Add configurable LLM provider adapters for email classification

app/llm_adapters.py

@@ -0,0 +1,92 @@
+from __future__ import annotations
+
+import json
+from typing import Protocol
+
+from anthropic import AsyncAnthropic
+from openai import AsyncOpenAI
+
+from app.config import LLMSettings
+from app.models import EmailData
+from app.prompts import SYSTEM_PROMPT
+
+
+class LLMAdapter(Protocol):
+    async def classify(self, email: EmailData) -> str: ...
+
+
+class OpenAICompatibleAdapter:
+    def __init__(self, settings: LLMSettings):
+        self.settings = settings
+        self.client = AsyncOpenAI(
+            base_url=settings.base_url,
+            api_key=settings.api_key,
+            timeout=settings.timeout_seconds,
+            max_retries=0,
+        )
+
+    async def classify(self, email: EmailData) -> str:
+        response = await self.client.chat.completions.create(
+            model=self.settings.model,
+            messages=[
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": f"Subject: {email.subject}\nBody: {email.body}"},
+            ],
+            temperature=self.settings.temperature,
+            response_format={"type": "json_object"},
+        )
+        return response.choices[0].message.content or ""
+
+
+class AnthropicCompatibleAdapter:
+    def __init__(self, settings: LLMSettings):
+        self.settings = settings
+        self.client = AsyncAnthropic(
+            base_url=settings.base_url,
+            api_key=settings.api_key,
+            timeout=settings.timeout_seconds,
+            max_retries=0,
+        )
+
+    async def classify(self, email: EmailData) -> str:
+        response = await self.client.messages.create(
+            model=self.settings.model,
+            max_tokens=500,
+            temperature=self.settings.temperature,
+            system=SYSTEM_PROMPT,
+            messages=[
+                {"role": "user", "content": f"Subject: {email.subject}\nBody: {email.body}"},
+            ],
+        )
+        chunks: list[str] = []
+        for block in response.content:
+            text = getattr(block, "text", None)
+            if text:
+                chunks.append(text)
+        return "\n".join(chunks)
+
+
+def build_adapter(settings: LLMSettings) -> LLMAdapter:
+    if settings.provider == "anthropic":
+        return AnthropicCompatibleAdapter(settings)
+    return OpenAICompatibleAdapter(settings)
+
+
+def coerce_json_text(raw: str) -> str:
+    text = raw.strip()
+    if not text:
+        return text
+    if text.startswith("```"):
+        lines = text.splitlines()
+        if len(lines) >= 3 and lines[0].startswith("```") and lines[-1].startswith("```"):
+            text = "\n".join(lines[1:-1]).strip()
+            if text.lower().startswith("json\n"):
+                text = text[5:].strip()
+    start = text.find("{")
+    end = text.rfind("}")
+    if start != -1 and end != -1 and end >= start:
+        candidate = text[start : end + 1]
+        json.loads(candidate)
+        return candidate
+    json.loads(text)
+    return text