Implementing the RAG flow

RAG Workflow Implementation Notes

Overview

• The Retrieval-Augmented Generation (RAG) flow is implemented through a five-step process using a vector database.

Step 1: Text Chunking

• The initial source text (e. g. , report. md) is read and broken down into smaller, manageable pieces called chunks.
• Chunking can be performed by section or other defined methods.

Step 2: Embedding Generation (Chunks)

• Each individual text chunk is passed through an embedding function.
• This function generates a numerical representation (an embedding) for the text.
• This process requires an API request for every chunk.

Step 3: Vector Store Creation and Population

• A vector store (e. g. , VectorIndex) is instantiated to hold the data.
• When storing data, it is essential to pair the generated embedding (the vector) with the original chunk text (or a unique ID).
• Purpose of pairing: The embedding itself is not meaningful to a human; the associated text is required for the final answer.

Step 4: Query Embedding

• When a user submits a question (the query), the same embedding function used in Step 2 is applied to the query string.
• This converts the user's natural language question into a query embedding.

Step 5: Retrieval (Search)

• The query embedding is used to search the vector store.
• The search function finds the chunks whose embeddings are most similar to the query embedding.
• The search allows the user to specify the number of most relevant results (e. g. , top 2).
• Relevance is often measured using metrics like cosine distance.

Takeaways

• The RAG workflow involves a five-step process utilizing a vector database.
• Source text must be broken into chunks and converted into numerical embeddings.
• The vector store pairs the generated embedding with the original chunk text to ensure the final answer is meaningful.
• User queries are converted into embeddings to enable similarity search against the stored vectors.
• Retrieval identifies the most relevant chunks by measuring the similarity between the query embedding and stored embeddings (e. g. , using cosine distance).