tomasonjo
commented
1 month ago Thats because there is an existing vector index, use a different index_name
V pet., 9. avg. 2024, 18:26 je oseba ramkumarsync @.***> napisala:
@tomasonjo https://github.com/tomasonjo I am trying to initialize the vector store from the existing graph to perform for vector similarity search. But whatever the value assigned for the "node_label" property wasn't considered.
In the below code example, I assign the "Codeexample" value to the "node_label". However, after the vector store initialization I printed the value of "node_label" and it simply return "Document" every time. This makes my vector similarity search to return empty results.
`from neo4j import GraphDatabase from langchain_openai import OpenAIEmbeddings from langchain.vectorstores import Neo4jVector
driver = GraphDatabase.driver(url, auth=(username, password)) Initialize LangChain components
embedding = OpenAIEmbeddings() vector_store = Neo4jVector.from_existing_graph( embedding=embedding, url=url, username=username, password=password,
node_label="Document",
text_node_properties=["id", "name", "title"],
node_label="Codeexample", text_node_properties=["id", "name", "description", "code"], embedding_node_property="embedding", ) Print Debug Information
print("Node Label:", vector_store.node_label) ----- Node Label: Document ------ Process natural language query
query = "My code example query" query_embedding = embedding.embed_query(query) Search in the knowledge graph
results = vector_store.similarity_search(query, k=1) results`
Please check this and let me know if I am missing anything here. Thanks in advance :)
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@tomasonjo I am trying to initialize the vector store from the existing graph to perform for vector similarity search. But whatever the value assigned for the "node_label" property wasn't considered.
In the below code example, I assign the "Codeexample" value to the "node_label". However, after the vector store initialization I printed the value of "node_label" and it simply return "Document" every time. This makes my vector similarity search to return empty results.
`from neo4j import GraphDatabase from langchain_openai import OpenAIEmbeddings from langchain.vectorstores import Neo4jVector
driver = GraphDatabase.driver(url, auth=(username, password))
Initialize LangChain components
embedding = OpenAIEmbeddings() vector_store = Neo4jVector.from_existing_graph( embedding=embedding, url=url, username=username, password=password, node_label="Codeexample", text_node_properties=["id", "name", "description", "code"], embedding_node_property="embedding", )
Print Debug Information
print("Node Label:", vector_store.node_label) ----- Node Label: Document ------
Process natural language query
query = "My code example query" query_embedding = embedding.embed_query(query)
Search in the knowledge graph
results = vector_store.similarity_search(query, k=1) results`
Please check this and let me know if I am missing anything here. Thanks in advance :)