無需程式碼，在CHATGPT中與PDF數據交流

無需程式碼，在CHATGPT中與PDF數據交流

Table of Contents

1. Introduction
2. Step 1: Creating an AI App with Flowwise
3. Step 2: Creating the Chat GPT Plugin using Lang Dock
4. Conclusion

Introduction

In this tutorial, we will guide You through the process of chatting with your PDF data inside Chat GPT without writing any code. We will break down the process into two steps. The first step involves creating an AI application that understands your private PDF file. The Second step involves creating the Chat GPT plugin itself. We will be using two tools, Flowwise AI for the first step and Lang Dock for the second step. Let's get started!

Step 1: Creating an AI App with Flowwise

To begin, we will use Flowwise AI to Create an AI application that can understand your private PDF file. In this example, we will be using a simple earnings report from a company called Triton. The earnings report contains information such as financial results and operating performance.

First, open Flowwise AI and add a new flow. We will start with a blank canvas and build the components from scratch. The first component we will add is a conversational retrieval Q&A chain, which will allow us to retrieve answers to questions. Next, we will specify the large language model we want to use, which in this case is OpenAI. We will then connect the conversational agent with the large language model.

Next, we need to build a vector store, which serves as the database for the information we want to query. We will use a service called Pinecone for this purpose. After connecting Pinecone to the conversational agent, we will add a PDF loader to specify the PDF file we want to use. This will be connected to the vector store.

To convert the text in the PDF file into something the vector store understands, we will add an embeddings component. We will use OpenAI's embeddings and specify the API key. We will then connect the embeddings component to both the text splitter and the vector store.

Once we have set up the flow, it will retrieve information from the PDF file and store it in the vector database. It will then be ready to answer questions using the conversational retrieval Q&A chain. Save the flow and start running it to ask questions about the earnings report. You can ask questions such as the earnings for the first quarter or the adjusted return on equity.

Pros:

• Allows you to create an AI application without writing code.
• Can understand and retrieve information from private PDF files.
• Provides a user-friendly interface for building and running the application.

Cons:

• Requires setting up and connecting multiple components.

Step 2: Creating the Chat GPT Plugin using Lang Dock

Now that we have created the AI application trained on the PDF data, let's move on to creating the Chat GPT plugin using Lang Dock. The Chat GPT plugin will allow users to Interact with the AI application in a chat-like manner.

To create the plugin, go to Lang Dock and create a new plugin. Give it a name, such as "Finance", and provide a description that explains what the plugin can do. In the description, highlight that the plugin is useful for getting earnings information about a company. Specify the arguments for the plugin, in this case, the question asked by the user.

Upload an icon for the plugin and save your progress. Next, create an API for the plugin by adding a new folder and starting a post request. Provide a summary and name for the API, and specify the URL to the Flowwise service. Define the response and create a schema for the question argument.

Save the API and deploy the plugin. Copy the URL generated by Lang Dock and go to the Chat GPT user interface. Install the plugin and enable it. You can now test the plugin by asking questions in the chat. Chat GPT will recognize that the plugin needs to be called and will retrieve the specific answer from the AI application.

Pros:

• Provides a seamless integration between Chat GPT and the AI application.
• Allows users to ask questions in a chat-like manner.
• Easy to create and deploy the plugin using Lang Dock.

Cons:

• Requires setting up the API and connecting it to the Flowwise service.

Conclusion

By following the steps outlined in this tutorial, you can chat with your PDF data inside Chat GPT without writing any code. The first step involves creating an AI application with Flowwise, which understands your private PDF file. The second step involves creating the Chat GPT plugin using Lang Dock, which allows users to interact with the AI application in a chat-like manner. Enjoy exploring the possibilities of using AI to analyze and retrieve information from PDF files!

Highlights

• Chat with your PDF data inside Chat GPT without coding.
• Step 1: Create an AI application with Flowwise to understand your private PDF file.
• Step 2: Build the Chat GPT plugin using Lang Dock for interactive chat-like interactions.
• Retrieve specific information from the PDF using conversational retrieval and embeddings.
• Easily deploy the plugin and enable it in Chat GPT.

FAQ

Q: Is any coding required to Chat with PDF data using Chat GPT? A: No, you can chat with your PDF data without writing a single line of code. The tutorial guides you through the process using Flowwise AI and Lang Dock.

Q: Can I use any PDF file for this tutorial? A: Yes, you can use any private PDF file. In the tutorial, we use a simple earnings report as an example.

Q: Are there any limitations to the types of questions I can ask in Chat GPT? A: You can ask a wide range of questions related to the PDF data. However, the accuracy of the answers depends on the AI application's training on the specific PDF file.

Q: Can I customize the Chat GPT plugin further? A: Yes, you can customize the plugin by modifying the API and adding additional functionality using Lang Dock.

Q: Are there any other tools I can use besides Flowwise AI and Lang Dock? A: Flowwise AI and Lang Dock are the tools used in this tutorial, but there are other similar tools available that offer similar functionalities. It ultimately depends on your preference and requirements.