[ ] I have searched the existing issues and this bug is not already filed.
[ ] My model is hosted on OpenAI or Azure. If not, please look at the "model providers" issue and don't file a new one here.
[ ] I believe this is a legitimate bug, not just a question. If this is a question, please use the Discussions area.
Describe the issue
Issue: When running a local query in GraphRAG, I got an error related to the nomic_embed_text model. The error message indicates that the model is not found, even though it appears in the list of available models. Here are the details:
python -m graphrag.query --root ./ragtest --method local "Who are the main demons Krishna defeated during his childhood?"
Error embedding chunk {'OpenAIEmbedding': 'Error code: 404 - {"error": {"message": "model 'nomic_embed_text' not found, try pulling it first", "type": "api_error", "param": null, "code": null}}'}
ZeroDivisionError: Weights sum to zero, can't be normalized
Steps to reproduce
The nomic_embed_text model is available and listed when I run the command ollama list.
I verified the embedding model API with a curl command, which successfully returned embeddings:
curl -X POST http://localhost:11434/v1/embeddings -H "Content-Type: application/json" -d '{"model": "nomic_embed_text", "input": "Test embedding generation with nomic model"}'
3.I've verified that the embedding API is correctly set in settings.yaml
embeddings:
llm:
model: nomic_embed_text
api_base: http://localhost:11434/v1
Global queries work fine, and embedding generation is successful in the global method.
GraphRAG Config Used
# Paste your config here
encoding_model: cl100k_base
skip_workflows: []
llm:
#api_key: ${GRAPHRAG_API_KEY}
type: openai_chat # or azure_openai_chat
#model: gpt-4-turbo-preview
model: mistral
model_supports_json: true # recommended if this is available for your model.
#max_tokens: 4000
# request_timeout: 180.0
api_base: http://localhost:11434/v1 #https://<instance>.openai.azure.com
# 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: 1
# 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
# temperature: 0 # temperature for sampling
# top_p: 1 # top-p sampling
# n: 1 # Number of completions to generate
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
# target: required # or all
# batch_size: 16 # 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
llm:
api_key: ${GRAPHRAG_API_KEY}
type: openai_embedding # or azure_openai_embedding
#model: text-embedding-3-small
model: nomic_embed_text
api_base: http://localhost:11434/v1 #https://<instance>.openai.azure.com
# api_version: 2024-02-15-preview
# organization: <organization_id>
# deployment_name: <azure_model_deployment_name>
# tokens_per_minute: 150_000 # set a leaky bucket throttle
# requests_per_minute: 10_000 # set a leaky bucket throttle
# max_retries: 10
# max_retry_wait: 10.0
# sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times
# concurrent_requests: 25 # the number of parallel inflight requests that may be made
chunks:
size: 1200
overlap: 100
group_by_columns: [id] # by default, we don't allow chunks to cross documents
input:
type: file # or blob
file_type: text # or csv
base_dir: "input"
file_encoding: utf-8
file_pattern: ".*\\.txt$"
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:
## strategy: fully override the entity extraction strategy.
## type: one of graph_intelligence, graph_intelligence_json and nltk
## 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: 1
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: 1
community_reports:
## 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
58,12 56%
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
# llm_temperature: 0 # temperature for sampling
# llm_top_p: 1 # top-p sampling
# llm_n: 1 # Number of completions to generate
# max_tokens: 12000
global_search:
# llm_temperature: 0 # temperature for sampling
# llm_top_p: 1 # top-p sampling
# llm_n: 1 # Number of completions to generate
# max_tokens: 12000
# data_max_tokens: 12000
# map_max_tokens: 1000
# reduce_max_tokens: 2000
# concurrency: 32
natoverse
commented
1 month ago
Do you need to file an issue?
Describe the issue
Issue: When running a local query in GraphRAG, I got an error related to the nomic_embed_text model. The error message indicates that the model is not found, even though it appears in the list of available models. Here are the details:
python -m graphrag.query --root ./ragtest --method local "Who are the main demons Krishna defeated during his childhood?"
Error embedding chunk {'OpenAIEmbedding': 'Error code: 404 - {"error": {"message": "model 'nomic_embed_text' not found, try pulling it first", "type": "api_error", "param": null, "code": null}}'} ZeroDivisionError: Weights sum to zero, can't be normalized
Steps to reproduce
The nomic_embed_text model is available and listed when I run the command ollama list.
I verified the embedding model API with a curl command, which successfully returned embeddings: curl -X POST http://localhost:11434/v1/embeddings -H "Content-Type: application/json" -d '{"model": "nomic_embed_text", "input": "Test embedding generation with nomic model"}' 3.I've verified that the embedding API is correctly set in settings.yaml embeddings: llm: model: nomic_embed_text api_base: http://localhost:11434/v1
Global queries work fine, and embedding generation is successful in the global method.
GraphRAG Config Used
Logs and screenshots
No response
Additional Information