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• Table of Contents
  • What's new in version3?
  • Introduction
  • What's LangChain?
  • How to Use docGPT?
  • How to Develop a docGPT with Streamlit?
  • Advanced - How to build a better model in langchain

• Main Development Software and Packages:
  • Python 3.10.11
  • Langchain 0.0.218
  • Streamlit 1.22.0
  • more

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• Introduction of integration with gpt4free, allowing users to use docGPT for free without needing to input API keys or make payments.

• If you choose to use the gpt4free model, you only need to select the Provider (default is g4f.provider.ChatgptAi). For more details about gpt4free, refer to the source project.

• Version 2:

  • Uses the openai model.
  • Requires an openai_api_key. You can obtain this key from the link.
  • If you have a serpapi_key, AI responses can include Google search results.

• Version 3:

  • Retains all the features of Version 2.
  • Introduces the gpt4free model, enabling completely free usage.
  • Users can choose between gpt4free and openai models:
    • gpt4free: Allows free access to OpenAI models through reverse engineering, but stability might be compromised.
    • openai: Offers stable access by using an API key.

• Project Purpose:

  • The purpose of this project is to create a powerful "LLM" model using LangChain and Streamlit. This model aims to surpass the capabilities of ChatGPT by enabling:
    • Connect with external data, such as PDF documents, through RetrievalQA techniques for the model to understand uploaded files.
    • Integrate LLM with other tools to achieve internet connectivity. exemplified by using Serp API for querying modern topics similar to Google search.
    • Integration with LLM Math model for accurate mathematical computations.

• This project consists of three main components:

  • DataConnection: Facilitates communication between LLM and external data, like reading PDF files. It also includes splitting large PDFs to avoid OpenAI's 4096 token limitation.
  • docGPT: The core element that helps the model understand PDF content. It involves embedding PDF text vectors and creating LangChain's retrievalQA model. For more details, refer to the documentation.
  • agent: Manages tools used by the model and automatically decides which tool to use based on user queries. Tools include:
    • SerpAI: Used for modern topics, enabling Google search functionality.
    • llm_math_chain: Used for mathematical calculations.
    • docGPT: Used for answering queries related to PDF document content, designed using retrievalQA.

• docGPT is developed based on Langchain and Streamlit.

• gpt4free Integration: Everyone can use docGPT for free without needing an OpenAI API key.
• Direct PDF URL Input: Users can input PDF URL links for parsing without uploading .pdf files.
• Langchain Agent: Enables AI to answer current questions and achieve Google search-like functionality.
• User-Friendly Environment: Easy-to-use interface for simple operations.

• LangChain is a framework for developing applications powered by language models. It supports the following applications:

  1. Connecting LLM models with external data sources.
  2. Interactive communication with LLM models.

• For more details about LangChain, refer to the official documentation.

For questions that ChatGPT can't answer, turn to LangChain!

LangChain fills in the gaps left by ChatGPT. Through the following example, you can understand the power of LangChain:

In cases where ChatGPT can't solve mathematical problems or answer questions about events after 2020 (e.g., "Who is the president in 2023?"):

• For mathematical problems: There's a math-LLM model dedicated to handling math queries.
• For modern topics: You can use Google search.

To create a comprehensive AI model, we need to combine "ChatGPT," "math-LLM," and "Google search" tools.

In the non-AI era, we used if...else... to categorize user queries and had users select the question type through UI.

In the AI era, users should be able to directly ask questions without preselecting the question type. With LangChain's agent:

• We provide tools to the agent, e.g., tools = ['chatgpt', 'math-llm', 'google-search'].
• Tools can include chains designed using LangChain, such as using a retrievalQA chain to answer questions from documents.
• The agent automatically decides which tool to use based on user queries (fully automated).

Through LangChain, you can create a universal AI model or tailor it for business applications.

1. 🎬Visit the application.

2. 🔑Enter your API_KEY (optional in Version 3, as you can use the gpt4free free model):

   • OpenAI API KEY: Ensure you have available usage.
   • SERPAPI API KEY: Required if you want to query content not present in the PDF.

3. 📁Upload a PDF file (choose one method)

   • Method 1: Browse and upload your own .pdf file from your local machine.
   • Method 2: Enter the PDF URL link directly.

4. 🚀Start asking questions!

A step-by-step tutorial to quickly build your own chatGPT!

First, clone the repository using git clone https://github.com/Lin-jun-xiang/docGPT-streamlit.git.

There are two methods:

• Local development:

  • pip install -r requirements.txt: Download the required packages for development.
  • streamlit run ./app.py: Start the service in the project's root directory.
  • Start exploring!

• Use Streamlit Community Cloud for free deployment, management, and sharing of applications:

  • Place your application in a public GitHub repository (ensure you have requirements.txt).
  • Log in to share.streamlit.io.
  • Click "Deploy an App," then paste your GitHub URL.
  • Complete deployment and share your application.

To build a powerful docGPT model in LangChain, consider these tips to enhance performance:

1. Language Model

   Select an appropriate LLM model, such as OpenAI's gpt-3.5-turbo or other models. Experiment with different models to find the best fit for your use case.

   # ./docGPT/docGPT.py
   llm = ChatOpenAI(
   temperature=0.2,
   max_tokens=2000,
   model_name='gpt-3.5-turbo'
   )

   Please note that there is no best or worst model. You need to try multiple models to find the one that suits your use case the best. For more OpenAI models, please refer to the documentation.

   (Some models support up to 16,000 tokens!)

2. PDF Loader

   Choose a suitable PDF loader. Consider using PyMuPDF for fast text extraction and PDFPlumber for extracting text from tables.

   (official Langchain documentation)

   • PyPDF: Simple and easy to use.
   • PyMuPDF: Reads the document very quickly and provides additional metadata such as page numbers and document dates.
   • PDFPlumber: Can extract text within tables. Similar to PyMuPDF, it provides metadata but takes longer to parse.

   If your document contains multiple tables and important information is within those tables, it is recommended to try PDFPlumber, which may give you unexpected results!

   Please do not overlook this detail, as without correctly parsing the text from the document, even the most powerful LLM model would be useless!

3. Tracking Token Usage

   Implement token usage tracking with callbacks in LangChain to monitor token and API key usage during the QA chain process.

   When using chain.run, you can try using the method provided by Langchain to track token usage here:

   from langchain.callbacks import get_openai_callback
   
   with get_openai_callback() as callback:
       response = self.qa_chain.run(query)
   
   print(callback)
   
   # Result of print
   """
   chain...
   ...
   > Finished chain.
   Total Tokens: 1506
   Prompt Tokens: 1350
   Completion Tokens: 156
   Total Cost (USD): $0.03012

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