magnus919_agent-skills/langchain/references/rag-strategies.md

LangChain RAG Strategies

The Canonical RAG Chain

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

# Load -> Split -> Embed -> Retrieve -> Generate
loader = WebBaseLoader("https://example.com/docs")
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
vectorstore = Chroma.from_documents(
    splitter.split_documents(loader.load()),
    OpenAIEmbeddings()
)
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

def fmt(docs):
    return "\n\n".join(d.page_content for d in docs)

rag_chain = (
    {"context": retriever | fmt, "question": RunnablePassthrough()}
    | ChatPromptTemplate.from_template("Answer using context:\n{context}\n\nQ: {question}")
    | ChatOpenAI()
    | StrOutputParser()
)

Document Loaders (150+ Sources)

Loader	Source	Package
WebBaseLoader	Web pages	langchain-community
PyPDFLoader	PDF files	langchain-community
TextLoader	Plain text	langchain-core
NotionDBLoader	Notion	langchain-community
S3FileLoader	AWS S3	langchain-community

Text Splitters

Splitter	Method	Best For
RecursiveCharacterTextSplitter	Recursive character	General purpose (default)
TokenTextSplitter	Token-count-based	LLM context optimization
MarkdownHeaderTextSplitter	Header-aware	Markdown documents
SemanticChunker	Embedding similarity	Coherent semantic units

Vector Store Integrations

All 40+ vector stores share the same interface: from_documents, as_retriever, similarity_search.

Store	Production	Install
Chroma	Local dev	pip install chromadb
Pinecone	Yes	pip install langchain-pinecone
pgvector	Yes	pip install langchain-postgres
Weaviate	Yes	pip install langchain-weaviate
Qdrant	Yes	pip install langchain-qdrant
FAISS	Local	pip install faiss-cpu

Advanced Retrieval Patterns

Technique	When to use	Implementation
Multi-query retrieval	Broad topics need diverse sources	Generate query variants, retrieve for each
ParentDocumentRetriever	Need small chunks + rich context	Retrieve child chunks, return parent documents
SelfQueryRetriever	Queries with filters	Extract semantic filter + query from natural language
EnsembleRetriever	Multiple retrieval methods	Weighted combination of BM25 + vector

Structured Document Chains (Migration Path)

These chain factories exist in langchain_classic.chains (the pre-v1.0 classic module):

Chain	Purpose	Import
create_history_aware_retriever	Rephrase question with chat history	langchain_classic.chains
create_stuff_documents_chain	LCEL-style Stuff documents chain	langchain.chains (v1.0 path)

v1.0 recommendation: Use LCEL directly rather than factory chains. The canonical RAG chain at the top of this page is the recommended pattern.