Boost Vector DB Search Speed with Caching - Step-by-Step Guide | Embeddings, Indexing, and Caching скачать в хорошем качестве

Boost Vector DB Search Speed with Caching - Step-by-Step Guide | Embeddings, Indexing, and Caching

Learn how to build a powerful and efficient vector search system using ChromaDB with HNSW indexing and MiniLM-L6-v2 text embeddings. In this tutorial, we take a deep dive into optimizing performance by implementing query caching and only hitting the database when necessary—based on similarity thresholds. 🚀 What You'll Learn: How to set up and use ChromaDB with default HNSW index Integrating MiniLM-L6-v2 embeddings for text vectorization Implementing smart query caching to reduce redundant vector searches Dynamically checking similarity scores before querying the DB Practical example & code walkthrough 📦 Tools & Technologies: ChromaDB Hugging Face Transformers - MiniLM-L6-v2 HNSW Indexing Python 🧠 Whether you're building a chatbot, semantic search engine, or any NLP app, this technique can save compute and speed up response times. Previous Video: Text Embedding:    • Text Embedding Guide: Query PDFs & Retriev...   📁 Code Repository: [https://github.com/mohitdb7/Youtube-T...] 👍 Like the video if it helped, and subscribe for more tutorials on AI, ML, and vector databases! #ChromaDB #VectorSearch #MiniLM #HNSW #SemanticSearch #Python #Embedding #AI #Tutorial #Caching #RAG