Forbes recently named Retrieval-Augmented Generation (RAG) applications the hottest trend in AI, and it’s not hard to see why. RAG workflows require minimal coding and are great for building user trust in large language models (LLMs). However, the challenge comes when managing both structured and unstructured text in these systems.
Unstructured text, often chunked or embedded, fits seamlessly into RAG workflows. However, structured data needs extra steps to ensure it’s accurate and relevant for your LLM to process. Some developers solve this by taking daily architecture snapshots and transforming them into LLM-readable text. A more efficient approach, though, is using knowledge graphs. Knowledge graphs store both structured and unstructured data in a single database, making it easier to provide LLMs with the contextual information they need to deliver precise responses.
In recent experiments at Cplus Soft, we explored hybrid retrieval using Neo4j, which proved highly effective when the graph contains comprehensive data. We also evaluated Microsoft’s GraphRAG, which, despite being more expensive, consistently ensures that no information from text files is missed—making it a valuable investment.
With the growing reliance on large language models (LLMs) for complex tasks, retrieval-augmented generation (RAG) is an excellent way to use these models alongside private databases. However, many RAG processes simplify their approach to vector-based searches. While vector searches are powerful, they sometimes need help to convey the desired context to the LLM effectively. This is where graphs step in.
Knowledge Graphs enhance response accuracy by providing a rich context that complements the model’s output. By combining both vector and full-text indexes, the Neo4j GraphRAG Python library’s HybridRetriever class refines this process further. It retrieves and ranks nodes from both indexes, merging their scores to return the top results. This approach makes retrieval more precise and opens up new possibilities compared to standard RAG methods.
An insightful article I found (link at the bottom) dives deeper into the power of GraphRAG and Hybrid Retrieval with Neo4j, explaining how this combination improves the accuracy of returned results. The use of vector indexes (embeddings) and full-text indexes (strings), along with Knowledge Graphs, allows for more targeted, precise information retrieval. It’s a powerful tool for getting the proper context when answering complex user queries, something a vector search alone might struggle with.
The Neo4j GraphRAG package is open source, and the HybridRetriever class is a game changer for those looking to enhance their AI applications. You can check out the package on GitHub for more details and sample code.
This approach is part of our continuous effort at Cplus Soft to push the boundaries of AI-powered solutions, making them more reliable and robust for real-world applications.
Stay tuned for more innovations in our next CplusNews edition!
Article Neo4J:https://neo4j.com/developer-blog/knowledge-graph-rag-application/
