[BUG] NanoGraphRAG Error: name 'EmbeddingFunc' is not defined

[vap0rtranz]

Description

I've enabled Nano in Docker image and I'm getting an "EmbeddingFunc is not defined" error.

This is the same issue closed in #458 . My UI looks the same as in that bug.

I pulled the latest main-full image this morning and running locally against Ollama.

I've tried both Llama3.1 8b and Qwen2.5 14b for the model.

Reproduction steps

1. Run latest image with Nano enabled:

`
docker run --platform linux/amd64 --net=host -e GRADIO_SERVER_NAME=0.0.0.0 -e GRADIO_SERVER_PORT=7860 -e USE_NANO_GRAPHRAG=true -e USE_CUSTOMIZED_GRAPHRAG_SETTING=false -p 7860:7860 -v ~/external-ssd/CodeReady/kotaemon/ktem_app_data:/app/ktem_app_data -it ghcr.io/cinnamon/kotaemon:main-full`

2. Setup Nomic Embed as default Embeddings model via Ollama, and set Qwen2.5 or Llama3.1 as default LLM via Ollama.

3. I don't change the Index Collections setup.  Nano is set to 'embedding: default'

3. Upload PDF File to NanoGraphRAG, and note UI error message on final step.

Screenshots

![DESCRIPTION](LINK.png)

Logs

Session reasoning type simple use mindmap False
Session LLM Qwen2_5
Reasoning class <class 'ktem.reasoning.simple.FullQAPipeline'>
Reasoning state {'app': {'regen': False}, 'pipeline': {}}
Thinking ...
Retrievers [DocumentRetrievalPipeline(DS=<kotaemon.storages.docstores.lancedb.LanceDBDocumentStore object at 0x7f69d1a51f00>, FSPath=PosixPath('/app/ktem_app_data/user_data/files/index_1'), Index=<class 'ktem.index.file.index.IndexTable'>, Source=<class 'ktem.index.file.index.Source'>, VS=<kotaemon.storages.vectorstores.chroma.ChromaVectorStore object at 0x7f69d1a528f0>, get_extra_table=False, llm_scorer=LLMTrulensScoring(concurrent=True, normalize=10, prompt_template=<kotaemon.llms.prompts.template.PromptTemplate object at 0x7f693565d840>, system_prompt_template=<kotaemon.llms.prompts.template.PromptTemplate object at 0x7f693565d9f0>, top_k=3, user_prompt_template=<kotaemon.llms.prompts.template.PromptTemplate object at 0x7f693565dc00>), mmr=False, rerankers=[CohereReranking(cohere_api_key='<COHERE_API_KEY>', model_name='rerank-multilingual-v2.0')], retrieval_mode='hybrid', top_k=10, user_id=1), GraphRAGRetrieverPipeline(DS=<theflow.base.unset_ object at 0x7f6ac85a85b0>, FSPath=<theflow.base.unset_ object at 0x7f6ac85a85b0>, Index=<class 'ktem.index.file.index.IndexTable'>, Source=<theflow.base.unset_ object at 0x7f6ac85a85b0>, VS=<theflow.base.unset_ object at 0x7f6ac85a85b0>, file_ids=[], user_id=<theflow.base.unset_ object at 0x7f6ac85a85b0>), NanoGraphRAGRetrieverPipeline(DS=<theflow.base.unset_ object at 0x7f6ac85a85b0>, FSPath=<theflow.base.unset_ object at 0x7f6ac85a85b0>, Index=<class 'ktem.index.file.index.IndexTable'>, Source=<theflow.base.unset_ object at 0x7f6ac85a85b0>, VS=<theflow.base.unset_ object at 0x7f6ac85a85b0>, file_ids=['b6d008a4-40b4-4d7e-80d6-c4e81bbd7862'], user_id=<theflow.base.unset_ object at 0x7f6ac85a85b0>)]
searching in doc_ids []
INFO:ktem.index.file.pipelines:Skip retrieval because of no selected files: DocumentRetrievalPipeline(
  (vector_retrieval): <function Function._prepare_child.<locals>.exec at 0x7f69cb7e9990>
  (embedding): <function Function._prepare_child.<locals>.exec at 0x7f69cb7e9cf0>
)
INFO:httpx:HTTP Request: POST http://localhost:11434/v1/embeddings "HTTP/1.1 200 OK"
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/site-packages/gradio/queueing.py", line 575, in process_events
    response = await route_utils.call_process_api(
  File "/usr/local/lib/python3.10/site-packages/gradio/route_utils.py", line 276, in call_process_api
    output = await app.get_blocks().process_api(
  File "/usr/local/lib/python3.10/site-packages/gradio/blocks.py", line 1923, in process_api
    result = await self.call_function(
  File "/usr/local/lib/python3.10/site-packages/gradio/blocks.py", line 1520, in call_function
    prediction = await utils.async_iteration(iterator)
  File "/usr/local/lib/python3.10/site-packages/gradio/utils.py", line 663, in async_iteration
    return await iterator.__anext__()
  File "/usr/local/lib/python3.10/site-packages/gradio/utils.py", line 656, in __anext__
    return await anyio.to_thread.run_sync(
  File "/usr/local/lib/python3.10/site-packages/anyio/to_thread.py", line 56, in run_sync
    return await get_async_backend().run_sync_in_worker_thread(
  File "/usr/local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 2441, in run_sync_in_worker_thread
    return await future
  File "/usr/local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 943, in run
    result = context.run(func, *args)
  File "/usr/local/lib/python3.10/site-packages/gradio/utils.py", line 639, in run_sync_iterator_async
    return next(iterator)
  File "/usr/local/lib/python3.10/site-packages/gradio/utils.py", line 801, in gen_wrapper
    response = next(iterator)
  File "/app/libs/ktem/ktem/pages/chat/__init__.py", line 835, in chat_fn
    for response in pipeline.stream(chat_input, conversation_id, chat_history):
  File "/app/libs/ktem/ktem/reasoning/simple.py", line 741, in stream
    docs, infos = self.retrieve(message, history)
  File "/app/libs/ktem/ktem/reasoning/simple.py", line 517, in retrieve
    retriever_docs = retriever_node(text=query)
  File "/usr/local/lib/python3.10/site-packages/theflow/base.py", line 1097, in __call__
    raise e from None
  File "/usr/local/lib/python3.10/site-packages/theflow/base.py", line 1088, in __call__
    output = self.fl.exec(func, args, kwargs)
  File "/usr/local/lib/python3.10/site-packages/theflow/backends/base.py", line 151, in exec
    return run(*args, **kwargs)
  File "/usr/local/lib/python3.10/site-packages/theflow/middleware.py", line 144, in __call__
    raise e from None
  File "/usr/local/lib/python3.10/site-packages/theflow/middleware.py", line 141, in __call__
    _output = self.next_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/site-packages/theflow/middleware.py", line 117, in __call__
    return self.next_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/site-packages/theflow/base.py", line 1017, in _runx
    return self.run(*args, **kwargs)
  File "/app/libs/ktem/ktem/index/file/graph/nano_pipelines.py", line 384, in run
    graphrag_func, query_params = self._build_graph_search()
  File "/app/libs/ktem/ktem/index/file/graph/nano_pipelines.py", line 316, in _build_graph_search
    llm_func, embedding_func, _, _ = get_default_models_wrapper()
  File "/app/libs/ktem/ktem/index/file/graph/nano_pipelines.py", line 101, in get_default_models_wrapper
    embedding_func = EmbeddingFunc(
NameError: name 'EmbeddingFunc' is not defined
INFO:httpx:HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
User-id: 1, can see public conversations: True

Browsers

Firefox

OS

Linux

Additional information

No response