Multi-model RAG with LangChain

In a blog post by Martin Schaer, the process of building a Retrieval-Augmented Generation (RAG) solution using SurrealDB and LangChain is explored, highlighting the importance of optimizing for accuracy and latency in AI applications. Schaer details his experiment, including using WhatsApp chat data to create a vector and graph store with SurrealDB, employing LangChain's document loaders for data ingestion, and utilizing Ollama's embeddings for vector storage. The use of prompt engineering and keyword inference is discussed to enhance semantic search capabilities, while Schaer shares insights on the limitations of simple graphs and the potential benefits of incorporating more complex relationships, as seen in LightRAG. He emphasizes the importance of structuring code to allow for testing variations and measuring results, suggesting that a multi-model RAG architecture can be tailored to specific use cases to improve document retrieval and provide efficient, cost-effective solutions. The post concludes with recommendations for further experimentation and optimization, encouraging readers to engage with the SurrealDB community for further discussion and exploration.