React&RAG LLama Crew

🚀 Overview

This project introduces an AI Agent Crew powered by LlamaIndex to analyze, generate, and process code efficiently. The system consists of multiple specialized LLMs and a ReActAgent, forming a collaborative AI team that enhances Retrieval-Augmented Generation (RAG), structured output parsing, and vector-based search.

🔥 Key Features

🏆 Multi-Agent AI Crew

• Llama3.2: Handles natural language understanding and analysis.
• CodeLlama: Specialized in code generation and improvement.
• Ollama: Supports enhanced query response and knowledge retrieval.
• ReActAgent: Coordinates tools, interacts with the user, and ensures logical reasoning.

🧠 Intelligent Code Analysis & Generation

• Generates, refines, and explains code based on user prompts.
• Understands complex queries and adapts responses accordingly.

📄 API Documentation Querying

• Vector-based search on API documentation for instant lookups.
• Automatic document processing for .pdf files.
• LlamaParse integration to extract meaningful text from documents.

🔎 Retrieval-Augmented Generation (RAG)

• Combines LLMs with real-time document retrieval to enhance code generation accuracy.
• Ensures responses are grounded in relevant documentation.
• Reduces hallucinations and improves factual correctness.

📂 Code Reader Functionality

• Reads and retrieves code files using the code_reader tool.
• Helps in debugging, refactoring, and understanding existing code.

🔗 LlamaIndex-Powered Query Pipeline

• VectorStoreIndex for efficient document retrieval.
• QueryEngineTool for answering API documentation queries.
• SimpleDirectoryReader to load and process data.

⚙️ Advanced Code Parsing & Output Structuring

• Uses PydanticOutputParser to structure output in JSON format.
• Generates clean, structured code with meaningful descriptions and filenames.

🎯 Robust & Reliable Execution

• Error handling & retry mechanism for resilient processing.
• Saves generated code automatically to the output directory.

🛠 How It Works

1. User Input: Enter a prompt to generate or analyze code.
2. Processing: The agent crew retrieves relevant documentation, reads code, and formulates responses.
3. Generation: The AI produces well-structured code and descriptions.
4. Output: The code is saved with an appropriate filename.

🏗 Technologies Used

• LlamaIndex: Efficient document indexing and retrieval.
• Ollama: Llama3.2 & CodeLlama models for text & code processing.
• Pydantic: Structured output parsing.
• HuggingFace Optimum: High-performance vector embeddings.
• Retrieval-Augmented Generation (RAG): Enhances AI responses with real-time document retrieval.
• Ast & Dotenv: Code execution & environment configuration.

📌 Usage

1. Place code and documentation files in the data/ folder.
2. Run the script:

   python main.py

3. Follow the prompt to analyze or generate code.
4. Output files are saved in the output/ folder.

📩 Contact

For improvements or issues, feel free to contribute or reach out!