Poly AI Research Insights 📚🤖

Poly AI Research bot is a user-friendly news research tool designed for effortless information retrieval. Users can input article URLs and ask questions to receive relevant insights from the market trends and financial domain.

📊 Features

• Load URLs or upload text files containing URLs to fetch article content.
• Process article content through LangChain's UnstructuredURL Loader
• Construct an embedding vector using OpenAI's embeddings and leverage FAISS, a powerful similarity search library, to enable swift and effective retrieval of relevant information
• Interact with the LLM's (Chatgpt) by inputting queries and receiving answers along with source URLs

Tech Stack

Streamlit: Powers the frontend, providing a seamless user interface. LangChain: Acts as the foundation for integrating the LLM into the web app. OpenAI: AI research and technology, powering natural language processing.

How to use Application

Website link:

Go through Here

Local Setup:

1. Clone this repository to your local machine using:

git clone https://github.com/saibattula93/PolyInnovate.git

2. Set Up a Virtual Environment:

#For Windows:

python -m venv venv

3. Activate the Virtual Environment:

#For Windows:

venv\Scripts\activate

4. Install Required Dependencies:

pip install -r requirements.txt

5. Set up your OpenAI API key by creating a .env file in the project root and adding your API:

openai_api_key = "OPEN AI API KEY"

6. Run Poly AI Bot:

streamlit run Home.py

After running the command, Streamlit will provide a local URL http://localhost:8501/ which you can open in your web browser to access Poly AI Bot.

The web app will open in your browser.

On the sidebar, you can input URLs directly.

• Initiate the data loading and processing by clicking "Process URLs."

• Observe the system as it performs text splitting, generates embedding vectors, and efficiently indexes them using FAISS.

• The embeddings will be stored and indexed using FAISS, enhancing retrieval speed.

• The FAISS index will be saved in a local file path in pickle format for future use.

• One can now ask a question and get the answer based on those news articles

Project Structure

• Home.py: The main Streamlit application script.
• requirements.txt: A list of required Python packages for the project.
• faiss_store_openai.pkl: A pickle file to store the FAISS index.
• .env: Configuration file for storing your OpenAI API key.