问题是什么？
我在graphrag中使用bge-m3模型，并使用以下参数：
embeddings:
## parallelization: override the global parallelization settings for embeddings
async_mode: asyncio
llm:
api_key:
type: openai_embedding # or azure_openai_embedding
model: chatfire/bge-m3:q8_0
api_base: http://localhost:11434/api返回以下错误：
17:11:30,126 httpx INFO HTTP Request: POST http://localhost:11434/api/embeddings "HTTP/1.1 200 OK"
17:11:30,129 datashaper.workflow.workflow ERROR Error executing verb "text_embed" in create_final_entities: 'NoneType' object is not iterable
Traceback (most recent call last):
File "E:\Langchain-Chatchat\glut\lib\site-packages\datashaper\workflow\workflow.py", line 415, in _execute_verb
result = await result
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\index\verbs\text\embed\text_embed.py", line 105, in text_embed
return await _text_embed_in_memory(
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\index\verbs\text\embed\text_embed.py", line 130, in _text_embed_in_memory
result = await strategy_exec(texts, callbacks, cache, strategy_args)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\index\verbs\text\embed\strategies\openai.py", line 61, in run
embeddings = await _execute(llm, text_batches, ticker, semaphore)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\index\verbs\text\embed\strategies\openai.py", line 105, in _execute
results = await asyncio.gather(*futures)
File "E:\Langchain-Chatchat\glut\lib\asyncio\tasks.py", line 304, in __wakeup
future.result()
File "E:\Langchain-Chatchat\glut\lib\asyncio\tasks.py", line 232, in __step
result = coro.send(None)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\index\verbs\text\embed\strategies\openai.py", line 99, in embed
chunk_embeddings = await llm(chunk)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\base\caching_llm.py", line 104, in __call__
result = await self._delegate(input, **kwargs)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\base\rate_limiting_llm.py", line 177, in __call__
result, start = await execute_with_retry()
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\base\rate_limiting_llm.py", line 159, in execute_with_retry
async for attempt in retryer:
File "E:\Langchain-Chatchat\glut\lib\site-packages\tenacity\_asyncio.py", line 71, in __anext__
do = self.iter(retry_state=self._retry_state)
File "E:\Langchain-Chatchat\glut\lib\site-packages\tenacity\__init__.py", line 314, in iter
return fut.result()
File "E:\Langchain-Chatchat\glut\lib\concurrent\futures\_base.py", line 451, in result
return self.__get_result()
File "E:\Langchain-Chatchat\glut\lib\concurrent\futures\_base.py", line 403, in __get_result
raise self._exception
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\base\rate_limiting_llm.py", line 165, in execute_with_retry
return await do_attempt(), start
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\base\rate_limiting_llm.py", line 147, in do_attempt
return await self._delegate(input, **kwargs)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\base\base_llm.py", line 49, in __call__
return await self._invoke(input, **kwargs)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\base\base_llm.py", line 53, in _invoke
output = await self._execute_llm(input, **kwargs)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\openai\openai_embeddings_llm.py", line 36, in _execute_llm
embedding = await self.client.embeddings.create(
File "E:\Langchain-Chatchat\glut\lib\site-packages\openai\resources\embeddings.py", line 215, in create
return await self._post(
File "E:\Langchain-Chatchat\glut\lib\site-packages\openai\_base_client.py", line 1826, in post
return await self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)
File "E:\Langchain-Chatchat\glut\lib\site-packages\openai\_base_client.py", line 1519, in request
return await self._request(
File "E:\Langchain-Chatchat\glut\lib\site-packages\openai\_base_client.py", line 1622, in _request
return await self._process_response(
File "E:\Langchain-Chatchat\glut\lib\site-packages\openai\_base_client.py", line 1714, in _process_response
return await api_response.parse()
File "E:\Langchain-Chatchat\glut\lib\site-packages\openai\_response.py", line 419, in parse
parsed = self._options.post_parser(parsed)
File "E:\Langchain-Chatchat\glut\lib\site-packages\openai\resources\embeddings.py", line 203, in parser
for embedding in obj.data:
TypeError: 'NoneType' object is not iterable
17:11:30,131 graphrag.index.reporting.file_workflow_callbacks INFO Error executing verb "text_embed" in create_final_entities: 'NoneType' object is not iterable details=None
17:11:30,142 graphrag.index.run ERROR error running workflow create_final_entities操作系统
Windows
GPU
Nvidia
CPU
AMD
Ollama版本
0.2.8
8条答案
按热度按时间6ioyuze21#
我切换到了xinference的嵌入API,它运行得很好！
7kjnsjlb2#
看起来请求http://localhost:11434/api/embeddings是有效的，但我不确定嵌入式模型为何无法正常工作。
jhkqcmku3#
OpenAI的接口调用嵌入模型是否还不支持？
wfsdck304#
The ollama嵌入端点是
localhost:11434/api/embed。与openai兼容的嵌入端点是
localhost:11434/v1/embeddings。返回的数据格式也不同(为了空间原因，我已从示例中删除了嵌入):
vyswwuz25#
localhost:11434/api/embeddings需要一个不同的请求(prompt而不是input),并生成一个与其他两种方法不同格式的响应，其中嵌入的精度也与这两种方法不同：q1qsirdb6#
GraphRAG也存在同样的问题，下一个版本是否会兼容OpenAI的嵌入API?
kgsdhlau7#
以下是您提供的文本内容的翻译结果：
$ curl -s localhost:11434/api/embed -d '{"model":"chatfire/bge-m3:q8_0","input":"Your text string goes here"}' | jq '.embeddings=[]'
{
"model": "chatfire/bge-m3:q8_0",
"embeddings": []
}
$ curl -s localhost:11434/v1/embeddings -d '{"model":"chatfire/bge-m3:q8_0","input":"Your text string goes here"}' | jq '.data[].embedding=[]'
{
"object": "list",
"data": [
{
"object": "embedding",
"embedding": [],
"index": 0
}
],
"model": "chatfire/bge-m3:q8_0"
}
text_embedder = OpenAIEmbedding(
api_key="ollama",
api_base="http://localhost:11434/v1",
model="chatfire/bge-m3:q8_0",
deployment_name="chatfire/bge-m3:q8_0",
api_type=OpenaiApiType.OpenAI,
max_retries=20,
)
qyyhg6bp8#
你的应用正在发送一个被ollama无法理解的请求。你需要查看这个请求是什么，可以通过让客户端记录它，或者使用如wireshark、tcpdump、tcpflow等工具来捕获网络流量。