Ollama deploy LLM to use Graphrag,meets configuration problems

[Actioner1]

I use Ollama to deploy LLM,choose qwen3:0.6b as model ,embedding model choose quentinz/bge-large-zh-v1.5:latest
executing the following instruction to build the index of the file: python -m graphrag index --root ./graphrag_ollama
but it comes errors:
Failed to validate language model (default_chat_model) params litellm. InternalServerError: InternalServerError: OpenAIException - Connection error.
anyone have the same problem?
following is my settings_yaml
models:
default_chat_model:
type: chat
model_provider: openai
auth_type: api_key # or azure_managed_identity
#api_key: ${GRAPHRAG_API_KEY} # set this in the generated .env file, or remove if managed identity
api_key: ollama
model: qwen3:0.6b
api_base: https://localhost:11434/v1
encoding_model: cl100k_base
max_tokens: 2000
# api_version: 2024-05-01-preview
model_supports_json: true # recommended if this is available for your model.
#concurrent_requests: 25
concurrent_requests: 1
async_mode: threaded # or asyncio
retry_strategy: exponential_backoff
max_retries: 10
tokens_per_minute: null
requests_per_minute: null
default_embedding_model:
type: embedding
model_provider: openai
auth_type: api_key
#api_key: ${GRAPHRAG_API_KEY}
api_key: ollama
model: quentinz/bge-large-zh-v1.5:latest
encoding_model: cl100k_base
api_base: https://localhost:11434/api
# api_base: https://.openai.azure.com
# api_version: 2024-05-01-preview
#concurrent_requests: 25
concurrent_requests: 1
async_mode: threaded # or asyncio
retry_strategy: exponential_backoff
max_retries: 1
#max_retries: 10
tokens_per_minute: null
requests_per_minute: null

Input settings

input:
storage:
type: file # or blob
base_dir: "input"
file_type: text # [csv, text, json]
file_encoding: utf-8

chunks:
#size: 200
#overlap: 50
size: 300
overlap: 100
group_by_columns: [id]

Output/storage settings

If blob storage is specified in the following four sections,

connection_string and container_name must be provided

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

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

reporting:
type: file # [file, blob]
base_dir: "logs"

vector_store:
default_vector_store:
type: lancedb
db_uri: output\lancedb
container_name: default

Workflow settings

embed_text:
model_id: default_embedding_model
vector_store_id: default_vector_store

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

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

extract_graph_nlp:
text_analyzer:
extractor_type: regex_english # [regex_english, syntactic_parser, cfg]
async_mode: threaded # or asyncio

cluster_graph:
max_cluster_size: 10

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

community_reports:
model_id: default_chat_model
graph_prompt: "prompts/community_report_graph.txt"
text_prompt: "prompts/community_report_text.txt"
max_length: 2000
max_input_length: 8000

embed_graph:
enabled: false # 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

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:
chat_model_id: default_chat_model
embedding_model_id: default_embedding_model
prompt: "prompts/local_search_system_prompt.txt"

global_search:
chat_model_id: default_chat_model
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:
chat_model_id: default_chat_model
embedding_model_id: default_embedding_model
prompt: "prompts/drift_search_system_prompt.txt"
reduce_prompt: "prompts/drift_search_reduce_prompt.txt"

basic_search:
chat_model_id: default_chat_model
embedding_model_id: default_embedding_model
prompt: "prompts/basic_search_system_prompt.txt"