Overview of Chains

In this lesson, we’ll explore two high-level chains that streamline common workflows in LangChain:

1. Summarization: Batch summarization using the Create Stuff Documents Chain
2. Retrieval: Retrieval-Augmented Generation (RAG) via the Create Retrieval Chain

These constructs let you process multiple documents without reinventing the wheel, whether you need to summarize everything at once or selectively retrieve relevant passages.

1. Summarization with Create Stuff Documents Chain

The Create Stuff Documents Chain merges a list of document chunks into a single prompt and sends it to your LLM. This is ideal when your combined content stays within the model’s context window.

Use cases:

• Summarize multiple documents in one pass
• Extract specific insights across all inputs

Example: Batch Summarization

from langchain.chains import create_stuff_documents_chain
from langchain.llms import OpenAI
from langchain.schema import Document

# Initialize your LLM
llm = OpenAI(model_name="gpt-4")

# Prepare document chunks
docs = [
    Document(page_content="Document text chunk 1..."),
    Document(page_content="Document text chunk 2..."),
    # ...
]

# Build the chain
chain = create_stuff_documents_chain(llm=llm)

# Run the summarization
summary = chain.run(input_documents=docs)
print("Summary:", summary)

2. Retrieval with Create Retrieval Chain

For larger corpora that exceed a single prompt window, the Create Retrieval Chain combines a retriever with the Stuff Documents logic. This effectively implements a simple RAG workflow:

1. Retriever retrieves the most relevant chunks.
2. Document Chain formats those chunks into a prompt.
3. LLM generates the final answer.

Example: Simple RAG Pipeline

from langchain.chains import create_retrieval_chain
from langchain.llms import OpenAI
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings

# Set up embeddings and vector store
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_texts(["Doc text A", "Doc text B"], embeddings)

# Build the retrieval chain
retrieval_chain = create_retrieval_chain(
    llm=OpenAI(model_name="gpt-4"),
    retriever=vectorstore.as_retriever()
)

# Run RAG
query = "What are the key takeaways from these documents?"
result = retrieval_chain.run(query)
print("Answer:", result)

Comparison of Built-In Chains

Next Steps

Now that you understand both the Summarization and Retrieval chains, let’s dive into hands-on demos to see them in action.

Links and References

• LangChain Documentation: Chains
• Retrieval-Augmented Generation (RAG) Overview
• OpenAI Python SDK