𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗿𝗮𝗽𝗵𝘀: 𝗧𝗵𝗲 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗣𝗶𝗲𝗰𝗲 𝗶𝗻 𝗥𝗔𝗚 𝗦𝘆𝘀𝘁𝗲𝗺𝘀

Most RAG systems follow a simple path. Documents go to chunks. Chunks turn into embeddings. Embeddings live in a vector database. Similarity search finds text. The LLM gives an answer.

This works for many chatbots. It works well for finding similar text. But it fails when you need to understand relationships.

Vector databases find things that look similar. They do not understand how things connect.

Look at these facts:

  • React is used in Project A.
  • Project A uses a RAG system.
  • The RAG system uses ChromaDB.

A human sees the link: React leads to Project A, which leads to RAG, which leads to ChromaDB. A vector database sees four separate pieces of text.

If you ask a vector search "Which projects use React and AI?", it might struggle. It looks for documents with those words. It does not follow the path between them.

This is where knowledge graphs help. A knowledge graph stores entities and their connections. It maps the links between concepts.

You do not have to replace your vector database. You should use both. This is called Graph-RAG.

The vector store handles semantic retrieval. The graph store handles relationship retrieval. Together, they give your AI agent better context.

Use a graph layer when your data includes:

  • Technical docs
  • Research notes
  • Product manuals
  • Enterprise knowledge

Users want agents that do more than find documents. They want agents that connect ideas. They want agents that explain dependencies.

Stop building systems that only search. Start building systems that reason.

Source: https://dev.to/vishdevwork/knowledge-graphs-the-missing-piece-in-most-rag-systems-1j75

Optional learning community: https://t.me/GyaanSetuAi