I am exploring property graph index.

I am exploring property graph index.

This code seems to work with Azure OpenAI LLM Settings.

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# Property Graph Construction: Implicit Extraction Method
from llama_index.core.indices.property_graph import ImplicitPathExtractor
import nest_asyncio
from llama_index.core import PropertyGraphIndex

# Apply nest_asyncio to avoid runtime errors in async environments
nest_asyncio.apply()

# Initialize Azure AI Search vector store
vector_store = AzureAISearchVectorStore(
    search_or_index_client=index_client,
    index_name=INDEX_NAME,
    index_management=IndexManagement.CREATE_IF_NOT_EXISTS,
    id_field_key="id",
    chunk_field_key="text",
    embedding_field_key="embedding",
    embedding_dimensionality=3072,  # Adjust to match embedding model output (like ada-002)
    metadata_string_field_key="metadata",
    doc_id_field_key="doc_id",
    language_analyzer="en.lucene",
    vector_algorithm_type="exhaustiveKnn",
    compression_type="binary"
)

# Construct the property graph index with implicit path extraction
index = PropertyGraphIndex.from_documents(
    documents,
    llm=llm,
    embed_model=embed_model,
    vector_store=vector_store,
    show_progress=True,
)

I can confirm my vector store in Azure AI SEarch looks correct.

10 comments

ffarzzy528

But this code using schema-guided extraction fails:

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# Schema-Guided Extraction Method
from typing import Literal
from llama_index.core.indices.property_graph import SchemaLLMPathExtractor

# Define the schema for entity types and relationships
entities = Literal["PERSON", "PLACE", "THING"]
relations = Literal["PART_OF", "HAS", "IS_A"]
schema = {
    "PERSON": ["HAS", "IS_A"],
    "PLACE": ["PART_OF", "HAS"],
    "THING": ["IS_A"],
}

# Initialize the schema-based extractor
kg_extractor = SchemaLLMPathExtractor(
    llm=llm,
    possible_entities=entities,
    possible_relations=relations,
    kg_validation_schema=schema,
    strict=True,  # Disallow extractions outside the schema
)

# Construct the property graph index using the schema-guided extractor
index_schema = PropertyGraphIndex.from_documents(
    documents,
    kg_extractors=[kg_extractor],
    vector_store=vector_store,
    show_progress=True,
)

AuthenticationError: Error code: 401 - {'error': {'message': 'Incorrect API key provided: c39162c5**5418. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}

---
It seems like the Settings.llm isn't being applied and is looking for an openai key vs azure openai key?

ffarzzy528

I can 100% confirm tis API key is correct as it works for creating a normal vector store.

LLogan M

Did you set Settings.llm and Settings.embed_model ? Hard to say if this error is from the LLM or the embed model

LLogan M

The stack trace would probably be helpful

ffarzzy528

fixed it.

ffarzzy528

On another note @Logan M , do I need to create multiple vector indexes (eg. llamaindex-simplellmpathextractor, llamaindex-schema-guidedextractor, llamaindex-free-formextractor,etc) or do I only need a single vector index and the changes on the type are on the property graph store?

LLogan M

I think you need a vector store (or collection/table in your vector store) for each PropertyGraphIndex you want to create

ffarzzy528

Not following exactly, here is what a node looks like inside of my search index after I run this:

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# Free-Form Extraction Method
from llama_index.core.indices.property_graph import SimpleLLMPathExtractor

# Initialize the free-form extractor
kg_extractor_free = SimpleLLMPathExtractor()

# Construct the property graph index using free-form extraction
index_free_form = PropertyGraphIndex.from_documents(
    documents,
    kg_extractors=[kg_extractor_free],
    vector_store=vector_store,
    show_progress=True,
)

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LLogan M

That looks fine to me? 👀

LLogan M

Maybe?

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I am exploring property graph index.