forked from phoenix/litellm-mirror
updated oobabooga to new api and support for embeddings
This commit is contained in:
dan
parent
31fbb095c2
commit
c4dfd9be7c
2 changed files with 86 additions and 59 deletions
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@ -7,7 +7,6 @@ from typing import Callable, Optional
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from litellm.utils import ModelResponse, Usage
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from .prompt_templates.factory import prompt_factory, custom_prompt
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class OobaboogaError(Exception):
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def __init__(self, status_code, message):
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self.status_code = status_code
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@ -16,7 +15,6 @@ class OobaboogaError(Exception):
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self.message
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) # Call the base class constructor with the parameters it needs
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def validate_environment(api_key):
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headers = {
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"accept": "application/json",
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@ -26,7 +24,6 @@ def validate_environment(api_key):
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headers["Authorization"] = f"Token {api_key}"
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return headers
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def completion(
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model: str,
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messages: list,
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@ -47,93 +44,113 @@ def completion(
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completion_url = model
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elif api_base:
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completion_url = api_base
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else:
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raise OobaboogaError(
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status_code=404,
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message="API Base not set. Set one via completion(..,api_base='your-api-url')",
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)
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else:
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raise OobaboogaError(status_code=404, message="API Base not set. Set one via completion(..,api_base='your-api-url')")
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model = model
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if model in custom_prompt_dict:
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# check if the model has a registered custom prompt
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model_prompt_details = custom_prompt_dict[model]
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prompt = custom_prompt(
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role_dict=model_prompt_details["roles"],
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initial_prompt_value=model_prompt_details["initial_prompt_value"],
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final_prompt_value=model_prompt_details["final_prompt_value"],
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messages=messages,
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)
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else:
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prompt = prompt_factory(model=model, messages=messages)
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completion_url = completion_url + "/api/v1/generate"
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completion_url = completion_url + "/v1/chat/completions"
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data = {
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"prompt": prompt,
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"messages": messages,
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**optional_params,
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}
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## LOGGING
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logging_obj.pre_call(
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input=prompt,
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api_key=api_key,
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additional_args={"complete_input_dict": data},
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)
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input=messages,
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api_key=api_key,
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additional_args={"complete_input_dict": data},
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)
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## COMPLETION CALL
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response = requests.post(
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completion_url,
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headers=headers,
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data=json.dumps(data),
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stream=optional_params["stream"] if "stream" in optional_params else False,
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completion_url, headers=headers, data=json.dumps(data), stream=optional_params["stream"] if "stream" in optional_params else False
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)
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if "stream" in optional_params and optional_params["stream"] == True:
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return response.iter_lines()
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else:
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## LOGGING
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logging_obj.post_call(
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input=prompt,
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api_key=api_key,
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original_response=response.text,
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additional_args={"complete_input_dict": data},
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)
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input=messages,
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api_key=api_key,
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original_response=response.text,
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additional_args={"complete_input_dict": data},
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)
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print_verbose(f"raw model_response: {response.text}")
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## RESPONSE OBJECT
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try:
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completion_response = response.json()
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except:
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raise OobaboogaError(
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message=response.text, status_code=response.status_code
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)
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raise OobaboogaError(message=response.text, status_code=response.status_code)
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if "error" in completion_response:
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raise OobaboogaError(
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message=completion_response["error"],
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status_code=response.status_code,
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)
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raise OobaboogaError(message=completion_response["error"],status_code=response.status_code,)
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else:
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try:
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model_response["choices"][0]["message"][
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"content"
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] = completion_response["results"][0]["text"]
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model_response["choices"][0]["message"]["content"] = completion_response["choices"][0]["message"]["content"]
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except:
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raise OobaboogaError(
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message=json.dumps(completion_response),
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status_code=response.status_code,
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)
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raise OobaboogaError(message=json.dumps(completion_response), status_code=response.status_code)
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## CALCULATING USAGE
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prompt_tokens = len(encoding.encode(prompt))
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completion_tokens = len(
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encoding.encode(model_response["choices"][0]["message"]["content"])
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)
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model_response["created"] = int(time.time())
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model_response["model"] = model
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usage = Usage(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=prompt_tokens + completion_tokens,
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prompt_tokens=completion_response["usage"]["prompt_tokens"],
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completion_tokens=completion_response["usage"]["completion_tokens"],
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total_tokens=completion_response["usage"]["total_tokens"],
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)
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model_response.usage = usage
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return model_response
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def embedding():
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# logic for parsing in - calling - parsing out model embedding calls
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pass
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def embedding(
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model: str,
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input: list,
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api_key: Optional[str] = None,
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api_base: str = None,
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logging_obj=None,
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model_response=None,
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optional_params=None,
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encoding=None,
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):
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# Create completion URL
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if "https" in model:
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embeddings_url = model
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elif api_base:
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embeddings_url = f"{api_base}/v1/embeddings"
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else:
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raise OobaboogaError(status_code=404, message="API Base not set. Set one via completion(..,api_base='your-api-url')")
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# Prepare request data
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data = {"input": input}
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if optional_params:
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data.update(optional_params)
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# Logging before API call
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if logging_obj:
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logging_obj.pre_call(input=input, api_key=api_key, additional_args={"complete_input_dict": data})
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# Send POST request
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headers = validate_environment(api_key)
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response = requests.post(embeddings_url, headers=headers, json=data)
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if not response.ok:
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raise OobaboogaError(message=response.text, status_code=response.status_code)
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completion_response = response.json()
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# Check for errors in response
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if "error" in completion_response:
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raise OobaboogaError(message=completion_response["error"], status_code=completion_response.get('status_code', 500))
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# Process response data
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model_response["data"]=[{"embedding": completion_response["data"][0]["embedding"], "index": 0, "object": "embedding"}]
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num_tokens = len(completion_response["data"][0]["embedding"])
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#Adding metadata to response
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model_response.usage = Usage(prompt_tokens=num_tokens,total_tokens=num_tokens)
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model_response["object"]="list"
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model_response["model"]=model
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return model_response
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@ -2313,6 +2313,16 @@ def embedding(
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optional_params=optional_params,
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model_response=EmbeddingResponse(),
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)
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elif custom_llm_provider == "oobabooga":
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response = oobabooga.embedding(
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model=model,
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input=input,
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encoding=encoding,
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api_base=api_base,
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logging_obj=logging,
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optional_params=optional_params,
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model_response= EmbeddingResponse()
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)
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elif custom_llm_provider == "ollama":
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if aembedding == True:
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response = ollama.ollama_aembeddings(
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