two version of R2R are here HEAD master

1 files changed, 769 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/huggingface/chat/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/huggingface/chat/handler.py
new file mode 100644
index 00000000..2b65e5b7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/huggingface/chat/handler.py
@@ -0,0 +1,769 @@
+## Uses the huggingface text generation inference API
+import json
+import os
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Tuple,
+    Union,
+    cast,
+    get_args,
+)
+
+import httpx
+
+import litellm
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
+from litellm.llms.custom_httpx.http_handler import (
+    AsyncHTTPHandler,
+    HTTPHandler,
+    _get_httpx_client,
+    get_async_httpx_client,
+)
+from litellm.llms.huggingface.chat.transformation import (
+    HuggingfaceChatConfig as HuggingfaceConfig,
+)
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import EmbeddingResponse
+from litellm.types.utils import Logprobs as TextCompletionLogprobs
+from litellm.types.utils import ModelResponse
+
+from ...base import BaseLLM
+from ..common_utils import HuggingfaceError
+
+hf_chat_config = HuggingfaceConfig()
+
+
+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=api_base)
+
+    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=api_base)
+
+    model_info_dict = model_info.json()
+
+    pipeline_tag: Optional[str] = model_info_dict.get("pipeline_tag", None)
+
+    return pipeline_tag
+
+
+async def make_call(
+    client: Optional[AsyncHTTPHandler],
+    api_base: str,
+    headers: dict,
+    data: str,
+    model: str,
+    messages: list,
+    logging_obj,
+    timeout: Optional[Union[float, httpx.Timeout]],
+    json_mode: bool,
+) -> Tuple[Any, httpx.Headers]:
+    if client is None:
+        client = litellm.module_level_aclient
+
+    try:
+        response = await client.post(
+            api_base, headers=headers, data=data, stream=True, timeout=timeout
+        )
+    except httpx.HTTPStatusError as e:
+        error_headers = getattr(e, "headers", None)
+        error_response = getattr(e, "response", None)
+        if error_headers is None and error_response:
+            error_headers = getattr(error_response, "headers", None)
+        raise HuggingfaceError(
+            status_code=e.response.status_code,
+            message=str(await e.response.aread()),
+            headers=cast(dict, error_headers) if error_headers else None,
+        )
+    except Exception as e:
+        for exception in litellm.LITELLM_EXCEPTION_TYPES:
+            if isinstance(e, exception):
+                raise e
+        raise HuggingfaceError(status_code=500, message=str(e))
+
+    # LOGGING
+    logging_obj.post_call(
+        input=messages,
+        api_key="",
+        original_response=response,  # Pass the completion stream for logging
+        additional_args={"complete_input_dict": data},
+    )
+
+    return response.aiter_lines(), response.headers
+
+
+class Huggingface(BaseLLM):
+    _client_session: Optional[httpx.Client] = None
+    _aclient_session: Optional[httpx.AsyncClient] = None
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def completion(  # noqa: PLR0915
+        self,
+        model: str,
+        messages: list,
+        api_base: Optional[str],
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        timeout: float,
+        encoding,
+        api_key,
+        logging_obj,
+        optional_params: dict,
+        litellm_params: dict,
+        custom_prompt_dict={},
+        acompletion: bool = False,
+        logger_fn=None,
+        client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        headers: dict = {},
+    ):
+        super().completion()
+        exception_mapping_worked = False
+        try:
+            task, model = hf_chat_config.get_hf_task_for_model(model)
+            litellm_params["task"] = task
+            headers = hf_chat_config.validate_environment(
+                api_key=api_key,
+                headers=headers,
+                model=model,
+                messages=messages,
+                optional_params=optional_params,
+            )
+            completion_url = hf_chat_config.get_api_base(api_base=api_base, model=model)
+            data = hf_chat_config.transform_request(
+                model=model,
+                messages=messages,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                headers=headers,
+            )
+
+            ## LOGGING
+            logging_obj.pre_call(
+                input=data,
+                api_key=api_key,
+                additional_args={
+                    "complete_input_dict": data,
+                    "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, timeout=timeout, messages=messages)  # type: ignore
+                else:
+                    ### ASYNC COMPLETION
+                    return self.acompletion(
+                        api_base=completion_url,
+                        data=data,
+                        headers=headers,
+                        model_response=model_response,
+                        encoding=encoding,
+                        model=model,
+                        optional_params=optional_params,
+                        timeout=timeout,
+                        litellm_params=litellm_params,
+                        logging_obj=logging_obj,
+                        api_key=api_key,
+                        messages=messages,
+                        client=(
+                            client
+                            if client is not None
+                            and isinstance(client, AsyncHTTPHandler)
+                            else None
+                        ),
+                    )
+            if client is None or not isinstance(client, HTTPHandler):
+                client = _get_httpx_client()
+            ### SYNC STREAMING
+            if "stream" in optional_params and optional_params["stream"] is True:
+                response = client.post(
+                    url=completion_url,
+                    headers=headers,
+                    data=json.dumps(data),
+                    stream=optional_params["stream"],
+                )
+                return response.iter_lines()
+            ### SYNC COMPLETION
+            else:
+                response = client.post(
+                    url=completion_url,
+                    headers=headers,
+                    data=json.dumps(data),
+                )
+
+                return hf_chat_config.transform_response(
+                    model=model,
+                    raw_response=response,
+                    model_response=model_response,
+                    logging_obj=logging_obj,
+                    api_key=api_key,
+                    request_data=data,
+                    messages=messages,
+                    optional_params=optional_params,
+                    encoding=encoding,
+                    json_mode=None,
+                    litellm_params=litellm_params,
+                )
+        except httpx.HTTPStatusError as e:
+            raise HuggingfaceError(
+                status_code=e.response.status_code,
+                message=e.response.text,
+                headers=e.response.headers,
+            )
+        except HuggingfaceError as e:
+            exception_mapping_worked = True
+            raise e
+        except Exception as e:
+            if exception_mapping_worked:
+                raise e
+            else:
+                import traceback
+
+                raise HuggingfaceError(status_code=500, message=traceback.format_exc())
+
+    async def acompletion(
+        self,
+        api_base: str,
+        data: dict,
+        headers: dict,
+        model_response: ModelResponse,
+        encoding: Any,
+        model: str,
+        optional_params: dict,
+        litellm_params: dict,
+        timeout: float,
+        logging_obj: LiteLLMLoggingObj,
+        api_key: str,
+        messages: List[AllMessageValues],
+        client: Optional[AsyncHTTPHandler] = None,
+    ):
+        response: Optional[httpx.Response] = None
+        try:
+            if client is None:
+                client = get_async_httpx_client(
+                    llm_provider=litellm.LlmProviders.HUGGINGFACE
+                )
+            ### ASYNC COMPLETION
+            http_response = await client.post(
+                url=api_base, headers=headers, data=json.dumps(data), timeout=timeout
+            )
+
+            response = http_response
+
+            return hf_chat_config.transform_response(
+                model=model,
+                raw_response=http_response,
+                model_response=model_response,
+                logging_obj=logging_obj,
+                api_key=api_key,
+                request_data=data,
+                messages=messages,
+                optional_params=optional_params,
+                encoding=encoding,
+                json_mode=None,
+                litellm_params=litellm_params,
+            )
+        except Exception as e:
+            if isinstance(e, httpx.TimeoutException):
+                raise HuggingfaceError(status_code=500, message="Request Timeout Error")
+            elif isinstance(e, HuggingfaceError):
+                raise e
+            elif response is not None and hasattr(response, "text"):
+                raise HuggingfaceError(
+                    status_code=500,
+                    message=f"{str(e)}\n\nOriginal Response: {response.text}",
+                    headers=response.headers,
+                )
+            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,
+        messages: List[AllMessageValues],
+        model: str,
+        timeout: float,
+        client: Optional[AsyncHTTPHandler] = None,
+    ):
+        completion_stream, _ = await make_call(
+            client=client,
+            api_base=api_base,
+            headers=headers,
+            data=json.dumps(data),
+            model=model,
+            messages=messages,
+            logging_obj=logging_obj,
+            timeout=timeout,
+            json_mode=False,
+        )
+        streamwrapper = CustomStreamWrapper(
+            completion_stream=completion_stream,
+            model=model,
+            custom_llm_provider="huggingface",
+            logging_obj=logging_obj,
+        )
+        return streamwrapper
+
+    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=embed_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 = HuggingfaceConfig().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=embed_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,
+        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 = hf_chat_config.validate_environment(
+            api_key=api_key,
+            headers=headers,
+            model=model,
+            optional_params=optional_params,
+            messages=[],
+        )
+        # 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}"
+
+        ## 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,
+        )
+
+    def _transform_logprobs(
+        self, hf_response: Optional[List]
+    ) -> Optional[TextCompletionLogprobs]:
+        """
+        Transform Hugging Face logprobs to OpenAI.Completion() format
+        """
+        if hf_response is None:
+            return None
+
+        # Initialize an empty list for the transformed logprobs
+        _logprob: TextCompletionLogprobs = TextCompletionLogprobs(
+            text_offset=[],
+            token_logprobs=[],
+            tokens=[],
+            top_logprobs=[],
+        )
+
+        # For each Hugging Face response, transform the logprobs
+        for response in hf_response:
+            # Extract the relevant information from the response
+            response_details = response["details"]
+            top_tokens = response_details.get("top_tokens", {})
+
+            for i, token in enumerate(response_details["prefill"]):
+                # Extract the text of the token
+                token_text = token["text"]
+
+                # Extract the logprob of the token
+                token_logprob = token["logprob"]
+
+                # Add the token information to the 'token_info' list
+                cast(List[str], _logprob.tokens).append(token_text)
+                cast(List[float], _logprob.token_logprobs).append(token_logprob)
+
+                # stub this to work with llm eval harness
+                top_alt_tokens = {"": -1.0, "": -2.0, "": -3.0}  # noqa: F601
+                cast(List[Dict[str, float]], _logprob.top_logprobs).append(
+                    top_alt_tokens
+                )
+
+            # For each element in the 'tokens' list, extract the relevant information
+            for i, token in enumerate(response_details["tokens"]):
+                # Extract the text of the token
+                token_text = token["text"]
+
+                # Extract the logprob of the token
+                token_logprob = token["logprob"]
+
+                top_alt_tokens = {}
+                temp_top_logprobs = []
+                if top_tokens != {}:
+                    temp_top_logprobs = top_tokens[i]
+
+                # top_alt_tokens should look like this: { "alternative_1": -1, "alternative_2": -2, "alternative_3": -3 }
+                for elem in temp_top_logprobs:
+                    text = elem["text"]
+                    logprob = elem["logprob"]
+                    top_alt_tokens[text] = logprob
+
+                # Add the token information to the 'token_info' list
+                cast(List[str], _logprob.tokens).append(token_text)
+                cast(List[float], _logprob.token_logprobs).append(token_logprob)
+                cast(List[Dict[str, float]], _logprob.top_logprobs).append(
+                    top_alt_tokens
+                )
+
+                # Add the text offset of the token
+                # This is computed as the sum of the lengths of all previous tokens
+                cast(List[int], _logprob.text_offset).append(
+                    sum(len(t["text"]) for t in response_details["tokens"][:i])
+                )
+
+        return _logprob