feat: add unified provider architecture with Amazon Bedrock support

Refactored LLM provider infrastructure to support sustainable additions of new providers with both embedding and text generation capabilities.

## Major Changes

### Unified Provider Architecture (ADR-015)
- Created `nextcloud_mcp_server/providers/` with unified Provider ABC
- Providers now support optional capabilities (embeddings and/or generation)
- Auto-detection registry with priority: Bedrock → Ollama → Simple
- Backward compatible - existing code continues to work

### New Providers
- **BedrockProvider**: Full Amazon Bedrock integration
  - Embeddings: Titan Embed, Cohere Embed models
  - Generation: Claude, Llama, Titan Text, Mistral models
  - Model-specific request/response handling
  - AWS credential chain integration
- **OllamaProvider**: Migrated with both capabilities support
- **AnthropicProvider**: Moved from test code to production providers
- **SimpleProvider**: Migrated in-memory fallback provider

### Breaking Changes
None - full backward compatibility maintained:
- `embedding.get_embedding_service()` still works
- RAG evaluation tests updated to use unified providers
- All existing tests pass (127 unit tests)

### Testing
- Added 9 comprehensive Bedrock unit tests with mocked boto3
- All existing unit tests pass
- Type checking (ty) and linting (ruff) pass
- Verified backward compatibility

### Documentation
- `docs/ADR-015-unified-provider-architecture.md`: Comprehensive ADR
- `docs/bedrock-setup.md`: AWS setup guide with IAM permissions
- `CLAUDE.md`: Updated with provider architecture section

### Dependencies
- Added `boto3>=1.35.0` to dev dependencies (optional)

## Environment Variables

### Bedrock
- `AWS_REGION`: AWS region (e.g., "us-east-1")
- `BEDROCK_EMBEDDING_MODEL`: Model ID for embeddings
- `BEDROCK_GENERATION_MODEL`: Model ID for generation
- `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`: Optional credentials

### Ollama
- `OLLAMA_BASE_URL`: API URL
- `OLLAMA_EMBEDDING_MODEL`: Embedding model (default: "nomic-embed-text")
- `OLLAMA_GENERATION_MODEL`: Generation model

## AWS Bedrock Permissions Required

Minimal IAM policy:
```json
{
  "Effect": "Allow",
  "Action": ["bedrock:InvokeModel"],
  "Resource": ["arn:aws:bedrock:*::foundation-model/*"]
}
```

See `docs/bedrock-setup.md` for detailed setup instructions.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

nextcloud_mcp_server/providers/ollama.py

@@ -0,0 +1,221 @@
+"""Unified Ollama provider for embeddings and text generation."""
+
+import logging
+
+import httpx
+
+from .base import Provider
+
+logger = logging.getLogger(__name__)
+
+
+class OllamaProvider(Provider):
+    """
+    Ollama provider supporting both embeddings and text generation.
+
+    Supports TLS, SSL verification, and automatic model loading.
+    """
+
+    def __init__(
+        self,
+        base_url: str,
+        embedding_model: str | None = None,
+        generation_model: str | None = None,
+        verify_ssl: bool = True,
+        timeout: httpx.Timeout | None = None,
+    ):
+        """
+        Initialize Ollama provider.
+
+        Args:
+            base_url: Ollama API base URL (e.g., https://ollama.internal.example.com:443)
+            embedding_model: Model for embeddings (e.g., "nomic-embed-text"). None disables embeddings.
+            generation_model: Model for text generation (e.g., "llama3.2:1b"). None disables generation.
+            verify_ssl: Verify SSL certificates (default: True)
+            timeout: HTTP timeout configuration
+        """
+        self.base_url = base_url.rstrip("/")
+        self.embedding_model = embedding_model
+        self.generation_model = generation_model
+        self.verify_ssl = verify_ssl
+
+        if timeout is None:
+            timeout = httpx.Timeout(timeout=120, connect=5)
+
+        self.client = httpx.AsyncClient(verify=verify_ssl, timeout=timeout)
+        self._dimension: int | None = None  # Detected dynamically for embeddings
+
+        logger.info(
+            f"Initialized Ollama provider: {base_url} "
+            f"(embedding_model={embedding_model}, generation_model={generation_model}, "
+            f"verify_ssl={verify_ssl})"
+        )
+
+        # Pre-check and auto-load models
+        if embedding_model:
+            self._check_model_is_loaded(embedding_model, autoload=True)
+        if generation_model:
+            self._check_model_is_loaded(generation_model, autoload=True)
+
+    @property
+    def supports_embeddings(self) -> bool:
+        """Whether this provider supports embedding generation."""
+        return self.embedding_model is not None
+
+    @property
+    def supports_generation(self) -> bool:
+        """Whether this provider supports text generation."""
+        return self.generation_model is not None
+
+    async def embed(self, text: str) -> list[float]:
+        """
+        Generate embedding vector for text.
+
+        Args:
+            text: Input text to embed
+
+        Returns:
+            Vector embedding as list of floats
+
+        Raises:
+            NotImplementedError: If embeddings not enabled (no embedding_model)
+        """
+        if not self.supports_embeddings:
+            raise NotImplementedError(
+                "Embedding not supported - no embedding_model configured"
+            )
+
+        response = await self.client.post(
+            f"{self.base_url}/api/embeddings",
+            json={"model": self.embedding_model, "prompt": text},
+        )
+        response.raise_for_status()
+        return response.json()["embedding"]
+
+    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
+        """
+        Generate embeddings for multiple texts (batched requests).
+
+        Note: Ollama doesn't have native batch API, so we send requests sequentially.
+
+        Args:
+            texts: List of texts to embed
+
+        Returns:
+            List of vector embeddings
+
+        Raises:
+            NotImplementedError: If embeddings not enabled (no embedding_model)
+        """
+        if not self.supports_embeddings:
+            raise NotImplementedError(
+                "Embedding not supported - no embedding_model configured"
+            )
+
+        embeddings = []
+        for text in texts:
+            embedding = await self.embed(text)
+            embeddings.append(embedding)
+        return embeddings
+
+    async def _detect_dimension(self):
+        """
+        Detect embedding dimension by generating a test embedding.
+
+        This method queries the model to determine the actual dimension
+        instead of relying on hardcoded values.
+        """
+        if self._dimension is None and self.supports_embeddings:
+            logger.debug(
+                f"Detecting embedding dimension for model {self.embedding_model}..."
+            )
+            test_embedding = await self.embed("test")
+            self._dimension = len(test_embedding)
+            logger.info(
+                f"Detected embedding dimension: {self._dimension} "
+                f"for model {self.embedding_model}"
+            )
+
+    def get_dimension(self) -> int:
+        """
+        Get embedding dimension.
+
+        Returns:
+            Vector dimension for the configured embedding model
+
+        Raises:
+            NotImplementedError: If embeddings not enabled (no embedding_model)
+            RuntimeError: If dimension not detected yet (call _detect_dimension first)
+        """
+        if not self.supports_embeddings:
+            raise NotImplementedError(
+                "Embedding not supported - no embedding_model configured"
+            )
+
+        if self._dimension is None:
+            raise RuntimeError(
+                f"Embedding dimension not detected yet for model {self.embedding_model}. "
+                "Call _detect_dimension() first or generate an embedding."
+            )
+        return self._dimension
+
+    async def generate(self, prompt: str, max_tokens: int = 500) -> str:
+        """
+        Generate text from a prompt.
+
+        Args:
+            prompt: The prompt to generate from
+            max_tokens: Maximum tokens to generate
+
+        Returns:
+            Generated text
+
+        Raises:
+            NotImplementedError: If generation not enabled (no generation_model)
+        """
+        if not self.supports_generation:
+            raise NotImplementedError(
+                "Text generation not supported - no generation_model configured"
+            )
+
+        response = await self.client.post(
+            f"{self.base_url}/api/generate",
+            json={
+                "model": self.generation_model,
+                "prompt": prompt,
+                "stream": False,
+                "options": {
+                    "num_predict": max_tokens,
+                    "temperature": 0.7,
+                },
+            },
+        )
+        response.raise_for_status()
+        data = response.json()
+        return data["response"]
+
+    def _check_model_is_loaded(self, model: str, autoload: bool = True):
+        """
+        Check if model is loaded in Ollama, optionally auto-loading it.
+
+        Args:
+            model: Model name to check
+            autoload: Whether to automatically pull the model if not loaded
+        """
+        response = httpx.get(f"{self.base_url}/api/tags")
+        response.raise_for_status()
+
+        models = [m["name"] for m in response.json().get("models", [])]
+        logger.info("Ollama has following models pre-loaded: %s", models)
+
+        if (model not in models) and autoload:
+            logger.warning(
+                "Model '%s' not yet available in ollama, attempting to pull now...",
+                model,
+            )
+            response = httpx.post(f"{self.base_url}/api/pull", json={"model": model})
+            response.raise_for_status()
+
+    async def close(self) -> None:
+        """Close HTTP client."""
+        await self.client.aclose()