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# Ref: https://github.com/zou-group/textgrad/blob/main/textgrad/engine/gemini.py
# Ref: https://ai.google.dev/gemini-api/docs/quickstart?lang=python
# Changed to use the new google-genai package May 25, 2025
# Ref: https://ai.google.dev/gemini-api/docs/migrate
try:
# import google.generativeai as genai
from google import genai
from google.genai import types
except ImportError:
raise ImportError("If you'd like to use Gemini models, please install the google-genai package by running `pip install google-genai`, and add 'GOOGLE_API_KEY' to your environment variables.")
import os
import platformdirs
from tenacity import (
retry,
stop_after_attempt,
wait_random_exponential,
)
import base64
import json
from typing import List, Union
from .base import EngineLM, CachedEngine
from .engine_utils import get_image_type_from_bytes
import io
from PIL import Image
class ChatGemini(EngineLM, CachedEngine):
SYSTEM_PROMPT = "You are a helpful, creative, and smart assistant."
def __init__(
self,
model_string="gemini-pro",
use_cache: bool=False,
system_prompt=SYSTEM_PROMPT,
is_multimodal: bool=False,
):
self.use_cache = use_cache
self.model_string = model_string
self.system_prompt = system_prompt
assert isinstance(self.system_prompt, str)
self.is_multimodal = is_multimodal
if self.use_cache:
root = platformdirs.user_cache_dir("agentflow")
cache_path = os.path.join(root, f"cache_gemini_{model_string}.db")
super().__init__(cache_path=cache_path)
if os.getenv("GOOGLE_API_KEY") is None:
raise ValueError("Please set the GOOGLE_API_KEY environment variable if you'd like to use Gemini models.")
self.client = genai.Client(api_key=os.getenv("GOOGLE_API_KEY"))
def __call__(self, prompt, **kwargs):
return self.generate(prompt, **kwargs)
@retry(wait=wait_random_exponential(min=1, max=5), stop=stop_after_attempt(5))
def generate(self, content: Union[str, List[Union[str, bytes]]], system_prompt=None, **kwargs):
try:
if isinstance(content, str):
return self._generate_from_single_prompt(content, system_prompt=system_prompt, **kwargs)
elif isinstance(content, list):
if all(isinstance(item, str) for item in content):
full_text = "\n".join(content)
return self._generate_from_single_prompt(full_text, system_prompt=system_prompt, **kwargs)
elif any(isinstance(item, bytes) for item in content):
if not self.is_multimodal:
raise NotImplementedError(
f"Multimodal generation is only supported for {self.model_string}. "
"Consider using a multimodal model like 'gpt-4o'."
)
return self._generate_from_multiple_input(content, system_prompt=system_prompt, **kwargs)
else:
raise ValueError("Unsupported content in list: only str or bytes are allowed.")
except Exception as e:
print(f"Error in generate method: {str(e)}")
print(f"Error type: {type(e).__name__}")
print(f"Error details: {e.args}")
return {
"error": type(e).__name__,
"message": str(e),
"details": getattr(e, 'args', None)
}
def _generate_from_single_prompt(
self, prompt: str, system_prompt=None, temperature=0., max_tokens=4000, top_p=0.99, **kwargs
):
sys_prompt_arg = system_prompt if system_prompt else self.system_prompt
if self.use_cache:
cache_or_none = self._check_cache(sys_prompt_arg + prompt)
if cache_or_none is not None:
return cache_or_none
# messages = [{'role': 'user', 'parts': [prompt]}]
messages = [prompt]
response = self.client.models.generate_content(
model=self.model_string,
contents=messages,
config=types.GenerateContentConfig(
system_instruction=sys_prompt_arg,
max_output_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
candidate_count=1,
)
)
response_text = response.text
if self.use_cache:
self._save_cache(sys_prompt_arg + prompt, response_text)
return response_text
def _format_content(self, content: List[Union[str, bytes]]) -> List[dict]:
formatted_content = []
for item in content:
if isinstance(item, bytes):
image_obj = Image.open(io.BytesIO(item))
formatted_content.append(image_obj)
elif isinstance(item, str):
formatted_content.append(item)
else:
raise ValueError(f"Unsupported input type: {type(item)}")
return formatted_content
def _generate_from_multiple_input(
self, content: List[Union[str, bytes]], system_prompt=None, temperature=0, max_tokens=4000, top_p=0.99, **kwargs
):
sys_prompt_arg = system_prompt if system_prompt else self.system_prompt
formatted_content = self._format_content(content)
if self.use_cache:
cache_key = sys_prompt_arg + json.dumps(formatted_content)
cache_or_none = self._check_cache(cache_key)
if cache_or_none is not None:
return cache_or_none
response = self.client.models.generate_content(
model=self.model_string,
contents=formatted_content,
config=types.GenerateContentConfig(
system_instruction=sys_prompt_arg,
max_output_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
candidate_count=1
)
)
response_text = response.text
if self.use_cache:
self._save_cache(cache_key, response_text)
return response_text |