Implementing the RAG flow

Overview

• Implementing a complete RAG (Retrieval-Augmented Generation) flow using a vector database
• Five sequential steps to process documents and answer user queries

Step 1: Chunking Text

• Read the source document (report. md)
• Split text into chunks organized by section using a chunk_by_section() function
• Each chunk represents a logical section of the document

Step 2: Generate Embeddings

• Create vector embeddings for each text chunk
• The embedding function accepts either a single string or a list of strings
• Returns a list of embeddings corresponding to each chunk

Step 3: Store Embeddings in Vector Database

• Create a vector store instance
• Loop through pairs of chunks and embeddings using zip()
• Insert each embedding into the store with associated metadata
• Store the original chunk text alongside each embedding as a dictionary with a "content" key
• This metadata is crucial because embeddings alone are not human-readable; you need the original text for retrieval

Step 4: Generate User Query Embedding

• When a user asks a question, convert it to an embedding using the same generate_embedding() function
• Example query: "What did the software engineering department do last year? "

Step 5: Search and Retrieve Relevant Documents

• Query the vector store with the user embedding
• Specify how many top results to return (e. g. , 2 most relevant chunks)
• Results include cosine distance scores and the original chunk content
• Lower distance scores indicate higher relevance

Key Insight

• Storing metadata (original text) with embeddings enables meaningful retrieval results for users

Takeaways

• Split documents into logical chunks by section, then generate vector embeddings for each chunk
• Store embeddings in a vector database alongside the original text as metadata—embeddings alone aren't human-readable
• Convert user queries to embeddings using the same function, then search the vector store to retrieve the most relevant chunks based on cosine distance scores
• Lower distance scores indicate higher relevance when retrieving results
• Metadata storage is essential because it allows you to return meaningful, readable content to users rather than just numerical vectors