https://github.com/aadityarajgupta/aethercare_chatbot
This repository contains a healthcare-based chatbot project that integrates advanced generative AI techniques with document retrieval for answering medical queries. It leverages vector-based search for relevant information retrieval and uses transformer-based models for generating responses.
https://github.com/aadityarajgupta/aethercare_chatbot
ai-chatbot document-retrieval flask generative-ai healthcare langchain-python machine-learning nlp pinecone transformers
Last synced: about 2 months ago
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This repository contains a healthcare-based chatbot project that integrates advanced generative AI techniques with document retrieval for answering medical queries. It leverages vector-based search for relevant information retrieval and uses transformer-based models for generating responses.
- Host: GitHub
- URL: https://github.com/aadityarajgupta/aethercare_chatbot
- Owner: AadityaRajGupta
- License: mit
- Created: 2024-10-17T04:49:12.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-18T05:01:06.000Z (over 1 year ago)
- Last Synced: 2025-06-12T15:52:04.934Z (about 1 year ago)
- Topics: ai-chatbot, document-retrieval, flask, generative-ai, healthcare, langchain-python, machine-learning, nlp, pinecone, transformers
- Language: Jupyter Notebook
- Homepage:
- Size: 13.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
---
# AetherCare_ChatBot Project
## Overview
This project implements a custom healthcare-focused question-answering system using **Pinecone Vector Search**, **Hugging Face Embeddings**, and a **LLM model**. The system retrieves relevant documents and generates answers based on user queries, using advanced machine-learning techniques.
## Project Structure
- `testing.ipynb`: A Jupyter notebook to run initial checks and tests for the project.
- `models/`: Stores local models like Llama used in the project.
- `data/`: Placeholder for any input or processed data files.
## Requirements
Before running the project, you need to set up the environment with the necessary dependencies. The following instructions detail the steps to do this.
## Installation
### Step 1: Create a Conda Environment
To ensure that all dependencies are correctly installed, start by creating a Conda environment using the provided `requirements.txt` file.
```bash
# Clone the repository
git clone https://github.com/AadityaRajGupta/AetherCare_ChatBot.git
cd AetherCare_ChatBot
# Create the environment
conda create -n health-bot python=3.8 -y
# Activate the environment
conda activate health-bot
# Download the Dependencies
pip install -r requirements.txt
```
### Step 2: Download the quantize model from the link provided in model folder & keep the model in the model directory:
```ini
## Download the Llama 2 Model:
llama-2-7b-chat.ggmlv3.q4_0.bin
## From the following link:
https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/tree/main
```
### Step 3: Set up API Keys
Make sure you have your **Pinecone API key** and any other necessary keys configured in your environment:
```bash
# Set Pinecone API key
PINECONE_API_KEY=your_pinecone_api_key
# Enter the index name
INDEX_NAME=your_index_name
```
### Step 4: Store String Data to Pinecone
Run the following command to store string data to Pinecone:
```bash
python store_index.py
```
### Step 5: Run the Application
After setting up the index, run the application using:
```bash
python app.py
```
## If You Want to Implement on Your Own, Follow These Steps
### Initial Testing
Before exploring the full project structure, run the initial test notebook to ensure everything is working as expected.
1. Open the `testing.ipynb` Jupyter notebook:
```bash
jupyter notebook testing.ipynb
```
2. Run the notebook cells to test:
- Setting up Pinecone.
- Creating embeddings using the Hugging Face model.
- Perform a similarity search and generate answers using the LLM model.
## Testing
For testing, utilize the Jupyter Notebook to ensure that:
- Embeddings are created correctly.
- The vector store is properly initialized.
- QA retrieval returns valid results.
Once satisfied with the initial tests, move the code to the organized repository structure and refine the functionality.
---
This README file outlines how to:
- Create the environment with Conda.
- Download dependencies.
- Test the project using the notebook.
- Transition to a more structured codebase for further development.
Make sure to replace placeholders (e.g., API keys, repository URLs) with the actual project details.
```
Feel free to adjust any parts as needed!