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Basic Information

PocketGroq is a Python SDK and toolkit for developers who want to integrate with the Groq API and build multimodal AI applications and autonomous agents. It exposes a central GroqProvider class and several managers and utilities to perform text generation, vision analysis, speech transcription and translation, web search and crawling, retrieval-augmented generation (RAG), and automated end-to-end agent workflows. The project is intended for developers building applications that need image and screen analysis, audio processing, web content extraction, document indexing, and autonomous request handling. Configuration is handled via environment variables such as GROQ_API_KEY and optional USER_AGENT. The README documents method signatures, usage examples, and an included test suite so developers can prototype, test, and extend capabilities for custom tools and persistence options.

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Features
The repository documents and implements a broad feature set centered on the GroqProvider interface. Vision capabilities include image URL analysis, desktop/screen capture and region analysis, and multi-turn image conversations. Speech features provide transcription and translation with selectable Whisper-style models and advanced options. Web tooling includes web_search, get_web_content, scrape_url, and crawl_website with format options. RAG support includes initialize_rag, document loading and querying, and optional Ollama integration with graceful degradation. There is an AutonomousAgent class that can research, search the web, evaluate sources, and produce answers. Additional utilities include conversation management, Chain of Thought reasoning helpers, response evaluation, tool registration, model discovery, error handling, and a comprehensive test menu.
Use Cases
PocketGroq simplifies building AI-driven applications by wrapping Groq API functionality into reusable, documented Python components. Developers can rapidly add multimodal input handling for images and audio, perform on-screen analysis for desktop integrations, and run multi-turn conversations that include images. Web crawling and scraping tools let agents gather source material for answers. RAG and document indexing enable context-aware retrieval and persistent knowledge stores when Ollama is available. The AutonomousAgent automates search and verification workflows, reducing manual orchestration. Built-in response evaluation and error handling assist with quality control. The included test suite and environment-driven configuration help teams validate features and run repeatable experiments when prototyping or deploying agent-powered services.

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