two version of R2R are here HEAD master

2 files changed, 267 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/clarifai/chat/transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/clarifai/chat/transformation.py
new file mode 100644
index 00000000..916da738
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/clarifai/chat/transformation.py
@@ -0,0 +1,261 @@
+import json
+from typing import TYPE_CHECKING, Any, AsyncIterator, Iterator, 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.base_model_iterator import FakeStreamResponseIterator
+from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import (
+    ChatCompletionToolCallChunk,
+    ChatCompletionUsageBlock,
+    Choices,
+    GenericStreamingChunk,
+    Message,
+    ModelResponse,
+    Usage,
+)
+from litellm.utils import token_counter
+
+from ..common_utils import ClarifaiError
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+
+    LoggingClass = LiteLLMLoggingObj
+else:
+    LoggingClass = Any
+
+
+class ClarifaiConfig(BaseConfig):
+    """
+    Reference: https://clarifai.com/meta/Llama-2/models/llama2-70b-chat
+    """
+
+    max_tokens: Optional[int] = None
+    temperature: Optional[int] = None
+    top_k: Optional[int] = None
+
+    def __init__(
+        self,
+        max_tokens: Optional[int] = None,
+        temperature: Optional[int] = None,
+        top_k: 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 get_supported_openai_params(self, model: str) -> list:
+        return [
+            "temperature",
+            "max_tokens",
+        ]
+
+    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 == "temperature":
+                optional_params["temperature"] = value
+            elif param == "max_tokens":
+                optional_params["max_tokens"] = value
+
+        return optional_params
+
+    def _completions_to_model(self, prompt: str, optional_params: dict) -> dict:
+        params = {}
+        if temperature := optional_params.get("temperature"):
+            params["temperature"] = temperature
+        if max_tokens := optional_params.get("max_tokens"):
+            params["max_tokens"] = max_tokens
+        return {
+            "inputs": [{"data": {"text": {"raw": prompt}}}],
+            "model": {"output_info": {"params": params}},
+        }
+
+    def _convert_model_to_url(self, model: str, api_base: str):
+        user_id, app_id, model_id = model.split(".")
+        return f"{api_base}/users/{user_id}/apps/{app_id}/models/{model_id}/outputs"
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        prompt = " ".join(convert_content_list_to_str(message) for message in messages)
+
+        ## Load Config
+        config = self.get_config()
+        for k, v in config.items():
+            if k not in optional_params:
+                optional_params[k] = v
+
+        data = self._completions_to_model(
+            prompt=prompt, optional_params=optional_params
+        )
+
+        return data
+
+    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"Bearer {api_key}"
+        return headers
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+    ) -> BaseLLMException:
+        return ClarifaiError(message=error_message, status_code=status_code)
+
+    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: str,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ModelResponse:
+        logging_obj.post_call(
+            input=messages,
+            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 httpx.HTTPStatusError as e:
+            raise ClarifaiError(
+                message=str(e),
+                status_code=raw_response.status_code,
+            )
+        except Exception as e:
+            raise ClarifaiError(
+                message=str(e),
+                status_code=422,
+            )
+        # print(completion_response)
+        try:
+            choices_list = []
+            for idx, item in enumerate(completion_response["outputs"]):
+                if len(item["data"]["text"]["raw"]) > 0:
+                    message_obj = Message(content=item["data"]["text"]["raw"])
+                else:
+                    message_obj = Message(content=None)
+                choice_obj = Choices(
+                    finish_reason="stop",
+                    index=idx + 1,  # check
+                    message=message_obj,
+                )
+                choices_list.append(choice_obj)
+            model_response.choices = choices_list  # type: ignore
+
+        except Exception as e:
+            raise ClarifaiError(
+                message=str(e),
+                status_code=422,
+            )
+
+        # Calculate Usage
+        prompt_tokens = token_counter(model=model, messages=messages)
+        completion_tokens = len(
+            encoding.encode(model_response["choices"][0]["message"].get("content"))
+        )
+        model_response.model = model
+        setattr(
+            model_response,
+            "usage",
+            Usage(
+                prompt_tokens=prompt_tokens,
+                completion_tokens=completion_tokens,
+                total_tokens=prompt_tokens + completion_tokens,
+            ),
+        )
+        return model_response
+
+    def get_model_response_iterator(
+        self,
+        streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
+        sync_stream: bool,
+        json_mode: Optional[bool] = False,
+    ) -> Any:
+        return ClarifaiModelResponseIterator(
+            model_response=streaming_response,
+            json_mode=json_mode,
+        )
+
+
+class ClarifaiModelResponseIterator(FakeStreamResponseIterator):
+    def __init__(
+        self,
+        model_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
+        json_mode: Optional[bool] = False,
+    ):
+        super().__init__(
+            model_response=model_response,
+            json_mode=json_mode,
+        )
+
+    def chunk_parser(self, chunk: dict) -> GenericStreamingChunk:
+        try:
+            text = ""
+            tool_use: Optional[ChatCompletionToolCallChunk] = None
+            is_finished = False
+            finish_reason = ""
+            usage: Optional[ChatCompletionUsageBlock] = None
+            provider_specific_fields = None
+
+            text = (
+                chunk.get("outputs", "")[0]
+                .get("data", "")
+                .get("text", "")
+                .get("raw", "")
+            )
+
+            index: int = 0
+
+            return GenericStreamingChunk(
+                text=text,
+                tool_use=tool_use,
+                is_finished=is_finished,
+                finish_reason=finish_reason,
+                usage=usage,
+                index=index,
+                provider_specific_fields=provider_specific_fields,
+            )
+        except json.JSONDecodeError:
+            raise ValueError(f"Failed to decode JSON from chunk: {chunk}")
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/clarifai/common_utils.py b/.venv/lib/python3.12/site-packages/litellm/llms/clarifai/common_utils.py
new file mode 100644
index 00000000..611d2ccf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/clarifai/common_utils.py
@@ -0,0 +1,6 @@
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+
+
+class ClarifaiError(BaseLLMException):
+    def __init__(self, status_code: int, message: str):
+        super().__init__(status_code=status_code, message=message)