initial commit
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Agent@BackendIM
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@ -1,14 +1,14 @@
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"""
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LLM service for converting natural language to structured task data.
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"""
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import json
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import logging
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from abc import ABC, abstractmethod
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from typing import Dict, List
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from typing import Dict, List, Optional
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from app.core.config import settings
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# Configure logger
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logger = logging.getLogger(__name__)
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@ -19,12 +19,26 @@ class LLMService(ABC):
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async def chat_to_tasks(self, prompt: str) -> List[Dict]:
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"""
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Convert natural language input to structured task objects.
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Must return a list of task dictionaries.
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"""
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pass
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def extract_json_from_response(text: str) -> Dict:
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"""Extract valid JSON from possibly markdown-wrapped LLM responses."""
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try:
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if "```json" in text:
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text = text.split("```json")[1].split("```")[0].strip()
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elif "```" in text:
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text = text.split("```")[1].strip()
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return json.loads(text)
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except Exception as e:
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logger.error(f"Failed to parse JSON: {e}\nRaw response: {text}")
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raise
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class OpenAIService(LLMService):
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"""OpenAI implementation of LLM service."""
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"""OpenAI implementation of the LLM service."""
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def __init__(self):
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try:
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@ -32,45 +46,45 @@ class OpenAIService(LLMService):
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self.client = openai.AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
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self.model = settings.OPENAI_MODEL
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except (ImportError, AttributeError) as e:
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logger.error(f"Failed to initialize OpenAI service: {e}")
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raise RuntimeError(f"OpenAI service initialization failed: {e}")
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logger.error(f"OpenAI service initialization failed: {e}")
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raise RuntimeError("OpenAI client setup failed.")
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async def chat_to_tasks(self, prompt: str) -> List[Dict]:
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system_prompt = """
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You are a task extraction assistant. Your job is to convert the user's natural language
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input into one or more structured task objects. Each task should have these properties:
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- title: A short, clear title for the task
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- description: A more detailed description of what needs to be done
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- due_date: When the task is due (ISO format date string, or null if not specified)
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- priority: The priority level (high, medium, low)
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- status: The status (defaults to "pending")
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Return ONLY a JSON array of task objects without any additional text or explanation.
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"""
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system_prompt = (
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"You are a task extraction assistant. Convert the user's message into structured task objects. "
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"Each task must include:\n"
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"- title: short title\n"
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"- description: detailed description\n"
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"- due_date: ISO 8601 date (YYYY-MM-DD) or null\n"
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"- priority: high, medium, or low\n"
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"- status: set to \"pending\"\n\n"
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"Respond ONLY with a JSON object in the format:\n"
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"{ \"tasks\": [ { ... }, { ... } ] }\n"
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"No extra commentary or text."
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)
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try:
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response = await self.client.chat.completions.create(
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model=self.model,
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temperature=0.2,
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt}
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],
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response_format={"type": "json_object"},
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temperature=0.2,
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{"role": "user", "content": prompt},
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]
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)
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content = response.choices[0].message.content
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result = json.loads(content)
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raw = response.choices[0].message.content.strip()
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result = extract_json_from_response(raw)
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if "tasks" in result:
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if "tasks" in result and isinstance(result["tasks"], list):
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return result["tasks"]
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return [result]
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raise ValueError("Missing or invalid 'tasks' key in response.")
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except Exception as e:
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logger.error(f"OpenAI API error: {e}")
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raise RuntimeError(f"Failed to process request with OpenAI: {e}")
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logger.error(f"OpenAI task extraction failed: {e}")
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raise RuntimeError("Failed to extract tasks from OpenAI response.")
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class GeminiService(LLMService):
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"""Google Gemini implementation of LLM service."""
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"""Google Gemini implementation of the LLM service."""
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def __init__(self):
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try:
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@ -78,60 +92,51 @@ class GeminiService(LLMService):
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genai.configure(api_key=settings.GEMINI_API_KEY)
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self.model = genai.GenerativeModel(settings.GEMINI_MODEL)
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except (ImportError, AttributeError) as e:
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logger.error(f"Failed to initialize Gemini service: {e}")
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raise RuntimeError(f"Gemini service initialization failed: {e}")
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logger.error(f"Gemini service initialization failed: {e}")
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raise RuntimeError("Gemini client setup failed.")
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async def chat_to_tasks(self, prompt: str) -> List[Dict]:
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system_prompt = (
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"You are a task extraction assistant. Your job is to convert the user's natural language "
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"input into one or more structured task objects. Each task should have these properties:\n"
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"- title: A short, clear title for the task\n"
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"- description: A more detailed description of what needs to be done\n"
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"- due_date: When the task is due (ISO format date string, or null if not specified)\n"
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"- priority: The priority level (high, medium, low)\n"
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"- status: The status (defaults to \"pending\")\n\n"
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"Return ONLY a JSON object with a \"tasks\" key that contains an array of task objects. "
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"Do not include any text or explanation outside the JSON."
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"You are a task extraction assistant. Convert the user's message into structured task objects. "
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"Each task must include:\n"
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"- title: short title\n"
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"- description: detailed description\n"
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"- due_date: ISO 8601 date (YYYY-MM-DD) or null\n"
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"- priority: high, medium, or low\n"
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"- status: set to \"pending\"\n\n"
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"Return only this JSON format: { \"tasks\": [ ... ] }\n"
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"No explanations, no markdown, no formatting – just pure JSON."
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)
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try:
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chat = self.model.start_chat(history=[
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{"role": "user", "parts": [system_prompt]}
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])
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response = await chat.send_message_async(prompt)
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content = response.text
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raw = response.text.strip()
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result = extract_json_from_response(raw)
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# Handle markdown-wrapped responses
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if "```json" in content:
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json_str = content.split("```json")[1].split("```")[0].strip()
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elif "```" in content:
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json_str = content.split("```")[1].strip()
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else:
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json_str = content.strip()
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result = json.loads(json_str)
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if "tasks" in result:
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if "tasks" in result and isinstance(result["tasks"], list):
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return result["tasks"]
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return [result]
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raise ValueError("Missing or invalid 'tasks' key in response.")
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except Exception as e:
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logger.error(f"Gemini API error: {e}")
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raise RuntimeError(f"Failed to process request with Gemini: {e}")
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logger.error(f"Gemini task extraction failed: {e}")
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raise RuntimeError("Failed to extract tasks from Gemini response.")
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class MockLLMService(LLMService):
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"""Mock LLM service for testing."""
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"""Mock LLM service for testing without external API calls."""
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async def chat_to_tasks(self, prompt: str) -> List[Dict]:
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words = prompt.lower().split()
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priority = "medium"
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if "urgent" in words or "important" in words:
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if any(word in words for word in ["urgent", "important"]):
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priority = "high"
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elif "low" in words or "minor" in words:
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elif any(word in words for word in ["minor", "low"]):
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priority = "low"
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return [{
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"title": prompt[:50] + ("..." if len(prompt) > 50 else ""),
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"title": (prompt[:50] + "...") if len(prompt) > 50 else prompt,
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"description": prompt,
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"due_date": None,
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"priority": priority,
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@ -140,6 +145,7 @@ class MockLLMService(LLMService):
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def get_llm_service() -> LLMService:
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"""Factory to return the appropriate LLM service based on settings."""
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llm_provider = settings.LLM_PROVIDER.lower()
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if llm_provider == "openai" and settings.OPENAI_API_KEY:
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@ -149,5 +155,7 @@ def get_llm_service() -> LLMService:
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elif llm_provider == "mock" or settings.environment == "test":
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return MockLLMService()
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else:
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logger.warning(f"LLM provider '{llm_provider}' not properly configured - using mock service")
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logger.warning(
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f"LLM provider '{llm_provider}' is not properly configured. Falling back to mock."
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)
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return MockLLMService()
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