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&A
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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