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Donger is a Python and Streamlit application for building, running, and managing Grok-powered analytical assistants. It supports text generation, image generation and analysis, image editing, audio transcription, audio translation, text-to-speech, embeddings, document question answering, Gemini file operations, file-search stores, Google Cloud bucket management, prompt engineering, SQLite-backed data management, and export workflows.

The application is designed for federal data analysis, budget execution support, document review, knowledge retrieval, prompt management, and multimodal artificial intelligence experimentation.

Documentation Map

Page Purpose
Getting Started Install Donger, configure the virtual environment, run Streamlit, and build the documentation.
Configuration Configure API keys, Google Cloud settings, paths, local storage, and documentation publishing values.
User Guide Use Donger's text, image, audio, embedding, document, files, stores, bucket, prompt, export, and data modes.
Architecture Understand the Streamlit shell, Gemini wrappers, configuration layer, persistence, retrieval, and documentation model.
API Reference Render Google-style docstrings from import-safe Python modules with mkdocstrings.

Core Capabilities

Capability Description
Text Generation Grok-backed chat and prompt response workflows with optional grounding and URL context.
Images Image generation, analysis, editing, aspect controls, MIME controls, and model-specific options.
Audio Transcription, translation, browser recording support, uploaded audio processing, and text-to-speech.
Embeddings Text normalization, chunking, token metrics, embedding generation, and vector inspection.
Document Q&A Local retrieval-augmented question answering using extracted document text and vector search.
Files and Stores Grok file upload, metadata workflows, file-search stores, and store file upload workflows.
Google Cloud Buckets Bucket creation, retrieval, deletion, and upload workflows.
Prompt Engineering SQLite-backed reusable prompt records and prompt-template workflows.
Data Management SQLite import, browse, CRUD, profiling, filtering, aggregation, visualization, administration, and safe SQL queries.