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260 | class KDBAIVectorStore(BasePydanticVectorStore):
"""
The KDBAI Vector Store.
In this vector store we store the text, its embedding and
its metadata in a KDBAI vector store table. This implementation
allows the use of an already existing table.
Args:
table kdbai.Table: The KDB.AI table to use as storage.
batch (int, optional): batch size to insert data.
Default is 100.
Returns:
KDBAIVectorStore: Vectorstore that supports add and query.
"""
stores_text: bool = True
flat_metadata: bool = True
hybrid_search: bool = False
batch_size: int
_table: Any = PrivateAttr()
_sparse_encoder: Optional[Callable] = PrivateAttr()
def __init__(
self,
table: Any = None,
hybrid_search: bool = False,
sparse_encoder: Optional[Callable] = None,
batch_size: int = DEFAULT_BATCH_SIZE,
**kwargs: Any,
) -> None:
"""Initialize params."""
try:
import kdbai_client as kdbai
logger.info("KDBAI client version: " + kdbai.__version__)
except ImportError:
raise ValueError(
"Could not import kdbai_client package."
"Please add it to the dependencies."
)
super().__init__(batch_size=batch_size, hybrid_search=hybrid_search)
if table is None:
raise ValueError("Must provide an existing KDB.AI table.")
else:
self._table = table
if hybrid_search:
if sparse_encoder is None:
self._sparse_encoder = default_sparse_encoder
else:
self._sparse_encoder = sparse_encoder
@property
def client(self) -> Any:
"""Return KDB.AI client."""
return self._table
@classmethod
def class_name(cls) -> str:
return "KDBAIVectorStore"
def add(
self,
nodes: List[BaseNode],
**add_kwargs: Any,
) -> List[str]:
"""
Add nodes to the KDBAI Vector Store.
Args:
nodes (List[BaseNode]): List of nodes to be added.
Returns:
List[str]: List of document IDs that were added.
"""
try:
import kdbai_client as kdbai
logger.info("KDBAI client version: " + kdbai.__version__)
except ImportError:
raise ValueError(
"Could not import kdbai_client package."
"Please add it to the dependencies."
)
df = pd.DataFrame()
docs = []
schema = self._table.schema
if self.hybrid_search:
schema = [item for item in schema if item["name"] != "sparseVectors"]
try:
for node in nodes:
doc = {
"document_id": node.node_id.encode("utf-8"),
"text": node.text.encode("utf-8"),
"embeddings": node.embedding,
}
if self.hybrid_search:
doc["sparseVectors"] = self._sparse_encoder(node.get_content())
# handle metadata columns
if len(schema) > len(DEFAULT_COLUMN_NAMES):
for column in [
item
for item in schema
if item["name"] not in DEFAULT_COLUMN_NAMES
]:
try:
doc[column["name"]] = node.metadata[column["name"]]
except Exception as e:
logger.error(
f"Error writing column {column['name']} as type {column['type']}: {e}."
)
docs.append(doc)
df = pd.DataFrame(docs)
for i in range((len(df) - 1) // self.batch_size + 1):
batch = df.iloc[i * self.batch_size : (i + 1) * self.batch_size]
try:
self._table.insert(batch)
logger.info(f"inserted batch {i}")
except Exception as e:
logger.exception(
f"Failed to insert batch {i} of documents into the datastore: {e}"
)
return [x.decode("utf-8") for x in df["document_id"].tolist()]
except Exception as e:
logger.error(f"Error preparing data for KDB.AI: {e}.")
def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
try:
import kdbai_client as kdbai
logger.info("KDBAI client version: " + kdbai.__version__)
except ImportError:
raise ValueError(
"Could not import kdbai_client package."
"Please add it to the dependencies."
)
if query.alpha:
raise ValueError(
"Could not run hybrid search. "
"Please remove alpha and provide KDBAI weights for the two indexes though the vector_store_kwargs."
)
if query.filters:
filter = query.filters
if kwargs.get("filter"):
filter.extend(kwargs.pop("filter"))
kwargs["filter"] = filter
if kwargs.get("index"):
index = kwargs.pop("index")
if self.hybrid_search:
indexSparse = kwargs.pop("indexSparse", None)
indexWeight = kwargs.pop("indexWeight", None)
indexSparseWeight = kwargs.pop("indexSparseWeight", None)
if not all([indexSparse, indexWeight, indexSparseWeight]):
raise ValueError(
"Could not run hybrid search. "
"Please provide KDBAI sparse index name and weights."
)
else:
raise ValueError(
"Could not run the search. " "Please provide KDBAI index name."
)
if self.hybrid_search:
sparse_vectors = [self._sparse_encoder(query.query_str)]
qry = {index: [query.query_embedding], indexSparse: sparse_vectors}
index_params = {
index: {"weight": indexWeight},
indexSparse: {"weight": indexSparseWeight},
}
results = self._table.search(
vectors=qry,
index_params=index_params,
n=query.similarity_top_k,
**kwargs,
)[0]
else:
results = self._table.search(
vectors={index: [query.query_embedding]},
n=query.similarity_top_k,
**kwargs,
)[0]
top_k_nodes = []
top_k_ids = []
top_k_scores = []
for result in results.to_dict(orient="records"):
metadata = {x: result[x] for x in result if x not in DEFAULT_COLUMN_NAMES}
node = TextNode(
text=result["text"], id_=result["document_id"], metadata=metadata
)
top_k_ids.append(result["document_id"])
top_k_nodes.append(node)
top_k_scores.append(result["__nn_distance"])
return VectorStoreQueryResult(
nodes=top_k_nodes, similarities=top_k_scores, ids=top_k_ids
)
def delete(self, **delete_kwargs: Any) -> None:
raise Exception("Not implemented.")
|
