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"""Weaviate Vector store index.
An index that that is built on top of an existing vector store.
"""
from typing import Any, List, Optional, cast
from gpt_index.readers.weaviate.data_structs import WeaviateNode
from gpt_index.readers.weaviate.utils import get_default_class_prefix
from gpt_index.vector_stores.types import (
NodeEmbeddingResult,
VectorStore,
VectorStoreQueryResult,
)
class WeaviateVectorStore(VectorStore):
"""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.
Args:
weaviate_client (weaviate.Client): WeaviateClient
instance from `weaviate-client` package
class_prefix (Optional[str]): prefix for Weaviate classes
"""
stores_text: bool = True
def __init__(
self,
weaviate_client: Optional[Any] = None,
class_prefix: Optional[str] = None,
**kwargs: Any,
) -> None:
"""Initialize params."""
import_err_msg = (
"`weaviate` package not found, please run `pip install weaviate-client`"
)
try:
import weaviate # noqa: F401
from weaviate import Client # noqa: F401
except ImportError:
raise ImportError(import_err_msg)
self._client = cast(Client, weaviate_client)
# validate class prefix starts with a capital letter
if class_prefix is not None and not class_prefix[0].isupper():
raise ValueError(
"Class prefix must start with a capital letter, e.g. 'Gpt'"
)
self._class_prefix = class_prefix or get_default_class_prefix()
# try to create schema
WeaviateNode.create_schema(self._client, self._class_prefix)
@property
def client(self) -> Any:
"""Get client."""
return self._client
@property
def config_dict(self) -> dict:
"""Get config dict."""
return {"class_prefix": self._class_prefix}
def add(
self,
embedding_results: List[NodeEmbeddingResult],
) -> List[str]:
"""Add embedding results to index.
Args
embedding_results: List[NodeEmbeddingResult]: list of embedding results
"""
for result in embedding_results:
node = result.node
embedding = result.embedding
# TODO: always store embedding in node
node.embedding = embedding
WeaviateNode.from_gpt_index_batch(
self._client, [r.node for r in embedding_results], self._class_prefix
)
return [result.id for result in embedding_results]
def delete(self, doc_id: str, **delete_kwargs: Any) -> None:
"""Delete a document.
Args:
doc_id (str): document id
"""
WeaviateNode.delete_document(self._client, doc_id, self._class_prefix)
def query(
self,
query_embedding: List[float],
similarity_top_k: int,
doc_ids: Optional[List[str]] = None,
) -> VectorStoreQueryResult:
"""Query index for top k most similar nodes.
Args:
query_embedding (List[float]): query embedding
similarity_top_k (int): top k most similar nodes
"""
nodes = WeaviateNode.to_gpt_index_list(
self.client,
self._class_prefix,
vector=query_embedding,
object_limit=similarity_top_k,
)
nodes = nodes[:similarity_top_k]
node_idxs = [str(i) for i in range(len(nodes))]
return VectorStoreQueryResult(nodes=nodes, ids=node_idxs)
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