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"""
Google AI Studio API backend
Uses Google's AI Studio API for Gemma and other Google models
"""
import asyncio
import time
import uuid
import json
from typing import AsyncGenerator, List, Dict, Any, Optional
from datetime import datetime
import httpx
from .base import ModelBackend, ModelLoadError, GenerationError, ModelNotLoadedError
from ...models.schemas import ChatMessage, ChatResponse, StreamChunk
from ...core.config import settings
class GoogleAIBackend(ModelBackend):
"""Google AI Studio API backend for Gemma and other Google models"""
def __init__(self, model_name: str, **kwargs):
super().__init__(model_name, **kwargs)
self.api_key = kwargs.get('api_key', settings.google_api_key)
self.base_url = "https://generativelanguage.googleapis.com/v1beta"
self.capabilities = ["chat", "streaming", "api_based"]
# Generation parameters
self.parameters = {
'temperature': kwargs.get('temperature', settings.temperature),
'max_tokens': kwargs.get('max_tokens', settings.max_new_tokens),
'top_p': kwargs.get('top_p', settings.top_p),
'top_k': kwargs.get('top_k', settings.top_k),
}
async def load_model(self) -> bool:
"""Initialize the Google AI API client"""
try:
if not self.api_key:
raise ModelLoadError("Google AI API key is required")
self.log_info("Initializing Google AI API client", model=self.model_name)
# Test the connection
await self._test_connection()
self.is_loaded = True
self.log_info("Google AI API client initialized successfully", model=self.model_name)
return True
except Exception as e:
self.log_error("Failed to initialize Google AI API client", error=str(e), model=self.model_name)
raise ModelLoadError(f"Failed to initialize Google AI API for {self.model_name}: {str(e)}")
async def unload_model(self) -> bool:
"""Clean up the API client"""
try:
self.is_loaded = False
self.log_info("Google AI API client cleaned up", model=self.model_name)
return True
except Exception as e:
self.log_error("Failed to cleanup Google AI API client", error=str(e), model=self.model_name)
return False
async def _test_connection(self):
"""Test the Google AI API connection"""
try:
url = f"{self.base_url}/models/{self.model_name}:generateContent"
test_data = {
"contents": [
{
"parts": [{"text": "Hello"}]
}
],
"generationConfig": {
"maxOutputTokens": 5,
"temperature": 0.1
}
}
async with httpx.AsyncClient() as client:
response = await client.post(
f"{url}?key={self.api_key}",
headers={'Content-Type': 'application/json'},
json=test_data,
timeout=10.0
)
if response.status_code != 200:
raise Exception(f"API test failed with status {response.status_code}: {response.text}")
self.log_info("Google AI API connection test successful", model=self.model_name)
except Exception as e:
self.log_error("Google AI API connection test failed", error=str(e), model=self.model_name)
raise
def _format_messages_for_api(self, messages: List[ChatMessage]) -> Dict[str, Any]:
"""Format messages for Google AI API"""
contents = []
system_instruction = None
for msg in messages:
if msg.role == "system":
system_instruction = msg.content
elif msg.role == "user":
contents.append({
"role": "user",
"parts": [{"text": msg.content}]
})
elif msg.role == "assistant":
contents.append({
"role": "model",
"parts": [{"text": msg.content}]
})
result = {"contents": contents}
if system_instruction:
result["systemInstruction"] = {"parts": [{"text": system_instruction}]}
return result
async def generate_response(
self,
messages: List[ChatMessage],
temperature: float = 0.7,
max_tokens: int = 512,
**kwargs
) -> ChatResponse:
"""Generate a complete response using Google AI API"""
if not self.is_loaded:
raise ModelNotLoadedError("Google AI API client not initialized")
start_time = time.time()
message_id = str(uuid.uuid4())
try:
# Validate parameters
params = self.validate_parameters(
temperature=temperature,
max_tokens=max_tokens,
**kwargs
)
# Format messages
api_data = self._format_messages_for_api(messages)
# Add generation config
api_data["generationConfig"] = {
"maxOutputTokens": params['max_tokens'],
"temperature": params['temperature'],
"topP": params.get('top_p', 0.9),
"topK": params.get('top_k', 40)
}
# Make API call
url = f"{self.base_url}/models/{self.model_name}:generateContent"
async with httpx.AsyncClient() as client:
response = await client.post(
f"{url}?key={self.api_key}",
headers={'Content-Type': 'application/json'},
json=api_data,
timeout=30.0
)
if response.status_code != 200:
raise GenerationError(f"API request failed with status {response.status_code}: {response.text}")
response_data = response.json()
# Extract response text
if 'candidates' in response_data and response_data['candidates']:
candidate = response_data['candidates'][0]
if 'content' in candidate and 'parts' in candidate['content']:
parts = candidate['content']['parts']
response_text = ''.join(part.get('text', '') for part in parts)
else:
response_text = str(response_data)
else:
response_text = str(response_data)
generation_time = time.time() - start_time
return ChatResponse(
message=response_text.strip(),
session_id=messages[-1].metadata.get('session_id', 'unknown') if messages[-1].metadata else 'unknown',
message_id=message_id,
model_name=self.model_name,
generation_time=generation_time,
token_count=len(response_text.split()), # Approximate token count
finish_reason="stop"
)
except Exception as e:
self.log_error("Google AI API generation failed", error=str(e), model=self.model_name)
raise GenerationError(f"Failed to generate response via Google AI API: {str(e)}")
async def generate_stream(
self,
messages: List[ChatMessage],
temperature: float = 0.7,
max_tokens: int = 512,
**kwargs
) -> AsyncGenerator[StreamChunk, None]:
"""Generate a streaming response using Google AI API"""
if not self.is_loaded:
raise ModelNotLoadedError("Google AI API client not initialized")
message_id = str(uuid.uuid4())
session_id = messages[-1].metadata.get('session_id', 'unknown') if messages[-1].metadata else 'unknown'
chunk_id = 0
try:
# Validate parameters
params = self.validate_parameters(
temperature=temperature,
max_tokens=max_tokens,
**kwargs
)
# Format messages
api_data = self._format_messages_for_api(messages)
# Add generation config
api_data["generationConfig"] = {
"maxOutputTokens": params['max_tokens'],
"temperature": params['temperature'],
"topP": params.get('top_p', 0.9),
"topK": params.get('top_k', 40)
}
# Make streaming API call
url = f"{self.base_url}/models/{self.model_name}:streamGenerateContent"
async with httpx.AsyncClient() as client:
async with client.stream(
'POST',
f"{url}?key={self.api_key}",
headers={'Content-Type': 'application/json'},
json=api_data,
timeout=60.0
) as response:
if response.status_code != 200:
raise GenerationError(f"Streaming request failed with status {response.status_code}")
async for line in response.aiter_lines():
if line.strip():
try:
# Google AI API returns JSON objects separated by newlines
data = json.loads(line)
if 'candidates' in data and data['candidates']:
candidate = data['candidates'][0]
if 'content' in candidate and 'parts' in candidate['content']:
parts = candidate['content']['parts']
content = ''.join(part.get('text', '') for part in parts)
if content:
yield StreamChunk(
content=content,
session_id=session_id,
message_id=message_id,
chunk_id=chunk_id,
is_final=False
)
chunk_id += 1
# Add small delay
await asyncio.sleep(settings.stream_delay)
except json.JSONDecodeError:
continue
# Send final chunk
yield StreamChunk(
content="",
session_id=session_id,
message_id=message_id,
chunk_id=chunk_id,
is_final=True
)
except Exception as e:
self.log_error("Google AI API streaming failed", error=str(e), model=self.model_name)
raise GenerationError(f"Failed to generate streaming response via Google AI API: {str(e)}")
def get_model_info(self) -> Dict[str, Any]:
"""Get information about the current model"""
return {
"name": self.model_name,
"type": "google_ai",
"loaded": self.is_loaded,
"provider": "Google AI Studio",
"capabilities": self.capabilities,
"parameters": self.parameters,
"requires_api_key": True,
"api_key_configured": bool(self.api_key),
"base_url": self.base_url
}
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