scottyakc

Trying to replicate the LLamaParse example here: https://github.com/run-llama/llama_parse/blob/main/examples/demo_advanced.ipynb

But instead of OpenAI trying to use LM Studio on my local machine:
from llama_index.embeddings.huggingface import HuggingFaceEmbedding


embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
llm = OpenAI(
    api_key="NULL",
    api_base="http://localhost:1234/v1",
    temperature=0.2,
)

Settings.llm = llm
Settings.embed_model = embed_model

node_parser = MarkdownElementNodeParser(llm=llm, num_workers=8)

...

sub_query_engine = SubQuestionQueryEngine.from_defaults(
    query_engine_tools=query_engine_tools,
    llm=llm,
    use_async=True,
)

response = sub_query_engine.query(
    "Which fund has the smallest minimum investment size?"
)

The api call is received by the server, but when the response is sent back, Llama Index throws the below error:

ValueError: Expected tool_calls in ai_message.additional_kwargs, but none found.

Anyone experience this?

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