LangChain Cheat Sheet

🧑‍🏫 Sabse pehle — simple mein samjho#

Ye ek quick reference page hai jab tum AI application build kar rahe ho. Ise bookmark karke rakho!

1. Initializing Models#

import os
os.environ["OPENAI_API_KEY"] = "sk-..."

from langchain_openai import ChatOpenAI

# Temperature 0 = strict/factual, 1 = creative/random
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

2. Chat Prompt Templates#

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are an expert on {topic}."),
    ("human", "Answer this question: {question}")
])

3. Basic LCEL Chain#

from langchain_core.output_parsers import StrOutputParser

# The pipe operator connects prompt -> model -> parser
chain = prompt | llm | StrOutputParser()

# Pass a dictionary containing the variables
answer = chain.invoke({"topic": "Space", "question": "Why is the sky dark?"})
print(answer)

4. RAG: Loaders & Splitters#

from langchain_community.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Load
loader = WebBaseLoader("https://example.com")
docs = loader.load()

# Split
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(docs)

5. RAG: Vector Store & Retrieval#

from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma

# Embed and Save to DB
vectorstore = Chroma.from_documents(chunks, OpenAIEmbeddings())

# Convert DB to Retriever (fetch top 3 matching chunks)
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

# Fetch manually
relevant_docs = retriever.invoke("User's question here")

6. Tools & Agents#

from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain import hub

# Define a tool (docstring is mandatory)
@tool
def get_weather(location: str) -> str:
    """Get the current weather in a given location."""
    return f"Sunny in {location}"

tools = [get_weather]

# Bind tools to model and pull standard prompt
llm_with_tools = llm.bind_tools(tools)
prompt = hub.pull("hwchase17/openai-functions-agent")

# Create and run Agent
agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)

response = agent_executor.invoke({"input": "What's the weather in Tokyo?"})

7. LangSmith Tracing Setup (in .env)#

LANGCHAIN_TRACING_V2="true"
LANGCHAIN_API_KEY="ls_your_api_key_here"
LANGCHAIN_PROJECT="My Custom Project Name"