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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)
inference_params.pop(
"stream", None
) # palm does not support streaming, so we handle this by fake streaming in main.py
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}")
response = _model.generate_content(
contents=prompt,
generation_config=genai.types.GenerationConfig(**inference_params),
)
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
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