❌ create_final_entities : Unable to run GraphRAG Pipeline

[Jainil-Gosalia]

Describe the issue

I was trying to run graphRAG using llama_cpp. Got the following issue:

❌ create_final_entities
⠼ GraphRAG Indexer
├── Loading Input (text) - 1 files loaded (0 filtered) ━━━━━━ 100% 0:00:… 0:00:…
├── create_base_text_units
├── create_base_extracted_entities
├── create_summarized_entities
├── create_base_entity_graph
None
⠴ GraphRAG Indexer
├── Loading Input (text) - 1 files loaded (0 filtered) ━━━━━━ 100% 0:00:… 0:00:…
├── create_base_text_units
├── create_base_extracted_entities
├── create_summarized_entities
├── create_base_entity_graph
⠴ GraphRAG Indexer
├── Loading Input (text) - 1 files loaded (0 filtered) ━━━━━━ 100% 0:00:… 0:00:…
├── create_base_text_units
├── create_base_extracted_entities
├── create_summarized_entities
├── create_base_entity_graph
└── create_final_entities
❌ Errors occurred during the pipeline run, see logs for more details.

Steps to reproduce

Use the settings.yaml file to replicate the issue

GraphRAG Config Used

The settings.yaml is as follows:

encoding_model: cl100k_base
skip_workflows: []
llm:
api_key: ${GRAPHRAG_API_KEY}
type: openai_chat # or azure_openai_chat
model: mistral

model_supports_json: false # recommended if this is available for your model.

max_tokens: 4000

request_timeout: 180.0

api_base: http://localhost:8000/v1

api_version: 2024-02-15-preview

organization: <organization_id>

deployment_name: <azure_model_deployment_name>

tokens_per_minute: 150_000 # set a leaky bucket throttle

requests_per_minute: 10_000 # set a leaky bucket throttle

max_retries: 10

max_retry_wait: 10.0

sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times

concurrent_requests: 1 # the number of parallel inflight requests that may be made

parallelization:
stagger: 0.3

num_threads: 50 # the number of threads to use for parallel processing

async_mode: threaded # or asyncio

embeddings:

parallelization: override the global parallelization settings for embeddings

async_mode: threaded # or asyncio
llm:
api_key: ${GRAPHRAG_API_KEY}
type: openai_embedding # or azure_openai_embedding
model: mistral
api_base: http://localhost:8000/v1
# api_version: 2024-02-15-preview
# organization: <organization_id>
# deployment_name: <azure_model_deployment_name>
# tokens_per_minute: 150_000 # set a leaky bucket throttle
# requests_per_minute: 10_000 # set a leaky bucket throttle
# max_retries: 10
# max_retry_wait: 10.0
# sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times
# concurrent_requests: 25 # the number of parallel inflight requests that may be made
batch_size: 1 # the number of documents to send in a single request
# batch_max_tokens: 8191 # the maximum number of tokens to send in a single request
# target: required # or optional

chunks:
size: 300
overlap: 100
group_by_columns: [id] # by default, we don't allow chunks to cross documents

input:
type: file # or blob
file_type: text # or csv
base_dir: "input"
file_encoding: utf-8
file_pattern: ".*\.txt$"

cache:
type: file # or blob
base_dir: "cache"

connection_string: <azure_blob_storage_connection_string>

container_name: <azure_blob_storage_container_name>

storage:
type: file # or blob
base_dir: "output/${timestamp}/artifacts"

connection_string: <azure_blob_storage_connection_string>

container_name: <azure_blob_storage_container_name>

reporting:
type: file # or console, blob
base_dir: "output/${timestamp}/reports"

connection_string: <azure_blob_storage_connection_string>

container_name: <azure_blob_storage_container_name>

entity_extraction:

llm: override the global llm settings for this task

parallelization: override the global parallelization settings for this task

async_mode: override the global async_mode settings for this task

prompt: "prompts/entity_extraction.txt"
entity_types: [organization,person,geo,event]
max_gleanings: 0

summarize_descriptions:

llm: override the global llm settings for this task

parallelization: override the global parallelization settings for this task

async_mode: override the global async_mode settings for this task

prompt: "prompts/summarize_descriptions.txt"
max_length: 500

claim_extraction:

llm: override the global llm settings for this task

parallelization: override the global parallelization settings for this task

async_mode: override the global async_mode settings for this task

enabled: true

prompt: "prompts/claim_extraction.txt"
description: "Any claims or facts that could be relevant to information discovery."
max_gleanings: 0

community_report:

llm: override the global llm settings for this task

parallelization: override the global parallelization settings for this task

async_mode: override the global async_mode settings for this task

prompt: "prompts/community_report.txt"
max_length: 2000
max_input_length: 8000

cluster_graph:
max_cluster_size: 10

embed_graph:
enabled: false # if true, will generate node2vec embeddings for nodes

num_walks: 10

walk_length: 40

window_size: 2

iterations: 3

random_seed: 597832

umap:
enabled: false # if true, will generate UMAP embeddings for nodes

snapshots:
graphml: false
raw_entities: false
top_level_nodes: false

local_search:

text_unit_prop: 0.5

community_prop: 0.1

conversation_history_max_turns: 5

top_k_mapped_entities: 10

top_k_relationships: 10

max_tokens: 12000

global_search:

max_tokens: 12000

data_max_tokens: 12000

map_max_tokens: 1000

reduce_max_tokens: 2000

concurrency: 32

Logs and screenshots

Indexing Engine Log file shows this:

04:36:19,87 datashaper.workflow.workflow ERROR Error executing verb "text_embed" in create_final_entities: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (16,) + inhomogeneous part.
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datashaper/workflow/workflow.py", line 415, in _execute_verb
result = await result
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/text_embed.py", line 105, in text_embed
return await _text_embed_in_memory(
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/text_embed.py", line 130, in _text_embed_in_memory
result = await strategy_exec(texts, callbacks, cache, strategy_args)
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/strategies/openai.py", line 61, in run
embeddings = await _execute(llm, text_batches, ticker, semaphore)
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/strategies/openai.py", line 105, in _execute
results = await asyncio.gather(*futures)
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/strategies/openai.py", line 100, in embed
result = np.array(chunk_embeddings.output)
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (16,) + inhomogeneous part.
04:36:19,92 graphrag.index.reporting.file_workflow_callbacks INFO Error executing verb "text_embed" in create_final_entities: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (16,) + inhomogeneous part. details=None
04:36:19,96 graphrag.index.run ERROR error running workflow create_final_entities
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/run.py", line 323, in run_pipeline
result = await workflow.run(context, callbacks)
File "/usr/local/lib/python3.10/dist-packages/datashaper/workflow/workflow.py", line 369, in run
timing = await self._execute_verb(node, context, callbacks)
File "/usr/local/lib/python3.10/dist-packages/datashaper/workflow/workflow.py", line 415, in _execute_verb
result = await result
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/text_embed.py", line 105, in text_embed
return await _text_embed_in_memory(
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/text_embed.py", line 130, in _text_embed_in_memory
result = await strategy_exec(texts, callbacks, cache, strategy_args)
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/strategies/openai.py", line 61, in run
embeddings = await _execute(llm, text_batches, ticker, semaphore)
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/strategies/openai.py", line 105, in _execute
results = await asyncio.gather(*futures)
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/strategies/openai.py", line 100, in embed
result = np.array(chunk_embeddings.output)
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (16,) + inhomogeneous part.
04:36:19,97 graphrag.index.reporting.file_workflow_callbacks INFO Error running pipeline! details=None

Logs.json File shows this:

{"type": "error", "data": "Error executing verb "text_embed" in create_final_entities: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (16,) + inhomogeneous part.", "stack": "Traceback (most recent call last):\n File "/usr/local/lib/python3.10/dist-packages/datashaper/workflow/workflow.py", line 415, in _execute_verb\n result = await result\n File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/text_embed.py", line 105, in text_embed\n return await _text_embed_in_memory(\n File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/text_embed.py", line 130, in _text_embed_in_memory\n result = await strategy_exec(texts, callbacks, cache, strategy_args)\n File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/strategies/openai.py", line 61, in run\n embeddings = await _execute(llm, text_batches, ticker, semaphore)\n File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/strategies/openai.py", line 105, in _execute\n results = await asyncio.gather(*futures)\n File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/strategies/openai.py", line 100, in embed\n result = np.array(chunk_embeddings.output)\nValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (16,) + inhomogeneous part.\n", "source": "setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (16,) + inhomogeneous part.", "details": null}

{"type": "error", "data": "Error running pipeline!", "stack": "Traceback (most recent call last):\n File "/usr/local/lib/python3.10/dist-packages/graphrag/index/run.py", line 323, in run_pipeline\n result = await workflow.run(context, callbacks)\n File "/usr/local/lib/python3.10/dist-packages/datashaper/workflow/workflow.py", line 369, in run\n timing = await self._execute_verb(node, context, callbacks)\n File "/usr/local/lib/python3.10/dist-packages/datashaper/workflow/workflow.py", line 415, in _execute_verb\n result = await result\n File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/text_embed.py", line 105, in text_embed\n return await _text_embed_in_memory(\n File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/text_embed.py", line 130, in _text_embed_in_memory\n result = await strategy_exec(texts, callbacks, cache, strategy_args)\n File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/strategies/openai.py", line 61, in run\n embeddings = await _execute(llm, text_batches, ticker, semaphore)\n File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/strategies/openai.py", line 105, in _execute\n results = await asyncio.gather(*futures)\n File "/usr/local/lib/python3.10/dist-packages/graphrag/index/verbs/text/embed/strategies/openai.py", line 100, in embed\n result = np.array(chunk_embeddings.output)\nValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (16,) + inhomogeneous part.\n", "source": "setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (16,) + inhomogeneous part.", "details": null}

Additional Information

• GraphRAG Version: v0.1.1
• Operating System: Ubuntu 20.04
• Python Version: 3.10.14
• Related Issues: 442

[rushizirpe]

In configuration(yaml), you are using mistral as an embedding model and that might be causing the inhomogeneous dimension. You can use models from nomic-ai or mixedbread.

When I faced the issue, I created a repository for deploying Hugging Face models to local endpoints, offering functionality similar to OpenAI APIs. You can find the repo here: https://github.com/rushizirpe/open-llm-server

Also, I've prepared a Colab notebook for the Graphrag Demo. You might want to take a look: https://colab.research.google.com/drive/1uhFDnih1WKrSRQHisU-L6xw6coapgR51?usp=sharing.
If you don't have access to GPUs like the A100, you'll need a GROQ_API_KEY (which is free with certain limitations), you can obtain it from: https://console.groq.com/keys

[natoverse]

Consolidating alternate model issues here: #657