Implementing the RAG flow

RAG Workflow Implementation using a Vector Database

• Purpose: The workflow demonstrates the five core steps of Retrieval-Augmented Generation (RAG) using a sample vector database implementation.

The RAG Workflow Steps:

• Chunking (Step 1):
• The source text (e. g. , report. md) is read and broken down into smaller, manageable pieces (chunks).
• Chunking is performed by section to maintain contextual integrity.

• Embedding Generation (Step 2):
• An embedding (a numerical representation) is generated for each individual text chunk using a function like generate embedding.

• Indexing and Storage (Step 3):
• A vector store instance is created.
• The system iterates through all pairs of (chunk, embedding).
• Each pair is inserted into the vector store using store add vector.
• Crucial Detail: When storing, the original chunk text (or a unique ID) must be included alongside the embedding. This allows the system to retrieve the meaningful text later, as the embedding alone is not human-readable.

• Query Embedding (Step 4):
• When a user submits a question, the question itself is passed through the embedding function to create a user embedding.

• Retrieval/Search (Step 5):
• The vector store is searched using the user embedding.
• The search function (results store dot search) finds the most relevant chunks based on similarity.
• The system can specify the number of results desired (e. g. , top two chunks).
• Output: The results include the retrieved document content and a similarity score (e. g. , cosine distance).

Key Concepts:

• Chunking: Breaking large documents into smaller, context-rich segments.
• Embedding: Converting text into high-dimensional numerical vectors.
• Vector Store: A database designed to efficiently store and search these numerical vectors.
• Retrieval: The process of finding the most semantically similar chunks to the user's query embedding.
• Similarity Score: A metric (like cosine distance) used to quantify how closely related the query embedding is to a stored chunk embedding.

Takeaways

• RAG begins by chunking large source documents into smaller, context-rich segments.
• Embeddings convert both the source text chunks and the user query into high-dimensional numerical vectors.
• A Vector Store indexes and stores these vectors, ensuring the original text is linked to its numerical representation.
• Retrieval works by searching the Vector Store to find chunks whose embeddings are most semantically similar to the query embedding.
• Similarity scores (like cosine distance) quantify the relationship between the query and the retrieved document chunks.