Using LocalPdfChatRAG to process PDF documents requires the completion of several major steps:
- Deployment::
- Cloning a project repository via Git
- Install the required Python dependencies using pip
- Configure the environment variable file (.env) to set the API key
- Starting services::
- Run rag_demo.py to start the local service
- By default a web interface will be opened locally
- Document Processing Flow::
- Accessing the Local Services Interface through a Browser
- Upload PDF files to be processed (supports multiple files)
- The system automatically parses the text and stores it in the database.
- Question Interaction::
- Enter natural language questions in the Q&A interface
- The system returns accurate answers based on the content of the document
The whole process is very intuitive, and users do not need to write code to complete complex document retrieval and analysis work. It should be noted that the first time you use it, you need to configure the SerpAPI key in order to realize the network search function.
This answer comes from the articleLocalPdfChatRAG: Intelligent Chat Tool to Support Local Multi-Source PDF Document Q&AThe
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