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
parent d540dc3f32
commit 330543f9ce
10 changed files with 281 additions and 0 deletions
@@ -10,6 +10,8 @@ from app.core.db import get_session
 from app.integrations.mailer.base import Mailer
 from app.integrations.mailer.console import ConsoleMailer
 from app.integrations.mailer.smtp import SMTPMailer
+from app.integrations.models.base import EmbeddingProvider, LLMProvider
+from app.integrations.models.null import NullEmbeddingProvider, NullLLMProvider
 from app.integrations.objectstore.base import ObjectStore
 from app.integrations.objectstore.s3 import S3ObjectStore
 from app.models.user import User
@@ -67,3 +69,28 @@ def get_objectstore() -> ObjectStore:


 ObjectStoreDep = Annotated[ObjectStore, Depends(get_objectstore)]
+
+
+def get_llm_provider() -> LLMProvider:
+    settings = get_settings()
+    if settings.model_provider == "anthropic" and settings.anthropic_api_key:
+        from app.integrations.models.anthropic_provider import AnthropicLLMProvider
+
+        return AnthropicLLMProvider(
+            api_key=settings.anthropic_api_key,
+            model=settings.llm_model,
+            max_tokens=settings.llm_max_tokens,
+        )
+    return NullLLMProvider()
+
+
+LLMProviderDep = Annotated[LLMProvider, Depends(get_llm_provider)]
+
+
+def get_embedding_provider() -> EmbeddingProvider:
+    # Only the null provider exists today; concrete embedders (Ollama/Voyage)
+    # implement the same interface and are selected here by settings.embedding_provider.
+    return NullEmbeddingProvider()
+
+
+EmbeddingProviderDep = Annotated[EmbeddingProvider, Depends(get_embedding_provider)]
@@ -60,6 +60,14 @@ class Settings(BaseSettings):
    smtp_password: str | None = None
    smtp_from: str = "Provenance <no-reply@provenance.local>"

+    # --- Model providers (AI assistant + match-ranking embeddings) ---
+    # Separate because Anthropic has no embeddings endpoint; either can be off.
+    model_provider: str = "null"  # null | anthropic
+    anthropic_api_key: str | None = None
+    llm_model: str = "claude-opus-4-8"
+    llm_max_tokens: int = 4096
+    embedding_provider: str = "null"  # null | (future: ollama, voyage, …)
+

@lru_cache
 def get_settings() -> Settings:
@@ -0,0 +1,24 @@
+"""Anthropic LLM provider (official SDK). Self-hosters who want everything to
+stay on their own metal would configure a local provider instead (e.g. Ollama) —
+that's a future implementation of the same LLMProvider interface."""
+
+from anthropic import AsyncAnthropic
+
+from app.integrations.models.base import LLMProvider
+
+
+class AnthropicLLMProvider(LLMProvider):
+    def __init__(self, *, api_key: str, model: str, max_tokens: int = 4096) -> None:
+        self._client = AsyncAnthropic(api_key=api_key)
+        self._model = model
+        self._max_tokens = max_tokens
+
+    async def complete(self, *, prompt: str, system: str | None = None) -> str:
+        resp = await self._client.messages.create(
+            model=self._model,
+            max_tokens=self._max_tokens,
+            system=system or "",
+            messages=[{"role": "user", "content": prompt}],
+        )
+        # content is a list of blocks; concatenate the text ones.
+        return "".join(b.text for b in resp.content if b.type == "text")
@@ -0,0 +1,36 @@
+"""Model-provider interfaces — the seam the AI assistant and match ranking plug
+into. LLM (text) and embeddings are *separate* abstractions: Anthropic offers no
+embeddings endpoint, so the two are configured independently (twelve-factor,
+CLAUDE.md #7) and a deployment may run one without the other.
+
+These providers are read-only text/vector producers. They MUST NOT mutate tree
+data — the assistant's writes go through a ChangeProposal a human approves
+(CLAUDE.md #1). Nothing here touches the database.
+"""
+
+from abc import ABC, abstractmethod
+
+
+class LLMProvider(ABC):
+    """Text in, text out. Implementations wrap a chat/completion model."""
+
+    @abstractmethod
+    async def complete(self, *, prompt: str, system: str | None = None) -> str:
+        """Return the model's text response to a single user prompt."""
+        ...
+
+
+class EmbeddingProvider(ABC):
+    """Text in, vectors out — for pgvector-backed match ranking."""
+
+    #: Dimensionality of the returned vectors (for the pgvector column).
+    dimensions: int
+
+    @abstractmethod
+    async def embed(self, texts: list[str]) -> list[list[float]]:
+        """Return one embedding vector per input text, in order."""
+        ...
+
+
+class ModelProviderNotConfigured(RuntimeError):
+    """Raised when an AI capability is used but no provider is configured."""
@@ -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."
+        )
@@ -14,6 +14,7 @@ dependencies = [
    "argon2-cffi>=23.1",
    "boto3>=1.35",
    "python-multipart>=0.0.12",
+    "anthropic>=0.108.0",
 ]

 [dependency-groups]
@@ -0,0 +1,43 @@
+"""Model-provider selection + the null-provider fail-loud behavior.
+
+No network: we only assert which provider the factory returns and that the null
+providers raise a clear error. (Live LLM/embedding calls aren't unit-tested.)
+"""
+
+import pytest
+
+from app.api.deps import get_embedding_provider, get_llm_provider
+from app.core.config import get_settings
+from app.integrations.models.anthropic_provider import AnthropicLLMProvider
+from app.integrations.models.base import ModelProviderNotConfigured
+from app.integrations.models.null import NullEmbeddingProvider, NullLLMProvider
+
+
+async def test_default_llm_is_null_and_fails_loud(monkeypatch):
+    monkeypatch.setattr(get_settings(), "model_provider", "null")
+    provider = get_llm_provider()
+    assert isinstance(provider, NullLLMProvider)
+    with pytest.raises(ModelProviderNotConfigured):
+        await provider.complete(prompt="hello")
+
+
+async def test_anthropic_selected_when_configured(monkeypatch):
+    s = get_settings()
+    monkeypatch.setattr(s, "model_provider", "anthropic")
+    monkeypatch.setattr(s, "anthropic_api_key", "sk-ant-test-key")
+    monkeypatch.setattr(s, "llm_model", "claude-opus-4-8")
+    assert isinstance(get_llm_provider(), AnthropicLLMProvider)  # no network call
+
+
+async def test_anthropic_without_key_falls_back_to_null(monkeypatch):
+    s = get_settings()
+    monkeypatch.setattr(s, "model_provider", "anthropic")
+    monkeypatch.setattr(s, "anthropic_api_key", None)
+    assert isinstance(get_llm_provider(), NullLLMProvider)
+
+
+async def test_embedding_default_is_null_and_fails_loud():
+    provider = get_embedding_provider()
+    assert isinstance(provider, NullEmbeddingProvider)
+    with pytest.raises(ModelProviderNotConfigured):
+        await provider.embed(["text"])
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 ]

+[[package]]
+name = "anthropic"
+version = "0.108.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+    { name = "distro" },
+    { name = "docstring-parser" },
+    { name = "httpx" },
+    { name = "jiter" },
+    { name = "pydantic" },
+    { name = "sniffio" },
+    { name = "typing-extensions" },
+]
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+]
+
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 name = "anyio"
 version = "4.13.0"
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+[[package]]
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 name = "fastapi"
 version = "0.136.3"
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+
 [[package]]
 name = "jmespath"
 version = "1.1.0"
@@ -482,6 +573,7 @@ version = "0.0.0"
 source = { virtual = "." }
 dependencies = [
    { name = "alembic" },
+    { name = "anthropic" },
    { name = "argon2-cffi" },
    { name = "asyncpg" },
    { name = "boto3" },
@@ -504,6 +596,7 @@ dev = [
 [package.metadata]
 requires-dist = [
    { name = "alembic", specifier = ">=1.14" },
+    { name = "anthropic", specifier = ">=0.108.0" },
    { name = "argon2-cffi", specifier = ">=23.1" },
    { name = "asyncpg", specifier = ">=0.30" },
    { name = "boto3", specifier = ">=1.35" },
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+version = "1.3.1"
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+
 [[package]]
 name = "sqlalchemy"
 version = "2.0.50"
@@ -57,6 +57,15 @@ SMTP_USERNAME=
 SMTP_PASSWORD=
 SMTP_FROM=

+# --- Model providers (AI assistant + embeddings; both optional, default off) ---
+# LLM: 'null' disables AI features; 'anthropic' uses the Claude API.
+MODEL_PROVIDER=null
+ANTHROPIC_API_KEY=
+LLM_MODEL=claude-opus-4-8
+LLM_MAX_TOKENS=4096
+# Embeddings are separate (Anthropic has no embeddings endpoint). 'null' for now.
+EMBEDDING_PROVIDER=null
+
 # --- Model providers — wired in Phase 4 (AI assistant). BYO key. ---
 # ANTHROPIC_API_KEY=
 # OPENAI_API_KEY=