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Master Vector Databases DevCourseWeb

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Files:
  1. Get Bonus Downloads Here.url 182 bytes
  2. 1. Introduction to Vector Database.mp4 86.3 MB
  3. 2. Vectors and Embeddings.mp4 40.7 MB
  4. 3. Explain vector database like I'm 5.mp4 29.9 MB
  5. 4. How vector database store data.mp4 32.1 MB
  6. 5. How do vector database works.mp4 26.8 MB
  7. 6. Vectors in 2D.mp4 37.0 MB
  8. 1. Create embeddings using OpenAI.mp4 92.4 MB
  9. 1.1 VD_Vector_Embedding_OpenAI.ipynb 47.5 KB
  10. 2. Sentence Embedding Models.html 3.5 KB
  11. 1. Setup and basic operations.mp4 55.3 MB
  12. 2. Creating, storing and retrieving vector data.mp4 142.8 MB
  13. 2.1 VD_SQLite_Vector_search.ipynb 16.8 KB
  14. 3. Finding nearest vector.mp4 48.3 MB
  15. 4. Vector search using sqlite-vss extension.mp4 149.2 MB
  16. 4.1 VD_SQLite_VSS.ipynb 12.0 KB
  17. 1. Introduction to ChromaDB.mp4 125.4 MB
  18. 2. Revolutionizing the Data access with Vector Database.html 5.2 KB
  19. 3. Methods on collections.mp4 60.6 MB
  20. 3.1 Vector_Database_ChromaDB.ipynb 2.5 KB
  21. 4. Storing The Matrix collections.mp4 103.9 MB
  22. 4.1 Vector_Database_ChromaDB__The_Matrix_.ipynb 25.2 KB
  23. 5. Adding document associated embeddings.mp4 96.0 MB
  24. 6. Query data with 'where' filter.mp4 96.0 MB
  25. 7. ChromaDB + Langchain - QA on multiple documents - Part 1.mp4 133.3 MB
  26. 7.1 VD_ChromaDB_+_Langchain_QA_Multiple_documents.ipynb 27.2 KB
  27. 8. ChromaDB + Langchain - QA on multiple documents - Part 2.mp4 92.1 MB
  28. 1. Introduction to FAISS.mp4 127.6 MB
  29. 2. Using similarity search for nearest neighbours.mp4 67.0 MB
  30. 1. Introduction to Pinecone.mp4 113.0 MB
  31. 10. Vector IDs must be string.mp4 51.1 MB
  32. 11. Sentence transformer embeddings.mp4 85.8 MB
  33. 12. Semantic search with metadata filtering - news articles.mp4 252.7 MB
  34. 2. Setup account, create an index, dashboard review.mp4 97.0 MB
  35. 3. Understanding index creation configuration.mp4 82.9 MB
  36. 4. Index management.mp4 114.9 MB
  37. 5. Insert vector data to an index.mp4 104.7 MB
  38. 6. Query vector data.mp4 94.3 MB
  39. 7. Upsert vector data in batches.mp4 100.3 MB
  40. 8. Upsert batches in parallel.mp4 17.7 MB
  41. 9. Upsert with metadata.html 381 bytes
  42. 1. Introduction to Qdrant vector database.mp4 82.2 MB
  43. 2. Connect with APIs.mp4 59.8 MB
  44. 3. Create a qdrant python client.mp4 37.0 MB
  45. 4. Create a collection.mp4 53.3 MB
  46. 5. Create a vector store.mp4 45.3 MB
  47. 6. Add document to vector store on the cloud.mp4 118.7 MB
  48. 7. Query the document.mp4 86.0 MB
  49. 8. Create a streamlit QA app.html 940 bytes
  50. 1. Your feedback is very valuable!.html 690 bytes
  51. Bonus Resources.txt 386 bytes

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