init

backend/app/llm/client.py

@@ -0,0 +1,112 @@
+import json
+from collections.abc import AsyncIterator
+from typing import Any
+
+from openai import AsyncOpenAI
+
+from app.config import get_settings
+
+
+class LLMClient:
+    def __init__(self) -> None:
+        settings = get_settings()
+        self.model = settings.openrouter_model
+        self.client = AsyncOpenAI(
+            api_key=settings.openrouter_api_key,
+            base_url=settings.openrouter_base_url,
+        )
+
+    async def stream_chat(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]] | None = None,
+    ) -> AsyncIterator[dict[str, Any]]:
+        kwargs: dict[str, Any] = {
+            "model": self.model,
+            "messages": messages,
+            "stream": True,
+            "temperature": 0.7,
+        }
+        if tools:
+            kwargs["tools"] = tools
+
+        stream = await self.client.chat.completions.create(**kwargs)
+
+        tool_calls: dict[int, dict[str, Any]] = {}
+
+        async for chunk in stream:
+            if not chunk.choices:
+                continue
+
+            choice = chunk.choices[0]
+            delta = choice.delta
+
+            if delta.content:
+                yield {"type": "content", "content": delta.content}
+
+            if delta.tool_calls:
+                for tool_call in delta.tool_calls:
+                    idx = tool_call.index
+                    if idx not in tool_calls:
+                        tool_calls[idx] = {
+                            "id": tool_call.id or "",
+                            "type": "function",
+                            "function": {"name": "", "arguments": ""},
+                        }
+                    if tool_call.id:
+                        tool_calls[idx]["id"] = tool_call.id
+                    if tool_call.function:
+                        if tool_call.function.name:
+                            tool_calls[idx]["function"]["name"] = tool_call.function.name
+                        if tool_call.function.arguments:
+                            tool_calls[idx]["function"]["arguments"] += tool_call.function.arguments
+
+            if choice.finish_reason:
+                if tool_calls:
+                    yield {"type": "tool_calls", "tool_calls": list(tool_calls.values())}
+                yield {"type": "done", "finish_reason": choice.finish_reason}
+
+    async def complete(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]] | None = None,
+    ) -> dict[str, Any]:
+        kwargs: dict[str, Any] = {
+            "model": self.model,
+            "messages": messages,
+            "temperature": 0.7,
+        }
+        if tools:
+            kwargs["tools"] = tools
+
+        response = await self.client.chat.completions.create(**kwargs)
+        message = response.choices[0].message
+
+        result: dict[str, Any] = {
+            "content": message.content or "",
+            "tool_calls": [],
+        }
+
+        if message.tool_calls:
+            result["tool_calls"] = [
+                {
+                    "id": tc.id,
+                    "type": "function",
+                    "function": {
+                        "name": tc.function.name,
+                        "arguments": tc.function.arguments,
+                    },
+                }
+                for tc in message.tool_calls
+            ]
+
+        return result
+
+    @staticmethod
+    def parse_tool_arguments(arguments: str) -> dict[str, Any]:
+        if not arguments:
+            return {}
+        try:
+            return json.loads(arguments)
+        except json.JSONDecodeError:
+            return {}