How do I set up LangChain in my Python environment?

To set up LangChain in your Python environment, begin by installing the library and creating a virtual environment. Start by running python -m venv langchain_env to create an isolated environment, then activate it using your terminal (e.g., source langchain_env/bin/activate on Unix). Install LangChain using pip with pip install langchain. This installs the core library, which includes base classes for chains, agents, and memory management. If you plan to use language models like OpenAI’s GPT, add their SDKs—for example, run pip install openai for OpenAI integration. LangChain’s modular design lets you install only the components you need, such as pip install langchain-community for third-party integrations or pip install langchain-core for core utilities.

Next, configure API keys and external services. For instance, to use OpenAI models, set your API key in the environment:

import os
os.environ["OPENAI_API_KEY"] = "your-api-key-here"

If you’re using Hugging Face models, install pip install huggingface_hub and set HUGGINGFACEHUB_API_TOKEN. LangChain supports multiple providers, so you can switch between services like Anthropic or Google’s Gemini by installing their respective packages and configuring keys. For local model testing, use pip install transformers and load models via Hugging Face pipelines. This flexibility allows you to prototype with cloud APIs and later switch to local models without rewriting your entire chain.

Finally, build a basic application. Start with a simple prompt template and chain:

from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI

prompt = PromptTemplate(template="Write a haiku about {topic}:", input_variables=["topic"])
llm = OpenAI(temperature=0.7)
chain = prompt | llm # Combine prompt and model
print(chain.invoke({"topic": "robots"}))

This example creates a chain that generates text based on a structured prompt. For more advanced use cases, explore agents (e.g., pip install langchain-experimental) to add decision-making logic or use memory modules to retain context between interactions. Refer to LangChain’s documentation for detailed examples, such as building retrieval-augmented generation (RAG) systems or connecting to vector databases. Keep dependencies updated, as LangChain’s ecosystem evolves frequently to support new tools and providers.