how can i convert an existing neo4j knowledge graph int...

rrawwerks

how can i convert an existing neo4j knowledge graph into a llamaindex property graph that is hosted on the same neo4j instance?

16 comments

kkapa.ai

Please stand by @rawwerks as I search the knowledge base. I normally take about 20 seconds or so. :wave:

kkapa.ai

----------------------
To convert an existing Neo4j knowledge graph into a LlamaIndex property graph hosted on the same Neo4j instance, you can follow these steps:

First, you need to connect to your existing Neo4j graph. You can do this using the Neo4jPropertyGraphStore class from LlamaIndex. You will need to provide the username, password, and URL for your Neo4j instance. Here is an example:

Plain Text

from llama_index.graph_stores.neo4j import Neo4jPropertyGraphStore

graph_store = Neo4jPropertyGraphStore(
    username="neo4j",
    password="your_password",
    url="bolt://localhost:7687",
)

Please replace "your_password" with your actual Neo4j password.

Once you have connected to your existing graph, you can create a PropertyGraphIndex from it using the from_existing method. This method also requires an LLM (Language Model) and an embedding model. Here is an example:

Plain Text

from llama_index.core import PropertyGraphIndex
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI

index = PropertyGraphIndex.from_existing(
    property_graph_store=graph_store,
    llm=OpenAI(model="gpt-3.5-turbo", temperature=0.3),
    embed_model=OpenAIEmbedding(model_name="text-embedding-3-small"),
)

In this example, the OpenAI GPT-3.5-turbo model is used as the LLM and the "text-embedding-3-small" model is used for embeddings.

After creating the PropertyGraphIndex, you can still insert more documents into it using the insert method. Here is an example:

Plain Text

from llama_index.core import Document

document = Document(text="LlamaIndex is great!")
index.insert(document)

In this example, a new document with the text "LlamaIndex is great!" is inserted into the index.

Finally, you can retrieve nodes from the index using the as_retriever and retrieve methods. Here is an example:

kkapa.ai

Plain Text

nodes = index.as_retriever(include_text=False).retrieve("LlamaIndex")
print(nodes[0].text)

In this example, nodes related to "LlamaIndex" are retrieved from the index and the text of the first node is printed.

Please note that if your graph was created outside of LlamaIndex, the most useful retrievers will be text to cypher or cypher templates. Other retrievers rely on properties that LlamaIndex inserts.

For more details, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

rrawwerks

@kapa.ai but i don't want to insert new documents, i just want to run the embeddings to convert the "knowledge graph" to the "property graph"

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
If you want to convert your existing Neo4j knowledge graph into a LlamaIndex property graph without inserting new documents, you can do so by following these steps:

Connect to your existing Neo4j graph using the Neo4jPropertyGraphStore class from LlamaIndex. Provide the username, password, and URL for your Neo4j instance:

Plain Text

from llama_index.graph_stores.neo4j import Neo4jPropertyGraphStore

graph_store = Neo4jPropertyGraphStore(
    username="neo4j",
    password="your_password",
    url="bolt://localhost:7687",
)

Replace "your_password" with your actual Neo4j password.

Create a PropertyGraphIndex from your existing graph using the from_existing method. This method also requires an LLM (Language Model) and an embedding model:

Plain Text

from llama_index.core import PropertyGraphIndex
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI

index = PropertyGraphIndex.from_existing(
    property_graph_store=graph_store,
    llm=OpenAI(model="gpt-3.5-turbo", temperature=0.3),
    embed_model=OpenAIEmbedding(model_name="text-embedding-3-small"),
)

In this example, the OpenAI GPT-3.5-turbo model is used as the LLM and the "text-embedding-3-small" model is used for embeddings.

At this point, your existing Neo4j knowledge graph has been converted into a LlamaIndex property graph. The embeddings have been run and no new documents have been inserted.

For more details, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

rrawwerks

hmm, ok i'll try that.

@Logan M - just to clarify , if i do:

Plain Text

index = PropertyGraphIndex.from_existing(
    property_graph_store=graph_store,
    llm=OpenAI(model="gpt-3.5-turbo", temperature=0.3),
    embed_model=OpenAIEmbedding(model_name="text-embedding-3-small"),
)

then is my neo4j graph store now saved as a "property graph"? does the .from_existing go find all the unprocessed nodes and embed them?

my use case is that neo4j built this really easy tool to make a graph from a bunch of files: https://dev-frontend-dcavk67s4a-uc.a.run.app/, so i want to use that instead of building my own document ingestion pipeline, but then i want to convert from a "regular" neo4j KG to a llamaindex property graph.

...and ideally any time i add more stuff to the KG directly, then llamaindex will just embed the new stuff...

LLogan M

it does not find stuff and embed it. from_existing is meant to load a graph that you created early with from_documents() or created from nodes

LLogan M

converting an existing graph to a llamaindex graph will be a little tricky. I would use cypher to query your existing graph, make a bunch of EntityNode and Relation objects, embed the entity nodes, and then upsert into the property graph

rrawwerks

OK, thanks for clarifying. In that case, I’ll just invest my time in making a document ingestion pipeline that I like, and do it from the beginning as a property graph

ddkatz123

@kapa.ai Running the following code results in some orphan nodes in the neo4j database, when these nodes should not be orphaned. Why is that?

from llama_index.core import PropertyGraphIndex

kg_extractor = SchemaLLMPathExtractor(
llm=llm,
possible_entities=entities,
possible_relations=relations,
kg_validation_schema=validation_schema,
# if false, allows for values outside of the schema
# useful for using the schema as a suggestion
strict=False,
)
NUMBER_OF_ARTICLES = 100

index = PropertyGraphIndex.from_documents(
documents[:NUMBER_OF_ARTICLES],
kg_extractors=[kg_extractor],
llm=llm,
embed_model=embed_model,
property_graph_store=graph_store,
show_progress=True,
)

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
The issue of orphan nodes appearing in your Neo4j database could be due to several reasons. Here are a few possibilities:

Incomplete Relationships: The SchemaLLMPathExtractor might be extracting entities without their corresponding relationships or vice versa. This could happen if the documents do not contain enough information to form complete relationships.

Strict Mode: You have set strict=False in your SchemaLLMPathExtractor. This means that entities and relationships that are not in your validation_schema can be included in the graph. If these entities or relationships do not form complete paths, they could result in orphan nodes.

Errors in the Extraction Process: There might be errors or exceptions occurring during the extraction process that cause some nodes to be inserted into the graph without their corresponding relationships.

To debug this issue, you could:

Check your documents to ensure they contain complete information for forming relationships in the graph.
Set strict=True in your SchemaLLMPathExtractor to only include entities and relationships that are in your validation_schema.
Monitor the extraction process for any errors or exceptions and handle them appropriately.

Unfortunately, without more specific information about your documents, entities, relations, and validation schema, it's hard to provide a more precise answer. If you continue to experience issues, you might find it helpful to consult the LlamaIndex documentation () or seek support from the LlamaIndex community or developers.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

ddkatz123

@kapa.ai How can I set a verbose mode on the code so I can see what's happening?

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
To enable verbose mode in your LlamaIndex code, you can use Python's built-in logging module. Here's how you can do it:

At the top of your script, import the logging module and configure it to output debug information:

Plain Text

import logging

logging.basicConfig(level=logging.DEBUG)

When creating your LlamaIndex components such as FunctionCallingAgentWorker, OpenAIAgent, etc., set the verbose parameter to True. This will cause these components to print out detailed information about their operations. Here are some examples from the knowledge sources:

Plain Text

# Creating a FunctionCallingAgentWorker with verbose output
worker = FunctionCallingAgentWorker.from_tools([lyft_tool, uber_tool], verbose=True)

# Creating an OpenAIAgent with verbose output
agent = OpenAIAgent.from_tools(tools, llm=llm, verbose=True)

Remember to replace lyft_tool, uber_tool, tools, and llm with your actual tools and LLM.

For more information, you can refer to the following knowledge sources:

LlamaIndex Starter Tutorial ()
LlamaIndex Agent Example ()
LlamaIndex Cassandra Tools Example ()

----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

Add a reply

Find answers from the community

how can i convert an existing neo4j knowledge graph into a llamaindex property graph that