Transform Your PDF Data with AI: Q&A Workflow Using LangChain, ChatGPT, and Pinecone on the Ubility Platform

How It Works: A Behind-the-Scenes Look

Here’s a closer look at how this setup works, broken down step-by-step:

1. Triggering the Workflow with a Webhook

   The workflow starts with a Webhook Trigger. Here’s how it works:

   • Method: We use the POST method to send the question in the body of the request. This makes it easy to customize questions dynamically.
   • Storing the Question as a Variable: Once received, the question is stored as a variable within the workflow, making it accessible in later steps

   

2. Passing the Question to the Q&A Connector

   Next, the question variable is directed to the Question and Answer Connector, where two powerful tools are put to work:

   • Chat Model (ChatGPT): Here, ChatGPT processes the question with its language comprehension abilities, allowing it to interpret and respond in a contextually relevant way.
   • Vector Store (Pinecone): Pinecone’s vector store retrieves the relevant content from the PDF, ensuring the response is both accurate and contextual.

   

   In the Q&A Connector’s question field, we use the stored question variable ${question}, linking the user's query to ChatGPT and Pinecone for processing.

3. Integrating ChatGPT with Pinecone for Precision

   By integrating ChatGPT, the model interprets and refines responses based on the PDF data, while Pinecone provides targeted retrieval of document content.

   • Model Choice: For this setup, we’re using gpt-3.5-turbo-16k, which allows for nuanced responses ideal for in-depth questions.
   • Embedding Model: Pinecone uses the OpenAI text-embedding-ada-002 model to convert the PDF content into a searchable format, matching the question to the most relevant sections.

   

4. Retrieving Data with Pinecone’s Vector Store

   Pinecone acts as the database, making the data search and retrieval process fast and precise.

   • Indexed Data: The PDF is preprocessed and indexed, allowing for real-time data retrieval whenever a question is asked.
   • Customizable Retrieval: Only the most pertinent information is pulled, meaning you get relevant answers without extra noise.

   

5. Testing the Workflow

   Once configured, you can test the workflow to ensure everything is set up correctly. A quick end-to-end test verifies that the webhook receives the question, sends it through the Q&A connector, and outputs the correct answer.

6. Outputting the Answer

   The workflow is designed to output a clear, structured answer based on your query. You can further customize this output, allowing it to fit seamlessly into any application, whether for research, customer support, or team communication.