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Agent@BackendIM 2025-05-17 09:22:58 +00:00
parent 5ad1c553ef
commit fc250eb8f8
1 changed files with 13 additions and 18 deletions
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27
app/services/llm_service.py
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@ -6,7 +6,6 @@ import logging
from abc import ABC, abstractmethod from abc import ABC, abstractmethod
from typing import Dict, List from typing import Dict, List
from app.core.config import settings from app.core.config import settings
# Configure logger # Configure logger
@ -37,7 +36,7 @@ class OpenAIService(LLMService):
"""Initialize the OpenAI service.""" """Initialize the OpenAI service."""
try: try:
import openai import openai
self.client = openai.AsyncOpenAI(api_key=settings.openai_api_key) self.client = openai.AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
self.model = settings.OPENAI_MODEL self.model = settings.OPENAI_MODEL
except (ImportError, AttributeError) as e: except (ImportError, AttributeError) as e:
logger.error(f"Failed to initialize OpenAI service: {e}") logger.error(f"Failed to initialize OpenAI service: {e}")
@ -122,21 +121,21 @@ class GeminiService(LLMService):
- priority: The priority level (high, medium, low) - priority: The priority level (high, medium, low)
- status: The status (defaults to "pending") - status: The status (defaults to "pending")
Return ONLY a JSON array of task objects without any additional text or explanation. Return ONLY a JSON object with a "tasks" key that contains an array of task objects.
Format your response as valid JSON with a "tasks" key that contains an array of task objects. Do not include any text or explanation outside the JSON.
""" """
try: try:
# Start the chat session with the system prompt
chat = self.model.start_chat(history=[ chat = self.model.start_chat(history=[
{"role": "user", "parts": [system_prompt]}, {"role": "system", "content": system_prompt}
{"role": "model", "parts": ["I understand. I'll convert user inputs into JSON task objects with the specified properties."]}
]) ])
# Send the user prompt asynchronously
response = await chat.send_message_async(prompt) response = await chat.send_message_async(prompt)
content = response.text content = response.text
# Extract JSON from the response # Remove possible markdown code blocks wrapping the JSON response
# This handles cases where the model might add markdown code blocks
if "```json" in content: if "```json" in content:
json_str = content.split("```json")[1].split("```")[0].strip() json_str = content.split("```json")[1].split("```")[0].strip()
elif "```" in content: elif "```" in content:
@ -144,12 +143,13 @@ class GeminiService(LLMService):
else: else:
json_str = content.strip() json_str = content.strip()
# Parse JSON response
result = json.loads(json_str) result = json.loads(json_str)
# Expect a "tasks" key in the response JSON # Return the list under "tasks" or the whole as single-item list
if "tasks" in result: if "tasks" in result:
return result["tasks"] return result["tasks"]
return [result] # Return as a single-item list if no "tasks" key return [result]
except Exception as e: except Exception as e:
logger.error(f"Gemini API error: {e}") logger.error(f"Gemini API error: {e}")
@ -169,7 +169,6 @@ class MockLLMService(LLMService):
Returns: Returns:
List of task dictionaries List of task dictionaries
""" """
# Simple parsing logic for testing
words = prompt.lower().split() words = prompt.lower().split()
priority = "medium" priority = "medium"
@ -178,7 +177,6 @@ class MockLLMService(LLMService):
elif "low" in words or "minor" in words: elif "low" in words or "minor" in words:
priority = "low" priority = "low"
# Create a basic task from the prompt
return [{ return [{
"title": prompt[:50] + ("..." if len(prompt) > 50 else ""), "title": prompt[:50] + ("..." if len(prompt) > 50 else ""),
"description": prompt, "description": prompt,
@ -188,7 +186,6 @@ class MockLLMService(LLMService):
}] }]
# Factory function to create the appropriate LLM service
def get_llm_service() -> LLMService: def get_llm_service() -> LLMService:
""" """
Factory function for LLM service dependency injection. Factory function for LLM service dependency injection.
@ -198,14 +195,12 @@ def get_llm_service() -> LLMService:
""" """
llm_provider = settings.LLM_PROVIDER.lower() llm_provider = settings.LLM_PROVIDER.lower()
if llm_provider == "openai" and settings.OPEANAI_API_KEY: if llm_provider == "openai" and settings.OPENAI_API_KEY:
return OpenAIService() return OpenAIService()
elif llm_provider == "gemini" and settings.GEMINI_API_KEY: elif llm_provider == "gemini" and settings.GEMINI_API_KEY:
return GeminiService() return GeminiService()
elif llm_provider == "mock" or settings.environment == "test": elif llm_provider == "mock" or settings.environment == "test":
# Use mock service for testing or when configured
return MockLLMService() return MockLLMService()
else: else:
# Default to mock service if configuration is incomplete
logger.warning(f"LLM provider '{llm_provider}' not properly configured - using mock service") logger.warning(f"LLM provider '{llm_provider}' not properly configured - using mock service")
return MockLLMService() return MockLLMService()