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This guide shows you how to add Snow Leopard data retrieval to a LangGraph workflow.

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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/langgraph
Install dependencies: 
pip install langgraph 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 langgraph_quickstart.py

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Bring your own agent

Already have a LangGraph workflow? Add Snow Leopard data retrieval with these steps.

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1. Install dependencies

pip install snowleopard

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2. Add Snow Leopard fields to your state

Add fields to your graph state to store query results: 
from typing_extensions import TypedDict

class GraphState(TypedDict):
    user_question: str
    query_result: str  # Add this field for Snow Leopard results
    # ... your other state fields

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3. Create a Snow Leopard query node

Create a node that calls Snow Leopard to retrieve data: 
from snowleopard import SnowLeopardClient

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

def query_database(state: GraphState) -> GraphState:
    """Query the database using Snow Leopard."""
    response = client.retrieve(
        datafile_id="{your-datafile-id}",
        user_query=state["user_question"]
    )
    state["query_result"] = str(response)
    return state

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4. Add the node to your workflow

Add the query node and connect it to your existing workflow: 
from langgraph.graph import StateGraph

workflow = StateGraph(GraphState)

# Add the Snow Leopard query node
workflow.add_node("query", query_database)

# Connect it to your workflow
workflow.set_entry_point("query")
workflow.add_edge("query", "your_next_node")

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Next steps