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160 | class WordliftVectorStore(VectorStore):
stores_text = True
vector_search_service: VectorSearchService
@staticmethod
def create(key: str):
return WordliftVectorStore(KeyProvider(key), VectorSearchService())
def __init__(
self,
key_provider: KeyProvider,
vector_search_service: VectorSearchService,
):
super(WordliftVectorStore, self).__init__(use_async=True)
self.vector_search_service = vector_search_service
self.key_provider = key_provider
def add(self, nodes: List[BaseNode], **add_kwargs: Any) -> List[str]:
log.debug("Add node(s)\n")
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
task = loop.create_task(self.async_add(nodes, **add_kwargs))
add = loop.run_until_complete(task)
loop.close()
return add
async def async_add(
self,
nodes: List[BaseNode],
**kwargs: Any,
) -> List[str]:
# Empty nodes, return empty list
if not nodes:
return []
log.debug("{0} node(s) received\n".format(len(nodes)))
# Get the key to use for the operation.
key = await self.key_provider.for_add(nodes)
requests = []
for node in nodes:
node_dict = node.dict()
metadata: Dict[str, Any] = node_dict.get("metadata", {})
entity_id = metadata.get("entity_id", None)
entry = NodeRequest(
entity_id=entity_id,
node_id=node.node_id,
embeddings=node.get_embedding(),
text=node.get_content(metadata_mode=MetadataMode.NONE) or "",
metadata=metadata,
)
requests.append(entry)
log.debug("Inserting data, using key {0}: {1}".format(key, requests))
try:
await self.vector_search_service.update_nodes_collection(
node_request=requests, key=key
)
except Exception:
print(traceback.format_exc())
return []
return [node.node_id for node in nodes]
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
raise NotImplementedError
def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
log.debug("Running in NON async mode")
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
task = loop.create_task(self.aquery(query, **kwargs))
query = loop.run_until_complete(task)
loop.close()
return query
async def aquery(
self, query: VectorStoreQuery, **kwargs: Any
) -> VectorStoreQueryResult:
request = VectorSearchQueryRequest(
query_embedding=query.query_embedding,
similarity_top_k=query.similarity_top_k,
)
# Get the key to use for the operation.
key = await self.key_provider.for_query(query)
try:
page = await self.vector_search_service.query_nodes_collection(
vector_search_query_request=request, key=key
)
except ServiceException as exception:
raise WordliftVectorQueryServiceException(
exception=exception, msg=exception.body
)
except Exception as exception:
print(traceback.format_exc())
raise WordliftVectorStoreException(
exception=exception, msg="Failed to fetch query results"
)
nodes: List[TextNode] = []
similarities: List[float] = []
ids: List[str] = []
for item in page.items:
nodes.append(
TextNode(
text=item.text,
id_=item.node_id,
embedding=item.embeddings,
metadata=item.metadata,
)
)
similarities.append(item.score)
ids.append(item.node_id)
return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)
|