Merge remote-tracking branch 'origin/master' into fix/chunk-context-indexed-lookup

# Conflicts:
#	nextcloud_mcp_server/api/visualization.py
#	nextcloud_mcp_server/auth/viz_routes.py

.claude/hooks/sonar-secrets/build-scripts/pretool-secrets.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+if ! command -v sonar &> /dev/null; then
+  exit 0
+fi
+sonar hook claude-pre-tool-use

.claude/hooks/sonar-secrets/build-scripts/prompt-secrets.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+if ! command -v sonar &> /dev/null; then
+  exit 0
+fi
+sonar hook claude-prompt-submit

.claude/settings.json

@@ -0,0 +1,28 @@
+{
+  "hooks": {
+    "PreToolUse": [
+      {
+        "matcher": "Read",
+        "hooks": [
+          {
+            "type": "command",
+            "command": ".claude/hooks/sonar-secrets/build-scripts/pretool-secrets.sh",
+            "timeout": 60
+          }
+        ]
+      }
+    ],
+    "UserPromptSubmit": [
+      {
+        "matcher": "*",
+        "hooks": [
+          {
+            "type": "command",
+            "command": ".claude/hooks/sonar-secrets/build-scripts/prompt-secrets.sh",
+            "timeout": 60
+          }
+        ]
+      }
+    ]
+  }
+}

.claude/skills/pre-push-review/SKILL.md

@@ -7,6 +7,7 @@ description: |
   in this repo's automated PR reviews. Use when the user is about to push, says "ready
   to push", "review my work", "check before PR", or invokes /pre-push-review.
   Report-only — does not modify code.
+model: sonnet
 allowed-tools:
   - Bash
   - Read

CHANGELOG.md

@@ -5,6 +5,30 @@ All notable changes to the Nextcloud MCP Server will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [PEP 440](https://peps.python.org/pep-0440/).
 
+## v0.83.0 (2026-05-08)
+
+### Feat
+
+- **vector**: replace inline page-image payloads with chunk_bbox (Deck #76)
+
+### Refactor
+
+- **vector**: address PR #775 review round 3 — fix unused var, harden boundary lookup, rename trace span
+- **vector**: address PR #775 review round 2 — drop dead page field, add omission tests
+- **vector**: address PR #775 review — drop unused payload key, fix resource leaks
+
+## v0.82.0 (2026-05-08)
+
+### Feat
+
+- **providers**: add Mistral embedding provider, route registry through dynaconf
+
+### Refactor
+
+- **providers**: address PR #772 review round 3 — hermetic test, lazy logging, defensive-guard tests
+- **providers**: address PR #772 review round 2 — guard, naming, docs, tests
+- **providers**: address PR #772 review — shared retry, cleaner imports, no-op close
+
 ## v0.81.0 (2026-05-07)
 
 ### Feat

docker-compose.yml

@@ -334,7 +334,7 @@ services:
       - claude-funnel
 
   qdrant:
-    image: docker.io/qdrant/qdrant:v1.17.1@sha256:94728574965d17c6485dd361aa3c0818b325b9016dac5ea6afec7b4b2700865f
+    image: docker.io/qdrant/qdrant:v1.18.0@sha256:1cf3e07c9f269030c7cfed0d5c0beea7bb081848a88e2ae13b35a613d4dd5019
     restart: always
     ports:
       - 127.0.0.1:6333:6333  # REST API

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
 
@@ -533,7 +583,21 @@ equivalent.** Operators who need a runtime toggle should open an issue.
 | `VECTOR_SYNC_QUEUE_MAX_SIZE` | ⚠️ Optional | `10000` | Max queued documents |
 | `OLLAMA_BASE_URL` | ⚠️ Optional | - | Ollama API endpoint for embeddings |
 | `OLLAMA_EMBEDDING_MODEL` | ⚠️ Optional | `nomic-embed-text` | Embedding model to use |
+| `OLLAMA_GENERATION_MODEL` | ⚠️ Optional | - | Ollama model for text generation |
 | `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 |
+| `OPENAI_GENERATION_MODEL` | ⚠️ Optional | - | OpenAI model for text generation |
+| `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) |
+| `AWS_ACCESS_KEY_ID` | ⚠️ Optional | - | AWS access key (boto3 credential chain fallback) |
+| `AWS_SECRET_ACCESS_KEY` | ⚠️ Optional | - | AWS secret key (boto3 credential chain fallback) |
+| `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) |
 

nextcloud_mcp_server/api/visualization.py

@@ -565,9 +565,10 @@ async def get_chunk_context(request: Request) -> JSONResponse:
                 status_code=404,
             )
 
-        # For PDF files, also fetch the highlighted page image from Qdrant if available
-        # This is useful for clients that want to show a pre-rendered image
-        highlighted_page_image = None
+        # For PDF files, also fetch the chunk's bounding box from Qdrant if
+        # available so the client can overlay a highlight on top of a
+        # render-on-demand page image (Deck #76).
+        chunk_bbox = None
         page_number = chunk_context.page_number
 
         if doc_type == "file":
@@ -575,7 +576,7 @@ async def get_chunk_context(request: Request) -> JSONResponse:
                 settings = get_settings()
                 qdrant_client = await get_qdrant_client()
 
-                # Prefer chunk_index for the highlighted-image lookup (always indexed);
+                # Prefer chunk_index for the chunk-bbox lookup (always indexed);
                 # fall back to (chunk_start_offset, chunk_end_offset) when not provided.
                 if chunk_index is not None:
                     points_response = await qdrant_client.scroll(
@@ -589,10 +590,6 @@ async def get_chunk_context(request: Request) -> JSONResponse:
                                 FieldCondition(
                                     key="user_id", match=MatchValue(value=user_id)
                                 ),
-                                FieldCondition(
-                                    key="doc_type",
-                                    match=MatchValue(value=doc_type),
-                                ),
                                 FieldCondition(
                                     key="chunk_index",
                                     match=MatchValue(value=chunk_index),
@@ -601,7 +598,7 @@ async def get_chunk_context(request: Request) -> JSONResponse:
                         ),
                         limit=1,
                         with_vectors=False,
-                        with_payload=["highlighted_page_image", "page_number"],
+                        with_payload=["chunk_bbox", "page_number"],
                     )
                 else:
                     points_response = await qdrant_client.scroll(
@@ -615,10 +612,6 @@ async def get_chunk_context(request: Request) -> JSONResponse:
                                 FieldCondition(
                                     key="user_id", match=MatchValue(value=user_id)
                                 ),
-                                FieldCondition(
-                                    key="doc_type",
-                                    match=MatchValue(value=doc_type),
-                                ),
                                 FieldCondition(
                                     key="chunk_start_offset",
                                     match=MatchValue(value=start),
@@ -631,19 +624,19 @@ async def get_chunk_context(request: Request) -> JSONResponse:
                         ),
                         limit=1,
                         with_vectors=False,
-                        with_payload=["highlighted_page_image", "page_number"],
+                        with_payload=["chunk_bbox", "page_number"],
                     )
 
                 if points_response[0]:
                     payload = points_response[0][0].payload
                     if payload:
-                        highlighted_page_image = payload.get("highlighted_page_image")
+                        chunk_bbox = payload.get("chunk_bbox")
                         # Trust Qdrant page number if available (might be more accurate than context expansion logic)
                         if payload.get("page_number") is not None:
                             page_number = payload.get("page_number")
 
             except Exception as e:
-                logger.warning(f"Failed to fetch highlighted image: {e}")
+                logger.warning(f"Failed to fetch chunk bbox: {e}")
 
         # Build response
         response_data = {
@@ -658,8 +651,8 @@ async def get_chunk_context(request: Request) -> JSONResponse:
             "total_chunks": chunk_context.total_chunks,
         }
 
-        if highlighted_page_image:
-            response_data["highlighted_page_image"] = highlighted_page_image
+        if chunk_bbox:
+            response_data["chunk_bbox"] = chunk_bbox
 
         return JSONResponse(response_data)
 

nextcloud_mcp_server/app.py

@@ -125,12 +125,13 @@ from nextcloud_mcp_server.server import (
 )
 from nextcloud_mcp_server.server.auth_tools import register_auth_tools
 from nextcloud_mcp_server.server.oauth_tools import register_oauth_tools
-from nextcloud_mcp_server.vector import processor_task, scanner_task
 from nextcloud_mcp_server.vector.oauth_sync import (
     oauth_processor_task,
     user_manager_task,
 )
+from nextcloud_mcp_server.vector.processor import processor_task
 from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
+from nextcloud_mcp_server.vector.scanner import scanner_task
 from nextcloud_mcp_server.vector.webhook_receiver import handle_nextcloud_webhook
 
 logger = logging.getLogger(__name__)

nextcloud_mcp_server/auth/viz_routes.py

@@ -623,8 +623,10 @@ async def chunk_context_endpoint(request: Request) -> JSONResponse:
             f"after_len={len(chunk_context.after_context)}"
         )
 
-        # For PDF files, also fetch the highlighted page image from Qdrant
-        highlighted_page_image = None
+        # For PDF files, also fetch the chunk bbox from Qdrant so the client
+        # can overlay a highlight on top of a render-on-demand page image
+        # (Deck #76).
+        chunk_bbox = None
         page_number = chunk_context.page_number
         if doc_type == "file":
             try:
@@ -632,7 +634,7 @@ async def chunk_context_endpoint(request: Request) -> JSONResponse:
                 qdrant_client = await get_qdrant_client()
                 username = request.user.display_name
 
-                # Prefer chunk_index for the highlighted-image lookup (always indexed);
+                # Prefer chunk_index for the chunk-bbox lookup (always indexed);
                 # fall back to (chunk_start_offset, chunk_end_offset) when not provided.
                 if chunk_index is not None:
                     points_response = await qdrant_client.scroll(
@@ -646,10 +648,6 @@ async def chunk_context_endpoint(request: Request) -> JSONResponse:
                                 FieldCondition(
                                     key="user_id", match=MatchValue(value=username)
                                 ),
-                                FieldCondition(
-                                    key="doc_type",
-                                    match=MatchValue(value=doc_type),
-                                ),
                                 FieldCondition(
                                     key="chunk_index",
                                     match=MatchValue(value=chunk_index),
@@ -658,7 +656,7 @@ async def chunk_context_endpoint(request: Request) -> JSONResponse:
                         ),
                         limit=1,
                         with_vectors=False,
-                        with_payload=["highlighted_page_image", "page_number"],
+                        with_payload=["chunk_bbox", "page_number"],
                     )
                 else:
                     points_response = await qdrant_client.scroll(
@@ -672,10 +670,6 @@ async def chunk_context_endpoint(request: Request) -> JSONResponse:
                                 FieldCondition(
                                     key="user_id", match=MatchValue(value=username)
                                 ),
-                                FieldCondition(
-                                    key="doc_type",
-                                    match=MatchValue(value=doc_type),
-                                ),
                                 FieldCondition(
                                     key="chunk_start_offset",
                                     match=MatchValue(value=start),
@@ -688,22 +682,20 @@ async def chunk_context_endpoint(request: Request) -> JSONResponse:
                         ),
                         limit=1,
                         with_vectors=False,
-                        with_payload=["highlighted_page_image", "page_number"],
+                        with_payload=["chunk_bbox", "page_number"],
                     )
 
                 points = points_response[0]
                 if points and points[0].payload:
-                    highlighted_page_image = points[0].payload.get(
-                        "highlighted_page_image"
-                    )
+                    chunk_bbox = points[0].payload.get("chunk_bbox")
                     page_number = points[0].payload.get("page_number")
-                    if highlighted_page_image:
+                    if chunk_bbox:
                         logger.info(
-                            f"Found highlighted image for chunk: "
-                            f"page={page_number}, image_size={len(highlighted_page_image)}"
+                            f"Found chunk bbox: page={page_number}, "
+                            f"rects={len(chunk_bbox)}"
                         )
             except Exception as e:
-                logger.warning(f"Failed to fetch highlighted image: {e}")
+                logger.warning(f"Failed to fetch chunk bbox: {e}")
 
         # Return response compatible with frontend expectations
         response_data: dict = {
@@ -718,8 +710,8 @@ async def chunk_context_endpoint(request: Request) -> JSONResponse:
             "total_chunks": chunk_context.total_chunks,
         }
 
-        if highlighted_page_image:
-            response_data["highlighted_page_image"] = highlighted_page_image
+        if chunk_bbox:
+            response_data["chunk_bbox"] = chunk_bbox
 
         return JSONResponse(response_data)
 

nextcloud_mcp_server/config.py

@@ -77,11 +77,25 @@ _DEFAULTS: dict[str, Any] = {
     # Ollama
     "ollama_base_url": None,
     "ollama_embedding_model": "nomic-embed-text",
+    "ollama_generation_model": None,
     "ollama_verify_ssl": True,
     # OpenAI
     "openai_api_key": None,
     "openai_base_url": None,
     "openai_embedding_model": "text-embedding-3-small",
+    "openai_generation_model": None,
+    # Bedrock (AWS)
+    "aws_region": None,
+    "aws_access_key_id": None,
+    "aws_secret_access_key": None,
+    "bedrock_embedding_model": None,
+    "bedrock_generation_model": None,
+    # Mistral
+    "mistral_api_key": None,
+    "mistral_embedding_model": "mistral-embed",
+    "mistral_base_url": None,
+    # Simple (fallback) embedding dimension
+    "simple_embedding_dimension": 384,
     # Document chunking
     "document_chunk_size": 2048,
     "document_chunk_overlap": 200,
@@ -486,15 +500,32 @@ class Settings:
     qdrant_api_key: str | None = None
     qdrant_collection: str = "nextcloud_content"
 
-    # Ollama settings (for embeddings)
+    # Ollama settings (embeddings + optional generation)
     ollama_base_url: str | None = None
     ollama_embedding_model: str = "nomic-embed-text"
+    ollama_generation_model: str | None = None
     ollama_verify_ssl: bool = True
 
-    # OpenAI settings (for embeddings)
+    # OpenAI settings (embeddings + optional generation)
     openai_api_key: str | None = None
     openai_base_url: str | None = None
     openai_embedding_model: str = "text-embedding-3-small"
+    openai_generation_model: str | None = None
+
+    # Bedrock (AWS) settings — boto3 also reads these from its credential chain
+    aws_region: str | None = None
+    aws_access_key_id: str | None = None
+    aws_secret_access_key: str | None = None
+    bedrock_embedding_model: str | None = None
+    bedrock_generation_model: str | None = None
+
+    # Mistral settings (embeddings only)
+    mistral_api_key: str | None = None
+    mistral_embedding_model: str = "mistral-embed"
+    mistral_base_url: str | None = None
+
+    # Simple (fallback) provider — dimension when no real provider configured
+    simple_embedding_dimension: int = 384
 
     # Document chunking settings (for vector embeddings)
     document_chunk_size: int = 2048  # Characters per chunk
@@ -573,23 +604,32 @@ class Settings:
         Get the active embedding model name based on provider priority.
 
         Priority order (same as ProviderRegistry):
-        1. OpenAI - if OPENAI_API_KEY is set
-        2. Ollama - if OLLAMA_BASE_URL is set
-        3. Simple - fallback (returns "simple-384")
+        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 (returns "simple-{dimension}")
 
         Returns:
             Active embedding model name
         """
-        # Check OpenAI first (higher priority than Ollama in registry)
+        if (
+            self.aws_region
+            or self.bedrock_embedding_model
+            or self.bedrock_generation_model
+        ):
+            return self.bedrock_embedding_model or "bedrock-default"
+
         if self.openai_api_key:
             return self.openai_embedding_model
 
-        # Check Ollama
+        if self.mistral_api_key:
+            return self.mistral_embedding_model
+
         if self.ollama_base_url:
             return self.ollama_embedding_model
 
-        # Fallback to simple provider indicator
-        return "simple-384"
+        return f"simple-{self.simple_embedding_dimension}"
 
     def get_collection_name(self) -> str:
         """
@@ -835,11 +875,25 @@ def get_settings() -> Settings:
         # Ollama settings
         "ollama_base_url": "OLLAMA_BASE_URL",
         "ollama_embedding_model": "OLLAMA_EMBEDDING_MODEL",
+        "ollama_generation_model": "OLLAMA_GENERATION_MODEL",
         "ollama_verify_ssl": "OLLAMA_VERIFY_SSL",
         # OpenAI settings
         "openai_api_key": "OPENAI_API_KEY",
         "openai_base_url": "OPENAI_BASE_URL",
         "openai_embedding_model": "OPENAI_EMBEDDING_MODEL",
+        "openai_generation_model": "OPENAI_GENERATION_MODEL",
+        # Bedrock (AWS) settings
+        "aws_region": "AWS_REGION",
+        "aws_access_key_id": "AWS_ACCESS_KEY_ID",
+        "aws_secret_access_key": "AWS_SECRET_ACCESS_KEY",
+        "bedrock_embedding_model": "BEDROCK_EMBEDDING_MODEL",
+        "bedrock_generation_model": "BEDROCK_GENERATION_MODEL",
+        # Mistral settings
+        "mistral_api_key": "MISTRAL_API_KEY",
+        "mistral_embedding_model": "MISTRAL_EMBEDDING_MODEL",
+        "mistral_base_url": "MISTRAL_BASE_URL",
+        # Simple provider
+        "simple_embedding_dimension": "SIMPLE_EMBEDDING_DIMENSION",
         # Document chunking settings
         "document_chunk_size": "DOCUMENT_CHUNK_SIZE",
         "document_chunk_overlap": "DOCUMENT_CHUNK_OVERLAP",

nextcloud_mcp_server/providers/__init__.py

@@ -3,6 +3,7 @@
 from .anthropic import AnthropicProvider
 from .base import Provider
 from .bedrock import BedrockProvider
+from .mistral import MistralProvider
 from .ollama import OllamaProvider
 from .openai import OpenAIProvider
 from .registry import get_provider, reset_provider
@@ -13,6 +14,7 @@ __all__ = [
     "OllamaProvider",
     "OpenAIProvider",
     "AnthropicProvider",
+    "MistralProvider",
     "SimpleProvider",
     "BedrockProvider",
     "get_provider",

nextcloud_mcp_server/providers/_retry.py

@@ -0,0 +1,78 @@
+"""Shared rate-limit retry helper for provider modules.
+
+OpenAI and Mistral both retry on 429 with the same exponential-backoff curve;
+extracting the loop here keeps the two provider modules thin and lets future
+providers (Bedrock throttling, etc.) reuse the same primitive.
+"""
+
+from __future__ import annotations
+
+import logging
+from collections.abc import Awaitable, Callable
+from functools import wraps
+from typing import Any, TypeVar
+
+import anyio
+
+logger = logging.getLogger(__name__)
+
+MAX_RETRIES = 5
+INITIAL_RETRY_DELAY = 2.0
+MAX_RETRY_DELAY = 60.0
+
+T = TypeVar("T")
+
+
+def retry_on_rate_limit(
+    exception_type: type[BaseException],
+    is_rate_limit: Callable[[BaseException], bool] = lambda _exc: True,
+    *,
+    provider_name: str = "provider",
+) -> Callable[[Callable[..., Awaitable[T]]], Callable[..., Awaitable[T]]]:
+    """Build a decorator that retries on rate-limit exceptions.
+
+    Args:
+        exception_type: Catch this exception class (e.g. ``openai.RateLimitError``,
+            ``mistralai.client.errors.SDKError``).
+        is_rate_limit: Predicate that decides whether a caught exception is
+            actually a rate-limit (vs. some other error of the same class).
+            Defaults to "always True" — appropriate when ``exception_type`` is
+            already a rate-limit-specific class.
+        provider_name: Used in log messages so operators can tell which
+            provider exhausted retries.
+    """
+
+    def decorator(func: Callable[..., Awaitable[T]]) -> Callable[..., Awaitable[T]]:
+        @wraps(func)
+        async def wrapper(*args: Any, **kwargs: Any) -> T:
+            retry_delay = INITIAL_RETRY_DELAY
+            last_error: BaseException | None = None
+
+            for attempt in range(1, MAX_RETRIES + 1):
+                try:
+                    return await func(*args, **kwargs)
+                except exception_type as e:
+                    if not is_rate_limit(e):
+                        raise
+                    last_error = e
+                    if attempt < MAX_RETRIES:
+                        logger.warning(
+                            "%s rate limit hit (attempt %d/%d), retrying in %.1fs...",
+                            provider_name,
+                            attempt,
+                            MAX_RETRIES,
+                            retry_delay,
+                        )
+                        await anyio.sleep(retry_delay)
+                        retry_delay = min(retry_delay * 2, MAX_RETRY_DELAY)
+
+            logger.error(
+                "%s rate limit exceeded after %d attempts", provider_name, MAX_RETRIES
+            )
+            if last_error is None:  # pragma: no cover — loop above always sets this
+                raise RuntimeError("retry loop exited without capturing an error")
+            raise last_error
+
+        return wrapper
+
+    return decorator

nextcloud_mcp_server/providers/mistral.py

@@ -0,0 +1,199 @@
+"""Mistral provider for embeddings.
+
+Currently supports embeddings only (``mistral-embed``, 1024-dim). Generation
+can be added later if needed; see ADR-015.
+"""
+
+import logging
+
+# mistralai 2.x ships no top-level __init__.py, so `from mistralai import …`
+# raises ImportError. The canonical public paths are `mistralai.client` (which
+# re-exports the SDK class via `client/__init__.py`) and `mistralai.client.errors`
+# (which lazy-loads SDKError). There is no `mistralai.models` subpackage either.
+from mistralai.client import Mistral
+from mistralai.client.errors import SDKError
+
+from ._retry import retry_on_rate_limit
+from .base import Provider
+
+logger = logging.getLogger(__name__)
+
+# Well-known Mistral embedding model dimensions
+MISTRAL_EMBEDDING_DIMENSIONS: dict[str, int] = {
+    "mistral-embed": 1024,
+}
+
+# Conservative chunk size for batch embeddings. Mistral allows large batches,
+# but we keep this in line with sibling providers (OpenAI=100, Ollama=32).
+BATCH_SIZE = 64
+
+_NO_EMBEDDING_MODEL_MSG = "Embedding not supported - no embedding_model configured"
+
+
+def _is_rate_limit(exc: BaseException) -> bool:
+    """True only for HTTP 429 SDKErrors."""
+    return getattr(exc, "status_code", None) == 429
+
+
+_retry_429 = retry_on_rate_limit(
+    SDKError, is_rate_limit=_is_rate_limit, provider_name="Mistral"
+)
+
+
+class MistralProvider(Provider):
+    """
+    Mistral provider — embeddings only.
+
+    Uses the official ``mistralai`` SDK. Lazy dimension detection mirrors the
+    OpenAI provider: known models populate the cached dimension at construction
+    time; unknown models get their dimension detected on the first ``embed()``
+    call.
+    """
+
+    def __init__(
+        self,
+        api_key: str,
+        embedding_model: str | None = "mistral-embed",
+        base_url: str | None = None,
+    ):
+        """
+        Initialize the Mistral provider.
+
+        Args:
+            api_key: Mistral API key.
+            embedding_model: Embedding model ID (default: ``mistral-embed``).
+                Pass ``None`` to disable embeddings (the provider will then
+                support no capabilities, which is mostly useful for tests).
+            base_url: Optional base URL override (e.g. proxies, on-prem).
+        """
+        self.embedding_model = embedding_model
+        self._dimension: int | None = None
+
+        self.client = Mistral(api_key=api_key, server_url=base_url)
+
+        if embedding_model and embedding_model in MISTRAL_EMBEDDING_DIMENSIONS:
+            self._dimension = MISTRAL_EMBEDDING_DIMENSIONS[embedding_model]
+
+        logger.info(
+            "Initialized Mistral provider: base_url=%s, embedding_model=%s, "
+            "dimension=%s",
+            base_url or "default",
+            embedding_model,
+            self._dimension,
+        )
+
+    @property
+    def supports_embeddings(self) -> bool:
+        return self.embedding_model is not None
+
+    @property
+    def supports_generation(self) -> bool:
+        return False
+
+    @_retry_429
+    async def embed(self, text: str) -> list[float]:
+        """Generate an embedding for a single text."""
+        if not self.supports_embeddings:
+            raise NotImplementedError(_NO_EMBEDDING_MODEL_MSG)
+
+        assert self.embedding_model is not None
+        response = await self.client.embeddings.create_async(
+            model=self.embedding_model,
+            inputs=[text],
+        )
+
+        if not response.data or response.data[0].embedding is None:
+            raise RuntimeError(
+                f"Mistral embeddings API returned no embedding for model "
+                f"{self.embedding_model}"
+            )
+
+        embedding = response.data[0].embedding
+
+        if self._dimension is None:
+            self._dimension = len(embedding)
+            logger.info(
+                "Detected embedding dimension: %d for model %s",
+                self._dimension,
+                self.embedding_model,
+            )
+
+        return embedding
+
+    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
+        """Generate embeddings for multiple texts, chunking by ``BATCH_SIZE``."""
+        if not self.supports_embeddings:
+            raise NotImplementedError(_NO_EMBEDDING_MODEL_MSG)
+
+        if not texts:
+            return []
+
+        all_embeddings: list[list[float]] = []
+        for i in range(0, len(texts), BATCH_SIZE):
+            batch = texts[i : i + BATCH_SIZE]
+            batch_embeddings = await self._embed_batch_request(batch)
+            all_embeddings.extend(batch_embeddings)
+
+            if self._dimension is None and batch_embeddings:
+                self._dimension = len(batch_embeddings[0])
+                logger.info(
+                    "Detected embedding dimension: %d for model %s",
+                    self._dimension,
+                    self.embedding_model,
+                )
+
+        return all_embeddings
+
+    @_retry_429
+    async def _embed_batch_request(self, batch: list[str]) -> list[list[float]]:
+        """Single batch request with rate-limit retry."""
+        assert self.embedding_model is not None
+        response = await self.client.embeddings.create_async(
+            model=self.embedding_model,
+            inputs=batch,
+        )
+
+        # Defensive: response.data items have Optional fields. Sort by index
+        # (default 0 if missing) and reject None embeddings explicitly.
+        sorted_data = sorted(response.data or [], key=lambda x: x.index or 0)
+        result: list[list[float]] = []
+        for item in sorted_data:
+            if item.embedding is None:
+                raise RuntimeError(
+                    f"Mistral embeddings API returned a null embedding for "
+                    f"model {self.embedding_model}"
+                )
+            result.append(item.embedding)
+
+        if len(result) != len(batch):
+            raise RuntimeError(
+                f"Mistral embeddings API returned {len(result)} embeddings "
+                f"for {len(batch)} inputs"
+            )
+        return result
+
+    def get_dimension(self) -> int:
+        if not self.supports_embeddings:
+            raise NotImplementedError(_NO_EMBEDDING_MODEL_MSG)
+
+        if self._dimension is None:
+            raise RuntimeError(
+                f"Embedding dimension not detected yet for model "
+                f"{self.embedding_model}. Call embed() first or use a known "
+                "model."
+            )
+        return self._dimension
+
+    async def generate(self, prompt: str, max_tokens: int = 500) -> str:
+        raise NotImplementedError(
+            "MistralProvider does not support generation. "
+            "Use OpenAI, Anthropic, or Bedrock for text generation."
+        )
+
+    async def close(self) -> None:
+        # The mistralai 2.x client (Speakeasy-generated) does not expose a
+        # public close()/aclose() — only the async-context-manager protocol
+        # (__aenter__/__aexit__). Calling __aexit__ directly is internal API
+        # and brittle across SDK patch versions; the underlying httpx client
+        # is closed during garbage collection, so we leave this as a no-op.
+        return None

nextcloud_mcp_server/providers/openai.py

@@ -7,46 +7,17 @@ Supports:
 """
 
 import logging
-from functools import wraps
 
-import anyio
 from openai import AsyncOpenAI, RateLimitError
 
+from ._retry import retry_on_rate_limit
 from .base import Provider
 
 logger = logging.getLogger(__name__)
 
-# Rate limit retry configuration
-MAX_RETRIES = 5
-INITIAL_RETRY_DELAY = 2.0  # seconds
-MAX_RETRY_DELAY = 60.0  # seconds
-
-
-def retry_on_rate_limit(func):
-    """Decorator to retry on OpenAI rate limit errors with exponential backoff."""
-
-    @wraps(func)
-    async def wrapper(*args, **kwargs):
-        retry_delay = INITIAL_RETRY_DELAY
-        last_error: Exception | None = None
-
-        for attempt in range(1, MAX_RETRIES + 1):
-            try:
-                return await func(*args, **kwargs)
-            except RateLimitError as e:
-                last_error = e
-                if attempt < MAX_RETRIES:
-                    logger.warning(
-                        f"Rate limit hit (attempt {attempt}/{MAX_RETRIES}), "
-                        f"retrying in {retry_delay:.1f}s..."
-                    )
-                    await anyio.sleep(retry_delay)
-                    retry_delay = min(retry_delay * 2, MAX_RETRY_DELAY)
-
-        logger.error(f"Rate limit exceeded after {MAX_RETRIES} attempts")
-        raise last_error  # type: ignore[misc]
-
-    return wrapper
+# OpenAI's RateLimitError is itself a 429-specific class, so the default
+# is_rate_limit predicate ("always True") matches the previous behavior.
+_retry_429 = retry_on_rate_limit(RateLimitError, provider_name="OpenAI")
 
 
 # Well-known embedding dimensions for OpenAI models
@@ -106,9 +77,12 @@ class OpenAIProvider(Provider):
             self._dimension = OPENAI_EMBEDDING_DIMENSIONS[embedding_model]
 
         logger.info(
-            f"Initialized OpenAI provider: base_url={base_url or 'default'} "
-            f"(embedding_model={embedding_model}, generation_model={generation_model}, "
-            f"dimension={self._dimension})"
+            "Initialized OpenAI provider: base_url=%s "
+            "(embedding_model=%s, generation_model=%s, dimension=%s)",
+            base_url or "default",
+            embedding_model,
+            generation_model,
+            self._dimension,
         )
 
     @property
@@ -121,7 +95,7 @@ class OpenAIProvider(Provider):
         """Whether this provider supports text generation."""
         return self.generation_model is not None
 
-    @retry_on_rate_limit
+    @_retry_429
     async def embed(self, text: str) -> list[float]:
         """
         Generate embedding vector for text.
@@ -152,8 +126,9 @@ class OpenAIProvider(Provider):
         if self._dimension is None:
             self._dimension = len(embedding)
             logger.info(
-                f"Detected embedding dimension: {self._dimension} "
-                f"for model {self.embedding_model}"
+                "Detected embedding dimension: %d for model %s",
+                self._dimension,
+                self.embedding_model,
             )
 
         return embedding
@@ -196,13 +171,14 @@ class OpenAIProvider(Provider):
             if self._dimension is None and batch_embeddings:
                 self._dimension = len(batch_embeddings[0])
                 logger.info(
-                    f"Detected embedding dimension: {self._dimension} "
-                    f"for model {self.embedding_model}"
+                    "Detected embedding dimension: %d for model %s",
+                    self._dimension,
+                    self.embedding_model,
                 )
 
         return all_embeddings
 
-    @retry_on_rate_limit
+    @_retry_429
     async def _embed_batch_request(self, batch: list[str]) -> list[list[float]]:
         """Make a single batch embedding request with retry logic."""
         assert self.embedding_model is not None  # Type narrowing
@@ -237,7 +213,7 @@ class OpenAIProvider(Provider):
             )
         return self._dimension
 
-    @retry_on_rate_limit
+    @_retry_429
     async def generate(self, prompt: str, max_tokens: int = 500) -> str:
         """
         Generate text from a prompt.

nextcloud_mcp_server/providers/registry.py

@@ -1,10 +1,11 @@
 """Provider registry and factory for auto-detection and instantiation."""
 
 import logging
-import os
 
+from ..config import get_settings
 from .base import Provider
 from .bedrock import BedrockProvider
+from .mistral import MistralProvider
 from .ollama import OllamaProvider
 from .openai import OpenAIProvider
 from .simple import SimpleProvider
@@ -16,117 +17,112 @@ class ProviderRegistry:
     """
     Registry for provider auto-detection and instantiation.
 
-    Checks environment variables in priority order and creates appropriate provider:
-    1. Bedrock (AWS_REGION + BEDROCK_*_MODEL)
-    2. OpenAI (OPENAI_API_KEY)
-    3. Ollama (OLLAMA_BASE_URL)
-    4. Simple (fallback for testing/development)
+    Reads configuration via dynaconf-backed Settings (see ``config.py``).
+    Checks provider settings in priority order and creates the appropriate
+    provider:
+
+    1. Bedrock (``AWS_REGION`` or ``BEDROCK_*_MODEL``)
+    2. OpenAI (``OPENAI_API_KEY``)
+    3. Mistral (``MISTRAL_API_KEY``)
+    4. Ollama (``OLLAMA_BASE_URL``)
+    5. Simple (fallback for testing/development)
     """
 
     @staticmethod
     def create_provider() -> Provider:
         """
-        Auto-detect and create provider based on environment variables.
+        Auto-detect and create provider based on configured settings.
+
+        Settings are sourced via :func:`nextcloud_mcp_server.config.get_settings`,
+        which reads from settings files and environment variables (env vars
+        always win, see ADR-024/025).
 
         Priority order:
-        1. Bedrock - if AWS_REGION or BEDROCK_EMBEDDING_MODEL is set
-        2. OpenAI - if OPENAI_API_KEY is set
-        3. Ollama - if OLLAMA_BASE_URL is set
-        4. Simple - fallback for testing/development
+
+        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 for testing/development
 
         Returns:
             Provider instance
-
-        Environment Variables:
-            Bedrock:
-                - AWS_REGION: AWS region (e.g., "us-east-1")
-                - AWS_ACCESS_KEY_ID: AWS access key (optional, uses credential chain)
-                - AWS_SECRET_ACCESS_KEY: AWS secret key (optional)
-                - 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: Base URL override (e.g., "https://models.github.ai/inference")
-                - OPENAI_EMBEDDING_MODEL: Model for embeddings (default: "text-embedding-3-small")
-                - OPENAI_GENERATION_MODEL: Model for text generation (e.g., "gpt-4o-mini")
-
-            Ollama:
-                - OLLAMA_BASE_URL: Ollama API base URL (e.g., "http://localhost:11434")
-                - OLLAMA_EMBEDDING_MODEL: Model for embeddings (default: "nomic-embed-text")
-                - OLLAMA_GENERATION_MODEL: Model for text generation (e.g., "llama3.2:1b")
-                - OLLAMA_VERIFY_SSL: Verify SSL certificates (default: "true")
-
-            Simple (no configuration needed, fallback):
-                - SIMPLE_EMBEDDING_DIMENSION: Embedding dimension (default: 384)
         """
-        # 1. Check for Bedrock
-        aws_region = os.getenv("AWS_REGION")
-        bedrock_embedding_model = os.getenv("BEDROCK_EMBEDDING_MODEL")
-        bedrock_generation_model = os.getenv("BEDROCK_GENERATION_MODEL")
+        settings = get_settings()
 
-        if aws_region or bedrock_embedding_model or bedrock_generation_model:
+        # 1. Bedrock
+        if (
+            settings.aws_region
+            or settings.bedrock_embedding_model
+            or settings.bedrock_generation_model
+        ):
             logger.info(
-                f"Using Bedrock provider: region={aws_region}, "
-                f"embedding_model={bedrock_embedding_model}, "
-                f"generation_model={bedrock_generation_model}"
+                "Using Bedrock provider: region=%s, embedding_model=%s, "
+                "generation_model=%s",
+                settings.aws_region,
+                settings.bedrock_embedding_model,
+                settings.bedrock_generation_model,
             )
             return BedrockProvider(
-                region_name=aws_region,
-                embedding_model=bedrock_embedding_model,
-                generation_model=bedrock_generation_model,
-                aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
-                aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
+                region_name=settings.aws_region,
+                embedding_model=settings.bedrock_embedding_model,
+                generation_model=settings.bedrock_generation_model,
+                aws_access_key_id=settings.aws_access_key_id,
+                aws_secret_access_key=settings.aws_secret_access_key,
             )
 
-        # 2. Check for OpenAI
-        openai_api_key = os.getenv("OPENAI_API_KEY")
-        if openai_api_key:
-            base_url = os.getenv("OPENAI_BASE_URL")
-            embedding_model = os.getenv(
-                "OPENAI_EMBEDDING_MODEL", "text-embedding-3-small"
-            )
-            generation_model = os.getenv("OPENAI_GENERATION_MODEL")
-
+        # 2. OpenAI
+        if settings.openai_api_key:
             logger.info(
-                f"Using OpenAI provider: base_url={base_url or 'default'}, "
-                f"embedding_model={embedding_model}, "
-                f"generation_model={generation_model}"
+                "Using OpenAI provider: base_url=%s, embedding_model=%s, "
+                "generation_model=%s",
+                settings.openai_base_url or "default",
+                settings.openai_embedding_model,
+                settings.openai_generation_model,
             )
             return OpenAIProvider(
-                api_key=openai_api_key,
-                base_url=base_url,
-                embedding_model=embedding_model,
-                generation_model=generation_model,
+                api_key=settings.openai_api_key,
+                base_url=settings.openai_base_url,
+                embedding_model=settings.openai_embedding_model,
+                generation_model=settings.openai_generation_model,
             )
 
-        # 3. Check for Ollama (local LLM)
-        ollama_url = os.getenv("OLLAMA_BASE_URL")
-        if ollama_url:
-            embedding_model = os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text")
-            generation_model = os.getenv("OLLAMA_GENERATION_MODEL")
-            verify_ssl = os.getenv("OLLAMA_VERIFY_SSL", "true").lower() == "true"
-
+        # 3. Mistral
+        if settings.mistral_api_key:
             logger.info(
-                f"Using Ollama provider: {ollama_url}, "
-                f"embedding_model={embedding_model}, "
-                f"generation_model={generation_model}"
+                "Using Mistral provider: base_url=%s, embedding_model=%s",
+                settings.mistral_base_url or "default",
+                settings.mistral_embedding_model,
+            )
+            return MistralProvider(
+                api_key=settings.mistral_api_key,
+                base_url=settings.mistral_base_url,
+                embedding_model=settings.mistral_embedding_model,
+            )
+
+        # 4. Ollama
+        if settings.ollama_base_url:
+            logger.info(
+                "Using Ollama provider: %s, embedding_model=%s, generation_model=%s",
+                settings.ollama_base_url,
+                settings.ollama_embedding_model,
+                settings.ollama_generation_model,
             )
             return OllamaProvider(
-                base_url=ollama_url,
-                embedding_model=embedding_model,
-                generation_model=generation_model,
-                verify_ssl=verify_ssl,
+                base_url=settings.ollama_base_url,
+                embedding_model=settings.ollama_embedding_model,
+                generation_model=settings.ollama_generation_model,
+                verify_ssl=settings.ollama_verify_ssl,
             )
 
-        # 4. Fallback to Simple provider for development/testing
-        dimension = int(os.getenv("SIMPLE_EMBEDDING_DIMENSION", "384"))
+        # 5. Simple (fallback)
         logger.warning(
-            "No provider configured (AWS_REGION, OPENAI_API_KEY, OLLAMA_BASE_URL not set). "
+            "No provider configured (AWS_REGION, OPENAI_API_KEY, "
+            "MISTRAL_API_KEY, OLLAMA_BASE_URL not set). "
             "Using SimpleProvider for testing/development. "
-            "For production, configure Bedrock, OpenAI, or Ollama."
+            "For production, configure Bedrock, OpenAI, Mistral, or Ollama."
         )
-        return SimpleProvider(dimension=dimension)
+        return SimpleProvider(dimension=settings.simple_embedding_dimension)
 
 
 # Singleton instance

nextcloud_mcp_server/search/pdf_highlighter.py

@@ -691,6 +691,122 @@ class PDFHighlighter:
                         f"Failed to delete temp directory {temp_pdf_path.parent}: {e}"
                     )
 
+    @staticmethod
+    def compute_chunk_bboxes_batch(
+        pdf_bytes: bytes,
+        chunks: list[tuple[int, int, int, int | None, str]],
+        page_boundaries: list[dict],
+        full_text: str,
+    ) -> dict[int, tuple[list[tuple[float, float, float, float]], int]]:
+        """Compute normalized bounding boxes for chunks without rendering.
+
+        Lightweight alternative to highlight_chunks_batch — opens the PDF,
+        locates each chunk on its assigned page using the same text-search
+        path as the highlighter (`_find_chunk_bbox`), and returns
+        page-normalized rectangles. Skips the get_pixmap + PIL pipeline
+        entirely, so no PNG bytes are produced.
+
+        Args:
+            pdf_bytes: PDF file bytes.
+            chunks: List of (chunk_index, start_offset, end_offset,
+                stored_page_number, chunk_text). chunk_index is the dict key.
+            page_boundaries: Pre-computed page boundaries from the document
+                processor; each entry is {"page", "start_offset", "end_offset"}.
+            full_text: Full document text (for cross-page chunk handling).
+
+        Returns:
+            dict mapping chunk_index to (normalized_bboxes, page_number).
+            Each bbox is (x0, y0, x1, y1) in [0, 1] relative to page width
+            and height, top-left origin. Chunks whose bbox cannot be located
+            are omitted from the result.
+        """
+        results: dict[int, tuple[list[tuple[float, float, float, float]], int]] = {}
+
+        if not chunks:
+            return results
+
+        temp_pdf_path = None
+        doc = None
+        try:
+            temp_dir = Path(tempfile.mkdtemp(prefix="pdf_bbox_batch_"))
+            temp_pdf_path = temp_dir / "pdf.pdf"
+            temp_pdf_path.write_bytes(pdf_bytes)
+
+            doc = pymupdf.open(temp_pdf_path)
+
+            for (
+                chunk_index,
+                start_offset,
+                end_offset,
+                _,
+                _,
+            ) in chunks:
+                chunk_page_info = PDFHighlighter.find_chunk_page(
+                    start_offset, end_offset, page_boundaries
+                )
+                if not chunk_page_info:
+                    logger.debug("Chunk %s: not found on any page", chunk_index)
+                    continue
+
+                page_num = chunk_page_info["page_num"]
+                page_boundary = next(
+                    (b for b in page_boundaries if b["page"] == page_num), None
+                )
+                if page_boundary is None:
+                    logger.debug(
+                        "Chunk %s: page %s not found in boundaries",
+                        chunk_index,
+                        page_num,
+                    )
+                    continue
+                page_text_length = (
+                    page_boundary["end_offset"] - page_boundary["start_offset"]
+                )
+
+                # Page-relative slice (handles chunks that span page boundaries)
+                chunk_start_on_page = max(start_offset, page_boundary["start_offset"])
+                chunk_end_on_page = min(end_offset, page_boundary["end_offset"])
+                page_relative_text = full_text[chunk_start_on_page:chunk_end_on_page]
+
+                page = doc[page_num - 1]
+                bbox = PDFHighlighter._find_chunk_bbox(
+                    page,
+                    page_relative_text,
+                    chunk_page_info["page_relative_start"],
+                    chunk_page_info["page_relative_end"],
+                    page_text_length,
+                )
+
+                if bbox is None:
+                    continue
+
+                page_rect = page.rect
+                w = page_rect.width or 1.0
+                h = page_rect.height or 1.0
+                normalized = (
+                    bbox[0] / w,
+                    bbox[1] / h,
+                    bbox[2] / w,
+                    bbox[3] / h,
+                )
+                results[chunk_index] = ([normalized], page_num)
+
+            logger.info(f"Computed bboxes for {len(results)}/{len(chunks)} chunks")
+            return results
+
+        except Exception as e:
+            logger.error(f"Error computing chunk bboxes: {e}", exc_info=True)
+            return results
+
+        finally:
+            if doc is not None:
+                doc.close()
+            if temp_pdf_path and temp_pdf_path.parent.exists():
+                try:
+                    shutil.rmtree(temp_pdf_path.parent)
+                except Exception as e:
+                    logger.warning(f"Failed to clean up temp dir: {e}")
+
     @staticmethod
     def highlight_chunks_batch(
         pdf_bytes: bytes,

nextcloud_mcp_server/vector/__init__.py

@@ -1,16 +1,17 @@
-"""Vector database and background sync package."""
+"""Vector database and background sync package.
+
+`processor` and `scanner` are intentionally NOT re-exported from this
+package init: they transitively import `server.semantic` ->
+`search.bm25_hybrid`, which forms an import cycle with
+`search.algorithms` -> `vector.placeholder` -> `vector/__init__`.
+Consumers that need those symbols import them from their submodules
+directly (e.g. `from nextcloud_mcp_server.vector.processor import ...`).
+"""
 
 from .document_chunker import DocumentChunker
-from .processor import process_document, processor_task
 from .qdrant_client import get_qdrant_client
-from .scanner import DocumentTask, scan_user_documents, scanner_task
 
 __all__ = [
     "get_qdrant_client",
     "DocumentChunker",
-    "scanner_task",
-    "scan_user_documents",
-    "DocumentTask",
-    "processor_task",
-    "process_document",
 ]

nextcloud_mcp_server/vector/processor.py

@@ -3,7 +3,6 @@
 Processes documents from stream: fetches content, generates embeddings, stores in Qdrant.
 """
 
-import base64
 import logging
 import time
 import uuid
@@ -538,7 +537,11 @@ async def _index_document(
     # Initialize results containers
     dense_embeddings: list = []
     sparse_embeddings: list = []
-    chunk_images: dict[int, dict] = {}
+    # chunk_index -> list[(x0, y0, x1, y1)] of normalized rectangles
+    # in [0, 1] relative to page width/height. The page is taken from
+    # `chunk.page_number` (offset-based) and stored as `page_number`
+    # in the Qdrant payload, so we don't carry an `actual_page_num` here.
+    chunk_bboxes: dict[int, list[tuple[float, float, float, float]]] = {}
 
     # Determine if we need PDF highlighting
     is_pdf = doc_task.doc_type == "file" and content_type == "application/pdf"
@@ -570,8 +573,8 @@ async def _index_document(
             sparse_embeddings = await bm25_service.encode_batch(chunk_texts)
 
     async def generate_highlights():
-        """Generate highlighted page images for PDF chunks (CPU-bound)."""
-        nonlocal chunk_images
+        """Compute chunk bounding boxes for PDF chunks (CPU-bound, no rendering)."""
+        nonlocal chunk_bboxes
         if not is_pdf:
             return
 
@@ -579,64 +582,42 @@ async def _index_document(
         assert content_bytes is not None
 
         with trace_operation(
-            "vector_sync.generate_highlights",
+            "vector_sync.compute_chunk_bboxes",
             attributes={
                 "vector_sync.chunk_count": len(chunks),
                 "vector_sync.pdf_size": len(content_bytes),
             },
         ):
-            # Build chunk data for batch processing
-            # Format: (chunk_index, start_offset, end_offset, page_number, chunk_text)
             chunk_data: list[tuple[int, int, int, int | None, str]] = [
                 (i, chunk.start_offset, chunk.end_offset, chunk.page_number, chunk.text)
                 for i, chunk in enumerate(chunks)
                 if chunk.page_number is not None
             ]
 
-            # Get pre-computed page boundaries from document processor
             page_boundaries = file_metadata.get("page_boundaries")
             if not page_boundaries:
-                logger.warning("No page boundaries available, skipping highlighting")
+                logger.warning(
+                    "No page boundaries available, skipping bbox computation"
+                )
                 return
 
-            # Type narrowing: page_boundaries is guaranteed to be list[dict] here
             page_boundaries_list = cast(list[dict[str, Any]], page_boundaries)
 
-            logger.info(
-                f"Batch generating highlighted page images for {len(chunk_data)} PDF chunks"
-            )
+            logger.info(f"Computing chunk bboxes for {len(chunk_data)} PDF chunks")
 
-            # Run CPU-bound highlighting in thread pool
-            # Pass pre-computed page boundaries and full text to avoid re-processing the PDF
             batch_results = await anyio.to_thread.run_sync(  # type: ignore[attr-defined]
-                lambda: PDFHighlighter.highlight_chunks_batch(
+                lambda: PDFHighlighter.compute_chunk_bboxes_batch(
                     pdf_bytes=content_bytes,
                     chunks=chunk_data,
                     page_boundaries=page_boundaries_list,
                     full_text=content,
-                    color="yellow",
-                    zoom=2.0,
                 )
             )
 
-            # Convert results to storage format
-            for chunk_index, (
-                png_bytes,
-                actual_page_num,
-                highlight_count,
-            ) in batch_results.items():
-                image_base64 = base64.b64encode(png_bytes).decode("utf-8")
-                chunk_images[chunk_index] = {
-                    "image": image_base64,
-                    "page": actual_page_num,
-                    "highlights": highlight_count,
-                    "size": len(png_bytes),
-                }
+            for chunk_index, (bboxes, _) in batch_results.items():
+                chunk_bboxes[chunk_index] = bboxes
 
-            logger.info(
-                f"Generated {len(chunk_images)}/{len(chunks)} highlighted page images "
-                f"(avg {sum(img['size'] for img in chunk_images.values()) // max(len(chunk_images), 1):,} bytes)"
-            )
+            logger.info(f"Computed bboxes for {len(chunk_bboxes)}/{len(chunks)} chunks")
 
     # Run all embedding/highlighting operations in parallel
     # - Dense embeddings: I/O bound (API call)
@@ -752,16 +733,11 @@ async def _index_document(
                         if doc_task.doc_type == "deck_card"
                         else {}
                     ),
-                    # Highlighted page image (PDF only)
-                    **(
-                        {
-                            "highlighted_page_image": chunk_images[i]["image"],
-                            "highlighted_page_number": chunk_images[i]["page"],
-                            "highlight_count": chunk_images[i]["highlights"],
-                        }
-                        if i in chunk_images
-                        else {}
-                    ),
+                    # Chunk bbox (PDF only) — normalized rectangles in [0,1]
+                    # relative to page width/height. Replaces the legacy
+                    # `highlighted_page_image` (Deck #76). The page number
+                    # comes from `page_number` (set above for PDF chunks).
+                    **({"chunk_bbox": chunk_bboxes[i]} if i in chunk_bboxes else {}),
                 },
             )
         )
@@ -780,15 +756,16 @@ async def _index_document(
             f"Failed to delete placeholder for {doc_task.doc_type}_{doc_task.doc_id}: {e}"
         )
 
-    # Upsert to Qdrant in batches to avoid timeout with large payloads
-    # Each batch is limited to avoid WriteTimeout when sending large image payloads
-    BATCH_SIZE = 10  # ~2MB per batch with images
+    # Upsert to Qdrant in batches. Now that we no longer embed PNG payloads,
+    # per-point payloads are small (chunk text + small metadata), so we can
+    # safely use a larger batch size.
+    BATCH_SIZE = 100
     with trace_operation(
         "vector_sync.qdrant_upsert",
         attributes={
             "vector_sync.point_count": len(points),
             "vector_sync.collection": settings.get_collection_name(),
-            "vector_sync.images_count": len(chunk_images),
+            "vector_sync.bboxes_count": len(chunk_bboxes),
             "vector_sync.batch_size": BATCH_SIZE,
         },
     ):

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "nextcloud-mcp-server"
-version = "0.81.0"
+version = "0.83.0"
 description = "Model Context Protocol (MCP) server for Nextcloud integration - enables AI assistants to interact with Nextcloud data"
 authors = [
     {name = "Chris Coutinho", email = "chris@coutinho.io"}
@@ -43,6 +43,7 @@ dependencies = [
     "pymupdf4llm>=0.2.2",
     "openai>=2.8.1",
     "dynaconf>=3.2.13,<4.0",
+    "mistralai>=2.4.5",
 ]
 classifiers = [
     "Development Status :: 4 - Beta",

scripts/purge_page_images.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+"""Purge legacy `highlighted_page_image` payloads from Qdrant (Deck #76).
+
+Iterates all points in the configured Qdrant collection and deletes the
+legacy payload keys `highlighted_page_image`, `highlighted_page_number`,
+and `highlight_count`. This relieves disk pressure caused by inline
+base64 PNGs that the new code path no longer writes.
+
+Idempotent: deleting non-existent keys is a no-op, so re-runs are safe.
+
+Usage:
+    uv run python scripts/purge_page_images.py [--dry-run] [--batch-size 256]
+
+Connection settings (Qdrant URL/API key, collection name) are read from
+the same `Settings` object the server uses.
+"""
+
+from __future__ import annotations
+
+import argparse
+import logging
+import sys
+from functools import partial
+
+import anyio
+from qdrant_client import AsyncQdrantClient
+
+from nextcloud_mcp_server.config import get_settings
+
+logger = logging.getLogger("purge_page_images")
+
+LEGACY_FIELDS = [
+    "highlighted_page_image",
+    "highlighted_page_number",
+    "highlight_count",
+]
+
+
+async def purge(dry_run: bool, batch_size: int) -> None:
+    settings = get_settings()
+    if not settings.qdrant_url:
+        raise SystemExit(
+            "qdrant_url is not configured. Set QDRANT_URL (and QDRANT_API_KEY "
+            "if required) before running this script."
+        )
+    collection = settings.get_collection_name()
+
+    # AsyncQdrantClient doesn't implement __aenter__/__aexit__, so use
+    # try/finally to guarantee the underlying aiohttp session is closed.
+    client = AsyncQdrantClient(
+        url=settings.qdrant_url,
+        api_key=settings.qdrant_api_key,
+        timeout=60,
+    )
+    try:
+        next_offset = None
+        total_seen = 0
+        total_updated = 0
+
+        logger.info(
+            "Scanning collection %s; will delete keys %s%s",
+            collection,
+            LEGACY_FIELDS,
+            " (dry run)" if dry_run else "",
+        )
+
+        while True:
+            points, next_offset = await client.scroll(
+                collection_name=collection,
+                limit=batch_size,
+                offset=next_offset,
+                with_payload=False,
+                with_vectors=False,
+            )
+            if not points:
+                break
+
+            ids = [p.id for p in points]
+            total_seen += len(ids)
+
+            if not dry_run:
+                await client.delete_payload(
+                    collection_name=collection,
+                    keys=LEGACY_FIELDS,
+                    points=ids,
+                )
+                total_updated += len(ids)
+
+            logger.info(
+                "Batch: ids=%d total_seen=%d total_updated=%d",
+                len(ids),
+                total_seen,
+                total_updated,
+            )
+
+            if next_offset is None:
+                break
+
+        logger.info(
+            "Done. total_seen=%d total_updated=%d%s",
+            total_seen,
+            total_updated,
+            " (dry run, no writes)" if dry_run else "",
+        )
+    finally:
+        await client.close()
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Scan only; do not write any changes.",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=256,
+        help="Points per scroll/update batch (default: 256).",
+    )
+    parser.add_argument(
+        "-v", "--verbose", action="store_true", help="Enable debug logging."
+    )
+    args = parser.parse_args()
+
+    logging.basicConfig(
+        level=logging.DEBUG if args.verbose else logging.INFO,
+        format="%(asctime)s %(levelname)s %(name)s: %(message)s",
+    )
+
+    anyio.run(partial(purge, dry_run=args.dry_run, batch_size=args.batch_size))
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

tests/unit/providers/test_mistral.py

@@ -0,0 +1,269 @@
+"""Unit tests for Mistral provider."""
+
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+from mistralai.client.errors import SDKError
+
+from nextcloud_mcp_server.providers.mistral import (
+    BATCH_SIZE,
+    MISTRAL_EMBEDDING_DIMENSIONS,
+    MistralProvider,
+    _is_rate_limit,
+)
+
+
+def _make_data(embedding: list[float], index: int) -> MagicMock:
+    """Build a mock EmbeddingResponseData entry."""
+    item = MagicMock()
+    item.embedding = embedding
+    item.index = index
+    return item
+
+
+def _make_response(embeddings: list[list[float]]) -> MagicMock:
+    """Build a mock EmbeddingResponse with `embeddings` indexed in order."""
+    response = MagicMock()
+    response.data = [_make_data(emb, i) for i, emb in enumerate(embeddings)]
+    return response
+
+
+@pytest.fixture
+def mock_mistral_client(mocker):
+    """Mock the Mistral SDK constructor."""
+    mock_client = MagicMock()
+    mock_client.embeddings = MagicMock()
+    mocker.patch(
+        "nextcloud_mcp_server.providers.mistral.Mistral", return_value=mock_client
+    )
+    return mock_client
+
+
+@pytest.mark.unit
+async def test_mistral_embedding_single(mock_mistral_client):
+    """Single text embed: round-trip through SDK with correct kwargs."""
+    mock_mistral_client.embeddings.create_async = AsyncMock(
+        return_value=_make_response([[0.1, 0.2, 0.3]])
+    )
+
+    provider = MistralProvider(api_key="test-key", embedding_model="mistral-embed")
+    embedding = await provider.embed("hello world")
+
+    assert embedding == [0.1, 0.2, 0.3]
+    mock_mistral_client.embeddings.create_async.assert_awaited_once_with(
+        model="mistral-embed",
+        inputs=["hello world"],
+    )
+
+
+@pytest.mark.unit
+async def test_mistral_embedding_batch_single_call(mock_mistral_client):
+    """Batch smaller than BATCH_SIZE issues a single API call."""
+    mock_mistral_client.embeddings.create_async = AsyncMock(
+        return_value=_make_response([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]])
+    )
+
+    provider = MistralProvider(api_key="test-key", embedding_model="mistral-embed")
+    embeddings = await provider.embed_batch(["a", "b", "c"])
+
+    assert embeddings == [[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]]
+    assert mock_mistral_client.embeddings.create_async.await_count == 1
+
+
+@pytest.mark.unit
+async def test_mistral_embedding_batch_chunking(mock_mistral_client):
+    """Batches exceeding BATCH_SIZE are split into multiple API calls."""
+
+    # Each call returns one embedding per input it received; capture by side
+    # effect so we can inspect lengths per chunk.
+    def _side_effect(*, model, inputs, **_kwargs):
+        return _make_response([[float(i)] for i in range(len(inputs))])
+
+    mock_mistral_client.embeddings.create_async = AsyncMock(side_effect=_side_effect)
+
+    provider = MistralProvider(api_key="test-key", embedding_model="mistral-embed")
+    total = BATCH_SIZE * 2 + 5  # forces three chunks: 64, 64, 5 (with default)
+    embeddings = await provider.embed_batch([f"text-{i}" for i in range(total)])
+
+    assert len(embeddings) == total
+    assert mock_mistral_client.embeddings.create_async.await_count == 3
+    # Verify the chunk sizes the SDK was actually called with.
+    chunk_sizes = [
+        len(call.kwargs["inputs"])
+        for call in mock_mistral_client.embeddings.create_async.await_args_list
+    ]
+    assert chunk_sizes == [BATCH_SIZE, BATCH_SIZE, 5]
+
+
+@pytest.mark.unit
+async def test_mistral_embedding_batch_order_preserved(mock_mistral_client):
+    """Out-of-order index in response data is sorted before returning."""
+    response = MagicMock()
+    response.data = [
+        _make_data([0.3, 0.3], 2),
+        _make_data([0.1, 0.1], 0),
+        _make_data([0.2, 0.2], 1),
+    ]
+    mock_mistral_client.embeddings.create_async = AsyncMock(return_value=response)
+
+    provider = MistralProvider(api_key="test-key", embedding_model="mistral-embed")
+    embeddings = await provider.embed_batch(["x", "y", "z"])
+
+    assert embeddings == [[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]
+
+
+@pytest.mark.unit
+async def test_mistral_supports_capabilities(mock_mistral_client):
+    """Mistral provider advertises embeddings only."""
+    provider = MistralProvider(api_key="test-key", embedding_model="mistral-embed")
+    assert provider.supports_embeddings is True
+    assert provider.supports_generation is False
+
+
+@pytest.mark.unit
+async def test_mistral_generate_not_implemented(mock_mistral_client):
+    """generate() always raises NotImplementedError."""
+    provider = MistralProvider(api_key="test-key", embedding_model="mistral-embed")
+    with pytest.raises(NotImplementedError, match="does not support generation"):
+        await provider.generate("test prompt")
+
+
+@pytest.mark.unit
+async def test_mistral_get_dimension_known_model(mock_mistral_client):
+    """Known model: dimension available without an API call."""
+    provider = MistralProvider(api_key="test-key", embedding_model="mistral-embed")
+    assert provider.get_dimension() == MISTRAL_EMBEDDING_DIMENSIONS["mistral-embed"]
+    mock_mistral_client.embeddings.create_async.assert_not_called()
+
+
+@pytest.mark.unit
+async def test_mistral_get_dimension_unknown_model_detected(mock_mistral_client):
+    """Unknown model: dimension detected on first embed() call."""
+    mock_mistral_client.embeddings.create_async = AsyncMock(
+        return_value=_make_response([[0.1] * 768])
+    )
+
+    provider = MistralProvider(api_key="test-key", embedding_model="custom-mistral")
+
+    with pytest.raises(RuntimeError, match="not detected yet"):
+        provider.get_dimension()
+
+    await provider.embed("test")
+    assert provider.get_dimension() == 768
+
+
+@pytest.mark.unit
+async def test_mistral_no_embeddings_disabled(mock_mistral_client):
+    """Setting embedding_model=None disables the embedding capability."""
+    provider = MistralProvider(api_key="test-key", embedding_model=None)
+    assert provider.supports_embeddings is False
+
+    with pytest.raises(NotImplementedError, match="no embedding_model configured"):
+        await provider.embed("test")
+    with pytest.raises(NotImplementedError, match="no embedding_model configured"):
+        await provider.embed_batch(["test"])
+    with pytest.raises(NotImplementedError, match="no embedding_model configured"):
+        provider.get_dimension()
+
+
+@pytest.mark.unit
+async def test_mistral_empty_batch(mock_mistral_client):
+    """An empty batch returns [] without calling the API."""
+    provider = MistralProvider(api_key="test-key", embedding_model="mistral-embed")
+    assert await provider.embed_batch([]) == []
+    mock_mistral_client.embeddings.create_async.assert_not_called()
+
+
+@pytest.mark.unit
+async def test_mistral_close_no_error(mock_mistral_client):
+    """close() is best-effort and does not raise."""
+    provider = MistralProvider(api_key="test-key", embedding_model="mistral-embed")
+    # No __aexit__ on the mock by default → close() should silently no-op.
+    await provider.close()
+
+
+@pytest.mark.unit
+async def test_mistral_base_url_passed_to_sdk(mocker):
+    """base_url is forwarded as server_url to the Mistral SDK constructor."""
+    mock_ctor = mocker.patch(
+        "nextcloud_mcp_server.providers.mistral.Mistral", return_value=MagicMock()
+    )
+
+    MistralProvider(
+        api_key="test-key",
+        embedding_model="mistral-embed",
+        base_url="https://example.com/mistral",
+    )
+    mock_ctor.assert_called_once_with(
+        api_key="test-key",
+        server_url="https://example.com/mistral",
+    )
+
+
+@pytest.mark.unit
+async def test_mistral_embed_raises_on_empty_response_data(mock_mistral_client):
+    """embed(): empty response.data triggers the defensive RuntimeError guard."""
+    empty_response = MagicMock()
+    empty_response.data = []
+    mock_mistral_client.embeddings.create_async = AsyncMock(return_value=empty_response)
+
+    provider = MistralProvider(api_key="test-key", embedding_model="mistral-embed")
+    with pytest.raises(RuntimeError, match="returned no embedding"):
+        await provider.embed("test")
+
+
+@pytest.mark.unit
+async def test_mistral_embed_raises_on_null_embedding(mock_mistral_client):
+    """embed(): a single response item with embedding=None is rejected."""
+    null_item = MagicMock()
+    null_item.embedding = None
+    null_item.index = 0
+    null_response = MagicMock()
+    null_response.data = [null_item]
+    mock_mistral_client.embeddings.create_async = AsyncMock(return_value=null_response)
+
+    provider = MistralProvider(api_key="test-key", embedding_model="mistral-embed")
+    with pytest.raises(RuntimeError, match="returned no embedding"):
+        await provider.embed("test")
+
+
+@pytest.mark.unit
+async def test_mistral_batch_raises_on_null_embedding(mock_mistral_client):
+    """_embed_batch_request: a null embedding inside a batch raises explicitly."""
+    good = _make_data([0.1, 0.2], 0)
+    bad = MagicMock()
+    bad.embedding = None
+    bad.index = 1
+    response = MagicMock()
+    response.data = [good, bad]
+    mock_mistral_client.embeddings.create_async = AsyncMock(return_value=response)
+
+    provider = MistralProvider(api_key="test-key", embedding_model="mistral-embed")
+    with pytest.raises(RuntimeError, match="null embedding"):
+        await provider.embed_batch(["a", "b"])
+
+
+@pytest.mark.unit
+async def test_mistral_batch_raises_on_count_mismatch(mock_mistral_client):
+    """_embed_batch_request: fewer embeddings returned than inputs sent."""
+    # Two inputs sent, one embedding returned.
+    response = _make_response([[0.1, 0.2]])
+    mock_mistral_client.embeddings.create_async = AsyncMock(return_value=response)
+
+    provider = MistralProvider(api_key="test-key", embedding_model="mistral-embed")
+    with pytest.raises(RuntimeError, match="returned 1 embeddings for 2 inputs"):
+        await provider.embed_batch(["a", "b"])
+
+
+@pytest.mark.unit
+def test_mistral_is_rate_limit_predicate():
+    """_is_rate_limit returns True only for SDKErrors with status_code == 429."""
+    err_429 = MagicMock(spec=SDKError)
+    err_429.status_code = 429
+    err_500 = MagicMock(spec=SDKError)
+    err_500.status_code = 500
+
+    assert _is_rate_limit(err_429) is True
+    assert _is_rate_limit(err_500) is False
+    # ValueError has no status_code attr → getattr returns None → False.
+    assert _is_rate_limit(ValueError()) is False

tests/unit/providers/test_registry.py

@@ -0,0 +1,136 @@
+"""Unit tests for ProviderRegistry — dynaconf-driven auto-detection."""
+
+import pytest
+
+from nextcloud_mcp_server.config import _reload_config
+from nextcloud_mcp_server.providers import (
+    BedrockProvider,
+    MistralProvider,
+    OllamaProvider,
+    OpenAIProvider,
+    SimpleProvider,
+    get_provider,
+    reset_provider,
+)
+from nextcloud_mcp_server.providers.bedrock import BOTO3_AVAILABLE
+
+
+def _clear_provider_envs(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Strip every provider-selection env var so each test starts clean."""
+    for name in (
+        "AWS_REGION",
+        "AWS_ACCESS_KEY_ID",
+        "AWS_SECRET_ACCESS_KEY",
+        "BEDROCK_EMBEDDING_MODEL",
+        "BEDROCK_GENERATION_MODEL",
+        "OPENAI_API_KEY",
+        "OPENAI_BASE_URL",
+        "OPENAI_EMBEDDING_MODEL",
+        "OPENAI_GENERATION_MODEL",
+        "MISTRAL_API_KEY",
+        "MISTRAL_BASE_URL",
+        "MISTRAL_EMBEDDING_MODEL",
+        "OLLAMA_BASE_URL",
+        "OLLAMA_EMBEDDING_MODEL",
+        "OLLAMA_GENERATION_MODEL",
+        "OLLAMA_VERIFY_SSL",
+        "SIMPLE_EMBEDDING_DIMENSION",
+    ):
+        monkeypatch.delenv(name, raising=False)
+
+
+@pytest.fixture
+def clean_provider_env(monkeypatch):
+    """Reset provider singleton + dynaconf cache around each test."""
+    _clear_provider_envs(monkeypatch)
+    reset_provider()
+    _reload_config()
+    yield monkeypatch
+    reset_provider()
+
+
+@pytest.mark.unit
+def test_registry_falls_back_to_simple(clean_provider_env):
+    """No provider env set → SimpleProvider (with default dimension)."""
+    provider = get_provider()
+    assert isinstance(provider, SimpleProvider)
+    assert provider.get_dimension() == 384
+
+
+@pytest.mark.unit
+def test_registry_picks_simple_with_custom_dimension(clean_provider_env):
+    """SIMPLE_EMBEDDING_DIMENSION flows through dynaconf to SimpleProvider."""
+    clean_provider_env.setenv("SIMPLE_EMBEDDING_DIMENSION", "512")
+    _reload_config()
+
+    provider = get_provider()
+    assert isinstance(provider, SimpleProvider)
+    assert provider.get_dimension() == 512
+
+
+@pytest.mark.unit
+def test_registry_picks_mistral_when_api_key_set(clean_provider_env, mocker):
+    """MISTRAL_API_KEY alone is enough to select MistralProvider."""
+    # MistralProvider eagerly constructs the SDK client in __init__; stub it
+    # so the test doesn't depend on the SDK accepting arbitrary keys.
+    mocker.patch("nextcloud_mcp_server.providers.mistral.Mistral")
+    clean_provider_env.setenv("MISTRAL_API_KEY", "test-key")
+    _reload_config()
+
+    provider = get_provider()
+    assert isinstance(provider, MistralProvider)
+
+
+@pytest.mark.unit
+def test_registry_picks_ollama_when_base_url_set(clean_provider_env, mocker):
+    """OLLAMA_BASE_URL selects OllamaProvider."""
+    # OllamaProvider eagerly probes /api/tags in __init__; stub it out.
+    mocker.patch(
+        "nextcloud_mcp_server.providers.ollama.OllamaProvider._check_model_is_loaded"
+    )
+    clean_provider_env.setenv("OLLAMA_BASE_URL", "http://localhost:11434")
+    _reload_config()
+
+    provider = get_provider()
+    assert isinstance(provider, OllamaProvider)
+
+
+@pytest.mark.unit
+def test_registry_openai_wins_over_mistral_and_ollama(clean_provider_env):
+    """OpenAI takes priority when multiple provider env vars are set."""
+    clean_provider_env.setenv("OPENAI_API_KEY", "openai-key")
+    clean_provider_env.setenv("MISTRAL_API_KEY", "mistral-key")
+    clean_provider_env.setenv("OLLAMA_BASE_URL", "http://localhost:11434")
+    _reload_config()
+
+    provider = get_provider()
+    assert isinstance(provider, OpenAIProvider)
+
+
+@pytest.mark.unit
+def test_registry_mistral_wins_over_ollama(clean_provider_env, mocker):
+    """Mistral takes priority over Ollama when both are configured."""
+    # Stub the Mistral SDK constructor for the same reason as the sibling
+    # picker test — keeps the registry test independent of SDK key validation.
+    mocker.patch("nextcloud_mcp_server.providers.mistral.Mistral")
+    clean_provider_env.setenv("MISTRAL_API_KEY", "mistral-key")
+    clean_provider_env.setenv("OLLAMA_BASE_URL", "http://localhost:11434")
+    _reload_config()
+
+    provider = get_provider()
+    assert isinstance(provider, MistralProvider)
+
+
+@pytest.mark.unit
+def test_registry_bedrock_wins_when_aws_region_set(clean_provider_env):
+    """AWS_REGION alone routes to Bedrock, even with other providers configured."""
+    if not BOTO3_AVAILABLE:
+        pytest.skip("boto3 not installed")
+
+    clean_provider_env.setenv("AWS_REGION", "us-east-1")
+    clean_provider_env.setenv("OPENAI_API_KEY", "openai-key")
+    clean_provider_env.setenv("MISTRAL_API_KEY", "mistral-key")
+    _reload_config()
+
+    provider = get_provider()
+    assert isinstance(provider, BedrockProvider)

tests/unit/providers/test_retry.py

@@ -0,0 +1,103 @@
+"""Unit tests for the shared rate-limit retry decorator."""
+
+from unittest.mock import AsyncMock
+
+import pytest
+
+from nextcloud_mcp_server.providers import _retry
+
+
+class _FakeError(Exception):
+    """Stand-in for an SDK exception with an HTTP status code attached."""
+
+    def __init__(self, status_code: int):
+        super().__init__(f"status {status_code}")
+        self.status_code = status_code
+
+
+@pytest.fixture(autouse=True)
+def _no_real_sleep(monkeypatch):
+    """Replace anyio.sleep with an awaitable no-op so retries don't waste time."""
+    monkeypatch.setattr(_retry.anyio, "sleep", AsyncMock(return_value=None))
+
+
+@pytest.mark.unit
+async def test_retry_succeeds_after_429():
+    """A 429 followed by success returns the success value."""
+    calls = {"n": 0}
+
+    @_retry.retry_on_rate_limit(
+        _FakeError, is_rate_limit=lambda e: e.status_code == 429
+    )
+    async def flaky():
+        calls["n"] += 1
+        if calls["n"] < 3:
+            raise _FakeError(429)
+        return "ok"
+
+    result = await flaky()
+    assert result == "ok"
+    assert calls["n"] == 3
+
+
+@pytest.mark.unit
+async def test_retry_reraises_non_rate_limit_immediately():
+    """A non-rate-limit error of the same class is re-raised on first hit."""
+    calls = {"n": 0}
+
+    @_retry.retry_on_rate_limit(
+        _FakeError, is_rate_limit=lambda e: e.status_code == 429
+    )
+    async def boom():
+        calls["n"] += 1
+        raise _FakeError(500)
+
+    with pytest.raises(_FakeError, match="status 500"):
+        await boom()
+    assert calls["n"] == 1  # No retries on non-429.
+
+
+@pytest.mark.unit
+async def test_retry_gives_up_after_max_retries():
+    """After MAX_RETRIES failed attempts the last error is re-raised."""
+    calls = {"n": 0}
+
+    @_retry.retry_on_rate_limit(
+        _FakeError, is_rate_limit=lambda e: e.status_code == 429
+    )
+    async def always_429():
+        calls["n"] += 1
+        raise _FakeError(429)
+
+    with pytest.raises(_FakeError, match="status 429"):
+        await always_429()
+    assert calls["n"] == _retry.MAX_RETRIES
+
+
+@pytest.mark.unit
+async def test_retry_default_predicate_treats_all_as_rate_limit():
+    """Default predicate (`lambda _: True`) retries every caught exception."""
+    calls = {"n": 0}
+
+    @_retry.retry_on_rate_limit(_FakeError)
+    async def fail_once():
+        calls["n"] += 1
+        if calls["n"] < 2:
+            raise _FakeError(503)
+        return "recovered"
+
+    result = await fail_once()
+    assert result == "recovered"
+    assert calls["n"] == 2
+
+
+@pytest.mark.unit
+async def test_retry_does_not_catch_unrelated_exceptions():
+    """Exceptions of a different class bypass the decorator entirely."""
+
+    @_retry.retry_on_rate_limit(_FakeError)
+    async def value_error():
+        raise ValueError("nope")
+
+    with pytest.raises(ValueError, match="nope"):
+        await value_error()

tests/unit/search/test_pdf_highlighter_bbox.py

@@ -0,0 +1,232 @@
+"""Unit tests for PDFHighlighter.compute_chunk_bboxes_batch (Deck #76).
+
+Replaces the legacy `highlight_chunks_batch`-+-base64 pipeline that inflated
+Qdrant payloads with per-chunk PNG screenshots. The new path returns
+normalized bounding boxes only.
+"""
+
+from __future__ import annotations
+
+import pymupdf
+import pytest
+
+from nextcloud_mcp_server.search.pdf_highlighter import PDFHighlighter
+
+
+def _make_pdf(pages: list[str]) -> bytes:
+    """Build an in-memory PDF whose pages contain the given text."""
+    doc = pymupdf.open()
+    for body in pages:
+        page = doc.new_page(width=595, height=842)  # A4
+        page.insert_text((50, 50), body)
+    pdf_bytes = doc.tobytes()
+    doc.close()
+    return pdf_bytes
+
+
+def _page_boundaries(pages: list[str]) -> tuple[list[dict], str]:
+    """Build (page_boundaries, full_text) compatible with the highlighter API."""
+    boundaries: list[dict] = []
+    cursor = 0
+    parts: list[str] = []
+    for i, body in enumerate(pages, start=1):
+        end = cursor + len(body)
+        boundaries.append({"page": i, "start_offset": cursor, "end_offset": end})
+        parts.append(body)
+        cursor = end
+    return boundaries, "".join(parts)
+
+
+@pytest.mark.unit
+def test_compute_chunk_bboxes_returns_normalized_rects():
+    """Each returned bbox should be 4 floats in [0, 1] tagged with the page."""
+    pages = [
+        "Chapter 1: Introduction. Nextcloud is a self-hosted collaboration platform "
+        "covering installation, configuration and maintenance topics.",
+        "Chapter 2: Installation. Download the package, extract it to the web "
+        "server directory, and configure the database connection.",
+    ]
+    pdf_bytes = _make_pdf(pages)
+    boundaries, full_text = _page_boundaries(pages)
+
+    chunks = [
+        (
+            0,
+            0,
+            len(pages[0]),
+            1,
+            "Chapter 1: Introduction. Nextcloud is a self-hosted collaboration platform.",
+        ),
+        (
+            1,
+            len(pages[0]),
+            len(pages[0]) + len(pages[1]),
+            2,
+            "Chapter 2: Installation. Download the package.",
+        ),
+    ]
+
+    results = PDFHighlighter.compute_chunk_bboxes_batch(
+        pdf_bytes=pdf_bytes,
+        chunks=chunks,
+        page_boundaries=boundaries,
+        full_text=full_text,
+    )
+
+    assert set(results) == {0, 1}
+
+    bboxes_p1, page_p1 = results[0]
+    bboxes_p2, page_p2 = results[1]
+
+    assert page_p1 == 1
+    assert page_p2 == 2
+
+    for rects in (bboxes_p1, bboxes_p2):
+        assert len(rects) >= 1
+        for rect in rects:
+            assert len(rect) == 4
+            x0, y0, x1, y1 = rect
+            assert 0.0 <= x0 < x1 <= 1.0
+            assert 0.0 <= y0 < y1 <= 1.0
+
+
+@pytest.mark.unit
+def test_compute_chunk_bboxes_empty_input():
+    assert (
+        PDFHighlighter.compute_chunk_bboxes_batch(
+            pdf_bytes=b"",
+            chunks=[],
+            page_boundaries=[],
+            full_text="",
+        )
+        == {}
+    )
+
+
+@pytest.mark.unit
+def test_compute_chunk_bboxes_omits_when_offsets_out_of_range():
+    """Chunks whose offsets fall outside every page boundary are omitted.
+
+    Verifies the docstring contract: *"Chunks whose bbox cannot be located
+    are omitted from the result."* (path: ``find_chunk_page`` returns None).
+    """
+    pages = ["Page one body text content here for the test."]
+    pdf_bytes = _make_pdf(pages)
+    boundaries, full_text = _page_boundaries(pages)
+
+    # Offsets way beyond the document end — no page boundary matches.
+    out_of_range_start = len(full_text) + 1000
+    out_of_range_end = out_of_range_start + 50
+    chunks = [(0, out_of_range_start, out_of_range_end, 1, "irrelevant")]
+
+    results = PDFHighlighter.compute_chunk_bboxes_batch(
+        pdf_bytes=pdf_bytes,
+        chunks=chunks,
+        page_boundaries=boundaries,
+        full_text=full_text,
+    )
+
+    assert results == {}
+
+
+@pytest.mark.unit
+def test_compute_chunk_bboxes_omits_when_text_not_in_pdf():
+    """Chunks whose page-relative text isn't on the page are omitted.
+
+    Verifies the second omission path: ``_find_chunk_bbox`` returns None
+    when the supplied text cannot be located on the rendered page.
+    """
+    pages = ["Hello world."]
+    pdf_bytes = _make_pdf(pages)
+    # Build boundaries from the real text but pass a *different* full_text
+    # so the page-relative slice is content that does not exist in the PDF.
+    boundaries, _ = _page_boundaries(pages)
+    bogus_full_text = "Z" * len(pages[0])
+
+    chunks = [(0, 0, len(pages[0]), 1, "ignored")]
+
+    results = PDFHighlighter.compute_chunk_bboxes_batch(
+        pdf_bytes=pdf_bytes,
+        chunks=chunks,
+        page_boundaries=boundaries,
+        full_text=bogus_full_text,
+    )
+
+    assert results == {}
+
+
+@pytest.mark.unit
+@pytest.mark.parametrize("page_index", [0, 1])
+def test_compute_chunk_bboxes_assigns_correct_page(page_index: int):
+    """Verify the page number returned matches the page the chunk lives on."""
+    pages = [
+        "Page one talks about apples and oranges in detail.",
+        "Page two discusses bananas and grapes thoroughly.",
+    ]
+    pdf_bytes = _make_pdf(pages)
+    boundaries, full_text = _page_boundaries(pages)
+
+    if page_index == 0:
+        chunk_text = "apples and oranges"
+        offsets = (0, len(pages[0]))
+    else:
+        chunk_text = "bananas and grapes"
+        offsets = (len(pages[0]), len(pages[0]) + len(pages[1]))
+
+    chunks = [(0, offsets[0], offsets[1], page_index + 1, chunk_text)]
+
+    results = PDFHighlighter.compute_chunk_bboxes_batch(
+        pdf_bytes=pdf_bytes,
+        chunks=chunks,
+        page_boundaries=boundaries,
+        full_text=full_text,
+    )
+
+    assert results, "expected a bbox for the chunk"
+    _, page_num = results[0]
+    assert page_num == page_index + 1
+
+
+@pytest.mark.unit
+def test_compute_chunk_bboxes_handles_unordered_page_boundaries():
+    """Page lookup must match by ``page`` key, not by list position.
+
+    Regression guard: an earlier implementation indexed
+    ``page_boundaries[page_num - 1]``, which silently produces a wrong
+    bbox if boundaries are passed out of order. Reverse the boundaries
+    and assert the result is identical to the in-order case.
+    """
+    pages = [
+        "Page one talks about apples and oranges in detail.",
+        "Page two discusses bananas and grapes thoroughly.",
+    ]
+    pdf_bytes = _make_pdf(pages)
+    boundaries, full_text = _page_boundaries(pages)
+
+    chunks = [
+        (0, 0, len(pages[0]), 1, "apples and oranges"),
+        (
+            1,
+            len(pages[0]),
+            len(pages[0]) + len(pages[1]),
+            2,
+            "bananas and grapes",
+        ),
+    ]
+
+    in_order = PDFHighlighter.compute_chunk_bboxes_batch(
+        pdf_bytes=pdf_bytes,
+        chunks=chunks,
+        page_boundaries=boundaries,
+        full_text=full_text,
+    )
+    reversed_order = PDFHighlighter.compute_chunk_bboxes_batch(
+        pdf_bytes=pdf_bytes,
+        chunks=chunks,
+        page_boundaries=list(reversed(boundaries)),
+        full_text=full_text,
+    )
+
+    assert in_order == reversed_order
+    assert reversed_order[0][1] == 1
+    assert reversed_order[1][1] == 2

uv.lock

@@ -769,6 +769,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/97/43/11d6e5d2c00bf000b5329717c74563bf76a9193f4a41cb0c4ef277dde4fa/dynaconf-3.2.13-py2.py3-none-any.whl", hash = "sha256:4305527aef4834bdba3e39479b23c005186e83fb85f65bcaa4bcea58fa26759b", size = 238041, upload-time = "2026-03-17T19:38:45.337Z" },
 ]
 
+[[package]]
+name = "eval-type-backport"
+version = "0.3.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/fb/a3/cafafb4558fd638aadfe4121dc6cefb8d743368c085acb2f521df0f3d9d7/eval_type_backport-0.3.1.tar.gz", hash = "sha256:57e993f7b5b69d271e37482e62f74e76a0276c82490cf8e4f0dffeb6b332d5ed", size = 9445, upload-time = "2025-12-02T11:51:42.987Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/cf/22/fdc2e30d43ff853720042fa15baa3e6122722be1a7950a98233ebb55cd71/eval_type_backport-0.3.1-py3-none-any.whl", hash = "sha256:279ab641905e9f11129f56a8a78f493518515b83402b860f6f06dd7c011fdfa8", size = 6063, upload-time = "2025-12-02T11:51:41.665Z" },
+]
+
 [[package]]
 name = "executing"
 version = "2.2.1"
@@ -1478,6 +1487,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/73/07/02e16ed01e04a374e644b575638ec7987ae846d25ad97bcc9945a3ee4b0e/jsonpatch-1.33-py2.py3-none-any.whl", hash = "sha256:0ae28c0cd062bbd8b8ecc26d7d164fbbea9652a1a3693f3b956c1eae5145dade", size = 12898, upload-time = "2023-06-16T21:01:28.466Z" },
 ]
 
+[[package]]
+name = "jsonpath-python"
+version = "1.1.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/98/18/4ca8742534a5993ff383f7602e325ce2d5d7cc93d72ac5e1cdedbea8a458/jsonpath_python-1.1.6.tar.gz", hash = "sha256:dded9932b4ec41fb8726e09c83afa4e6be618f938c2db287cc2a81723c639671", size = 88178, upload-time = "2026-05-07T01:26:34.482Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/55/8a/1270a6803bd821cbfcdda387eaa13cb41a7b1f7b9bd145979b3bfb9d6cb7/jsonpath_python-1.1.6-py3-none-any.whl", hash = "sha256:a1c50afd8d3fbbaf47a4873bc890dcb3c15da96f5c020327977d844d8731a2d4", size = 14453, upload-time = "2026-05-07T01:26:33.306Z" },
+]
+
 [[package]]
 name = "jsonpointer"
 version = "3.0.0"
@@ -1842,6 +1860,25 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979, upload-time = "2022-08-14T12:40:09.779Z" },
 ]
 
+[[package]]
+name = "mistralai"
+version = "2.4.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "eval-type-backport" },
+    { name = "httpx" },
+    { name = "jsonpath-python" },
+    { name = "opentelemetry-api" },
+    { name = "opentelemetry-semantic-conventions" },
+    { name = "pydantic" },
+    { name = "python-dateutil" },
+    { name = "typing-inspection" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8e/3f/5624d57c5897c83c55d3e4c7dd4127de42ad14fd3183e26566cdc7dca1bf/mistralai-2.4.5.tar.gz", hash = "sha256:ef165bb004ec4423cbf19a440bf0983ca0c3fc92ab12a35ebca097bdf418e33a", size = 424611, upload-time = "2026-05-07T11:46:43.888Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1b/48/2c5c4f853dec32a625c1a3d23809b80cf2e135c3441fe1764f72910dfea9/mistralai-2.4.5-py3-none-any.whl", hash = "sha256:bf3b6550258ab16dec8547b90e9c18bebf9099f55b7fc25a884bf0bbeffced0f", size = 995999, upload-time = "2026-05-07T11:46:41.915Z" },
+]
+
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