WeaviateVectorStore

pydantic model llama_index.vector_stores.WeaviateVectorStore

Weaviate vector store.

In this vector store, embeddings and docs are stored within a Weaviate collection.

During query time, the index uses Weaviate to query for the top k most similar nodes.

Parameters

• weaviate_client (weaviate.Client) – WeaviateClient instance from weaviate-client package

• index_name (Optional[str]) – name for Weaviate classes

Show JSON schema

{
   "title": "WeaviateVectorStore",
   "description": "Weaviate vector store.\n\nIn this vector store, embeddings and docs are stored within a\nWeaviate collection.\n\nDuring query time, the index uses Weaviate to query for the top\nk most similar nodes.\n\nArgs:\n    weaviate_client (weaviate.Client): WeaviateClient\n        instance from `weaviate-client` package\n    index_name (Optional[str]): name for Weaviate classes",
   "type": "object",
   "properties": {
      "stores_text": {
         "title": "Stores Text",
         "default": true,
         "type": "boolean"
      },
      "is_embedding_query": {
         "title": "Is Embedding Query",
         "default": true,
         "type": "boolean"
      },
      "index_name": {
         "title": "Index Name",
         "type": "string"
      },
      "url": {
         "title": "Url",
         "type": "string"
      },
      "text_key": {
         "title": "Text Key",
         "type": "string"
      },
      "auth_config": {
         "title": "Auth Config",
         "type": "object"
      },
      "client_kwargs": {
         "title": "Client Kwargs",
         "type": "object"
      },
      "class_name": {
         "title": "Class Name",
         "type": "string",
         "default": "WeaviateVectorStore"
      }
   },
   "required": [
      "index_name",
      "text_key"
   ]
}

Config

• schema_extra: function = <function BaseComponent.Config.schema_extra at 0x7ff1e41e53a0>

Fields

• auth_config (Dict[str, Any])

• client_kwargs (Dict[str, Any])

• index_name (str)

• stores_text (bool)

• text_key (str)

• url (Optional[str])

field auth_config: Dict[str, Any] [Optional]

field client_kwargs: Dict[str, Any] [Optional]

field index_name: str [Required]

field stores_text: bool = True

field text_key: str [Required]

field url: Optional[str] = None

add(nodes: List[BaseNode], **add_kwargs: Any) → List[str]

Add nodes to index.

Parameters

nodes – List[BaseNode]: list of nodes with embeddings

classmethod class_name() → str

Get the class name, used as a unique ID in serialization.

This provides a key that makes serialization robust against actual class name changes.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

classmethod from_params(url: str, auth_config: Any, index_name: Optional[str] = None, text_key: str = 'text', client_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Any) → WeaviateVectorStore

Create WeaviateVectorStore from config.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query index for top k most similar nodes.

property client: Any

Get client.