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This guide shows you how to add Snow Leopard data retrieval to a LangChain agent.

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Prerequisites

Don’t have data? Use our sample superheroes dataset to get started, or choose from our other sample datasets.

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Start from scratch

Clone and run the complete working example: 
git clone https://github.com/SnowLeopard-AI/snowy-examples.git
cd snowy-examples/quickstart/langchain
Install dependencies: 
pip install langchain langchain-openai snowleopard
Set your environment variables: 
export OPENAI_API_KEY=your-openai-key
export SNOWLEOPARD_API_KEY=your-snowleopard-key
export SNOWLEOPARD_DATAFILE_ID=your-datafile-id
Run the example: 
python langchain_quickstart.py

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Bring your own agent

Already have a LangChain agent? Add Snow Leopard data retrieval with these steps.

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1. Install dependencies

pip install snowleopard

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2. Create the Snow Leopard tool

Create a LangChain Tool that calls Snow Leopard to retrieve data: 
from langchain_core.tools import Tool
from snowleopard import SnowLeopardClient

client = SnowLeopardClient(api_key="{your-snowleopard-api-key}")

def query_data(natural_language_query: str) -> str:
    """Query your database using natural language."""
    response = client.retrieve(
        datafile_id="{your-datafile-id}",
        user_query=natural_language_query
    )
    return str(response)

snowleopard_tool = Tool(
    name="query_database",
    func=query_data,
    description="Query your database with natural language questions. "
                "Describe your data here - this becomes part of the agent's context."
)

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3. Add the tool to your agent

Add the Snow Leopard tool to your agent’s tool list: 
from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4o")
tools = [snowleopard_tool]  # Add to your existing tools

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant that can query databases."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)

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Next steps