Tensorlake

export TENSORLAKE_API_KEY="your-tensorlake-api-key"
export OPENAI_API_KEY = "your-openai-api-key"

from langchain_tensorlake import document_markdown_tool
from langchain.agents import create_agent
import asyncio
import os

async def main(question):
    # Create the agent with the Tensorlake tool
    agent = create_agent(
            model="openai:gpt-4o-mini",
            tools=[document_markdown_tool],
            prompt=(
                """
                I have a document that needs to be parsed. \n\nPlease parse this document and answer the question about it.
                """
            ),
            name="real-estate-agent",
        )

    # Run the agent
    result = await agent.ainvoke({"messages": [{"role": "user", "content": question}]})

    # Print the result
    print(result["messages"][-1].content)

# Define the path to the document to be parsed
path = "path/to/your/document.pdf"

# Define the question to be asked and create the agent
question = f"What contextual information can you extract about the signatures in my document found at {path}?"

if __name__ == "__main__":
    asyncio.run(main(question))