# Weaviate -- Core Patterns

> Connection setup, collection management, object CRUD, and basic queries. Reference from [SKILL.md](../SKILL.md).

**Related examples:**

- [search.md](search.md) -- nearText, nearVector, hybrid, bm25, filters, generative search (RAG)
- [multi-tenancy.md](multi-tenancy.md) -- Tenant management, batch imports, cross-references

---

## Connection: Weaviate Cloud

```typescript
import weaviate from "weaviate-client";

const QUERY_TIMEOUT_SECONDS = 30;
const INSERT_TIMEOUT_SECONDS = 120;
const INIT_TIMEOUT_SECONDS = 5;

async function createCloudClient() {
  const url = process.env.WEAVIATE_URL;
  const apiKey = process.env.WEAVIATE_API_KEY;
  if (!url || !apiKey) {
    throw new Error(
      "WEAVIATE_URL and WEAVIATE_API_KEY environment variables are required",
    );
  }

  const client = await weaviate.connectToWeaviateCloud(url, {
    authCredentials: new weaviate.ApiKey(apiKey),
    headers: {
      "X-OpenAI-Api-Key": process.env.OPENAI_API_KEY ?? "",
    },
    timeout: {
      query: QUERY_TIMEOUT_SECONDS,
      insert: INSERT_TIMEOUT_SECONDS,
      init: INIT_TIMEOUT_SECONDS,
    },
  });

  return client;
}

export { createCloudClient };
```

**Why good:** Environment variable validation, named timeout constants, API key header for vectorizer module, `authCredentials` for Weaviate Cloud authentication

```typescript
// Bad Example -- Leaked connections, missing headers
import weaviate from "weaviate-client";

const client = await weaviate.connectToWeaviateCloud(
  "https://my-instance.weaviate.network",
  {
    authCredentials: new weaviate.ApiKey("hardcoded-key"),
  },
);
// No X-OpenAI-Api-Key header -- nearText queries will fail silently
// No client.close() -- gRPC connection leaks
// Hardcoded credentials -- leak in version control
```

**Why bad:** Hardcoded credentials, missing vectorizer API key header causes silent search failures, no `client.close()` leaks gRPC connections

---

## Connection: Local Docker

```typescript
import weaviate from "weaviate-client";

const LOCAL_HTTP_PORT = 8080;
const LOCAL_GRPC_PORT = 50051;

async function createLocalClient() {
  const client = await weaviate.connectToLocal({
    port: LOCAL_HTTP_PORT,
    grpcPort: LOCAL_GRPC_PORT,
    headers: {
      "X-OpenAI-Api-Key": process.env.OPENAI_API_KEY ?? "",
    },
  });

  return client;
}

export { createLocalClient };
```

**Why good:** Explicit ports with named constants, headers still provided for vectorizer modules even locally

---

## Connection: Cleanup Pattern

Always close the client when done. Use try/finally in scripts or shutdown hooks in servers.

```typescript
// Script pattern -- try/finally
const client = await createCloudClient();
try {
  // ... perform operations
} finally {
  client.close();
}
```

```typescript
// Server pattern -- shutdown hook
const client = await createCloudClient();

process.on("SIGTERM", () => {
  client.close();
  process.exit(0);
});

process.on("SIGINT", () => {
  client.close();
  process.exit(0);
});
```

---

## Collection: Basic Creation

```typescript
import { vectors, dataType, generative } from "weaviate-client";

async function createArticleCollection(client: WeaviateClient) {
  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: "author", dataType: dataType.TEXT },
      { name: "publishedAt", dataType: dataType.DATE },
      { name: "wordCount", dataType: dataType.INT },
    ],
  });
}

export { createArticleCollection };
```

**Why good:** Explicit property data types prevent auto-detection surprises, vectorizer and generative model configured at creation

---

## Collection: Named Vectors

Use named vectors when objects need multiple embedding representations (e.g., title vs body, or different models).

```typescript
import { vectors, dataType, configure } from "weaviate-client";

await client.collections.create({
  name: "Product",
  vectorizers: [
    vectors.text2VecOpenAI({
      name: "title_vector",
      sourceProperties: ["title", "brand"],
      vectorIndexConfig: configure.vectorIndex.hnsw(),
    }),
    vectors.text2VecOpenAI({
      name: "description_vector",
      sourceProperties: ["description"],
      vectorIndexConfig: configure.vectorIndex.hnsw(),
    }),
    vectors.selfProvided({
      name: "image_vector",
      vectorIndexConfig: configure.vectorIndex.hnsw(),
    }),
  ],
  properties: [
    { name: "title", dataType: dataType.TEXT },
    { name: "brand", dataType: dataType.TEXT },
    { name: "description", dataType: dataType.TEXT },
    { name: "price", dataType: dataType.NUMBER },
  ],
});
```

**Why good:** Separate vectors for different semantic fields, `selfProvided` for externally computed image embeddings, `sourceProperties` controls which fields each vector covers

```typescript
// Bad Example -- Named vectors without targetVector in query
const products = client.collections.use("Product");
const result = await products.query.nearText("leather jacket", { limit: 5 });
// Defaults to first named vector -- may search title_vector when you wanted description_vector
```

**Why bad:** Without `targetVector`, Weaviate uses the first named vector, which may not be the intended search field

---

## Collection: Vectorizer Property Controls

Skip vectorization for properties that shouldn't influence search, or include the property name in the embedding.

```typescript
import { vectors, dataType, tokenization } from "weaviate-client";

await client.collections.create({
  name: "Document",
  vectorizers: vectors.text2VecOpenAI(),
  properties: [
    {
      name: "title",
      dataType: dataType.TEXT,
      vectorizePropertyName: true, // "title: My Article" vectorized together
      tokenization: tokenization.LOWERCASE,
    },
    {
      name: "body",
      dataType: dataType.TEXT,
      tokenization: tokenization.WHITESPACE,
    },
    {
      name: "internalId",
      dataType: dataType.TEXT,
      skipVectorization: true, // Don't include in embedding
    },
    {
      name: "createdAt",
      dataType: dataType.DATE,
      skipVectorization: true,
    },
  ],
});
```

**Why good:** `skipVectorization` on non-semantic fields prevents noise in embeddings, `vectorizePropertyName` adds context for short fields, tokenization controls keyword search behavior

---

## Collection: Check and Delete

```typescript
// Check if collection exists before creating
const exists = await client.collections.exists("Article");
if (!exists) {
  await client.collections.create({ name: "Article" /* ... */ });
}

// Get collection configuration
const articles = client.collections.use("Article");
const config = await articles.config.get();
console.log(config);

// List all collections
const allCollections = await client.collections.listAll();

// Delete collection (permanent -- deletes all data)
await client.collections.delete("Article");
```

---

## Object: Insert Single

```typescript
const articles = client.collections.use("Article");

const uuid = await articles.data.insert({
  title: "Introduction to Vector Databases",
  body: "Vector databases store data alongside embeddings...",
  category: "technology",
  author: "Jane Smith",
  publishedAt: new Date("2024-06-15").toISOString(),
  wordCount: 1500,
});

console.log("Inserted:", uuid);
```

---

## Object: Insert with Explicit ID

Use `generateUuid5` for deterministic, idempotent IDs based on content.

```typescript
import { generateUuid5 } from "weaviate-client";

const articles = client.collections.use("Article");
const data = {
  title: "Deterministic ID Example",
  body: "Content here...",
  category: "tutorial",
};

const deterministicId = generateUuid5("Article", JSON.stringify(data));

const uuid = await articles.data.insert({
  properties: data,
  id: deterministicId,
});
```

**Why good:** `generateUuid5` produces the same UUID for the same input -- safe to retry without creating duplicates

---

## Object: Insert with Pre-Computed Vector

```typescript
const articles = client.collections.use("Article");
const EMBEDDING_DIM = 1536;

// Single default vector
await articles.data.insert({
  properties: { title: "Custom Vector Example", body: "..." },
  vectors: myEmbeddingArray, // number[] matching collection vector dimension
});

// Named vectors
const products = client.collections.use("Product");
await products.data.insert({
  properties: { title: "Jacket", description: "Warm winter jacket" },
  vectors: {
    title_vector: titleEmbedding,
    description_vector: descEmbedding,
    image_vector: imageEmbedding,
  },
});
```

---

## Object: Update vs Replace

```typescript
const articles = client.collections.use("Article");
const objectId = "ed89d9e7-4c9d-4a6a-8d20-095cb0026f54";

// Update (merge) -- preserves properties NOT included
await articles.data.update({
  id: objectId,
  properties: {
    wordCount: 2000, // Only this property changes
  },
});

// Replace (overwrite) -- DELETES properties NOT included
await articles.data.replace({
  id: objectId,
  properties: {
    title: "Replaced Title",
    body: "Replaced body",
    // category, author, publishedAt, wordCount are DELETED
  },
});
```

**Why good:** Clear distinction between merge and overwrite semantics

```typescript
// Bad Example -- Using replace when update was intended
await articles.data.replace({
  id: objectId,
  properties: { wordCount: 2000 },
});
// All other properties (title, body, category, etc.) are now DELETED
```

**Why bad:** `replace` deletes every property not explicitly provided -- use `update` for partial changes

---

## Object: Delete

```typescript
const articles = client.collections.use("Article");

// Delete single object by ID
await articles.data.deleteById("ed89d9e7-4c9d-4a6a-8d20-095cb0026f54");

// Delete multiple objects matching a filter
const deleteResult = await articles.data.deleteMany(
  articles.filter.byProperty("category").equal("draft"),
);
console.log("Deleted:", deleteResult);

// Delete by ID list
const idsToDelete = ["id-1", "id-2", "id-3"];
await articles.data.deleteMany(articles.filter.byId().containsAny(idsToDelete));

// Dry run -- check what would be deleted without deleting
const dryResult = await articles.data.deleteMany(
  articles.filter.byProperty("category").equal("old"),
  { dryRun: true, verbose: true },
);
console.log("Would delete:", dryResult);
```

---

## Object: Iterate Over Entire Collection

Use `iterator()` to process all objects without loading everything into memory.

```typescript
const articles = client.collections.use("Article");

for await (const item of articles.iterator()) {
  console.log(item.uuid, item.properties.title);
}

// With specific return properties
for await (const item of articles.iterator({
  returnProperties: ["title", "category"],
})) {
  processArticle(item);
}
```

**Why good:** Memory-efficient -- streams objects in batches internally, unlike `fetchObjects` which loads a fixed page

---

## TypeScript Generics

Use generics for compile-time type safety on collection objects.

```typescript
interface Article {
  title: string;
  body: string;
  category: string;
  author: string;
  publishedAt: string;
  wordCount: number;
}

const articles = client.collections.use<Article>("Article");

// Now insert and query methods are typed
const uuid = await articles.data.insert({
  title: "Typed Insert",
  body: "This is type-checked at compile time",
  category: "tutorial",
  author: "Developer",
  publishedAt: new Date().toISOString(),
  wordCount: 500,
  // misspelledField: "error" // TypeScript error!
});

const result = await articles.query.fetchObjects({ limit: 5 });
for (const obj of result.objects) {
  // obj.properties is typed as Article
  console.log(obj.properties.title); // string, not unknown
}
```

**Why good:** Compile-time type checking catches property name typos, wrong types, and missing fields before runtime

---

_Full skill documentation: [SKILL.md](../SKILL.md) | Quick reference: [reference.md](../reference.md)_

# Weaviate -- Multi-Tenancy & Batch Examples

> Tenant management, batch imports, and cross-references. See [core.md](core.md) for connection and collection setup.

**Related examples:**

- [core.md](core.md) -- Connection, collection setup, object CRUD
- [search.md](search.md) -- nearText, nearVector, hybrid, bm25, filters, generative search

---

## Pattern 1: Enable Multi-Tenancy

Multi-tenancy must be enabled at collection creation time.

```typescript
import weaviate from "weaviate-client";
import { vectors, dataType } from "weaviate-client";

await client.collections.create({
  name: "CustomerDocument",
  multiTenancy: weaviate.configure.multiTenancy({
    enabled: true,
    autoTenantCreation: true, // Create tenants on first insert
  }),
  vectorizers: vectors.text2VecOpenAI(),
  properties: [
    { name: "title", dataType: dataType.TEXT },
    { name: "content", dataType: dataType.TEXT },
    { name: "docType", dataType: dataType.TEXT },
  ],
});
```

**Why good:** `autoTenantCreation: true` avoids manual tenant creation before inserts, useful for dynamic SaaS applications

```typescript
// Bad Example -- Querying multi-tenant collection without tenant context
const docs = client.collections.use("CustomerDocument");
const result = await docs.query.fetchObjects({ limit: 10 });
// FAILS: multi-tenant collection requires .withTenant()
```

**Why bad:** All operations on multi-tenant collections require `.withTenant()` -- queries without tenant context throw an error

---

## Pattern 2: Tenant Lifecycle Management

```typescript
const docs = client.collections.use("CustomerDocument");

// Create tenants manually
await docs.tenants.create([
  { name: "tenant-acme" },
  { name: "tenant-globex" },
  { name: "tenant-initech" },
]);

// List all tenants
const allTenants = await docs.tenants.get();
console.log("Tenants:", Object.keys(allTenants));

// Get specific tenants
const specific = await docs.tenants.getByNames([
  "tenant-acme",
  "tenant-globex",
]);
console.log(specific);

// Get single tenant
const acme = await docs.tenants.getByName("tenant-acme");
console.log(acme);

// Remove tenants (non-existent names are silently ignored)
await docs.tenants.remove([
  { name: "tenant-initech" },
  { name: "tenant-nonexistent" }, // Ignored
]);
```

---

## Pattern 3: Tenant State Management

Deactivate tenants to free memory, reactivate when needed.

```typescript
const docs = client.collections.use("CustomerDocument");

// Deactivate tenant (data stays on disk, freed from memory)
await docs.tenants.update({
  name: "tenant-acme",
  activityStatus: "INACTIVE",
});

// Offload tenant to cold storage (cloud deployments only)
await docs.tenants.update({
  name: "tenant-globex",
  activityStatus: "OFFLOADED",
});

// Reactivate tenant before querying
await docs.tenants.update({
  name: "tenant-acme",
  activityStatus: "ACTIVE",
});

// Now queries work again
const acmeDocs = docs.withTenant("tenant-acme");
const result = await acmeDocs.query.fetchObjects({ limit: 10 });
```

**Why good:** Tenant states manage memory for large multi-tenant deployments -- inactive tenants consume no memory

### Auto-Tenant Activation

```typescript
import weaviate from "weaviate-client";

// Enable auto-activation so queries automatically activate inactive tenants
const docs = client.collections.use("CustomerDocument");
await docs.config.update({
  multiTenancy: weaviate.reconfigure.multiTenancy({
    autoTenantActivation: true,
  }),
});
```

---

## Pattern 4: Multi-Tenant CRUD Operations

All data operations require tenant context via `.withTenant()`.

```typescript
const docs = client.collections.use("CustomerDocument");
const acmeDocs = docs.withTenant("tenant-acme");

// Insert
const uuid = await acmeDocs.data.insert({
  title: "Q3 Report",
  content: "Revenue increased by 15%...",
  docType: "report",
});

// Query
const result = await acmeDocs.query.nearText("quarterly revenue", {
  limit: 5,
  returnMetadata: ["distance"],
});

// Update
await acmeDocs.data.update({
  id: uuid,
  properties: { docType: "financial-report" },
});

// Delete
await acmeDocs.data.deleteById(uuid);

// Delete many
await acmeDocs.data.deleteMany(
  acmeDocs.filter.byProperty("docType").equal("draft"),
);
```

---

## Pattern 5: Batch Import with insertMany

Use `insertMany` for bulk data loading. Always check for errors -- partial failures are silent.

```typescript
const articles = client.collections.use("Article");
const BATCH_SIZE = 100;

interface ArticleData {
  title: string;
  body: string;
  category: string;
}

async function batchImport(
  collection: ReturnType<typeof client.collections.use>,
  data: ArticleData[],
) {
  // Process in chunks to avoid memory pressure
  for (let i = 0; i < data.length; i += BATCH_SIZE) {
    const chunk = data.slice(i, i + BATCH_SIZE);

    const response = await collection.data.insertMany(chunk);

    // CRITICAL: Check for partial failures
    if (response.hasErrors) {
      for (const err of Object.values(response.errors)) {
        console.error(`Insert error at index ${err.index}:`, err.message);
      }
      throw new Error(
        `Batch insert failed with ${Object.keys(response.errors).length} errors`,
      );
    }

    console.log(
      `Imported ${Math.min(i + BATCH_SIZE, data.length)}/${data.length}`,
    );
  }
}

export { batchImport };
```

**Why good:** Chunked processing avoids memory pressure for large datasets, `hasErrors` check catches partial failures that would otherwise be silent

```typescript
// Bad Example -- Ignoring insertMany errors
const response = await articles.data.insertMany(largeDataset);
// response.hasErrors might be true, but we never check
// Some objects silently failed to insert
```

**Why bad:** `insertMany` can partially fail -- some objects insert, some don't. Without checking `hasErrors`, you have incomplete data

---

## Pattern 6: Batch Import with Deterministic IDs

Use `generateUuid5` for idempotent imports -- safe to retry without duplicates.

```typescript
import { generateUuid5 } from "weaviate-client";

const COLLECTION_NAME = "Article";

function prepareObjects(data: ArticleData[]) {
  return data.map((item) => ({
    properties: item,
    id: generateUuid5(COLLECTION_NAME, item.title), // Deterministic UUID from title
  }));
}

const articles = client.collections.use(COLLECTION_NAME);
const objects = prepareObjects(rawData);
const response = await articles.data.insertMany(objects);

if (response.hasErrors) {
  // Objects with duplicate IDs will fail -- expected on retry
  const realErrors = Object.values(response.errors).filter(
    (err) => !err.message.includes("already exists"),
  );
  if (realErrors.length > 0) {
    throw new Error(`Import failed: ${realErrors.length} non-duplicate errors`);
  }
}
```

**Why good:** Deterministic IDs make imports idempotent, filtering "already exists" errors enables safe retries

---

## Pattern 7: Batch Import with Custom Vectors

```typescript
const products = client.collections.use("Product");
const BATCH_SIZE = 100;

interface ProductWithEmbedding {
  properties: { title: string; description: string; price: number };
  vectors: number[];
}

async function importWithVectors(data: ProductWithEmbedding[]) {
  for (let i = 0; i < data.length; i += BATCH_SIZE) {
    const chunk = data.slice(i, i + BATCH_SIZE);
    const response = await products.data.insertMany(chunk);

    if (response.hasErrors) {
      console.error("Batch errors:", response.errors);
      throw new Error("Import with vectors failed");
    }
  }
}
```

### Named Vectors in Batch

```typescript
const multiVectorObjects = data.map((item) => ({
  properties: { title: item.title, description: item.description },
  vectors: {
    title_vector: item.titleEmbedding,
    description_vector: item.descEmbedding,
  },
}));

await products.data.insertMany(multiVectorObjects);
```

---

## Pattern 8: Cross-References

Link objects across collections. Requires adding a reference property to the collection.

```typescript
import { dataType } from "weaviate-client";

// Create collections
await client.collections.create({
  name: "Author",
  vectorizers: vectors.text2VecOpenAI(),
  properties: [
    { name: "name", dataType: dataType.TEXT },
    { name: "bio", dataType: dataType.TEXT },
  ],
});

await client.collections.create({
  name: "Article",
  vectorizers: vectors.text2VecOpenAI(),
  properties: [
    { name: "title", dataType: dataType.TEXT },
    { name: "body", dataType: dataType.TEXT },
  ],
});

// Add cross-reference property
const articles = client.collections.use("Article");
await articles.config.addReference({
  name: "writtenBy",
  targetCollection: "Author",
});
```

### Create and Query References

```typescript
const authors = client.collections.use("Author");
const articles = client.collections.use("Article");

// Insert an author
const authorId = await authors.data.insert({
  name: "Jane Smith",
  bio: "AI researcher and writer",
});

// Insert an article with reference
const articleId = await articles.data.insert({
  properties: { title: "AI Ethics", body: "..." },
  references: { writtenBy: authorId },
});

// OR add reference after creation
await articles.data.referenceAdd({
  fromUuid: articleId,
  fromProperty: "writtenBy",
  to: authorId,
});

// Query with resolved references
const result = await articles.query.fetchObjects({
  limit: 5,
  returnReferences: [
    {
      linkOn: "writtenBy",
      returnProperties: ["name", "bio"],
    },
  ],
});

for (const obj of result.objects) {
  console.log("Article:", obj.properties.title);
  console.log(
    "Author:",
    obj.references?.writtenBy?.objects[0]?.properties.name,
  );
}
```

### Replace and Delete References

```typescript
// Replace all references on a property
await articles.data.referenceReplace({
  fromUuid: articleId,
  fromProperty: "writtenBy",
  to: [newAuthorId], // Replaces all existing references
});

// Delete specific reference
await articles.data.referenceDelete({
  fromUuid: articleId,
  fromProperty: "writtenBy",
  to: authorId,
});
```

---

## Pattern 9: Multi-Tenant Cross-References

Cross-references in multi-tenant collections can only point to objects in the same tenant or in non-multi-tenant collections.

```typescript
const tenantDocs = docs.withTenant("tenant-acme");

// Add reference property
await docs.config.addReference({
  name: "hasCategory",
  targetCollection: "Category", // Non-multi-tenant collection
});

// Create cross-reference
await tenantDocs.data.referenceAdd({
  fromUuid: documentId,
  fromProperty: "hasCategory",
  to: categoryId, // Category object in non-MT collection
});
```

**Why good:** Non-multi-tenant reference targets work across all tenants (shared lookup data)

```typescript
// Bad Example -- Cross-tenant reference
const acmeDocs = docs.withTenant("tenant-acme");
await acmeDocs.data.referenceAdd({
  fromUuid: acmeDocId,
  fromProperty: "relatedDoc",
  to: globexDocId, // Object in tenant-globex -- FAILS
});
```

**Why bad:** Multi-tenant cross-references cannot span tenants -- only same-tenant or non-multi-tenant targets

---

_Full skill documentation: [SKILL.md](../SKILL.md) | Quick reference: [reference.md](../reference.md)_

# Weaviate -- Search & Filtering Examples

> Search types, filtering, and generative search (RAG). See [core.md](core.md) for connection and collection setup.

**Related examples:**

- [core.md](core.md) -- Connection, collection setup, object CRUD
- [multi-tenancy.md](multi-tenancy.md) -- Tenant management, batch imports, cross-references

---

## Pattern 1: nearText (Semantic Search)

Search by natural language query. Requires a vectorizer module configured on the collection.

```typescript
const articles = client.collections.use("Article");
const SEARCH_LIMIT = 10;
const MAX_DISTANCE = 0.3;

// Basic nearText
const result = await articles.query.nearText("climate change policy", {
  limit: SEARCH_LIMIT,
  returnMetadata: ["distance"],
});

for (const obj of result.objects) {
  console.log(obj.properties.title, "distance:", obj.metadata?.distance);
}
```

**Why good:** `returnMetadata: ['distance']` enables relevance debugging, named constant for limit

### With Distance Threshold

```typescript
// Only return results within distance threshold
const result = await articles.query.nearText("renewable energy", {
  distance: MAX_DISTANCE,
  returnMetadata: ["distance"],
});
```

**When to use:** When you want to ensure a minimum relevance level rather than a fixed count.

### With Named Vector Target

```typescript
const products = client.collections.use("Product");

const result = await products.query.nearText("comfortable running shoes", {
  limit: SEARCH_LIMIT,
  targetVector: "description_vector", // Explicitly target the description embedding
  returnMetadata: ["distance"],
});
```

**When to use:** Collections with named vectors -- always specify `targetVector` to avoid defaulting to the first vector.

---

## Pattern 2: nearVector (Vector Search)

Search with a pre-computed embedding vector. No vectorizer module needed.

```typescript
const articles = client.collections.use("Article");
const SEARCH_LIMIT = 5;

// Use your own embedding
const queryVector = await myEmbeddingModel.encode("search query");

const result = await articles.query.nearVector(queryVector, {
  limit: SEARCH_LIMIT,
  returnMetadata: ["distance"],
});

for (const obj of result.objects) {
  console.log(obj.properties.title, obj.metadata?.distance);
}
```

**When to use:** When you compute embeddings externally (e.g., using your own embedding service or a model Weaviate doesn't support).

---

## Pattern 3: BM25 (Keyword Search)

Traditional keyword search based on term frequency. No vector component.

```typescript
const articles = client.collections.use("Article");
const SEARCH_LIMIT = 10;

// Basic BM25
const result = await articles.query.bm25("vector database comparison", {
  limit: SEARCH_LIMIT,
  returnMetadata: ["score"],
});

for (const obj of result.objects) {
  console.log(obj.properties.title, "score:", obj.metadata?.score);
}
```

### BM25 with Property Targeting and Boosting

```typescript
// Search specific properties with weight boosting
const result = await articles.query.bm25("machine learning", {
  limit: SEARCH_LIMIT,
  queryProperties: ["title^3", "body"], // title weighted 3x
  returnMetadata: ["score"],
});
```

**Why good:** Property boosting (`title^3`) prioritizes matches in important fields

---

## Pattern 4: Hybrid Search

Combines vector search and keyword search with configurable weighting.

```typescript
import { Filters } from "weaviate-client";

const articles = client.collections.use("Article");
const SEARCH_LIMIT = 10;
const HYBRID_ALPHA = 0.75; // 0.0 = pure keyword, 1.0 = pure vector

const result = await articles.query.hybrid("neural network architecture", {
  alpha: HYBRID_ALPHA,
  limit: SEARCH_LIMIT,
  returnMetadata: ["score", "explainScore"],
});

for (const obj of result.objects) {
  console.log(obj.properties.title);
  console.log("Score:", obj.metadata?.score);
  console.log("Explanation:", obj.metadata?.explainScore);
}
```

**Why good:** `explainScore` shows the vector and keyword contributions, helping tune alpha

### Hybrid with Fusion Type

```typescript
// RelativeScore uses actual similarity scores (default since v1.24)
const result = await articles.query.hybrid("search query", {
  fusionType: "RelativeScore", // or "Ranked"
  alpha: HYBRID_ALPHA,
  limit: SEARCH_LIMIT,
});
```

**When to use:** `RelativeScore` (default) for most cases. `Ranked` when you want rank-based fusion regardless of actual similarity distances.

### Hybrid with Keyword Operator Control

```typescript
import { Bm25Operator } from "weaviate-client";

// Require at least 2 of 3 query tokens to match
const result = await articles.query.hybrid("Australian mammal cute", {
  bm25Operator: Bm25Operator.or({ minimumMatch: 2 }),
  alpha: HYBRID_ALPHA,
  limit: SEARCH_LIMIT,
});

// Require ALL query tokens to match
const strictResult = await articles.query.hybrid("neural network training", {
  bm25Operator: Bm25Operator.and(),
  alpha: HYBRID_ALPHA,
  limit: SEARCH_LIMIT,
});
```

---

## Pattern 5: Filtering

Filters work with all search methods. They narrow results AFTER vector/keyword retrieval.

### Single Filter

```typescript
const articles = client.collections.use("Article");

const result = await articles.query.nearText("technology trends", {
  limit: SEARCH_LIMIT,
  filters: articles.filter.byProperty("category").equal("technology"),
  returnMetadata: ["distance"],
});
```

### Combined Filters

```typescript
import { Filters } from "weaviate-client";

const result = await articles.query.hybrid("AI research", {
  alpha: HYBRID_ALPHA,
  limit: SEARCH_LIMIT,
  filters: Filters.and(
    articles.filter.byProperty("category").equal("technology"),
    articles.filter.byProperty("wordCount").greaterThan(500),
    Filters.not(articles.filter.byProperty("author").equal("Bot")),
  ),
});
```

**Why good:** Flat argument list to `Filters.and()`, not nested arrays

```typescript
// Bad Example -- Array argument to Filters.and
const result = await articles.query.hybrid("search", {
  filters: Filters.and([filterA, filterB]), // WRONG: expects flat args, not array
});
```

**Why bad:** `Filters.and()` takes variadic arguments, not an array -- `Filters.and(a, b, c)` not `Filters.and([a, b, c])`

### Nested Filters

```typescript
const result = await articles.query.fetchObjects({
  filters: Filters.and(
    articles.filter.byProperty("category").equal("technology"),
    Filters.or(
      articles.filter.byProperty("wordCount").greaterThan(2000),
      articles.filter.byProperty("wordCount").lessThan(500),
    ),
  ),
  limit: SEARCH_LIMIT,
});
```

### Filter with Like (Wildcard)

```typescript
const result = await articles.query.fetchObjects({
  filters: articles.filter.byProperty("title").like("*machine learning*"),
  limit: SEARCH_LIMIT,
});
```

### Filter by Date

```typescript
const cutoffDate = new Date("2024-01-01");
const result = await articles.query.fetchObjects({
  filters: articles.filter.byProperty("publishedAt").greaterThan(cutoffDate),
  limit: SEARCH_LIMIT,
});
```

### Filter by Cross-Reference

```typescript
const result = await articles.query.fetchObjects({
  filters: articles.filter
    .byRef("hasCategory")
    .byProperty("title")
    .equal("Science"),
  returnReferences: [
    {
      linkOn: "hasCategory",
      returnProperties: ["title"],
    },
  ],
  limit: SEARCH_LIMIT,
});
```

### Filter by Object ID

```typescript
const result = await articles.query.fetchObjects({
  filters: articles.filter.byId().equal(targetId),
});
```

### Filter by Property Length

```typescript
// Second arg `true` enables length-based filtering
const result = await articles.query.fetchObjects({
  filters: articles.filter.byProperty("title", true).greaterThan(20),
  limit: SEARCH_LIMIT,
});
```

---

## Pattern 6: Pagination

### Offset-Based Pagination

```typescript
const PAGE_SIZE = 20;

async function fetchPage(collection: Collection, page: number) {
  return collection.query.fetchObjects({
    limit: PAGE_SIZE,
    offset: page * PAGE_SIZE,
  });
}

// Page 0 (first 20), Page 1 (next 20), etc.
const page0 = await fetchPage(articles, 0);
const page1 = await fetchPage(articles, 1);
```

**When to use:** Simple pagination for user-facing pages. Offset has a 10,000 limit by default.

### Cursor-Based Pagination

```typescript
// Use after cursor for deep pagination beyond offset limits
const firstPage = await articles.query.fetchObjects({
  limit: PAGE_SIZE,
  returnMetadata: ["creationTime"],
});

// Get the last object's ID for cursor
const lastId = firstPage.objects[firstPage.objects.length - 1]?.uuid;
if (lastId) {
  const nextPage = await articles.query.fetchObjects({
    limit: PAGE_SIZE,
    after: lastId,
  });
}
```

**When to use:** Deep pagination or iterating large result sets where offset would be slow.

---

## Pattern 7: Return Options

```typescript
const articles = client.collections.use("Article");

// Return specific properties only
const result = await articles.query.fetchObjects({
  limit: SEARCH_LIMIT,
  returnProperties: ["title", "category"],
});

// Include vector in response
const withVector = await articles.query.fetchObjects({
  limit: SEARCH_LIMIT,
  includeVector: true,
});

// Return metadata (distance, score, creation time, etc.)
const withMeta = await articles.query.nearText("search term", {
  limit: SEARCH_LIMIT,
  returnMetadata: ["distance", "creationTime"],
});

// Return cross-references
const withRefs = await articles.query.fetchObjects({
  limit: SEARCH_LIMIT,
  returnReferences: [
    {
      linkOn: "hasCategory",
      returnProperties: ["title"],
    },
  ],
});
```

---

## Pattern 8: Generative Search (RAG) -- Single Prompt

Each search result is individually processed by the LLM. Requires a generative model configured on the collection.

```typescript
const articles = client.collections.use("Article");
const RAG_LIMIT = 5;

const result = await articles.generate.nearText(
  "climate change solutions",
  {
    singlePrompt: "Summarize this article in one tweet: {title} - {body}",
  },
  {
    limit: RAG_LIMIT,
    returnMetadata: ["distance"],
  },
);

for (const obj of result.objects) {
  console.log("Source:", obj.properties.title);
  console.log("Generated:", obj.generative?.text);
}
```

**Why good:** `{title}` and `{body}` are interpolated from each object's properties -- no manual string building needed

### With Metadata and Debug

```typescript
import { generativeParameters } from "weaviate-client";

const result = await articles.generate.nearText(
  "renewable energy",
  {
    singlePrompt: {
      prompt: "Extract 3 key points from: {title} - {body}",
      metadata: true,
      debug: true,
    },
    config: generativeParameters.openAI({ model: "gpt-4o" }),
  },
  { limit: RAG_LIMIT },
);

for (const obj of result.objects) {
  console.log("Generated:", obj.generative?.text);
  console.log("Debug:", obj.generative?.debug);
  console.log("Metadata:", obj.generative?.metadata);
}
```

---

## Pattern 9: Generative Search (RAG) -- Grouped Task

All search results are sent to the LLM as a single context. One output for the entire group.

```typescript
const articles = client.collections.use("Article");
const RAG_LIMIT = 5;

const result = await articles.generate.nearText(
  "artificial intelligence ethics",
  {
    groupedTask:
      "Based on these articles, write a summary of the main ethical concerns in AI.",
  },
  { limit: RAG_LIMIT },
);

// Single generated response for all results
console.log("Combined summary:", result.generative?.text);

// Individual source objects are still available
for (const obj of result.objects) {
  console.log("Source:", obj.properties.title);
}
```

**Why good:** `result.generative?.text` gives one combined output, individual `obj.properties` still accessible for citations

### Grouped Task with Selected Properties

```typescript
const result = await articles.generate.nearText(
  "machine learning applications",
  {
    groupedTask: "Compare and contrast these articles.",
    groupedProperties: ["title", "body"], // Only send title and body to LLM
  },
  { limit: RAG_LIMIT },
);
```

**When to use:** When you want to limit what properties the LLM sees, reducing token usage and improving focus.

---

## Pattern 10: Generative Search with Override Model

Override the collection's default generative model at query time.

```typescript
import { generativeParameters } from "weaviate-client";

const result = await articles.generate.nearText(
  "quantum computing",
  {
    singlePrompt: "Explain this to a 5-year-old: {title}",
    config: generativeParameters.anthropic({
      model: "claude-haiku-4-5",
      maxTokens: 200,
    }),
  },
  { limit: RAG_LIMIT },
);
```

**When to use:** When you need a different model for specific queries (e.g., faster model for summaries, more capable model for analysis).

---

_Full skill documentation: [SKILL.md](../SKILL.md) | Quick reference: [reference.md](../reference.md)_