Graph RAG vs. Classical RAG:
A Comparative Analysis

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Key takeaways

End-to-End POC Development Services

We specialize in Proof of Concept (POC) development designed to rapidly transform your ideas into tangible prototypes. Our expert team takes your concepts from vision to reality, helping you validate technological feasibility, assess potential risks, and showcase business value to stakeholders.

• Classical RAG: It was implemented using the LangChain framework, with FAISS employed for embedding-based document retrieval. In this setup, user queries are matched against vectorised document chunks based on semantic similarity. Relevant sections are then retrieved and passed to GPT-4, generating responses grounded in the source material.
• Neural Search: This method leverages transformer-based models to directly rank passages, providing more nuanced results than classical embeddings. GPT-4 then summarizes the best-matching content.