LangChain

git clone https://github.com/SnowLeopard-AI/snowy-examples.git
cd snowy-examples/quickstart/langchain

pip install langchain langchain-openai snowleopard

export OPENAI_API_KEY=your-openai-key
export SNOWLEOPARD_API_KEY=your-snowleopard-key
export SNOWLEOPARD_DATAFILE_ID=your-datafile-id

python langchain_quickstart.py

pip install snowleopard

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."
)

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)