Introduction to RAG Pipelines for Document-Based Chatbots

RAG (Retrieval-Augmented Generation) pipelines provide an effective way to build chatbots that can retrieve and generate responses based on large document collections. This article summarizes a video introducing the basic concepts and code implementation of RAG pipelines.

The Basic RAG Pipeline Architecture

Document Loader

Loads documents from a source like a PDF file

Chunk Splitter

Splits documents into smaller chunks for processing

Embedding Computation

Calculates numeric embeddings for each chunk

Vector Store

Stores chunk embeddings for fast retrieval

Semantic Search

Searches vector store for relevant chunks

Response Generation

Uses search results to generate natural language responses

Advanced Techniques

Hybrid Search

Combines keyword and embedding-based searches

Ensemble Retriever

Assigns weights to different search methods

Code Example

The video provides an example RAG pipeline implementation using libraries like RankBM25, huggingface, and ChromaDB.

Processing Documents

The example loads a PDF, splits it into chunks, and processes the chunks through the RAG pipeline.

The video invites viewing a series on building RAG pipelines to develop conversational systems using document collections.


Q: What is the purpose of a RAG pipeline?

A: A RAG pipeline allows chatbots to retrieve relevant passages from documents and use them to generate natural language responses, providing a scalable way to incorporate large document collections into conversational systems.

Q: What are some common techniques for improving search in a RAG pipeline?

A: Two techniques mentioned are hybrid search, which combines keyword and embedding-based searches, and ensemble retriever, which assigns different weights to multiple search methods.

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