rag-chatbot

A chatbot that can interact with multiple PDF files locally.

CommonProductProgrammingchatbotquestion-answering

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.

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

\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.\

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.

1. Clone the project locally: Use the git clone command to clone the repository.
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.

Visit

rag-chatbot Visit Over Time

Monthly Visits

488643166

Bounce Rate

37.28%

Page per Visit

5.7

Visit Duration

00:06:37

rag-chatbot Visit Trend

rag-chatbot Visit Geography

rag-chatbot Traffic Sources

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