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Rag Chatbot
Overview :
rag-chatbot is an AI-based chatbot model that allows users to interact with multiple PDF files using natural language. This model leverages the latest machine learning technologies, such as Hugging Face and Ollama, to comprehend PDF content and generate responses. Its significance lies in its ability to process extensive document information, providing users with rapid and accurate question-answering services. The background information indicates that this is an open-source project aimed at enhancing document processing efficiency through technological innovation. Currently, the project is free and primarily targets developers and tech enthusiasts.
Target Users :
The target audience includes developers, data scientists, and machine learning engineers who often need to handle and analyze large amounts of PDF documents. rag-chatbot can assist them in quickly extracting information, thereby boosting work efficiency.
Use Cases
Developers can use it to create a question-answering system based on PDF content.
Data scientists can leverage it to quickly retrieve key information from research reports.
Machine learning engineers can employ it to build and test new conversational system models.
Features
Supports easy local or Kaggle operation
Utilizes any models available from Hugging Face and Ollama
Capable of processing multiple PDF inputs
Multilingual chat support (coming soon)
Features a simple user interface built with Gradio
Supports deployment and access using tools like Docker and Ngrok
How to Use
1. Clone the project locally: Use the git clone command to clone the repository.
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2. Install dependencies: Follow the project documentation to install the required dependencies, either via Docker or manually.
3. Run the model: Execute the appropriate script to launch the chatbot.
4. Access the interface: Open the specified address in your browser to start interacting with the PDF files.
5. Import PDF: Upload the PDF files that need to be processed into the chatbot.
6. Ask questions and get answers: Input your questions to receive answers generated by the bot based on the PDF content.
7. Adjust the model as needed: Developers can modify and optimize the model's performance according to their requirements.
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