\n\nQuick Guide: Use Weaviate for semantic search and RAG applications. Use weaviate-client (v3.x) as the TypeScript client -- it uses gRPC for performance and provides full type safety with generics. Connect via
\nconnectToWeaviateCloud()for managed instances orconnectToLocal()for Docker. Collections are the central abstraction -- configure vectorizers at collection level, not per-query. Usecollection.query.*for search,collection.generate.*for RAG, andcollection.data.*for CRUD. Always callclient.close()when done. Increase query timeout to 60s+ when using generative search. The v3 client does NOT support browsers or Embedded Weaviate.
<critical_requirements>
\n\n\nAll code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering,
\nimport type, named constants)
(You MUST call client.close() when done with the Weaviate client -- it maintains persistent gRPC connections that will leak if not closed)
(You MUST configure vectorizers at the COLLECTION level during client.collections.create() -- you cannot add a vectorizer after creation, only add new named vectors)
(You MUST use a SEPARATE client.collections.use() call with .withTenant() for multi-tenant queries -- queries without tenant context on multi-tenant collections will fail)
(You MUST increase query timeout to 60+ seconds when using generate.* (RAG) submodule -- generative model calls are slow and the default timeout causes failures)
</critical_requirements>
\nAdditional resources:
\nAuto-detection: Weaviate, weaviate-client, connectToWeaviateCloud, connectToLocal, nearText, nearVector, hybrid search, bm25, vector database, semantic search, RAG, generative search, generate.nearText, insertMany, vectorizer, text2vec, multi-tenancy, withTenant, collection.query, collection.generate, collection.data
\nWhen to use:
\nKey patterns covered:
\nWhen NOT to use:
\n<decision_framework>
\nWhat kind of search do I need?\n├─ Natural language query, semantic meaning? -> nearText (requires vectorizer module)\n├─ Have pre-computed vector embedding? -> nearVector\n├─ Exact keyword matching? -> bm25\n├─ Both semantic and keyword relevance? -> hybrid (alpha controls blend)\n├─ Just list/filter objects without search? -> fetchObjects\n└─ Search + LLM generation? -> generate.nearText / generate.hybrid\nWhich vectorizer module should I use?\n├─ OpenAI models (text-embedding-3-small/large)? -> text2VecOpenAI\n├─ Cohere models (embed-v3)? -> text2VecCohere\n├─ Self-hosted models? -> text2VecOllama or text2VecTransformers\n├─ Bring your own embeddings? -> none (use selfProvided for named vectors)\n├─ Multimodal (images + text)? -> multi2VecClip or multi2VecBind\n└─ Multiple embedding strategies? -> Named vectors (array of vectorizers)\nHow many vector representations do I need?\n├─ One embedding per object (most common)? -> Single default vectorizer\n├─ Different embeddings for different properties? -> Named vectors\n├─ Mix of auto-vectorized and self-provided? -> Named vectors with selfProvided\n└─ Different models for different search use cases? -> Named vectors\nDo I need data isolation?\n├─ Each customer/user needs isolated data? -> Enable multi-tenancy\n├─ Shared dataset, filter by user? -> Single tenant with filters\n├─ Need to offload inactive tenants? -> Multi-tenancy with tenant states\n└─ Small number of distinct datasets? -> Separate collections may be simpler\n</decision_framework>
\n<red_flags>
\nHigh Priority Issues:
\nclient.close() -- gRPC connections persist and leak memory/file descriptorscreate(). Only named vectors can be added later with config.addVector().withTenant() -- all operations fail with an errorgenerate.* -- generative calls need 60+ seconds; default is often too shortMedium Priority Issues:
\nreplace() when update() is intended -- replace deletes all properties not included in the call; update mergesinsertMany response for errors -- partial failures are silent; check response.hasErrors and response.errorsalpha: 1.0 to hybrid search -- equivalent to pure vector search; use nearText instead for claritytargetVector with named vectors -- queries default to the first vector, which may not be the intended oneCommon Mistakes:
\nclient.schema.classCreator()) with v3 client -- the API is completely different; v3 uses client.collections.create()X-OpenAI-Api-Key, X-Cohere-Api-Key etc. must be in connection headersconnectToWCS() (deprecated) instead of connectToWeaviateCloud()Gotchas & Edge Cases:
\ninsertMany uses server-side batching but the TS client does NOT have a streaming batch API -- for very large imports (100K+), chunk into batches of 100-1000 objectsFilters.and() and Filters.or() take a flat list of filter conditions, NOT nested arrays -- Filters.and(a, b, c) not Filters.and([a, b, c])fetchObjects() without limit returns 25 objects by default (server-side default), not all objectsdistance metadata varies by vector distance metric -- cosine distance range [0, 2], not [0, 1]deleteMany has a server-side maximum of 10,000 objects per call (configurable via QUERY_MAXIMUM_RESULTS)fetchObjectById returns null for non-existent IDs, not an empty object -- always check for null before accessing properties</red_flags>
\n<critical_reminders>
\n\n\nAll code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering,
\nimport type, named constants)
(You MUST call client.close() when done with the Weaviate client -- it maintains persistent gRPC connections that will leak if not closed)
(You MUST configure vectorizers at the COLLECTION level during client.collections.create() -- you cannot add a vectorizer after creation, only add new named vectors)
(You MUST use a SEPARATE client.collections.use() call with .withTenant() for multi-tenant queries -- queries without tenant context on multi-tenant collections will fail)
(You MUST increase query timeout to 60+ seconds when using generate.* (RAG) submodule -- generative model calls are slow and the default timeout causes failures)
Failure to follow these rules will cause connection leaks, missing vectorization, multi-tenant query failures, and RAG timeouts.
\n</critical_reminders>
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