langchainlisted
Install: claude install-skill Makiya1202/ai-agents-skills
# LangChain & LangGraph
Build sophisticated LLM applications with composable chains and agent graphs.
## Quick Start
```bash
pip install langchain langchain-openai langchain-anthropic langgraph
```
```python
from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate
# Simple chain
llm = ChatAnthropic(model="claude-3-sonnet-20240229")
prompt = ChatPromptTemplate.from_template("Explain {topic} in simple terms.")
chain = prompt | llm
response = chain.invoke({"topic": "quantum computing"})
```
## LCEL (LangChain Expression Language)
Compose chains with the pipe operator:
```python
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
# Chain with parsing
chain = (
{"topic": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
result = chain.invoke("machine learning")
```
## RAG Pipeline
```python
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
# Create vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents, embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
# RAG prompt
prompt = ChatPromptTemplate.from_template("""
Answer based on the following context:
{context}
Question: {question}
""")
# RAG chain
rag_chain = (
{"conte