LangChain
Framework for building applications with large language models.
When to Use
- •Building chatbots and AI agents
- •RAG (Retrieval-Augmented Generation)
- •Multi-step LLM workflows
- •Tool-using AI agents
Quick Start
python
from langchain_openai import ChatOpenAI from langchain_core.messages import HumanMessage llm = ChatOpenAI(model="gpt-4o") response = llm.invoke([HumanMessage(content="Hello!")]) print(response.content)
Core Concepts
Chains
python
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
prompt = ChatPromptTemplate.from_template(
"Summarize this text in {language}: {text}"
)
chain = prompt | ChatOpenAI() | StrOutputParser()
result = chain.invoke({
"language": "Spanish",
"text": "Long English text..."
})
RAG Pipeline
python
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.runnables import RunnablePassthrough
# Create vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents, embeddings)
retriever = vectorstore.as_retriever()
# RAG chain
rag_chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
answer = rag_chain.invoke("What is the main topic?")
Common Patterns
Agents with Tools
python
from langchain_openai import ChatOpenAI
from langchain.agents import create_react_agent, AgentExecutor
from langchain_core.tools import tool
@tool
def search_database(query: str) -> str:
"""Search the database for information."""
return db.search(query)
@tool
def calculate(expression: str) -> str:
"""Calculate a math expression."""
return str(eval(expression))
tools = [search_database, calculate]
agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)
result = executor.invoke({"input": "What is 25 * 4?"})
Memory
python
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
history = ChatMessageHistory()
chain_with_history = RunnableWithMessageHistory(
chain,
lambda session_id: history,
input_messages_key="input",
history_messages_key="history"
)
Best Practices
Do:
- •Use LCEL for chain composition
- •Implement proper error handling
- •Add observability with LangSmith
- •Cache expensive operations
Don't:
- •Chain too many models together
- •Skip input validation
- •Ignore token limits
- •Trust agent outputs blindly
Troubleshooting
| Issue | Cause | Solution |
|---|---|---|
| Slow chain | Multiple LLM calls | Parallelize or cache |
| Context overflow | Too many documents | Implement summarization |
| Agent loops | Bad tool descriptions | Improve tool docstrings |