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228 | class NeptuneAnalyticsVectorStore(VectorStore):
stores_text: bool = True
flat_metadata = True
def __init__(
self,
graph_identifier: str,
embedding_dimension: int,
client: Any = None,
credentials_profile_name: Optional[str] = None,
region_name: Optional[str] = None,
hybrid_search: bool = False,
node_label: str = "Chunk",
text_node_property: str = "text",
retrieval_query: str = None,
**kwargs: Any,
) -> None:
"""Create a new Neptune Analytics graph wrapper instance."""
self.node_label = node_label
self.graph_identifier = graph_identifier
self.embedding_dimension = embedding_dimension
self.text_node_property = text_node_property
self.hybrid_search = hybrid_search
self.retrieval_query = retrieval_query
try:
if client is not None:
self._client = client
else:
import boto3
if credentials_profile_name is not None:
session = boto3.Session(profile_name=credentials_profile_name)
else:
# use default credentials
session = boto3.Session()
if region_name:
self._client = session.client(
"neptune-graph", region_name=region_name
)
else:
self._client = session.client("neptune-graph")
except ImportError:
raise ModuleNotFoundError(
"Could not import boto3 python package. "
"Please install it with `pip install boto3`."
)
except Exception as e:
if type(e).__name__ == "UnknownServiceError":
raise ModuleNotFoundError(
"NeptuneGraph requires a boto3 version 1.34.40 or greater."
"Please install it with `pip install -U boto3`."
) from e
else:
raise ValueError(
"Could not load credentials to authenticate with AWS client. "
"Please check that credentials in the specified "
"profile name are valid."
) from e
# Verify that the analytics graph has a vector search index and that the dimensions match
self._verify_vectorIndex()
def _verify_vectorIndex(self) -> None:
"""
Check if the connected Neptune Analytics graph has VSS enabled and that the dimensions are the same.
"""
resp = self._client.get_graph(graphIdentifier=self.graph_identifier)
if "vectorSearchConfiguration" in resp:
if (
not resp["vectorSearchConfiguration"]["dimension"]
== self.embedding_dimension
):
raise ValueError(
f"Vector search index dimension for Neptune Analytics graph does not match the provided value."
)
else:
raise ValueError(
f"Vector search index does not exist for the Neptune Analytics graph."
)
def database_query(
self, query: str, params: Optional[dict] = None
) -> List[Dict[str, Any]]:
"""
This method sends a query to the Neptune Analytics graph
and returns the results as a list of dictionaries.
Args:
query (str): The openCypher query to execute.
params (dict, optional): Dictionary of query parameters. Defaults to {}.
Returns:
List[Dict[str, Any]]: List of dictionaries containing the query results.
"""
try:
logger.debug(f"query() query: {query} parameters: {json.dumps(params)}")
resp = self._client.execute_query(
graphIdentifier=self.graph_identifier,
queryString=query,
parameters=params,
language="OPEN_CYPHER",
)
return json.loads(resp["payload"].read().decode("UTF-8"))["results"]
except Exception as e:
raise NeptuneVectorQueryException(
{
"message": "An error occurred while executing the query.",
"details": str(e),
}
)
def add(self, nodes: List[BaseNode], **add_kwargs: Any) -> List[str]:
ids = [r.node_id for r in nodes]
for r in nodes:
import_query = (
f"MERGE (c:`{self.node_label}` {{`~id`: $id}}) "
"SET c += $data "
"WITH c "
f"CALL neptune.algo.vectors.upsert(c, {r.embedding}) "
"YIELD node "
"RETURN id(node) as id"
)
resp = self.database_query(
import_query,
params=self.__clean_params(r),
)
print("Nodes added")
return ids
def _get_search_index_query(self, hybrid: bool, k: int = 10) -> str:
if not hybrid:
return (
"WITH $embedding as emb "
"CALL neptune.algo.vectors.topKByEmbedding(emb, {topK: "
+ str(k)
+ "}) YIELD embedding, node, score "
)
else:
raise NotImplementedError
def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
default_retrieval = (
f"RETURN node.`{self.text_node_property}` AS text, score, "
"id(node) AS id, "
f"node AS metadata"
)
retrieval_query = self.retrieval_query or default_retrieval
read_query = (
self._get_search_index_query(self.hybrid_search, query.similarity_top_k)
+ retrieval_query
)
parameters = {
"embedding": query.query_embedding,
}
results = self.database_query(read_query, params=parameters)
nodes = []
similarities = []
ids = []
for record in results:
node = metadata_dict_to_node(record["metadata"]["~properties"])
node.set_content(str(record["text"]))
nodes.append(node)
similarities.append(record["score"])
ids.append(record["id"])
return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
self.database_query(
f"MATCH (n:`{self.node_label}`) WHERE n.ref_doc_id = $id DETACH DELETE n",
params={"id": ref_doc_id},
)
def __clean_params(self, record: BaseNode) -> List[Dict[str, Any]]:
"""Convert BaseNode object to a dictionary to be imported into Neo4j."""
text = record.get_content(metadata_mode=MetadataMode.NONE)
id = record.node_id
metadata = node_to_metadata_dict(record, remove_text=True, flat_metadata=False)
# Remove redundant metadata information
for k in ["document_id", "doc_id"]:
del metadata[k]
return {"id": id, "data": {self.text_node_property: text, "id": id, **metadata}}
|