🔐Free GPT-3.5 chat with your docs (PDF, WORD, CSV, TXT)

English | 中文版

Free docGPT allows you to chat with your documents (.pdf, .docx, .csv, .txt), without the need for any keys or fees.

Additionally, you can deploy the app anywhere based on the document.

• Table of Contents
  • Introduction
  • Features
  • 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

If you like this project, please give it a ⭐Star to support the developers~

• Upload a Document link from your local device (.pdf, .docx, .csv, .txt) and query docGPT about the content of the Document. For example, you can ask GPT to summarize an article.

• Provide two models:

  • gpt4free
    • Completely free, allowing users to use the application without the need for API keys or payments.
    • Select the Provider. For more details about gpt4free, please refer to the source project.
  • openai
    • Requires an openai_api_key, which you can obtain from this link.
    • If you have an serpapi_key, AI responses can include Google search results.

• gpt4free Integration: Everyone can use docGPT for free without needing an OpenAI API key.
• Support docx, pdf, csv, txt file: Users can upload PDF, Word, CSV, txt file.
• Direct Document URL Input: Users can input Document URL links for parsing without uploading document files(see the demo).
• 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 Document.

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

   • Method 1: Browse and upload your own .pdf, .docx, .csv, .txt file from your local machine.
   • Method 2: Enter the Document 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 few methods:

• Local development without docker:

  • Download the required packages for development.

    pip install -r requirements.txt
    

  • Start the service in the project's root directory.

    streamlit run ./app.py
    

  • Start exploring! You server will now be running at http://localhost:8501.

• Local development with docker:

  • Start the service using Docker Compose

    docker-compose up
    

    You server will now be running at http://localhost:8501. You can interact with the docGPT or run your tests as you would normally.

  • To stop the Docker containers, simply run:

    docker-compose down
    

• 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.

Due to the limitations of the free version of Streamlit Cloud and its reliance on server resources, docGPT may experience some latency. We recommend users to consider deploying it locally for a smoother experience

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

Back to top