Weaviate

Import from @anvia/weaviate.

WeaviateVectorStoreConnectOptions

type WeaviateDistance = "cosine" | "dot" | "l2" | "manhattan" | "hamming";

type WeaviateVectorStoreConnectOptions = {
  client?: WeaviateClientLike;
  className: string;
  vectorSize: number;
  createIfMissing?: boolean;
  distance?: WeaviateDistance;
};

Purpose: connection options for a Weaviate collection.

Return behavior: consumed by WeaviateVectorStore.connect(...).

Notable errors: missing collections reject when createIfMissing is false; collection creation requires vectorSize.

Design note: connect(...) performs async collection lookup or creation before returning a store. This keeps constructors synchronous and side-effect free while making connection and configuration failures happen before ingestion or search. Uses the Weaviate v3 client API with collections.create(...) and collections.exists(...).

WeaviateVectorStore

class WeaviateVectorStore<T, Metadata extends VectorMetadata = VectorMetadata> {
  static connect<T, Metadata extends VectorMetadata = VectorMetadata>(
    options: WeaviateVectorStoreConnectOptions,
  ): Promise<WeaviateVectorStore<T, Metadata>>;
  upsertDocuments(documents: Array<EmbeddedDocument<T, Metadata>>): Promise<void>;
  index(model: EmbeddingModel): WeaviateVectorIndex<T, Metadata>;
}

Purpose: Weaviate-backed document storage.

Return behavior: connect(...) resolves a store; index(...) binds it to an embedding model.

Notable errors: connection and upsert calls reject on Weaviate errors; upsertDocuments(...) throws when a document has no embeddings or metadata uses reserved __anvia_* keys.

WeaviateVectorIndex

class WeaviateVectorIndex<T, Metadata extends VectorMetadata = VectorMetadata>
  implements VectorSearchIndex<T, Metadata> {
  search(request: VectorSearchRequest): Promise<Array<VectorSearchResult<T, Metadata>>>;
  searchIds(request: VectorSearchRequest): Promise<Array<{ score: number; id: string }>>;
  asTool(options: VectorSearchToolOptions): Tool<{ query: string; topK?: number }, unknown>;
}

Purpose: query-time Weaviate search adapter.

Return behavior: embeds the query, calls Weaviate via nearVector, deduplicates multi-embedding document IDs, and returns normalized results.

Notable errors: embedding or Weaviate query failures reject.

filterToWeaviateWhere

function filterToWeaviateWhere(filter: VectorFilter | undefined): unknown;

Purpose: convert Anvia vector filters to Weaviate where filter objects.

Return behavior: returns undefined when no filter is supplied.

Notable errors: none directly.

WeaviateClientLike

type WeaviateClientLike = {
  collections: WeaviateCollectionsLike;
  batch: WeaviateBatchLike;
};

Purpose: duck-typed interface for a Weaviate v3 client.

WeaviateCollectionLike

type WeaviateCollectionLike = {
  query: {
    nearVector(params: NearVectorParams): Promise<Array<Record<string, unknown>>>;
  };
};

Purpose: duck-typed interface for a Weaviate collection, exposing vector query capabilities.

WeaviateBatcherLike

type WeaviateBatcherLike = {
  withObject(obj: Record<string, unknown>): WeaviateBatcherLike;
  do(): Promise<unknown>;
};

Purpose: duck-typed interface for a Weaviate batch inserter.