Initial commit: CI Agent competitive-intelligence monitoring app

FastAPI + Celery + Next.js + Postgres/Redis app with company monitoring,
source collection, LLM-based change analysis, enrichment, and account
security (Turnstile, escalating lockout, email verification).

apps/api/app/analysis/llm/anthropic_provider.py

@@ -0,0 +1,82 @@
+"""Anthropic provider: structured output via forced tool-use (the response
+schema becomes the tool's input_schema, so the model can only "call" it with
+arguments matching the shape we asked for), with a bounded repair loop for
+the rare malformed response.
+"""
+
+from __future__ import annotations
+
+from anthropic import AsyncAnthropic
+from pydantic import BaseModel, ValidationError
+
+from app.analysis.llm.base import LLMResponseError
+from app.core.config import Settings
+
+_TOOL_NAME = "emit_result"
+
+
+class AnthropicLLMProvider:
+    provider_name = "anthropic"
+
+    def __init__(self, settings: Settings) -> None:
+        self._settings = settings
+        self._client = AsyncAnthropic(api_key=settings.anthropic_api_key)
+
+    async def generate_structured[T: BaseModel](
+        self, system_prompt: str, user_prompt: str, response_model: type[T]
+    ) -> T:
+        tools = [
+            {
+                "name": _TOOL_NAME,
+                "description": f"Emit the result matching the {response_model.__name__} schema.",
+                "input_schema": response_model.model_json_schema(),
+            }
+        ]
+
+        last_error: Exception | None = None
+        messages: list[dict] = [{"role": "user", "content": user_prompt}]
+
+        for attempt in range(self._settings.llm_max_retries + 1):
+            if attempt > 0 and last_error is not None:
+                messages = [
+                    *messages,
+                    {
+                        "role": "user",
+                        "content": (
+                            f"Your previous response did not match the required schema: "
+                            f"{last_error}. Please try again."
+                        ),
+                    },
+                ]
+
+            response = await self._client.messages.create(
+                model=self._settings.anthropic_model,
+                max_tokens=self._settings.llm_max_tokens_per_request,
+                system=system_prompt,
+                tools=tools,
+                tool_choice={"type": "tool", "name": _TOOL_NAME},
+                messages=messages,
+            )
+            tool_use = next((b for b in response.content if b.type == "tool_use"), None)
+            if tool_use is None:
+                last_error = ValueError("No tool_use block in the model's response")
+                continue
+            try:
+                return response_model.model_validate(tool_use.input)
+            except ValidationError as exc:
+                last_error = exc
+                continue
+
+        raise LLMResponseError(
+            f"Anthropic provider failed to produce a valid {response_model.__name__} after "
+            f"{self._settings.llm_max_retries + 1} attempt(s): {last_error}"
+        )
+
+    async def generate_text(self, system_prompt: str, user_prompt: str) -> str:
+        response = await self._client.messages.create(
+            model=self._settings.anthropic_model,
+            max_tokens=self._settings.llm_max_tokens_per_request,
+            system=system_prompt,
+            messages=[{"role": "user", "content": user_prompt}],
+        )
+        return "\n".join(block.text for block in response.content if block.type == "text")