llama_index/llama-index-core/llama_index...

The schema (allowed entity and relation names) are used to create a pydantic object on the fly

This pydantic object is provided to the LLM, to inform it what type of graph should be extracted

Specifically, its this line of code, which runs extraction per chunk
https://github.com/run-llama/llama_index/blob/08c748f0111bfa1d772176e68532d1948b30a267/llama-index-core/llama_index/core/indices/property_graph/transformations/schema_llm.py#L354

@Logan M Can you explain a little more on how to customize the prompt. Should I just look at the default prompt above for reference or something else?
With the SchemaLLMPathExtractors, I see no EntityNodes extracted, so really want to understand why.
Additionally, is it possible to define a directional relationships for this schema? I saw that the type for schema - kg_validation_schema (Dict[str, str], optional) - is Dict, so not sure how to make it directional...

I first build the KG with strict=False, then put all the extracted entities and relations there into my schema and set back to strict=True but the it still extracts nothing.... Do you know anyways I could make the models extract more nodes?

If the LLM is not outputting proper json, then there will be no output. This is very common for open-source LLMs

I think you might have an older version, the type for kg_validation_schema is kg_validation_schema: Union[Dict[str, str], List[Triple]], Its directional becaue you can do kg_validation_schema=[("ENTITY1", "REL", "ENTITY2"), ...]

Got it! Thank you!