two version of R2R are hereHEADmaster

2 files changed, 652 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/predibase/chat/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/predibase/chat/handler.py
new file mode 100644
index 00000000..43f4b067
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/predibase/chat/handler.py
@@ -0,0 +1,472 @@
+# What is this?
+## Controller file for Predibase Integration - https://predibase.com/
+
+import json
+import os
+import time
+from functools import partial
+from typing import Callable, Optional, Union
+
+import httpx  # type: ignore
+
+import litellm
+import litellm.litellm_core_utils
+import litellm.litellm_core_utils.litellm_logging
+from litellm.litellm_core_utils.core_helpers import map_finish_reason
+from litellm.litellm_core_utils.prompt_templates.factory import (
+    custom_prompt,
+    prompt_factory,
+)
+from litellm.llms.custom_httpx.http_handler import (
+    AsyncHTTPHandler,
+    get_async_httpx_client,
+)
+from litellm.types.utils import LiteLLMLoggingBaseClass
+from litellm.utils import Choices, CustomStreamWrapper, Message, ModelResponse, Usage
+
+from ..common_utils import PredibaseError
+
+
+async def make_call(
+    client: AsyncHTTPHandler,
+    api_base: str,
+    headers: dict,
+    data: str,
+    model: str,
+    messages: list,
+    logging_obj,
+    timeout: Optional[Union[float, httpx.Timeout]],
+):
+    response = await client.post(
+        api_base, headers=headers, data=data, stream=True, timeout=timeout
+    )
+
+    if response.status_code != 200:
+        raise PredibaseError(status_code=response.status_code, message=response.text)
+
+    completion_stream = response.aiter_lines()
+    # LOGGING
+    logging_obj.post_call(
+        input=messages,
+        api_key="",
+        original_response=completion_stream,  # Pass the completion stream for logging
+        additional_args={"complete_input_dict": data},
+    )
+
+    return completion_stream
+
+
+class PredibaseChatCompletion:
+    def __init__(self) -> None:
+        super().__init__()
+
+    def output_parser(self, generated_text: str):
+        """
+        Parse the output text to remove any special characters. In our current approach we just check for ChatML tokens.
+
+        Initial issue that prompted this - https://github.com/BerriAI/litellm/issues/763
+        """
+        chat_template_tokens = [
+            "<|assistant|>",
+            "<|system|>",
+            "<|user|>",
+            "<s>",
+            "</s>",
+        ]
+        for token in chat_template_tokens:
+            if generated_text.strip().startswith(token):
+                generated_text = generated_text.replace(token, "", 1)
+            if generated_text.endswith(token):
+                generated_text = generated_text[::-1].replace(token[::-1], "", 1)[::-1]
+        return generated_text
+
+    def process_response(  # noqa: PLR0915
+        self,
+        model: str,
+        response: httpx.Response,
+        model_response: ModelResponse,
+        stream: bool,
+        logging_obj: LiteLLMLoggingBaseClass,
+        optional_params: dict,
+        api_key: str,
+        data: Union[dict, str],
+        messages: list,
+        print_verbose,
+        encoding,
+    ) -> ModelResponse:
+        ## LOGGING
+        logging_obj.post_call(
+            input=messages,
+            api_key=api_key,
+            original_response=response.text,
+            additional_args={"complete_input_dict": data},
+        )
+        print_verbose(f"raw model_response: {response.text}")
+        ## RESPONSE OBJECT
+        try:
+            completion_response = response.json()
+        except Exception:
+            raise PredibaseError(message=response.text, status_code=422)
+        if "error" in completion_response:
+            raise PredibaseError(
+                message=str(completion_response["error"]),
+                status_code=response.status_code,
+            )
+        else:
+            if not isinstance(completion_response, dict):
+                raise PredibaseError(
+                    status_code=422,
+                    message=f"'completion_response' is not a dictionary - {completion_response}",
+                )
+            elif "generated_text" not in completion_response:
+                raise PredibaseError(
+                    status_code=422,
+                    message=f"'generated_text' is not a key response dictionary - {completion_response}",
+                )
+            if len(completion_response["generated_text"]) > 0:
+                model_response.choices[0].message.content = self.output_parser(  # type: ignore
+                    completion_response["generated_text"]
+                )
+            ## GETTING LOGPROBS + FINISH REASON
+            if (
+                "details" in completion_response
+                and "tokens" in completion_response["details"]
+            ):
+                model_response.choices[0].finish_reason = map_finish_reason(
+                    completion_response["details"]["finish_reason"]
+                )
+                sum_logprob = 0
+                for token in completion_response["details"]["tokens"]:
+                    if token["logprob"] is not None:
+                        sum_logprob += token["logprob"]
+                setattr(
+                    model_response.choices[0].message,  # type: ignore
+                    "_logprob",
+                    sum_logprob,  # [TODO] move this to using the actual logprobs
+                )
+            if "best_of" in optional_params and optional_params["best_of"] > 1:
+                if (
+                    "details" in completion_response
+                    and "best_of_sequences" in completion_response["details"]
+                ):
+                    choices_list = []
+                    for idx, item in enumerate(
+                        completion_response["details"]["best_of_sequences"]
+                    ):
+                        sum_logprob = 0
+                        for token in item["tokens"]:
+                            if token["logprob"] is not None:
+                                sum_logprob += token["logprob"]
+                        if len(item["generated_text"]) > 0:
+                            message_obj = Message(
+                                content=self.output_parser(item["generated_text"]),
+                                logprobs=sum_logprob,
+                            )
+                        else:
+                            message_obj = Message(content=None)
+                        choice_obj = Choices(
+                            finish_reason=map_finish_reason(item["finish_reason"]),
+                            index=idx + 1,
+                            message=message_obj,
+                        )
+                        choices_list.append(choice_obj)
+                    model_response.choices.extend(choices_list)
+
+        ## CALCULATING USAGE
+        prompt_tokens = 0
+        try:
+            prompt_tokens = litellm.token_counter(messages=messages)
+        except Exception:
+            # this should remain non blocking we should not block a response returning if calculating usage fails
+            pass
+        output_text = model_response["choices"][0]["message"].get("content", "")
+        if output_text is not None and len(output_text) > 0:
+            completion_tokens = 0
+            try:
+                completion_tokens = len(
+                    encoding.encode(
+                        model_response["choices"][0]["message"].get("content", "")
+                    )
+                )  ##[TODO] use a model-specific tokenizer
+            except Exception:
+                # this should remain non blocking we should not block a response returning if calculating usage fails
+                pass
+        else:
+            completion_tokens = 0
+
+        total_tokens = prompt_tokens + completion_tokens
+
+        model_response.created = int(time.time())
+        model_response.model = model
+        usage = Usage(
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            total_tokens=total_tokens,
+        )
+        model_response.usage = usage  # type: ignore
+
+        ## RESPONSE HEADERS
+        predibase_headers = response.headers
+        response_headers = {}
+        for k, v in predibase_headers.items():
+            if k.startswith("x-"):
+                response_headers["llm_provider-{}".format(k)] = v
+
+        model_response._hidden_params["additional_headers"] = response_headers
+
+        return model_response
+
+    def completion(
+        self,
+        model: str,
+        messages: list,
+        api_base: str,
+        custom_prompt_dict: dict,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        encoding,
+        api_key: str,
+        logging_obj,
+        optional_params: dict,
+        tenant_id: str,
+        timeout: Union[float, httpx.Timeout],
+        acompletion=None,
+        litellm_params=None,
+        logger_fn=None,
+        headers: dict = {},
+    ) -> Union[ModelResponse, CustomStreamWrapper]:
+        headers = litellm.PredibaseConfig().validate_environment(
+            api_key=api_key,
+            headers=headers,
+            messages=messages,
+            optional_params=optional_params,
+            model=model,
+        )
+        completion_url = ""
+        input_text = ""
+        base_url = "https://serving.app.predibase.com"
+
+        if "https" in model:
+            completion_url = model
+        elif api_base:
+            base_url = api_base
+        elif "PREDIBASE_API_BASE" in os.environ:
+            base_url = os.getenv("PREDIBASE_API_BASE", "")
+
+        completion_url = f"{base_url}/{tenant_id}/deployments/v2/llms/{model}"
+
+        if optional_params.get("stream", False) is True:
+            completion_url += "/generate_stream"
+        else:
+            completion_url += "/generate"
+
+        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["roles"],
+                initial_prompt_value=model_prompt_details["initial_prompt_value"],
+                final_prompt_value=model_prompt_details["final_prompt_value"],
+                messages=messages,
+            )
+        else:
+            prompt = prompt_factory(model=model, messages=messages)
+
+        ## Load Config
+        config = litellm.PredibaseConfig.get_config()
+        for k, v in config.items():
+            if (
+                k not in optional_params
+            ):  # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
+                optional_params[k] = v
+
+        stream = optional_params.pop("stream", False)
+
+        data = {
+            "inputs": prompt,
+            "parameters": optional_params,
+        }
+        input_text = prompt
+        ## LOGGING
+        logging_obj.pre_call(
+            input=input_text,
+            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 stream is True:
+                return self.async_streaming(
+                    model=model,
+                    messages=messages,
+                    data=data,
+                    api_base=completion_url,
+                    model_response=model_response,
+                    print_verbose=print_verbose,
+                    encoding=encoding,
+                    api_key=api_key,
+                    logging_obj=logging_obj,
+                    optional_params=optional_params,
+                    litellm_params=litellm_params,
+                    logger_fn=logger_fn,
+                    headers=headers,
+                    timeout=timeout,
+                )  # type: ignore
+            else:
+                ### ASYNC COMPLETION
+                return self.async_completion(
+                    model=model,
+                    messages=messages,
+                    data=data,
+                    api_base=completion_url,
+                    model_response=model_response,
+                    print_verbose=print_verbose,
+                    encoding=encoding,
+                    api_key=api_key,
+                    logging_obj=logging_obj,
+                    optional_params=optional_params,
+                    stream=False,
+                    litellm_params=litellm_params,
+                    logger_fn=logger_fn,
+                    headers=headers,
+                    timeout=timeout,
+                )  # type: ignore
+
+        ### SYNC STREAMING
+        if stream is True:
+            response = litellm.module_level_client.post(
+                completion_url,
+                headers=headers,
+                data=json.dumps(data),
+                stream=stream,
+                timeout=timeout,  # type: ignore
+            )
+            _response = CustomStreamWrapper(
+                response.iter_lines(),
+                model,
+                custom_llm_provider="predibase",
+                logging_obj=logging_obj,
+            )
+            return _response
+        ### SYNC COMPLETION
+        else:
+            response = litellm.module_level_client.post(
+                url=completion_url,
+                headers=headers,
+                data=json.dumps(data),
+                timeout=timeout,  # type: ignore
+            )
+        return self.process_response(
+            model=model,
+            response=response,
+            model_response=model_response,
+            stream=optional_params.get("stream", False),
+            logging_obj=logging_obj,  # type: ignore
+            optional_params=optional_params,
+            api_key=api_key,
+            data=data,
+            messages=messages,
+            print_verbose=print_verbose,
+            encoding=encoding,
+        )
+
+    async def async_completion(
+        self,
+        model: str,
+        messages: list,
+        api_base: str,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        encoding,
+        api_key,
+        logging_obj,
+        stream,
+        data: dict,
+        optional_params: dict,
+        timeout: Union[float, httpx.Timeout],
+        litellm_params=None,
+        logger_fn=None,
+        headers={},
+    ) -> ModelResponse:
+
+        async_handler = get_async_httpx_client(
+            llm_provider=litellm.LlmProviders.PREDIBASE,
+            params={"timeout": timeout},
+        )
+        try:
+            response = await async_handler.post(
+                api_base, headers=headers, data=json.dumps(data)
+            )
+        except httpx.HTTPStatusError as e:
+            raise PredibaseError(
+                status_code=e.response.status_code,
+                message="HTTPStatusError - received status_code={}, error_message={}".format(
+                    e.response.status_code, e.response.text
+                ),
+            )
+        except Exception as e:
+            for exception in litellm.LITELLM_EXCEPTION_TYPES:
+                if isinstance(e, exception):
+                    raise e
+            raise PredibaseError(
+                status_code=500, message="{}".format(str(e))
+            )  # don't use verbose_logger.exception, if exception is raised
+        return self.process_response(
+            model=model,
+            response=response,
+            model_response=model_response,
+            stream=stream,
+            logging_obj=logging_obj,
+            api_key=api_key,
+            data=data,
+            messages=messages,
+            print_verbose=print_verbose,
+            optional_params=optional_params,
+            encoding=encoding,
+        )
+
+    async def async_streaming(
+        self,
+        model: str,
+        messages: list,
+        api_base: str,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        encoding,
+        api_key,
+        logging_obj,
+        data: dict,
+        timeout: Union[float, httpx.Timeout],
+        optional_params=None,
+        litellm_params=None,
+        logger_fn=None,
+        headers={},
+    ) -> CustomStreamWrapper:
+        data["stream"] = True
+
+        streamwrapper = CustomStreamWrapper(
+            completion_stream=None,
+            make_call=partial(
+                make_call,
+                api_base=api_base,
+                headers=headers,
+                data=json.dumps(data),
+                model=model,
+                messages=messages,
+                logging_obj=logging_obj,
+                timeout=timeout,
+            ),
+            model=model,
+            custom_llm_provider="predibase",
+            logging_obj=logging_obj,
+        )
+        return streamwrapper
+
+    def embedding(self, *args, **kwargs):
+        pass
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/predibase/chat/transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/predibase/chat/transformation.py
new file mode 100644
index 00000000..f5742386
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/predibase/chat/transformation.py
@@ -0,0 +1,180 @@
+from typing import TYPE_CHECKING, Any, List, Literal, Optional, Union
+
+from httpx import Headers, Response
+
+from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import ModelResponse
+
+from ..common_utils import PredibaseError
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+    LiteLLMLoggingObj = Any
+
+
+class PredibaseConfig(BaseConfig):
+    """
+    Reference:  https://docs.predibase.com/user-guide/inference/rest_api
+    """
+
+    adapter_id: Optional[str] = None
+    adapter_source: Optional[Literal["pbase", "hub", "s3"]] = None
+    best_of: Optional[int] = None
+    decoder_input_details: Optional[bool] = None
+    details: bool = True  # enables returning logprobs + best of
+    max_new_tokens: int = (
+        256  # openai default - requests hang if max_new_tokens not given
+    )
+    repetition_penalty: Optional[float] = None
+    return_full_text: Optional[bool] = (
+        False  # by default don't return the input as part of the output
+    )
+    seed: Optional[int] = None
+    stop: Optional[List[str]] = None
+    temperature: Optional[float] = None
+    top_k: Optional[int] = None
+    top_p: Optional[int] = None
+    truncate: Optional[int] = None
+    typical_p: Optional[float] = None
+    watermark: Optional[bool] = None
+
+    def __init__(
+        self,
+        best_of: Optional[int] = None,
+        decoder_input_details: Optional[bool] = None,
+        details: Optional[bool] = None,
+        max_new_tokens: Optional[int] = None,
+        repetition_penalty: Optional[float] = None,
+        return_full_text: Optional[bool] = None,
+        seed: Optional[int] = None,
+        stop: Optional[List[str]] = None,
+        temperature: Optional[float] = None,
+        top_k: Optional[int] = None,
+        top_p: Optional[int] = None,
+        truncate: Optional[int] = None,
+        typical_p: Optional[float] = None,
+        watermark: Optional[bool] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return super().get_config()
+
+    def get_supported_openai_params(self, model: str):
+        return [
+            "stream",
+            "temperature",
+            "max_completion_tokens",
+            "max_tokens",
+            "top_p",
+            "stop",
+            "n",
+            "response_format",
+        ]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        for param, value in non_default_params.items():
+            # temperature, top_p, n, stream, stop, max_tokens, n, presence_penalty default to None
+            if param == "temperature":
+                if value == 0.0 or value == 0:
+                    # hugging face exception raised when temp==0
+                    # Failed: Error occurred: HuggingfaceException - Input validation error: `temperature` must be strictly positive
+                    value = 0.01
+                optional_params["temperature"] = value
+            if param == "top_p":
+                optional_params["top_p"] = value
+            if param == "n":
+                optional_params["best_of"] = value
+                optional_params["do_sample"] = (
+                    True  # Need to sample if you want best of for hf inference endpoints
+                )
+            if param == "stream":
+                optional_params["stream"] = value
+            if param == "stop":
+                optional_params["stop"] = value
+            if param == "max_tokens" or param == "max_completion_tokens":
+                # HF TGI raises the following exception when max_new_tokens==0
+                # Failed: Error occurred: HuggingfaceException - Input validation error: `max_new_tokens` must be strictly positive
+                if value == 0:
+                    value = 1
+                optional_params["max_new_tokens"] = value
+            if param == "echo":
+                # https://huggingface.co/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.text_generation.decoder_input_details
+                #  Return the decoder input token logprobs and ids. You must set details=True as well for it to be taken into account. Defaults to False
+                optional_params["decoder_input_details"] = True
+            if param == "response_format":
+                optional_params["response_format"] = value
+        return optional_params
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: Response,
+        model_response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        request_data: dict,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: str,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ModelResponse:
+        raise NotImplementedError(
+            "Predibase transformation currently done in handler.py. Need to migrate to this file."
+        )
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        raise NotImplementedError(
+            "Predibase transformation currently done in handler.py. Need to migrate to this file."
+        )
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, Headers]
+    ) -> BaseLLMException:
+        return PredibaseError(
+            status_code=status_code, message=error_message, headers=headers
+        )
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        if api_key is None:
+            raise ValueError(
+                "Missing Predibase API Key - A call is being made to predibase but no key is set either in the environment variables or via params"
+            )
+
+        default_headers = {
+            "content-type": "application/json",
+            "Authorization": "Bearer {}".format(api_key),
+        }
+        if headers is not None and isinstance(headers, dict):
+            headers = {**default_headers, **headers}
+        return headers