Fix #215: pluggable LLM + embedding provider abstraction

Adds the vendor-agnostic seam the AI assistant + match-ranking plug into:
- LLMProvider / EmbeddingProvider ABCs (base.py). LLM and embeddings are
  SEPARATE abstractions — Anthropic has no embeddings endpoint, so each is
  configured independently and either can be off.
- NullLLMProvider / NullEmbeddingProvider — the default; fail loud with a clear
  "not configured" error so AI-off deployments don't silently no-op.
- AnthropicLLMProvider — first concrete LLM impl, via the official anthropic SDK
  (default model claude-opus-4-8). A local provider (e.g. Ollama) would be
  another subclass of the same interface.
- Factory in deps.py (get_llm_provider / get_embedding_provider) selects by
  env (MODEL_PROVIDER / EMBEDDING_PROVIDER); documented in .env.example.

Providers are read-only text/vector producers — they never touch the DB, so the
"AI never writes autonomously" invariant (CLAUDE.md #1) holds; writes will go
through ChangeProposal (#214).

Tests: provider selection (null default, anthropic when keyed, fallback without
key) + null providers raise. 81 passed.

Closes #215

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Signed-off-by: Justin Paul <justin@jpaul.me>

backend/app/integrations/models/null.py

@@ -0,0 +1,31 @@
+"""Default providers when no model backend is configured — AI features are off.
+
+They fail loudly (rather than silently doing nothing) so a caller that reaches
+for an unconfigured capability gets a clear, actionable error.
+"""
+
+from app.integrations.models.base import (
+    EmbeddingProvider,
+    LLMProvider,
+    ModelProviderNotConfigured,
+)
+
+_MSG = (
+    "No model provider configured. Set MODEL_PROVIDER (e.g. 'anthropic') and the "
+    "provider's credentials to enable AI features."
+)
+
+
+class NullLLMProvider(LLMProvider):
+    async def complete(self, *, prompt: str, system: str | None = None) -> str:
+        raise ModelProviderNotConfigured(_MSG)
+
+
+class NullEmbeddingProvider(EmbeddingProvider):
+    dimensions = 0
+
+    async def embed(self, texts: list[str]) -> list[list[float]]:
+        raise ModelProviderNotConfigured(
+            "No embedding provider configured. Set EMBEDDING_PROVIDER and its "
+            "credentials to enable match ranking."
+        )