Introduction to docGPT-langchain
Introduction
The docGPT-langchain project is a cutting-edge tool that offers users the ability to interact with their documents in an innovative way. By uploading various document formats like PDFs, Word files (.docx), CSVs, and text files (.txt), users can ask the docGPT system questions about the content. For instance, users can request the system to summarize articles or extract specific information, making document processing more intuitive and interactive.
This application doesn't require any keys or fees to use the basic functionalities. With two model options available—gpt4free and openai—it presents both a free version and a version requiring an OpenAI API key for more advanced features, hence introducing flexibility based on user needs and preferences.
Features
gpt4freeIntegration: Enables completely free usage without needing an OpenAI API key.- Support for Multiple File Types: Facilitates uploads of documents in .pdf, .docx, .csv, and .txt formats.
- Direct Document URL Input: Allows entering URLs for document parsing, reducing the need to upload files manually.
- Langchain Agent: Employs AI to answer questions, integrating functionality similar to Google search.
- User-Friendly Interface: Designed to ensure ease of use and accessibility for all users.
What's LangChain?
Langchain is a framework built for applications that incorporate language models. It helps by linking these models to external data sources and facilitating interactive communication. LangChain shines in areas where traditional tools like ChatGPT might fall short, particularly when dealing with mathematical problems or answering recent queries beyond ChatGPT's knowledge cut-off. By integrating tools like math-LLM models and Google search, LangChain aims to create more robust and capable AI systems.
How to Use docGPT?
To utilize docGPT, users can access the application online and optionally enter an API key to unlock additional features. Users can then upload documents either by directly browsing files on their local devices or by providing a URL. After uploading, users are free to query the document's content, enhancing document interaction with AI insights.
How to Develop a docGPT with Streamlit?
Development of docGPT can be carried out locally or using Streamlit Community Cloud for deployment. Locally, users can clone the repo and start the development server with Python or Docker commands. For cloud deployment, the app can be shared via Streamlit by linking it to a GitHub repository, making it convenient for deployment and sharing.
Advanced - How to Build a Better Model in LangChain
Building a sophisticated docGPT model with LangChain involves selecting the right language model and PDF loader, as well as tracking token usage. Different model configurations such as gpt-3.5-turbo can be explored, and various PDF extraction tools like PyMuPDF and PDFPlumber can be employed based on the document’s content requirements. Properly managing these configurations can greatly enhance the docGPT's performance and efficiency.
Overall, the docGPT-langchain project opens up new possibilities for document interaction, offering a user-friendly interface and versatile features for both casual users and advanced developers.