feat(providers): add Mistral embedding provider, route registry through dynaconf

Adds a hosted Mistral embedding option (mistral-embed, 1024-dim) alongside
the existing Bedrock / OpenAI / Ollama / Simple providers. Implementation
mirrors OpenAIProvider: lazy dimension detection with a known-models lookup,
chunked batch requests, defensive index sort, and a 429-aware retry decorator.

In the same change, ProviderRegistry switches from os.getenv to the
dynaconf-backed Settings dataclass so all five providers share a single
configuration path. config.py gains the previously-uncovered Bedrock keys,
the new Mistral keys, the missing OPENAI_GENERATION_MODEL /
OLLAMA_GENERATION_MODEL, and SIMPLE_EMBEDDING_DIMENSION.

Auto-detection priority: Bedrock → OpenAI → Mistral → Ollama → Simple.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

docs/ADR-015-unified-provider-architecture.md

@@ -118,10 +118,16 @@ class ProviderRegistry:
     @staticmethod
     def create_provider() -> Provider:
         # 1. Bedrock (AWS_REGION or BEDROCK_*_MODEL)
-        # 2. Ollama (OLLAMA_BASE_URL)
-        # 3. Simple (fallback)
+        # 2. OpenAI (OPENAI_API_KEY)
+        # 3. Mistral (MISTRAL_API_KEY)
+        # 4. Ollama (OLLAMA_BASE_URL)
+        # 5. Simple (fallback)
 ```
 
+Configuration is sourced via the dynaconf-backed `Settings` dataclass in
+`config.py`; the registry reads `get_settings()` rather than `os.getenv`
+directly, so settings files and env vars share one resolution path.
+
 **Environment Variables:**
 
 **Bedrock:**
@@ -131,6 +137,17 @@ class ProviderRegistry:
 - `BEDROCK_EMBEDDING_MODEL`: Model ID for embeddings (e.g., "amazon.titan-embed-text-v2:0")
 - `BEDROCK_GENERATION_MODEL`: Model ID for text generation (e.g., "anthropic.claude-3-sonnet-20240229-v1:0")
 
+**OpenAI:**
+- `OPENAI_API_KEY`: OpenAI API key (or `GITHUB_TOKEN` for GitHub Models)
+- `OPENAI_BASE_URL`: Optional base URL override for OpenAI-compatible APIs
+- `OPENAI_EMBEDDING_MODEL`: Embedding model (default: "text-embedding-3-small")
+- `OPENAI_GENERATION_MODEL`: Generation model (e.g., "gpt-4o-mini")
+
+**Mistral (embeddings only):**
+- `MISTRAL_API_KEY`: Mistral API key from console.mistral.ai
+- `MISTRAL_EMBEDDING_MODEL`: Embedding model (default: "mistral-embed", 1024-dim)
+- `MISTRAL_BASE_URL`: Optional server URL override (proxies, on-prem)
+
 **Ollama:**
 - `OLLAMA_BASE_URL`: Ollama API base URL (e.g., "http://localhost:11434")
 - `OLLAMA_EMBEDDING_MODEL`: Model for embeddings (default: "nomic-embed-text")

docs/configuration.md

@@ -410,9 +410,16 @@ DOCUMENT_CHUNK_OVERLAP=50             # Overlapping words between chunks (defaul
 
 ### Embedding Service Configuration
 
-The server uses an embedding service to generate vector representations. Two options are available:
+The server picks an embedding provider via auto-detection. Priority order
+(see `nextcloud_mcp_server/providers/registry.py`):
 
-#### Ollama (Recommended)
+1. **Bedrock** — if `AWS_REGION` or `BEDROCK_EMBEDDING_MODEL` is set
+2. **OpenAI** — if `OPENAI_API_KEY` is set
+3. **Mistral** — if `MISTRAL_API_KEY` is set
+4. **Ollama** — if `OLLAMA_BASE_URL` is set
+5. **Simple** — fallback when nothing else is configured
+
+#### Ollama (Recommended for self-hosted)
 
 Use a local Ollama instance for embeddings:
 
@@ -422,9 +429,52 @@ OLLAMA_EMBEDDING_MODEL=nomic-embed-text  # Default model
 OLLAMA_VERIFY_SSL=true                   # Verify SSL certificates
 ```
 
+#### OpenAI
+
+Hosted OpenAI embeddings (or any OpenAI-compatible API via `OPENAI_BASE_URL`):
+
+```dotenv
+OPENAI_API_KEY=sk-...
+OPENAI_EMBEDDING_MODEL=text-embedding-3-small  # default
+# OPENAI_BASE_URL=https://models.github.ai/inference  # optional
+```
+
+#### Mistral
+
+Hosted Mistral embeddings. Requires a Mistral API key from
+[console.mistral.ai](https://console.mistral.ai). Currently embeddings only
+(no text generation).
+
+```dotenv
+MISTRAL_API_KEY=...
+MISTRAL_EMBEDDING_MODEL=mistral-embed   # default; produces 1024-dim vectors
+# MISTRAL_BASE_URL=https://api.mistral.ai  # optional override (proxies, on-prem)
+```
+
+Switching to or from Mistral forces a new Qdrant collection because the
+collection name encodes the model (see "Qdrant Collection Naming" above).
+
+#### Amazon Bedrock
+
+Bedrock provides hosted embedding models (Titan, Cohere) and uses the AWS
+credential chain (env vars, profiles, or IAM role):
+
+```dotenv
+AWS_REGION=us-east-1
+BEDROCK_EMBEDDING_MODEL=amazon.titan-embed-text-v2:0
+# AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY are optional — boto3 will use
+# the standard credential chain if not set.
+```
+
 #### Simple Embedding Provider (Fallback)
 
-If `OLLAMA_BASE_URL` is not set, the server uses a simple random embedding provider for testing. This is **not suitable for production** as it generates random embeddings with no semantic meaning.
+If no provider env var is set, the server falls back to a simple deterministic
+embedding provider for testing. This is **not suitable for production** as
+its embeddings have no semantic meaning.
+
+```dotenv
+SIMPLE_EMBEDDING_DIMENSION=384  # optional; default 384
+```
 
 ### Document Chunking Configuration
 
@@ -534,6 +584,16 @@ equivalent.** Operators who need a runtime toggle should open an issue.
 | `OLLAMA_BASE_URL` | ⚠️ Optional | - | Ollama API endpoint for embeddings |
 | `OLLAMA_EMBEDDING_MODEL` | ⚠️ Optional | `nomic-embed-text` | Embedding model to use |
 | `OLLAMA_VERIFY_SSL` | ⚠️ Optional | `true` | Verify SSL certificates |
+| `OPENAI_API_KEY` | ⚠️ Optional | - | OpenAI API key (selects OpenAI provider) |
+| `OPENAI_BASE_URL` | ⚠️ Optional | - | OpenAI base URL override (for compatible APIs) |
+| `OPENAI_EMBEDDING_MODEL` | ⚠️ Optional | `text-embedding-3-small` | OpenAI embedding model |
+| `MISTRAL_API_KEY` | ⚠️ Optional | - | Mistral API key (selects Mistral provider) |
+| `MISTRAL_EMBEDDING_MODEL` | ⚠️ Optional | `mistral-embed` | Mistral embedding model (1024-dim) |
+| `MISTRAL_BASE_URL` | ⚠️ Optional | - | Mistral base URL override (proxies, on-prem) |
+| `AWS_REGION` | ⚠️ Optional | - | AWS region (selects Bedrock provider) |
+| `BEDROCK_EMBEDDING_MODEL` | ⚠️ Optional | - | Bedrock embedding model ID |
+| `BEDROCK_GENERATION_MODEL` | ⚠️ Optional | - | Bedrock generation model ID |
+| `SIMPLE_EMBEDDING_DIMENSION` | ⚠️ Optional | `384` | Dimension for the fallback Simple provider |
 | `DOCUMENT_CHUNK_SIZE` | ⚠️ Optional | `512` | Words per chunk for document embedding |
 | `DOCUMENT_CHUNK_OVERLAP` | ⚠️ Optional | `50` | Overlapping words between chunks (must be < chunk size) |