DeepSeek-V2
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20 hours ago •
deepseek-ai
deepseek-ai
怎么用dspy里的方法来调用deepseek？
我最近有一项dspy的prompt实验，想对比一下不同llm的效果，我不知道该如何接入deepseek，使用官方的OpenAI方法会报错：
deepseek = dspy.OpenAI(api_base ='https://api.deepseek.com/v1',
api_key='*********',
model='deepseek-chat',
model_type = "chat",
)
gpt4 = dspy.OpenAI(api_base ='https://openkey.cloud/v1',
api_key='**********',
model='gpt-4o',
max_tokens = 4096,
model_type = "chat",
)
上面gpt4方法可以正常调用，deepseek会报下面这个错误：
NotFoundError: Error code: 404 - {'error': {'message': 'Invalid URL (POST /v1chat/completions)', 'type': 'invalid_request_error', 'param': '', 'code': ''}}
具体错误为：
Cell In[21], line 1
----> 1 deepseek("日经跌了1000点，分析一下原因并给出相关依据")
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/dsp/modules/gpt3.py:178, in GPT3.__call__(self, prompt, only_completed, return_sorted, **kwargs)
170 assert return_sorted is False, "for now"
172 # if kwargs.get("n", 1) > 1:
173 # if self.model_type == "chat":
174 # kwargs = {**kwargs}
175 # else:
176 # kwargs = {**kwargs, "logprobs": 5}
--> 178 response = self.request(prompt, **kwargs)
180 self.log_usage(response)
181 choices = response["choices"]
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/backoff/_sync.py:105, in retry_exception.<locals>.retry(*args, **kwargs)
96 details = {
97 "target": target,
98 "args": args,
(...)
101 "elapsed": elapsed,
102 }
104 try:
--> 105 ret = target(*args, **kwargs)
106 except exception as e:
107 max_tries_exceeded = (tries == max_tries_value)
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/dsp/modules/gpt3.py:144, in GPT3.request(self, prompt, **kwargs)
141 if "model_type" in kwargs:
142 del kwargs["model_type"]
--> 144 return self.basic_request(prompt, **kwargs)
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/dsp/modules/gpt3.py:117, in GPT3.basic_request(self, prompt, **kwargs)
115 kwargs["messages"] = messages
116 kwargs = {"stringify_request": json.dumps(kwargs)}
--> 117 response = chat_request(**kwargs)
119 else:
120 kwargs["prompt"] = prompt
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/dsp/modules/gpt3.py:269, in chat_request(**kwargs)
266 if OPENAI_LEGACY:
267 return _cached_gpt3_turbo_request_v2_wrapped(**kwargs)
--> 269 return v1_cached_gpt3_turbo_request_v2_wrapped(**kwargs).model_dump()
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/dsp/modules/cache_utils.py:16, in noop_decorator.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
14 @wraps(func)
15 def wrapper(*args, **kwargs):
---> 16 return func(*args, **kwargs)
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/dsp/modules/gpt3.py:262, in v1_cached_gpt3_turbo_request_v2_wrapped(**kwargs)
259 @functools.lru_cache(maxsize=None if cache_turn_on else 0)
260 @NotebookCacheMemory.cache
261 def v1_cached_gpt3_turbo_request_v2_wrapped(**kwargs):
--> 262 return v1_cached_gpt3_turbo_request_v2(**kwargs)
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/joblib/memory.py:655, in MemorizedFunc.__call__(self, *args, **kwargs)
654 def __call__(self, *args, **kwargs):
--> 655 return self._cached_call(args, kwargs)[0]
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/joblib/memory.py:598, in MemorizedFunc._cached_call(self, args, kwargs, shelving)
595 must_call = True
597 if must_call:
--> 598 out, metadata = self.call(*args, **kwargs)
599 if self.mmap_mode is not None:
600 # Memmap the output at the first call to be consistent with
601 # later calls
602 if self._verbose:
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/joblib/memory.py:856, in MemorizedFunc.call(self, *args, **kwargs)
854 if self._verbose > 0:
855 print(format_call(self.func, args, kwargs))
--> 856 output = self.func(*args, **kwargs)
857 self.store_backend.dump_item(
858 [func_id, args_id], output, verbose=self._verbose)
860 duration = time.time() - start_time
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/dsp/modules/gpt3.py:256, in v1_cached_gpt3_turbo_request_v2(**kwargs)
254 if "stringify_request" in kwargs:
255 kwargs = json.loads(kwargs["stringify_request"])
--> 256 return openai.chat.completions.create(**kwargs)
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/openai/_utils/_utils.py:277, in required_args.<locals>.inner.<locals>.wrapper(*args, **kwargs)
275 msg = f"Missing required argument: {quote(missing[0])}"
276 raise TypeError(msg)
--> 277 return func(*args, **kwargs)
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/openai/resources/chat/completions.py:643, in Completions.create(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, parallel_tool_calls, presence_penalty, response_format, seed, service_tier, stop, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)
609 @required_args(["messages", "model"], ["messages", "model", "stream"])
610 def create(
611 self,
(...)
641 timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
642 ) -> ChatCompletion | Stream[ChatCompletionChunk]:
--> 643 return self._post(
644 "/chat/completions",
645 body=maybe_transform(
646 {
647 "messages": messages,
648 "model": model,
649 "frequency_penalty": frequency_penalty,
650 "function_call": function_call,
651 "functions": functions,
652 "logit_bias": logit_bias,
653 "logprobs": logprobs,
654 "max_tokens": max_tokens,
655 "n": n,
656 "parallel_tool_calls": parallel_tool_calls,
657 "presence_penalty": presence_penalty,
658 "response_format": response_format,
659 "seed": seed,
660 "service_tier": service_tier,
661 "stop": stop,
662 "stream": stream,
663 "stream_options": stream_options,
664 "temperature": temperature,
665 "tool_choice": tool_choice,
666 "tools": tools,
667 "top_logprobs": top_logprobs,
668 "top_p": top_p,
669 "user": user,
670 },
671 completion_create_params.CompletionCreateParams,
672 ),
673 options=make_request_options(
674 extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
675 ),
676 cast_to=ChatCompletion,
677 stream=stream or False,
678 stream_cls=Stream[ChatCompletionChunk],
679 )
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/openai/_base_client.py:1266, in SyncAPIClient.post(self, path, cast_to, body, options, files, stream, stream_cls)
1252 def post(
1253 self,
1254 path: str,
(...)
1261 stream_cls: type[_StreamT] | None = None,
1262 ) -> ResponseT | _StreamT:
1263 opts = FinalRequestOptions.construct(
1264 method="post", url=path, json_data=body, files=to_httpx_files(files), **options
1265 )
-> 1266 return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/openai/_base_client.py:942, in SyncAPIClient.request(self, cast_to, options, remaining_retries, stream, stream_cls)
933 def request(
934 self,
935 cast_to: Type[ResponseT],
(...)
940 stream_cls: type[_StreamT] | None = None,
941 ) -> ResponseT | _StreamT:
--> 942 return self._request(
943 cast_to=cast_to,
944 options=options,
945 stream=stream,
946 stream_cls=stream_cls,
947 remaining_retries=remaining_retries,
948 )
File ~/anaconda3/envs/llms_py310/lib/python3.10/site-packages/openai/_base_client.py:1046, in SyncAPIClient._request(self, cast_to, options, remaining_retries, stream, stream_cls)
1043 err.response.read()
1045 log.debug("Re-raising status error")
-> 1046 raise self._make_status_error_from_response(err.response) from None
1048 return self._process_response(
1049 cast_to=cast_to,
1050 options=options,
(...)
1053 stream_cls=stream_cls,
1054 )
请问该怎么解决这个问题？
I made a custom model for DeepSeek:
import requests
from dsp import LM
class DeepSeek(LM):
def __init__(self, model, api_key, **kwargs,):
self.model = model
self.api_key = api_key
self.provider = "default"
self.history = []
self.kwargs = {
"temperature": kwargs.get("temperature", 0.0),
"max_tokens": min(kwargs.get("max_tokens", 4096), 4096),
"top_p": kwargs.get("top_p", 0.95),
"top_k": kwargs.get("top_k", 1),
"n": kwargs.pop("n", kwargs.pop("num_generations", 1)),
**kwargs,
}
self.kwargs["model"] = model
self.base_url = "https://api.deepseek.com/chat/completions"
def basic_request(self, prompt: str, **kwargs):
headers = {
"Authorization": "Bearer " + self.api_key,
"Accept": "application/json",
"Content-Type": "application/json"
}
data = {
**kwargs,
"model": self.model,
"messages": [
{"role": "user", "content": prompt}
],
"steam": False
}
response = requests.post(self.base_url, headers=headers, json=data)
response = response.json()
self.history.append({
"prompt": prompt,
"response": response,
"kwargs": kwargs,
})
return response
def __call__(self, prompt, **kwargs):
response = self.request(prompt, **kwargs)
completions = [result['message']['content'] for result in response["choices"]]
return completions
Then I could run it by importing the class:
import dspy
import os
from DeepSeek import DeepSeek
lm = DeepSeek(model='deepseek-coder', api_key=os.getenv("DEEPSEEK_PALTFORM_API_KEY"))
dspy.configure(lm=lm)
qa = dspy.ChainOfThought('question -> answer')
response = qa(question="What is the capital of Paris?")
print(response.answer)
@Lekssays Thanks Ahmed. however, this is deprecated now:
*** In DSPy 2.5, all LM clients except `dspy.LM` are deprecated, underperform, and are about to be deleted. ***
You are using the client DeepSeek, which will be removed in DSPy 2.6.
current solution:
deep = dspy.LM("deepseek/deepseek-chat", api_key='API-key-here', cache=True)
dspy.configure(lm=deep)
qa = dspy.Predict('question -> answer')
response = qa(question="are you built by openAI or Deepseek?")
print(response.answer)
@Lekssays Thanks Ahmed. however, this is deprecated now:
*** In DSPy 2.5, all LM clients except `dspy.LM` are deprecated, underperform, and are about to be deleted. *** You are using the client DeepSeek, which will be removed in DSPy 2.6.current solution:
deep = dspy.LM("deepseek/deepseek-chat", api_key='API-key-here', cache=True) dspy.configure(lm=deep) qa = dspy.Predict('question -> answer') response = qa(question="are you built by openAI or Deepseek?") print(response.answer)
Thanks @mohamedScikitLearn this works well.
Basically, dspy is using LiteLLM to call LLM, so check https://docs.litellm.ai/docs/providers/deepseek to see all LLM supported by LiteLLM and their model name.
