Model providers: OpenAI/xAI/Ollama + run several at once (registry)

Extends the #215 abstraction:
- OpenAICompatibleLLMProvider / OpenAICompatibleEmbeddingProvider — one impl (via
  the official openai SDK) covers OpenAI, xAI (api.x.ai/v1), Ollama
  (…:11434/v1), OpenRouter, etc.; they differ only by base_url, key, and model.
- Registry factory: build_llm_providers() / build_embedding_providers() return
  every provider whose credentials are configured, so you can run several
  concurrently. get_llm_provider(name)/get_embedding_provider(name) select by
  name, falling back to default_*_provider, then Null.
- Per-provider env config (ANTHROPIC_*, OPENAI_*, XAI_*, OLLAMA_*) +
  DEFAULT_LLM_PROVIDER / DEFAULT_EMBEDDING_PROVIDER; documented in .env.example.
  Defaults keep AI off (empty registry).

Embeddings now have real backends (OpenAI/Ollama), still separate from the LLM
since Anthropic offers no embeddings endpoint. Tests cover multi-provider
selection, default resolution, disabled-without-credentials, and null fail-loud.
Full suite 87 passed.

Relates to #215.

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

backend/app/api/deps.py

@@ -71,26 +71,66 @@ 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
+def build_llm_providers() -> dict[str, LLMProvider]:
+    """Every LLM provider whose credentials are configured, keyed by name. Run
+    several at once; pick one with get_llm_provider(name)."""
+    from app.integrations.models.anthropic_provider import AnthropicLLMProvider
+    from app.integrations.models.openai_compat import OpenAICompatibleLLMProvider
 
-        return AnthropicLLMProvider(
-            api_key=settings.anthropic_api_key,
-            model=settings.llm_model,
-            max_tokens=settings.llm_max_tokens,
+    s = get_settings()
+    providers: dict[str, LLMProvider] = {}
+    if s.anthropic_api_key:
+        providers["anthropic"] = AnthropicLLMProvider(
+            api_key=s.anthropic_api_key, model=s.anthropic_model, max_tokens=s.llm_max_tokens
         )
-    return NullLLMProvider()
+    if s.openai_api_key:
+        providers["openai"] = OpenAICompatibleLLMProvider(
+            api_key=s.openai_api_key, base_url=s.openai_base_url, model=s.openai_model,
+            max_tokens=s.llm_max_tokens,
+        )
+    if s.xai_api_key:
+        providers["xai"] = OpenAICompatibleLLMProvider(
+            api_key=s.xai_api_key, base_url=s.xai_base_url, model=s.xai_model,
+            max_tokens=s.llm_max_tokens,
+        )
+    if s.ollama_enabled:
+        providers["ollama"] = OpenAICompatibleLLMProvider(
+            api_key=None, base_url=s.ollama_base_url, model=s.ollama_model,
+            max_tokens=s.llm_max_tokens,
+        )
+    return providers
+
+
+def get_llm_provider(name: str | None = None) -> LLMProvider:
+    """The named LLM provider, or the configured default, or Null if unconfigured."""
+    providers = build_llm_providers()
+    return providers.get(name or get_settings().default_llm_provider) or 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()
+def build_embedding_providers() -> dict[str, EmbeddingProvider]:
+    from app.integrations.models.openai_compat import OpenAICompatibleEmbeddingProvider
+
+    s = get_settings()
+    providers: dict[str, EmbeddingProvider] = {}
+    if s.openai_api_key:
+        providers["openai"] = OpenAICompatibleEmbeddingProvider(
+            api_key=s.openai_api_key, base_url=s.openai_base_url,
+            model=s.openai_embedding_model, dimensions=s.embedding_dimensions,
+        )
+    if s.ollama_enabled:
+        providers["ollama"] = OpenAICompatibleEmbeddingProvider(
+            api_key=None, base_url=s.ollama_base_url,
+            model=s.ollama_embedding_model, dimensions=s.embedding_dimensions,
+        )
+    return providers
+
+
+def get_embedding_provider(name: str | None = None) -> EmbeddingProvider:
+    providers = build_embedding_providers()
+    return providers.get(name or get_settings().default_embedding_provider) or NullEmbeddingProvider()
 
 
 EmbeddingProviderDep = Annotated[EmbeddingProvider, Depends(get_embedding_provider)]