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import json
import os
from typing import Any, Callable, Dict, List, Literal, Optional, Union, get_args
import httpx
import litellm
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.custom_httpx.http_handler import (
AsyncHTTPHandler,
HTTPHandler,
get_async_httpx_client,
)
from litellm.types.utils import EmbeddingResponse
from ...base import BaseLLM
from ..common_utils import HuggingFaceError
from .transformation import HuggingFaceEmbeddingConfig
config = HuggingFaceEmbeddingConfig()
HF_HUB_URL = "https://huggingface.co"
hf_tasks_embeddings = Literal[ # pipeline tags + hf tei endpoints - https://huggingface.github.io/text-embeddings-inference/#/
"sentence-similarity", "feature-extraction", "rerank", "embed", "similarity"
]
def get_hf_task_embedding_for_model(
model: str, task_type: Optional[str], api_base: str
) -> Optional[str]:
if task_type is not None:
if task_type in get_args(hf_tasks_embeddings):
return task_type
else:
raise Exception(
"Invalid task_type={}. Expected one of={}".format(
task_type, hf_tasks_embeddings
)
)
http_client = HTTPHandler(concurrent_limit=1)
model_info = http_client.get(url=f"{api_base}/api/models/{model}")
model_info_dict = model_info.json()
pipeline_tag: Optional[str] = model_info_dict.get("pipeline_tag", None)
return pipeline_tag
async def async_get_hf_task_embedding_for_model(
model: str, task_type: Optional[str], api_base: str
) -> Optional[str]:
if task_type is not None:
if task_type in get_args(hf_tasks_embeddings):
return task_type
else:
raise Exception(
"Invalid task_type={}. Expected one of={}".format(
task_type, hf_tasks_embeddings
)
)
http_client = get_async_httpx_client(
llm_provider=litellm.LlmProviders.HUGGINGFACE,
)
model_info = await http_client.get(url=f"{api_base}/api/models/{model}")
model_info_dict = model_info.json()
pipeline_tag: Optional[str] = model_info_dict.get("pipeline_tag", None)
return pipeline_tag
class HuggingFaceEmbedding(BaseLLM):
_client_session: Optional[httpx.Client] = None
_aclient_session: Optional[httpx.AsyncClient] = None
def __init__(self) -> None:
super().__init__()
def _transform_input_on_pipeline_tag(
self, input: List, pipeline_tag: Optional[str]
) -> dict:
if pipeline_tag is None:
return {"inputs": input}
if pipeline_tag == "sentence-similarity" or pipeline_tag == "similarity":
if len(input) < 2:
raise HuggingFaceError(
status_code=400,
message="sentence-similarity requires 2+ sentences",
)
return {"inputs": {"source_sentence": input[0], "sentences": input[1:]}}
elif pipeline_tag == "rerank":
if len(input) < 2:
raise HuggingFaceError(
status_code=400,
message="reranker requires 2+ sentences",
)
return {"inputs": {"query": input[0], "texts": input[1:]}}
return {"inputs": input} # default to feature-extraction pipeline tag
async def _async_transform_input(
self,
model: str,
task_type: Optional[str],
embed_url: str,
input: List,
optional_params: dict,
) -> dict:
hf_task = await async_get_hf_task_embedding_for_model(
model=model, task_type=task_type, api_base=HF_HUB_URL
)
data = self._transform_input_on_pipeline_tag(input=input, pipeline_tag=hf_task)
if len(optional_params.keys()) > 0:
data["options"] = optional_params
return data
def _process_optional_params(self, data: dict, optional_params: dict) -> dict:
special_options_keys = config.get_special_options_params()
special_parameters_keys = [
"min_length",
"max_length",
"top_k",
"top_p",
"temperature",
"repetition_penalty",
"max_time",
]
for k, v in optional_params.items():
if k in special_options_keys:
data.setdefault("options", {})
data["options"][k] = v
elif k in special_parameters_keys:
data.setdefault("parameters", {})
data["parameters"][k] = v
else:
data[k] = v
return data
def _transform_input(
self,
input: List,
model: str,
call_type: Literal["sync", "async"],
optional_params: dict,
embed_url: str,
) -> dict:
data: Dict = {}
## TRANSFORMATION ##
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": input[1:]}}
else:
data = {"inputs": input}
task_type = optional_params.pop("input_type", None)
if call_type == "sync":
hf_task = get_hf_task_embedding_for_model(
model=model, task_type=task_type, api_base=HF_HUB_URL
)
elif call_type == "async":
return self._async_transform_input(
model=model, task_type=task_type, embed_url=embed_url, input=input
) # type: ignore
data = self._transform_input_on_pipeline_tag(
input=input, pipeline_tag=hf_task
)
if len(optional_params.keys()) > 0:
data = self._process_optional_params(
data=data, optional_params=optional_params
)
return data
def _process_embedding_response(
self,
embeddings: dict,
model_response: EmbeddingResponse,
model: str,
input: List,
encoding: Any,
) -> EmbeddingResponse:
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
}
)
elif isinstance(embedding, list) and isinstance(embedding[0], 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))
setattr(
model_response,
"usage",
litellm.Usage(
prompt_tokens=input_tokens,
completion_tokens=input_tokens,
total_tokens=input_tokens,
prompt_tokens_details=None,
completion_tokens_details=None,
),
)
return model_response
async def aembedding(
self,
model: str,
input: list,
model_response: litellm.utils.EmbeddingResponse,
timeout: Union[float, httpx.Timeout],
logging_obj: LiteLLMLoggingObj,
optional_params: dict,
api_base: str,
api_key: Optional[str],
headers: dict,
encoding: Callable,
client: Optional[AsyncHTTPHandler] = None,
):
## TRANSFORMATION ##
data = self._transform_input(
input=input,
model=model,
call_type="sync",
optional_params=optional_params,
embed_url=api_base,
)
## LOGGING
logging_obj.pre_call(
input=input,
api_key=api_key,
additional_args={
"complete_input_dict": data,
"headers": headers,
"api_base": api_base,
},
)
## COMPLETION CALL
if client is None:
client = get_async_httpx_client(
llm_provider=litellm.LlmProviders.HUGGINGFACE,
)
response = await client.post(api_base, 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"])
## PROCESS RESPONSE ##
return self._process_embedding_response(
embeddings=embeddings,
model_response=model_response,
model=model,
input=input,
encoding=encoding,
)
def embedding(
self,
model: str,
input: list,
model_response: EmbeddingResponse,
optional_params: dict,
litellm_params: dict,
logging_obj: LiteLLMLoggingObj,
encoding: Callable,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
timeout: Union[float, httpx.Timeout] = httpx.Timeout(None),
aembedding: Optional[bool] = None,
client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
headers={},
) -> EmbeddingResponse:
super().embedding()
headers = config.validate_environment(
api_key=api_key,
headers=headers,
model=model,
optional_params=optional_params,
messages=[],
litellm_params=litellm_params,
)
task_type = optional_params.pop("input_type", None)
task = get_hf_task_embedding_for_model(
model=model, task_type=task_type, api_base=HF_HUB_URL
)
# 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://router.huggingface.co/hf-inference/pipeline/{task}/{model}"
)
## ROUTING ##
if aembedding is True:
return self.aembedding(
input=input,
model_response=model_response,
timeout=timeout,
logging_obj=logging_obj,
headers=headers,
api_base=embed_url, # type: ignore
api_key=api_key,
client=client if isinstance(client, AsyncHTTPHandler) else None,
model=model,
optional_params=optional_params,
encoding=encoding,
)
## TRANSFORMATION ##
data = self._transform_input(
input=input,
model=model,
call_type="sync",
optional_params=optional_params,
embed_url=embed_url,
)
## LOGGING
logging_obj.pre_call(
input=input,
api_key=api_key,
additional_args={
"complete_input_dict": data,
"headers": headers,
"api_base": embed_url,
},
)
## COMPLETION CALL
if client is None or not isinstance(client, HTTPHandler):
client = HTTPHandler(concurrent_limit=1)
response = client.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"])
## PROCESS RESPONSE ##
return self._process_embedding_response(
embeddings=embeddings,
model_response=model_response,
model=model,
input=input,
encoding=encoding,
)
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