api-vector-db-weaviate

# Weaviate Patterns > **Quick 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 `connectToWeaviateCloud()` for managed instances or `connectToLocal()` for Docker. Collections are the central abstraction -- configure vectorizers at collection level, not per-query. Use `collection.query.*` for search, `collection.generate.*` for RAG, and `collection.data.*` for CRUD. Always call `client.close()` when done. Increase query timeout to 60s+ when using generative search. The v3 client does NOT support browsers or Embedded Weaviate. --- <critical_requirements> ## CRITICAL: Before Using This Skill > **All code must follow project conventions in CLAUDE.md** (kebab-case, named exports, import ordering, `import 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> --- ## Examples - [Core Patterns](examples/core.md) -- Connection, collection setup, object CRUD, basic search - [Search & Filtering](examples/search.md) -- nearText, nearVector, hybrid, bm25, filters, generative search (RAG) - [Multi-Tenancy & Batch](examples/multi-tenancy.md) -- Tenant management, batch imports, cross-references **Additional resources:** - [reference.md](reference.md) -- API cheat sheet, vectorizer comparison, data types, decision frameworks --- **Auto-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 **When to use:** - Semantic search over text, images, or multimodal data - Retrieval Augmented Generation (RAG) with built-in generative search - Hybrid search combining vector similarity and keyword (BM25) ranking - Multi-tenant applications needing isolated vector stores per customer - Applications requiring built-in vectorization (no external embedding pipeline) - Real-time similarity search with filtering on structured properties **Key patterns covered:** - weaviate-client v3 connection setup and configuration - Collection management with vectorizer modules (text2vec-openai, text2vec-cohere, etc.) - Object CRUD (insert, insertMany, update, replace, deleteById, deleteMany) - Search types (nearText, nearVector, hybrid, bm25, fetchObjects) - Filtering with operators (equal, greaterThan, like, containsAny, and/or/not) - Generative search (RAG) with singlePrompt and groupedTask - Multi-tenancy with tenant lifecycle management - Batch imports with insertMany and error handling - Cross-references between collections - Named vectors for multi-vector collections **When NOT to use:** - Relational data with complex joins (use PostgreSQL) - Full-text search without vector component (use Elasticsearch/Meilisearch) - Key-value caching (use Redis) - Time-series data (use TimescaleDB/InfluxDB) - Graph traversal queries (use Neo4j) - Browser-side applications (v3 client is Node.js only) --- <philosophy> ## Philosophy Weaviate is a **vector database** that stores data objects alongside their vector embeddings. The core principle: **configure once at the collection level, then query with simple method calls.** **Core principles:** 1. **Collection-centric design** -- All configuration (vectorizer, generative model, reranker, properties) is set at collection creation. Queries operate on collection objects obtained via `client.collections.use()`. 2. **Built-in vectorization** -- Weaviate can vectorize data automatically using configured modules (text2vec-openai, text2vec-cohere, etc.). You don't need an external embedding pipeline unless you want one. 3. **Search is a spectrum** -- Use `nearText` for semantic similarity, `bm25` for keyword matching, `hybrid` for a weighted combination. The `alpha` parameter controls the vector-vs-keyword balance in hybrid search. 4. **RAG is a search mode, not a separate system** -- Switch from `collection.query.nearText()` to `collection.generate.nearText()` to add LLM generation on top of search results. 5. **Filters are additive** -- Filters narrow results after vector/keyword retrieval. Combine with `Filters.and()` and `Filters.or()` for complex conditions. </philosophy> --- <patterns> ## Core Patterns ### Pattern 1: Connection Setup Connect to Weaviate Cloud or local Docker instance. Always close the client when done. See [examples/core.md](examples/core.md) for full examples. ```typescript // Good Example -- Cloud connection with API key headers import weaviate from "weaviate-client"; const QUERY_TIMEOUT_SECONDS = 30; const INSERT_TIMEOUT_SECONDS = 120; async function createWeaviateClient() { const client = await weaviate.connectToWeaviateCloud( process.env.WEAVIATE_URL!, { authCredentials: new weaviate.ApiKey(process.env.WEAVIATE_API_KEY!), headers: { "X-OpenAI-Api-Key": process.env.OPENAI_API_KEY!, }, timeout: { query: QUERY_TIMEOUT_SECONDS, insert: INSERT_TIMEOUT_SECONDS, }, }, ); return client; } export { createWeaviateClient }; ``` **Why good:** Environment variables for credentials, explicit timeouts, API key headers for vectorizer modules ```typescript // Bad Example -- Missing cleanup, no timeout config import weaviate from "weaviate-client"; const client = await weaviate.connectToLocal(); // No client.close() -- gRPC connections leak // No timeout config -- generative queries will timeout ``` **Why bad:** Missing `client.close()` leaks gRPC connections, default timeout too short for RAG queries --- ### Pattern 2: Collection with Vectorizer Configure vectorizer and properties at creation time. See [examples/core.md](examples/core.md) for named vectors and advanced configuration. ```typescript // Good Example -- Collection with vectorizer and generative model import { vectors, dataType, generative } from "weaviate-client"; await client.collections.create({ name: "Article", vectorizers: vectors.text2VecOpenAI({ model: "text-embedding-3-small", }), generative: generative.openAI({ model: "gpt-4o", }), properties: [ { name: "title", dataType: dataType.TEXT }, { name: "body", dataType: dataType.TEXT }, { name: "category", dataType: dataType.TEXT }, { name: "publishedAt", dataType: dataType.DATE }, ], }); ``` **Why good:** Vectorizer and generative model configured at collection level, typed properties with explicit data types ```typescript // Bad Example -- Trying to add vectorizer after creation await client.collections.create({ name: "Article" }); // No way to add a vectorizer to an existing collection without named vectors // Must delete and recreate, or use addVector() for named vectors only ``` **Why bad:** Vectorizer must be set at creation time; cannot be added to an existing default vector after the fact --- ### Pattern 3: Hybrid Search with Filters Combine vector and keyword search with property filters. See [examples/search.md](examples/search.md) for all search types. ```typescript // Good Example -- Hybrid search with filter import { Filters } from "weaviate-client"; const articles = client.collections.use("Article"); const SEARCH_LIMIT = 10; const HYBRID_ALPHA = 0.75; // Favor vector search const result = await articles.query.hybrid("machine learning trends", { alpha: HYBRID_ALPHA, limit: SEARCH_LIMIT, filters: Filters.and( articles.filter.byProperty("category").equal("technology"), articles.filter .byProperty("publishedAt") .greaterThan(new Date("2024-01-01")), ), returnMetadata: ["score", "explainScore"], }); for (const obj of result.objects) { console.log(obj.properties.title, obj.metadata?.score); } ``` **Why good:** Named constants for limits and alpha, combined filter with `Filters.and()`, metadata for debugging relevance --- ### Pattern 4: Generative Search (RAG) Switch from `query.*` to `generate.*` for RAG. See [examples/search.md](examples/search.md) for singlePrompt and groupedTask patterns. ```typescript // Good Example -- RAG with single prompt per result const articles = client.collections.use("Article"); const RAG_RESULT_LIMIT = 5; const result = await articles.generate.nearText( "climate change policy", { singlePrompt: "Summarize this article in one sentence: {title} - {body}", }, { limit: RAG_RESULT_LIMIT, returnMetadata: ["distance"], }, ); for (const obj of result.objects) { console.log("Source:", obj.properties.title); console.log("Generated:", obj.generative?.text); } ``` **Why good:** Uses property interpolation `{title}` in prompt, accesses generated text via `obj.generative?.text` ```typescript // Bad Example -- Using query instead of generate for RAG const result = await articles.query.nearText("climate change", { limit: 5 }); // Then manually calling OpenAI API with results // Weaviate does this natively with generate.* ``` **Why bad:** Misses Weaviate's built-in RAG -- extra network hops, no automatic prompt interpolation </patterns> --- <decision_framework> ## Decision Framework ### Which Search Type? ``` What kind of search do I need? ├─ Natural language query, semantic meaning? -> nearText (requires vectorizer module) ├─ Have pre-computed vector embedding? -> nearVector ├─ Exact keyword matching? -> bm25 ├─ Both semantic and keyword relevance? -> hybrid (alpha controls blend) ├─ Just list/filter objects without search? -> fetchObjects └─ Search + LLM generation? -> generate.nearText / generate.hybrid ``` ### Which Vectorizer? ``` Which vectorizer module should I use? ├─ OpenAI models (text-embedding-3-small/large)? -> text2VecOpenAI ├─ Cohere models (embed-v3)? -> text2VecCohere ├─ Self-hosted models? -> text2VecOllama or text2VecTransformers ├─ Bring your own embeddings? -> none (use selfProvided for named vectors) ├─ Multimodal (images + text)? -> multi2VecClip or multi2VecBind └─ Multiple embedding strategies? -> Named vectors (array of vectorizers) ``` ### Single vs Named Vectors? ``` How many vector representations do I need? ├─ One embedding per object (most common)? -> Single default vectorizer ├─ Different embeddings for different properties? -> Named vectors ├─ Mix of auto-vectorized and self-provided? -> Named vectors with selfProvided └─ Different models for different search use cases? -> Named vectors ``` ### When to Use Multi-Tenancy? ``` Do I need data isolation? ├─ Each customer/user needs isolated data? -> Enable multi-tenancy ├─ Shared dataset, filter by user? -> Single tenant with filters ├─ Need to offload inactive tenants? -> Multi-tenancy with tenant states └─ Small number of distinct datasets? -> Separate collections may be simpler ``` </decision_framework> --- <red_flags> ## RED FLAGS **High Priority Issues:** - Missing `client.close()` -- gRPC connections persist and leak memory/file descriptors - Trying to add a default vectorizer after collection creation -- vectorizer must be configured in `create()`. Only named vectors can be added later with `config.addVector()` - Querying a multi-tenant collection without `.withTenant()` -- all operations fail with an error - Using default query timeout with `generate.*` -- generative calls need 60+ seconds; default is often too short **Medium Priority Issues:** - Using `replace()` when `update()` is intended -- `replace` deletes all properties not included in the call; `update` merges - Not checking `insertMany` response for errors -- partial failures are silent; check `response.hasErrors` and `response.errors` - Passing `alpha: 1.0` to hybrid search -- equivalent to pure vector search; use `nearText` instead for clarity - Not specifying `targetVector` with named vectors -- queries default to the first vector, which may not be the intended one **Common Mistakes:** - Using v2 class-based API (`client.schema.classCreator()`) with v3 client -- the API is completely different; v3 uses `client.collections.create()` - Forgetting to pass API key headers for vectorizer modules -- `X-OpenAI-Api-Key`, `X-Cohere-Api-Key` etc. must be in connection headers - Using `connectToWCS()` (deprecated) instead of `connectToWeaviateCloud()` - Adding a property after data import without reindexing -- pre-existing objects won't have that property indexed **Gotchas & Edge Cases:** - `insertMany` 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 objects - `Filters.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 objects - Property names in Weaviate must start with a lowercase letter -- the client silently lowercases the first character - `distance` 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`) - Weaviate auto-detects property types on first insert if not defined in the schema -- this can cause type mismatches if first object has atypical data - `fetchObjectById` returns `null` for non-existent IDs, not an empty object -- always check for null before accessing properties - Cross-references in multi-tenant collections can only reference objects in the same tenant or in non-multi-tenant collections </red_flags> --- <critical_reminders> ## CRITICAL REMINDERS > **All code must follow project conventions in CLAUDE.md** (kebab-case, named exports, import ordering, `import 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.** </critical_reminders>