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justin 330543f9ce 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>
2026-06-09 12:51:01 -04:00

32 lines
978 B
Python

"""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."
)