LangChain RAG Agent

LangChain RAG Agent

Build a sophisticated Retrieval-Augmented Generation (RAG) agent using LangChain for intelligent document question-answering.

Architecture

This example demonstrates a complete RAG pipeline:

1. Document Loading: Ingest documents from various sources
2. Text Splitting: Break documents into manageable chunks
3. Embedding: Convert text to vector representations
4. Vector Storage: Store embeddings in a vector database
5. Retrieval: Find relevant documents based on queries
6. Generation: Generate answers using retrieved context

Key Components

python
1from langchain.chains import RetrievalQA
2from langchain.embeddings import OpenAIEmbeddings
3from langchain.vectorstores import Chroma
4from langchain.llms import OpenAI
5
6# Initialize components
7embeddings = OpenAIEmbeddings()
8vectorstore = Chroma(embedding_function=embeddings)
9qa_chain = RetrievalQA.from_chain_type(
10    llm=OpenAI(),
11    retriever=vectorstore.as_retriever()
12)

Applications

• Internal knowledge base systems
• Research paper analysis
• Legal document review
• Technical documentation Q&A