LangChain / LangGraph

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│  User Query  │────▶│  LangChain   │────▶│  Response    │
│              │     │  / LangGraph │     │  + Sources   │
└──────────────┘     └──────┬───────┘     └──────────────┘
                            │
           ┌────────────────┼────────────────┐
           ▼                ▼                ▼
    ┌──────────────┐ ┌──────────────┐ ┌──────────────┐
    │  LLM         │ │  Vector DB   │ │  Tools       │
    │  (GPT-5.6 /  │ │  (Pinecone / │ │  (APIs,      │
    │   Claude)    │ │   pgvector)  │ │   Functions) │
    └──────────────┘ └──────────────┘ └──────────────┘
           │                                  │
           ▼                                  ▼
    ┌──────────────┐                   ┌──────────────┐
    │  LangSmith   │                   │  LangGraph   │
    │  (Traces +   │                   │  (Multi-Agent│
    │   Evals)     │                   │   State)     │
    └──────────────┘                   └──────────────┘

# LangChain RAG Pipeline: Python
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_pinecone import PineconeVectorStore
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

# Components
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = PineconeVectorStore(index_name="knowledge-base", embedding=embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
llm = ChatOpenAI(model="gpt-5.6-sol", temperature=0.1)

# RAG Chain
prompt = ChatPromptTemplate.from_template("""
Answer based on the context below. Cite sources.
Context: {context}
Question: {question}
""")

rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
)

# Usage
response = rag_chain.invoke("What is our refund policy?")