GraphRAG with Ollama, entity_extraction directory is not empty but errors come... Columns must be same length as key . How to solve?

thank you

Awesome, thank you!

thanks

thank you

You're welcome

Thank you

thaks. You would have to hack the source code to change the vector store. Yes local search also worked. Just have to replace global keyword with local.

I think graphrag-accelerator requires Azure. If its API based, I would rather go directly to OpenAI and I have already done a video on it.

sure you can use it.

It has its own built-in vector store. For migration, I would suggest installing it from scratch in cloud.

Yes, you would have to run the index procedure. Thanks.

plz check the logs in output directory and see what the error is. Also make sure that command is correct

That's on basis of trial at the moment of creating video.

yes

@@fahdmirza if possible please make a video ,that will be helpful for me

Sure, go to this directory ~/ragtest/output/20240711-055438/reports . The date directory would vary as per your run. You would log files there. Thanks.

@@fahdmirza raise ValueError(\"Columns must be same length as key\") ValueError: Columns must be same length as key ", "source": "Columns must be same length as key", "details": null , I FACE SAME ERROR , AND I FOUND THE LOG FILES , THEY SAID

@@jiangnanfan8944 I also face the same error "ValueError(\"Columns must be same length as key\", "details": null)

Same issue here...below states to use "ollama" as API key. In which file should this be indicated?

which model you are using?

I got the same error as well while using local method. And also, Error embedding chunk {'OpenAIEmbedding': "'NoneType' object is not iterable"} I am using mistral and nomic-embed-text:latest for embeddings.

I am also getting same error while using local method

same error with groq api llama3 8b and nomic embed text, any solution to this?

Issue is that `--method local` does not work out of the box with open source embedding models. It is because of the way how OpenAI's `text-embedding-3-small` model is working. It is using token IDs as input, while open source models like `nomic-embed-text` are working with text as input. So you need to convert token IDs to text before using open source models. Solution is to add one line to package's `graphrag/query/llm/oai/embedding.py` "embed" function : ```python ... def embed(self, text: str, **kwargs: Any) -> list[float]: """ Embed text using OpenAI Embedding's sync function. For text longer than max_tokens, chunk texts into max_tokens, embed each chunk, then combine using weighted average. Please refer to: github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb """ token_chunks = chunk_text( text=text, token_encoder=self.token_encoder, max_tokens=self.max_tokens ) chunk_embeddings = [] chunk_lens = [] for chunk in token_chunks: # decode chunk from token ids to text (added line after row 83) chunk = self.token_encoder.decode(chunk) try: embedding, chunk_len = self._embed_with_retry(chunk, **kwargs) chunk_embeddings.append(embedding) chunk_lens.append(chunk_len) # TODO: catch a more specific exception except Exception as e: # noqa BLE001 self._reporter.error( message="Error embedding chunk", details={self.__class__.__name__: str(e)}, ) continue chunk_embeddings = np.average(chunk_embeddings, axis=0, weights=chunk_lens) chunk_embeddings = chunk_embeddings / np.linalg.norm(chunk_embeddings) return chunk_embeddings.tolist() ... ```

That can be done too in various ways, but the purpose of this video to do it all in Ollama. Thanks for comment.

would you change this in the .env file or directly in the setting.yaml. I have the same issue as above where _config.py requires API key

yeah just did. Thanks.

@@fahdmirza Thanks, I appreciate your work.

Its the same process as any data. The cleaner your data is, the better your responses will be.

Please explore this repo github.com/microsoft/graphrag for underlying tech. Thanks.

Its the same process, you just need to replace global with local

@@fahdmirza no it fails to build community reports:just tested again with mistral to make sure i have the exact same set up as you. look in the index-engine.log. 5:48:44,679 graphrag.index.graph.extractors.community_reports.community_reports_extractor ERROR error generating community report Traceback (most recent call last): File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/index/graph/extractors/community_reports/community_reports_extractor.py", line 58, in __call__ await self._llm( File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/llm/openai/json_parsing_llm.py", line 34, in __call__ result = await self._delegate(input, **kwargs) File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/llm/openai/openai_token_replacing_llm.py", line 37, in __call__ return await self._delegate(input, **kwargs) File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/llm/openai/openai_history_tracking_llm.py", line 33, in __call__ output = await self._delegate(input, **kwargs) File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/llm/base/caching_llm.py", line 104, in __call__ result = await self._delegate(input, **kwargs) File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/llm/base/rate_limiting_llm.py", line 177, in __call__ result, start = await execute_with_retry() File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/llm/base/rate_limiting_llm.py", line 159, in execute_with_retry async for attempt in retryer: File "/home/shawn/.local/lib/python3.10/site-packages/tenacity/asyncio/__init__.py", line 166, in __anext__ do = await self.iter(retry_state=self._retry_state) File "/home/shawn/.local/lib/python3.10/site-packages/tenacity/asyncio/__init__.py", line 153, in iter result = await action(retry_state) File "/home/shawn/.local/lib/python3.10/site-packages/tenacity/_utils.py", line 99, in inner return call(*args, **kwargs) File "/home/shawn/.local/lib/python3.10/site-packages/tenacity/__init__.py", line 398, in self._add_action_func(lambda rs: rs.outcome.result()) File "/usr/lib/python3.10/concurrent/futures/_base.py", line 451, in result return self.__get_result() File "/usr/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result raise self._exception File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/llm/base/rate_limiting_llm.py", line 165, in execute_with_retry return await do_attempt(), start File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/llm/base/rate_limiting_llm.py", line 147, in do_attempt return await self._delegate(input, **kwargs) File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/llm/base/base_llm.py", line 48, in __call__ return await self._invoke_json(input, **kwargs) File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/llm/openai/openai_chat_llm.py", line 90, in _invoke_json raise RuntimeError(FAILED_TO_CREATE_JSON_ERROR).......python -m graphrag.query --root . --method global "what are the top themes in this story?" INFO: Reading settings from settings.yaml creating llm client with {'api_key': 'REDACTED,len=56', 'type': "openai_chat", 'model': 'mistral', 'max_tokens': 4000, 'request_timeout': 180.0, 'api_base': 'localhost:11434/v1', 'api_version': None, 'organization': None, 'proxy': None, 'cognitive_services_endpoint': None, 'deployment_name': None, 'model_supports_json': True, 'tokens_per_minute': 0, 'requests_per_minute': 0, 'max_retries': 10, 'max_retry_wait': 10.0, 'sleep_on_rate_limit_recommendation': True, 'concurrent_requests': 25} SUCCESS: Global Search Response: In the story, the main themes revolve around the transition of young people from formal education to practical work, specifically through apprenticeship under Ebenezer Scrooge. This transition is evident in various scenes and actions [Data: Scenes (1, 2, 3); Actions (4)]. During their apprenticeship, the young people are engaged in specific tasks or responsibilities that are likely related to Scrooge's business [Data: Actions (1-5)]. It is also suggested that Scrooge may act as a mentor or supervisor to these apprentices during this period [Data: Relationships (1-23)]. The young people are involved in various activities related to their apprenticeship, which could include tasks such as bookkeeping, accounting, or business management [Data: Actions (1-5)]. However, the exact nature of these activities is not explicitly detailed in the provided data. It is important to note that the information provided is based on the analysis of multiple reports and does not necessarily cover all aspects of the story. For a more comprehensive understanding, additional research or analysis may be required. shawn@pop-os:~/Documents/GRAPHRAG$ python -m graphrag.query --root . --method local "who is scrooge, and what are his main relationships?" INFO: Reading settings from settings.yaml creating llm client with {'api_key': 'REDACTED,len=56', 'type': "openai_chat", 'model': 'mistral', 'max_tokens': 4000, 'request_timeout': 180.0, 'api_base': 'localhost:11434/v1', 'api_version': None, 'organization': None, 'proxy': None, 'cognitive_services_endpoint': None, 'deployment_name': None, 'model_supports_json': True, 'tokens_per_minute': 0, 'requests_per_minute': 0, 'max_retries': 10, 'max_retry_wait': 10.0, 'sleep_on_rate_limit_recommendation': True, 'concurrent_requests': 25} creating embedding llm client with {'api_key': 'REDACTED,len=56', 'type': "openai_embedding", 'model': 'nomic_embed_text', 'max_tokens': 4000, 'request_timeout': 180.0, 'api_base': 'localhost:11434/api', 'api_version': None, 'organization': None, 'proxy': None, 'cognitive_services_endpoint': None, 'deployment_name': None, 'model_supports_json': None, 'tokens_per_minute': 0, 'requests_per_minute': 0, 'max_retries': 10, 'max_retry_wait': 10.0, 'sleep_on_rate_limit_recommendation': True, 'concurrent_requests': 25} Error embedding chunk {'OpenAIEmbedding': 'Error code: 404 - {\'error\': "model \'nomic_embed_text\' not found, try pulling it first"}'} Traceback (most recent call last): File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "/usr/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/query/__main__.py", line 75, in run_local_search( File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/query/cli.py", line 154, in run_local_search result = search_engine.search(query=query) File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/query/structured_search/local_search/search.py", line 118, in search context_text, context_records = self.context_builder.build_context( File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/query/structured_search/local_search/mixed_context.py", line 139, in build_context selected_entities = map_query_to_entities( File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/query/context_builder/entity_extraction.py", line 55, in map_query_to_entities search_results = text_embedding_vectorstore.similarity_search_by_text( File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/vector_stores/lancedb.py", line 118, in similarity_search_by_text query_embedding = text_embedder(text) File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/query/context_builder/entity_extraction.py", line 57, in text_embedder=lambda t: text_embedder.embed(t), File "/home/shawn/.local/lib/python3.10/site-packages/graphrag/query/llm/oai/embedding.py", line 96, in embed chunk_embeddings = np.average(chunk_embeddings, axis=0, weights=chunk_lens) File "/home/shawn/.local/lib/python3.10/site-packages/numpy/lib/function_base.py", line 550, in average raise ZeroDivisionError( ZeroDivisionError: Weights sum to zero, can't be normalized

got the same error