LocalPdfChatRAG: Intelligent Chat Tool to Support Local Multi-Source PDF Document Q&A

General Introduction

LocalPdfChatRAG is an open source project that aims to enable intelligent chat by combining local PDF documents with Retrieval Augmented Generation (RAG) models. The project allows users to upload PDF documents , and through natural language questions to obtain relevant information from the document . localPdfChatRAG utilizes advanced natural language processing technology to provide efficient and accurate document content retrieval and Q&A services for a wide range of scenarios, including academic research and enterprise document management.

Function List

• PDF document upload: Users can upload local PDF documents, the system will automatically parse and extract the text content.
• natural language quiz: Users can ask questions in natural language, and the system will retrieve relevant information from the uploaded PDF document and generate an answer.
• multi-source information integration: Support for combining local PDF documents and web search results to provide more comprehensive answers.
• vectorization: Vectorization of text using embedding models to improve retrieval and Q&A accuracy.
• Environment variable configuration: Supports configuration of API keys and other parameters via .env files for easy customization of settings.

Using Help

Installation process

1. cloning project: Run the following command in the terminal to clone the project code:

   git clone https://github.com/weiwill88/Local_Pdf_Chat_RAG.git

2. Installation of dependencies: Go to the project directory and install the required dependencies:

   cd Local_Pdf_Chat_RAG
pip install -r requirements.txt

3. Configuring Environment Variables: Create a.envfile and add the following:

   SERPAPI_KEY=your_serpapi_key

commander-in-chief (military)your_serpapi_keyReplace it with your SerpAPI key.

Usage Process

1. Starting services: Run the following command in the terminal to start the service:

   python rag_demo.py

2. Upload PDF documents: Open your browser to access the local service address and upload the PDF document you need to process.
3. ask questions: Enter your question in the input box and the system will retrieve the relevant information from the uploaded PDF document and generate an answer.

Detailed Function Operation

• PDF document upload: Click the Upload button to select a local PDF file, the system will automatically parse the document content and store it in the database.
• natural language quiz: Enter a question in the input box, e.g. "What are the main conclusions of this paper?". The system will extract the relevant paragraphs from the PDF document and generate an answer.
• multi-source information integration: The system will not only retrieve information from local PDF documents, but will also conduct web searches through SerpAPI, integrating multiple sources of information to provide more comprehensive answers.
• vectorization: The system uses the SentenceTransformer model to vectorize the text to ensure high accuracy in retrieval and Q&A.
• Environment variable configuration: Users can modify the parameters in the .env file to configure API keys, search engines, etc. to meet individual needs.