Use ollama and deepseek-r1:1.5b can't generate graph

[qpb8023]

The error occurs in the langchain_experimental/graph_transformers/llm.py aprocess_response function.
How to fix it ?

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{'head': '作者', 'head_type': '相关人物', 'relation': {'涉及人物': {'描述': '与相关的人物角色有关'}}, 'tail': '研究成果', 'tail_type': '成果'}
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2025-02-24 17:26:41,801 - Deleted File Path: /Users/tiutiutiu/Desktop/llm-graph-builder/backend/merged_files/《教育心理学》.pdf and Deleted File Name : 《教育心理学》.pdf
[ERROR]{'api_name': 'extract', 'message': 'Failed To Process File:《教育心理学》.pdf or LLM Unable To Parse Content ', 'file_created_at': '2025-02-24 16:21:15 ', 'error_message': "1 validation error for Relationship\ntype\n  Input should be a valid string [type=string_type, input_value={'涉及人物': {'描述. input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.9/v/string_type", 'file_name': '《教育心理学》.pdf', 'status': 'Failed', 'db_url': 'neo4j://192.168.6.6:7688', 'userName': 'neo4j', 'database': 'neo4j', 'failed_count': 1, 'source_type': 'local file', 'source_url': None, 'wiy': None, 'logging_time': '2025-02-24 09:26:41 UTC', 'email': None}
2025-02-24 17:26:41,809 - File Failed in extraction: 1 validation error for Relationship
type
  Input should be a valid string [type=string_type, input_value={'涉及人物': {'描述...的人物角色有关'}}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.9/v/string_type
Traceback (most recent call last):
  File "/Users/tiutiutiu/Desktop/llm-graph-builder/backend/score.py", line 222, in extract_knowledge_graph_from_file
    uri_latency, result = await extract_graph_from_file_local_file(uri, userName, password, database, model, merged_file_path, file_name, allowedNodes, allowedRelationship, token_chunk_size, chunk_overlap, chunks_to_combine, retry_condition, additional_instructions)
  File "/Users/tiutiutiu/Desktop/llm-graph-builder/backend/src/main.py", line 238, in extract_graph_from_file_local_file
    return await processing_source(uri, userName, password, database, model, fileName, [], allowedNodes, allowedRelationship, token_chunk_size, chunk_overlap, chunks_to_combine, True, merged_file_path, retry_condition, additional_instructions=additional_instructions)
  File "/Users/tiutiutiu/Desktop/llm-graph-builder/backend/src/main.py", line 383, in processing_source
    node_count,rel_count,latency_processed_chunk = await processing_chunks(selected_chunks,graph,uri, userName, password, database,file_name,model,allowedNodes,allowedRelationship,chunks_to_combine,node_count, rel_count, additional_instructions)
  File "/Users/tiutiutiu/Desktop/llm-graph-builder/backend/src/main.py", line 478, in processing_chunks
    graph_documents =  await get_graph_from_llm(model, chunkId_chunkDoc_list, allowedNodes, allowedRelationship, chunks_to_combine, additional_instructions)
  File "/Users/tiutiutiu/Desktop/llm-graph-builder/backend/src/llm.py", line 225, in get_graph_from_llm
    graph_document_list = await get_graph_document_list(
  File "/Users/tiutiutiu/Desktop/llm-graph-builder/backend/src/llm.py", line 208, in get_graph_document_list
    graph_document_list = await llm_transformer.aconvert_to_graph_documents(combined_chunk_document_list)
  File "/Users/tiutiutiu/Desktop/llm-graph-builder/backend/venv/lib/python3.10/site-packages/langchain_experimental/graph_transformers/llm.py", line 1034, in aconvert_to_graph_documents
    results = await asyncio.gather(*tasks)
  File "/Users/tiutiutiu/.pyenv/versions/3.10.16/lib/python3.10/asyncio/tasks.py", line 304, in __wakeup
    future.result()
  File "/Users/tiutiutiu/.pyenv/versions/3.10.16/lib/python3.10/asyncio/tasks.py", line 232, in __step
    result = coro.send(None)
  File "/Users/tiutiutiu/Desktop/llm-graph-builder/backend/venv/lib/python3.10/site-packages/langchain_experimental/graph_transformers/llm.py", line 977, in aprocess_response
    Relationship(
  File "/Users/tiutiutiu/Desktop/llm-graph-builder/backend/venv/lib/python3.10/site-packages/langchain_core/load/serializable.py", line 125, in __init__
    super().__init__(*args, **kwargs)
  File "/Users/tiutiutiu/Desktop/llm-graph-builder/backend/venv/lib/python3.10/site-packages/pydantic/main.py", line 212, in __init__
    validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
pydantic_core._pydantic_core.ValidationError: 1 validation error for Relationship
type
  Input should be a valid string [type=string_type, input_value={'涉及人物': {'描述...的人物角色有关'}}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.9/v/string_type

[seemakurthy]

Similar issue using locally host Ollama.
[ERROR]{'api_name': 'extract', 'message': 'Failed To Process File:A_Compact_GuideTo_RAG.pdf or LLM Unable To Parse Content ', 'file_created_at': '2025-02-28 09:18:43 ', 'error_message': "Ollama call failed with status code 400. Details: <bound method ClientResponse.text of <ClientResponse(http://localhost:11434/api/chat) [400 Bad Request]>\n<CIMultiDictProxy('Content-Type': 'application/json; charset=utf-8', 'Date': 'Fri, 28 Feb 2025 17:28:28 GMT', 'Content-Length': '29')>\n>", 'file_name': 'A_Compact_GuideTo_RAG.pdf', 'status': 'Failed', 'db_url': 'neo4j://localhost:7687', 'userName': 'neo4j', 'database': 'neo4j', 'failed_count': 1, 'source_typINFO: 127.0.0.1:51052 - "GET /health HTTP/1.1" 200 OK. The graph is created with Chuck nodes and after that it is not able to feed back the LLM for further breaking down.

[kaustubh-darekar]

Currently, the DeepSeek LLM model is not producing structured output as expected when used with LLM Graph Builder. We are actively investigating this issue and working on a resolution. We appreciate your patience and will provide updates as we make progress.

[jinshuaiwang]

@kaustubh-darekar has the problem already solved？looking forward your reply.

[kaustubh-darekar]

Hi @jinshuaiwang , we havent found a solution yet but you can try
https://python.langchain.com/docs/integrations/chat/deepseek/ (If you have deepseek api key)
as mentioned there you can keep model ="deepseek-chat" for structured output.

let us know if it works.

[jinshuaiwang]

@kaustubh-darekar I run ollama locally，use ChatOllama and OllamaEmbeddings, when run 'docs = doc_retriever.invoke({"messages": messages}, {"callbacks": [handler]})' this line，Error retrieving documents: {code: Neo.ClientError.Statement.ParameterMissing} {message: Expected parameter(s): embedding} raise this error，what‘s the problem，how can i solve it？

[kaustubh-darekar]

Hi @jinshuaiwang

Check langchain_neo4j version you are using.

with latest version embeddings are changed to query_vector
So we need to change that in all queries used in constants.py ( You can refer dev branch constants.py )

[jinshuaiwang]

@kaustubh-darekar Thanks a lot, I will try

[damiannqn90]

@jinshuaiwang Let me know if you have any updates. I'm currently trying to run the app locally with Ollama, but I'm running into some issues. If you manage to get it working or already have a working setup, would you mind sharing your .env files for both the frontend and backend?

I'm a bit confused because there are several different answers in past discussions, and it's not entirely clear how the LLM_MODEL_CONFIG_... should be structured with Ollama.

Thanks a lot in advance!