v0.1.0

Reflexion RAG Engine v0.1.0

Date: June 22, 2025

We are thrilled to announce the inaugural release of the Reflexion RAG Engine v0.1.0. This initial version introduces a production-ready, retrieval-augmented generation system designed for complex reasoning tasks that require multi-step analysis and comprehensive knowledge synthesis.

This release establishes a powerful foundation for building intelligent applications that can reason, self-correct, and interact with real-time information.

🧠 Advanced Reflexion Architecture

• Iterative Self-Correction: The engine employs a multi-cycle reasoning loop where it generates an initial response, evaluates its own confidence and completeness, and automatically triggers follow-up queries to fill knowledge gaps.
• Dynamic Decision Engine: A sophisticated evaluation module decides whether to COMPLETE a response, CONTINUE refining it, or REFINE_QUERY for better results, ensuring comprehensive and accurate answers.
• Confidence Scoring: Every generated response is scored for confidence, providing a transparent measure of answer quality.

🔄 Multi-LLM Orchestration

• Specialized Model Roles: The system orchestrates multiple large language models, assigning specialized roles for generation, evaluation, and final synthesis to optimize for both quality and performance.
• Flexible Model Support: Integrates with the GitHub Models ecosystem, providing access to a wide range of state-of-the-art models. For a list of compatible models, please refer to Github Models

🌐 Hybrid Retrieval System

• High-Performance Vector Store: Built on SurrealDB with native HNSW indexing for fast, scalable, and production-ready vector search over local documents.
• Real-Time Web Search: Integrated Google Custom Search allows the engine to augment its knowledge base with up-to-the-minute information from the web, which can be enabled for every reasoning cycle.
• Advanced Content Extraction: Utilizes sophisticated content extraction to pull clean, relevant text from web pages, filtering out noise and low-quality content.

🚀 Developer & Operational Excellence

• Fully Asynchronous Pipeline: The entire engine is built on Python's asyncio, ensuring high throughput and non-blocking I/O from document ingestion to query processing.
• Streaming Responses: Delivers answers in real-time as they are generated, providing a responsive user experience.
• Comprehensive CLI: An intuitive command-line interface powered by Typer for interactive chat, document ingestion, and system configuration management.
• Modular & Extensible API: A clean, interface-driven architecture allows for easy extension and integration into larger applications[1].

🛠 Getting Started

1. Clone & Install:

   git clone https://github.com/cloaky233/rag_new.git
   cd rag_new
   uv venv && source .venv/bin/activate
   uv sync

2. Configure: Copy the .env.example to .env and populate it with your credentials for GitHub, SurrealDB, and Google Search.
3. Ingest & Run:

   # Ingest your local documents
   uv run rag.py ingest --docs_path=./docs
   
   # Start the interactive chat
   uv run rag.py chat

For detailed setup instructions, please see the Installation Guide

🛣️ What's Next?

This is just the beginning. Our roadmap includes integrating the Model Context Protocol (MCP) for standardized tool use, enhancing web search with more sources, and optimizing critical performance paths with Rust extensions.

For more details, please see our public Roadmap

🙏 Acknowledgements

A special thanks to the teams behind GitHub Models, SurrealDB, and Azure AI Inference for providing the powerful infrastructure that makes this project possible.

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Authored by Lay Sheth @cloaky233