【失败了】LazyGraphRAG利用本地ollama提供Embedding model服务和火山引擎的deepseek API构建本地知识库
LazyGraphRAG测试结果如下
数据:
curl https://www.gutenberg.org/cache/epub/24022/pg24022.txt -o ./ragtest/input/book.txt
失败了
气死我也!!!对deepseek-V3也不是很友好啊,我没钱prompt 微调啊,晕死
将模型从deepseek切换为豆包后成功!
明日继续研究更新
错误log:
主要是ds的json遵循能力还是有点弱啊
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/site-packages/fnllm/base/base_llm.py", line 144, in __call__
return await self._decorated_target(prompt, **kwargs)
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/site-packages/fnllm/base/services/json.py", line 77, in invoke
return await this.invoke_json(delegate, prompt, kwargs)
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/site-packages/fnllm/base/services/json.py", line 100, in invoke_json
raise FailedToGenerateValidJsonError from error
fnllm.base.services.errors.FailedToGenerateValidJsonError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/site-packages/graphrag/index/operations/summarize_communities/community_reports_extractor.py", line 80, in __call__
response = await self._model.achat(
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/site-packages/graphrag/language_model/providers/fnllm/models.py", line 81, in achat
response = await self.model(prompt, **kwargs)
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/site-packages/fnllm/openai/llm/openai_chat_llm.py", line 94, in __call__
return await self._text_chat_llm(prompt, **kwargs)
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/site-packages/fnllm/openai/services/openai_tools_parsing.py", line 130, in __call__
return await self._delegate(prompt, **kwargs)
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/site-packages/fnllm/base/base_llm.py", line 148, in __call__
await self._events.on_error(
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/site-packages/graphrag/language_model/providers/fnllm/events.py", line 26, in on_error
self._on_error(error, traceback, arguments)
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/site-packages/graphrag/language_model/providers/fnllm/utils.py", line 45, in on_error
callbacks.error("Error Invoking LLM", error, stack, details)
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/site-packages/graphrag/callbacks/workflow_callbacks_manager.py", line 64, in error
callback.error(message, cause, stack, details)
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/site-packages/graphrag/callbacks/file_workflow_callbacks.py", line 37, in error
json.dumps(
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/json/__init__.py", line 238, in dumps
**kw).encode(obj)
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/json/encoder.py", line 201, in encode
chunks = list(chunks)
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/json/encoder.py", line 431, in _iterencode
yield from _iterencode_dict(o, _current_indent_level)
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/json/encoder.py", line 405, in _iterencode_dict
yield from chunks
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/json/encoder.py", line 405, in _iterencode_dict
yield from chunks
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/json/encoder.py", line 405, in _iterencode_dict
yield from chunks
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/json/encoder.py", line 438, in _iterencode
o = _default(o)
File "/home/zli/miniconda3/envs/graphrag/lib/python3.10/json/encoder.py", line 179, in default
raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type ModelMetaclass is not JSON serializable
22:53:43,292 graphrag.callbacks.file_workflow_callbacks INFO Community Report Extraction Error details=None
22:53:43,293 graphrag.index.operations.summarize_communities.strategies WARNING No report found for community: 8.0
配置如下:
models:
default_chat_model:
type: openai_chat # or azure_openai_chat
api_base: https://ark.cn-beijing.volces.com/api/v3/
# api_version: 2024-05-01-preview
auth_type: api_key # or azure_managed_identity
api_key: ${GRAPHRAG_API_KEY} # set this in the generated .env file
# audience: "https://cognitiveservices.azure.com/.default"
# organization: <organization_id>
model: deepseek-v3-241226
# deployment_name: <azure_model_deployment_name>
encoding_model: cl100k_base # automatically set by tiktoken if left undefined
model_supports_json: true # recommended if this is available for your model.
concurrent_requests: 25 # max number of simultaneous LLM requests allowed
async_mode: threaded # or asyncio
retry_strategy: native
max_retries: -1 # set to -1 for dynamic retry logic (most optimal setting based on server response)
tokens_per_minute: 0 # set to 0 to disable rate limiting
requests_per_minute: 0 # set to 0 to disable rate limiting
default_embedding_model:
type: openai_embedding # or azure_openai_embedding
api_base: http://localhost:11434/v1/
# api_version: 2024-05-01-preview
#auth_type: api_key # or azure_managed_identity
#type: openai_chat
api_key: ollama
# audience: "https://cognitiveservices.azure.com/.default"
# organization: <organization_id>
model: bge-m3
# deployment_name: <azure_model_deployment_name>
encoding_model: cl100k_base # automatically set by tiktoken if left undefined
model_supports_json: true # recommended if this is available for your model.
concurrent_requests: 25 # max number of simultaneous LLM requests allowed
async_mode: threaded # or asyncio
retry_strategy: native
max_retries: -1 # set to -1 for dynamic retry logic (most optimal setting based on server response)
tokens_per_minute: 0 # set to 0 to disable rate limiting
requests_per_minute: 0 # set to 0 to disable rate limiting
vector_store:
default_vector_store:
type: lancedb
db_uri: output/lancedb
container_name: default
overwrite: True
embed_text:
model_id: default_embedding_model
vector_store_id: default_vector_store
### Input settings ###
input:
type: file # or blob
file_type: text #[csv, text, json]
base_dir: "input"
chunks:
size: 1200
overlap: 100
group_by_columns: [id]
### Output settings ###
## If blob storage is specified in the following four sections,
## connection_string and container_name must be provided
cache:
type: file # [file, blob, cosmosdb]
base_dir: "cache"
reporting:
type: file # [file, blob, cosmosdb]
base_dir: "logs"
output:
type: file # [file, blob, cosmosdb]
base_dir: "output"
### Workflow settings ###
#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]
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: 8000
max_input_length: 4000
cluster_graph:
max_cluster_size: 10
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"