feat: add NORTH_STAR.md and MVP core loop (Telegram bot + LLM + proposal generator)

steward/llm/client.py

@@ -0,0 +1,70 @@
+"""LLM client wrapper (OpenAI-compatible)."""
+
+import logging
+from collections.abc import AsyncIterator
+
+from openai import AsyncOpenAI
+
+from steward.config import Settings
+
+logger = logging.getLogger(__name__)
+
+
+class LLMClient:
+    """Thin async wrapper around the OpenAI chat-completions API."""
+
+    def __init__(self, settings: Settings) -> None:
+        self._settings = settings
+        self._client = AsyncOpenAI(
+            api_key=settings.openai_api_key,
+            base_url=settings.openai_base_url,
+        )
+
+    async def chat(
+        self,
+        user_message: str,
+        *,
+        history: list[dict[str, str]] | None = None,
+        system_prompt: str | None = None,
+    ) -> str:
+        """Send a user message (with optional history) and return the assistant reply."""
+        system = system_prompt or self._settings.openai_system_prompt
+        messages: list[dict[str, str]] = [{"role": "system", "content": system}]
+        if history:
+            messages.extend(history)
+        messages.append({"role": "user", "content": user_message})
+
+        logger.debug(
+            "LLM request: model=%s messages=%d", self._settings.openai_model, len(messages)
+        )
+        response = await self._client.chat.completions.create(
+            model=self._settings.openai_model,
+            messages=messages,  # type: ignore[arg-type]
+        )
+        reply = response.choices[0].message.content or ""
+        logger.debug("LLM reply: %d chars", len(reply))
+        return reply
+
+    async def stream(
+        self,
+        user_message: str,
+        *,
+        history: list[dict[str, str]] | None = None,
+        system_prompt: str | None = None,
+    ) -> AsyncIterator[str]:
+        """Stream the assistant reply token by token."""
+        system = system_prompt or self._settings.openai_system_prompt
+        messages: list[dict[str, str]] = [{"role": "system", "content": system}]
+        if history:
+            messages.extend(history)
+        messages.append({"role": "user", "content": user_message})
+
+        stream = await self._client.chat.completions.create(
+            model=self._settings.openai_model,
+            messages=messages,  # type: ignore[arg-type]
+            stream=True,
+        )
+        async for chunk in stream:
+            delta = chunk.choices[0].delta.content
+            if delta:
+                yield delta