jmanhype / SemantiQBot

This name highlights the semantic clustering aspect of the chatbot and suggests its ability to understand and generate contextually relevant responses.

SemantiQBot

This project is a chatbot that uses Pinecone, a vector search engine, and GPT-3, a powerful language model, to answer user queries. The chatbot clusters text data based on semantic similarity, creates conjunctions for the clusters, generates new knowledge base (KB) articles, and adds them to the Pinecone index. It then uses GPT-3 to generate a response based on the most relevant KB article.

Getting Started

Prerequisites

• Python 3.6 or higher
• Pinecone API key
• OpenAI API key

Installation

1. Clone the repository:

git clone https://github.com/yourusername/semantic-chatbot.git

2. Install the required packages:

pip install -r requirements.txt

3. Set up environment variables for Pinecone and OpenAI API keys:

export PINECONE_API_KEY=your_pinecone_api_key
export OPENAI_API_KEY=your_openai_api_key

Usage

1. Run the main script:

python main.py

2. Interact with the chatbot by typing your queries:

User: What is the capital of France?
Chatbot: The capital of France is Paris.

Project Structure

• main.py: The main script that sets up the Pinecone index, initializes data structures, and runs the chatbot.
• clustering.py: Contains the Rollup, Chunk, Cluster, and Clustering classes for representing and clustering text data.
• gpt3.py: Contains the generate_response function that uses the OpenAI API to generate a response from GPT-3.
• pinecone_index.py: Contains the PineconeIndex class for managing the Pinecone index.
• reindexing_event.py: Contains the ReindexingEvent class for running the reindexing process on a set of roll-ups.
• sparse_priming.py: Contains the create_conjunctions function for creating conjunctions for a given cluster of roll-ups.