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349 | class VearchVectorStore(BasePydanticVectorStore):
"""
Vearch vector store:
embeddings are stored within a Vearch table.
when query, the index uses Vearch to query for the top
k most similar nodes.
Args:
chroma_collection (chromadb.api.models.Collection.Collection):
ChromaDB collection instance
"""
flat_metadata: bool = True
stores_text: bool = True
using_db_name: str
using_table_name: str
url: str
_vearch: vearch_cluster.VearchCluster = PrivateAttr()
def __init__(
self,
path_or_url: Optional[str] = None,
table_name: str = _DEFAULT_TABLE_NAME,
db_name: str = _DEFAULT_CLUSTER_DB_NAME,
**kwargs: Any,
) -> None:
"""Initialize vearch vector store."""
if path_or_url is None:
raise ValueError("Please input url of cluster")
if not db_name:
db_name = _DEFAULT_CLUSTER_DB_NAME
db_name += "_"
db_name += str(uuid.uuid4()).split("-")[-1]
if not table_name:
table_name = _DEFAULT_TABLE_NAME
table_name += "_"
table_name += str(uuid.uuid4()).split("-")[-1]
super().__init__(
using_db_name=db_name,
using_table_name=table_name,
url=path_or_url,
)
self._vearch = vearch_cluster.VearchCluster(path_or_url)
@classmethod
def class_name(cls) -> str:
return "VearchVectorStore"
@property
def client(self) -> Any:
"""Get client."""
return self._vearch
def _get_matadata_field(self, metadatas: Optional[List[dict]] = None) -> None:
field_list = []
if metadatas:
for key, value in metadatas[0].items():
if isinstance(value, int):
field_list.append({"field": key, "type": "int"})
continue
if isinstance(value, str):
field_list.append({"field": key, "type": "str"})
continue
if isinstance(value, float):
field_list.append({"field": key, "type": "float"})
continue
else:
raise ValueError("Please check data type,support int, str, float")
self.field_list = field_list
def _add_texts(
self,
ids: Iterable[str],
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
embeddings: Optional[List[List[float]]] = None,
**kwargs: Any,
) -> List[str]:
"""
Returns:
List of ids from adding the texts into the vectorstore.
"""
if embeddings is None:
raise ValueError("embeddings is None")
self._get_matadata_field(metadatas)
dbs_list = self._vearch.list_dbs()
if self.using_db_name not in dbs_list:
create_db_code = self._vearch.create_db(self.using_db_name)
if not create_db_code:
raise ValueError("create db failed!!!")
space_list = self._vearch.list_spaces(self.using_db_name)
if self.using_table_name not in space_list:
create_space_code = self._create_space(len(embeddings[0]))
if not create_space_code:
raise ValueError("create space failed!!!")
docid = []
if embeddings is not None and metadatas is not None:
meta_field_list = [i["field"] for i in self.field_list]
for text, metadata, embed, id_d in zip(texts, metadatas, embeddings, ids):
profiles: typing.Dict[str, Any] = {}
profiles["ref_doc_id"] = id_d
profiles["text"] = text
for f in meta_field_list:
profiles[f] = metadata[f]
embed_np = np.array(embed)
profiles["text_embedding"] = {
"feature": (embed_np / np.linalg.norm(embed_np)).tolist()
}
insert_res = self._vearch.insert_one(
self.using_db_name, self.using_table_name, profiles
)
if insert_res["status"] == 200:
docid.append(insert_res["_id"])
continue
else:
retry_insert = self._vearch.insert_one(
self.using_db_name, self.using_table_name, profiles
)
docid.append(retry_insert["_id"])
continue
return docid
def _create_space(
self,
dim: int = 1024,
) -> int:
"""
Create Cluster VectorStore space.
Args:
dim:dimension of vector.
Return:
code,0 failed for ,1 for success.
"""
type_dict = {"int": "integer", "str": "string", "float": "float"}
space_config = {
"name": self.using_table_name,
"partition_num": 1,
"replica_num": 1,
"engine": {
"index_size": 1,
"retrieval_type": "HNSW",
"retrieval_param": {
"metric_type": "InnerProduct",
"nlinks": -1,
"efConstruction": -1,
},
},
}
tmp_proer = {
"ref_doc_id": {"type": "string"},
"text": {"type": "string"},
"text_embedding": {
"type": "vector",
"index": True,
"dimension": dim,
"store_type": "MemoryOnly",
},
}
for item in self.field_list:
tmp_proer[item["field"]] = {"type": type_dict[item["type"]]}
space_config["properties"] = tmp_proer
return self._vearch.create_space(self.using_db_name, space_config)
def add(
self,
nodes: List[BaseNode],
**add_kwargs: Any,
) -> List[str]:
if not self._vearch:
raise ValueError("Vearch Engine is not initialized")
embeddings = []
metadatas = []
ids = []
texts = []
for node in nodes:
embeddings.append(node.get_embedding())
metadatas.append(
node_to_metadata_dict(
node, remove_text=True, flat_metadata=self.flat_metadata
)
)
ids.append(node.node_id)
texts.append(node.get_content(metadata_mode=MetadataMode.NONE) or "")
return self._add_texts(
ids=ids,
texts=texts,
metadatas=metadatas,
embeddings=embeddings,
)
def query(
self,
query: VectorStoreQuery,
**kwargs: Any,
) -> VectorStoreQueryResult:
"""
Query index for top k most similar nodes.
Args:
query : vector store query.
Returns:
VectorStoreQueryResult: Query results.
"""
meta_filters = {}
if query.filters is not None:
for filter_ in query.filters.legacy_filters():
meta_filters[filter_.key] = filter_.value
if self.flag:
meta_field_list = self._vearch.get_space(
self.using_db_name, self.using_table_name
)
meta_field_list.remove("text_embedding")
embed = query.query_embedding
if embed is None:
raise ValueError("query.query_embedding is None")
k = query.similarity_top_k
query_data = {
"query": {
"sum": [
{
"field": "text_embedding",
"feature": (embed / np.linalg.norm(embed)).tolist(),
}
],
},
"retrieval_param": {"metric_type": "InnerProduct", "efSearch": 64},
"size": k,
"fields": meta_field_list,
}
query_result = self._vearch.search(
self.using_db_name, self.using_table_name, query_data
)
res = query_result["hits"]["hits"]
nodes = []
similarities = []
ids = []
for item in res:
content = ""
meta_data = {}
node_id = ""
score = item["_score"]
item = item["_source"]
for item_key in item:
if item_key == "text":
content = item[item_key]
continue
elif item_key == "_id":
node_id = item[item_key]
ids.append(node_id)
continue
meta_data[item_key] = item[item_key]
similarities.append(score)
try:
node = metadata_dict_to_node(meta_data)
node.set_content(content)
except Exception:
metadata, node_info, relationships = legacy_metadata_dict_to_node(
meta_data
)
node = TextNode(
text=content,
id_=node_id,
metadata=metadata,
start_char_idx=node_info.get("start", None),
end_char_idx=node_info.get("end", None),
relationships=relationships,
)
nodes.append(node)
return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)
def _delete(
self,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> None:
"""
Delete the documents which have the specified ids.
Args:
ids: The ids of the embedding vectors.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
Optional[bool]: True if deletion is successful.
False otherwise, None if not implemented.
"""
if ids is None or len(ids) == 0:
return
for _id in ids:
queries = {
"query": {
"filter": [{"term": {"ref_doc_id": [_id], "operator": "and"}}]
},
"size": 10000,
}
self._vearch.delete_by_query(
self, self.using_db_name, self.using_table_name, queries
)
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
"""Delete nodes using with ref_doc_id.
Args:
ref_doc_id (str): The doc_id of the document to delete.
Returns:
None
"""
if len(ref_doc_id) == 0:
return
ids: List[str] = []
ids.append(ref_doc_id)
self._delete(ids)
|
