[Question]: read neo4j data into llama-index without local persist file

[zydmtaichi]

Question Validation

• [X] I have searched both the documentation and discord for an answer.

Question

Surpose we have a neo4j database with concern data, and no persist file of json format on local. Is it possible to read data only relay on neo4jgraphstore and convert the data to index?

I try to pass the graph store merely to Storagecontext and use load_index_from_storage to get the index like below code but it's not working at all. the infer_index is empty

storage_context = StorageContext.from_defaults(graph_store=graph_store)

infer_index = load_index_from_storage(storage_context=storage_context, sercice_context=sercice_context)

[dosubot[bot]]

To read data directly from a Neo4j database using neo4jgraphstore and convert it to an index without relying on a local persist file, you can use the Neo4jGraphStore class to interact with the database and then create an index from the retrieved documents. Here is an example of how you can achieve this:

from llama_index.core.indices.property_graph import PropertyGraphIndex
from llama_index.graph_stores.neo4j import Neo4jGraphStore

# Initialize the Neo4jGraphStore
neo4j_store = Neo4jGraphStore(
    username="your_username",
    password="your_password",
    url="your_neo4j_url",
    database="your_database"
)

# Assuming you have a list of documents to index
documents = [
    # Your documents here
]

# Create the index from the documents
index = PropertyGraphIndex.from_documents(
    documents,
    property_graph_store=neo4j_store,
)

# Print the index
print(index)

This code initializes the Neo4jGraphStore, retrieves the documents, and then creates an index from those documents. Make sure to replace "your_username", "your_password", "your_neo4j_url", "your_database", and the documents list with your actual Neo4j credentials and the documents you want to index. Additionally, ensure that the APOC plugin is installed and properly configured in your Neo4j instance, as it is required for schema operations ^[1][2][3].

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[zydmtaichi]

I notice the PropertyGraphIndex has the from_existing method, but it's not suitable for other kind index like KnowledgeGraphIndex, so i want to know the general method for load data from neo4j, not just for PropertyGraph

[dosubot[bot]]

To load data from a Neo4j database into various types of indexes, such as KnowledgeGraphIndex, without relying on a local persist file, you can follow these steps:

1. Install Required Packages:

   %pip install llama-index-llms-openai
   %pip install llama-index-graph-stores-neo4j
   %pip install llama-index-embeddings-openai
   %pip install llama-index-llms-azure-openai
   %pip install neo4j

2. Set Up LLM and Embeddings:

   import os
   from llama_index.llms.openai import OpenAI
   from llama_index.embeddings.openai import OpenAIEmbedding
   from llama_index.core import Settings
   
   os.environ["OPENAI_API_KEY"] = "API_KEY_HERE"
   
   llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
   embedding_llm = OpenAIEmbedding(model="text-embedding-ada-002")
   
   Settings.llm = llm
   Settings.embed_model = embedding_llm
   Settings.chunk_size = 512

3. Prepare Neo4j Connection:

   username = "neo4j"
   password = "your_password"
   url = "bolt://your_neo4j_url:7687"
   database = "neo4j"

4. Instantiate Neo4jGraphStore and Load Data:

   from llama_index.graph_stores.neo4j import Neo4jGraphStore
   from llama_index.core import StorageContext, KnowledgeGraphIndex, SimpleDirectoryReader
   
   graph_store = Neo4jGraphStore(
      username=username,
      password=password,
      url=url,
      database=database,
   )
   
   storage_context = StorageContext.from_defaults(graph_store=graph_store)
   
   documents = SimpleDirectoryReader("path_to_your_documents").load_data()
   
   index = KnowledgeGraphIndex.from_documents(
      documents,
      storage_context=storage_context,
      max_triplets_per_chunk=2,
   )

5. Query the Knowledge Graph:

   query_engine = index.as_query_engine(include_text=False, response_mode="tree_summarize")
   response = query_engine.query("Tell me more about Interleaf")

6. Display the Response:

   from IPython.display import Markdown, display
   display(Markdown(f"<b>{response}</b>"))

This process allows you to load data directly from a Neo4j database into a KnowledgeGraphIndex without the need for a local persist file. The steps are similar to those used for Amazon Neptune, but adapted for Neo4j ^[1][2].

For other types of indexes, such as PropertyGraphIndex, you can follow a similar approach:

1. Create the Index:

   from llama_index.core import PropertyGraphIndex
   
   index = PropertyGraphIndex.from_documents(
      documents,
   )

2. Use the Index:

   retriever = index.as_retriever(
      include_text=True,
      similarity_top_k=2,
   )
   nodes = retriever.retrieve("Test")
   
   query_engine = index.as_query_engine(
      include_text=True,
      similarity_top_k=2,
   )
   response = query_engine.query("Test")

3. Load from Existing Graph Store:

   index = PropertyGraphIndex.from_existing(
      property_graph_store=graph_store,
   )

This approach provides flexibility in constructing and querying property graphs, allowing you to leverage the capabilities of Neo4j without relying on local storage ^[3].

To continue talking to Dosu, mention @dosu.

[zydmtaichi]

per my test, use PropertyGraphIndex.from_existing on non-PropertyGraph neo4j graph store cause a error, paste info below:

Traceback (most recent call last):
  File "f:\test\llamaidxinfertest.py", line 38, in <module>
    infer_index = PropertyGraphIndex.from_existing(property_graph_store=graph_store, llm=llm, embed_model=embed_model)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "E:\anaconda\envs\test\Lib\site-packages\llama_index\core\indices\property_graph\base.py", line 163, in from_existing
    return cls(
           ^^^^
  File "E:\anaconda\envs\test\Lib\site-packages\llama_index\core\indices\property_graph\base.py", line 111, in __init__
    storage_context.property_graph_store.supports_vector_queries
AttributeError: 'Neo4jGraphStore' object has no attribute 'supports_vector_queries'

[zydmtaichi]

@dosubot it's still relay on local documents to read from neo4j

[dosubot[bot]]

To address the error you're encountering and ensure compatibility with the PropertyGraphIndex, you need to use the Neo4jPropertyGraphStore instead of Neo4jGraphStore. The Neo4jPropertyGraphStore class has the supports_vector_queries attribute set to True, which is required by the PropertyGraphIndex.

Here's how you can modify your code:

1. Install Required Packages:

   %pip install llama-index-llms-openai
   %pip install llama-index-graph-stores-neo4j
   %pip install llama-index-embeddings-openai
   %pip install llama-index-llms-azure-openai
   %pip install neo4j

2. Set Up LLM and Embeddings:

   import os
   from llama_index.llms.openai import OpenAI
   from llama_index.embeddings.openai import OpenAIEmbedding
   from llama_index.core import Settings
   
   os.environ["OPENAI_API_KEY"] = "API_KEY_HERE"
   
   llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
   embedding_llm = OpenAIEmbedding(model="text-embedding-ada-002")
   
   Settings.llm = llm
   Settings.embed_model = embedding_llm
   Settings.chunk_size = 512

3. Prepare Neo4j Connection:

   username = "neo4j"
   password = "your_password"
   url = "bolt://your_neo4j_url:7687"
   database = "neo4j"

4. Instantiate Neo4jPropertyGraphStore and Load Data:

   from llama_index.graph_stores.neo4j import Neo4jPropertyGraphStore
   from llama_index.core import StorageContext, PropertyGraphIndex
   
   graph_store = Neo4jPropertyGraphStore(
      username=username,
      password=password,
      url=url,
      database=database,
   )
   
   storage_context = StorageContext.from_defaults(graph_store=graph_store)
   
   # Load the index from the existing graph store
   index = PropertyGraphIndex.from_existing(
      property_graph_store=graph_store,
      llm=llm,
      embed_model=embedding_llm,
   )

This setup ensures that the Neo4jPropertyGraphStore is used, which has the necessary supports_vector_queries attribute, making it compatible with the PropertyGraphIndex ^[1][2].

To continue talking to Dosu, mention @dosu.