RAG & Code Validation
Empowers AI agents to learn from the web and validate their generated code against a knowledge base, effectively preventing hallucinations.
About
This comprehensive framework enhances AI agent capabilities through a dual approach. It features a dynamic Retrieval-Augmented Generation (RAG) pipeline that crawls websites, documentation, and GitHub repositories, storing processed content in a vector database for AI agents to access up-to-date, contextual information. Furthermore, it offers an advanced knowledge graph-based code validation system, analyzing code repositories to build a detailed map of functions and relationships, which it then uses to check AI-generated Python code for correctness and prevent 'hallucinations' like non-existent APIs or incorrect parameters. This combined functionality creates more reliable and trustworthy AI assistants.
Key Features
- Smart Web Crawler (Crawl4AI) for diverse content ingestion
- 2 GitHub stars
- Knowledge Graph-based AI code validation using Neo4j
- Flexible API provider integration for embeddings and responses (Gemini, OpenAI)
- Agentic RAG for dedicated indexing and retrieval of code examples
- Hybrid Search combining keyword and semantic vector retrieval
Use Cases
- Providing AI agents with real-time, contextually relevant information from diverse web sources.
- Automated validation of AI-generated Python code to prevent hallucinations and ensure correctness.
- Building more reliable and trustworthy AI assistants by combining dynamic knowledge retrieval with output verification.
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