Langchain

LangChain

Overview

Build production-grade LLM applications using LangChain's composable framework. This skill covers chains, agents, retrieval-augmented generation (RAG), tool integration, memory, and deployment — using modern LCEL patterns (not legacy LLMChain).

Instructions

Step 1: Project Setup

Determine the user's runtime (Python or TypeScript) and initialize the project:

Python:

pip install langchain langchain-core langchain-openai langchain-community
# For RAG:
pip install langchain-chroma sentence-transformers
# For document loading:
pip install unstructured pypdf docx2txt

TypeScript:

npm install langchain @langchain/core @langchain/openai @langchain/community
# For RAG:
npm install @langchain/chroma

Verify the LLM provider API key is set:

echo $OPENAI_API_KEY  # or ANTHROPIC_API_KEY, etc.

Step 2: Understand LCEL (LangChain Expression Language)

All modern LangChain code uses LCEL — the pipe (|) operator for composing runnables:

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant specialized in {domain}."),
    ("human", "{question}")
])

chain = prompt | ChatOpenAI(model="gpt-4o") | StrOutputParser()

result = chain.invoke({"domain": "Python", "question": "Explain decorators"})

Key LCEL concepts:

• Runnables: Any component that implements .invoke(), .stream(), .batch()
• Pipe operator: a | b means output of a feeds into b
• RunnablePassthrough: Pass input through unchanged
• RunnableLambda: Wrap any function as a runnable
• RunnableParallel: Run multiple chains simultaneously

Step 3: Implement Core Patterns

Simple Chain

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_template("Summarize this text in {language}: {text}")
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
chain = prompt | llm

result = chain.invoke({"language": "Spanish", "text": "..."})

Structured Output

from pydantic import BaseModel, Field

class ExtractedInfo(BaseModel):
    name: str = Field(description="Person's full name")
    role: str = Field(description="Job title or role")
    sentiment: str = Field(description="Overall sentiment: positive, negative, neutral")

llm_structured = llm.with_structured_output(ExtractedInfo)
chain = prompt | llm_structured
# Returns ExtractedInfo object, not raw text

Retrieval-Augmented Generation (RAG)

from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_core.runnables import RunnablePassthrough

# Load and split documents
loader = PyPDFLoader("docs/manual.pdf")
docs = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = splitter.split_documents(docs)

# Create vector store
vectorstore = Chroma.from_documents(splits, OpenAIEmbeddings())
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})

# RAG chain
rag_prompt = ChatPromptTemplate.from_template(
    "Answer based on context:\n\n{context}\n\nQuestion: {question}"
)

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

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

answer = rag_chain.invoke("What is the return policy?")

Tool-Calling Agent

from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

@tool
def search_database(query: str) -> str:
    """Search the product database for matching items."""
    # Implementation here
    return f"Found 3 results for '{query}'"

@tool
def calculate_discount(price: float, percent: float) -> float:
    """Calculate discounted price."""
    return price * (1 - percent / 100)

tools = [search_database, calculate_discount]
llm = ChatOpenAI(model="gpt-4o")

# Modern approach uses LangGraph for agents
agent = create_react_agent(llm, tools)
result = agent.invoke({"messages": [("human", "Find laptops under $1000 and apply 15% discount")]})

Conversational Memory

from langchain_core.chat_history import InMemoryChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory

store = {}

def get_session_history(session_id: str):
    if session_id not in store:
        store[session_id] = InMemoryChatMessageHistory()
    return store[session_id]

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("placeholder", "{history}"),
    ("human", "{input}")
])

chain = prompt | llm | StrOutputParser()

chain_with_history = RunnableWithMessageHistory(
    chain,
    get_session_history,
    input_messages_key="input",
    history_messages_key="history",
)

# Each call remembers previous messages
response = chain_with_history.invoke(
    {"input": "My name is Alice"},
    config={"configurable": {"session_id": "user-123"}}
)

Step 4: Document Loaders and Text Splitters

Common loaders:

from langchain_community.document_loaders import (
    PyPDFLoader,           # PDF files
    TextLoader,            # Plain text
    CSVLoader,             # CSV files
    DirectoryLoader,       # Entire directories
    WebBaseLoader,         # Web pages
    UnstructuredHTMLLoader,# HTML files
    Docx2txtLoader,        # Word documents
    JSONLoader,            # JSON files
)

Splitting strategies:

from langchain_text_splitters import (
    RecursiveCharacterTextSplitter,  # General purpose (recommended)
    TokenTextSplitter,               # Token-aware splitting
    MarkdownHeaderTextSplitter,      # Split by markdown headers
    HTMLHeaderTextSplitter,          # Split by HTML headers
)

# Best default:
splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
    separators=["\n\n", "\n", ". ", " ", ""]
)

Step 5: Vector Stores

# Chroma (local dev), FAISS (fast in-memory), Pinecone (managed production)
from langchain_chroma import Chroma
vectorstore = Chroma.from_documents(docs, embeddings, persist_directory="./chroma_db")

# Use MMR retrieval for diversity over pure similarity
retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 5, "fetch_k": 20})

Step 6: Production Patterns

# Streaming
async for chunk in chain.astream({"question": "Explain quantum computing"}):
    print(chunk, end="", flush=True)

# Batch processing with concurrency control
results = chain.batch([
    {"question": "What is Python?"},
    {"question": "What is Rust?"},
], config={"max_concurrency": 3})

# Fallbacks: use a different provider if primary fails
from langchain_anthropic import ChatAnthropic
llm_with_fallback = ChatOpenAI(model="gpt-4o").with_fallback([ChatAnthropic(model="claude-sonnet-4-20250514")])

# Caching: avoid duplicate LLM calls
from langchain_core.globals import set_llm_cache
from langchain_community.cache import SQLiteCache
set_llm_cache(SQLiteCache(database_path=".langchain_cache.db"))

Examples

Example 1: Build a RAG pipeline over internal documentation

User prompt: "I have 40 PDF files of internal engineering docs in ./docs/. Build a RAG pipeline so I can ask questions about our architecture and get accurate answers with source citations."

The agent will create a Python script that loads all PDFs from ./docs/ using DirectoryLoader with PyPDFLoader, splits them with RecursiveCharacterTextSplitter (chunk_size=1000, chunk_overlap=200), creates a Chroma vector store persisted to ./chroma_db/ using OpenAIEmbeddings, and builds an LCEL RAG chain. The chain uses RunnableParallel to pass the question through while retrieving the top 5 documents via MMR search. The prompt template instructs the LLM to answer based on the provided context and cite which document each fact comes from. The output includes the answer and a "Sources" list with filenames and page numbers. Running python rag.py "How does our authentication service handle token refresh?" returns a grounded answer referencing auth-service-design.pdf page 12.

Example 2: Create a tool-calling agent that queries a database and sends Slack messages

User prompt: "Build a LangChain agent that can query our PostgreSQL analytics database and post summaries to Slack channel #weekly-metrics."

The agent will create a Python script using LangGraph's create_react_agent with two custom tools. The query_analytics_db tool accepts a SQL query string, connects to PostgreSQL using psycopg2 with connection parameters from DATABASE_URL, executes read-only queries (with a 10-second timeout), and returns formatted results. The send_slack_message tool takes a channel name and message body, posts via the Slack Web API using SLACK_BOT_TOKEN. The agent uses ChatOpenAI(model="gpt-4o") and is invoked with prompts like "What was our total revenue last week? Post the summary to #weekly-metrics." The agent first calls query_analytics_db with SELECT SUM(amount) FROM transactions WHERE created_at >= '2026-02-10', formats the result as a readable summary, then calls send_slack_message to post it. Error handling wraps both tools with try/except to return informative error messages instead of crashing.

Guidelines

1. Use LCEL, not legacy chains — LLMChain, SequentialChain are deprecated
2. Use langchain-{provider} packages — not monolithic langchain imports
3. Structured output over output parsers — .with_structured_output() is more reliable
4. LangGraph for agents — AgentExecutor is legacy; use create_react_agent from langgraph
5. Chunk size matters — too small loses context, too large dilutes relevance; test with 500-1500
6. Always add overlap — 10-20% overlap prevents splitting mid-sentence
7. Use MMR retrieval — better diversity than pure similarity search
8. Stream in production — reduces perceived latency significantly
9. Cache LLM calls — identical prompts hit cache instead of API
10. Test chains with .invoke() first — before adding streaming or async