How to Build a RAG Pipeline with LangChain and Claude in 2026

TechPulse Team
Published September 9, 2026 · 13 min read

Retrieval-Augmented Generation (RAG) is still the most reliable way to get an LLM to answer accurately from your own documents instead of hallucinating from training data. This guide builds a complete RAG pipeline using LangChain for orchestration, a local vector store for retrieval, and Claude for generation — from raw documents to a working question-answering endpoint.

Table of Contents

• What RAG Actually Solves
• Pipeline Architecture
• Project Setup
• Document Ingestion and Chunking
• Generating Embeddings and Indexing
• Retrieval Chain
• Generation With Claude
• Full Pipeline Together
• Improving Retrieval Quality
• FAQ

What RAG Actually Solves

An LLM’s training data has a cutoff and no knowledge of your private documents. RAG closes that gap by retrieving relevant chunks of your own content at query time and feeding them into the model’s context window alongside the question, so the model answers from your actual source material rather than guessing. This matters for internal documentation search, customer support bots grounded in your knowledge base, and any use case where factual accuracy against a specific corpus is non-negotiable.

Pipeline Architecture

A RAG pipeline has two distinct phases that run independently:

1. Ingestion (offline, run once or on a schedule): load documents → split into chunks → embed each chunk → store vectors in an index.
2. Query (online, runs per user question): embed the question → retrieve the most similar chunks → pass them plus the question to Claude → return the grounded answer.

Project Setup

pip install langchain langchain-anthropic langchain-community \
    chromadb sentence-transformers pypdf

# set your API key
export ANTHROPIC_API_KEY="your-key-here"

Document Ingestion and Chunking

Chunk size is the single most impactful tuning knob in a RAG pipeline. Chunks too large dilute relevance signal; chunks too small lose surrounding context. 500-1000 characters with overlap is a reasonable default for prose documentation:

from langchain_community.document_loaders import DirectoryLoader, PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

loader = DirectoryLoader('./docs', glob='**/*.pdf', loader_cls=PyPDFLoader)
raw_documents = loader.load()

splitter = RecursiveCharacterTextSplitter(
    chunk_size=800,
    chunk_overlap=120,
    separators=['\n\n', '\n', '. ', ' ', '']
)
chunks = splitter.split_documents(raw_documents)
print('Split into', len(chunks), 'chunks')

Generating Embeddings and Indexing

Embeddings turn each text chunk into a vector so semantic similarity search works. A local embedding model keeps this step fast and free; Chroma gives you a persistent local vector store with no external service to run:

from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma

embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')

vectorstore = Chroma.from_documents(
    documents=chunks,
    embedding=embeddings,
    persist_directory='./chroma_db'
)
vectorstore.persist()

For production scale (millions of chunks), swap Chroma for a managed vector database like Pinecone, Weaviate, or pgvector on Postgres — the LangChain interface stays nearly identical, only the vectorstore class changes.

Retrieval Chain

A retriever wraps the vector store and handles the similarity search at query time. k controls how many chunks come back per question — too few misses context, too many wastes context window and can dilute relevance:

retriever = vectorstore.as_retriever(
    search_type='similarity',
    search_kwargs={'k': 4}
)

results = retriever.invoke('How do I configure rate limiting?')
for doc in results:
    print(doc.metadata.get('source'), '-', doc.page_content[:80])

Generation With Claude

Retrieved chunks get injected into a prompt template alongside the user’s question, then passed to Claude for a grounded answer:

from langchain_anthropic import ChatAnthropic
from langchain.prompts import ChatPromptTemplate

llm = ChatAnthropic(model='claude-sonnet-5', temperature=0)

prompt = ChatPromptTemplate.from_template('''
Answer the question using ONLY the context below.
If the answer isn't in the context, say you don't know.

Context:
{context}

Question: {question}
''')

chain = prompt | llm

Full Pipeline Together

Wiring retrieval and generation into a single callable chain:

from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser

def format_docs(docs):
    return '\n\n'.join(doc.page_content for doc in docs)

rag_chain = (
    {'context': retriever | format_docs, 'question': RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

answer = rag_chain.invoke('How do I configure rate limiting?')
print(answer)

Wrap this in a FastAPI endpoint and you have a working RAG API in under 80 lines total.

Improving Retrieval Quality

• Hybrid search. Combine vector similarity with keyword (BM25) search — catches exact terms like product names or error codes that pure semantic search sometimes misses.
• Re-ranking. Retrieve more candidates than you need (e.g. 20), then re-rank with a cross-encoder model and keep the top 4-5 — noticeably improves precision over raw vector similarity alone.
• Metadata filtering. Tag chunks with source, date, or category metadata and filter before similarity search when the query implies scope (e.g. “in the 2026 docs”).
• Chunk overlap tuning. If answers frequently cut off mid-thought, increase overlap or switch to semantic chunking that splits on topic boundaries instead of fixed character counts.
• Evaluation. Build a small labeled test set of questions and expected answers, and measure retrieval precision/recall before and after any pipeline change — tuning blind is how RAG pipelines quietly get worse.

Frequently Asked Questions

Do I need LangChain, or can I build RAG without it?

You can build RAG from raw API calls; LangChain just standardizes the plumbing (loaders, splitters, vector store interfaces) so switching components later is a one-line change instead of a rewrite. For simple pipelines, going framework-free is also reasonable.

Which vector database should I use?

Chroma or FAISS for local prototyping and small-to-medium corpora. Pinecone, Weaviate, or pgvector for production scale with high query volume or when you need managed infrastructure.

How do I keep the index updated as documents change?

Re-run ingestion on a schedule or trigger it on document change events, and use stable document IDs so you can delete-and-reinsert just the changed chunks instead of rebuilding the whole index.

Why does my RAG bot still hallucinate sometimes?

Usually the retriever is returning irrelevant chunks, not a generation failure — check what’s actually being retrieved before assuming the model is at fault. A strict prompt instruction (“say you don’t know if it’s not in the context”) also meaningfully reduces this.

How large can my document corpus be?

Vector search scales well into the millions of chunks with a proper vector database; the practical bottleneck is usually ingestion pipeline design and embedding cost, not retrieval-time performance.

Related Reading

• How to Fine-Tune LLMs Locally
• How to Build a REST API with FastAPI
• Is AI-Assisted Coding Making Developers Worse in 2026?