This News research tool 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 stock market and financial domain.
- 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.
- Run the Streamlit app by executing:
streamlit run main.py
2.The web app will open in your browser.
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On the sidebar, you can input URLs directly.
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Initiate the data loading and processing by clicking "Process URLs."
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Observe the system as it performs text splitting, generates embedding vectors, and efficiently indexes them using FAISS.
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The embeddings will be stored and indexed using FAISS, enhancing retrieval speed.
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The FAISS index will be saved in a local file path in pickle format for future use.
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One can now ask a question and get the answer based on those news articles
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In video tutorial, we used following news articles
- https://www.moneycontrol.com/news/business/tata-motors-mahindra-gain-certificates-for-production-linked-payouts-11281691.html
- https://www.moneycontrol.com/news/business/tata-motors-launches-punch-icng-price-starts-at-rs-7-1-lakh-11098751.html
- https://www.moneycontrol.com/news/business/stocks/buy-tata-motors-target-of-rs-743-kr-choksey-11080811.html
- main.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.