Added custom gemini provider for llm source file. Will be added to li…

…tellm fork later

automation-api/yival_experiments/custom-litellm-llms-gemini.py

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+import os, types, traceback, copy
+import json
+from enum import Enum
+import time
+from typing import Callable, Optional
+from litellm.utils import ModelResponse, get_secret, Choices, Message, Usage
+import litellm
+import sys, httpx
+from .prompt_templates.factory import prompt_factory, custom_prompt
+
+
+class GeminiError(Exception):
+    def __init__(self, status_code, message):
+        self.status_code = status_code
+        self.message = message
+        self.request = httpx.Request(
+            method="POST",
+            url="https://developers.generativeai.google/api/python/google/generativeai/chat",
+        )
+        self.response = httpx.Response(status_code=status_code, request=self.request)
+        super().__init__(
+            self.message
+        )  # Call the base class constructor with the parameters it needs
+
+
+class GeminiConfig:
+    """
+    Reference: https://ai.google.dev/api/python/google/generativeai/GenerationConfig
+
+    The class `GeminiConfig` provides configuration for the Gemini's API interface. Here are the parameters:
+
+    - `candidate_count` (int): Number of generated responses to return.
+
+    - `stop_sequences` (List[str]): The set of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response.
+
+    - `max_output_tokens` (int): The maximum number of tokens to include in a candidate. If unset, this will default to output_token_limit specified in the model's specification.
+
+    - `temperature` (float): Controls the randomness of the output. Note: The default value varies by model, see the Model.temperature attribute of the Model returned the genai.get_model function. Values can range from [0.0,1.0], inclusive. A value closer to 1.0 will produce responses that are more varied and creative, while a value closer to 0.0 will typically result in more straightforward responses from the model.
+
+    - `top_p` (float): Optional. The maximum cumulative probability of tokens to consider when sampling.
+
+    - `top_k` (int): Optional. The maximum number of tokens to consider when sampling.
+    """
+
+    candidate_count: Optional[int] = None
+    stop_sequences: Optional[list] = None
+    max_output_tokens: Optional[int] = None
+    temperature: Optional[float] = None
+    top_p: Optional[float] = None
+    top_k: Optional[int] = None
+
+    def __init__(
+        self,
+        candidate_count: Optional[int] = None,
+        stop_sequences: Optional[list] = None,
+        max_output_tokens: Optional[int] = None,
+        temperature: Optional[float] = None,
+        top_p: Optional[float] = None,
+        top_k: Optional[int] = None,
+    ) -> None:
+        locals_ = locals()
+        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 {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+
+def completion(
+    model: str,
+    messages: list,
+    model_response: ModelResponse,
+    print_verbose: Callable,
+    api_key,
+    encoding,
+    logging_obj,
+    custom_prompt_dict: dict,
+    acompletion: bool = False,
+    optional_params=None,
+    litellm_params=None,
+    logger_fn=None,
+):
+    try:
+        import google.generativeai as genai
+    except:
+        raise Exception(
+            "Importing google.generativeai failed, please run 'pip install -q google-generativeai"
+        )
+    genai.configure(api_key=api_key)
+
+    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, custom_llm_provider="gemini"
+        )
+
+    ## Load Config
+    inference_params = copy.deepcopy(optional_params)
+    stream = inference_params.pop("stream", None)
+    safety_settings = optional_params.pop("safety_settings", None)
+    config = litellm.GeminiConfig.get_config()
+    for k, v in config.items():
+        if (
+            k not in inference_params
+        ):  # completion(top_k=3) > gemini_config(top_k=3) <- allows for dynamic variables to be passed in
+            inference_params[k] = v
+
+    ## LOGGING
+    logging_obj.pre_call(
+        input=prompt,
+        api_key="",
+        additional_args={"complete_input_dict": {"inference_params": inference_params}},
+    )
+    ## COMPLETION CALL
+    try:
+        _model = genai.GenerativeModel(f"models/{model}")
+        if stream != True:
+            response = _model.generate_content(
+                contents=prompt,
+                generation_config=genai.types.GenerationConfig(**inference_params),
+                safety_settings=safety_settings,
+            )
+        else:
+            response = _model.generate_content(
+                contents=prompt,
+                generation_config=genai.types.GenerationConfig(**inference_params),
+                safety_settings=safety_settings,
+                stream=True,
+            )
+            return response
+    except Exception as e:
+        raise GeminiError(
+            message=str(e),
+            status_code=500,
+        )
+
+    ## LOGGING
+    logging_obj.post_call(
+        input=prompt,
+        api_key="",
+        original_response=response,
+        additional_args={"complete_input_dict": {}},
+    )
+    print_verbose(f"raw model_response: {response}")
+    ## RESPONSE OBJECT
+    completion_response = response
+    try:
+        choices_list = []
+        for idx, item in enumerate(completion_response.candidates):
+            if len(item.content.parts) > 0:
+                message_obj = Message(content=item.content.parts[0].text)
+            else:
+                message_obj = Message(content=None)
+            choice_obj = Choices(index=idx + 1, message=message_obj)
+            choices_list.append(choice_obj)
+        model_response["choices"] = choices_list
+    except Exception as e:
+        traceback.print_exc()
+        raise GeminiError(
+            message=traceback.format_exc(), status_code=response.status_code
+        )
+
+    try:
+        completion_response = model_response["choices"][0]["message"].get("content")
+        if completion_response is None:
+            raise Exception
+    except:
+        original_response = f"response: {response}"
+        if hasattr(response, "candidates"):
+            original_response = f"response: {response.candidates}"
+            if "SAFETY" in original_response:
+                original_response += (
+                    "\nThe candidate content was flagged for safety reasons."
+                )
+            elif "RECITATION" in original_response:
+                original_response += (
+                    "\nThe candidate content was flagged for recitation reasons."
+                )
+        raise GeminiError(
+            status_code=400,
+            message=f"No response received. Original response - {original_response}",
+        )
+
+    ## CALCULATING USAGE
+    prompt_str = ""
+    for m in messages:
+        if isinstance(m["content"], str):
+            prompt_str += m["content"]
+        elif isinstance(m["content"], list):
+            for content in m["content"]:
+                if content["type"] == "text":
+                    prompt_str += content["text"]
+    prompt_tokens = len(encoding.encode(prompt_str))
+    completion_tokens = len(
+        encoding.encode(model_response["choices"][0]["message"].get("content", ""))
+    )
+
+    model_response["created"] = int(time.time())
+    model_response["model"] = "gemini/" + model
+    usage = Usage(
+        prompt_tokens=prompt_tokens,
+        completion_tokens=completion_tokens,
+        total_tokens=prompt_tokens + completion_tokens,
+    )
+    model_response.usage = usage
+    return model_response
+
+
+def embedding():
+    # logic for parsing in - calling - parsing out model embedding calls
+    pass