Fix asyncio event loop mismatch in LLM service
The genai.Client and AsyncOpenAI clients were being created at module import time, before the Quart/Hypercorn event loop existed. This caused "Future attached to a different loop" errors when async calls were made, resulting in autonomous focus group conversations stopping with "excessive_silence". Changed to lazy initialization - clients are now created on first use within the running event loop context via get_gemini_client() and get_openai_client() helper functions. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
michael
parent
bb16165a83
commit
45c7a52aeb
1 changed files with 65 additions and 49 deletions
|
|
@ -17,13 +17,29 @@ from typing import Dict, Any, Optional, Union, List
|
|||
from PIL import Image
|
||||
import io
|
||||
|
||||
# Set up the Gemini API key and client
|
||||
# Set up the Gemini API key (client created lazily to avoid event loop issues)
|
||||
GEMINI_API_KEY = os.environ.get('GEMINI_API_KEY', 'AIzaSyAc50jzC3k9K1PmKT1vGFi0sCdhhnqsvl0')
|
||||
gemini_client = genai.Client(api_key=GEMINI_API_KEY)
|
||||
_gemini_client = None
|
||||
|
||||
# Set up OpenAI API key
|
||||
# Set up OpenAI API key (client created lazily to avoid event loop issues)
|
||||
OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY', 'REDACTED_OPENAI_KEY')
|
||||
openai_client = AsyncOpenAI(api_key=OPENAI_API_KEY, timeout=600.0)
|
||||
_openai_client = None
|
||||
|
||||
|
||||
def get_gemini_client():
|
||||
"""Get or create the Gemini client lazily within the running event loop."""
|
||||
global _gemini_client
|
||||
if _gemini_client is None:
|
||||
_gemini_client = genai.Client(api_key=GEMINI_API_KEY)
|
||||
return _gemini_client
|
||||
|
||||
|
||||
def get_openai_client():
|
||||
"""Get or create the OpenAI client lazily within the running event loop."""
|
||||
global _openai_client
|
||||
if _openai_client is None:
|
||||
_openai_client = AsyncOpenAI(api_key=OPENAI_API_KEY, timeout=600.0)
|
||||
return _openai_client
|
||||
|
||||
# The default model we're using
|
||||
DEFAULT_MODEL = "gemini-3-pro-preview"
|
||||
|
|
@ -202,45 +218,45 @@ class LLMService:
|
|||
|
||||
# Note: GPT-5 Responses API does not support max_tokens parameter
|
||||
|
||||
response = await openai_client.responses.create(**kwargs)
|
||||
response = await get_openai_client().responses.create(**kwargs)
|
||||
result = LLMService._extract_responses_api_content(response)
|
||||
|
||||
|
||||
else:
|
||||
# Use Chat Completions API for non-GPT-5 models
|
||||
messages = []
|
||||
if system_prompt:
|
||||
messages.append({"role": "system", "content": system_prompt})
|
||||
messages.append({"role": "user", "content": prompt})
|
||||
|
||||
|
||||
kwargs = {
|
||||
"model": actual_model,
|
||||
"messages": messages,
|
||||
"temperature": temperature,
|
||||
}
|
||||
|
||||
|
||||
if max_tokens:
|
||||
kwargs["max_tokens"] = max_tokens
|
||||
|
||||
response = await openai_client.chat.completions.create(**kwargs)
|
||||
|
||||
response = await get_openai_client().chat.completions.create(**kwargs)
|
||||
result = response.choices[0].message.content.strip()
|
||||
|
||||
|
||||
else:
|
||||
# New Google GenAI SDK - async call
|
||||
config = genai.types.GenerateContentConfig(
|
||||
temperature=temperature,
|
||||
)
|
||||
|
||||
|
||||
if max_tokens:
|
||||
config.max_output_tokens = max_tokens
|
||||
|
||||
|
||||
# Prepare the prompt - combine system prompt with user prompt if needed
|
||||
if system_prompt:
|
||||
combined_prompt = f"System: {system_prompt}\n\nUser: {prompt}"
|
||||
else:
|
||||
combined_prompt = prompt
|
||||
|
||||
|
||||
# Make async call to new GenAI SDK
|
||||
response = await gemini_client.aio.models.generate_content(
|
||||
response = await get_gemini_client().aio.models.generate_content(
|
||||
model=actual_model,
|
||||
contents=combined_prompt,
|
||||
config=config
|
||||
|
|
@ -488,70 +504,70 @@ class LLMService:
|
|||
}
|
||||
|
||||
# Note: GPT-5 Responses API does not support max_tokens parameter
|
||||
|
||||
response = await openai_client.responses.create(**kwargs)
|
||||
|
||||
response = await get_openai_client().responses.create(**kwargs)
|
||||
result = LLMService._extract_responses_api_content(response)
|
||||
|
||||
|
||||
else:
|
||||
# Use Chat Completions API for non-GPT-5 models
|
||||
content = [{"type": "text", "text": prompt}]
|
||||
content.extend(image_content)
|
||||
|
||||
|
||||
kwargs = {
|
||||
"model": actual_model,
|
||||
"messages": [{"role": "user", "content": content}],
|
||||
"temperature": temperature,
|
||||
}
|
||||
|
||||
|
||||
if max_tokens:
|
||||
kwargs["max_tokens"] = max_tokens
|
||||
|
||||
response = await openai_client.chat.completions.create(**kwargs)
|
||||
|
||||
response = await get_openai_client().chat.completions.create(**kwargs)
|
||||
result = response.choices[0].message.content.strip()
|
||||
|
||||
|
||||
else:
|
||||
# New Google GenAI SDK - multimodal async call
|
||||
config = genai.types.GenerateContentConfig(
|
||||
temperature=temperature,
|
||||
)
|
||||
|
||||
|
||||
if max_tokens:
|
||||
config.max_output_tokens = max_tokens
|
||||
|
||||
|
||||
# Prepare multimodal content for new SDK
|
||||
content_parts = []
|
||||
|
||||
|
||||
# Add text prompt
|
||||
content_parts.append(genai.types.Part.from_text(prompt))
|
||||
|
||||
|
||||
# Add images
|
||||
for image_path in image_paths:
|
||||
try:
|
||||
if not os.path.exists(image_path):
|
||||
raise LLMServiceError(f"Image file not found: {image_path}")
|
||||
|
||||
|
||||
# Read image data for new SDK
|
||||
with open(image_path, 'rb') as img_file:
|
||||
image_data = img_file.read()
|
||||
|
||||
|
||||
# Determine MIME type from file extension
|
||||
ext = os.path.splitext(image_path)[1].lower()
|
||||
mime_type = {
|
||||
'.jpg': 'image/jpeg',
|
||||
'.jpeg': 'image/jpeg',
|
||||
'.jpeg': 'image/jpeg',
|
||||
'.png': 'image/png',
|
||||
'.gif': 'image/gif',
|
||||
'.webp': 'image/webp'
|
||||
}.get(ext, 'image/jpeg') # Default to JPEG
|
||||
|
||||
|
||||
content_parts.append(genai.types.Part.from_bytes(image_data, mime_type=mime_type))
|
||||
logger.debug(f"Successfully loaded image for new GenAI SDK: {image_path}")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
raise LLMServiceError(f"Failed to load image {image_path}: {str(e)}")
|
||||
|
||||
|
||||
# Make async call to new GenAI SDK with multimodal content
|
||||
response = await gemini_client.aio.models.generate_content(
|
||||
response = await get_gemini_client().aio.models.generate_content(
|
||||
model=actual_model,
|
||||
contents=content_parts,
|
||||
config=config
|
||||
|
|
@ -713,54 +729,54 @@ class LLMService:
|
|||
}
|
||||
|
||||
# Note: GPT-5 Responses API does not support max_tokens parameter
|
||||
|
||||
response = await openai_client.responses.create(**kwargs)
|
||||
|
||||
response = await get_openai_client().responses.create(**kwargs)
|
||||
result = LLMService._extract_responses_api_content(response)
|
||||
|
||||
|
||||
else:
|
||||
# Use Chat Completions API for non-GPT-5 models
|
||||
content = [{"type": "text", "text": full_prompt}]
|
||||
content.extend(image_content)
|
||||
|
||||
|
||||
kwargs = {
|
||||
"model": actual_model,
|
||||
"messages": [{"role": "user", "content": content}],
|
||||
"temperature": temperature,
|
||||
}
|
||||
|
||||
|
||||
if max_tokens:
|
||||
kwargs["max_tokens"] = max_tokens
|
||||
|
||||
response = await openai_client.chat.completions.create(**kwargs)
|
||||
|
||||
response = await get_openai_client().chat.completions.create(**kwargs)
|
||||
result = response.choices[0].message.content.strip()
|
||||
|
||||
|
||||
else:
|
||||
# New Google GenAI SDK - contextual multimodal async call
|
||||
config = genai.types.GenerateContentConfig(
|
||||
temperature=temperature,
|
||||
)
|
||||
|
||||
|
||||
if max_tokens:
|
||||
config.max_output_tokens = max_tokens
|
||||
|
||||
|
||||
# Prepare content parts for new SDK
|
||||
new_content_parts = []
|
||||
|
||||
|
||||
# Add text prompt
|
||||
new_content_parts.append(genai.types.Part.from_text(full_prompt))
|
||||
|
||||
|
||||
# Convert PIL image parts to new SDK format
|
||||
for img in image_parts:
|
||||
# Convert PIL image to bytes
|
||||
buffer = io.BytesIO()
|
||||
img.save(buffer, format='PNG')
|
||||
image_data = buffer.getvalue()
|
||||
|
||||
|
||||
# Add as image part in new SDK format
|
||||
new_content_parts.append(genai.types.Part.from_bytes(image_data, mime_type='image/png'))
|
||||
|
||||
|
||||
# Make async call to new GenAI SDK
|
||||
response = await gemini_client.aio.models.generate_content(
|
||||
response = await get_gemini_client().aio.models.generate_content(
|
||||
model=actual_model,
|
||||
contents=new_content_parts,
|
||||
config=config
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue