[Question]: KnowledgeGraphIndex

[BunnyRivennn]

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When i use the function to build graph on neo4j: kg_index = KnowledgeGraphIndex.from_documents( documents=documents, storage_context=storage_context, max_triplets_per_chunk=3, show_progress=True, include_embeddings=True, kg_triplet_extract_fn=extract_triplets) I set the param include_embeddings is True, kg_triplet_extract_fn=extract_triplets, the Triple show on the neo4j, and now, i want to store the embeddings on the db(qdrant/milvus...), and next load th kg, how to do it?

[dosubot[bot]]

To store embeddings in a database like Qdrant or Milvus when using the KnowledgeGraphIndex function to build a graph on Neo4j, you can follow these steps:

1. Set Up the Environment:

   Ensure you have the necessary packages installed for working with Neo4j and Qdrant. You can install them using pip:

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

2. Create the Index with Neo4j and Qdrant:

   You can create a PropertyGraphIndex using Neo4j as the graph store and Qdrant as the vector store. This allows you to store the graph structure in Neo4j and the embeddings in Qdrant.

   from llama_index.core import PropertyGraphIndex
   from llama_index.graph_stores.neo4j import Neo4jPropertyGraphStore
   from llama_index.vector_stores.qdrant import QdrantVectorStore
   from qdrant_client import QdrantClient, AsyncQdrantClient
   
   vector_store = QdrantVectorStore(
      "graph_collection",
      client=QdrantClient(...),
      aclient=AsyncQdrantClient(...),
   )
   
   graph_store = Neo4jPropertyGraphStore(
      username="neo4j",
      password="<password>",
      url="bolt://localhost:7687",
   )
   
   # Create an index
   index = PropertyGraphIndex.from_documents(
      documents,
      property_graph_store=graph_store,
      vector_store=vector_store,
      embed_kg_nodes=True,
   )

3. Load the Index from Existing Stores:

   If you have an existing graph and vector store, you can load the index from these stores.

   index = PropertyGraphIndex.from_existing(
      property_graph_store=graph_store,
      vector_store=vector_store,
      embed_kg_nodes=True,
   )

4. Querying the Knowledge Graph:

   You can query the knowledge graph using the as_query_engine method, which allows you to specify whether to include text and how to handle embeddings.

   query_engine = index.as_query_engine(
      include_text=True,
      response_mode="tree_summarize",
      embedding_mode="hybrid",
      similarity_top_k=5,
   )
   response = query_engine.query("Tell me more about what the author worked on at Interleaf")

This setup allows you to store and manage the graph structure in Neo4j while using Qdrant to handle the vector embeddings, and provides flexibility in querying the knowledge graph with or without embeddings ^[1][2].

To continue talking to Dosu, mention @dosu.

[BunnyRivennn]

But, how to define the param kg_triplet_extract_fn=extract_triplets, i have my triple way. I set graph_store = Neo4jPropertyGraphStore( username=username, password=password, url=url,

show_progress=True,

kg_triplet_extract_fn=extract_triplets, ), its error.

[dosubot[bot]]

To define and use the kg_triplet_extract_fn parameter, you need to ensure that your extract_triplets function is a callable that takes a single string argument (the text from which to extract triplets) and returns a list of triplets. Each triplet should be a tuple of three strings, representing the subject, predicate, and object.

Here's a basic example of how you might define such a function:

def extract_triplets(text: str) -> list[tuple[str, str, str]]:
    # Your custom logic to extract triplets from the text
    # For example, this could be a simple regex or a more complex NLP model
    triplets = []
    # Example triplet extraction logic
    # triplets.append(("subject", "predicate", "object"))
    return triplets

Make sure that your extract_triplets function adheres to this signature and returns the expected format. If you encounter errors, check that the function is correctly defined and that it handles the input text appropriately. Also, ensure that the function is accessible in the scope where you are passing it to the Neo4jPropertyGraphStore or KnowledgeGraphIndex ^[1].

To continue talking to Dosu, mention @dosu.

[BunnyRivennn]

graph_store = Neo4jPropertyGraphStore( username=username, password=password, url=url,

show_progress=True,

kg_triplet_extract_fn=extract_triplets, ), its error. The error is "neo4j.exceptions.ConfigurationError: Unexpected config keys: kg_triplet_extract_fn". So i want to know how to define my triple may on the function of Neo4jPropertyGraphStore?

[dosubot[bot]]

The Neo4jPropertyGraphStore class does not support a kg_triplet_extract_fn parameter. This is why you're encountering the "Unexpected config keys" error. The valid configuration keys for Neo4jPropertyGraphStore are username, password, url, database, refresh_schema, sanitize_query_output, enhanced_schema, and **neo4j_kwargs ^[1].

To use a custom triplet extraction function, you should define it separately and use it when processing your documents before storing them in the graph. You can integrate your custom triplet extraction logic with the KnowledgeGraphIndex or PropertyGraphIndex by passing your function to the kg_triplet_extract_fn parameter during their initialization, not in the Neo4jPropertyGraphStore ^[2].

Here's a brief example of how you might set this up:

def extract_triplets(text: str) -> list[tuple[str, str, str]]:
    # Your custom logic to extract triplets
    return [("Subject", "Predicate", "Object")]

# Use the custom function with KnowledgeGraphIndex or PropertyGraphIndex
kg_index = KnowledgeGraphIndex(
    kg_triplet_extract_fn=extract_triplets,
    # other parameters...
)

Ensure that your custom function is correctly defined and used in the appropriate context.

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[BunnyRivennn]

i using the KnowledgeGraphIndex and set my custom triplet extraction function, kg_triplet_extract_fn=extract_triplets, and next, how store the embedding to the db(qdrant/milvus)? and load it?

[dosubot[bot]]

To store embeddings in a database like Qdrant or Milvus using the KnowledgeGraphIndex, you need to configure the vector store to handle the embeddings. Here's a general approach:

1. Set Up the Vector Store:

   First, ensure you have the necessary packages installed for Qdrant or Milvus. You can install them using pip if needed.

2. Configure the Vector Store:

   Set up the vector store with the appropriate configuration parameters. For example, for Qdrant:

   from llama_index.vector_stores.qdrant import QdrantVectorStore
   from qdrant_client import QdrantClient
   
   vector_store = QdrantVectorStore(
      collection_name="your_collection_name",
      client=QdrantClient(url="http://localhost:6333", api_key="your_api_key"),
   )

   For Milvus, the setup would be similar but using Milvus-specific parameters.

3. Create the KnowledgeGraphIndex:

   When creating the KnowledgeGraphIndex, pass the vector store as a parameter. This will allow the index to store embeddings in the specified vector store.

   kg_index = KnowledgeGraphIndex.from_documents(
      documents=your_documents,
      storage_context=storage_context,
      vector_store=vector_store,
      kg_triplet_extract_fn=extract_triplets,
      include_embeddings=True,
   )

4. Load the Index:

   To load the index from the existing vector store, you can use:

   kg_index = KnowledgeGraphIndex.from_existing(
      vector_store=vector_store,
      # other parameters...
   )

This setup will enable you to store the embeddings in Qdrant or Milvus and load them as needed. Make sure your vector store is properly configured with the necessary connection details and authentication credentials.

To continue talking to Dosu, mention @dosu.

[BunnyRivennn]

kg_index = KnowledgeGraphIndex.from_documents(
    documents=batch_docs,
    storage_context=storage_context,
    vector_store=vector_store,
    max_triplets_per_chunk=3,
    show_progress=True,
    include_embeddings=True,
    kg_triplet_extract_fn=extract_triplets,
)

the graph show on the neo4j sucessful, but, the embedding is not store on the qdrant, why and how to do it?

[dosubot[bot]]

To ensure that embeddings are stored in Qdrant when using KnowledgeGraphIndex, verify the following:

1. Vector Store Configuration: Ensure that your QdrantVectorStore is correctly configured. This includes setting the collection_name and providing a valid QdrantClient instance. Double-check the URL and API key if applicable.

   from llama_index.vector_stores.qdrant import QdrantVectorStore
   from qdrant_client import QdrantClient
   
   vector_store = QdrantVectorStore(
      collection_name="your_collection_name",
      client=QdrantClient(url="http://localhost:6333", api_key="your_api_key"),
   )

2. Embedding Model: Ensure that the embedding model is correctly set up and integrated with the KnowledgeGraphIndex. The embeddings need to be generated and passed to the vector store.

3. Check for Errors: Look for any error messages or logs that might indicate why the embeddings are not being stored. This could include connection issues with Qdrant or misconfigurations in the vector store setup.

4. Include Embeddings: Confirm that include_embeddings=True is set when creating the KnowledgeGraphIndex, which you have already done.

5. Batch Processing: If you are processing documents in batches, ensure that each batch is correctly processed and that embeddings are generated for each document.

If all configurations are correct and the issue persists, consider checking the Qdrant logs for any errors or warnings that might provide more insight into the problem.

To continue talking to Dosu, mention @dosu.