The process for preparing documents for retrieval-augmented generation (RAG) involves five steps: (1) Chunk the text by splitting documents into multiple chunks, (2) Generate embeddings by using a text embedding model to create vector representations of the text chunks, (3) Encode the user query by converting the input question into a vector at query time, (4) Perform similarity search by applying algorithms like cosine similarity to compare the distance between the user input vector and the embedded text chunks, and (5) Retrieve top matches by returning the most similar documents to provide context to the large language model.

Authors

• Person: Not available Organization: Neo4j
  How to Improve Multi-Hop Reasoning With Knowledge Graphs and ...

Sources

• How to Improve Multi-Hop Reasoning With Knowledge Graphs and ... neo4j.com Neo4j via serper

Referenced by nodes (3)

• Retrieval-Augmented Generation (RAG) concept
• embeddings concept
• cosine similarity concept