mirror of
https://github.com/BerriAI/litellm.git
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refactor(huggingface_restapi.py): moving async completion + streaming to real async calls
This commit is contained in:
Krrish Dholakia
parent
77394e7987
commit
1a705bfbcb
5 changed files with 464 additions and 365 deletions
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@ -3,6 +3,7 @@ import os, copy, types
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import json
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from enum import Enum
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import httpx, requests
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from .base import BaseLLM
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import time
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import litellm
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from typing import Callable, Dict, List, Any
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@ -67,19 +68,6 @@ class HuggingfaceConfig():
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and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod))
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and v is not None}
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def validate_environment(api_key, headers):
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default_headers = {
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"content-type": "application/json",
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}
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if api_key and headers is None:
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default_headers["Authorization"] = f"Bearer {api_key}" # Huggingface Inference Endpoint default is to accept bearer tokens
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headers = default_headers
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elif headers:
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headers=headers
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else:
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headers = default_headers
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return headers
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def output_parser(generated_text: str):
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"""
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Parse the output text to remove any special characters. In our current approach we just check for ChatML tokens.
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@ -94,8 +82,6 @@ def output_parser(generated_text: str):
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generated_text = generated_text[::-1].replace(token[::-1], "", 1)[::-1]
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return generated_text
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tgi_models_cache = None
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conv_models_cache = None
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def read_tgi_conv_models():
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@ -144,365 +130,470 @@ def get_hf_task_for_model(model):
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else:
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return "text-generation-inference" # default to tgi
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def completion(
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model: str,
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messages: list,
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api_base: Optional[str],
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headers: Optional[dict],
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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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api_key,
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logging_obj,
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custom_prompt_dict={},
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optional_params=None,
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litellm_params=None,
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logger_fn=None,
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):
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exception_mapping_worked = False
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try:
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headers = validate_environment(api_key, headers)
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task = get_hf_task_for_model(model)
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print_verbose(f"{model}, {task}")
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completion_url = ""
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input_text = None
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if "https" in model:
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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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elif "HF_API_BASE" in os.environ:
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completion_url = os.getenv("HF_API_BASE", "")
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elif "HUGGINGFACE_API_BASE" in os.environ:
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completion_url = os.getenv("HUGGINGFACE_API_BASE", "")
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else:
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completion_url = f"https://api-inference.huggingface.co/models/{model}"
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class Huggingface(BaseLLM):
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_client_session: Optional[httpx.Client] = None
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_aclient_session: Optional[httpx.AsyncClient] = None
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## Load Config
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config=litellm.HuggingfaceConfig.get_config()
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for k, v in config.items():
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if k not in optional_params: # completion(top_k=3) > huggingfaceConfig(top_k=3) <- allows for dynamic variables to be passed in
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optional_params[k] = v
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def __init__(self) -> None:
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super().__init__()
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### MAP INPUT PARAMS
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if task == "conversational":
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inference_params = copy.deepcopy(optional_params)
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inference_params.pop("details")
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inference_params.pop("return_full_text")
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past_user_inputs = []
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generated_responses = []
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text = ""
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for message in messages:
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if message["role"] == "user":
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if text != "":
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past_user_inputs.append(text)
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text = message["content"]
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elif message["role"] == "assistant" or message["role"] == "system":
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generated_responses.append(message["content"])
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data = {
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"inputs": {
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"text": text,
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"past_user_inputs": past_user_inputs,
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"generated_responses": generated_responses
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},
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"parameters": inference_params
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}
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input_text = "".join(message["content"] for message in messages)
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elif task == "text-generation-inference":
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# always send "details" and "return_full_text" as params
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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.get("roles", None),
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initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""),
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final_prompt_value=model_prompt_details.get("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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data = {
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"inputs": prompt,
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"parameters": optional_params,
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"stream": True if "stream" in optional_params and optional_params["stream"] == True else False,
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}
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input_text = prompt
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else:
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# Non TGI and Conversational llms
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# We need this branch, it removes 'details' and 'return_full_text' from params
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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.get("roles", {}),
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initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""),
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final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
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bos_token=model_prompt_details.get("bos_token", ""),
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eos_token=model_prompt_details.get("eos_token", ""),
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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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inference_params = copy.deepcopy(optional_params)
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inference_params.pop("details")
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inference_params.pop("return_full_text")
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data = {
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"inputs": prompt,
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"parameters": inference_params,
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"stream": True if "stream" in optional_params and optional_params["stream"] == True else False,
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}
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input_text = prompt
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## LOGGING
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logging_obj.pre_call(
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input=input_text,
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api_key=api_key,
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additional_args={"complete_input_dict": data, "task": task, "headers": headers, "api_base": completion_url},
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)
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## COMPLETION CALL
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if "stream" in optional_params and optional_params["stream"] == True:
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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"]
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)
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return response.iter_lines()
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else:
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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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)
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def validate_environment(self, api_key, headers):
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default_headers = {
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"content-type": "application/json",
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}
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if api_key and headers is None:
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default_headers["Authorization"] = f"Bearer {api_key}" # Huggingface Inference Endpoint default is to accept bearer tokens
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headers = default_headers
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elif headers:
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headers=headers
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else:
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headers = default_headers
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return headers
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## Some servers might return streaming responses even though stream was not set to true. (e.g. Baseten)
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is_streamed = False
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if response.__dict__['headers'].get("Content-Type", "") == "text/event-stream":
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is_streamed = True
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# iterate over the complete streamed response, and return the final answer
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if is_streamed:
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streamed_response = CustomStreamWrapper(completion_stream=response.iter_lines(), model=model, custom_llm_provider="huggingface", logging_obj=logging_obj)
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content = ""
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for chunk in streamed_response:
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content += chunk["choices"][0]["delta"]["content"]
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completion_response: List[Dict[str, Any]] = [{"generated_text": content}]
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## LOGGING
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logging_obj.post_call(
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input=input_text,
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api_key=api_key,
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original_response=completion_response,
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additional_args={"complete_input_dict": data, "task": task},
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)
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else:
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## LOGGING
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logging_obj.post_call(
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input=input_text,
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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, "task": task},
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)
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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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import traceback
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raise HuggingfaceError(
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message=f"Original Response received: {response.text}; Stacktrace: {traceback.format_exc()}", status_code=response.status_code
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)
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print_verbose(f"response: {completion_response}")
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if isinstance(completion_response, dict) and "error" in completion_response:
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print_verbose(f"completion error: {completion_response['error']}")
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print_verbose(f"response.status_code: {response.status_code}")
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raise HuggingfaceError(
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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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else:
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if task == "conversational":
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if len(completion_response["generated_text"]) > 0: # type: ignore
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model_response["choices"][0]["message"][
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"content"
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] = completion_response["generated_text"] # type: ignore
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elif task == "text-generation-inference":
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if len(completion_response[0]["generated_text"]) > 0:
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model_response["choices"][0]["message"][
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"content"
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] = output_parser(completion_response[0]["generated_text"])
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## GETTING LOGPROBS + FINISH REASON
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if "details" in completion_response[0] and "tokens" in completion_response[0]["details"]:
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model_response.choices[0].finish_reason = completion_response[0]["details"]["finish_reason"]
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def convert_to_model_response_object(self,
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completion_response,
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model_response,
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task,
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optional_params,
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encoding,
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input_text,
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model):
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if task == "conversational":
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if len(completion_response["generated_text"]) > 0: # type: ignore
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model_response["choices"][0]["message"][
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"content"
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] = completion_response["generated_text"] # type: ignore
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elif task == "text-generation-inference":
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if len(completion_response[0]["generated_text"]) > 0:
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model_response["choices"][0]["message"][
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"content"
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] = output_parser(completion_response[0]["generated_text"])
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## GETTING LOGPROBS + FINISH REASON
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if "details" in completion_response[0] and "tokens" in completion_response[0]["details"]:
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model_response.choices[0].finish_reason = completion_response[0]["details"]["finish_reason"]
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sum_logprob = 0
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for token in completion_response[0]["details"]["tokens"]:
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if token["logprob"] != None:
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sum_logprob += token["logprob"]
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model_response["choices"][0]["message"]._logprob = sum_logprob
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if "best_of" in optional_params and optional_params["best_of"] > 1:
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if "details" in completion_response[0] and "best_of_sequences" in completion_response[0]["details"]:
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choices_list = []
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for idx, item in enumerate(completion_response[0]["details"]["best_of_sequences"]):
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sum_logprob = 0
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for token in completion_response[0]["details"]["tokens"]:
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for token in item["tokens"]:
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if token["logprob"] != None:
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sum_logprob += token["logprob"]
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model_response["choices"][0]["message"]._logprob = sum_logprob
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if "best_of" in optional_params and optional_params["best_of"] > 1:
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if "details" in completion_response[0] and "best_of_sequences" in completion_response[0]["details"]:
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choices_list = []
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for idx, item in enumerate(completion_response[0]["details"]["best_of_sequences"]):
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sum_logprob = 0
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for token in item["tokens"]:
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if token["logprob"] != None:
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sum_logprob += token["logprob"]
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if len(item["generated_text"]) > 0:
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message_obj = Message(content=output_parser(item["generated_text"]), logprobs=sum_logprob)
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else:
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message_obj = Message(content=None)
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choice_obj = Choices(finish_reason=item["finish_reason"], index=idx+1, message=message_obj)
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choices_list.append(choice_obj)
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model_response["choices"].extend(choices_list)
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else:
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if len(completion_response[0]["generated_text"]) > 0:
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model_response["choices"][0]["message"][
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"content"
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] = output_parser(completion_response[0]["generated_text"])
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## CALCULATING USAGE
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prompt_tokens = 0
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if len(item["generated_text"]) > 0:
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message_obj = Message(content=output_parser(item["generated_text"]), logprobs=sum_logprob)
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else:
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message_obj = Message(content=None)
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choice_obj = Choices(finish_reason=item["finish_reason"], index=idx+1, message=message_obj)
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choices_list.append(choice_obj)
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model_response["choices"].extend(choices_list)
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else:
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if len(completion_response[0]["generated_text"]) > 0:
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model_response["choices"][0]["message"][
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"content"
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] = output_parser(completion_response[0]["generated_text"])
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## CALCULATING USAGE
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prompt_tokens = 0
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try:
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prompt_tokens = len(
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encoding.encode(input_text)
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) ##[TODO] use the llama2 tokenizer here
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except:
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# this should remain non blocking we should not block a response returning if calculating usage fails
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pass
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output_text = model_response["choices"][0]["message"].get("content", "")
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if output_text is not None and len(output_text) > 0:
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completion_tokens = 0
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try:
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prompt_tokens = len(
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encoding.encode(input_text)
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completion_tokens = len(
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encoding.encode(model_response["choices"][0]["message"].get("content", ""))
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) ##[TODO] use the llama2 tokenizer here
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except:
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# this should remain non blocking we should not block a response returning if calculating usage fails
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pass
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print_verbose(f'output: {model_response["choices"][0]["message"]}')
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output_text = model_response["choices"][0]["message"].get("content", "")
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if output_text is not None and len(output_text) > 0:
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completion_tokens = 0
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try:
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completion_tokens = len(
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encoding.encode(model_response["choices"][0]["message"].get("content", ""))
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) ##[TODO] use the llama2 tokenizer here
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except:
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# this should remain non blocking we should not block a response returning if calculating usage fails
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pass
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else:
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completion_tokens = 0
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model_response["created"] = 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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)
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model_response.usage = usage
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model_response._hidden_params["original_response"] = completion_response
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return model_response
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except HuggingfaceError as e:
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exception_mapping_worked = True
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raise e
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except Exception as e:
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if exception_mapping_worked:
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raise e
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else:
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import traceback
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raise HuggingfaceError(status_code=500, message=traceback.format_exc())
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completion_tokens = 0
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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: Optional[str] = None,
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logging_obj=None,
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model_response=None,
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encoding=None,
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):
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headers = validate_environment(api_key, headers=None)
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# print_verbose(f"{model}, {task}")
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embed_url = ""
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if "https" in model:
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embed_url = model
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elif api_base:
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embed_url = api_base
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elif "HF_API_BASE" in os.environ:
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embed_url = os.getenv("HF_API_BASE", "")
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elif "HUGGINGFACE_API_BASE" in os.environ:
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embed_url = os.getenv("HUGGINGFACE_API_BASE", "")
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else:
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embed_url = f"https://api-inference.huggingface.co/models/{model}"
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if "sentence-transformers" in model:
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if len(input) == 0:
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raise HuggingfaceError(status_code=400, message="sentence transformers requires 2+ sentences")
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data = {
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"inputs": {
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"source_sentence": input[0],
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"sentences": [ "That is a happy dog", "That is a very happy person", "Today is a sunny day" ]
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}
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}
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else:
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data = {
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"inputs": input # type: ignore
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}
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## LOGGING
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logging_obj.pre_call(
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input=input,
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api_key=api_key,
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additional_args={"complete_input_dict": data},
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model_response["created"] = 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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)
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## COMPLETION CALL
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response = requests.post(
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embed_url, headers=headers, data=json.dumps(data)
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)
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model_response.usage = usage
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model_response._hidden_params["original_response"] = completion_response
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return model_response
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## LOGGING
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logging_obj.post_call(
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input=input,
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api_key=api_key,
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additional_args={"complete_input_dict": data},
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original_response=response,
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)
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def completion(self,
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model: str,
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messages: list,
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api_base: Optional[str],
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headers: Optional[dict],
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model_response: ModelResponse,
|
||||
print_verbose: Callable,
|
||||
encoding,
|
||||
api_key,
|
||||
logging_obj,
|
||||
custom_prompt_dict={},
|
||||
acompletion: bool = False,
|
||||
optional_params=None,
|
||||
litellm_params=None,
|
||||
logger_fn=None,
|
||||
):
|
||||
super().completion()
|
||||
exception_mapping_worked = False
|
||||
try:
|
||||
headers = self.validate_environment(api_key, headers)
|
||||
task = get_hf_task_for_model(model)
|
||||
print_verbose(f"{model}, {task}")
|
||||
completion_url = ""
|
||||
input_text = None
|
||||
if "https" in model:
|
||||
completion_url = model
|
||||
elif api_base:
|
||||
completion_url = api_base
|
||||
elif "HF_API_BASE" in os.environ:
|
||||
completion_url = os.getenv("HF_API_BASE", "")
|
||||
elif "HUGGINGFACE_API_BASE" in os.environ:
|
||||
completion_url = os.getenv("HUGGINGFACE_API_BASE", "")
|
||||
else:
|
||||
completion_url = f"https://api-inference.huggingface.co/models/{model}"
|
||||
|
||||
## Load Config
|
||||
config=litellm.HuggingfaceConfig.get_config()
|
||||
for k, v in config.items():
|
||||
if k not in optional_params: # completion(top_k=3) > huggingfaceConfig(top_k=3) <- allows for dynamic variables to be passed in
|
||||
optional_params[k] = v
|
||||
|
||||
embeddings = response.json()
|
||||
|
||||
if "error" in embeddings:
|
||||
raise HuggingfaceError(status_code=500, message=embeddings['error'])
|
||||
|
||||
output_data = []
|
||||
if "similarities" in embeddings:
|
||||
for idx, embedding in embeddings["similarities"]:
|
||||
output_data.append(
|
||||
{
|
||||
"object": "embedding",
|
||||
"index": idx,
|
||||
"embedding": embedding # flatten list returned from hf
|
||||
}
|
||||
)
|
||||
else:
|
||||
for idx, embedding in enumerate(embeddings):
|
||||
if isinstance(embedding, float):
|
||||
output_data.append(
|
||||
{
|
||||
"object": "embedding",
|
||||
"index": idx,
|
||||
"embedding": embedding # flatten list returned from hf
|
||||
}
|
||||
### MAP INPUT PARAMS
|
||||
if task == "conversational":
|
||||
inference_params = copy.deepcopy(optional_params)
|
||||
inference_params.pop("details")
|
||||
inference_params.pop("return_full_text")
|
||||
past_user_inputs = []
|
||||
generated_responses = []
|
||||
text = ""
|
||||
for message in messages:
|
||||
if message["role"] == "user":
|
||||
if text != "":
|
||||
past_user_inputs.append(text)
|
||||
text = message["content"]
|
||||
elif message["role"] == "assistant" or message["role"] == "system":
|
||||
generated_responses.append(message["content"])
|
||||
data = {
|
||||
"inputs": {
|
||||
"text": text,
|
||||
"past_user_inputs": past_user_inputs,
|
||||
"generated_responses": generated_responses
|
||||
},
|
||||
"parameters": inference_params
|
||||
}
|
||||
input_text = "".join(message["content"] for message in messages)
|
||||
elif task == "text-generation-inference":
|
||||
# always send "details" and "return_full_text" as params
|
||||
if model in custom_prompt_dict:
|
||||
# check if the model has a registered custom prompt
|
||||
model_prompt_details = custom_prompt_dict[model]
|
||||
prompt = custom_prompt(
|
||||
role_dict=model_prompt_details.get("roles", None),
|
||||
initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""),
|
||||
final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
|
||||
messages=messages
|
||||
)
|
||||
else:
|
||||
prompt = prompt_factory(model=model, messages=messages)
|
||||
data = {
|
||||
"inputs": prompt,
|
||||
"parameters": optional_params,
|
||||
"stream": True if "stream" in optional_params and optional_params["stream"] == True else False,
|
||||
}
|
||||
input_text = prompt
|
||||
else:
|
||||
# Non TGI and Conversational llms
|
||||
# We need this branch, it removes 'details' and 'return_full_text' from params
|
||||
if model in custom_prompt_dict:
|
||||
# check if the model has a registered custom prompt
|
||||
model_prompt_details = custom_prompt_dict[model]
|
||||
prompt = custom_prompt(
|
||||
role_dict=model_prompt_details.get("roles", {}),
|
||||
initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""),
|
||||
final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
|
||||
bos_token=model_prompt_details.get("bos_token", ""),
|
||||
eos_token=model_prompt_details.get("eos_token", ""),
|
||||
messages=messages,
|
||||
)
|
||||
else:
|
||||
prompt = prompt_factory(model=model, messages=messages)
|
||||
inference_params = copy.deepcopy(optional_params)
|
||||
inference_params.pop("details")
|
||||
inference_params.pop("return_full_text")
|
||||
data = {
|
||||
"inputs": prompt,
|
||||
"parameters": inference_params,
|
||||
"stream": True if "stream" in optional_params and optional_params["stream"] == True else False,
|
||||
}
|
||||
input_text = prompt
|
||||
## LOGGING
|
||||
logging_obj.pre_call(
|
||||
input=input_text,
|
||||
api_key=api_key,
|
||||
additional_args={"complete_input_dict": data, "task": task, "headers": headers, "api_base": completion_url, "acompletion": acompletion},
|
||||
)
|
||||
## COMPLETION CALL
|
||||
if acompletion is True:
|
||||
### ASYNC STREAMING
|
||||
if optional_params.get("stream", False):
|
||||
return self.async_streaming(logging_obj=logging_obj, api_base=completion_url, data=data, headers=headers, model_response=model_response, model=model)
|
||||
else:
|
||||
### ASYNC COMPLETION
|
||||
return self.acompletion(api_base=completion_url, data=data, headers=headers, model_response=model_response, task=task, encoding=encoding, input_text=input_text, model=model, optional_params=optional_params)
|
||||
### SYNC STREAMING
|
||||
if "stream" in optional_params and optional_params["stream"] == True:
|
||||
response = requests.post(
|
||||
completion_url,
|
||||
headers=headers,
|
||||
data=json.dumps(data),
|
||||
stream=optional_params["stream"]
|
||||
)
|
||||
return response.iter_lines()
|
||||
### SYNC COMPLETION
|
||||
else:
|
||||
response = requests.post(
|
||||
completion_url,
|
||||
headers=headers,
|
||||
data=json.dumps(data)
|
||||
)
|
||||
|
||||
## Some servers might return streaming responses even though stream was not set to true. (e.g. Baseten)
|
||||
is_streamed = False
|
||||
if response.__dict__['headers'].get("Content-Type", "") == "text/event-stream":
|
||||
is_streamed = True
|
||||
|
||||
# iterate over the complete streamed response, and return the final answer
|
||||
if is_streamed:
|
||||
streamed_response = CustomStreamWrapper(completion_stream=response.iter_lines(), model=model, custom_llm_provider="huggingface", logging_obj=logging_obj)
|
||||
content = ""
|
||||
for chunk in streamed_response:
|
||||
content += chunk["choices"][0]["delta"]["content"]
|
||||
completion_response: List[Dict[str, Any]] = [{"generated_text": content}]
|
||||
## LOGGING
|
||||
logging_obj.post_call(
|
||||
input=input_text,
|
||||
api_key=api_key,
|
||||
original_response=completion_response,
|
||||
additional_args={"complete_input_dict": data, "task": task},
|
||||
)
|
||||
else:
|
||||
## LOGGING
|
||||
logging_obj.post_call(
|
||||
input=input_text,
|
||||
api_key=api_key,
|
||||
original_response=response.text,
|
||||
additional_args={"complete_input_dict": data, "task": task},
|
||||
)
|
||||
## RESPONSE OBJECT
|
||||
try:
|
||||
completion_response = response.json()
|
||||
except:
|
||||
import traceback
|
||||
raise HuggingfaceError(
|
||||
message=f"Original Response received: {response.text}; Stacktrace: {traceback.format_exc()}", status_code=response.status_code
|
||||
)
|
||||
print_verbose(f"response: {completion_response}")
|
||||
if isinstance(completion_response, dict) and "error" in completion_response:
|
||||
print_verbose(f"completion error: {completion_response['error']}")
|
||||
print_verbose(f"response.status_code: {response.status_code}")
|
||||
raise HuggingfaceError(
|
||||
message=completion_response["error"],
|
||||
status_code=response.status_code,
|
||||
)
|
||||
|
||||
return self.convert_to_model_response_object(
|
||||
completion_response=completion_response,
|
||||
model_response=model_response,
|
||||
task=task,
|
||||
optional_params=optional_params,
|
||||
encoding=encoding,
|
||||
input_text=input_text,
|
||||
model=model
|
||||
)
|
||||
except HuggingfaceError as e:
|
||||
exception_mapping_worked = True
|
||||
raise e
|
||||
except Exception as e:
|
||||
if exception_mapping_worked:
|
||||
raise e
|
||||
else:
|
||||
output_data.append(
|
||||
{
|
||||
"object": "embedding",
|
||||
"index": idx,
|
||||
"embedding": embedding[0][0] # flatten list returned from hf
|
||||
}
|
||||
)
|
||||
model_response["object"] = "list"
|
||||
model_response["data"] = output_data
|
||||
model_response["model"] = model
|
||||
input_tokens = 0
|
||||
for text in input:
|
||||
input_tokens+=len(encoding.encode(text))
|
||||
import traceback
|
||||
raise HuggingfaceError(status_code=500, message=traceback.format_exc())
|
||||
|
||||
model_response["usage"] = {
|
||||
"prompt_tokens": input_tokens,
|
||||
"total_tokens": input_tokens,
|
||||
}
|
||||
return model_response
|
||||
async def acompletion(self,
|
||||
api_base: str,
|
||||
data: dict,
|
||||
headers: dict,
|
||||
model_response: ModelResponse,
|
||||
task: str,
|
||||
encoding: Any,
|
||||
input_text: str,
|
||||
model: str,
|
||||
optional_params: dict):
|
||||
if self._aclient_session is None:
|
||||
self._aclient_session = self.create_aclient_session()
|
||||
client = self._aclient_session
|
||||
try:
|
||||
response = await client.post(url=api_base, json=data, headers=headers)
|
||||
response_json = response.json()
|
||||
if response.status_code != 200:
|
||||
raise HuggingfaceError(status_code=response.status_code, message=response.text, request=response.request, response=response)
|
||||
|
||||
## RESPONSE OBJECT
|
||||
return self.convert_to_model_response_object(completion_response=response_json,
|
||||
model_response=model_response,
|
||||
task=task,
|
||||
encoding=encoding,
|
||||
input_text=input_text,
|
||||
model=model,
|
||||
optional_params=optional_params)
|
||||
except Exception as e:
|
||||
if isinstance(e,httpx.TimeoutException):
|
||||
raise HuggingfaceError(status_code=500, message="Request Timeout Error")
|
||||
elif response and hasattr(response, "text"):
|
||||
raise HuggingfaceError(status_code=500, message=f"{str(e)}\n\nOriginal Response: {response.text}")
|
||||
else:
|
||||
raise HuggingfaceError(status_code=500, message=f"{str(e)}")
|
||||
|
||||
async def async_streaming(self,
|
||||
logging_obj,
|
||||
api_base: str,
|
||||
data: dict,
|
||||
headers: dict,
|
||||
model_response: ModelResponse,
|
||||
model: str):
|
||||
if self._aclient_session is None:
|
||||
self._aclient_session = self.create_aclient_session()
|
||||
client = self._aclient_session
|
||||
async with client.stream(
|
||||
url=f"{api_base}",
|
||||
json=data,
|
||||
headers=headers,
|
||||
method="POST"
|
||||
) as response:
|
||||
if response.status_code != 200:
|
||||
raise HuggingfaceError(status_code=response.status_code, message="An error occurred while streaming")
|
||||
|
||||
streamwrapper = CustomStreamWrapper(completion_stream=response.aiter_lines(), model=model, custom_llm_provider="huggingface",logging_obj=logging_obj)
|
||||
async for transformed_chunk in streamwrapper:
|
||||
yield transformed_chunk
|
||||
|
||||
def embedding(self,
|
||||
model: str,
|
||||
input: list,
|
||||
api_key: Optional[str] = None,
|
||||
api_base: Optional[str] = None,
|
||||
logging_obj=None,
|
||||
model_response=None,
|
||||
encoding=None,
|
||||
):
|
||||
super().embedding()
|
||||
headers = self.validate_environment(api_key, headers=None)
|
||||
# print_verbose(f"{model}, {task}")
|
||||
embed_url = ""
|
||||
if "https" in model:
|
||||
embed_url = model
|
||||
elif api_base:
|
||||
embed_url = api_base
|
||||
elif "HF_API_BASE" in os.environ:
|
||||
embed_url = os.getenv("HF_API_BASE", "")
|
||||
elif "HUGGINGFACE_API_BASE" in os.environ:
|
||||
embed_url = os.getenv("HUGGINGFACE_API_BASE", "")
|
||||
else:
|
||||
embed_url = f"https://api-inference.huggingface.co/models/{model}"
|
||||
|
||||
if "sentence-transformers" in model:
|
||||
if len(input) == 0:
|
||||
raise HuggingfaceError(status_code=400, message="sentence transformers requires 2+ sentences")
|
||||
data = {
|
||||
"inputs": {
|
||||
"source_sentence": input[0],
|
||||
"sentences": [ "That is a happy dog", "That is a very happy person", "Today is a sunny day" ]
|
||||
}
|
||||
}
|
||||
else:
|
||||
data = {
|
||||
"inputs": input # type: ignore
|
||||
}
|
||||
|
||||
## LOGGING
|
||||
logging_obj.pre_call(
|
||||
input=input,
|
||||
api_key=api_key,
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
## COMPLETION CALL
|
||||
response = requests.post(
|
||||
embed_url, headers=headers, data=json.dumps(data)
|
||||
)
|
||||
|
||||
|
||||
## LOGGING
|
||||
logging_obj.post_call(
|
||||
input=input,
|
||||
api_key=api_key,
|
||||
additional_args={"complete_input_dict": data},
|
||||
original_response=response,
|
||||
)
|
||||
|
||||
|
||||
embeddings = response.json()
|
||||
|
||||
if "error" in embeddings:
|
||||
raise HuggingfaceError(status_code=500, message=embeddings['error'])
|
||||
|
||||
output_data = []
|
||||
if "similarities" in embeddings:
|
||||
for idx, embedding in embeddings["similarities"]:
|
||||
output_data.append(
|
||||
{
|
||||
"object": "embedding",
|
||||
"index": idx,
|
||||
"embedding": embedding # flatten list returned from hf
|
||||
}
|
||||
)
|
||||
else:
|
||||
for idx, embedding in enumerate(embeddings):
|
||||
if isinstance(embedding, float):
|
||||
output_data.append(
|
||||
{
|
||||
"object": "embedding",
|
||||
"index": idx,
|
||||
"embedding": embedding # flatten list returned from hf
|
||||
}
|
||||
)
|
||||
else:
|
||||
output_data.append(
|
||||
{
|
||||
"object": "embedding",
|
||||
"index": idx,
|
||||
"embedding": embedding[0][0] # flatten list returned from hf
|
||||
}
|
||||
)
|
||||
model_response["object"] = "list"
|
||||
model_response["data"] = output_data
|
||||
model_response["model"] = model
|
||||
input_tokens = 0
|
||||
for text in input:
|
||||
input_tokens+=len(encoding.encode(text))
|
||||
|
||||
model_response["usage"] = {
|
||||
"prompt_tokens": input_tokens,
|
||||
"total_tokens": input_tokens,
|
||||
}
|
||||
return model_response
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue