Tensorlake is the AI Data Cloud that reliably transforms data from unstructured sources into ingestion-ready formats for AI Applications. The langchain-tensorlake package provides seamless integration between Tensorlake and LangChain, enabling you to build sophisticated document processing agents with enhanced parsing features, like signature detection.

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Tensorlake feature overview

Tensorlake gives you tools to:
  • Extract: Schema-driven structured data extraction to pull out specific fields from documents.
  • Parse: Convert documents to markdown to build RAG/Knowledge Graph systems.
  • Orchestrate: Build programmable workflows for large-scale ingestion and enrichment of Documents, Text, Audio, Video and more.
Learn more at docs.tensorlake.ai

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Installation

pip install -U langchain-tensorlake

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Examples

Follow a full tutorial on how to detect signatures in unstructured documents using the langchain-tensorlake tool. Or check out this colab notebook for a quick start.

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Quick Start

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1. Set up your environment

You should configure credentials for Tensorlake and OpenAI by setting the following environment variables: 
export TENSORLAKE_API_KEY="your-tensorlake-api-key"
export OPENAI_API_KEY = "your-openai-api-key"
Get your Tensorlake API key from the Tensorlake Cloud Console. New users get 100 free credits.

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2. Import necessary packages

from langchain_tensorlake import document_markdown_tool
from langgraph.prebuilt import create_react_agent
import asyncio
import os

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3. Build a Signature Detection Agent

async def main(question):
    # Create the agent with the Tensorlake tool
    agent = create_react_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)
Note: We highly recommend using openai as the agent model to ensure the agent sets the right parsing parameters

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4. Example Usage

# 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))

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Need help?

Reach out to us on Slack or on the package repository on GitHub directly.