two version of R2R are here HEAD master

1 files changed, 227 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/nlp_cloud/chat/transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/nlp_cloud/chat/transformation.py
new file mode 100644
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+++ b/.venv/lib/python3.12/site-packages/litellm/llms/nlp_cloud/chat/transformation.py
@@ -0,0 +1,227 @@
+import json
+import time
+from typing import TYPE_CHECKING, Any, List, Optional, Union
+
+import httpx
+
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    convert_content_list_to_str,
+)
+from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
+from litellm.types.llms.openai import AllMessageValues
+from litellm.utils import ModelResponse, Usage
+
+from ..common_utils import NLPCloudError
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+
+    LoggingClass = LiteLLMLoggingObj
+else:
+    LoggingClass = Any
+
+
+class NLPCloudConfig(BaseConfig):
+    """
+    Reference: https://docs.nlpcloud.com/#generation
+
+    - `max_length` (int): Optional. The maximum number of tokens that the generated text should contain.
+
+    - `length_no_input` (boolean): Optional. Whether `min_length` and `max_length` should not include the length of the input text.
+
+    - `end_sequence` (string): Optional. A specific token that should be the end of the generated sequence.
+
+    - `remove_end_sequence` (boolean): Optional. Whether to remove the `end_sequence` string from the result.
+
+    - `remove_input` (boolean): Optional. Whether to remove the input text from the result.
+
+    - `bad_words` (list of strings): Optional. List of tokens that are not allowed to be generated.
+
+    - `temperature` (float): Optional. Temperature sampling. It modulates the next token probabilities.
+
+    - `top_p` (float): Optional. Top P sampling. Below 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.
+
+    - `top_k` (int): Optional. Top K sampling. The number of highest probability vocabulary tokens to keep for top k filtering.
+
+    - `repetition_penalty` (float): Optional. Prevents the same word from being repeated too many times.
+
+    - `num_beams` (int): Optional. Number of beams for beam search.
+
+    - `num_return_sequences` (int): Optional. The number of independently computed returned sequences.
+    """
+
+    max_length: Optional[int] = None
+    length_no_input: Optional[bool] = None
+    end_sequence: Optional[str] = None
+    remove_end_sequence: Optional[bool] = None
+    remove_input: Optional[bool] = None
+    bad_words: Optional[list] = None
+    temperature: Optional[float] = None
+    top_p: Optional[float] = None
+    top_k: Optional[int] = None
+    repetition_penalty: Optional[float] = None
+    num_beams: Optional[int] = None
+    num_return_sequences: Optional[int] = None
+
+    def __init__(
+        self,
+        max_length: Optional[int] = None,
+        length_no_input: Optional[bool] = None,
+        end_sequence: Optional[str] = None,
+        remove_end_sequence: Optional[bool] = None,
+        remove_input: Optional[bool] = None,
+        bad_words: Optional[list] = None,
+        temperature: Optional[float] = None,
+        top_p: Optional[float] = None,
+        top_k: Optional[int] = None,
+        repetition_penalty: Optional[float] = None,
+        num_beams: Optional[int] = None,
+        num_return_sequences: Optional[int] = 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 validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        headers = {
+            "accept": "application/json",
+            "content-type": "application/json",
+        }
+        if api_key:
+            headers["Authorization"] = f"Token {api_key}"
+        return headers
+
+    def get_supported_openai_params(self, model: str) -> List:
+        return [
+            "max_tokens",
+            "stream",
+            "temperature",
+            "top_p",
+            "presence_penalty",
+            "frequency_penalty",
+            "n",
+            "stop",
+        ]
+
+    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():
+            if param == "max_tokens":
+                optional_params["max_length"] = value
+            if param == "stream":
+                optional_params["stream"] = value
+            if param == "temperature":
+                optional_params["temperature"] = value
+            if param == "top_p":
+                optional_params["top_p"] = value
+            if param == "presence_penalty":
+                optional_params["presence_penalty"] = value
+            if param == "frequency_penalty":
+                optional_params["frequency_penalty"] = value
+            if param == "n":
+                optional_params["num_return_sequences"] = value
+            if param == "stop":
+                optional_params["stop_sequences"] = value
+        return optional_params
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+    ) -> BaseLLMException:
+        return NLPCloudError(
+            status_code=status_code, message=error_message, headers=headers
+        )
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        text = " ".join(convert_content_list_to_str(message) for message in messages)
+
+        data = {
+            "text": text,
+            **optional_params,
+        }
+
+        return data
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ModelResponse,
+        logging_obj: LoggingClass,
+        request_data: dict,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ModelResponse:
+        ## LOGGING
+        logging_obj.post_call(
+            input=None,
+            api_key=api_key,
+            original_response=raw_response.text,
+            additional_args={"complete_input_dict": request_data},
+        )
+
+        ## RESPONSE OBJECT
+        try:
+            completion_response = raw_response.json()
+        except Exception:
+            raise NLPCloudError(
+                message=raw_response.text, status_code=raw_response.status_code
+            )
+        if "error" in completion_response:
+            raise NLPCloudError(
+                message=completion_response["error"],
+                status_code=raw_response.status_code,
+            )
+        else:
+            try:
+                if len(completion_response["generated_text"]) > 0:
+                    model_response.choices[0].message.content = (  # type: ignore
+                        completion_response["generated_text"]
+                    )
+            except Exception:
+                raise NLPCloudError(
+                    message=json.dumps(completion_response),
+                    status_code=raw_response.status_code,
+                )
+
+        ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here.
+        prompt_tokens = completion_response["nb_input_tokens"]
+        completion_tokens = completion_response["nb_generated_tokens"]
+
+        model_response.created = int(time.time())
+        model_response.model = model
+        usage = Usage(
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens,
+        )
+        setattr(model_response, "usage", usage)
+        return model_response