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Build an Automatic Ai Data Extraction Agent

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Introduction

Imagine having an AI agent that can scan thousands of pages of documents and extract the necessary data while you relax. In this article, I’ll guide you through setting up an AI agent that pulls data and organizes it into clean new files. This will streamline your workflow by eliminating repetitive and time-consuming tasks. If you follow along until the end, I’ll provide you with a complete template for this AI agent for free, which you can even sell to other businesses for a hefty profit.

Getting Started

We will be utilizing Flowwise AI, a free open-source framework. Begin by accessing the dashboard and navigating to "Agent Flows", then click "Add New Agent". We will start with the core of our system: a Supervisor Node.

Setting Up the Supervisor Node

  1. Search for the Supervisor Node in the nodes section.
  2. Drag it onto the canvas. This node will oversee all tasks, functioning as the manager of our agents.

Now, we need to equip our supervisor with a language model:

  1. Go to Chat Models and select the OpenAI chat model.
  2. Set up your credentials and choose the GPT 4.0 Mini model.
  3. Ensure the temperature parameter is set to zero to maintain consistent output.

Creating the Worker Agent

Next, let’s create a Worker Agent to extract the data from your files:

  1. Return to the nodes section and select a worker agent.
  2. Name this worker Data Reader.
  3. Open the worker's prompt and input the instructions: "You are responsible for extracting specific data from a file provided by the user."
  4. Save the agent and connect it to the supervisor. This enables the supervisor to delegate tasks to the data reader.

Setting Up the File Retrieval Tool

To manage file uploads and store them in a database, we’ll create a retrieval tool:

  1. Name this retrieval tool Read File Tool and add the description: "Retrieve and provide information to answer users' questions."
  2. Activate the Vector Database by selecting Pinecone from the nodes section.

After signing into Pinecone:

  1. Go to the dashboard and create a new index named Flowwise.
  2. Set the dimensions type to 1536 and use AWS as the cloud provider.
  3. Create the index, then copy your API key.

Connecting Pinecone with Flowwise

Return to Flowwise to connect the credentials:

  1. Create a new connection, name it, and paste your copied API key.
  2. Connect Pinecone to your retrieval tool.

File Upload and Chunking

To handle file uploads and split data into manageable chunks:

  1. Find the File Loader in the nodes section, drag it onto the canvas, and connect it to Pinecone.
  2. Next, locate the OpenAI Embeddings Node, use the same credentials, and connect it to the Pinecone node.

Now we need to implement a Text Splitter:

  1. Select the Recursive Character Text Splitter and connect it to the file loader.

Final Adjustments

  1. Open Pinecone's additional parameters, toggle the switch for file uploads, and set the top K parameter to 20000.

Save Your Agent

Save and name your agent Data Extractor.

Testing the System

To test, open the chat window and upload a document, such as an Nvidia 10K report. Request the statement of income, and the supervisor will delegate the task to the data reader. You should see the extracted information appear promptly.

Quality Control and Data Saving

To enhance our system, we'll add a Quality Control Agent:

  1. Duplicate the previous worker and name it Quality Controller.
  2. Instruct it to check the quality of the extracted data before further processing.
  3. Connect it to the supervisor.

Next, we’ll set up an agent that writes clean data to a new file:

  1. Duplicate the worker again and name it CSV Writer.
  2. Connect it to a Write File Tool and set the base path to your preferred output folder.

Finalize Supervisor Instructions

Finalize the supervisor's instruction set to ensure tasks are executed in the correct order:

  1. In the supervisor settings, add delegation instructions for first the data reader, followed by the quality controller, and finally the CSV writer.

Conclusion

You’re almost there! By following these steps, your system is now ready to process data efficiently, saving you valuable time.

As a reward for making it this far, comment "template" below, and I’ll send you the free download link for the complete template you can use and repurpose.

Keywords

  • AI Agent
  • Data Extraction
  • Flowwise AI
  • Supervisor Node
  • Worker Agent
  • Pinecone
  • Quality Control
  • CSV Writer
  • File Loader

FAQ

Q1: What is Flowwise AI?
A1: Flowwise AI is a free open-source framework that allows users to create AI agents for various data processing and extraction tasks.

Q2: What is the purpose of the Supervisor Node?
A2: The Supervisor Node oversees and manages the tasks performed by worker agents to ensure streamlined operations.

Q3: Why do we need a Quality Control Agent?
A3: A Quality Control Agent ensures the accuracy and reliability of the data extracted from documents before further processing.

Q4: Can I use the provided template for commercial purposes?
A4: Yes, you can use the template for free and even sell it to other businesses.

Q5: What do I need to set up before using the AI agent?
A5: You will need to sign up for a Pinecone account, set up OpenAI credentials, and configure the Flowwise AI framework as described in the article.