Project Title: Advanced Chatbot System

Overview

This project showcases an advanced chatbot system built with Flask, Pinecone, RAG(Retrieval-Augmented Generation) and Langchain. It leverages OpenAI embeddings for semantic understanding and integrates a retrieval-based chat system with agents and memory capabilities to handle complex conversations and follow-up questions. The system is designed to retrieve relevant information from a Pinecone vector store and utilize SerpAPI for fetching web search results when necessary.

The response generated with our model using Langchain, OpenAI, RAG

Able to catch the memory, as I have not specified the drugs for which disease, competent to comprehend chat history.

If the chatbot cannot find the response from the given data, our Serp-API agents come into play and give you the web search results.

Features

• Conversational AI: Harnesses the power of RAG alongside Langchain and OpenAI's LLM to craft nuanced and contextually relevant responses, generating coherent and context-aware responses.
• Information Retrieval: Utilizes Pinecone for efficient storage and retrieval of document embeddings, enabling the chatbot to fetch relevant information based on user queries.
• Semantic Understanding: Incorporates OpenAI embeddings to comprehend the nuances of user inputs, enhancing the accuracy of retrieved information.
• Agent Integration: Implements custom agents for specialized functionalities, such as leveraging SerpAPI to fetch web search results when the internal knowledge base is insufficient.
• Memory Management: Equipped with a memory component that allows the chatbot to remember past interactions and ask follow-up questions, improving the flow of conversations.

Tech Stack

• Frontend: HTML, CSS, Bootstrap
• Backend: Flask
• Vector Storage: Pinecone
• Natural Language Processing: Langchain, OpenAI, RAG
• Search API: SerpAPI

Setup Instructions

Step 1: Create a Virtual Environment

1. Open your terminal or command prompt.
2. Navigate to the project directory.
3. Run python -m venv venv to create a virtual environment named venv.

Step 2: Activate the Virtual Environment

• On Windows, run .\venv\Scripts\activate.
• On macOS/Linux, run source venv/bin/activate.

Step 3: Install Dependencies

Run pip install -r requirements.txt to install all necessary packages.

Step 4: Configure Environment Variables

Create a .env file in the root of your project and add the following lines:

OPENAI_API_KEY="your_openai_api_key_here"
PINECONE_API_KEY="your_pinecone_api_key_here"
PINECONE_API_ENV="your_pinecone_api_env_here"
SERPAPI_KEY="your_serpapi_key_here"
INDEXNAME="your_index_name_here"

Replace the placeholders with your actual API keys and environment-specific values.

Step 5: Update User Image (Optional)

• To change the user image, replace the existing profile.jpeg in the static/img folder with your desired image.
• Ensure the image name remains profile.jpeg for consistency.

Step 6: Running the Flask App

• To use the default medical chatbot, simply run python app.py in your terminal.
• For a customized chatbot, add or append your data in PDF format to the data folder.
• Run python store_index.py to update your vector database and store its embeddings.
• Finally, run python app.py again to see the customized results.

Usage

• Access the chatbot by navigating to http://localhost:5000/.
• Interact with the chatbot by typing your queries in the input field and pressing Enter to receive responses.

Contributing

Contributions are welcome. Feel free to submit pull requests or open issues for discussions.

Thank You