Hello. I am trying to use

At a glance

The community member is trying to use PropertyGraphIndex with SimplePropertyGraphStore to store data from a networkx graph. They can retrieve data from the store using methods like graph_store.get, but they are having trouble querying the index. The index is created using PropertyGraphIndex.from_existing(), and the query engine is created using LLMSynonymRetriever, VectorContextRetriever, and index.as_query_engine(). However, the query_engine.query() always returns an empty response.

The comments suggest that the community member may not be saving and loading the vector store, which is necessary for the query engine to work properly. The community member also asks about which vector store to use, and the comments indicate that the types can be handled under the hood, and the default is SimpleVectorStore.

The community member provides more detailed code, including setting up the LLM, embedding model, and vector store, and creating the graph store, index, and query engine. However, they still have issues with the query engine returning empty responses or not being able to retrieve information about a specific user.

There is no explicitly marked answer in the comments, and the community members are still trying to figure out how to properly query the index and retrieve the desired information.

aandriyko

Hello. I am trying to use PropertyGraphIndex with SimplePropertyGraphStore to store data from netwokx graph. The nodes and edges from the networkx graph are upserted into the graph store as EntityNode and Relation instances. I can get data from the store using methods like graph_store.get using properties or ids. But I don't understand how to query the index. The index is created using index = PropertyGraphIndex.from_existing(property_graph_store=graph_store).

Then, the query engine is created as

Plain Text

llm_synonym_retriever = LLMSynonymRetriever(
    index.property_graph_store,
    llm=llm,
    include_text=False,
)
vector_context_retriever = VectorContextRetriever(
    index.property_graph_store,
    embed_model=HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5"),
    include_text=False,
) 
query_engine = index.as_query_engine(
    sub_retrievers=[
        vector_context_retriever,
        llm_synonym_retriever,
    ],
    llm=llm,
)
# llm is Ollama(model="llama3")

The problem is that query_engine.query() always returns empty response.

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response = query_engine.query("What is the email address of user Fname Lname?")

11 comments

LLogan M

Are you saving and loading the vector store though?

Without the vector store, you are relying on the synoym/keyword retrieval, which isn't always great

aandriyko

which vector store should i use? vetor store wants nodes to be of type BaseNode, while graph nodes are EntityNode inheriting from pydantic model

LLogan M

The types all get handled under the hood -- use whichever vector store you want?

The default is SimpleVectorStore, it was probably used when you first created the index, but it seems like you either didn't save it or didn't load it

aandriyko

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llm = Ollama(
    model="llama3",
    request_timeout=300.0,
    additional_kwargs={
        "trust_remote_code": True,
        "generate_kwargs": {"temperature": 0.0, "do_sample": False},
    },
)
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")

llm.context_window = 4096
Settings.llm = llm
Settings.chunk_size = 1024
Settings.embed_model = embed_model

vector_store = SimpleVectorStore()
storage_context = StorageContext.from_defaults(vector_store=vector_store)
storage_context.vector_stores["default"] = vector_store

user = {
    "id": "user_1",
    "name": "Stepan Ivan",
    "email": "user@example.com",
    "type": "user",
}

market = {
    "id": "market_1",
    "name": "Canada",
    "type": "market",
}

relation = Relation(
    label="market_member", source_id=user["id"], target_id=market["id"], properties={}
)

graph_store = SimplePropertyGraphStore()
graph_store.upsert_nodes(
    [
        EntityNode(label=user["type"], name=user["id"], properties=user),
        EntityNode(label=market["type"], name=market["id"], properties=market),
    ]
)
graph_store.upsert_relations([relation])

index = PropertyGraphIndex.from_existing(
    llm=llm,
    property_graph_store=graph_store,
    storage_context=storage_context,
    embed_kg_nodes=True,
    show_progress=True,
)

llm_synonym_retriever = LLMSynonymRetriever(
    index.property_graph_store,
    llm=llm,
    include_text=False,
)

vector_context_retriever = VectorContextRetriever(
    graph_store=index.property_graph_store,
    embed_model=embed_model,
    vector_store=index.vector_store,
    include_text=False,
)

query_engine = index.as_query_engine(
    sub_retrievers=[
        vector_context_retriever,
        llm_synonym_retriever,
    ],
    llm=llm,
)

aandriyko

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nodes = graph_store.get(properties={"email": "user@example.com"})

this works as expected

aandriyko

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query_engine.query("What is Stepan's last name?")

this doesn't work

aandriyko

the model either returns something like "I'm happy to help! However, I don't see any information about a person named Stepan. The context only mentions user_1, market_member, and market_1, but no mention of Stepan or their last name. Therefore, I cannot provide an answer to the query."

aandriyko

or, returns empty response if I upsert thousands of nodes

LLogan M

You can check what the retriever is retrieving

retriever.retrieve("query")

Or check the source nodes on the response

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response = query_engine.query(...)
for node in response.source_nodes:
  print(node.text)

aandriyko

retriever node user_1 -> market_member -> market_1 and response node user_1 -> market_member -> market_1. It looks like entity nodes are not quired at all?

aandriyko

The strange thing is that when I dump the information about nodes into a plain text file - one sentence that describes users' attributes like name, id, email, etc and load using SimpleDirectoryReader, then I get almost correct response. The model is able to determine user's email by user name. But it works because SimpleDirectoryReader creates documents and nodes with relations where each node is a property. For example User -> Has email -> Email, which I think is wrong because I would expect to get one node with all the properties and then another node lets say Market with relation User -> Member Of -> Market.

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