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 -- Connection, collection setup, object CRUD, basic search
• Search & Filtering -- nearText, nearVector, hybrid, bm25, filters, generative search (RAG)
• Multi-Tenancy & Batch -- Tenant management, batch imports, cross-references

Additional resources:

• 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 a relational database)
• Full-text search without vector component (use a dedicated search engine)
• Key-value caching (use a key-value store)
• Time-series data (use a time-series database)
• Graph traversal queries (use a graph database)
• Browser-side applications (v3 client is Node.js only)

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.

Core Patterns

Pattern 1: Connection Setup

Connect to Weaviate Cloud or local Docker instance. Always close the client when done. See examples/core.md for full examples.

// 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

// 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 for named vectors and advanced configuration.

// 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

// 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 for all search types.

// Good Example -- Hybrid search with filter
import { Filters } from "weaviate-client";

const articles = clie