97fa6781cf
Image processing pipeline added OpenAI GPT-4.1 support Chat tool prompt enhancement Lint and code quality improvements
356 lines
14 KiB
Python
356 lines
14 KiB
Python
"""Gemini model provider implementation."""
|
|
|
|
import base64
|
|
import logging
|
|
import os
|
|
import time
|
|
from typing import Optional
|
|
|
|
from google import genai
|
|
from google.genai import types
|
|
|
|
from .base import ModelCapabilities, ModelProvider, ModelResponse, ProviderType, RangeTemperatureConstraint
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class GeminiModelProvider(ModelProvider):
|
|
"""Google Gemini model provider implementation."""
|
|
|
|
# Model configurations
|
|
SUPPORTED_MODELS = {
|
|
"gemini-2.5-flash-preview-05-20": {
|
|
"context_window": 1_048_576, # 1M tokens
|
|
"supports_extended_thinking": True,
|
|
"max_thinking_tokens": 24576, # Flash 2.5 thinking budget limit
|
|
"supports_images": True, # Vision capability
|
|
"max_image_size_mb": 20.0, # Conservative 20MB limit for reliability
|
|
},
|
|
"gemini-2.5-pro-preview-06-05": {
|
|
"context_window": 1_048_576, # 1M tokens
|
|
"supports_extended_thinking": True,
|
|
"max_thinking_tokens": 32768, # Pro 2.5 thinking budget limit
|
|
"supports_images": True, # Vision capability
|
|
"max_image_size_mb": 32.0, # Higher limit for Pro model
|
|
},
|
|
# Shorthands
|
|
"flash": "gemini-2.5-flash-preview-05-20",
|
|
"pro": "gemini-2.5-pro-preview-06-05",
|
|
}
|
|
|
|
# Thinking mode configurations - percentages of model's max_thinking_tokens
|
|
# These percentages work across all models that support thinking
|
|
THINKING_BUDGETS = {
|
|
"minimal": 0.005, # 0.5% of max - minimal thinking for fast responses
|
|
"low": 0.08, # 8% of max - light reasoning tasks
|
|
"medium": 0.33, # 33% of max - balanced reasoning (default)
|
|
"high": 0.67, # 67% of max - complex analysis
|
|
"max": 1.0, # 100% of max - full thinking budget
|
|
}
|
|
|
|
def __init__(self, api_key: str, **kwargs):
|
|
"""Initialize Gemini provider with API key."""
|
|
super().__init__(api_key, **kwargs)
|
|
self._client = None
|
|
self._token_counters = {} # Cache for token counting
|
|
|
|
@property
|
|
def client(self):
|
|
"""Lazy initialization of Gemini client."""
|
|
if self._client is None:
|
|
self._client = genai.Client(api_key=self.api_key)
|
|
return self._client
|
|
|
|
def get_capabilities(self, model_name: str) -> ModelCapabilities:
|
|
"""Get capabilities for a specific Gemini model."""
|
|
# Resolve shorthand
|
|
resolved_name = self._resolve_model_name(model_name)
|
|
|
|
if resolved_name not in self.SUPPORTED_MODELS:
|
|
raise ValueError(f"Unsupported Gemini model: {model_name}")
|
|
|
|
# Check if model is allowed by restrictions
|
|
from utils.model_restrictions import get_restriction_service
|
|
|
|
restriction_service = get_restriction_service()
|
|
if not restriction_service.is_allowed(ProviderType.GOOGLE, resolved_name, model_name):
|
|
raise ValueError(f"Gemini model '{model_name}' is not allowed by restriction policy.")
|
|
|
|
config = self.SUPPORTED_MODELS[resolved_name]
|
|
|
|
# Gemini models support 0.0-2.0 temperature range
|
|
temp_constraint = RangeTemperatureConstraint(0.0, 2.0, 0.7)
|
|
|
|
return ModelCapabilities(
|
|
provider=ProviderType.GOOGLE,
|
|
model_name=resolved_name,
|
|
friendly_name="Gemini",
|
|
context_window=config["context_window"],
|
|
supports_extended_thinking=config["supports_extended_thinking"],
|
|
supports_system_prompts=True,
|
|
supports_streaming=True,
|
|
supports_function_calling=True,
|
|
supports_images=config.get("supports_images", False),
|
|
max_image_size_mb=config.get("max_image_size_mb", 0.0),
|
|
temperature_constraint=temp_constraint,
|
|
)
|
|
|
|
def generate_content(
|
|
self,
|
|
prompt: str,
|
|
model_name: str,
|
|
system_prompt: Optional[str] = None,
|
|
temperature: float = 0.7,
|
|
max_output_tokens: Optional[int] = None,
|
|
thinking_mode: str = "medium",
|
|
images: Optional[list[str]] = None,
|
|
**kwargs,
|
|
) -> ModelResponse:
|
|
"""Generate content using Gemini model."""
|
|
# Validate parameters
|
|
resolved_name = self._resolve_model_name(model_name)
|
|
self.validate_parameters(resolved_name, temperature)
|
|
|
|
# Prepare content parts (text and potentially images)
|
|
parts = []
|
|
|
|
# Add system and user prompts as text
|
|
if system_prompt:
|
|
full_prompt = f"{system_prompt}\n\n{prompt}"
|
|
else:
|
|
full_prompt = prompt
|
|
|
|
parts.append({"text": full_prompt})
|
|
|
|
# Add images if provided and model supports vision
|
|
if images and self._supports_vision(resolved_name):
|
|
for image_path in images:
|
|
try:
|
|
image_part = self._process_image(image_path)
|
|
if image_part:
|
|
parts.append(image_part)
|
|
except Exception as e:
|
|
logger.warning(f"Failed to process image {image_path}: {e}")
|
|
# Continue with other images and text
|
|
continue
|
|
elif images and not self._supports_vision(resolved_name):
|
|
logger.warning(f"Model {resolved_name} does not support images, ignoring {len(images)} image(s)")
|
|
|
|
# Create contents structure
|
|
contents = [{"parts": parts}]
|
|
|
|
# Prepare generation config
|
|
generation_config = types.GenerateContentConfig(
|
|
temperature=temperature,
|
|
candidate_count=1,
|
|
)
|
|
|
|
# Add max output tokens if specified
|
|
if max_output_tokens:
|
|
generation_config.max_output_tokens = max_output_tokens
|
|
|
|
# Add thinking configuration for models that support it
|
|
capabilities = self.get_capabilities(resolved_name)
|
|
if capabilities.supports_extended_thinking and thinking_mode in self.THINKING_BUDGETS:
|
|
# Get model's max thinking tokens and calculate actual budget
|
|
model_config = self.SUPPORTED_MODELS.get(resolved_name)
|
|
if model_config and "max_thinking_tokens" in model_config:
|
|
max_thinking_tokens = model_config["max_thinking_tokens"]
|
|
actual_thinking_budget = int(max_thinking_tokens * self.THINKING_BUDGETS[thinking_mode])
|
|
generation_config.thinking_config = types.ThinkingConfig(thinking_budget=actual_thinking_budget)
|
|
|
|
# Retry logic with exponential backoff
|
|
max_retries = 2 # Total of 2 attempts (1 initial + 1 retry)
|
|
base_delay = 1.0 # Start with 1 second delay
|
|
|
|
last_exception = None
|
|
|
|
for attempt in range(max_retries):
|
|
try:
|
|
# Generate content
|
|
response = self.client.models.generate_content(
|
|
model=resolved_name,
|
|
contents=contents,
|
|
config=generation_config,
|
|
)
|
|
|
|
# Extract usage information if available
|
|
usage = self._extract_usage(response)
|
|
|
|
return ModelResponse(
|
|
content=response.text,
|
|
usage=usage,
|
|
model_name=resolved_name,
|
|
friendly_name="Gemini",
|
|
provider=ProviderType.GOOGLE,
|
|
metadata={
|
|
"thinking_mode": thinking_mode if capabilities.supports_extended_thinking else None,
|
|
"finish_reason": (
|
|
getattr(response.candidates[0], "finish_reason", "STOP") if response.candidates else "STOP"
|
|
),
|
|
},
|
|
)
|
|
|
|
except Exception as e:
|
|
last_exception = e
|
|
|
|
# Check if this is a retryable error
|
|
error_str = str(e).lower()
|
|
is_retryable = any(
|
|
term in error_str
|
|
for term in [
|
|
"timeout",
|
|
"connection",
|
|
"network",
|
|
"temporary",
|
|
"unavailable",
|
|
"retry",
|
|
"429",
|
|
"500",
|
|
"502",
|
|
"503",
|
|
"504",
|
|
]
|
|
)
|
|
|
|
# If this is the last attempt or not retryable, give up
|
|
if attempt == max_retries - 1 or not is_retryable:
|
|
break
|
|
|
|
# Calculate delay with exponential backoff
|
|
delay = base_delay * (2**attempt)
|
|
|
|
# Log retry attempt (could add logging here if needed)
|
|
# For now, just sleep and retry
|
|
time.sleep(delay)
|
|
|
|
# If we get here, all retries failed
|
|
error_msg = f"Gemini API error for model {resolved_name} after {max_retries} attempts: {str(last_exception)}"
|
|
raise RuntimeError(error_msg) from last_exception
|
|
|
|
def count_tokens(self, text: str, model_name: str) -> int:
|
|
"""Count tokens for the given text using Gemini's tokenizer."""
|
|
self._resolve_model_name(model_name)
|
|
|
|
# For now, use a simple estimation
|
|
# TODO: Use actual Gemini tokenizer when available in SDK
|
|
# Rough estimation: ~4 characters per token for English text
|
|
return len(text) // 4
|
|
|
|
def get_provider_type(self) -> ProviderType:
|
|
"""Get the provider type."""
|
|
return ProviderType.GOOGLE
|
|
|
|
def validate_model_name(self, model_name: str) -> bool:
|
|
"""Validate if the model name is supported and allowed."""
|
|
resolved_name = self._resolve_model_name(model_name)
|
|
|
|
# First check if model is supported
|
|
if resolved_name not in self.SUPPORTED_MODELS or not isinstance(self.SUPPORTED_MODELS[resolved_name], dict):
|
|
return False
|
|
|
|
# Then check if model is allowed by restrictions
|
|
from utils.model_restrictions import get_restriction_service
|
|
|
|
restriction_service = get_restriction_service()
|
|
if not restriction_service.is_allowed(ProviderType.GOOGLE, resolved_name, model_name):
|
|
logger.debug(f"Gemini model '{model_name}' -> '{resolved_name}' blocked by restrictions")
|
|
return False
|
|
|
|
return True
|
|
|
|
def supports_thinking_mode(self, model_name: str) -> bool:
|
|
"""Check if the model supports extended thinking mode."""
|
|
capabilities = self.get_capabilities(model_name)
|
|
return capabilities.supports_extended_thinking
|
|
|
|
def get_thinking_budget(self, model_name: str, thinking_mode: str) -> int:
|
|
"""Get actual thinking token budget for a model and thinking mode."""
|
|
resolved_name = self._resolve_model_name(model_name)
|
|
model_config = self.SUPPORTED_MODELS.get(resolved_name, {})
|
|
|
|
if not model_config.get("supports_extended_thinking", False):
|
|
return 0
|
|
|
|
if thinking_mode not in self.THINKING_BUDGETS:
|
|
return 0
|
|
|
|
max_thinking_tokens = model_config.get("max_thinking_tokens", 0)
|
|
if max_thinking_tokens == 0:
|
|
return 0
|
|
|
|
return int(max_thinking_tokens * self.THINKING_BUDGETS[thinking_mode])
|
|
|
|
def _resolve_model_name(self, model_name: str) -> str:
|
|
"""Resolve model shorthand to full name."""
|
|
# Check if it's a shorthand
|
|
shorthand_value = self.SUPPORTED_MODELS.get(model_name.lower())
|
|
if isinstance(shorthand_value, str):
|
|
return shorthand_value
|
|
return model_name
|
|
|
|
def _extract_usage(self, response) -> dict[str, int]:
|
|
"""Extract token usage from Gemini response."""
|
|
usage = {}
|
|
|
|
# Try to extract usage metadata from response
|
|
# Note: The actual structure depends on the SDK version and response format
|
|
if hasattr(response, "usage_metadata"):
|
|
metadata = response.usage_metadata
|
|
if hasattr(metadata, "prompt_token_count"):
|
|
usage["input_tokens"] = metadata.prompt_token_count
|
|
if hasattr(metadata, "candidates_token_count"):
|
|
usage["output_tokens"] = metadata.candidates_token_count
|
|
if "input_tokens" in usage and "output_tokens" in usage:
|
|
usage["total_tokens"] = usage["input_tokens"] + usage["output_tokens"]
|
|
|
|
return usage
|
|
|
|
def _supports_vision(self, model_name: str) -> bool:
|
|
"""Check if the model supports vision (image processing)."""
|
|
# Gemini 2.5 models support vision
|
|
vision_models = {
|
|
"gemini-2.5-flash-preview-05-20",
|
|
"gemini-2.5-pro-preview-06-05",
|
|
"gemini-2.0-flash",
|
|
"gemini-1.5-pro",
|
|
"gemini-1.5-flash",
|
|
}
|
|
return model_name in vision_models
|
|
|
|
def _process_image(self, image_path: str) -> Optional[dict]:
|
|
"""Process an image for Gemini API."""
|
|
try:
|
|
if image_path.startswith("data:image/"):
|
|
# Handle data URL: data:image/png;base64,iVBORw0...
|
|
header, data = image_path.split(",", 1)
|
|
mime_type = header.split(";")[0].split(":")[1]
|
|
return {"inline_data": {"mime_type": mime_type, "data": data}}
|
|
else:
|
|
# Handle file path - translate for Docker environment
|
|
from utils.file_types import get_image_mime_type
|
|
from utils.file_utils import translate_path_for_environment
|
|
|
|
translated_path = translate_path_for_environment(image_path)
|
|
logger.debug(f"Translated image path from '{image_path}' to '{translated_path}'")
|
|
|
|
if not os.path.exists(translated_path):
|
|
logger.warning(f"Image file not found: {translated_path} (original: {image_path})")
|
|
return None
|
|
|
|
# Use translated path for all subsequent operations
|
|
image_path = translated_path
|
|
|
|
# Detect MIME type from file extension using centralized mappings
|
|
ext = os.path.splitext(image_path)[1].lower()
|
|
mime_type = get_image_mime_type(ext)
|
|
|
|
# Read and encode the image
|
|
with open(image_path, "rb") as f:
|
|
image_data = base64.b64encode(f.read()).decode()
|
|
|
|
return {"inline_data": {"mime_type": mime_type, "data": image_data}}
|
|
except Exception as e:
|
|
logger.error(f"Error processing image {image_path}: {e}")
|
|
return None
|