How to generate only create_final_community_reports.parquet file

[ultrageopro]

how to generate it using cache?
skip_workflows and --resume parameters don't work
my configuration

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

llm:
  api_key: ${GRAPHRAG_API_KEY} # set this in the generated .env file
  type: openai_chat # or azure_openai_chat
  model: gpt-4o-mini
  model_supports_json: true # recommended if this is available for your model.
  # audience: "https://cognitiveservices.azure.com/.default"
  # api_base: https://<instance>.openai.azure.com
  # api_version: 2024-02-15-preview
  # organization: <organization_id>
  # deployment_name: <azure_model_deployment_name>
  temperature: 0.0
  requests_per_minute: 450

parallelization:
  stagger: 0.3

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: ${GRAPHRAG_API_KEY}
    type: openai_embedding # or azure_openai_embedding
    model: text-embedding-3-small
    # api_base: https://<instance>.openai.azure.com
    # 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: "data-txt"
  file_encoding: utf-8
  file_pattern: ".*\\.txt$"

chunks:
  size: 5000
  overlap: 200
  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",
      "technology",
      "task",
      "product",
      "service",
    ]
  max_gleanings: 1

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

claim_extraction:
  enabled: true
  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: 8000

cluster_graph:
  max_cluster_size: 10

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

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

snapshots:
  graphml: false
  embeddings: false
  transient: false

### 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"
  temperature: 0.0

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"
  temperature: 0.0

drift_search:
  prompt: "prompts/drift_search_system_prompt.txt"
  reduce_prompt: "prompts/drift_search_reduce_prompt.txt"
  temperature: 0.0
  local_search_temperature: 0.0

basic_search:
  prompt: "prompts/basic_search_system_prompt.txt"