Advanced LangChain: Integrating GenAI with External Data

Generative AI becomes truly useful when it can act on real knowledge — not just pre-trained patterns. LangChain’s modular design makes this possible.

Why External Data Matters

By pairing with retrieval systems like vector stores or SQL, you make responses accurate and context-aware.

Vector Store Integration

from langchain.vectorstores import Chroma

vectorstore = Chroma.from_documents(documents, OpenAIEmbeddings())

Combining Tools & APIs

def get_weather(city: str) -> str:
    return requests.get(`https://weather.api/${city}`).text

agent = create_agent(model="gpt-4o", tools=[get_weather])

Deployment & Scaling

LangChain’s ecosystem supports monitoring and production deployment of GenAI workflows.

Summary

By integrating external data and services, LangChain empowers developers to build generative AI that’s knowledgeable and actionable.