session_state.get( 'audio_system_instructions', '' ) )

	response_format = get_audio_response_format_value(
		task=task,
		selected_format=st.session_state.get( 'audio_response_format' ),
		selected_mime_type=st.session_state.get( 'audio_mime_type' ) )

	model = st.session_state.get( 'audio_model' )
	language = st.session_state.get( 'audio_language' )
	temperature = st.session_state.get( 'audio_temperature' )
	include = st.session_state.get( 'audio_include', [ ] )

	if task == 'Transcribe':
		try:
			result_text = transcriber.transcribe(
				path=file_path,
				model=model or 'gpt-4o-transcribe',
				language=language or None,
				prompt=prompt_value,
				format=response_format,
				temperature=temperature,
				include=include )
		except TypeError:
			result_text = transcriber.transcribe(
				path=file_path,
				model=model or 'gemini-3-flash-preview',
				language=language or None )

		st.session_state[ 'audio_output' ] = result_text or ''
		st.session_state[ 'audio_last_result' ] = getattr(
			transcriber, 'normalized_result', { } ) or { }
		st.session_state[ 'audio_last_usage' ] = extract_audio_usage(
			getattr( transcriber, 'response', None ) )

		return result_text

	if task == 'Translate':
		try:
			result_text = translator.translate(
				filepath=file_path,
				model=model or 'whisper-1',
				prompt=prompt_value,
				format=response_format,
				temperature=temperature,
				language=language or None )
		except TypeError:
			try:
				result_text = translator.translate(
					path=file_path,
					model=model or 'gemini-3-flash-preview',
					language=language or None )
			except TypeError:
				result_text = translator.translate(
					file_path,
					model=model or 'gemini-3-flash-preview',
					language=language or None )

		st.session_state[ 'audio_output' ] = result_text or ''
		st.session_state[ 'audio_last_result' ] = getattr(
			translator, 'normalized_result', { } ) or { }
		st.session_state[ 'audio_last_usage' ] = extract_audio_usage(
			getattr( translator, 'response', None ) )

		return result_text

	st.info( 'Use the Text-to-Speech input area to generate speech from text.' )
	return None

run_audio_tts_task

run_audio_tts_task(
    text: str | None, tts: Any
) -> bytes | None

Run audio tts task.

Purpose

Executes the run audio tts task workflow using the current provider, document, prompt, and session-state configuration.

Parameters:

Name	Type	Description	Default
text	str | None	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required
tts	Any	Tts value used by the application workflow.	required

Returns:

Type	Description
bytes | None	bytes | None: Normalized result produced for the active application workflow.

Source code in app.py

def run_audio_tts_task( text: str | None, tts: Any ) -> bytes | None:
	"""Run audio tts task.

	Purpose:
	    Executes the run audio tts task workflow using the current provider, document, prompt,
	    and session-state configuration.

	Args:
	    text: Text value supplied to the prompt, conversion, retrieval, or provider workflow.
	    tts: Tts value used by the application workflow.

	Returns:
	    bytes | None: Normalized result produced for the active application workflow.
	"""
	if not isinstance( text, str ) or not text.strip( ):
		st.warning( 'Enter text before generating speech.' )
		return None

	model = st.session_state.get( 'audio_model' )
	voice = st.session_state.get( 'audio_voice' )
	speed = float( st.session_state.get( 'audio_speed', 1.0 ) or 1.0 )
	response_format = get_audio_response_format_value(
		task='Text-to-Speech',
		selected_format=st.session_state.get( 'audio_response_format' ),
		selected_mime_type=st.session_state.get( 'audio_mime_type' ) )

	instructions = get_audio_prompt_value(
		task='Text-to-Speech',
		prompt=st.session_state.get( 'audio_system_instructions', '' ) )

	audio_bytes = tts.create_speech(
		text=text.strip( ),
		model=model or 'gpt-4o-mini-tts',
		format=response_format or 'mp3',
		speed=speed,
		voice=voice or 'alloy',
		instruct=instructions )

	st.session_state[ 'audio_output_bytes' ] = audio_bytes
	st.session_state[ 'audio_output' ] = text.strip( )
	st.session_state[ 'audio_last_result' ] = {
			'text': text.strip( ),
			'format': response_format or 'mp3',
			'voice': voice or 'alloy',
			'speed': speed,
	}
	st.session_state[ 'audio_last_usage' ] = extract_audio_usage(
		getattr( tts, 'response', None ) )

	return audio_bytes

render_audio_text_result

render_audio_text_result(
    title: str, result_text: str | None
) -> None

Render audio text result.

Purpose

Renders render audio text result in the Streamlit interface while preserving the active session state and provider workflow context.

Parameters:

Name	Type	Description	Default
title	str	Title value used by the application workflow.	required
result_text	str | None	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required

Source code in app.py

def render_audio_text_result( title: str, result_text: str | None ) -> None:
	"""Render audio text result.

	Purpose:
	    Renders render audio text result in the Streamlit interface while preserving the active
	    session state and provider workflow context.

	Args:
	    title: Title value used by the application workflow.
	    result_text: Text value supplied to the prompt, conversion, retrieval, or provider
	        workflow.
	"""
	if isinstance( result_text, str ) and result_text.strip( ):
		st.text_area( title, value=result_text.strip( ), height=300, width='stretch' )
	else:
		st.warning( 'No text output was returned.' )

render_audio_bytes

render_audio_bytes(
    audio_bytes: bytes | None,
    response_format: str | None = None,
) -> None

Render audio bytes.

Purpose

Renders render audio bytes in the Streamlit interface while preserving the active session state and provider workflow context.

Parameters:

Name	Type	Description	Default
audio_bytes	bytes | None	Audio Bytes value used by the application workflow.	required
response_format	str | None	Response Format value used by the application workflow.	None

Source code in app.py

def render_audio_bytes( audio_bytes: bytes | None, response_format: str | None = None ) -> None:
	"""Render audio bytes.

	Purpose:
	    Renders render audio bytes in the Streamlit interface while preserving the active
	    session state and provider workflow context.

	Args:
	    audio_bytes: Audio Bytes value used by the application workflow.
	    response_format: Response Format value used by the application workflow.
	"""
	if not audio_bytes:
		st.warning( 'No audio output was returned.' )
		return

	format_value = response_format or 'mp3'
	if format_value.startswith( 'audio/' ):
		mime = format_value
	else:
		mime = f'audio/{format_value}'

	st.audio( audio_bytes, format=mime )

get_embedding_model_options

get_embedding_model_options(embedding: Any) -> List[str]

Get embedding model options.

Purpose

Retrieves get embedding model options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Parameters:

Name	Type	Description	Default
embedding	Any	Embedding value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_embedding_model_options( embedding: Any ) -> List[ str ]:
	"""Get embedding model options.

	Purpose:
	    Retrieves get embedding model options for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.

	Args:
	    embedding: Embedding value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	options = getattr( embedding, 'model_options', None )
	if isinstance( options, list ) and len( options ) > 0:
		return [ '' ] + options

	provider_name = get_provider_name( )

	if provider_name == 'GPT':
		return [
				'',
				'text-embedding-3-small',
				'text-embedding-3-large',
				'text-embedding-ada-002',
		]

	if provider_name == 'Gemini':
		return [
				'',
				'gemini-embedding-001',
				'text-embedding-004',
		]

	return [ '' ]

get_embedding_encoding_options

get_embedding_encoding_options(embedding: Any) -> List[str]

Get embedding encoding options.

Purpose

Retrieves get embedding encoding options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Parameters:

Name	Type	Description	Default
embedding	Any	Embedding value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_embedding_encoding_options( embedding: Any ) -> List[ str ]:
	"""Get embedding encoding options.

	Purpose:
	    Retrieves get embedding encoding options for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.

	Args:
	    embedding: Embedding value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	options = getattr( embedding, 'encoding_options', None )
	if isinstance( options, list ) and len( options ) > 0:
		return options

	provider_name = get_provider_name( )

	if provider_name == 'GPT':
		return [ 'float', 'base64' ]

	if provider_name == 'Gemini':
		return [ 'float' ]

	return [ 'float' ]

get_embedding_max_dimensions

get_embedding_max_dimensions(
    model: str | None, embedding: Any
) -> int

Get embedding max dimensions.

Purpose

Retrieves get embedding max dimensions for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Parameters:

Name	Type	Description	Default
model	str | None	Provider model identifier selected for the active workflow.	required
embedding	Any	Embedding value used by the application workflow.	required

Returns:

Name	Type	Description
int	int	Normalized result produced for the active application workflow.

Source code in app.py

def get_embedding_max_dimensions( model: str | None, embedding: Any ) -> int:
	"""Get embedding max dimensions.

	Purpose:
	    Retrieves get embedding max dimensions for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.

	Args:
	    model: Provider model identifier selected for the active workflow.
	    embedding: Embedding value used by the application workflow.

	Returns:
	    int: Normalized result produced for the active application workflow.
	"""
	if not isinstance( model, str ) or not model.strip( ):
		return 1536

	try:
		return int( embedding.get_max_dimensions( model.strip( ) ) )
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'get_embedding_max_dimensions'
		exception.method = 'get_embedding_max_dimensions( model, embedding ) -> int'
		Logger( ).write( exception )
		pass

	if model == 'text-embedding-3-large':
		return 3072

	if model == 'gemini-embedding-001':
		return 3072

	return 1536

embedding_model_supports_dimensions

embedding_model_supports_dimensions(
    model: str | None, embedding: Any
) -> bool

Embedding model supports dimensions.

Purpose

Evaluates whether embedding model supports dimensions is enabled or safe for the current provider, model, table, query, or workflow context.

Parameters:

Name	Type	Description	Default
model	str | None	Provider model identifier selected for the active workflow.	required
embedding	Any	Embedding value used by the application workflow.	required

Returns:

Name	Type	Description
bool	bool	True when the requested condition is satisfied; otherwise, False.

Source code in app.py

def embedding_model_supports_dimensions( model: str | None, embedding: Any ) -> bool:
	"""Embedding model supports dimensions.

	Purpose:
	    Evaluates whether embedding model supports dimensions is enabled or safe for the
	    current provider, model, table, query, or workflow context.

	Args:
	    model: Provider model identifier selected for the active workflow.
	    embedding: Embedding value used by the application workflow.

	Returns:
	    bool: True when the requested condition is satisfied; otherwise, False.
	"""
	if not isinstance( model, str ) or not model.strip( ):
		return False

	support = getattr( embedding, 'model_dimension_support', None )
	if isinstance( support, dict ):
		return bool( support.get( model.strip( ), False ) )

	return model.strip( ) in [ 'text-embedding-3-small', 'text-embedding-3-large',
	                           'gemini-embedding-001', ]

normalize_embedding_dimensions

normalize_embedding_dimensions(
    model: str | None,
    dimensions: int | None,
    embedding: Any,
) -> int | None

Normalize embedding dimensions.

Purpose

Transforms normalize embedding dimensions inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
model	str | None	Provider model identifier selected for the active workflow.	required
dimensions	int | None	Dimensions value used by the application workflow.	required
embedding	Any	Embedding value used by the application workflow.	required

Returns:

Type	Description
int | None	int | None: Normalized result produced for the active application workflow.

Source code in app.py

def normalize_embedding_dimensions( model: str | None, dimensions: int | None,
		embedding: Any ) -> int | None:
	"""Normalize embedding dimensions.

	Purpose:
	    Transforms normalize embedding dimensions inputs into a normalized representation used
	    by provider calls, document retrieval, data management, or UI rendering.

	Args:
	    model: Provider model identifier selected for the active workflow.
	    dimensions: Dimensions value used by the application workflow.
	    embedding: Embedding value used by the application workflow.

	Returns:
	    int | None: Normalized result produced for the active application workflow.
	"""
	if not isinstance( model, str ) or not model.strip( ):
		return None

	try:
		value = int( dimensions or 0 )
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'normalize_embedding_dimensions'
		exception.method = 'normalize_embedding_dimensions( model, dimensions, embedding ) -> int | None'
		Logger( ).write( exception )
		return None

	if value <= 0:
		return None

	if not embedding_model_supports_dimensions( model, embedding ):
		return None

	max_dimensions = get_embedding_max_dimensions( model, embedding )
	if value > max_dimensions:
		return max_dimensions

	return value

normalize_embedding_chunk_settings

normalize_embedding_chunk_settings(
    chunk_size: int | None, overlap_amount: int | None
) -> Tuple[int, int]

Normalize embedding chunk settings.

Purpose

Transforms normalize embedding chunk settings inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
chunk_size	int | None	Chunk Size value used by the application workflow.	required
overlap_amount	int | None	Overlap Amount value used by the application workflow.	required

Returns:

Type	Description
Tuple[int, int]	Tuple[int, int]: Normalized result produced for the active application workflow.

Source code in app.py

def normalize_embedding_chunk_settings( chunk_size: int | None, overlap_amount: int | None ) -> \
Tuple[ int, int ]:
	"""Normalize embedding chunk settings.

	Purpose:
	    Transforms normalize embedding chunk settings inputs into a normalized representation
	    used by provider calls, document retrieval, data management, or UI rendering.

	Args:
	    chunk_size: Chunk Size value used by the application workflow.
	    overlap_amount: Overlap Amount value used by the application workflow.

	Returns:
	    Tuple[int, int]: Normalized result produced for the active application workflow.
	"""
	try:
		chunk_value = int( chunk_size or 800 )
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'normalize_embedding_chunk_settings'
		exception.method = 'normalize_embedding_chunk_settings( chunk_size, overlap_amount ) -> Tuple[int, int]'
		Logger( ).write( exception )
		chunk_value = 800

	try:
		overlap_value = int( overlap_amount or 0 )
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'normalize_embedding_chunk_settings'
		exception.method = 'normalize_embedding_chunk_settings( chunk_size, overlap_amount ) -> Tuple[int, int]'
		Logger( ).write( exception )
		overlap_value = 0

	if chunk_value <= 0:
		chunk_value = 800

	if chunk_value > 8192:
		chunk_value = 8192

	if overlap_value < 0:
		overlap_value = 0

	if overlap_value >= chunk_value:
		overlap_value = max( 0, chunk_value // 5 )

	return chunk_value, overlap_value

chunk_text_for_embeddings

chunk_text_for_embeddings(
    text: str,
    chunk_size: int = 800,
    overlap_amount: int = 0,
    encoding_name: str = "cl100k_base",
) -> List[str]

Chunk text for embeddings.

Purpose

Supports the chunk text for embeddings application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
text	str	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required
chunk_size	int	Chunk Size value used by the application workflow.	800
overlap_amount	int	Overlap Amount value used by the application workflow.	0
encoding_name	str	Encoding Name value used by the application workflow.	'cl100k_base'

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def chunk_text_for_embeddings( text: str, chunk_size: int = 800,
		overlap_amount: int = 0, encoding_name: str = 'cl100k_base' ) -> List[ str ]:
	"""Chunk text for embeddings.

	Purpose:
	    Supports the chunk text for embeddings application workflow by coordinating validated
	    inputs, Streamlit session state, provider configuration, and local data processing.

	Args:
	    text: Text value supplied to the prompt, conversion, retrieval, or provider workflow.
	    chunk_size: Chunk Size value used by the application workflow.
	    overlap_amount: Overlap Amount value used by the application workflow.
	    encoding_name: Encoding Name value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	if not isinstance( text, str ) or not text.strip( ):
		return [ ]

	chunk_value, overlap_value = normalize_embedding_chunk_settings(
		chunk_size=chunk_size,
		overlap_amount=overlap_amount )

	encoding = tiktoken.get_encoding( encoding_name )
	tokens = encoding.encode( text )

	if len( tokens ) == 0:
		return [ ]

	chunks: List[ str ] = [ ]
	start = 0
	step = max( 1, chunk_value - overlap_value )

	while start < len( tokens ):
		end = min( start + chunk_value, len( tokens ) )
		chunk_tokens = tokens[ start:end ]
		chunk_text_value = encoding.decode( chunk_tokens ).strip( )

		if chunk_text_value:
			chunks.append( chunk_text_value )

		if end >= len( tokens ):
			break

		start += step

	return chunks

normalize_embedding_vectors

normalize_embedding_vectors(vectors: Any) -> List[Any]

Normalize embedding vectors.

Purpose

Transforms normalize embedding vectors inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
vectors	Any	Vectors value used by the application workflow.	required

Returns:

Type	Description
List[Any]	List[Any]: List of normalized values used by the application workflow.

Source code in app.py

def normalize_embedding_vectors( vectors: Any ) -> List[ Any ]:
	"""Normalize embedding vectors.

	Purpose:
	    Transforms normalize embedding vectors inputs into a normalized representation used by
	    provider calls, document retrieval, data management, or UI rendering.

	Args:
	    vectors: Vectors value used by the application workflow.

	Returns:
	    List[Any]: List of normalized values used by the application workflow.
	"""
	if vectors is None:
		return [ ]

	if isinstance( vectors, str ):
		return [ vectors ]

	if isinstance( vectors, list ):
		if len( vectors ) == 0:
			return [ ]

		if all( isinstance( value, (int, float) ) for value in vectors ):
			return [ vectors ]

		return vectors

	return [ vectors ]

build_embeddings_dataframe

build_embeddings_dataframe(
    chunks: List[str],
    vectors: Any,
    encoding_format: str = "float",
) -> pd.DataFrame

Build embeddings dataframe.

Purpose

Builds build embeddings dataframe from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
chunks	List[str]	Chunks value used by the application workflow.	required
vectors	Any	Vectors value used by the application workflow.	required
encoding_format	str	Encoding Format value used by the application workflow.	'float'

Returns:

Type	Description
DataFrame	pd.DataFrame: DataFrame produced for application display, profiling, retrieval, or export.

Source code in app.py

def build_embeddings_dataframe( chunks: List[ str ], vectors: Any,
		encoding_format: str = 'float' ) -> pd.DataFrame:
	"""Build embeddings dataframe.

	Purpose:
	    Builds build embeddings dataframe from validated runtime inputs and prepares the
	    resulting object, payload, table, or display structure for later application
	    processing.

	Args:
	    chunks: Chunks value used by the application workflow.
	    vectors: Vectors value used by the application workflow.
	    encoding_format: Encoding Format value used by the application workflow.

	Returns:
	    pd.DataFrame: DataFrame produced for application display, profiling, retrieval, or
	        export.
	"""
	outputs = normalize_embedding_vectors( vectors )
	if len( outputs ) == 0:
		return pd.DataFrame( )

	rows: List[ Dict[ str, Any ] ] = [ ]
	format_value = encoding_format if isinstance( encoding_format, str ) else 'float'
	if format_value == 'base64':
		for index, item in enumerate( outputs ):
			chunk = chunks[ index ] if index < len( chunks ) else ''
			rows.append( {
					'ChunkIndex': index + 1,
					'Chunk': chunk,
					'EmbeddingBase64': item if isinstance( item, str ) else str( item ),
			} )

		return pd.DataFrame( rows )

	for index, vector in enumerate( outputs ):
		chunk = chunks[ index ] if index < len( chunks ) else ''

		if not isinstance( vector, list ):
			rows.append( {
					'ChunkIndex': index + 1,
					'Chunk': chunk,
					'Embedding': str( vector ),
			} )
			continue

		row: Dict[ str, Any ] = {
				'ChunkIndex': index + 1,
				'Chunk': chunk,
		}

		for dim_index, value in enumerate( vector ):
			row[ f'dim_{dim_index}' ] = value

		rows.append( row )

	return pd.DataFrame( rows )

get_embedding_vector_dimension

get_embedding_vector_dimension(vectors: Any) -> int

Get embedding vector dimension.

Purpose

Retrieves get embedding vector dimension for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Parameters:

Name	Type	Description	Default
vectors	Any	Vectors value used by the application workflow.	required

Returns:

Name	Type	Description
int	int	Normalized result produced for the active application workflow.

Source code in app.py

def get_embedding_vector_dimension( vectors: Any ) -> int:
	"""Get embedding vector dimension.

	Purpose:
	    Retrieves get embedding vector dimension for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.

	Args:
	    vectors: Vectors value used by the application workflow.

	Returns:
	    int: Normalized result produced for the active application workflow.
	"""
	outputs = normalize_embedding_vectors( vectors )
	if len( outputs ) == 0:
		return 0

	first = outputs[ 0 ]
	if isinstance( first, list ):
		return len( first )

	return 0

extract_embedding_usage

extract_embedding_usage(response: Any) -> Dict[str, Any]

Extract embedding usage.

Purpose

Transforms extract embedding usage inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
response	Any	Response value used by the application workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Source code in app.py

def extract_embedding_usage( response: Any ) -> Dict[ str, Any ]:
	"""Extract embedding usage.

	Purpose:
	    Transforms extract embedding usage inputs into a normalized representation used by
	    provider calls, document retrieval, data management, or UI rendering.

	Args:
	    response: Response value used by the application workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.
	"""
	if response is None:
		return { }

	try:
		raw = getattr( response, 'usage', None )
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'extract_embedding_usage'
		exception.method = 'extract_embedding_usage( response ) -> Dict[str, Any]'
		Logger( ).write( exception )
		raw = None

	if raw is None:
		try:
			raw = getattr( response, 'usage_metadata', None )
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'extract_embedding_usage'
			exception.method = 'extract_embedding_usage( response ) -> Dict[str, Any]'
			Logger( ).write( exception )
			raw = None

	if raw is None:
		return { }

	if isinstance( raw, dict ):
		return raw

	if hasattr( raw, 'model_dump' ):
		try:
			return raw.model_dump( )
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'extract_embedding_usage'
			exception.method = 'extract_embedding_usage( response ) -> Dict[str, Any]'
			Logger( ).write( exception )
			return { 'raw': str( raw ) }

	return { 'raw': str( raw ) }

build_embedding_metrics

build_embedding_metrics(
    source_text: str,
    normalized_text: str,
    chunks: List[str],
    vectors: Any,
    usage: Dict[str, Any] | None = None,
) -> Dict[str, Any]

Build embedding metrics.

Purpose

Builds build embedding metrics from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
source_text	str	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required
normalized_text	str	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required
chunks	List[str]	Chunks value used by the application workflow.	required
vectors	Any	Vectors value used by the application workflow.	required
usage	Dict[str, Any] | None	Usage value used by the application workflow.	None

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Source code in app.py

def build_embedding_metrics( source_text: str, normalized_text: str, chunks: List[ str ],
		vectors: Any, usage: Dict[ str, Any ] | None = None ) -> Dict[ str, Any ]:
	"""Build embedding metrics.

	Purpose:
	    Builds build embedding metrics from validated runtime inputs and prepares the resulting
	    object, payload, table, or display structure for later application processing.

	Args:
	    source_text: Text value supplied to the prompt, conversion, retrieval, or provider
	        workflow.
	    normalized_text: Text value supplied to the prompt, conversion, retrieval, or provider
	        workflow.
	    chunks: Chunks value used by the application workflow.
	    vectors: Vectors value used by the application workflow.
	    usage: Usage value used by the application workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.
	"""
	source_value = source_text if isinstance( source_text, str ) else ''
	normalized_value = normalized_text if isinstance( normalized_text, str ) else ''
	outputs = normalize_embedding_vectors( vectors )
	words = normalized_value.split( )
	unique_words = set( words )
	token_total = count_tokens( normalized_value ) if normalized_value else 0
	vector_dimension = get_embedding_vector_dimension( outputs )

	return {
			'characters': len( source_value ),
			'normalized_characters': len( normalized_value ),
			'words': len( words ),
			'unique_words': len( unique_words ),
			'type_token_ratio': round( len( unique_words ) / len( words ), 4 )
			if len( words ) else 0.0,
			'tokens': token_total,
			'chunks': len( chunks ),
			'embeddings': len( outputs ),
			'vector_dimension': vector_dimension,
			'encoding_format': st.session_state.get( 'embeddings_encoding_format', 'float' ),
			'usage': usage if isinstance( usage, dict ) else { },
	}

render_embedding_metrics

render_embedding_metrics(
    metrics: Dict[str, Any] | None,
) -> None

Render embedding metrics.

Purpose

Renders render embedding metrics in the Streamlit interface while preserving the active session state and provider workflow context.

Parameters:

Name	Type	Description	Default
metrics	Dict[str, Any] | None	Metrics value used by the application workflow.	required

Source code in app.py

def render_embedding_metrics( metrics: Dict[ str, Any ] | None ) -> None:
	"""Render embedding metrics.

	Purpose:
	    Renders render embedding metrics in the Streamlit interface while preserving the active
	    session state and provider workflow context.

	Args:
	    metrics: Metrics value used by the application workflow.
	"""
	if not isinstance( metrics, dict ) or len( metrics ) == 0:
		return

	metric_c1, metric_c2, metric_c3, metric_c4, metric_c5 = st.columns(
		[ 0.20, 0.20, 0.20, 0.20, 0.20 ], border=True, gap='xxsmall' )

	with metric_c1:
		st.metric( 'Tokens', metrics.get( 'tokens', 0 ) )

	with metric_c2:
		st.metric( 'Chunks', metrics.get( 'chunks', 0 ) )

	with metric_c3:
		st.metric( 'Embeddings', metrics.get( 'embeddings', 0 ) )

	with metric_c4:
		st.metric( 'Dimensions', metrics.get( 'vector_dimension', 0 ) )

	with metric_c5:
		st.metric( 'Words', metrics.get( 'words', 0 ) )

render_embeddings_dataframe

render_embeddings_dataframe(
    df_embeddings: DataFrame,
) -> None

Render embeddings dataframe.

Purpose

Renders render embeddings dataframe in the Streamlit interface while preserving the active session state and provider workflow context.

Parameters:

Name	Type	Description	Default
df_embeddings	DataFrame	DataFrame supplied to the application data-management or display workflow.	required

Source code in app.py

def render_embeddings_dataframe( df_embeddings: pd.DataFrame ) -> None:
	"""Render embeddings dataframe.

	Purpose:
	    Renders render embeddings dataframe in the Streamlit interface while preserving the
	    active session state and provider workflow context.

	Args:
	    df_embeddings: DataFrame supplied to the application data-management or display
	        workflow.
	"""
	if df_embeddings is None or df_embeddings.empty:
		st.info( 'No embeddings available.' )
		return

	st.data_editor( df_embeddings, use_container_width=True, hide_index=True )

reset_embeddings_controls

reset_embeddings_controls() -> None

Reset embeddings controls.

Purpose

Maintains application runtime state for reset embeddings controls by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_embeddings_controls( ) -> None:
	"""Reset embeddings controls.

	Purpose:
	    Maintains application runtime state for reset embeddings controls by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	for key in [ 'embedding_model', 'embeddings_dimensions',
	             'embeddings_chunk_size', 'embeddings_overlap_amount',
	             'embeddings_encoding_format', 'embeddings_user' ]:
		if key in st.session_state:
			del st.session_state[ key ]

clear_embeddings_output

clear_embeddings_output() -> None

Clear embeddings output.

Purpose

Maintains application runtime state for clear embeddings output by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_embeddings_output( ) -> None:
	"""Clear embeddings output.

	Purpose:
	    Maintains application runtime state for clear embeddings output by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'embeddings' ] = [ ]
	st.session_state[ 'embeddings_chunks' ] = [ ]
	st.session_state[ 'embeddings_df' ] = pd.DataFrame( )
	st.session_state[ 'embedding_metrics' ] = { }
	st.session_state[ 'embedding_usage' ] = { }

reset_embeddings_all

reset_embeddings_all() -> None

Reset embeddings all.

Purpose

Maintains application runtime state for reset embeddings all by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_embeddings_all( ) -> None:
	"""Reset embeddings all.

	Purpose:
	    Maintains application runtime state for reset embeddings all by initializing, clearing,
	    or restoring the session values used by the active Streamlit workflow.
	"""
	reset_embeddings_controls( )
	clear_embeddings_output( )

	if 'embeddings_input_text' in st.session_state:
		del st.session_state[ 'embeddings_input_text' ]

create_provider_embeddings

create_provider_embeddings(
    embedding: Any,
    chunks: List[str],
    model: str,
    encoding_format: str,
    dimensions: int | None,
    user_value: str | None,
) -> Any

Create provider embeddings.

Purpose

Builds create provider embeddings from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
embedding	Any	Embedding value used by the application workflow.	required
chunks	List[str]	Chunks value used by the application workflow.	required
model	str	Provider model identifier selected for the active workflow.	required
encoding_format	str	Encoding Format value used by the application workflow.	required
dimensions	int | None	Dimensions value used by the application workflow.	required
user_value	str | None	User Value value used by the application workflow.	required

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def create_provider_embeddings( embedding: Any, chunks: List[ str ], model: str,
		encoding_format: str, dimensions: int | None, user_value: str | None ) -> Any:
	"""Create provider embeddings.

	Purpose:
	    Builds create provider embeddings from validated runtime inputs and prepares the
	    resulting object, payload, table, or display structure for later application
	    processing.

	Args:
	    embedding: Embedding value used by the application workflow.
	    chunks: Chunks value used by the application workflow.
	    model: Provider model identifier selected for the active workflow.
	    encoding_format: Encoding Format value used by the application workflow.
	    dimensions: Dimensions value used by the application workflow.
	    user_value: User Value value used by the application workflow.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	provider_name = get_provider_name( )

	if provider_name == 'Gemini':
		return embedding.create(
			text=chunks,
			model=model,
			dimensions=dimensions,
			encoding_format=encoding_format,
			task_type='RETRIEVAL_DOCUMENT' )

	if provider_name == 'GPT':
		return embedding.create(
			text=chunks,
			model=model,
			format=encoding_format,
			dimensions=dimensions,
			user=user_value )

	return embedding.create(
		text=chunks,
		model=model,
		dimensions=dimensions )

clear_docqna_messages

clear_docqna_messages() -> None

Clear docqna messages.

Purpose

Maintains application runtime state for clear docqna messages by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_docqna_messages( ) -> None:
	"""Clear docqna messages.

	Purpose:
	    Maintains application runtime state for clear docqna messages by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'docqna_messages' ] = [ ]

clear_docqna_outputs

clear_docqna_outputs() -> None

Clear docqna outputs.

Purpose

Maintains application runtime state for clear docqna outputs by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_docqna_outputs( ) -> None:
	"""Clear docqna outputs.

	Purpose:
	    Maintains application runtime state for clear docqna outputs by initializing, clearing,
	    or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'docqna_last_answer' ] = ''
	st.session_state[ 'docqna_last_hits' ] = [ ]
	st.session_state[ 'docqna_last_sources' ] = [ ]
	st.session_state[ 'docqna_context' ] = ''
	st.session_state[ 'last_answer' ] = ''
	st.session_state[ 'last_sources' ] = [ ]

unload_docqna_documents

unload_docqna_documents() -> None

Unload docqna documents.

Purpose

Supports the unload docqna documents application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Source code in app.py

def unload_docqna_documents( ) -> None:
	"""Unload docqna documents.

	Purpose:
	    Supports the unload docqna documents application workflow by coordinating validated
	    inputs, Streamlit session state, provider configuration, and local data processing.
	"""
	st.session_state[ 'docqna_uploaded' ] = None
	st.session_state[ 'docqna_files' ] = [ ]
	st.session_state[ 'docqna_active_docs' ] = [ ]
	st.session_state[ 'active_docs' ] = [ ]
	st.session_state[ 'docqna_bytes' ] = None
	st.session_state[ 'doc_bytes' ] = { }
	st.session_state[ 'docqna_texts' ] = { }
	st.session_state[ 'docqna_chunks' ] = [ ]
	st.session_state[ 'docqna_vec_ready' ] = False
	st.session_state[ 'docqna_fingerprint' ] = ''
	st.session_state[ 'docqna_chunk_count' ] = 0
	st.session_state[ 'docqna_index_status' ] = 'Not indexed'
	clear_docqna_outputs( )

reset_docqna_controls

reset_docqna_controls() -> None

Reset docqna controls.

Purpose

Maintains application runtime state for reset docqna controls by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_docqna_controls( ) -> None:
	"""Reset docqna controls.

	Purpose:
	    Maintains application runtime state for reset docqna controls by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	for key in [ 'docqna_model', 'docqna_source', 'docqna_file_id',
	             'docqna_vector_store_id', 'docqna_multi_mode', 'docqna_top_k',
	             'docqna_chunk_size', 'docqna_chunk_overlap',
	             'docqna_show_diagnostics', 'docqna_temperature',
	             'docqna_top_percent', 'docqna_max_tokens',
	             'docqna_response_format', 'docqna_tool_choice',
	             'docqna_reasoning', 'docqna_file_search_store_names_input',
	             'docqna_file_search_store_names' ]:
		if key in st.session_state:
			del st.session_state[ key ]

reset_docqna_all

reset_docqna_all() -> None

Reset docqna all.

Purpose

Maintains application runtime state for reset docqna all by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_docqna_all( ) -> None:
	"""Reset docqna all.

	Purpose:
	    Maintains application runtime state for reset docqna all by initializing, clearing, or
	    restoring the session values used by the active Streamlit workflow.
	"""
	reset_docqna_controls( )
	unload_docqna_documents( )
	clear_docqna_messages( )

clear_docqna_instructions

clear_docqna_instructions() -> None

Clear docqna instructions.

Purpose

Maintains application runtime state for clear docqna instructions by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_docqna_instructions( ) -> None:
	"""Clear docqna instructions.

	Purpose:
	    Maintains application runtime state for clear docqna instructions by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'docqna_system_instructions' ] = ''
	st.session_state[ 'instructions' ] = ''

load_docqna_instruction

load_docqna_instruction() -> None

Load docqna instruction.

Purpose

Retrieves load docqna instruction for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Source code in app.py

def load_docqna_instruction( ) -> None:
	"""Load docqna instruction.

	Purpose:
	    Retrieves load docqna instruction for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.
	"""
	name = st.session_state.get( 'instructions' )
	if name and name != 'No Templates Found':
		prompt_text = fetch_prompt_text( cfg.DB_PATH, name )
		if prompt_text is not None:
			st.session_state[ 'docqna_system_instructions' ] = prompt_text

convert_docqna_instructions

convert_docqna_instructions() -> None

Convert docqna instructions.

Purpose

Transforms convert docqna instructions inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Source code in app.py

def convert_docqna_instructions( ) -> None:
	"""Convert docqna instructions.

	Purpose:
	    Transforms convert docqna instructions inputs into a normalized representation used by
	    provider calls, document retrieval, data management, or UI rendering.
	"""
	text_value = st.session_state.get( 'docqna_system_instructions', '' )
	if not isinstance( text_value, str ) or not text_value.strip( ):
		return

	source = text_value.strip( )
	if cfg.XML_BLOCK_PATTERN.search( source ):
		converted = convert_xml( source )
	else:
		converted = convert_markdown( source )

	st.session_state[ 'docqna_system_instructions' ] = converted

get_docqna_sources

get_docqna_sources() -> List[str]

Get docqna sources.

Purpose

Retrieves get docqna sources for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_docqna_sources( ) -> List[ str ]:
	"""Get docqna sources.

	Purpose:
	    Retrieves get docqna sources for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	provider_name = get_provider_name( )

	options = [ 'Local Upload' ]

	if provider_name == 'GPT':
		options.extend( [ 'OpenAI File ID', 'OpenAI Vector Store ID' ] )

	elif provider_name == 'Gemini':
		options.extend( [ 'Gemini File Search Store' ] )

	elif provider_name == 'Grok':
		options.extend( [ 'xAI Collection' ] )

	return options

get_docqna_extension

get_docqna_extension(filename: str | None) -> str

Get docqna extension.

Purpose

Retrieves get docqna extension for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
filename	str | None	File, upload, or path value used by the document or storage workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_docqna_extension( filename: str | None ) -> str:
	"""Get docqna extension.

	Purpose:
	    Retrieves get docqna extension for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    filename: File, upload, or path value used by the document or storage workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not isinstance( filename, str ) or not filename.strip( ):
		return ''

	return Path( filename ).suffix.lower( )

compute_fingerprint

compute_fingerprint(documents: List[Dict[str, Any]]) -> str

Compute fingerprint.

Purpose

Transforms compute fingerprint inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
documents	List[Dict[str, Any]]	Documents value used by the application workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def compute_fingerprint( documents: List[ Dict[ str, Any ] ] ) -> str:
	"""Compute fingerprint.

	Purpose:
	    Transforms compute fingerprint inputs into a normalized representation used by provider
	    calls, document retrieval, data management, or UI rendering.

	Args:
	    documents: Documents value used by the application workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	hasher = hashlib.sha256( )

	if not isinstance( documents, list ):
		return ''

	for doc in documents:
		if not isinstance( doc, dict ):
			continue

		name = str( doc.get( 'name', '' ) )
		content = doc.get( 'bytes', b'' )
		hasher.update( name.encode( 'utf-8', errors='ignore' ) )

		if isinstance( content, bytes ):
			hasher.update( content )

	return hasher.hexdigest( )

extract_pdf_text

extract_pdf_text(file_bytes: bytes) -> str

Extract pdf text.

Purpose

Transforms extract pdf text inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
file_bytes	bytes	File, upload, or path value used by the document or storage workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def extract_pdf_text( file_bytes: bytes ) -> str:
	"""Extract pdf text.

	Purpose:
	    Transforms extract pdf text inputs into a normalized representation used by provider
	    calls, document retrieval, data management, or UI rendering.

	Args:
	    file_bytes: File, upload, or path value used by the document or storage workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not isinstance( file_bytes, bytes ) or len( file_bytes ) == 0:
		return ''

	try:
		import fitz

		pages: List[ str ] = [ ]
		with fitz.open( stream=file_bytes, filetype='pdf' ) as doc:
			for page in doc:
				pages.append( page.get_text( 'text' ) or '' )

		return '\n\n'.join( pages ).strip( )
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'extract_pdf_text'
		exception.method = 'extract_pdf_text( file_bytes ) -> str'
		Logger( ).write( exception )
		return extract_text_bytes( file_bytes )

extract_text_bytes

extract_text_bytes(file_bytes: bytes) -> str

Extract text bytes.

Purpose

Transforms extract text bytes inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
file_bytes	bytes	File, upload, or path value used by the document or storage workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def extract_text_bytes( file_bytes: bytes ) -> str:
	"""Extract text bytes.

	Purpose:
	    Transforms extract text bytes inputs into a normalized representation used by provider
	    calls, document retrieval, data management, or UI rendering.

	Args:
	    file_bytes: File, upload, or path value used by the document or storage workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not isinstance( file_bytes, bytes ) or len( file_bytes ) == 0:
		return ''

	for encoding in [ 'utf-8', 'utf-8-sig', 'cp1252', 'latin-1' ]:
		try:
			return file_bytes.decode( encoding ).strip( )
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'extract_text_bytes'
			exception.method = 'extract_text_bytes( file_bytes ) -> str'
			Logger( ).write( exception )
			continue

	return ''

extract_docx_text

extract_docx_text(file_bytes: bytes) -> str

Extract docx text.

Purpose

Transforms extract docx text inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
file_bytes	bytes	File, upload, or path value used by the document or storage workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def extract_docx_text( file_bytes: bytes ) -> str:
	"""Extract docx text.

	Purpose:
	    Transforms extract docx text inputs into a normalized representation used by provider
	    calls, document retrieval, data management, or UI rendering.

	Args:
	    file_bytes: File, upload, or path value used by the document or storage workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not isinstance( file_bytes, bytes ) or len( file_bytes ) == 0:
		return ''

	try:
		with zipfile.ZipFile( io.BytesIO( file_bytes ) ) as archive:
			xml_bytes = archive.read( 'word/document.xml' )

		root = ET.fromstring( xml_bytes )
		namespace = '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}'
		paragraphs: List[ str ] = [ ]

		for paragraph in root.iter( f'{namespace}p' ):
			parts: List[ str ] = [ ]

			for node in paragraph.iter( f'{namespace}t' ):
				if node.text:
					parts.append( node.text )

			text = ''.join( parts ).strip( )
			if text:
				paragraphs.append( text )

		return '\n\n'.join( paragraphs ).strip( )
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'extract_docx_text'
		exception.method = 'extract_docx_text( file_bytes ) -> str'
		Logger( ).write( exception )
		return ''

extract_docqna_text

extract_docqna_text(
    filename: str, file_bytes: bytes
) -> str

Extract docqna text.

Purpose

Transforms extract docqna text inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
filename	str	File, upload, or path value used by the document or storage workflow.	required
file_bytes	bytes	File, upload, or path value used by the document or storage workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def extract_docqna_text( filename: str, file_bytes: bytes ) -> str:
	"""Extract docqna text.

	Purpose:
	    Transforms extract docqna text inputs into a normalized representation used by provider
	    calls, document retrieval, data management, or UI rendering.

	Args:
	    filename: File, upload, or path value used by the document or storage workflow.
	    file_bytes: File, upload, or path value used by the document or storage workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	extension = get_docqna_extension( filename )

	if extension == '.pdf':
		return extract_pdf_text( file_bytes )

	if extension == '.docx':
		return extract_docx_text( file_bytes )

	if extension in [ '.txt', '.md', '.csv', '.json', '.xml', '.py', '.cs', '.sql',
	                  '.yaml', '.yml', '.html', '.css', '.js', '.ts' ]:
		return extract_text_bytes( file_bytes )

	return extract_text_bytes( file_bytes )

normalize_docqna_text

normalize_docqna_text(text: str) -> str

Normalize docqna text.

Purpose

Transforms normalize docqna text inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
text	str	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def normalize_docqna_text( text: str ) -> str:
	"""Normalize docqna text.

	Purpose:
	    Transforms normalize docqna text inputs into a normalized representation used by
	    provider calls, document retrieval, data management, or UI rendering.

	Args:
	    text: Text value supplied to the prompt, conversion, retrieval, or provider workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not isinstance( text, str ):
		return ''

	value = text.replace( '\x00', ' ' )
	value = re.sub( r'[ \t]+', ' ', value )
	value = re.sub( r'\n{3,}', '\n\n', value )
	return value.strip( )

chunk_docqna_text

chunk_docqna_text(
    text: str,
    chunk_size: int = 900,
    chunk_overlap: int = 150,
) -> List[str]

Chunk docqna text.

Purpose

Supports the chunk docqna text application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
text	str	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required
chunk_size	int	Chunk Size value used by the application workflow.	900
chunk_overlap	int	Chunk Overlap value used by the application workflow.	150

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def chunk_docqna_text( text: str, chunk_size: int = 900, chunk_overlap: int = 150 ) -> List[ str ]:
	"""Chunk docqna text.

	Purpose:
	    Supports the chunk docqna text application workflow by coordinating validated inputs,
	    Streamlit session state, provider configuration, and local data processing.

	Args:
	    text: Text value supplied to the prompt, conversion, retrieval, or provider workflow.
	    chunk_size: Chunk Size value used by the application workflow.
	    chunk_overlap: Chunk Overlap value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	if not isinstance( text, str ) or not text.strip( ):
		return [ ]

	try:
		size = int( chunk_size )
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'chunk_docqna_text'
		exception.method = 'chunk_docqna_text( text, chunk_size, chunk_overlap ) -> List[str]'
		Logger( ).write( exception )
		size = 900

	try:
		overlap = int( chunk_overlap )
	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'chunk_docqna_text'
		exception.method = 'chunk_docqna_text( text, chunk_size, chunk_overlap ) -> List[str]'
		Logger( ).write( exception )
		overlap = 150

	if size <= 0:
		size = 900

	if overlap < 0:
		overlap = 0

	if overlap >= size:
		overlap = max( 0, size // 5 )

	words = text.split( )
	if len( words ) == 0:
		return [ ]

	chunks: List[ str ] = [ ]
	step = max( 1, size - overlap )
	start = 0

	while start < len( words ):
		end = min( start + size, len( words ) )
		chunk = ' '.join( words[ start:end ] ).strip( )

		if chunk:
			chunks.append( chunk )

		if end >= len( words ):
			break

		start += step

	return chunks

load_docqna_file

load_docqna_file(uploaded: Any) -> List[Dict[str, Any]]

Load docqna file.

Purpose

Retrieves load docqna file for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
uploaded	Any	File, upload, or path value used by the document or storage workflow.	required

Returns:

Type	Description
List[Dict[str, Any]]	List[Dict[str, Any]]: List of normalized values used by the application workflow.

Source code in app.py

def load_docqna_file( uploaded: Any ) -> List[ Dict[ str, Any ] ]:
	"""Load docqna file.

	Purpose:
	    Retrieves load docqna file for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    uploaded: File, upload, or path value used by the document or storage workflow.

	Returns:
	    List[Dict[str, Any]]: List of normalized values used by the application workflow.
	"""
	if uploaded is None:
		return [ ]

	files = uploaded if isinstance( uploaded, list ) else [ uploaded ]
	active_docs: List[ Dict[ str, Any ] ] = [ ]
	texts: Dict[ str, str ] = { }
	doc_bytes: Dict[ str, bytes ] = { }

	for item in files:
		if item is None:
			continue

		name = getattr( item, 'name', 'uploaded_document' )

		try:
			content = item.getvalue( ) if hasattr( item, 'getvalue' ) else item.read( )
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'load_docqna_file'
			exception.method = 'load_docqna_file( uploaded ) -> List[Dict[str, Any]]'
			Logger( ).write( exception )
			content = None

		if not isinstance( content, bytes ) or len( content ) == 0:
			continue

		text = extract_docqna_text( filename=name, file_bytes=content )

		active_docs.append(
			{
					'name': name,
					'extension': get_docqna_extension( name ),
					'bytes': content,
					'text': text,
					'size': len( content ),
			} )

		texts[ name ] = text
		doc_bytes[ name ] = content

	st.session_state[ 'docqna_uploaded' ] = [ doc.get( 'name', '' ) for doc in active_docs ]
	st.session_state[ 'docqna_files' ] = active_docs
	st.session_state[ 'docqna_active_docs' ] = active_docs
	st.session_state[ 'active_docs' ] = [ doc.get( 'name', '' ) for doc in active_docs ]
	st.session_state[ 'docqna_texts' ] = texts
	st.session_state[ 'doc_bytes' ] = doc_bytes

	if len( active_docs ) > 0:
		st.session_state[ 'docqna_bytes' ] = active_docs[ 0 ].get( 'bytes' )
	else:
		st.session_state[ 'docqna_bytes' ] = None

	fingerprint = compute_fingerprint( active_docs )
	if fingerprint != st.session_state.get( 'docqna_fingerprint', '' ):
		st.session_state[ 'docqna_vec_ready' ] = False
		st.session_state[ 'docqna_fingerprint' ] = fingerprint
		st.session_state[ 'docqna_index_status' ] = 'Loaded; not indexed'

	return active_docs

get_document_names

get_document_names() -> List[str]

Get document names.

Purpose

Retrieves get document names for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_document_names( ) -> List[ str ]:
	"""Get document names.

	Purpose:
	    Retrieves get document names for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	docs = st.session_state.get( 'docqna_active_docs', [ ] )

	if not isinstance( docs, list ):
		return [ ]

	return [ doc.get( 'name', '' ) for doc in docs if
	         isinstance( doc, dict ) and doc.get( 'name' ) ]

render_document_preview

render_document_preview() -> None

Render document preview.

Purpose

Renders render document preview in the Streamlit interface while preserving the active session state and provider workflow context.

Source code in app.py

def render_document_preview( ) -> None:
	"""Render document preview.

	Purpose:
	    Renders render document preview in the Streamlit interface while preserving the active
	    session state and provider workflow context.
	"""
	docs = st.session_state.get( 'docqna_active_docs', [ ] )

	if not isinstance( docs, list ) or len( docs ) == 0:
		st.info( 'No active document loaded.' )
		return

	for doc in docs:
		if not isinstance( doc, dict ):
			continue

		name = doc.get( 'name', 'Document' )
		extension = doc.get( 'extension', '' )
		content = doc.get( 'bytes', b'' )
		text = doc.get( 'text', '' )

		with st.expander( label=f'Preview: {name}', icon='📄',
				expanded=False, width='stretch' ):
			st.caption(
				f'File type: {extension or "unknown"} | Size: {doc.get( "size", 0 )} bytes' )

			if extension == '.pdf' and isinstance( content, bytes ):
				try:
					st.pdf( content, height=420 )
				except Exception as e:
					exception = Error( e )
					exception.module = 'app'
					exception.cause = 'render_document_preview'
					exception.method = 'render_document_preview( ) -> None'
					Logger( ).write( exception )
					st.text_area( label='Extracted Text Preview',
						value=text[ :12000 ] if isinstance( text, str ) else '',
						height=300, width='stretch', disabled=True )
			elif extension == '.md' and isinstance( text, str ):
				st.markdown( text[ :12000 ] )
			elif isinstance( text, str ) and text.strip( ):
				st.text_area( label='Extracted Text Preview',
					value=text[ :12000 ],
					height=300, width='stretch', disabled=True )
			else:
				st.warning( 'No readable text preview is available for this file.' )

rebuild_docqna_index

rebuild_docqna_index() -> List[Dict[str, Any]]

Rebuild docqna index.

Purpose

Supports the rebuild docqna index application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Returns:

Type	Description
List[Dict[str, Any]]	List[Dict[str, Any]]: List of normalized values used by the application workflow.

Source code in app.py

def rebuild_docqna_index( ) -> List[ Dict[ str, Any ] ]:
	"""Rebuild docqna index.

	Purpose:
	    Supports the rebuild docqna index application workflow by coordinating validated
	    inputs, Streamlit session state, provider configuration, and local data processing.

	Returns:
	    List[Dict[str, Any]]: List of normalized values used by the application workflow.
	"""
	docs = st.session_state.get( 'docqna_active_docs', [ ] )

	if not isinstance( docs, list ) or len( docs ) == 0:
		st.session_state[ 'docqna_chunks' ] = [ ]
		st.session_state[ 'docqna_vec_ready' ] = False
		st.session_state[ 'docqna_chunk_count' ] = 0
		st.session_state[ 'docqna_index_status' ] = 'No documents loaded'
		return [ ]

	chunk_records: List[ Dict[ str, Any ] ] = [ ]
	chunk_size = st.session_state.get( 'docqna_chunk_size', 900 )
	chunk_overlap = st.session_state.get( 'docqna_chunk_overlap', 150 )

	for doc in docs:
		if not isinstance( doc, dict ):
			continue

		name = doc.get( 'name', 'Document' )
		text = normalize_docqna_text( doc.get( 'text', '' ) )

		for index, chunk in enumerate( chunk_docqna_text(
				text=text, chunk_size=chunk_size, chunk_overlap=chunk_overlap ) ):
			chunk_records.append(
				{
						'document': name,
						'chunk_index': index + 1,
						'text': chunk,
						'tokens': count_tokens( chunk ),
				} )

	st.session_state[ 'docqna_chunks' ] = chunk_records
	st.session_state[ 'docqna_chunk_count' ] = len( chunk_records )
	st.session_state[ 'docqna_index_status' ] = (
			f'Indexed {len( chunk_records )} chunks'
			if len( chunk_records ) > 0 else 'No readable chunks'
	)
	st.session_state[ 'docqna_vec_ready' ] = len( chunk_records ) > 0

	return chunk_records

score_chunk

score_chunk(query: str, chunk: str) -> float

Score chunk.

Purpose

Transforms score chunk inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
query	str	Query string used by the database, retrieval, or provider workflow.	required
chunk	str	Chunk value used by the application workflow.	required

Returns:

Name	Type	Description
float	float	Normalized result produced for the active application workflow.

Source code in app.py

def score_chunk( query: str, chunk: str ) -> float:
	"""Score chunk.

	Purpose:
	    Transforms score chunk inputs into a normalized representation used by provider calls,
	    document retrieval, data management, or UI rendering.

	Args:
	    query: Query string used by the database, retrieval, or provider workflow.
	    chunk: Chunk value used by the application workflow.

	Returns:
	    float: Normalized result produced for the active application workflow.
	"""
	if not isinstance( query, str ) or not isinstance( chunk, str ):
		return 0.0

	query_terms = {
			term.lower( )
			for term in re.findall( r'\b\w+\b', query )
			if len( term ) > 2
	}

	if len( query_terms ) == 0:
		return 0.0

	chunk_terms = re.findall( r'\b\w+\b', chunk.lower( ) )
	if len( chunk_terms ) == 0:
		return 0.0

	chunk_set = set( chunk_terms )
	overlap = len( query_terms.intersection( chunk_set ) )
	density = overlap / max( 1, len( query_terms ) )

	return float( density )

retrieve_chunks

retrieve_chunks(
    query: str, top_k: int = 6
) -> List[Dict[str, Any]]

Retrieve chunks.

Purpose

Retrieves retrieve chunks for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
query	str	Query string used by the database, retrieval, or provider workflow.	required
top_k	int	Numeric control value that constrains the operation.	6

Returns:

Type	Description
List[Dict[str, Any]]	List[Dict[str, Any]]: List of normalized values used by the application workflow.

Source code in app.py

def retrieve_chunks( query: str, top_k: int = 6 ) -> List[ Dict[ str, Any ] ]:
	"""Retrieve chunks.

	Purpose:
	    Retrieves retrieve chunks for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    query: Query string used by the database, retrieval, or provider workflow.
	    top_k: Numeric control value that constrains the operation.

	Returns:
	    List[Dict[str, Any]]: List of normalized values used by the application workflow.
	"""
	chunks = st.session_state.get( 'docqna_chunks', [ ] )

	if not isinstance( chunks, list ) or len( chunks ) == 0:
		chunks = rebuild_docqna_index( )

	results: List[ Dict[ str, Any ] ] = [ ]

	for item in chunks:
		if not isinstance( item, dict ):
			continue

		text_value = item.get( 'text', '' )
		score = score_chunk( query, text_value )

		if score <= 0:
			continue

		result = dict( item )
		result[ 'score' ] = score
		results.append( result )

	results.sort( key=lambda row: row.get( 'score', 0.0 ), reverse=True )

	if len( results ) == 0 and isinstance( chunks, list ):
		results = [
				{
						**item,
						'score': 0.0,
				}
				for item in chunks[ : int( top_k ) ]
				if isinstance( item, dict )
		]

	return results[ : int( top_k ) ]

build_docqna_local_prompt

build_docqna_local_prompt(
    query: str, hits: List[Dict[str, Any]]
) -> str

Build docqna local prompt.

Purpose

Builds build docqna local prompt from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
query	str	Query string used by the database, retrieval, or provider workflow.	required
hits	List[Dict[str, Any]]	Hits value used by the application workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def build_docqna_local_prompt( query: str, hits: List[ Dict[ str, Any ] ] ) -> str:
	"""Build docqna local prompt.

	Purpose:
	    Builds build docqna local prompt from validated runtime inputs and prepares the
	    resulting object, payload, table, or display structure for later application
	    processing.

	Args:
	    query: Query string used by the database, retrieval, or provider workflow.
	    hits: Hits value used by the application workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	system = str( st.session_state.get( 'docqna_system_instructions', '' ) or '' ).strip( )
	context_blocks: List[ str ] = [ ]

	for hit in hits:
		if not isinstance( hit, dict ):
			continue

		doc_name = hit.get( 'document', 'Document' )
		chunk_index = hit.get( 'chunk_index', '' )
		text_value = hit.get( 'text', '' )

		if isinstance( text_value, str ) and text_value.strip( ):
			context_blocks.append(
				f'[Document: {doc_name} | Chunk: {chunk_index}]\n{text_value}'.strip( ) )

	context = '\n\n'.join( context_blocks ).strip( )
	prompt_parts: List[ str ] = [ ]

	if system:
		prompt_parts.append( system )

	if context:
		prompt_parts.append(
			'Use the following document excerpts to answer the question. If the excerpts do '
			'not contain the answer, say you do not have enough information.\n\n'
			f'{context}' )
	else:
		prompt_parts.append(
			'No relevant document excerpts were retrieved. If the document context does not '
			'contain the answer, say you do not have enough information.' )

	prompt_parts.append( f'Question:\n{query}\n\nAnswer:' )

	return '\n\n'.join( prompt_parts ).strip( )

run_docqna_query

run_docqna_query(query: str) -> str

Run docqna query.

Purpose

Executes the run docqna query workflow using the current provider, document, prompt, and session-state configuration.

Parameters:

Name	Type	Description	Default
query	str	Query string used by the database, retrieval, or provider workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def run_docqna_query( query: str ) -> str:
	"""Run docqna query.

	Purpose:
	    Executes the run docqna query workflow using the current provider, document, prompt,
	    and session-state configuration.

	Args:
	    query: Query string used by the database, retrieval, or provider workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	provider_name = get_provider_name( )
	chat = get_chat_module( )
	top_k = int( st.session_state.get( 'docqna_top_k', 6 ) or 6 )
	hits = retrieve_chunks( query=query, top_k=top_k )

	st.session_state[ 'docqna_last_hits' ] = hits
	st.session_state[ 'docqna_last_sources' ] = [ {
			'document': hit.get( 'document' ),
			'chunk_index': hit.get( 'chunk_index' ),
			'score': hit.get( 'score' ),
			'tokens': hit.get( 'tokens' ),
	}
			for hit in hits
			if isinstance( hit, dict ) ]

	prompt = build_docqna_local_prompt( query=query, hits=hits )

	if provider_name == 'Gemini':
		apply_gemini_runtime_config( )
		result = chat.generate_text(
			prompt=prompt,
			model=st.session_state.get( 'docqna_model' )
			      or st.session_state.get( 'text_model' )
			      or 'gemini-2.5-flash-lite',
			temperature=st.session_state.get( 'docqna_temperature' ),
			top_p=st.session_state.get( 'docqna_top_percent' ),
			top_k=st.session_state.get( 'docqna_top_k' ),
			max_tokens=st.session_state.get( 'docqna_max_tokens' ),
			instruct=st.session_state.get( 'docqna_system_instructions' ),
			stream=False )
		return str( result or '' ).strip( )

	if provider_name == 'GPT':
		result = chat.generate_text(
			prompt=prompt,
			model=st.session_state.get( 'docqna_model' )
			      or st.session_state.get( 'text_model' )
			      or 'gpt-5-nano',
			temperature=st.session_state.get( 'docqna_temperature' ),
			top_p=st.session_state.get( 'docqna_top_percent' ),
			frequency=st.session_state.get( 'docqna_frequency_penalty' ),
			presence=st.session_state.get( 'docqna_presence_penalty' ),
			max_tokens=st.session_state.get( 'docqna_max_tokens' ),
			store=st.session_state.get( 'docqna_store' ),
			stream=False,
			instruct=st.session_state.get( 'docqna_system_instructions' ) )
		return str( result or '' ).strip( )

	if provider_name == 'Grok':
		result = chat.generate_text(
			prompt=prompt,
			model=st.session_state.get( 'docqna_model' )
			      or st.session_state.get( 'text_model' )
			      or 'grok-4.3',
			temperature=st.session_state.get( 'docqna_temperature' ),
			top_p=st.session_state.get( 'docqna_top_percent' ),
			frequency=st.session_state.get( 'docqna_frequency_penalty' ),
			presence=st.session_state.get( 'docqna_presence_penalty' ),
			max_tokens=st.session_state.get( 'docqna_max_tokens' ) or 10000,
			store=bool( st.session_state.get( 'docqna_store', True ) ),
			stream=False,
			instruct=st.session_state.get( 'docqna_system_instructions' ),
			reasoning=st.session_state.get( 'docqna_reasoning' ) or 'high',
			response_format=st.session_state.get( 'docqna_response_format' ) or 'text' )

		response_obj = getattr( chat, 'response', None )
		st.session_state[ 'docqna_last_sources' ] = extract_sources( response_obj )
		st.session_state[ 'last_sources' ] = st.session_state[ 'docqna_last_sources' ]

		return str( result or '' ).strip( )

	return ''

run_remote_query

run_remote_query(query: str) -> str

Run remote query.

Purpose

Executes the run remote query workflow using the current provider, document, prompt, and session-state configuration.

Parameters:

Name	Type	Description	Default
query	str	Query string used by the database, retrieval, or provider workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def run_remote_query( query: str ) -> str:
	"""Run remote query.

	Purpose:
	    Executes the run remote query workflow using the current provider, document, prompt,
	    and session-state configuration.

	Args:
	    query: Query string used by the database, retrieval, or provider workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	provider_name = get_provider_name( )
	chat = get_chat_module( )
	source = st.session_state.get( 'docqna_source', 'Local Upload' )

	if provider_name == 'GPT':
		selected_vector_store_ids = get_store_ids( 'GPT' )
		manual_vector_store_ids = [ ]

		if source == 'OpenAI Vector Store ID':
			manual_vector_store_ids = split_text_values(
				st.session_state.get( 'docqna_vector_store_id', '' ),
				delimiter=',' )

		vector_store_ids = merge_unique_strings(
			primary=manual_vector_store_ids,
			secondary=selected_vector_store_ids )

		tools: List[ Dict[ str, Any ] ] = [ ]

		if len( vector_store_ids ) > 0:
			tools.append(
				{
						'type': 'file_search',
						'vector_store_ids': vector_store_ids,
				} )

		if source == 'OpenAI File ID' and not st.session_state.get( 'docqna_file_id' ):
			return 'Enter an OpenAI file ID before asking a file-based question.'

		if source == 'OpenAI Vector Store ID' and len( vector_store_ids ) == 0:
			return 'Enter or select an OpenAI vector store ID before asking a vector-store question.'

		result = chat.generate_text(
			prompt=query,
			model=st.session_state.get( 'docqna_model' ) or st.session_state.get(
				'text_model' ) or 'gpt-5-nano',
			temperature=st.session_state.get( 'docqna_temperature' ),
			top_p=st.session_state.get( 'docqna_top_percent' ),
			frequency=st.session_state.get( 'docqna_frequency_penalty' ),
			presence=st.session_state.get( 'docqna_presence_penalty' ),
			max_tokens=st.session_state.get( 'docqna_max_tokens' ),
			store=st.session_state.get( 'docqna_store' ),
			stream=False,
			instruct=st.session_state.get( 'docqna_system_instructions' ),
			tools=tools,
			include=[ 'file_search_call.results' ] if len( tools ) > 0 else [ ],
			vector_store_ids=vector_store_ids )

		response_obj = getattr( chat, 'response', None )
		st.session_state[ 'docqna_last_sources' ] = extract_sources( response_obj )
		st.session_state[ 'last_sources' ] = st.session_state[ 'docqna_last_sources' ]

		return str( result or '' ).strip( )

	if provider_name == 'Gemini':
		apply_gemini_runtime_config( )

		manual_store_names = [ ]

		if source == 'Gemini File Search Store':
			manual_store_names = split_text_values(
				st.session_state.get( 'docqna_file_search_store_names_input', '' ),
				delimiter=',' )

		selected_store_names = get_active_gemini_file_search_store_names( 'Gemini' )

		store_names = merge_unique_strings(
			primary=manual_store_names,
			secondary=selected_store_names )

		st.session_state[ 'docqna_file_search_store_names' ] = store_names

		if get_gemini_vector_backend( ) == 'Cloud Buckets' and len( store_names ) == 0:
			return (
					'The selected Gemini backend is Cloud Buckets. Cloud Buckets are storage '
					'objects, not Gemini File Search Store resources. Select a Gemini File '
					'Search Store backend or use Local Upload after downloading/loading the object.'
			)

		if source == 'Gemini File Search Store' and len( store_names ) == 0:
			return 'Enter or select at least one Gemini File Search Store resource name before asking.'

		result = chat.generate_text(
			prompt=query,
			model=st.session_state.get( 'docqna_model' ) or st.session_state.get(
				'text_model' ) or 'gemini-2.5-flash-lite',
			temperature=st.session_state.get( 'docqna_temperature' ),
			top_p=st.session_state.get( 'docqna_top_percent' ),
			top_k=st.session_state.get( 'docqna_top_k' ),
			max_tokens=st.session_state.get( 'docqna_max_tokens' ),
			instruct=st.session_state.get( 'docqna_system_instructions' ),
			file_search_store_names=store_names,
			stream=False )

		return str( result or '' ).strip( )

	if provider_name == 'Grok':
		collection_ids = get_active_grok_collection_ids( 'Grok' )

		if len( collection_ids ) == 0:
			return 'Select an xAI Collection in Vector Stores mode before using Grok remote Document Q&A.'

		vectorstores = get_vectorstores_module( 'Grok' )
		result = vectorstores.search(
			prompt=query,
			store_id=collection_ids[ 0 ] )

		if isinstance( result, str ) and result.strip( ):
			return result.strip( )

		if isinstance( result, dict ):
			return json.dumps( result, indent=2, default=str )

		return str( result or '' ).strip( )

	return run_docqna_query( query )

render_hits

render_hits() -> None

Render hits.

Purpose

Renders render hits in the Streamlit interface while preserving the active session state and provider workflow context.

Source code in app.py

def render_hits( ) -> None:
	"""Render hits.

	Purpose:
	    Renders render hits in the Streamlit interface while preserving the active session
	    state and provider workflow context.
	"""
	hits = st.session_state.get( 'docqna_last_hits', [ ] )
	sources = st.session_state.get( 'docqna_last_sources', [ ] )

	if isinstance( hits, list ) and len( hits ) > 0:
		with st.expander( label='Retrieved Chunks', icon='🧩',
				expanded=False, width='stretch' ):
			df_hits = pd.DataFrame( hits )
			st.data_editor( df_hits, use_container_width=True, hide_index=True )

	if isinstance( sources, list ) and len( sources ) > 0:
		with st.expander( label='Document Sources', icon='📌',
				expanded=False, width='stretch' ):
			df_sources = pd.DataFrame( sources )
			st.data_editor( df_sources, use_container_width=True, hide_index=True )

get_docqna_avatar

get_docqna_avatar(provider_name: str) -> str

Get docqna avatar.

Purpose

Retrieves get docqna avatar for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	str	Provider name used to route the operation.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_docqna_avatar( provider_name: str ) -> str:
	"""Get docqna avatar.

	Purpose:
	    Retrieves get docqna avatar for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if provider_name == 'GPT':
		return getattr( cfg, 'GPT_AVATAR', getattr( cfg, 'BUDDY', '🧠' ) )

	if provider_name == 'Gemini':
		return getattr( cfg, 'GEMINI_AVATAR', getattr( cfg, 'BUDDY', '🧠' ) )

	if provider_name == 'Grok':
		return getattr( cfg, 'GROK_AVATAR', getattr( cfg, 'BUDDY', '🧠' ) )

	return getattr( cfg, 'BUDDY', '🧠' )

ensure_runtime_state

ensure_runtime_state() -> None

Ensure runtime state.

Purpose

Maintains application runtime state for ensure runtime state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_runtime_state( ) -> None:
	"""Ensure runtime state.

	Purpose:
	    Maintains application runtime state for ensure runtime state by initializing, clearing,
	    or restoring the session values used by the active Streamlit workflow.
	"""
	ensure_files_mode_state( )
	ensure_key( 'files_filter_purpose', '' )
	ensure_key( 'files_table_data', [ ] )
	ensure_key( 'files_metadata', { } )
	ensure_key( 'files_content', '' )
	ensure_key( 'files_delete_result', { } )
	ensure_key( 'files_last_answer', '' )
	ensure_key( 'files_last_upload', { } )
	ensure_key( 'files_last_list', [ ] )
	ensure_key( 'files_last_operation', '' )
	ensure_key( 'files_system_instructions', '' )

clear_files_messages

clear_files_messages() -> None

Clear files messages.

Purpose

Maintains application runtime state for clear files messages by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_files_messages( ) -> None:
	"""Clear files messages.

	Purpose:
	    Maintains application runtime state for clear files messages by initializing, clearing,
	    or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'files_messages' ] = [ ]

clear_files_outputs

clear_files_outputs() -> None

Clear files outputs.

Purpose

Maintains application runtime state for clear files outputs by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_files_outputs( ) -> None:
	"""Clear files outputs.

	Purpose:
	    Maintains application runtime state for clear files outputs by initializing, clearing,
	    or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'files_table_data' ] = [ ]
	st.session_state[ 'files_metadata' ] = { }
	st.session_state[ 'files_content' ] = ''
	st.session_state[ 'files_delete_result' ] = { }
	st.session_state[ 'files_last_answer' ] = ''
	st.session_state[ 'files_last_upload' ] = { }
	st.session_state[ 'files_last_list' ] = [ ]
	st.session_state[ 'files_last_operation' ] = ''
	st.session_state[ 'last_answer' ] = ''

reset_files_controls

reset_files_controls() -> None

Reset files controls.

Purpose

Maintains application runtime state for reset files controls by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_files_controls( ) -> None:
	"""Reset files controls.

	Purpose:
	    Maintains application runtime state for reset files controls by initializing, clearing,
	    or restoring the session values used by the active Streamlit workflow.
	"""
	for key in [ 'files_model', 'files_purpose', 'files_filter_purpose',
	             'files_id', 'files_url', 'files_type', 'files_table',
	             'files_temperature', 'files_top_percent', 'files_max_tokens',
	             'files_response_format', 'files_reasoning', 'files_tool_choice',
	             'files_store', 'files_stream', 'files_background' ]:
		if key in st.session_state:
			del st.session_state[ key ]

reset_files_all

reset_files_all() -> None

Reset files all.

Purpose

Maintains application runtime state for reset files all by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_files_all( ) -> None:
	"""Reset files all.

	Purpose:
	    Maintains application runtime state for reset files all by initializing, clearing, or
	    restoring the session values used by the active Streamlit workflow.
	"""
	reset_files_controls( )
	clear_files_outputs( )
	clear_files_messages( )

clear_files_instructions

clear_files_instructions() -> None

Clear files instructions.

Purpose

Maintains application runtime state for clear files instructions by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_files_instructions( ) -> None:
	"""Clear files instructions.

	Purpose:
	    Maintains application runtime state for clear files instructions by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'files_system_instructions' ] = ''
	st.session_state[ 'instructions' ] = ''

load_files_instruction

load_files_instruction() -> None

Load files instruction.

Purpose

Retrieves load files instruction for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Source code in app.py

def load_files_instruction( ) -> None:
	"""Load files instruction.

	Purpose:
	    Retrieves load files instruction for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.
	"""
	name = st.session_state.get( 'instructions' )
	if name and name != 'No Templates Found':
		prompt_text = fetch_prompt_text( cfg.DB_PATH, name )
		if prompt_text is not None:
			st.session_state[ 'files_system_instructions' ] = prompt_text

convert_files_instructions

convert_files_instructions() -> None

Convert files instructions.

Purpose

Transforms convert files instructions inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Source code in app.py

def convert_files_instructions( ) -> None:
	"""Convert files instructions.

	Purpose:
	    Transforms convert files instructions inputs into a normalized representation used by
	    provider calls, document retrieval, data management, or UI rendering.
	"""
	text_value = st.session_state.get( 'files_system_instructions', '' )
	if not isinstance( text_value, str ) or not text_value.strip( ):
		return

	source = text_value.strip( )
	if cfg.XML_BLOCK_PATTERN.search( source ):
		converted = convert_xml( source )
	else:
		converted = convert_markdown( source )

	st.session_state[ 'files_system_instructions' ] = converted

get_purpose_options

get_purpose_options(files: Any) -> List[str]

Get purpose options.

Purpose

Retrieves get purpose options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
files	Any	File, upload, or path value used by the document or storage workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_purpose_options( files: Any ) -> List[ str ]:
	"""Get purpose options.

	Purpose:
	    Retrieves get purpose options for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    files: File, upload, or path value used by the document or storage workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	options = getattr( files, 'upload_purpose_options', None )
	if isinstance( options, list ) and len( options ) > 0:
		return options

	provider_name = get_provider_name( )

	if provider_name == 'GPT':
		return [ 'assistants', 'batch', 'fine-tune', 'vision', 'user_data' ]

	if provider_name == 'Gemini':
		return [ 'user_data' ]

	return [ 'user_data' ]

get_filter_options

get_filter_options(files: Any) -> List[str]

Get filter options.

Purpose

Retrieves get filter options for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
files	Any	File, upload, or path value used by the document or storage workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_filter_options( files: Any ) -> List[ str ]:
	"""Get filter options.

	Purpose:
	    Retrieves get filter options for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    files: File, upload, or path value used by the document or storage workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	options = getattr( files, 'file_purpose_options', None )
	if isinstance( options, list ) and len( options ) > 0:
		return [ '' ] + options

	provider_name = get_provider_name( )

	if provider_name == 'GPT':
		return [ '', 'assistants', 'batch', 'fine-tune', 'vision', 'user_data' ]

	return [ '' ]

normalize_files_object

normalize_files_object(value: Any) -> Dict[str, Any]

Normalize files object.

Purpose

Transforms normalize files object inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
value	Any	Value value used by the application workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Source code in app.py

def normalize_files_object( value: Any ) -> Dict[ str, Any ]:
	"""Normalize files object.

	Purpose:
	    Transforms normalize files object inputs into a normalized representation used by
	    provider calls, document retrieval, data management, or UI rendering.

	Args:
	    value: Value value used by the application workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.
	"""
	if value is None:
		return { }

	if isinstance( value, dict ):
		return value

	if hasattr( value, 'model_dump' ):
		try:
			return value.model_dump( )
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'normalize_files_object'
			exception.method = 'normalize_files_object( value ) -> Dict[str, Any]'
			Logger( ).write( exception )
			pass

	if hasattr( value, '__dict__' ):
		try:
			data = { }
			for key, item in vars( value ).items( ):
				if key.startswith( '_' ):
					continue

				if item is None or isinstance( item, (str, int, float, bool) ):
					data[ key ] = item
				else:
					data[ key ] = str( item )

			return data
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'normalize_files_object'
			exception.method = 'normalize_files_object( value ) -> Dict[str, Any]'
			Logger( ).write( exception )
			pass

	return { 'value': str( value ) }

normalize_files_list

normalize_files_list(value: Any) -> List[Dict[str, Any]]

Normalize files list.

Purpose

Transforms normalize files list inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
value	Any	Value value used by the application workflow.	required

Returns:

Type	Description
List[Dict[str, Any]]	List[Dict[str, Any]]: List of normalized values used by the application workflow.

Source code in app.py

def normalize_files_list( value: Any ) -> List[ Dict[ str, Any ] ]:
	"""Normalize files list.

	Purpose:
	    Transforms normalize files list inputs into a normalized representation used by
	    provider calls, document retrieval, data management, or UI rendering.

	Args:
	    value: Value value used by the application workflow.

	Returns:
	    List[Dict[str, Any]]: List of normalized values used by the application workflow.
	"""
	if value is None:
		return [ ]

	if isinstance( value, list ):
		return [ normalize_files_object( item ) for item in value ]

	data = getattr( value, 'data', None )
	if isinstance( data, list ):
		return [ normalize_files_object( item ) for item in data ]

	files = getattr( value, 'files', None )
	if isinstance( files, list ):
		return [ normalize_files_object( item ) for item in files ]

	file_list = getattr( value, 'file_list', None )
	if isinstance( file_list, list ):
		return [ normalize_files_object( item ) for item in file_list ]

	return [ normalize_files_object( value ) ]

get_files_id

get_files_id(row: Dict[str, Any]) -> str

Get files id.

Purpose

Retrieves get files id for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
row	Dict[str, Any]	Row value used by the application workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_files_id( row: Dict[ str, Any ] ) -> str:
	"""Get files id.

	Purpose:
	    Retrieves get files id for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    row: Row value used by the application workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not isinstance( row, dict ):
		return ''

	for key in [ 'id', 'file_id', 'name', 'uri', 'resource_name' ]:
		value = row.get( key )
		if isinstance( value, str ) and value.strip( ):
			return value.strip( )

	return ''

build_selector_options

build_selector_options(
    rows: List[Dict[str, Any]],
) -> List[str]

Build selector options.

Purpose

Builds build selector options from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
rows	List[Dict[str, Any]]	Rows value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def build_selector_options( rows: List[ Dict[ str, Any ] ] ) -> List[ str ]:
	"""Build selector options.

	Purpose:
	    Builds build selector options from validated runtime inputs and prepares the resulting
	    object, payload, table, or display structure for later application processing.

	Args:
	    rows: Rows value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	options: List[ str ] = [ ]

	for row in rows:
		if not isinstance( row, dict ):
			continue

		file_id = get_files_id( row )
		name = (row.get( 'filename' )
		        or row.get( 'display_name' )
		        or row.get( 'name' )
		        or row.get( 'id' )
		        or 'file')

		if file_id:
			options.append( f'{name} — {file_id}' )

	return options

get_option_id

get_option_id(option: str | None) -> str

Get option id.

Purpose

Retrieves get option id for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
option	str | None	Option value used by the application workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_option_id( option: str | None ) -> str:
	"""Get option id.

	Purpose:
	    Retrieves get option id for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    option: Option value used by the application workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not isinstance( option, str ) or not option.strip( ):
		return ''

	if ' — ' in option:
		return option.rsplit( ' — ', 1 )[ 1 ].strip( )

	return option.strip( )

upload_provider_file

upload_provider_file(
    files: Any, path: str, purpose: str | None = None
) -> Any

Upload provider file.

Purpose

Applies the upload provider file operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
files	Any	File, upload, or path value used by the document or storage workflow.	required
path	str	File, upload, or path value used by the document or storage workflow.	required
purpose	str | None	Purpose value used by the application workflow.	None

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def upload_provider_file( files: Any, path: str, purpose: str | None = None ) -> Any:
	"""Upload provider file.

	Purpose:
	    Applies the upload provider file operation to application-managed data, files, prompts,
	    or provider resources while preserving the surrounding workflow state.

	Args:
	    files: File, upload, or path value used by the document or storage workflow.
	    path: File, upload, or path value used by the document or storage workflow.
	    purpose: Purpose value used by the application workflow.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	provider_name = get_provider_name( )

	if provider_name == 'Gemini':
		apply_gemini_runtime_config( )

		try:
			return files.upload( path=path )
		except TypeError:
			try:
				return files.upload( file_path=path )
			except TypeError:
				return files.upload( path )

	try:
		return files.upload( path=path, purpose=purpose or 'user_data' )
	except TypeError:
		try:
			return files.upload( filepath=path, purpose=purpose or 'user_data' )
		except TypeError:
			return files.upload( path, purpose or 'user_data' )

list_provider_files

list_provider_files(
    files: Any, purpose: str | None = None
) -> List[Dict[str, Any]]

List provider files.

Purpose

Retrieves list provider files for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
files	Any	File, upload, or path value used by the document or storage workflow.	required
purpose	str | None	Purpose value used by the application workflow.	None

Returns:

Type	Description
List[Dict[str, Any]]	List[Dict[str, Any]]: List of normalized values used by the application workflow.

Source code in app.py

def list_provider_files( files: Any, purpose: str | None = None ) -> List[ Dict[ str, Any ] ]:
	"""List provider files.

	Purpose:
	    Retrieves list provider files for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    files: File, upload, or path value used by the document or storage workflow.
	    purpose: Purpose value used by the application workflow.

	Returns:
	    List[Dict[str, Any]]: List of normalized values used by the application workflow.
	"""
	provider_name = get_provider_name( )

	if provider_name == 'Gemini':
		apply_gemini_runtime_config( )

		if hasattr( files, 'list_files' ):
			result = files.list_files( )
		else:
			result = files.list( )

		return normalize_files_list( result )

	try:
		result = files.list( purpose=purpose if purpose else None )
	except TypeError:
		result = files.list( )

	return normalize_files_list( result )

retrieve_provider_file

retrieve_provider_file(
    files: Any, file_id: str
) -> Dict[str, Any]

Retrieve provider file.

Purpose

Retrieves retrieve provider file for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
files	Any	File, upload, or path value used by the document or storage workflow.	required
file_id	str	File, upload, or path value used by the document or storage workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Source code in app.py

def retrieve_provider_file( files: Any, file_id: str ) -> Dict[ str, Any ]:
	"""Retrieve provider file.

	Purpose:
	    Retrieves retrieve provider file for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    files: File, upload, or path value used by the document or storage workflow.
	    file_id: File, upload, or path value used by the document or storage workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.
	"""
	if not isinstance( file_id, str ) or not file_id.strip( ):
		return { }

	provider_name = get_provider_name( )

	if provider_name == 'Gemini':
		apply_gemini_runtime_config( )

		try:
			result = files.retrieve( file_id=file_id.strip( ) )
		except TypeError:
			result = files.retrieve( file_id.strip( ) )

		return normalize_files_object( result )

	try:
		result = files.retrieve( id=file_id.strip( ) )
	except TypeError:
		try:
			result = files.retrieve( file_id=file_id.strip( ) )
		except TypeError:
			result = files.retrieve( file_id.strip( ) )

	return normalize_files_object( result )

extract_file_content

extract_file_content(files: Any, file_id: str) -> str

Extract file content.

Purpose

Transforms extract file content inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
files	Any	File, upload, or path value used by the document or storage workflow.	required
file_id	str	File, upload, or path value used by the document or storage workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def extract_file_content( files: Any, file_id: str ) -> str:
	"""Extract file content.

	Purpose:
	    Transforms extract file content inputs into a normalized representation used by
	    provider calls, document retrieval, data management, or UI rendering.

	Args:
	    files: File, upload, or path value used by the document or storage workflow.
	    file_id: File, upload, or path value used by the document or storage workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not isinstance( file_id, str ) or not file_id.strip( ):
		return ''

	if not hasattr( files, 'extract' ):
		return ''

	try:
		result = files.extract( id=file_id.strip( ) )
	except TypeError:
		try:
			result = files.extract( file_id=file_id.strip( ) )
		except TypeError:
			result = files.extract( file_id.strip( ) )

	if isinstance( result, bytes ):
		try:
			return result.decode( 'utf-8' )
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'extract_file_content'
			exception.method = 'extract_file_content( files, file_id ) -> str'
			Logger( ).write( exception )
			return str( result )

	if isinstance( result, str ):
		return result

	if isinstance( result, dict ):
		return json.dumps( result, indent=2, default=str )

	return str( result )

delete_provider_file

delete_provider_file(
    files: Any, file_id: str
) -> Dict[str, Any]

Delete provider file.

Purpose

Applies the delete provider file operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
files	Any	File, upload, or path value used by the document or storage workflow.	required
file_id	str	File, upload, or path value used by the document or storage workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Source code in app.py

def delete_provider_file( files: Any, file_id: str ) -> Dict[ str, Any ]:
	"""Delete provider file.

	Purpose:
	    Applies the delete provider file operation to application-managed data, files, prompts,
	    or provider resources while preserving the surrounding workflow state.

	Args:
	    files: File, upload, or path value used by the document or storage workflow.
	    file_id: File, upload, or path value used by the document or storage workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.
	"""
	if not isinstance( file_id, str ) or not file_id.strip( ):
		return { }

	provider_name = get_provider_name( )

	if provider_name == 'Gemini':
		apply_gemini_runtime_config( )

	try:
		result = files.delete( id=file_id.strip( ) )
	except TypeError:
		try:
			result = files.delete( file_id=file_id.strip( ) )
		except TypeError:
			result = files.delete( file_id.strip( ) )

	return normalize_files_object( result )

analyze_provider_file

analyze_provider_file(
    files: Any,
    prompt: str,
    file_id: str | None = None,
    model: str | None = None,
) -> str

Analyze provider file.

Purpose

Executes the analyze provider file workflow using the current provider, document, prompt, and session-state configuration.

Parameters:

Name	Type	Description	Default
files	Any	File, upload, or path value used by the document or storage workflow.	required
prompt	str	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required
file_id	str | None	File, upload, or path value used by the document or storage workflow.	None
model	str | None	Provider model identifier selected for the active workflow.	None

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def analyze_provider_file( files: Any, prompt: str, file_id: str | None = None,
		model: str | None = None ) -> str:
	"""Analyze provider file.

	Purpose:
	    Executes the analyze provider file workflow using the current provider, document,
	    prompt, and session-state configuration.

	Args:
	    files: File, upload, or path value used by the document or storage workflow.
	    prompt: Text value supplied to the prompt, conversion, retrieval, or provider workflow.
	    file_id: File, upload, or path value used by the document or storage workflow.
	    model: Provider model identifier selected for the active workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not isinstance( prompt, str ) or not prompt.strip( ):
		return ''

	provider_name = get_provider_name( )
	clean_prompt = prompt.strip( )
	clean_file_id = file_id.strip( ) if isinstance( file_id, str ) and file_id.strip( ) else None

	if provider_name == 'GPT':
		selected_model = model or st.session_state.get( 'files_model' ) or 'gpt-5-nano'
	elif provider_name == 'Gemini':
		selected_model = model or st.session_state.get( 'files_model' ) or 'gemini-2.5-flash-lite'
	elif provider_name == 'Grok':
		selected_model = model or st.session_state.get( 'files_model' ) or 'grok-4.3'
	else:
		selected_model = model or st.session_state.get( 'files_model' )

	if clean_file_id and hasattr( files, 'summarize' ):
		try:
			return str( files.summarize(
				id=clean_file_id,
				prompt=clean_prompt,
				model=selected_model ) or '' ).strip( )
		except TypeError:
			try:
				return str( files.summarize(
					file_id=clean_file_id,
					prompt=clean_prompt,
					model=selected_model ) or '' ).strip( )
			except TypeError:
				pass

	if clean_file_id and hasattr( files, 'survey' ):
		try:
			result = files.survey( id=clean_file_id )
			return json.dumps( result, indent=2, default=str )
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'analyze_provider_file'
			exception.method = 'analyze_provider_file( *args ) -> str'
			Logger( ).write( exception )
			pass

	if clean_file_id:
		content = extract_file_content( files=files, file_id=clean_file_id )
		if content:
			query = (
					f'{st.session_state.get( "files_system_instructions", "" )}\n\n'
					f'Use the following file content to answer the user request.\n\n'
					f'File Content:\n{content[ :12000 ]}\n\n'
					f'User Request:\n{clean_prompt}'
			).strip( )

			chat = get_chat_module( )

			try:
				answer = chat.generate_text(
					prompt=query,
					model=selected_model,
					temperature=st.session_state.get( 'files_temperature' ),
					top_p=st.session_state.get( 'files_top_percent' ),
					max_tokens=st.session_state.get( 'files_max_tokens' ),
					instruct=st.session_state.get( 'files_system_instructions' ) )
				return str( answer or '' ).strip( )
			except Exception as e:
				exception = Error( e )
				exception.module = 'app'
				exception.cause = 'analyze_provider_file'
				exception.method = 'analyze_provider_file( *args ) -> str'
				Logger( ).write( exception )
				return content[ :12000 ]

	return ''

ensure_storage_state

ensure_storage_state() -> None

Ensure storage state.

Purpose

Maintains application runtime state for ensure storage state by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def ensure_storage_state( ) -> None:
	"""Ensure storage state.

	Purpose:
	    Maintains application runtime state for ensure storage state by initializing, clearing,
	    or restoring the session values used by the active Streamlit workflow.
	"""
	ensure_vectorstores_mode_state( )
	ensure_file_search_mode_state( )
	ensure_cloudbuckets_mode_state( )
	ensure_key( 'storage_operation_result', { } )
	ensure_key( 'storage_table_data', [ ] )
	ensure_key( 'storage_last_operation', '' )
	ensure_key( 'storage_selected_option', '' )
	ensure_key( 'storage_last_answer', '' )

clear_storage_outputs

clear_storage_outputs() -> None

Clear storage outputs.

Purpose

Maintains application runtime state for clear storage outputs by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def clear_storage_outputs( ) -> None:
	"""Clear storage outputs.

	Purpose:
	    Maintains application runtime state for clear storage outputs by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state[ 'storage_operation_result' ] = { }
	st.session_state[ 'storage_table_data' ] = [ ]
	st.session_state[ 'storage_last_operation' ] = ''
	st.session_state[ 'storage_selected_option' ] = ''
	st.session_state[ 'storage_last_answer' ] = ''

reset_storage_controls

reset_storage_controls() -> None

Reset storage controls.

Purpose

Maintains application runtime state for reset storage controls by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_storage_controls( ) -> None:
	"""Reset storage controls.

	Purpose:
	    Maintains application runtime state for reset storage controls by initializing,
	    clearing, or restoring the session values used by the active Streamlit workflow.
	"""
	for key in [ 'stores_id', 'stores_name', 'stores_file_id', 'stores_file_ids',
	             'stores_path', 'stores_operation', 'filestore_id', 'filestore_name',
	             'filestore_selected_label', 'bucket_id', 'bucket_name',
	             'bucket_object_name', 'bucket_path', 'bucket_operation',
	             'selected_bucket_id', 'selected_bucket_label' ]:
		if key in st.session_state:
			del st.session_state[ key ]

reset_storage_all

reset_storage_all() -> None

Reset storage all.

Purpose

Maintains application runtime state for reset storage all by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_storage_all( ) -> None:
	"""Reset storage all.

	Purpose:
	    Maintains application runtime state for reset storage all by initializing, clearing, or
	    restoring the session values used by the active Streamlit workflow.
	"""
	reset_storage_controls( )
	clear_storage_outputs( )

normalize_storage_object

normalize_storage_object(value: Any) -> Dict[str, Any]

Normalize storage object.

Purpose

Transforms normalize storage object inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
value	Any	Value value used by the application workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Source code in app.py

def normalize_storage_object( value: Any ) -> Dict[ str, Any ]:
	"""Normalize storage object.

	Purpose:
	    Transforms normalize storage object inputs into a normalized representation used by
	    provider calls, document retrieval, data management, or UI rendering.

	Args:
	    value: Value value used by the application workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.
	"""
	if value is None:
		return { }

	if isinstance( value, dict ):
		return value

	if hasattr( value, 'model_dump' ):
		try:
			return value.model_dump( )
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'normalize_storage_object'
			exception.method = 'normalize_storage_object( value ) -> Dict[str, Any]'
			Logger( ).write( exception )
			pass

	if hasattr( value, '__dict__' ):
		try:
			data: Dict[ str, Any ] = { }

			for key, item in vars( value ).items( ):
				if key.startswith( '_' ):
					continue

				if item is None or isinstance( item, (str, int, float, bool) ):
					data[ key ] = item
				else:
					data[ key ] = str( item )

			return data
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'normalize_storage_object'
			exception.method = 'normalize_storage_object( value ) -> Dict[str, Any]'
			Logger( ).write( exception )
			pass

	return { 'value': str( value ) }

normalize_storage_list

normalize_storage_list(value: Any) -> List[Dict[str, Any]]

Normalize storage list.

Purpose

Transforms normalize storage list inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
value	Any	Value value used by the application workflow.	required

Returns:

Type	Description
List[Dict[str, Any]]	List[Dict[str, Any]]: List of normalized values used by the application workflow.

Source code in app.py

def normalize_storage_list( value: Any ) -> List[ Dict[ str, Any ] ]:
	"""Normalize storage list.

	Purpose:
	    Transforms normalize storage list inputs into a normalized representation used by
	    provider calls, document retrieval, data management, or UI rendering.

	Args:
	    value: Value value used by the application workflow.

	Returns:
	    List[Dict[str, Any]]: List of normalized values used by the application workflow.
	"""
	if value is None:
		return [ ]

	if isinstance( value, list ):
		return [ normalize_storage_object( item ) for item in value ]

	data = getattr( value, 'data', None )
	if isinstance( data, list ):
		return [ normalize_storage_object( item ) for item in data ]

	items = getattr( value, 'items', None )
	if isinstance( items, list ):
		return [ normalize_storage_object( item ) for item in items ]

	buckets = getattr( value, 'buckets', None )
	if isinstance( buckets, list ):
		return [ normalize_storage_object( item ) for item in buckets ]

	files = getattr( value, 'files', None )
	if isinstance( files, list ):
		return [ normalize_storage_object( item ) for item in files ]

	return [ normalize_storage_object( value ) ]

get_storage_rowid

get_storage_rowid(row: Dict[str, Any]) -> str

Get storage rowid.

Purpose

Retrieves get storage rowid for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
row	Dict[str, Any]	Row value used by the application workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_storage_rowid( row: Dict[ str, Any ] ) -> str:
	"""Get storage rowid.

	Purpose:
	    Retrieves get storage rowid for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    row: Row value used by the application workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not isinstance( row, dict ):
		return ''

	for key in [ 'id', 'name', 'resource_name', 'uri', 'bucket', 'bucket_name' ]:
		value = row.get( key )
		if isinstance( value, str ) and value.strip( ):
			return value.strip( )

	return ''

build_storage_selectors

build_storage_selectors(
    rows: List[Dict[str, Any]],
) -> List[str]

Build storage selectors.

Purpose

Builds build storage selectors from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
rows	List[Dict[str, Any]]	Rows value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def build_storage_selectors( rows: List[ Dict[ str, Any ] ] ) -> List[ str ]:
	"""Build storage selectors.

	Purpose:
	    Builds build storage selectors from validated runtime inputs and prepares the resulting
	    object, payload, table, or display structure for later application processing.

	Args:
	    rows: Rows value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	options: List[ str ] = [ ]
	for row in rows:
		if not isinstance( row, dict ):
			continue

		resource_id = get_storage_rowid( row )
		name = (row.get( 'display_name' )
		        or row.get( 'name' )
		        or row.get( 'id' )
		        or row.get( 'bucket_name' )
		        or 'resource')

		if resource_id:
			options.append( f'{name} — {resource_id}' )

	return options

get_storage_id_from_option

get_storage_id_from_option(option: str | None) -> str

Get storage id from option.

Purpose

Retrieves get storage id from option for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
option	str | None	Option value used by the application workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def get_storage_id_from_option( option: str | None ) -> str:
	"""Get storage id from option.

	Purpose:
	    Retrieves get storage id from option for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    option: Option value used by the application workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	if not isinstance( option, str ) or not option.strip( ):
		return ''

	if ' — ' in option:
		return option.rsplit( ' — ', 1 )[ 1 ].strip( )

	return option.strip( )

create_openai_vector_store

create_openai_vector_store(
    vectorstores: Any, name: str
) -> Dict[str, Any]

Create openai vector store.

Purpose

Builds create openai vector store from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
vectorstores	Any	Vectorstores value used by the application workflow.	required
name	str	Name value used by the application workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def create_openai_vector_store( vectorstores: Any, name: str ) -> Dict[ str, Any ]:
	"""Create openai vector store.

	Purpose:
	    Builds create openai vector store from validated runtime inputs and prepares the
	    resulting object, payload, table, or display structure for later application
	    processing.

	Args:
	    vectorstores: Vectorstores value used by the application workflow.
	    name: Name value used by the application workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not isinstance( name, str ) or not name.strip( ):
		raise ValueError( 'Vector store name is required.' )

	try:
		result = vectorstores.create( name=name.strip( ) )
	except TypeError:
		result = vectorstores.create( name.strip( ) )

	return normalize_storage_object( result )

list_openai_vector_stores

list_openai_vector_stores(
    vectorstores: Any,
) -> List[Dict[str, Any]]

List openai vector stores.

Purpose

Retrieves list openai vector stores for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
vectorstores	Any	Vectorstores value used by the application workflow.	required

Returns:

Type	Description
List[Dict[str, Any]]	List[Dict[str, Any]]: List of normalized values used by the application workflow.

Source code in app.py

def list_openai_vector_stores( vectorstores: Any ) -> List[ Dict[ str, Any ] ]:
	"""List openai vector stores.

	Purpose:
	    Retrieves list openai vector stores for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    vectorstores: Vectorstores value used by the application workflow.

	Returns:
	    List[Dict[str, Any]]: List of normalized values used by the application workflow.
	"""
	if hasattr( vectorstores, 'list' ):
		result = vectorstores.list( )
	else:
		result = vectorstores.list_stores( )

	return normalize_storage_list( result )

retrieve_openai_vector_store

retrieve_openai_vector_store(
    vectorstores: Any, store_id: str
) -> Dict[str, Any]

Retrieve openai vector store.

Purpose

Retrieves retrieve openai vector store for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Parameters:

Name	Type	Description	Default
vectorstores	Any	Vectorstores value used by the application workflow.	required
store_id	str	Store Id value used by the application workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def retrieve_openai_vector_store( vectorstores: Any, store_id: str ) -> Dict[ str, Any ]:
	"""Retrieve openai vector store.

	Purpose:
	    Retrieves retrieve openai vector store for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.

	Args:
	    vectorstores: Vectorstores value used by the application workflow.
	    store_id: Store Id value used by the application workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not isinstance( store_id, str ) or not store_id.strip( ):
		raise ValueError( 'Vector store ID is required.' )

	try:
		result = vectorstores.retrieve( id=store_id.strip( ) )
	except TypeError:
		try:
			result = vectorstores.retrieve( vector_store_id=store_id.strip( ) )
		except TypeError:
			result = vectorstores.retrieve( store_id.strip( ) )

	return normalize_storage_object( result )

delete_openai_vector_store

delete_openai_vector_store(
    vectorstores: Any, store_id: str
) -> Dict[str, Any]

Delete openai vector store.

Purpose

Applies the delete openai vector store operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
vectorstores	Any	Vectorstores value used by the application workflow.	required
store_id	str	Store Id value used by the application workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def delete_openai_vector_store( vectorstores: Any, store_id: str ) -> Dict[ str, Any ]:
	"""Delete openai vector store.

	Purpose:
	    Applies the delete openai vector store operation to application-managed data, files,
	    prompts, or provider resources while preserving the surrounding workflow state.

	Args:
	    vectorstores: Vectorstores value used by the application workflow.
	    store_id: Store Id value used by the application workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not isinstance( store_id, str ) or not store_id.strip( ):
		raise ValueError( 'Vector store ID is required.' )

	try:
		result = vectorstores.delete( id=store_id.strip( ) )
	except TypeError:
		try:
			result = vectorstores.delete( vector_store_id=store_id.strip( ) )
		except TypeError:
			result = vectorstores.delete( store_id.strip( ) )

	return normalize_storage_object( result )

attach_file_to_openai_vector_store

attach_file_to_openai_vector_store(
    vectorstores: Any, store_id: str, file_id: str
) -> Dict[str, Any]

Attach file to openai vector store.

Purpose

Applies the attach file to openai vector store operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
vectorstores	Any	Vectorstores value used by the application workflow.	required
store_id	str	Store Id value used by the application workflow.	required
file_id	str	File, upload, or path value used by the document or storage workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def attach_file_to_openai_vector_store( vectorstores: Any, store_id: str,
		file_id: str ) -> Dict[ str, Any ]:
	"""Attach file to openai vector store.

	Purpose:
	    Applies the attach file to openai vector store operation to application-managed data,
	    files, prompts, or provider resources while preserving the surrounding workflow state.

	Args:
	    vectorstores: Vectorstores value used by the application workflow.
	    store_id: Store Id value used by the application workflow.
	    file_id: File, upload, or path value used by the document or storage workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not isinstance( store_id, str ) or not store_id.strip( ):
		raise ValueError( 'Vector store ID is required.' )

	if not isinstance( file_id, str ) or not file_id.strip( ):
		raise ValueError( 'File ID is required.' )

	for method_name in [ 'attach_file', 'add_file', 'create_file', 'upload_file' ]:
		if hasattr( vectorstores, method_name ):
			method = getattr( vectorstores, method_name )
			try:
				result = method( vector_store_id=store_id.strip( ), file_id=file_id.strip( ) )
			except TypeError:
				try:
					result = method( store_id.strip( ), file_id.strip( ) )
				except TypeError:
					continue

			return normalize_storage_object( result )

	raise AttributeError( 'VectorStores wrapper does not expose a file attachment method.' )

create_gemini_file_search_store

create_gemini_file_search_store(
    filestore: Any, name: str
) -> Dict[str, Any]

Create gemini file search store.

Purpose

Builds create gemini file search store from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
filestore	Any	File, upload, or path value used by the document or storage workflow.	required
name	str	Name value used by the application workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def create_gemini_file_search_store( filestore: Any, name: str ) -> Dict[ str, Any ]:
	"""Create gemini file search store.

	Purpose:
	    Builds create gemini file search store from validated runtime inputs and prepares the
	    resulting object, payload, table, or display structure for later application
	    processing.

	Args:
	    filestore: File, upload, or path value used by the document or storage workflow.
	    name: Name value used by the application workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not isinstance( name, str ) or not name.strip( ):
		raise ValueError( 'File Search Store name is required.' )

	apply_gemini_runtime_config( )
	for method_name in [ 'create', 'create_store', 'create_file_search_store' ]:
		if hasattr( filestore, method_name ):
			method = getattr( filestore, method_name )
			try:
				result = method( name=name.strip( ) )
			except TypeError:
				result = method( name.strip( ) )

			return normalize_storage_object( result )

	raise AttributeError( 'Gemini FileSearch wrapper does not expose a create method.' )

list_gemini_file_search_stores

list_gemini_file_search_stores(
    filestore: Any,
) -> List[Dict[str, Any]]

List gemini file search stores.

Purpose

Retrieves list gemini file search stores for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Parameters:

Name	Type	Description	Default
filestore	Any	File, upload, or path value used by the document or storage workflow.	required

Returns:

Type	Description
List[Dict[str, Any]]	List[Dict[str, Any]]: List of normalized values used by the application workflow.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def list_gemini_file_search_stores( filestore: Any ) -> List[ Dict[ str, Any ] ]:
	"""List gemini file search stores.

	Purpose:
	    Retrieves list gemini file search stores for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.

	Args:
	    filestore: File, upload, or path value used by the document or storage workflow.

	Returns:
	    List[Dict[str, Any]]: List of normalized values used by the application workflow.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	apply_gemini_runtime_config( )
	for method_name in [ 'list', 'list_stores', 'list_file_search_stores' ]:
		if hasattr( filestore, method_name ):
			result = getattr( filestore, method_name )( )
			return normalize_storage_list( result )

	raise AttributeError( 'Gemini FileSearch wrapper does not expose a list method.' )

retrieve_gemini_file_search_store

retrieve_gemini_file_search_store(
    filestore: Any, store_id: str
) -> Dict[str, Any]

Retrieve gemini file search store.

Purpose

Retrieves retrieve gemini file search store for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Parameters:

Name	Type	Description	Default
filestore	Any	File, upload, or path value used by the document or storage workflow.	required
store_id	str	Store Id value used by the application workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def retrieve_gemini_file_search_store( filestore: Any, store_id: str ) -> Dict[ str, Any ]:
	"""Retrieve gemini file search store.

	Purpose:
	    Retrieves retrieve gemini file search store for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.

	Args:
	    filestore: File, upload, or path value used by the document or storage workflow.
	    store_id: Store Id value used by the application workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not isinstance( store_id, str ) or not store_id.strip( ):
		raise ValueError( 'File Search Store ID or resource name is required.' )

	apply_gemini_runtime_config( )
	for method_name in [ 'retrieve', 'get', 'get_store', 'get_file_search_store' ]:
		if hasattr( filestore, method_name ):
			method = getattr( filestore, method_name )
			try:
				result = method( name=store_id.strip( ) )
			except TypeError:
				try:
					result = method( id=store_id.strip( ) )
				except TypeError:
					result = method( store_id.strip( ) )

			return normalize_storage_object( result )

	raise AttributeError( 'Gemini FileSearch wrapper does not expose a retrieve method.' )

delete_gemini_file_search_store

delete_gemini_file_search_store(
    filestore: Any, store_id: str
) -> Dict[str, Any]

Delete gemini file search store.

Purpose

Applies the delete gemini file search store operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
filestore	Any	File, upload, or path value used by the document or storage workflow.	required
store_id	str	Store Id value used by the application workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def delete_gemini_file_search_store( filestore: Any, store_id: str ) -> Dict[ str, Any ]:
	"""Delete gemini file search store.

	Purpose:
	    Applies the delete gemini file search store operation to application-managed data,
	    files, prompts, or provider resources while preserving the surrounding workflow state.

	Args:
	    filestore: File, upload, or path value used by the document or storage workflow.
	    store_id: Store Id value used by the application workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not isinstance( store_id, str ) or not store_id.strip( ):
		raise ValueError( 'File Search Store ID or resource name is required.' )

	apply_gemini_runtime_config( )
	for method_name in [ 'delete', 'delete_store', 'delete_file_search_store' ]:
		if hasattr( filestore, method_name ):
			method = getattr( filestore, method_name )
			try:
				result = method( name=store_id.strip( ) )
			except TypeError:
				try:
					result = method( id=store_id.strip( ) )
				except TypeError:
					result = method( store_id.strip( ) )

			return normalize_storage_object( result )

	raise AttributeError( 'Gemini FileSearch wrapper does not expose a delete method.' )

create_google_cloud_bucket

create_google_cloud_bucket(
    buckets: Any, bucket_name: str
) -> Dict[str, Any]

Create google cloud bucket.

Purpose

Builds create google cloud bucket from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
buckets	Any	Buckets value used by the application workflow.	required
bucket_name	str	Bucket Name value used by the application workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def create_google_cloud_bucket( buckets: Any, bucket_name: str ) -> Dict[ str, Any ]:
	"""Create google cloud bucket.

	Purpose:
	    Builds create google cloud bucket from validated runtime inputs and prepares the
	    resulting object, payload, table, or display structure for later application
	    processing.

	Args:
	    buckets: Buckets value used by the application workflow.
	    bucket_name: Bucket Name value used by the application workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not isinstance( bucket_name, str ) or not bucket_name.strip( ):
		raise ValueError( 'Bucket name is required.' )

	apply_gemini_runtime_config( )
	for method_name in [ 'create', 'create_bucket' ]:
		if hasattr( buckets, method_name ):
			method = getattr( buckets, method_name )
			try:
				result = method( name=bucket_name.strip( ) )
			except TypeError:
				try:
					result = method( bucket_name=bucket_name.strip( ) )
				except TypeError:
					result = method( bucket_name.strip( ) )

			return normalize_storage_object( result )

	raise AttributeError( 'CloudBuckets wrapper does not expose a create method.' )

list_google_cloud_buckets

list_google_cloud_buckets(
    buckets: Any,
) -> List[Dict[str, Any]]

List google cloud buckets.

Purpose

Retrieves list google cloud buckets for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
buckets	Any	Buckets value used by the application workflow.	required

Returns:

Type	Description
List[Dict[str, Any]]	List[Dict[str, Any]]: List of normalized values used by the application workflow.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def list_google_cloud_buckets( buckets: Any ) -> List[ Dict[ str, Any ] ]:
	"""List google cloud buckets.

	Purpose:
	    Retrieves list google cloud buckets for the Streamlit application workflow and returns
	    the normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    buckets: Buckets value used by the application workflow.

	Returns:
	    List[Dict[str, Any]]: List of normalized values used by the application workflow.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	apply_gemini_runtime_config( )
	for method_name in [ 'list', 'list_buckets' ]:
		if hasattr( buckets, method_name ):
			result = getattr( buckets, method_name )( )
			return normalize_storage_list( result )

	raise AttributeError( 'CloudBuckets wrapper does not expose a list method.' )

upload_to_google_cloud_bucket

upload_to_google_cloud_bucket(
    buckets: Any,
    bucket_name: str,
    object_name: str,
    path: str,
) -> Dict[str, Any]

Upload to google cloud bucket.

Purpose

Applies the upload to google cloud bucket operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
buckets	Any	Buckets value used by the application workflow.	required
bucket_name	str	Bucket Name value used by the application workflow.	required
object_name	str	Object Name value used by the application workflow.	required
path	str	File, upload, or path value used by the document or storage workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def upload_to_google_cloud_bucket( buckets: Any, bucket_name: str,
		object_name: str, path: str ) -> Dict[ str, Any ]:
	"""Upload to google cloud bucket.

	Purpose:
	    Applies the upload to google cloud bucket operation to application-managed data, files,
	    prompts, or provider resources while preserving the surrounding workflow state.

	Args:
	    buckets: Buckets value used by the application workflow.
	    bucket_name: Bucket Name value used by the application workflow.
	    object_name: Object Name value used by the application workflow.
	    path: File, upload, or path value used by the document or storage workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not isinstance( bucket_name, str ) or not bucket_name.strip( ):
		raise ValueError( 'Bucket name is required.' )

	if not isinstance( object_name, str ) or not object_name.strip( ):
		raise ValueError( 'Object name is required.' )

	if not isinstance( path, str ) or not path.strip( ):
		raise ValueError( 'Upload path is required.' )

	apply_gemini_runtime_config( )
	for method_name in [ 'upload', 'upload_file', 'upload_blob' ]:
		if hasattr( buckets, method_name ):
			method = getattr( buckets, method_name )
			try:
				result = method(
					bucket_name=bucket_name.strip( ),
					object_name=object_name.strip( ),
					path=path.strip( ) )
			except TypeError:
				try:
					result = method( bucket_name.strip( ), object_name.strip( ), path.strip( ) )
				except TypeError:
					result = method( bucket_name.strip( ), path.strip( ) )

			return normalize_storage_object( result )

	raise AttributeError( 'CloudBuckets wrapper does not expose an upload method.' )

delete_google_cloud_bucket_object

delete_google_cloud_bucket_object(
    buckets: Any, bucket_name: str, object_name: str
) -> Dict[str, Any]

Delete google cloud bucket object.

Purpose

Applies the delete google cloud bucket object operation to application-managed data, files, prompts, or provider resources while preserving the surrounding workflow state.

Parameters:

Name	Type	Description	Default
buckets	Any	Buckets value used by the application workflow.	required
bucket_name	str	Bucket Name value used by the application workflow.	required
object_name	str	Object Name value used by the application workflow.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Raises:

Type	Description
Exception	Raised when validation, provider execution, file processing, or database handling fails.

Source code in app.py

def delete_google_cloud_bucket_object( buckets: Any, bucket_name: str,
		object_name: str ) -> Dict[ str, Any ]:
	"""Delete google cloud bucket object.

	Purpose:
	    Applies the delete google cloud bucket object operation to application-managed data,
	    files, prompts, or provider resources while preserving the surrounding workflow state.

	Args:
	    buckets: Buckets value used by the application workflow.
	    bucket_name: Bucket Name value used by the application workflow.
	    object_name: Object Name value used by the application workflow.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

	Raises:
	    Exception: Raised when validation, provider execution, file processing, or database
	        handling fails.
	"""
	if not isinstance( bucket_name, str ) or not bucket_name.strip( ):
		raise ValueError( 'Bucket name is required.' )

	if not isinstance( object_name, str ) or not object_name.strip( ):
		raise ValueError( 'Object name is required.' )

	apply_gemini_runtime_config( )
	for method_name in [ 'delete_object', 'delete_blob', 'delete_file' ]:
		if hasattr( buckets, method_name ):
			method = getattr( buckets, method_name )
			try:
				result = method(
					bucket_name=bucket_name.strip( ),
					object_name=object_name.strip( ) )
			except TypeError:
				result = method( bucket_name.strip( ), object_name.strip( ) )

			return normalize_storage_object( result )

	raise AttributeError( 'CloudBuckets wrapper does not expose an object delete method.' )

get_retrieval_backend

get_retrieval_backend(
    provider_name: Optional[str] = None,
) -> Dict[str, Any]

Get retrieval backend.

Purpose

Retrieves get retrieval backend for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Dictionary containing normalized workflow configuration or results.

Source code in app.py

def get_retrieval_backend( provider_name: Optional[ str ] = None ) -> Dict[ str, Any ]:
	"""Get retrieval backend.

	Purpose:
	    Retrieves get retrieval backend for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    Dict[str, Any]: Dictionary containing normalized workflow configuration or results.
	"""
	provider = get_provider_name( provider_name )
	backend = ''
	resource_id = ''
	is_retrieval_ready = False

	if provider == 'GPT':
		backend = 'OpenAI Vector Stores'
		resource_id = get_selected_store_id(
			manual_key='stores_manual_id',
			selected_key='stores_selected_id',
			fallback_key='stores_id' )
		is_retrieval_ready = bool( resource_id )

	elif provider == 'Grok':
		backend = 'xAI Collections'
		resource_id = get_selected_store_id(
			manual_key='stores_manual_id',
			selected_key='stores_selected_id',
			fallback_key='stores_id' )
		is_retrieval_ready = bool( resource_id )

	elif provider == 'Gemini':
		selected_backend = get_gemini_vector_backend( )
		backend = f'Gemini {selected_backend}'

		if selected_backend == 'File Search Stores':
			resource_id = get_selected_store_id(
				manual_key='filestore_id',
				selected_key='filestore_selected_id',
				fallback_key='filestore_id' )
			is_retrieval_ready = bool( resource_id )
		else:
			resource_id = get_selected_store_id(
				manual_key='bucket_name',
				selected_key='bucket_selected_id',
				fallback_key='bucket_name' )
			is_retrieval_ready = False

	return {
			'provider': provider,
			'backend': backend,
			'resource_id': resource_id,
			'is_retrieval_ready': is_retrieval_ready,
			'gemini_backend': get_gemini_vector_backend( )
			if provider == 'Gemini' else '',
	}

get_store_ids

get_store_ids(
    provider_name: Optional[str] = None,
) -> List[str]

Get store ids.

Purpose

Retrieves get store ids for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_store_ids( provider_name: Optional[ str ] = None ) -> List[ str ]:
	"""Get store ids.

	Purpose:
	    Retrieves get store ids for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	provider = get_provider_name( provider_name )

	if provider != 'GPT':
		return [ ]

	backend = get_retrieval_backend( provider )
	resource_id = backend.get( 'resource_id', '' )

	return parse_storage_ids( resource_id )

get_active_grok_collection_ids

get_active_grok_collection_ids(
    provider_name: Optional[str] = None,
) -> List[str]

Get active grok collection ids.

Purpose

Retrieves get active grok collection ids for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document- processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_active_grok_collection_ids( provider_name: Optional[ str ] = None ) -> List[ str ]:
	"""Get active grok collection ids.

	Purpose:
	    Retrieves get active grok collection ids for the Streamlit application workflow and
	    returns the normalized value used by downstream UI, provider, database, or document-
	    processing steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	provider = get_provider_name( provider_name )

	if provider != 'Grok':
		return [ ]

	backend = get_retrieval_backend( provider )
	resource_id = backend.get( 'resource_id', '' )

	return parse_storage_ids( resource_id )

get_active_gemini_file_search_store_names

get_active_gemini_file_search_store_names(
    provider_name: Optional[str] = None,
) -> List[str]

Get active gemini file search store names.

Purpose

Retrieves get active gemini file search store names for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def get_active_gemini_file_search_store_names( provider_name: Optional[ str ] = None ) -> List[ str ]:
	"""Get active gemini file search store names.

	Purpose:
	    Retrieves get active gemini file search store names for the Streamlit application
	    workflow and returns the normalized value used by downstream UI, provider, database, or
	    document-processing steps.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	provider = get_provider_name( provider_name )

	if provider != 'Gemini':
		return [ ]

	if get_gemini_vector_backend( ) != 'File Search Stores':
		return [ ]

	backend = get_retrieval_backend( provider )
	resource_id = backend.get( 'resource_id', '' )

	return parse_storage_ids( resource_id )

merge_unique_strings

merge_unique_strings(
    primary: List[str], secondary: List[str]
) -> List[str]

Merge unique strings.

Purpose

Transforms merge unique strings inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
primary	List[str]	Primary value used by the application workflow.	required
secondary	List[str]	Secondary value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[str]: List of normalized values used by the application workflow.

Source code in app.py

def merge_unique_strings( primary: List[ str ], secondary: List[ str ] ) -> List[ str ]:
	"""Merge unique strings.

	Purpose:
	    Transforms merge unique strings inputs into a normalized representation used by
	    provider calls, document retrieval, data management, or UI rendering.

	Args:
	    primary: Primary value used by the application workflow.
	    secondary: Secondary value used by the application workflow.

	Returns:
	    List[str]: List of normalized values used by the application workflow.
	"""
	merged: List[ str ] = [ ]

	for source in [ primary, secondary ]:
		if not isinstance( source, list ):
			continue

		for item in source:
			text = str( item or '' ).strip( )

			if text and text not in merged:
				merged.append( text )

	return merged

build_provider_retrieval_summary

build_provider_retrieval_summary(
    provider_name: Optional[str] = None,
) -> str

Build provider retrieval summary.

Purpose

Builds build provider retrieval summary from validated runtime inputs and prepares the resulting object, payload, table, or display structure for later application processing.

Parameters:

Name	Type	Description	Default
provider_name	Optional[str]	Provider name used to route the operation.	None

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def build_provider_retrieval_summary( provider_name: Optional[ str ] = None ) -> str:
	"""Build provider retrieval summary.

	Purpose:
	    Builds build provider retrieval summary from validated runtime inputs and prepares the
	    resulting object, payload, table, or display structure for later application
	    processing.

	Args:
	    provider_name: Provider name used to route the operation.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	backend = get_retrieval_backend( provider_name )
	provider = backend.get( 'provider', '' )
	backend_name = backend.get( 'backend', '' )
	resource_id = backend.get( 'resource_id', '' )
	ready = backend.get( 'is_retrieval_ready', False )

	if not resource_id:
		return f'{provider}: no Vector Stores resource selected.'

	if ready:
		return f'{provider}: using {backend_name} resource {resource_id}.'

	return f'{provider}: selected {backend_name} resource {resource_id}, but it is not a retrieval store.'

text_model_options

text_model_options(chat: object) -> List[str]

Text model options.

Purpose

Supports the text model options application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
chat	object	object Chat value used by the application workflow.	required

Returns:

Type	Description
List[str]	List[ str ]: Text value produced for the active application workflow.

Source code in app.py

def text_model_options( chat: object ) -> List[ str ]:
	"""Text model options.

	Purpose:
	    Supports the text model options application workflow by coordinating validated inputs,
	    Streamlit session state, provider configuration, and local data processing.

	Args:
	    chat: object Chat value used by the application workflow.

	Returns:
	    List[ str ]: Text value produced for the active application workflow.
	"""
	if _provider( ) == 'GPT':
		return _safe( 'gpt', 'model_options', chat.model_options )
	if _provider( ) == 'Gemini':
		return _safe( 'gemini', 'model_options', chat.model_options )
	if _provider( ) == 'Grok':
		return _safe( 'grok', 'model_options', chat.model_options )
	return chat.model_options

image_model_options

image_model_options(image: Any) -> Any

Image model options.

Purpose

Supports the image model options application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
image	Any	Image value used by the application workflow.	required

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def image_model_options( image: Any ) -> Any:
	"""Image model options.

	Purpose:
	    Supports the image model options application workflow by coordinating validated inputs,
	    Streamlit session state, provider configuration, and local data processing.

	Args:
	    image: Image value used by the application workflow.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	if _provider( ) == 'GPT':
		return _safe( 'gpt', 'image_model_options', image.model_options )
	if _provider( ) == 'Gemini':
		return _safe( 'gemini', 'image_model_options', image.model_options )
	if _provider( ) == 'Grok':
		return _safe( 'grok', 'model_options', image.model_options )
	return image.model_options

image_size_or_aspect_options

image_size_or_aspect_options(image: Any) -> Any

Image size or aspect options.

Purpose

Supports the image size or aspect options application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
image	Any	Image value used by the application workflow.	required

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def image_size_or_aspect_options( image: Any ) -> Any:
	"""Image size or aspect options.

	Purpose:
	    Supports the image size or aspect options application workflow by coordinating
	    validated inputs, Streamlit session state, provider configuration, and local data
	    processing.

	Args:
	    image: Image value used by the application workflow.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	if _provider( ) == 'GPT':
		return _safe( 'gpt', 'aspect_options', image.size_options )
	if _provider( ) == 'Gemini':
		return _safe( 'gemini', 'aspect_options', image.size_options )
	if _provider( ) == 'Grok':
		return _safe( 'grok', 'model_options', image.size_options )
	return image.size_options

audio_model_options

audio_model_options(transcriber: Any) -> Any

Audio model options.

Purpose

Supports the audio model options application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
transcriber	Any	Transcriber value used by the application workflow.	required

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def audio_model_options( transcriber: Any ) -> Any:
	"""Audio model options.

	Purpose:
	    Supports the audio model options application workflow by coordinating validated inputs,
	    Streamlit session state, provider configuration, and local data processing.

	Args:
	    transcriber: Transcriber value used by the application workflow.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	if _provider( ) == 'GPT':
		return _safe( 'gpt', 'audio_model_options', transcriber.model_options )
	if _provider( ) == 'Gemini':
		return _safe( 'gemini', 'audio_model_options', transcriber.model_options )
	return transcriber.model_options

audio_language_options

audio_language_options(transcriber: Any) -> Any

Audio language options.

Purpose

Supports the audio language options application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
transcriber	Any	Transcriber value used by the application workflow.	required

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def audio_language_options( transcriber: Any ) -> Any:
	"""Audio language options.

	Purpose:
	    Supports the audio language options application workflow by coordinating validated
	    inputs, Streamlit session state, provider configuration, and local data processing.

	Args:
	    transcriber: Transcriber value used by the application workflow.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	if _provider( ) == 'GPT':
		return _safe( 'gpt', 'language_options', transcriber.language_options )
	if _provider( ) == 'Gemini':
		return _safe( 'gemini', 'language_options', transcriber.language_options )
	return transcriber.language_options

embedding_model_options

embedding_model_options(embed: Any) -> Any

Embedding model options.

Purpose

Supports the embedding model options application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
embed	Any	Embed value used by the application workflow.	required

Returns:

Name	Type	Description
Any	Any	Normalized result produced for the active application workflow.

Source code in app.py

def embedding_model_options( embed: Any ) -> Any:
	"""Embedding model options.

	Purpose:
	    Supports the embedding model options application workflow by coordinating validated
	    inputs, Streamlit session state, provider configuration, and local data processing.

	Args:
	    embed: Embed value used by the application workflow.

	Returns:
	    Any: Normalized result produced for the active application workflow.
	"""
	if _provider( ) == 'GPT':
		return _safe( 'gpt', 'embedding_model_options', embed.model_options )
	if _provider( ) == 'Gemini':
		return _safe( 'gemini', 'embedding_model_options', embed.model_options )
	return embed.model_options

route_document_query

route_document_query(prompt: str) -> str

Route document query.

Purpose

Executes the route document query workflow using the current provider, document, prompt, and session-state configuration.

Parameters:

Name	Type	Description	Default
prompt	str	Text value supplied to the prompt, conversion, retrieval, or provider workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def route_document_query( prompt: str ) -> str:
	"""Route document query.

	Purpose:
	    Executes the route document query workflow using the current provider, document,
	    prompt, and session-state configuration.

	Args:
	    prompt: Text value supplied to the prompt, conversion, retrieval, or provider workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	source = st.session_state.get( 'doc_source' )
	active_docs = st.session_state.get( 'docqna_active_docs', [ ] )
	doc_bytes = st.session_state.get( 'doc_bytes', { } )
	instructions = st.session_state.get( 'docqna_system_instructions' )

	if not source:
		return 'No document source selected.'

	if not active_docs:
		return 'No document selected.'

	# --------------------------------------------------
	# LOCAL DOCUMENT → Chat (single or multi)
	# --------------------------------------------------
	if source == 'uploadlocal':
		chat = get_chat_module( )

		# Single document
		if len( active_docs ) == 1:
			name = active_docs[ 0 ]
			file_bytes = doc_bytes.get( name )

			if not file_bytes:
				return 'Document content not available.'

			text = extract_text_from_bytes( file_bytes )

			full_prompt = f"""
				{instructions}

				Use the following document to answer the question.
				Be precise and cite relevant portions when possible.

				DOCUMENT:
				{text}

				QUESTION:
				{prompt}
				"""
			return chat.generate_text( prompt=full_prompt )

		# Multi-document injection
		combined_text = ""

		for name in active_docs:
			file_bytes = doc_bytes.get( name )
			if not file_bytes:
				continue

			text = extract_text_from_bytes( file_bytes )

			combined_text += f"\n\n===== DOCUMENT: {name} =====\n\n{text}\n"

		if not combined_text.strip( ):
			return 'No readable document content available.'

		full_prompt = f"""
			{instructions}

			You are analyzing multiple documents.

			Use the content below to answer the question.
			If multiple documents are relevant, compare them.
			Cite document names when possible.

			DOCUMENT SET:
			{combined_text}

			QUESTION:
			{prompt}
			"""

		return chat.generate_text( prompt=full_prompt )

	# --------------------------------------------------
	# FILES API → Files class
	# --------------------------------------------------
	if source == "filesapi":
		files = get_files_module( )

		# Single file search
		if len( active_docs ) == 1:
			return files.search( prompt, active_docs[ 0 ] )

		# Multi-file survey
		return files.survey( prompt )

	# --------------------------------------------------
	# VECTOR STORE → VectorStores class
	# --------------------------------------------------
	if source == 'vectorstore':
		vectorstores = get_vectorstores_module( )

		# Single store
		if len( active_docs ) == 1:
			return vectorstores.search( prompt, active_docs[ 0 ] )

		# Multi-store aggregation
		responses = [ ]
		for store_id in active_docs:
			result = vectorstores.search( prompt, store_id )
			if result:
				responses.append( result )

		if not responses:
			return 'No results found across selected vector stores.'

		return "\n\n".join( responses )

	return 'Unsupported document source.'

extract_text_from_bytes

extract_text_from_bytes(file_bytes: bytes) -> str

Extract text from bytes.

Purpose

Transforms extract text from bytes inputs into a normalized representation used by provider calls, document retrieval, data management, or UI rendering.

Parameters:

Name	Type	Description	Default
file_bytes	bytes	File, upload, or path value used by the document or storage workflow.	required

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def extract_text_from_bytes( file_bytes: bytes ) -> str:
	"""Extract text from bytes.

	Purpose:
	    Transforms extract text from bytes inputs into a normalized representation used by
	    provider calls, document retrieval, data management, or UI rendering.

	Args:
	    file_bytes: File, upload, or path value used by the document or storage workflow.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	try:
		import fitz  # PyMuPDF

		doc = fitz.open( stream=file_bytes, filetype="pdf" )
		text = ""
		for page in doc:
			text += page.get_text( )
		return text.strip( )

	except Exception as e:
		exception = Error( e )
		exception.module = 'app'
		exception.cause = 'extract_text_from_bytes'
		exception.method = 'extract_text_from_bytes( file_bytes ) -> str'
		Logger( ).write( exception )
		try:
			return file_bytes.decode( errors="ignore" )
		except Exception as e:
			exception = Error( e )
			exception.module = 'app'
			exception.cause = 'extract_text_from_bytes'
			exception.method = 'extract_text_from_bytes( file_bytes ) -> str'
			Logger( ).write( exception )
			return ""

summarize_document

summarize_document() -> str

Summarize document.

Purpose

Executes the summarize document workflow using the current provider, document, prompt, and session-state configuration.

Returns:

Name	Type	Description
str	str	Text value produced for the active application workflow.

Source code in app.py

def summarize_document( ) -> str:
	"""Summarize document.

	Purpose:
	    Executes the summarize document workflow using the current provider, document, prompt,
	    and session-state configuration.

	Returns:
	    str: Text value produced for the active application workflow.
	"""
	doc_instructions = st.session_state.get( "doc_instructions", "" )
	summary_prompt = """
		Provide a clear, structured summary of this document.
		Include:
		- Purpose
		- Key themes
		- Major conclusions
		- Important data points (if any)
		- Policy implications (if applicable)

		Be precise and concise.
		"""
	if doc_instructions:
		summary_prompt = f"{doc_instructions}\n\n{summary_prompt}"

	return route_document_query( summary_prompt.strip( ) )

on_stream_chunk

on_stream_chunk(chunk: str) -> None

On stream chunk.

Purpose

Supports the on stream chunk application workflow by coordinating validated inputs, Streamlit session state, provider configuration, and local data processing.

Parameters:

Name	Type	Description	Default
chunk	str	Chunk value used by the application workflow.	required

Source code in app.py

def on_stream_chunk( chunk: str ) -> None:
	"""On stream chunk.

	Purpose:
	    Supports the on stream chunk application workflow by coordinating validated
	    inputs, Streamlit session state, provider configuration, and local data
	    processing.

	Args:
	    chunk: Chunk value used by the application workflow.
	"""
	if chunk is None:
		return

	stream_buffer.append( str( chunk ) )
	stream_placeholder.markdown( ''.join( stream_buffer ) + '▌' )

get_conn

get_conn() -> sqlite3.Connection

Get conn.

Purpose

Retrieves get conn for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Returns:

Type	Description
Connection	sqlite3.Connection: SQLite connection used by the local data-management workflow.

Source code in app.py

def get_conn( ) -> sqlite3.Connection:
	"""Get conn.

	Purpose:
	    Retrieves get conn for the Streamlit application workflow and returns the normalized
	    value used by downstream UI, provider, database, or document-processing steps.

	Returns:
	    sqlite3.Connection: SQLite connection used by the local data-management workflow.
	"""
	return sqlite3.connect( cfg.DB_PATH )

reset_selection

reset_selection()

Reset selection.

Purpose

Maintains application runtime state for reset selection by initializing, clearing, or restoring the session values used by the active Streamlit workflow.

Source code in app.py

def reset_selection( ):
	"""Reset selection.

	Purpose:
	    Maintains application runtime state for reset selection by initializing, clearing, or
	    restoring the session values used by the active Streamlit workflow.
	"""
	st.session_state.pe_selected_id = None
	st.session_state.pe_caption = ''
	st.session_state.pe_name = ''
	st.session_state.pe_text = ''
	st.session_state.pe_version = ''
	st.session_state.pe_id = 0

load_prompt

load_prompt(pid: int) -> None

Load prompt.

Purpose

Retrieves load prompt for the Streamlit application workflow and returns the normalized value used by downstream UI, provider, database, or document-processing steps.

Parameters:

Name	Type	Description	Default
pid	int	Pid value used by the application workflow.	required

Source code in app.py

def load_prompt( pid: int ) -> None:
	"""Load prompt.

	Purpose:
	    Retrieves load prompt for the Streamlit application workflow and returns the
	    normalized value used by downstream UI, provider, database, or document-processing
	    steps.

	Args:
	    pid: Pid value used by the application workflow.
	"""
	with get_conn( ) as conn:
		_select = f"SELECT Caption, Name, Text, Version, ID FROM {TABLE} WHERE PromptsId=?"
		cur = conn.execute( _select, (pid,), )
		row = cur.fetchone( )
		if not row:
			return
		st.session_state.pe_caption = row[ 0 ]
		st.session_state.pe_name = row[ 1 ]
		st.session_state.pe_text = row[ 2 ]
		st.session_state.pe_version = row[ 3 ]
		st.session_state.pe_id = row[ 4 ]