[Issue]: Error running pipeline!, if no data to return

[lingfan]

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• 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

{
"type": "error",
"data": "Error running pipeline!",
"stack": "Traceback (most recent call last):\n File "/opt/conda/lib/python3.11/site-packages/graphrag/index/run/run_workflows.py", line 166, in _run_workflows\n result = await run_workflow(\n ^^^^^^^^^^^^^^^^^^^\n File "/opt/conda/lib/python3.11/site-packages/graphrag/index/workflows/extract_graph.py", line 45, in run_workflow\n base_entity_nodes, base_relationship_edges = await extract_graph(\n ^^^^^^^^^^^^^^^^^^^^\n File "/opt/conda/lib/python3.11/site-packages/graphrag/index/flows/extract_graph.py", line 33, in extract_graph\n entities, relationships = await extract_entities(\n ^^^^^^^^^^^^^^^^^^^^^^^\n File "/opt/conda/lib/python3.11/site-packages/graphrag/index/operations/extract_entities/extract_entities.py", line 137, in extract_entities\n relationships = _merge_relationships(relationship_dfs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "/opt/conda/lib/python3.11/site-packages/graphrag/index/operations/extract_entities/extract_entities.py", line 178, in _merge_relationships\n .agg(\n ^^^^\n File "/opt/conda/lib/python3.11/site-packages/pandas/core/groupby/generic.py", line 1432, in aggregate\n result = op.agg()\n ^^^^^^^^\n File "/opt/conda/lib/python3.11/site-packages/pandas/core/apply.py", line 190, in agg\n return self.agg_dict_like()\n ^^^^^^^^^^^^^^^^^^^^\n File "/opt/conda/lib/python3.11/site-packages/pandas/core/apply.py", line 423, in agg_dict_like\n return self.agg_or_apply_dict_like(op_name="agg")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "/opt/conda/lib/python3.11/site-packages/pandas/core/apply.py", line 1608, in agg_or_apply_dict_like\n result_index, result_data = self.compute_dict_like(\n ^^^^^^^^^^^^^^^^^^^^^^^\n File "/opt/conda/lib/python3.11/site-packages/pandas/core/apply.py", line 462, in compute_dict_like\n func = self.normalize_dictlike_arg(op_name, selected_obj, func)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "/opt/conda/lib/python3.11/site-packages/pandas/core/apply.py", line 663, in normalize_dictlike_arg\n raise KeyError(f"Column(s) {list(cols)} do not exist")\nKeyError: "Column(s) ['description', 'source_id', 'weight'] do not exist"\n",
"source": ""Column(s) ['description', 'source_id', 'weight'] do not exist"",
"details": null
}

Steps to reproduce

No response

GraphRAG Config Used

### This config file contains required core defaults that must be set, along with a handful of common optional settings.
### For a full list of available settings, see https://microsoft.github.io/graphrag/config/yaml/

### LLM settings ###
## There are a number of settings to tune the threading and token limits for LLM calls - check the docs.

encoding_model: cl100k_base # this needs to be matched to your model!

llm:
  api_key: lm-studio # set this in the generated .env file
  type: openai_chat # or azure_openai_chat
  #model: deepseek-r1:32b
  #max_tokens: 4000
  model_supports_json: false # recommended if this is available for your model.
  #model: deepseek-r1
  #api_base: http://192.168.2.131:11434/v1/
  # audience: "https://cognitiveservices.azure.com/.default"
  #model: deepseek-r1-distill-qwen-7b
  #api_base: http://192.168.2.131:1234/v1/
  model: Qwen/Qwen2.5-1.5B-Instruct
  api_base: http://192.168.2.131:30000/v1/
  # api_version: 2024-02-15-preview
  # organization: <organization_id>
  # deployment_name: <azure_model_deployment_name>

parallelization:
  stagger: 0.3
  num_threads: 50

async_mode: threaded # or asyncio

embeddings:
  async_mode: threaded # or asyncio
  vector_store: 
    type: lancedb # one of [lancedb, azure_ai_search, cosmosdb]
    db_uri: 'output/lancedb'
    collection_name: default
    overwrite: true
  llm:
    api_key: lm-studio
    type: openai_embedding # or azure_openai_embedding
    #model: quentinz/bge-large-zh-v1.5
    #api_base: http://192.168.2.131:11434/api
    model: text-embedding-bge-m3
    api_base: http://192.168.2.131:1234/v1
    #model: BAAI/bge-m3
    #api_base: http://192.168.2.131:30000/v1/
    max_tokens: 1024
    # api_version: 2024-02-15-preview
    # audience: "https://cognitiveservices.azure.com/.default"
    # organization: <organization_id>
    # deployment_name: <azure_model_deployment_name>

### Input settings ###

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

chunks:
  size: 4096
  overlap: 100
  group_by_columns: [id]

### Storage settings ###
## If blob storage is specified in the following four sections,
## connection_string and container_name must be provided

cache:
  type: file # one of [blob, cosmosdb, file]
  base_dir: "cache"

reporting:
  type: file # or console, blob
  base_dir: "logs"

storage:
  type: file # one of [blob, cosmosdb, file]
  base_dir: "output"

## only turn this on if running `graphrag index` with custom settings
## we normally use `graphrag update` with the defaults
update_index_storage:
  # type: file # or blob
  # base_dir: "update_output"

### Workflow settings ###

skip_workflows: []

entity_extraction:
  prompt: "prompts/entity_extraction.txt"
  entity_types: [organization,person,geo,event]
  max_gleanings: 1

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

claim_extraction:
  enabled: false
  prompt: "prompts/claim_extraction.txt"
  description: "Any claims or facts that could be relevant to information discovery."
  max_gleanings: 1

community_reports:
  prompt: "prompts/community_report.txt"
  max_length: 2000
  max_input_length: 4000

cluster_graph:
  max_cluster_size: 10

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

umap:
  enabled: true # if true, will generate UMAP embeddings for nodes (embed_graph must also be enabled)

snapshots:
  graphml: true
  embeddings: true
  transient: true

### Query settings ###
## The prompt locations are required here, but each search method has a number of optional knobs that can be tuned.
## See the config docs: https://microsoft.github.io/graphrag/config/yaml/#query

local_search:
  prompt: "prompts/local_search_system_prompt.txt"

global_search:
  map_prompt: "prompts/global_search_map_system_prompt.txt"
  reduce_prompt: "prompts/global_search_reduce_system_prompt.txt"
  knowledge_prompt: "prompts/global_search_knowledge_system_prompt.txt"

drift_search:
  prompt: "prompts/drift_search_system_prompt.txt"
  reduce_prompt: "prompts/drift_search_reduce_prompt.txt"

basic_search:
  prompt: "prompts/basic_search_system_prompt.txt"

Logs and screenshots

Additional Information

• GraphRAG Version:1.2.0
• Operating System:ubuntu
• Python Version:3.11
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