Source Code Analysis of Jupyter Notebooks using Natural Language Processing
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Updated
Jun 26, 2025 - Jupyter Notebook
Source Code Analysis of Jupyter Notebooks using Natural Language Processing
An end-to-end Jupyter Notebook guide to building, saving, and querying a FAISS vector store with LangChain for RAG and advanced semantic search.
This project is a RAG Q&A bot built in a jupyter notebook. It allows users to upload a custom document, which is then processed and stored in a Pinecone vector database using OpenAI embeddings. Users can ask questions about the document's content and receive accurate, context-aware answers generated by an OpenAI language model.
Add a description, image, and links to the vector-embeddings topic page so that developers can more easily learn about it.
To associate your repository with the vector-embeddings topic, visit your repo's landing page and select "manage topics."