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Supabase

Getting Started with Supabase Vector Databases

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Transcription de Getting Started with Supabase Vector Databases

Transcrit par un modèle vocal à partir du son du film, et non rédigé par la marque. Cliquez sur une ligne pour y amener la lecture.

  1. 0:00A lot of AI apps have the same hidden challenge.
  2. 0:02They need the right context before they can give a useful answer.
  3. 0:06That context might live in the docs, support tickets, product data, messages, images, or internal knowledge base.
  4. 0:12Vector databases make that data searchable by meaning.
  5. 0:15In this video, we'll cover what embeddings are, what vector databases do, and why Superbase is a natural place to use them.
  6. 0:25Before vector databases make sense, we need to understand embeddings.
  7. 0:29An embedding is a numerical representation of meaning.
  8. 0:32You give content to an embedding model and it returns a list of numbers.
  9. 0:37Those numbers are like coordinates in a semantic space.
  10. 0:40Similar ideas end up together, different ideas end up farther apart.
  11. 0:44So, how do I reset my password and I forgot my login may be close even though the wording is different.
  12. 0:51Embeddings do not match text character by character.
  13. 0:54They capture patterns, words, phrases, concepts, and context.
  14. 0:58Embeddings are vectors.
  15. 1:00A vector database is what makes those vectors usable in an application.
  16. 1:04It stores each vector alongside the original record and its metadata, such as the document,
  17. 1:09product, workspace, category, or permissions it belongs to.
  18. 1:12When a query vector comes in, the database compares it with the stored vectors, ranks
  19. 1:17the closest records, and returns the best matches.
  20. 1:21As the dataset grows, specialized vector indexes help it find nearby vectors quickly instead
  21. 1:26instead of comparing every vector one by one.
  22. 1:28And because the vectors are stored alongside the normal database records, similarity search
  23. 1:33can still use filters, access rules, and the rest of your application data.
  24. 1:38The embedding model creates the vectors, the vector databases store, indexes, filters,
  25. 1:43and retrieves them.
  26. 1:44Now how does similarity search work?
  27. 1:47The flow is simple.
  28. 1:49First, take the content you want to search, docs, support articles, products, notes, whatever.
  29. 1:54Generate an embedding for each of them and store it in your database.
  30. 1:58Then when a user performs a search, generate an embedding for their query data.
  31. 2:03Now compare the query embeddings against the stored embeddings.
  32. 2:06The closest vectors are the closest matches.
  33. 2:10Superbase is built on Postgres and Postgres supports Vector Search with PGVector.
  34. 2:15So with Superbase, Vector Search is supported natively.
  35. 2:19You can store embeddings, query them with SQL and keep using the database patterns you
  36. 2:24you already know.
  37. 2:25You also get the rest of Superbase platform around it,
  38. 2:28migrations, client libraries, edge functions, auth, storage,
  39. 2:31dashboard, and many more.
  40. 2:33What kind of apps can you actually
  41. 2:34build with vector databases?
  42. 2:36The obvious use case is semantic search.
  43. 2:39Let users search docs, help centers, products, categories,
  44. 2:43or app content with natural language.
  45. 2:46Another big one is RAG, Retrieval Augmented Generation.
  46. 2:50That's when an AI assistant retrieve
  47. 2:52relevant content from your database before generating an answer. You can also
  48. 2:56build recommendations, image search, duplicate detection, and matching systems.
  49. 3:01In real apps, search is rarely just find similar things. It's usually more specific,
  50. 3:06find similar things that the users can access in this workspace, in this category,
  51. 3:11or in the last 30 days. That's why having vectors in Postgres is useful. Your
  52. 3:16semantic search can work with permissions, filters, metadata, ownership, and
  53. 3:21business logic. Vectors are not replacement for every search problem. Use exact phrases when
  54. 3:27exactness matters. IDs, emails, usernames, codes, precise phrases. Use vector search when meaning
  55. 3:34matters. Questions, descriptions, support issues, docs, recommendations. And often the best answer
  56. 3:40is both. Keyword search gives you precision. Vector search gives you semantic understanding.
  57. 3:45Hybrid search combines them. And that's the core idea. Embeddings turn content into numerical
  58. 3:50meaning a vector database stores those embeddings and searches by similarity.
  59. 3:55And Superbase lets you do that right inside Postgres, next to the rest of your
  60. 3:59app data. Once you understand that, vector databases feel much less mysterious.
  61. 4:04They're a way to help your app understand what your data means, not just
  62. 4:08what words it contains.
62 lignes · 650 mots

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