[BUG] the usage issue of the graphrag feature

[yuqiao9]

Description

After I upload the file and execute graphrag collection, the error log is as follows. May I ask if I am missing any key steps.
I have set up the api_key, set USE_CUSTOMIZED_GRAPHRAG_SETTING=true, and mounted settings.yaml.example

Reproduction steps

1. Go to '...'
2. Click on '....'
3. Scroll down to '....'
4. See error

Screenshots

![DESCRIPTION](LINK.png)

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[yuqiao9]

I noticed there was an error when uploading the file. Could you please tell me what might be causing this

[yuqiao9]

[taprosoft]

Could you double check if:

• You are using OpenAI LLM and has GRAPHRAG_API_KEY env var set.
• You are using custom model through settings.yaml.example and make sure the URL & setting in there are correct.

If you use GraphRAG with Kotaemon Docker version you might need to change the host name to communicate with Ollama or other service on the host properly. See https://stackoverflow.com/questions/31324981/how-to-access-host-port-from-docker-container

[yuqiao9]

您能否仔细检查一下：

• 您使用的是 OpenAI LLM 并设置了 GRAPHRAG_API_KEY 环境变量。
• 您正在使用自定义模型，并确保那里的URL和设置正确。settings.yaml.example

如果您将 GraphRAG 与 Kotaemon Docker 版本一起使用，则可能需要更改主机名才能与主机上的 Ollama 或其他服务正确通信。查看 https://stackoverflow.com/questions/31324981/how-to-access-host-port-from-docker-container

I believe my path is configured correctly. Can you help me identify what might be wrong with my configuration?

encoding_model: cl100k_base
skip_workflows: []
llm:
api_key: ollama
type: openai_chat # or azure_openai_chat
api_base: http://192.168.8.101:11434/v1
model: llama3.1
model_supports_json: true # recommended if this is available for your model.

max_tokens: 4000

request_timeout: 1800.0

api_base: https://.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: 5 # 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_base: http://192.168.8.101:11434/v1
api_key: ollama
model: nomic-embed-text
type: openai_embedding

[yuqiao9]