Retrieval Augmented Generation (RAG): Coding ChromaDB for Multiple Documents Vector Storage & Search
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🚀 Coding ChromaDB for Multiple Documents: Vector Storage & Similarity Search
Welcome to Murat Karakaya Akademi! 🌟
In this exciting tutorial series, we are developing a Retrieval Augmented Generation (RAG) application. If you missed the first part where we covered how to code the GEMINI API for text generation and chat, be sure to check that out. In this second part, we dive into coding with ChromaDB for vector storage and similarity search with multiple documents.
In This Video, You Will Learn:
How Does RAG Work? – Understand the fundamentals of Retrieval Augmented Generation.
Upload Single and Multiple PDF Documents to ChromaDB – Learn to handle various document types.
Convert PDF Content to Text Format – Extract text from PDFs efficiently.
Convert Text from Pages to Chunks – Organize text for better processing.
Tokenize the Text – Prepare your text data for machine learning.
Use the Sentence Transformers Library – Implement advanced text encoding techniques.
Understand Vector Embedding – Grasp the concept of embedding vectors.
Create a ChromaDB Instance for Single and Multiple Files/Documents – Set up your database for efficient storage.
Query and Retrieve Chunks from ChromaDB – Learn to search and retrieve relevant information.
Filtering Results – Refine your search results.
Remove Less Related/Less Similar Chunks – Improve the accuracy of your retrieval process.
All the above steps will be implemented and coded in Python on Google Colab. Follow along step-by-step to master these techniques and enhance your data processing capabilities.
🔗 Watch the first part of the series here: GEMINI API FOR TEXT GENERATION & CHAT
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#ChromaDB #VectorStorage #SimilaritySearch #RAGApplication #Python #GoogleColab #DataScience #MuratKarakayaAkademi
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