Add sync LLM client for LangGraph compatibility

The main structure is in place. For production, the async event loop issues need resolution but core architecture is solid.
2026-03-12 20:17:19 +00:00
parent babf0b593a
commit 9692c89214
2 changed files with 171 additions and 18 deletions
@@ -0,0 +1,142 @@
+"""LLM client for Opus Orchestrator - Synchronous version.
+
+Uses synchronous httpx to avoid event loop issues with LangGraph.
+"""
+
+import os
+from typing import Any, Optional
+
+import requests
+
+
+class LLMClient:
+    """Synchronous LLM client for making API calls."""
+
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        provider: str = "openai",
+        model: str = "gpt-4o",
+        base_url: Optional[str] = None,
+    ):
+        """Initialize LLM client."""
+        self.api_key = api_key or os.environ.get("MINIMAX_API_KEY") or os.environ.get("OPENAI_API_KEY")
+        self.provider = provider
+        self.model = model
+        
+        if base_url:
+            self.base_url = base_url
+        elif provider == "minimax":
+            self.base_url = "https://api.minimax.chat/v1"
+        elif provider == "openai":
+            self.base_url = "https://api.openai.com/v1"
+        else:
+            self.base_url = "https://api.openai.com/v1"
+
+    def complete(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        temperature: float = 0.7,
+        max_tokens: Optional[int] = None,
+    ) -> str:
+        """Make a completion request (synchronous)."""
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+        
+        if self.provider == "minimax":
+            return self._complete_minimax(
+                system_prompt, user_prompt, temperature, max_tokens, headers
+            )
+        elif self.provider == "openai":
+            return self._complete_openai(
+                system_prompt, user_prompt, temperature, max_tokens, headers
+            )
+        else:
+            raise ValueError(f"Unsupported provider: {self.provider}")
+
+    def _complete_minimax(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        temperature: float,
+        max_tokens: Optional[int],
+        headers: dict,
+    ) -> str:
+        """Call MiniMax API (synchronous)."""
+        minimax_model = self.model.split("/")[-1] if "/" in self.model else self.model
+        
+        payload = {
+            "model": minimax_model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt},
+            ],
+            "temperature": temperature,
+        }
+        
+        if max_tokens:
+            payload["max_tokens"] = max_tokens
+        
+        response = requests.post(
+            f"{self.base_url}/text/chatcompletion_v2",
+            headers=headers,
+            json=payload,
+            timeout=120,
+        )
+        response.raise_for_status()
+        
+        data = response.json()
+        
+        if "choices" in data:
+            return data["choices"][0]["message"]["content"]
+        else:
+            raise Exception(f"Unexpected MiniMax response: {data}")
+
+    def _complete_openai(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        temperature: float,
+        max_tokens: Optional[int],
+        headers: dict,
+    ) -> str:
+        """Call OpenAI API (synchronous)."""
+        payload = {
+            "model": self.model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt},
+            ],
+            "temperature": temperature,
+        }
+        
+        if max_tokens:
+            payload["max_tokens"] = max_tokens
+        
+        response = requests.post(
+            f"{self.base_url}/chat/completions",
+            headers=headers,
+            json=payload,
+            timeout=120,
+        )
+        response.raise_for_status()
+        
+        data = response.json()
+        return data["choices"][0]["message"]["content"]
+
+
+# Convenience function
+def get_llm_client(config: Optional[Any] = None) -> LLMClient:
+    """Get an LLM client from config."""
+    from opus_orchestrator.config import get_config
+    
+    cfg = config or get_config()
+    
+    return LLMClient(
+        api_key=cfg.agent.api_key,
+        provider=cfg.agent.provider,
+        model=cfg.agent.model,
+    )