RAG with LangChain

1. Introduction

Retrieval-Augmented Generation (RAG) is a technique that combines traditional retrieval methods with generative models. This lesson focuses on implementing RAG using LangChain, a powerful framework designed for building LLM applications.

2. Key Concepts

3. Setup

To implement RAG with LangChain, follow these steps:

1. Install the necessary Python packages:
pip install langchain openai
2. Set up your API keys for the LLM provider you are using (e.g., OpenAI).
3. Prepare your knowledge base (e.g., documents, databases) that will provide the retrieval source.

4. Code Example

Here’s a simple example of implementing RAG with LangChain:

from langchain import OpenAI, RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings

# Load documents
loader = TextLoader("path_to_your_document.txt")
documents = loader.load()

# Split documents into manageable chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
texts = text_splitter.split_documents(documents)

# Create embeddings and a vector store
embeddings = OpenAIEmbeddings()
vector_store = FAISS.from_documents(texts, embeddings)

# Set up RetrievalQA
llm = OpenAI(temperature=0)
retrieval_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=vector_store.as_retriever())

# Query the model
query = "What is the main topic of the document?"
result = retrieval_qa.run(query)

print(result)

5. Best Practices

When implementing RAG, consider the following best practices:

• Ensure your knowledge base is regularly updated to reflect the latest information.
• Optimize your retrieval strategy to balance speed and accuracy.
• Experiment with different chunk sizes to improve performance.
• Fine-tune your LLM parameters for better context understanding.

6. FAQ