https://github.com/punnerud/Local_Knowledge_Graph

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punnerud/Local_Knowledge_Graph

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templates         templates                            
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  * README

Local Knowledge Graph

 

Example

This application uses a local Llama model to answer queries, build
embeddings, and create a knowledge graph for exploring related
questions and answers.

Description

 

The Local Knowledge Graph is a Flask-based web application that
leverages a local Llama language model to process user queries,
generate step-by-step reasoning, and visualize the thought process as
an interactive knowledge graph. It also finds and displays related
questions and answers based on semantic similarity.

Features

 

  * Interactive web interface for submitting queries
  * Step-by-step reasoning process displayed in real-time
  * Dynamic knowledge graph visualization of the reasoning steps
  * Calculation and display of the strongest reasoning path
  * Related questions and answers based on semantic similarity
  * Local processing using a Llama language model

Usage

 

 1. Ensure you have all the required dependencies installed.
 2. Start the Flask application by running app.py.
 3. Open a web browser and navigate to http://localhost:5100 (or the
    appropriate port if modified).
 4. Enter your query in the input field and click "Submit".
 5. Watch as the application generates a step-by-step reasoning
    process, updating the knowledge graph in real-time.
 6. Review the final answer and the strongest reasoning path.
 7. Explore related questions and answers displayed below the main
    response.

Requirements

 

  * Python 3.7+
  * Flask
  * NumPy
  * scikit-learn
  * Annoy
  * NetworkX
  * A local Llama language model (e.g., llama3.1:8b) running on http:
    //localhost:11434

Installation

 

 1. Clone this repository.
 2. Install the required Python packages using the requirements.txt
    file:

    pip install -r requirements.txt

 3. Ensure you have a local Llama model running and accessible.
 4. Run the Flask application:

    python app.py

Note

 

This application requires a local Llama language model to be running
and accessible. Make sure you have the appropriate model set up and
running before using this application.

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