ArticleIQ is a user-friendly news research tool designed to facilitate effortless information retrieval across diverse domains. Users can easily input article URLs and ask questions, receiving relevant insights spanning a wide range of topics. Whether you're delving into technology, health, science, or any other domain, ArticleIQ empowers users with a seamless and intuitive platform for informed decision-making based on comprehensive research
- Load URLs to fetch article content.
- Process article content through LangChain's WebbasedURL Loader
- Construct an embedding vector using MistralAI embeddings and leverage FAISS, a powerful similarity search library, to enable swift and effective retrieval of relevant information
- Interact with the LLM by inputting queries and receiving answers along with source URLs.
- Run the Streamlit app by executing:
python -m 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
- 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 HuggingFace API key.
Contributions to this repository are welcome. If you have suggestions or improvements, feel free to open an issue or submit a pull request.
This repository is licensed under the MIT License.