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Update neo4j vector procedures (#26775)

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Tomaz Bratanic 2024-10-01 07:45:09 +10:00 committed by GitHub
parent 870bd42b0d
commit 446144e7c6
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1 changed files with 18 additions and 10 deletions
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libs/community/langchain_community/vectorstores/neo4j_vector.py
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@ -444,6 +444,18 @@ class Neo4jVector(VectorStore):
embedding: Any embedding function implementing
`langchain.embeddings.base.Embeddings` interface.
distance_strategy: The distance strategy to use. (default: COSINE)
search_type: The type of search to be performed, either
'vector' or 'hybrid'
node_label: The label used for nodes in the Neo4j database.
(default: "Chunk")
embedding_node_property: The property name in Neo4j to store embeddings.
(default: "embedding")
text_node_property: The property name in Neo4j to store the text.
(default: "text")
retrieval_query: The Cypher query to be used for customizing retrieval.
If empty, a default query will be used.
index_type: The type of index to be used, either
'NODE' or 'RELATIONSHIP'
pre_delete_collection: If True, will delete existing data if it exists.
(default: False). Useful for testing.
@ -581,7 +593,7 @@ class Neo4jVector(VectorStore):
self.query(
f"MATCH (n:`{self.node_label}`) "
"CALL { WITH n DETACH DELETE n } "
"CALL (n) { DETACH DELETE n } "
"IN TRANSACTIONS OF 10000 ROWS;"
)
# Delete index
@ -753,18 +765,14 @@ class Neo4jVector(VectorStore):
to create a new vector index in Neo4j.
"""
index_query = (
"CALL db.index.vector.createNodeIndex("
"$index_name,"
"$node_label,"
"$embedding_node_property,"
"toInteger($embedding_dimension),"
"$similarity_metric )"
f"CREATE VECTOR INDEX {self.index_name} IF NOT EXISTS "
f"FOR (m:`{self.node_label}`) ON m.`{self.embedding_node_property}` "
"OPTIONS { indexConfig: { "
"`vector.dimensions`: toInteger($embedding_dimension), "
"`vector.similarity_function`: $similarity_metric }}"
)
parameters = {
"index_name": self.index_name,
"node_label": self.node_label,
"embedding_node_property": self.embedding_node_property,
"embedding_dimension": self.embedding_dimension,
"similarity_metric": DISTANCE_MAPPING[self._distance_strategy],
}