init

reelforge/services/llm.py

@@ -0,0 +1,97 @@
+"""
+LLM (Large Language Model) Service
+"""
+
+from typing import Optional
+
+from reelforge.services.base import BaseService
+
+
+class LLMService(BaseService):
+    """
+    LLM (Large Language Model) service
+    
+    Provides unified access to various LLM providers (Qwen, OpenAI, DeepSeek, Ollama, etc.)
+    
+    Usage:
+        # Direct call (recommended)
+        answer = await reelforge.llm("Explain atomic habits")
+        
+        # With parameters
+        answer = await reelforge.llm(
+            prompt="Explain atomic habits in 3 sentences",
+            temperature=0.7,
+            max_tokens=2000
+        )
+        
+        # Explicit call syntax
+        answer = await reelforge.llm.call(prompt="Hello")
+        
+        # Check active LLM
+        print(f"Using: {reelforge.llm.active}")
+        
+        # List available LLMs
+        print(f"Available: {reelforge.llm.available}")
+    """
+    
+    def __init__(self, router):
+        super().__init__(router, "llm")
+    
+    async def __call__(
+        self,
+        prompt: str,
+        api_key: Optional[str] = None,
+        base_url: Optional[str] = None,
+        model: Optional[str] = None,
+        temperature: Optional[float] = None,
+        max_tokens: Optional[int] = None,
+        **kwargs
+    ) -> str:
+        """
+        Generate text using LLM
+        
+        Args:
+            prompt: The prompt to generate from
+            api_key: API key (optional, uses config if not provided)
+            base_url: Base URL (optional, uses config if not provided)
+            model: Model name (optional, uses config if not provided)
+            temperature: Sampling temperature (0.0-2.0). Lower is more deterministic.
+            max_tokens: Maximum tokens to generate
+            **kwargs: Additional provider-specific parameters
+        
+        Returns:
+            Generated text
+        
+        Examples:
+            # Use config from config.yaml
+            answer = await reelforge.llm("Explain atomic habits")
+            
+            # Override with custom parameters
+            answer = await reelforge.llm(
+                "Summarize the book 'Atomic Habits' in 3 sentences",
+                api_key="sk-custom-key",
+                base_url="https://api.custom.com/v1",
+                model="custom-model",
+                temperature=0.7,
+                max_tokens=500
+            )
+        """
+        params = {"prompt": prompt}
+        
+        # Add optional LLM parameters (will override config if provided)
+        if api_key is not None:
+            params["api_key"] = api_key
+        if base_url is not None:
+            params["base_url"] = base_url
+        if model is not None:
+            params["model"] = model
+        
+        if temperature is not None:
+            params["temperature"] = temperature
+        if max_tokens is not None:
+            params["max_tokens"] = max_tokens
+        
+        params.update(kwargs)
+        
+        return await self._config_manager.call(self._capability_type, **params)
+