bump: version 0.40.0 → 0.41.0

Feature/cleanup

.dockerignore

@@ -5,3 +5,4 @@
 !uv.lock
 
 !nextcloud_mcp_server/**/*.py
+!nextcloud_mcp_server/**/*.html

.github/workflows/test.yml

@@ -85,4 +85,4 @@ jobs:
           NEXTCLOUD_USERNAME: "admin"
           NEXTCLOUD_PASSWORD: "admin"
         run: |
-          uv run pytest -v --log-cli-level=WARN -m smoke
+          uv run pytest -v --log-cli-level=WARN -m unit -m smoke

CHANGELOG.md

@@ -1,3 +1,38 @@
+## v0.41.0 (2025-11-17)
+
+### Feat
+
+- add configurable fusion algorithms for BM25 hybrid search
+- add chunk position tracking to vector indexing and search
+- add vector viz template and chunk context endpoint
+
+### Fix
+
+- prevent infinite loop in DocumentChunker with position tracking
+- Relax SearchResult validation to support DBSF fusion scores > 1.0
+
+## v0.40.0 (2025-11-16)
+
+### Feat
+
+- add unified provider architecture with Amazon Bedrock support
+
+### Fix
+
+- suppress Starlette middleware type warnings in ty checker
+
+## v0.39.0 (2025-11-16)
+
+### Feat
+
+- Implement BM25 hybrid search with native Qdrant RRF fusion
+
+### Fix
+
+- Handle named vectors in visualization and semantic search
+- Update vizApp to use bm25_hybrid algorithm and remove deprecated weights
+- Update viz routes to use BM25 hybrid search after refactor
+
 ## v0.38.0 (2025-11-16)
 
 ### Feat

CLAUDE.md

@@ -61,8 +61,60 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 - `nextcloud_mcp_server/server/` - MCP tool/resource definitions
 - `nextcloud_mcp_server/auth/` - OAuth/OIDC authentication
 - `nextcloud_mcp_server/models/` - Pydantic response models
+- `nextcloud_mcp_server/providers/` - Unified LLM provider infrastructure (embeddings + generation)
 - `tests/` - Layered test suite (unit, smoke, integration, load)
 
+### Provider Architecture (ADR-015)
+
+**Unified Provider System** for embeddings and text generation:
+
+**Location:** `nextcloud_mcp_server/providers/`
+- `base.py` - `Provider` ABC with optional capabilities
+- `registry.py` - Auto-detection and factory pattern
+- `ollama.py` - Ollama provider (embeddings + generation)
+- `anthropic.py` - Anthropic provider (generation only)
+- `bedrock.py` - Amazon Bedrock provider (embeddings + generation)
+- `simple.py` - Simple in-memory provider (embeddings only, fallback)
+
+**Usage:**
+```python
+from nextcloud_mcp_server.providers import get_provider
+
+provider = get_provider()  # Auto-detects from environment
+
+# Check capabilities
+if provider.supports_embeddings:
+    embeddings = await provider.embed_batch(texts)
+
+if provider.supports_generation:
+    text = await provider.generate("prompt", max_tokens=500)
+```
+
+**Environment Variables:**
+
+Bedrock:
+- `AWS_REGION` - AWS region (e.g., "us-east-1")
+- `BEDROCK_EMBEDDING_MODEL` - Embedding model ID (e.g., "amazon.titan-embed-text-v2:0")
+- `BEDROCK_GENERATION_MODEL` - Generation model ID (e.g., "anthropic.claude-3-sonnet-20240229-v1:0")
+- `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY` - Optional, uses AWS credential chain
+
+Ollama:
+- `OLLAMA_BASE_URL` - API URL (e.g., "http://localhost:11434")
+- `OLLAMA_EMBEDDING_MODEL` - Embedding model (default: "nomic-embed-text")
+- `OLLAMA_GENERATION_MODEL` - Generation model (e.g., "llama3.2:1b")
+- `OLLAMA_VERIFY_SSL` - SSL verification (default: "true")
+
+Simple (fallback, no config needed):
+- `SIMPLE_EMBEDDING_DIMENSION` - Dimension (default: 384)
+
+**Auto-Detection Priority:** Bedrock → Ollama → Simple
+
+**Backward Compatibility:**
+- Old code using `nextcloud_mcp_server.embedding.get_embedding_service()` still works
+- `EmbeddingService` now wraps `get_provider()` internally
+
+**For Details:** See `docs/ADR-015-unified-provider-architecture.md`
+
 ## Development Commands (Quick Reference)
 
 ### Testing

Dockerfile

@@ -1,6 +1,6 @@
-FROM python:3.12-slim-trixie
+FROM docker.io/library/python:3.12-slim-trixie@sha256:d86b4c74b936c438cd4cc3a9f7256b9a7c27ad68c7caf8c205e18d9845af0164
 
-COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
+COPY --from=ghcr.io/astral-sh/uv:latest@sha256:f6e3549ed287fee0ddde2460a2a74a2d74366f84b04aaa34c1f19fec40da8652 /uv /uvx /bin/
 
 # Install dependencies
 # 1. git (required for caldav dependency from git)

charts/nextcloud-mcp-server/Chart.yaml

@@ -2,8 +2,8 @@ apiVersion: v2
 name: nextcloud-mcp-server
 description: A Helm chart for Nextcloud MCP Server - enables AI assistants to interact with Nextcloud
 type: application
-version: 0.38.0
-appVersion: "0.38.0"
+version: 0.41.0
+appVersion: "0.41.0"
 keywords:
   - nextcloud
   - mcp

docs/ADR-014-bm25-search.md

@@ -1,8 +1,4 @@
-Here is a complete Architectural Decision Record (ADR) template based on your requirements. You can copy, paste, and adapt this directly.
-
----
-
-## ADR-007: Replace Custom Keyword Search with BM25 Hybrid Search via Qdrant
+# ADR-014: Replace Custom Keyword Search with BM25 Hybrid Search via Qdrant
 
 **Date:** 2025-11-16
 
@@ -151,7 +147,95 @@ This decision consolidates our retrieval logic, eliminates the data consistency
 
 **Benefits Realized:**
 - ✅ Consolidated architecture (single Qdrant database for both dense + sparse)
-- ✅ Native RRF fusion (database-level, more efficient)
+- ✅ Native fusion algorithms (database-level, more efficient)
 - ✅ Industry-standard BM25 (replaces custom keyword search)
 - ✅ Simplified codebase (removed 736 lines of legacy code)
 - ✅ Better relevance (handles both semantic and keyword queries)
+- ✅ Configurable fusion methods (RRF and DBSF)
+
+---
+
+### 7. Fusion Algorithm Options
+
+**Update: 2025-11-16**
+
+The BM25 hybrid search now supports two fusion algorithms for combining dense (semantic) and sparse (BM25) search results:
+
+#### Reciprocal Rank Fusion (RRF)
+
+**Default fusion method.** RRF is a widely-used, well-established algorithm that combines rankings from multiple retrieval systems using the reciprocal rank formula:
+
+```
+RRF(doc) = Σ 1/(k + rank_i(doc))
+```
+
+where `k` is a constant (typically 60) and `rank_i(doc)` is the rank of the document in retrieval system `i`.
+
+**Characteristics:**
+- ✅ **General-purpose**: Works well across diverse query types and document collections
+- ✅ **Rank-based**: Focuses on relative rankings rather than absolute scores
+- ✅ **Established**: Well-tested, documented, and understood in IR literature
+- ✅ **Robust**: Less sensitive to score distribution differences between systems
+
+**When to use RRF:**
+- Default choice for most use cases
+- When you have mixed query types (semantic + keyword)
+- When retrieval systems have very different score ranges
+- When you want predictable, well-understood behavior
+
+#### Distribution-Based Score Fusion (DBSF)
+
+**Alternative fusion method.** DBSF normalizes scores from each retrieval system using distribution statistics before combining them:
+
+1. **Normalization**: For each query, calculates mean (μ) and standard deviation (σ) of scores
+2. **Outlier handling**: Uses μ ± 3σ as normalization bounds
+3. **Fusion**: Sums normalized scores across systems
+
+**Characteristics:**
+- ✅ **Score-aware**: Uses actual relevance scores, not just rankings
+- ✅ **Statistical**: Normalizes based on score distribution properties
+- ⚠️ **Experimental**: Newer algorithm, less battle-tested than RRF
+- ⚠️ **Sensitive**: May behave differently depending on score distributions
+
+**When to use DBSF:**
+- When retrieval systems have vastly different score ranges that RRF doesn't balance well
+- When you want to experiment with score-based (vs rank-based) fusion
+- When statistical normalization better matches your use case
+- For A/B testing against RRF to measure retrieval quality improvements
+
+#### Configuration
+
+Both fusion algorithms are exposed via the `fusion` parameter in MCP tools:
+
+```python
+# Use RRF (default)
+response = await nc_semantic_search(
+    query="async programming",
+    fusion="rrf"  # Can be omitted, RRF is default
+)
+
+# Use DBSF
+response = await nc_semantic_search(
+    query="async programming",
+    fusion="dbsf"
+)
+```
+
+The `nc_semantic_search_answer` tool also supports the `fusion` parameter and passes it through to the underlying search.
+
+#### Future: Configurable Weights
+
+**Current limitation**: Neither RRF nor DBSF currently support per-system weights (e.g., 0.8 for semantic, 0.2 for BM25). This is a Qdrant platform limitation tracked in [qdrant/qdrant#6067](https://github.com/qdrant/qdrant/issues/6067).
+
+When Qdrant adds weight support, the `fusion` parameter can be extended to accept weight configurations:
+
+```python
+# Hypothetical future API
+response = await nc_semantic_search(
+    query="async programming",
+    fusion="rrf",
+    fusion_weights={"dense": 0.7, "sparse": 0.3}  # Not yet implemented
+)
+```
+
+**Recommendation**: Start with RRF (default). If you encounter cases where keyword matches are under- or over-weighted, experiment with DBSF. Monitor [qdrant/qdrant#6067](https://github.com/qdrant/qdrant/issues/6067) for configurable weight support.

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

@@ -0,0 +1,380 @@
+# ADR-015: Unified Provider Architecture for Embeddings and Text Generation
+
+**Status:** Accepted
+**Date:** 2025-01-16
+**Deciders:** Development Team
+**Related:** ADR-003 (Vector Database), ADR-008 (MCP Sampling), ADR-013 (RAG Evaluation)
+
+## Context
+
+Prior to this refactoring, the codebase had two separate provider systems:
+
+1. **Embedding Providers** (`nextcloud_mcp_server/embedding/`)
+   - Used `EmbeddingProvider` ABC with methods: `embed()`, `embed_batch()`, `get_dimension()`
+   - Had auto-detection via `EmbeddingService._detect_provider()`
+   - Used for semantic search and vector indexing (production)
+
+2. **LLM Providers** (`tests/rag_evaluation/llm_providers.py`)
+   - Used `LLMProvider` Protocol with method: `generate()`
+   - Had separate factory function `create_llm_provider()`
+   - Used only for RAG evaluation tests (not production)
+
+This fragmentation created several problems:
+
+### Problems with Dual Provider Systems
+
+1. **Code Duplication**
+   - Ollama configuration appeared in both `embedding/service.py` and `tests/rag_evaluation/llm_providers.py`
+   - Similar provider detection logic in multiple places
+   - Separate singleton patterns for each system
+
+2. **Limited Extensibility**
+   - Hard-coded provider detection in `EmbeddingService._detect_provider()`
+   - No support for providers that offer both capabilities (like Bedrock)
+   - Adding new providers required modifying multiple files
+
+3. **Inconsistent Patterns**
+   - BM25 provider didn't follow `EmbeddingProvider` ABC
+   - Different method names across providers (`embed` vs `encode`)
+   - ABC vs Protocol for type checking
+
+4. **Difficult Scaling**
+   - Adding Amazon Bedrock (our third provider) would exacerbate all issues
+   - No clear path for future providers (OpenAI, Cohere, etc.)
+
+### Amazon Bedrock Requirements
+
+Bedrock naturally supports **both** embeddings and text generation:
+- **Embeddings**: `amazon.titan-embed-text-v1/v2`, `cohere.embed-*`
+- **Text Generation**: `anthropic.claude-*`, `meta.llama3-*`, `amazon.titan-text-*`
+- **Unified API**: Single `invoke_model()` method via bedrock-runtime
+
+This made it the perfect opportunity to establish a unified provider architecture.
+
+## Decision
+
+We refactored the provider infrastructure to use a **unified Provider ABC** with optional capabilities:
+
+### 1. Unified Provider Interface
+
+**New Structure:**
+```
+nextcloud_mcp_server/providers/
+├── __init__.py
+├── base.py              # Provider ABC with optional capabilities
+├── registry.py          # Auto-detection and factory
+├── ollama.py            # Supports both embedding + generation
+├── anthropic.py         # Generation only
+├── bedrock.py           # Supports both embedding + generation
+└── simple.py            # Embedding only (testing fallback)
+```
+
+**Base Class (`providers/base.py`):**
+```python
+class Provider(ABC):
+    @property
+    @abstractmethod
+    def supports_embeddings(self) -> bool:
+        """Whether this provider supports embedding generation."""
+        pass
+
+    @property
+    @abstractmethod
+    def supports_generation(self) -> bool:
+        """Whether this provider supports text generation."""
+        pass
+
+    @abstractmethod
+    async def embed(self, text: str) -> list[float]:
+        """Generate embedding (raises NotImplementedError if not supported)."""
+        pass
+
+    @abstractmethod
+    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
+        """Generate batch embeddings (raises NotImplementedError if not supported)."""
+        pass
+
+    @abstractmethod
+    def get_dimension(self) -> int:
+        """Get embedding dimension (raises NotImplementedError if not supported)."""
+        pass
+
+    @abstractmethod
+    async def generate(self, prompt: str, max_tokens: int = 500) -> str:
+        """Generate text (raises NotImplementedError if not supported)."""
+        pass
+
+    @abstractmethod
+    async def close(self) -> None:
+        """Close provider and release resources."""
+        pass
+```
+
+### 2. Provider Registry
+
+**Auto-Detection Priority** (`providers/registry.py`):
+```python
+class ProviderRegistry:
+    @staticmethod
+    def create_provider() -> Provider:
+        # 1. Bedrock (AWS_REGION or BEDROCK_*_MODEL)
+        # 2. Ollama (OLLAMA_BASE_URL)
+        # 3. Simple (fallback)
+```
+
+**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")
+
+**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, fallback):**
+- `SIMPLE_EMBEDDING_DIMENSION`: Embedding dimension (default: 384)
+
+### 3. Backward Compatibility
+
+**Old Code Continues to Work:**
+```python
+# Old way (still works)
+from nextcloud_mcp_server.embedding import get_embedding_service
+
+service = get_embedding_service()  # Returns singleton Provider
+embeddings = await service.embed_batch(texts)
+```
+
+**New Way (recommended):**
+```python
+# New way (cleaner)
+from nextcloud_mcp_server.providers import get_provider
+
+provider = get_provider()  # Returns singleton Provider
+embeddings = await provider.embed_batch(texts)
+
+# Can also use generation if provider supports it
+if provider.supports_generation:
+    text = await provider.generate("prompt")
+```
+
+**Migration Path:**
+- `embedding/service.py` now wraps `providers.get_provider()` for compatibility
+- `tests/rag_evaluation/llm_providers.py` now uses unified providers
+- Old imports still work, marked as deprecated in docstrings
+
+### 4. Amazon Bedrock Implementation
+
+**Features:**
+- Supports both embeddings and text generation
+- Model-specific request/response handling for:
+  - Titan Embed (amazon.titan-embed-text-*)
+  - Cohere Embed (cohere.embed-*)
+  - Claude (anthropic.claude-*)
+  - Llama (meta.llama3-*)
+  - Titan Text (amazon.titan-text-*)
+  - Mistral (mistral.*)
+- Uses boto3 bedrock-runtime client
+- Graceful degradation if boto3 not installed
+- Async implementation matching existing patterns
+
+**Model-Specific Handling:**
+```python
+# Bedrock embedding request (Titan)
+{"inputText": text}
+
+# Bedrock generation request (Claude)
+{
+    "anthropic_version": "bedrock-2023-05-31",
+    "max_tokens": max_tokens,
+    "temperature": 0.7,
+    "messages": [{"role": "user", "content": prompt}]
+}
+```
+
+## Consequences
+
+### Positive
+
+1. **Sustainable Provider Additions**
+   - New providers only need to implement `Provider` ABC
+   - Auto-detection via environment variables
+   - No modifications to existing code required
+
+2. **Code Consolidation**
+   - Single provider interface instead of two
+   - Unified configuration pattern
+   - Eliminated duplication
+
+3. **Better Extensibility**
+   - Providers can support one or both capabilities
+   - Clear capability detection via properties
+   - Registry pattern simplifies auto-detection
+
+4. **Improved Testing**
+   - RAG evaluation can use any provider (Ollama, Anthropic, Bedrock)
+   - Comprehensive unit tests for all providers
+   - Mocked boto3 tests for Bedrock
+
+5. **Production-Ready Bedrock Support**
+   - Full embedding and generation support
+   - Multiple model families supported
+   - AWS credential chain integration
+
+### Neutral
+
+1. **Optional Boto3 Dependency**
+   - boto3 is dev dependency only (not required for core functionality)
+   - Bedrock provider gracefully fails if boto3 not installed
+   - Users who want Bedrock must `pip install boto3`
+
+2. **Capability Properties**
+   - All providers must implement capability properties
+   - Methods raise `NotImplementedError` if capability not supported
+   - Clear error messages guide users to alternatives
+
+### Negative
+
+1. **Migration Effort**
+   - Existing code must be migrated to new imports (optional, backward compatible)
+   - Documentation needs updating
+   - Users must learn new environment variables
+
+2. **Increased Complexity**
+   - Provider base class has more methods (embedding + generation)
+   - More environment variables to configure
+   - Capability detection adds runtime checks
+
+## Implementation
+
+### Files Created
+
+**New Provider Infrastructure:**
+- `nextcloud_mcp_server/providers/__init__.py`
+- `nextcloud_mcp_server/providers/base.py`
+- `nextcloud_mcp_server/providers/registry.py`
+- `nextcloud_mcp_server/providers/ollama.py`
+- `nextcloud_mcp_server/providers/anthropic.py`
+- `nextcloud_mcp_server/providers/bedrock.py`
+- `nextcloud_mcp_server/providers/simple.py`
+
+**Tests:**
+- `tests/unit/providers/__init__.py`
+- `tests/unit/providers/test_bedrock.py` (9 unit tests)
+
+**Documentation:**
+- `docs/ADR-015-unified-provider-architecture.md` (this file)
+
+### Files Modified
+
+**Backward Compatibility:**
+- `nextcloud_mcp_server/embedding/service.py` - Now wraps `get_provider()`
+- `tests/rag_evaluation/llm_providers.py` - Uses unified providers
+
+**Dependencies:**
+- `pyproject.toml` - Added `boto3>=1.35.0` to dev dependencies
+
+### Testing Results
+
+**Unit Tests:** 127 passed (including 9 new Bedrock tests)
+**Type Checking:** All checks passed (ty)
+**Linting:** All checks passed (ruff)
+**Backward Compatibility:** Verified - existing embedding tests work
+
+## Alternatives Considered
+
+### Alternative 1: Keep Separate Provider Systems
+
+**Pros:**
+- No refactoring needed
+- Simpler short-term
+
+**Cons:**
+- Bedrock would need to be implemented twice
+- Continued code duplication
+- No long-term scalability
+
+**Decision:** Rejected - technical debt would continue to grow
+
+### Alternative 2: Separate Embedding and Generation Providers
+
+Use composition instead of unified interface:
+```python
+class CombinedProvider:
+    def __init__(self, embedding: EmbeddingProvider, generation: LLMProvider):
+        self.embedding = embedding
+        self.generation = generation
+```
+
+**Pros:**
+- Clearer separation of concerns
+- Simpler individual providers
+
+**Cons:**
+- Bedrock and Ollama naturally do both - artificial separation
+- More complex configuration (two providers to configure)
+- More boilerplate code
+
+**Decision:** Rejected - unified interface better matches provider capabilities
+
+### Alternative 3: Plugin System
+
+Dynamic provider registration via entry points:
+```python
+# setup.py
+entry_points={
+    'nextcloud_mcp.providers': [
+        'ollama = nextcloud_mcp_server.providers.ollama:OllamaProvider',
+        'bedrock = nextcloud_mcp_server.providers.bedrock:BedrockProvider',
+    ]
+}
+```
+
+**Pros:**
+- Most extensible
+- Third-party providers possible
+
+**Cons:**
+- Over-engineered for current needs
+- Added complexity
+- No immediate benefit
+
+**Decision:** Deferred - can add later if needed
+
+## Future Work
+
+1. **Additional Providers**
+   - OpenAI (embeddings + generation)
+   - Cohere (embeddings + generation)
+   - Google Vertex AI
+   - Azure OpenAI
+
+2. **Provider Features**
+   - Streaming generation support
+   - Batch API optimization (when available)
+   - Model-specific optimizations
+   - Cost tracking and metrics
+
+3. **Configuration Improvements**
+   - Provider profiles (development, production)
+   - Model aliasing (e.g., "small", "large")
+   - Fallback provider chains
+
+4. **Testing**
+   - Integration tests with real Bedrock endpoints
+   - Performance benchmarking across providers
+   - Cost comparison analysis
+
+## References
+
+- [boto3 Bedrock Runtime Documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-runtime.html)
+- [Amazon Bedrock User Guide](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html)
+- ADR-003: Vector Database and Semantic Search
+- ADR-008: MCP Sampling for Semantic Search
+- ADR-013: RAG Evaluation Framework

docs/bedrock-setup.md

@@ -0,0 +1,338 @@
+# Amazon Bedrock Setup Guide
+
+This guide covers how to configure the Nextcloud MCP Server to use Amazon Bedrock for embeddings and text generation.
+
+## Prerequisites
+
+1. **AWS Account** with access to Amazon Bedrock
+2. **boto3 library** installed: `pip install boto3` or `uv sync --group dev`
+3. **Model Access** - Request access to models in AWS Bedrock console
+
+## Required AWS Permissions
+
+### IAM Policy for Bedrock Access
+
+The AWS IAM user or role needs the following permissions:
+
+```json
+{
+  "Version": "2012-10-17",
+  "Statement": [
+    {
+      "Sid": "BedrockInvokeModels",
+      "Effect": "Allow",
+      "Action": [
+        "bedrock:InvokeModel",
+        "bedrock:InvokeModelWithResponseStream"
+      ],
+      "Resource": [
+        "arn:aws:bedrock:*::foundation-model/*"
+      ]
+    }
+  ]
+}
+```
+
+### Minimal Permissions (Production)
+
+For production deployments, restrict to specific models:
+
+```json
+{
+  "Version": "2012-10-17",
+  "Statement": [
+    {
+      "Sid": "BedrockEmbeddings",
+      "Effect": "Allow",
+      "Action": [
+        "bedrock:InvokeModel"
+      ],
+      "Resource": [
+        "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v2:0"
+      ]
+    },
+    {
+      "Sid": "BedrockGeneration",
+      "Effect": "Allow",
+      "Action": [
+        "bedrock:InvokeModel"
+      ],
+      "Resource": [
+        "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0"
+      ]
+    }
+  ]
+}
+```
+
+### Additional Permissions (Optional)
+
+For advanced use cases:
+
+```json
+{
+  "Version": "2012-10-17",
+  "Statement": [
+    {
+      "Sid": "BedrockListModels",
+      "Effect": "Allow",
+      "Action": [
+        "bedrock:ListFoundationModels",
+        "bedrock:GetFoundationModel"
+      ],
+      "Resource": "*"
+    },
+    {
+      "Sid": "BedrockAsyncInvoke",
+      "Effect": "Allow",
+      "Action": [
+        "bedrock:InvokeModelAsync",
+        "bedrock:GetAsyncInvoke",
+        "bedrock:ListAsyncInvokes"
+      ],
+      "Resource": [
+        "arn:aws:bedrock:*::foundation-model/*"
+      ]
+    }
+  ]
+}
+```
+
+## Model Access
+
+Before using Bedrock models, you must request access in the AWS Console:
+
+1. Navigate to **Amazon Bedrock** → **Model access**
+2. Click **Manage model access**
+3. Select models you want to use:
+   - **Embeddings:** Amazon Titan Embed Text, Cohere Embed
+   - **Text Generation:** Anthropic Claude, Meta Llama, Amazon Titan Text
+4. Click **Request model access**
+5. Wait for approval (usually instant for most models)
+
+## Supported Models
+
+### Embedding Models
+
+| Provider | Model ID | Dimensions | Best For |
+|----------|----------|------------|----------|
+| Amazon Titan | `amazon.titan-embed-text-v1` | 1,536 | General purpose |
+| Amazon Titan | `amazon.titan-embed-text-v2:0` | 1,024 | Latest, improved quality |
+| Cohere | `cohere.embed-english-v3` | 1,024 | English text |
+| Cohere | `cohere.embed-multilingual-v3` | 1,024 | Multilingual |
+
+### Text Generation Models
+
+| Provider | Model ID | Context | Best For |
+|----------|----------|---------|----------|
+| Anthropic | `anthropic.claude-3-sonnet-20240229-v1:0` | 200K | Balanced performance |
+| Anthropic | `anthropic.claude-3-haiku-20240307-v1:0` | 200K | Fast, cost-effective |
+| Anthropic | `anthropic.claude-3-opus-20240229-v1:0` | 200K | Highest quality |
+| Meta | `meta.llama3-8b-instruct-v1:0` | 8K | Fast, open-source |
+| Meta | `meta.llama3-70b-instruct-v1:0` | 8K | High quality |
+| Amazon | `amazon.titan-text-express-v1` | 8K | Fast, low cost |
+| Mistral | `mistral.mistral-7b-instruct-v0:2` | 32K | Efficient |
+
+## Configuration
+
+### Environment Variables
+
+**Required:**
+```bash
+AWS_REGION=us-east-1
+```
+
+**Optional (at least one model required):**
+```bash
+# For embeddings
+BEDROCK_EMBEDDING_MODEL=amazon.titan-embed-text-v2:0
+
+# For text generation (RAG evaluation)
+BEDROCK_GENERATION_MODEL=anthropic.claude-3-sonnet-20240229-v1:0
+```
+
+**AWS Credentials (choose one method):**
+
+**Method 1: Environment Variables**
+```bash
+AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE
+AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
+```
+
+**Method 2: AWS Credentials File** (`~/.aws/credentials`)
+```ini
+[default]
+aws_access_key_id = AKIAIOSFODNN7EXAMPLE
+aws_secret_access_key = wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
+```
+
+**Method 3: IAM Role** (when running on AWS EC2/ECS/Lambda)
+- No credentials needed, uses instance/task role automatically
+
+### Docker Configuration
+
+Add to your `docker-compose.yml`:
+
+```yaml
+services:
+  mcp:
+    environment:
+      - AWS_REGION=us-east-1
+      - BEDROCK_EMBEDDING_MODEL=amazon.titan-embed-text-v2:0
+      - BEDROCK_GENERATION_MODEL=anthropic.claude-3-sonnet-20240229-v1:0
+      - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
+      - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
+```
+
+Or use AWS credentials file volume mount:
+
+```yaml
+services:
+  mcp:
+    volumes:
+      - ~/.aws:/root/.aws:ro
+    environment:
+      - AWS_REGION=us-east-1
+      - BEDROCK_EMBEDDING_MODEL=amazon.titan-embed-text-v2:0
+```
+
+## Usage Examples
+
+### Embeddings Only
+
+```bash
+export AWS_REGION=us-east-1
+export BEDROCK_EMBEDDING_MODEL=amazon.titan-embed-text-v2:0
+export AWS_ACCESS_KEY_ID=your-key
+export AWS_SECRET_ACCESS_KEY=your-secret
+
+uv run nextcloud-mcp-server
+```
+
+### Both Embeddings and Generation
+
+```bash
+export AWS_REGION=us-east-1
+export BEDROCK_EMBEDDING_MODEL=amazon.titan-embed-text-v2:0
+export BEDROCK_GENERATION_MODEL=anthropic.claude-3-sonnet-20240229-v1:0
+
+# For RAG evaluation with Bedrock
+export RAG_EVAL_PROVIDER=bedrock
+export RAG_EVAL_BEDROCK_MODEL=anthropic.claude-3-sonnet-20240229-v1:0
+
+uv run python -m tests.rag_evaluation.evaluate
+```
+
+### Programmatic Usage
+
+```python
+from nextcloud_mcp_server.providers import BedrockProvider
+
+# Embeddings only
+provider = BedrockProvider(
+    region_name="us-east-1",
+    embedding_model="amazon.titan-embed-text-v2:0",
+)
+
+embeddings = await provider.embed_batch(["text1", "text2"])
+
+# Both capabilities
+provider = BedrockProvider(
+    region_name="us-east-1",
+    embedding_model="amazon.titan-embed-text-v2:0",
+    generation_model="anthropic.claude-3-sonnet-20240229-v1:0",
+)
+
+# Generate embeddings
+embedding = await provider.embed("query text")
+
+# Generate text
+response = await provider.generate("Write a summary", max_tokens=500)
+```
+
+## Cost Considerations
+
+### Embedding Costs (as of Jan 2025)
+
+| Model | Price per 1K tokens |
+|-------|---------------------|
+| Titan Embed Text v2 | $0.0001 |
+| Cohere Embed English v3 | $0.0001 |
+
+### Generation Costs (as of Jan 2025)
+
+| Model | Input (per 1K tokens) | Output (per 1K tokens) |
+|-------|----------------------|------------------------|
+| Claude 3 Haiku | $0.00025 | $0.00125 |
+| Claude 3 Sonnet | $0.003 | $0.015 |
+| Claude 3 Opus | $0.015 | $0.075 |
+| Llama 3 8B | $0.0003 | $0.0006 |
+| Titan Text Express | $0.0002 | $0.0006 |
+
+**Note:** Prices vary by region. Check [AWS Bedrock Pricing](https://aws.amazon.com/bedrock/pricing/) for current rates.
+
+## Troubleshooting
+
+### Error: "Executable doesn't exist" or boto3 not found
+
+**Solution:**
+```bash
+uv sync --group dev  # Installs boto3
+```
+
+### Error: "AccessDeniedException"
+
+**Causes:**
+1. IAM permissions missing
+2. Model access not requested
+3. Wrong AWS region
+
+**Solution:**
+1. Verify IAM policy includes `bedrock:InvokeModel`
+2. Request model access in Bedrock console
+3. Check model is available in your region
+
+### Error: "ResourceNotFoundException"
+
+**Cause:** Invalid model ID or model not available in region
+
+**Solution:**
+- Verify model ID matches exactly (case-sensitive)
+- Check model availability in your AWS region
+- Use `aws bedrock list-foundation-models` to see available models
+
+### Error: "ThrottlingException"
+
+**Cause:** Rate limit exceeded
+
+**Solution:**
+- Reduce request rate
+- Request quota increase via AWS Support
+- Use batch operations where possible
+
+## Security Best Practices
+
+1. **Use IAM Roles** when running on AWS infrastructure
+2. **Rotate Access Keys** regularly if using IAM users
+3. **Restrict Permissions** to only required models
+4. **Enable CloudTrail** for audit logging
+5. **Use AWS Secrets Manager** for credential management
+6. **Monitor Costs** with AWS Cost Explorer and Budgets
+
+## Regional Availability
+
+Amazon Bedrock is available in:
+- **US East (N. Virginia)**: `us-east-1` ✅ Most models
+- **US West (Oregon)**: `us-west-2` ✅ Most models
+- **Asia Pacific (Singapore)**: `ap-southeast-1`
+- **Asia Pacific (Tokyo)**: `ap-northeast-1`
+- **Europe (Frankfurt)**: `eu-central-1`
+
+**Note:** Model availability varies by region. Check the [AWS Bedrock documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) for current availability.
+
+## References
+
+- [AWS Bedrock Documentation](https://docs.aws.amazon.com/bedrock/)
+- [AWS Bedrock Pricing](https://aws.amazon.com/bedrock/pricing/)
+- [boto3 Bedrock Runtime API](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-runtime.html)
+- [Provider Architecture ADR](./ADR-015-unified-provider-architecture.md)

nextcloud_mcp_server/app.py

@@ -1478,6 +1478,7 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
         vector_sync_status_fragment,
     )
     from nextcloud_mcp_server.auth.viz_routes import (
+        chunk_context_endpoint,
         vector_visualization_html,
         vector_visualization_search,
     )
@@ -1509,6 +1510,11 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
             vector_visualization_search,
             methods=["GET"],
         ),  # /app/vector-viz/search
+        Route(
+            "/chunk-context",
+            chunk_context_endpoint,
+            methods=["GET"],
+        ),  # /app/chunk-context
         # Webhook management routes (admin-only)
         Route("/webhooks", webhook_management_pane, methods=["GET"]),  # /app/webhooks
         Route(
@@ -1523,7 +1529,7 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
 
     browser_app = Starlette(routes=browser_routes)
     browser_app.add_middleware(
-        AuthenticationMiddleware,
+        AuthenticationMiddleware,  # type: ignore[invalid-argument-type]
         backend=SessionAuthBackend(oauth_enabled=oauth_enabled),
     )
 
@@ -1613,7 +1619,7 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
 
     # Add CORS middleware to allow browser-based clients like MCP Inspector
     app.add_middleware(
-        CORSMiddleware,
+        CORSMiddleware,  # type: ignore[invalid-argument-type]
         allow_origins=["*"],  # Allow all origins for development
         allow_credentials=True,
         allow_methods=["*"],
@@ -1623,7 +1629,7 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
 
     # Add observability middleware (metrics + tracing)
     if settings.metrics_enabled or settings.otel_exporter_otlp_endpoint:
-        app.add_middleware(ObservabilityMiddleware)
+        app.add_middleware(ObservabilityMiddleware)  # type: ignore[invalid-argument-type]
         logger.info("Observability middleware enabled (metrics and/or tracing)")
 
     # Add exception handler for scope challenges (OAuth mode only)

nextcloud_mcp_server/auth/templates/vector_viz.html

@@ -0,0 +1,323 @@
+<style>
+    .viz-card {
+        background: white;
+        border-radius: 8px;
+        padding: 20px;
+        margin-bottom: 20px;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    }
+    .viz-controls {
+        margin-bottom: 20px;
+    }
+    .viz-control-row {
+        display: grid;
+        grid-template-columns: 2fr 1fr auto;
+        gap: 12px;
+        margin-bottom: 12px;
+        align-items: end;
+    }
+    .viz-control-group {
+        margin-bottom: 15px;
+    }
+    .viz-control-group label {
+        display: block;
+        margin-bottom: 5px;
+        font-weight: 500;
+        color: #333;
+    }
+    .viz-control-group input[type="text"],
+    .viz-control-group input[type="number"],
+    .viz-control-group select {
+        width: 100%;
+        padding: 8px 12px;
+        border: 1px solid #ddd;
+        border-radius: 4px;
+        font-size: 14px;
+    }
+    .viz-control-group input[type="range"] {
+        width: 100%;
+    }
+    .viz-control-group select[multiple] {
+        min-height: 100px;
+    }
+    .viz-weight-display {
+        display: inline-block;
+        min-width: 40px;
+        text-align: right;
+        color: #666;
+    }
+    .viz-btn {
+        background: #0066cc;
+        color: white;
+        border: none;
+        padding: 10px 20px;
+        border-radius: 4px;
+        cursor: pointer;
+        font-size: 14px;
+        font-weight: 500;
+    }
+    .viz-btn:hover {
+        background: #0052a3;
+    }
+    .viz-btn-secondary {
+        background: #6c757d;
+        color: white;
+        border: none;
+        padding: 6px 12px;
+        border-radius: 4px;
+        cursor: pointer;
+        font-size: 13px;
+        margin-bottom: 12px;
+    }
+    .viz-btn-secondary:hover {
+        background: #5a6268;
+    }
+    #viz-plot-container {
+        width: 100%;
+        height: 600px;
+        position: relative;
+    }
+    #viz-plot {
+        width: 100%;
+        height: 100%;
+    }
+    .viz-loading {
+        text-align: center;
+        padding: 40px;
+        color: #666;
+    }
+    .viz-loading-overlay {
+        position: absolute;
+        inset: 0;
+        display: flex;
+        align-items: center;
+        justify-content: center;
+        background: white;
+        color: #666;
+    }
+    .viz-no-results {
+        text-align: center;
+        padding: 40px;
+        color: #666;
+        font-style: italic;
+    }
+    .viz-advanced-section {
+        margin-top: 16px;
+        padding: 16px;
+        background: #f8f9fa;
+        border-radius: 4px;
+        border: 1px solid #dee2e6;
+    }
+    .viz-advanced-grid {
+        display: grid;
+        grid-template-columns: 1fr 1fr;
+        gap: 20px;
+    }
+    .viz-info-box {
+        background: #e3f2fd;
+        border-left: 4px solid #2196f3;
+        padding: 12px;
+        margin-bottom: 20px;
+        font-size: 14px;
+    }
+    .chunk-toggle-btn {
+        background: #6c757d;
+        color: white;
+        border: none;
+        padding: 4px 10px;
+        border-radius: 3px;
+        cursor: pointer;
+        font-size: 12px;
+        margin-top: 6px;
+    }
+    .chunk-toggle-btn:hover {
+        background: #5a6268;
+    }
+    .chunk-context {
+        background: #f8f9fa;
+        border: 1px solid #dee2e6;
+        border-radius: 4px;
+        padding: 12px;
+        margin-top: 8px;
+        font-family: monospace;
+        font-size: 13px;
+        line-height: 1.6;
+        white-space: pre-wrap;
+        word-wrap: break-word;
+    }
+    .chunk-text {
+        color: #666;
+    }
+    .chunk-matched {
+        background: #fff3cd;
+        border: 1px solid #ffc107;
+        padding: 2px 4px;
+        border-radius: 2px;
+        font-weight: 500;
+        color: #333;
+    }
+    .chunk-ellipsis {
+        color: #999;
+        font-style: italic;
+    }
+</style>
+
+<div x-data="vizApp()">
+    <div class="viz-card">
+        <h2>Vector Visualization</h2>
+        <div class="viz-info-box">
+            Testing search algorithms on your indexed documents. User: <strong>{{ username }}</strong>
+        </div>
+
+        <form @submit.prevent="executeSearch">
+            <div class="viz-controls">
+                <!-- Main Controls -->
+                <div class="viz-control-group">
+                    <label>Search Query</label>
+                    <input type="text" x-model="query" placeholder="Enter search query..." required />
+                </div>
+
+                <div class="viz-control-row">
+                    <div class="viz-control-group" style="margin-bottom: 0;">
+                        <label>Algorithm</label>
+                        <select x-model="algorithm">
+                            <option value="semantic">Semantic (Dense Vectors)</option>
+                            <option value="bm25_hybrid" selected>BM25 Hybrid (Dense + Sparse)</option>
+                        </select>
+                    </div>
+
+                    <div class="viz-control-group" style="margin-bottom: 0;" x-show="algorithm === 'bm25_hybrid'">
+                        <label>Fusion Method</label>
+                        <select x-model="fusion">
+                            <option value="rrf" selected>RRF (Reciprocal Rank Fusion)</option>
+                            <option value="dbsf">DBSF (Distribution-Based Score Fusion)</option>
+                        </select>
+                    </div>
+
+                    <div style="display: flex; align-items: flex-end;">
+                        <button type="submit" class="viz-btn" style="width: 100%;">Search & Visualize</button>
+                    </div>
+
+                    <div style="display: flex; align-items: flex-end;">
+                        <button type="button" class="viz-btn-secondary" @click="showAdvanced = !showAdvanced" style="white-space: nowrap;">
+                            <span x-text="showAdvanced ? 'Hide Advanced' : 'Advanced'"></span>
+                        </button>
+                    </div>
+                </div>
+
+                <!-- Advanced Options (Collapsible) -->
+                <div class="viz-advanced-section" x-show="showAdvanced" x-transition.opacity.duration.200ms>
+                    <h3 style="margin-top: 0; margin-bottom: 16px; font-size: 16px;">Advanced Options</h3>
+
+                    <div class="viz-advanced-grid">
+                        <div class="viz-control-group">
+                            <label>Document Types</label>
+                            <select x-model="docTypes" multiple>
+                                <option value="">All Types (cross-app search)</option>
+                                <option value="note">Notes</option>
+                                <option value="file">Files</option>
+                                <option value="calendar">Calendar Events</option>
+                                <option value="contact">Contacts</option>
+                                <option value="deck">Deck Cards</option>
+                            </select>
+                            <small style="color: #666; display: block; margin-top: 4px;">
+                                Hold Ctrl/Cmd to select multiple
+                            </small>
+                        </div>
+
+                        <div>
+                            <div class="viz-control-group">
+                                <label>Score Threshold (Semantic/Hybrid)</label>
+                                <input type="number" x-model.number="scoreThreshold" min="0" max="1" step="0.1" />
+                            </div>
+
+                            <div class="viz-control-group">
+                                <label>Result Limit</label>
+                                <input type="number" x-model.number="limit" min="1" max="100" />
+                            </div>
+                        </div>
+                    </div>
+
+                    <!-- Info: BM25 Hybrid fusion methods -->
+                    <div x-show="algorithm === 'bm25_hybrid'" style="margin-top: 16px; padding: 12px; background: #e9ecef; border-radius: 4px;">
+                        <p style="margin: 0; font-size: 14px; color: #666;">
+                            <strong>BM25 Hybrid Search:</strong> Combines dense semantic vectors with sparse BM25 keyword vectors.
+                        </p>
+                        <p style="margin: 8px 0 0 0; font-size: 13px; color: #666;">
+                            <strong>RRF:</strong> Reciprocal Rank Fusion - Rank-based fusion producing scores in [0.0, 1.0]
+                        </p>
+                        <p style="margin: 4px 0 0 0; font-size: 13px; color: #666;">
+                            <strong>DBSF:</strong> Distribution-Based Score Fusion - Sums normalized scores (can exceed 1.0)
+                        </p>
+                    </div>
+                </div>
+            </div>
+        </form>
+    </div>
+
+    <div class="viz-card">
+        <div id="viz-plot-container">
+            <div x-show="loading" class="viz-loading-overlay" x-transition.opacity.duration.200ms>
+                Executing search and computing PCA projection...
+            </div>
+            <div id="viz-plot" x-show="!loading" x-transition.opacity.duration.200ms></div>
+        </div>
+    </div>
+
+    <div class="viz-card">
+        <h3>Search Results (<span x-text="loading ? '...' : results.length"></span>)</h3>
+
+        <div x-show="loading" class="viz-loading" x-transition.opacity.duration.200ms>
+            Loading results...
+        </div>
+
+        <div x-show="!loading && results.length === 0" class="viz-no-results" x-transition.opacity.duration.200ms>
+            No results found. Try a different query or adjust your search parameters.
+        </div>
+
+        <template x-if="!loading && results.length > 0">
+            <div x-transition.opacity.duration.200ms>
+                <template x-for="result in results" :key="result.id">
+                    <div style="padding: 12px; border-bottom: 1px solid #eee;">
+                        <a :href="getNextcloudUrl(result)" target="_blank" style="font-weight: 500; color: #0066cc; text-decoration: none;">
+                            <span x-text="result.title"></span>
+                        </a>
+                        <div style="font-size: 14px; color: #666; margin-top: 4px;" x-text="result.excerpt"></div>
+                        <div style="font-size: 12px; color: #999; margin-top: 4px;">
+                            Score: <span x-text="result.score.toFixed(3)"></span> |
+                            Type: <span x-text="result.doc_type"></span>
+                        </div>
+
+                        <!-- Show Chunk button (only if chunk position is available) -->
+                        <template x-if="hasChunkPosition(result)">
+                            <button
+                                class="chunk-toggle-btn"
+                                @click="toggleChunk(result)"
+                                x-text="isChunkExpanded(`${result.doc_type}_${result.id}`) ? 'Hide Chunk' : 'Show Chunk'"
+                            ></button>
+                        </template>
+
+                        <!-- Chunk context (expanded inline) -->
+                        <template x-if="isChunkExpanded(`${result.doc_type}_${result.id}`)">
+                            <div class="chunk-context" x-transition.opacity.duration.200ms>
+                                <template x-if="chunkLoading[`${result.doc_type}_${result.id}`]">
+                                    <div style="color: #666; font-style: italic;">Loading chunk...</div>
+                                </template>
+                                <template x-if="!chunkLoading[`${result.doc_type}_${result.id}`]">
+                                    <div>
+                                        <template x-if="expandedChunks[`${result.doc_type}_${result.id}`]?.has_more_before">
+                                            <span class="chunk-ellipsis">...</span>
+                                        </template>
+                                        <span class="chunk-text" x-text="expandedChunks[`${result.doc_type}_${result.id}`]?.before_context"></span><span class="chunk-matched" x-text="expandedChunks[`${result.doc_type}_${result.id}`]?.chunk_text"></span><span class="chunk-text" x-text="expandedChunks[`${result.doc_type}_${result.id}`]?.after_context"></span><template x-if="expandedChunks[`${result.doc_type}_${result.id}`]?.has_more_after">
+                                            <span class="chunk-ellipsis">...</span>
+                                        </template>
+                                    </div>
+                                </template>
+                            </div>
+                        </template>
+                    </div>
+                </template>
+            </div>
+        </template>
+    </div>
+</div>

nextcloud_mcp_server/auth/userinfo_routes.py

@@ -677,12 +677,15 @@ async def user_info_html(request: Request) -> HTMLResponse:
                 return {{
                     query: '',
                     algorithm: 'bm25_hybrid',
+                    fusion: 'rrf',  // Default fusion method for BM25 Hybrid
                     showAdvanced: false,
                     docTypes: [''],  // Default to "All Types"
                     limit: 50,
                     scoreThreshold: 0.0,
                     loading: false,
                     results: [],
+                    expandedChunks: {{}},  // Track which chunks are expanded (result_id -> chunk data)
+                    chunkLoading: {{}},    // Track loading state per result
 
                     async executeSearch() {{
                         this.loading = true;
@@ -696,6 +699,11 @@ async def user_info_html(request: Request) -> HTMLResponse:
                                 score_threshold: this.scoreThreshold,
                             }});
 
+                            // Add fusion parameter for BM25 Hybrid
+                            if (this.algorithm === 'bm25_hybrid') {{
+                                params.append('fusion', this.fusion);
+                            }}
+
                             // Add doc_types parameter (filter out empty string for "All Types")
                             const selectedTypes = this.docTypes.filter(t => t !== '');
                             if (selectedTypes.length > 0) {{
@@ -778,6 +786,51 @@ async def user_info_html(request: Request) -> HTMLResponse:
                             default:
                                 return `${{baseUrl}}`;
                         }}
+                    }},
+
+                    hasChunkPosition(result) {{
+                        // Check if result has position metadata
+                        return result.chunk_start_offset != null && result.chunk_end_offset != null;
+                    }},
+
+                    isChunkExpanded(resultKey) {{
+                        return this.expandedChunks[resultKey] !== undefined;
+                    }},
+
+                    async toggleChunk(result) {{
+                        const resultKey = `${{result.doc_type}}_${{result.id}}`;
+
+                        // If already expanded, collapse
+                        if (this.isChunkExpanded(resultKey)) {{
+                            delete this.expandedChunks[resultKey];
+                            return;
+                        }}
+
+                        // Otherwise, fetch and expand
+                        this.chunkLoading[resultKey] = true;
+
+                        try {{
+                            const params = new URLSearchParams({{
+                                doc_type: result.doc_type,
+                                doc_id: result.id,
+                                start: result.chunk_start_offset,
+                                end: result.chunk_end_offset,
+                                context: 500  // 500 chars before/after
+                            }});
+
+                            const response = await fetch(`/app/chunk-context?${{params}}`);
+                            const data = await response.json();
+
+                            if (data.success) {{
+                                this.expandedChunks[resultKey] = data;
+                            }} else {{
+                                alert('Failed to load chunk: ' + data.error);
+                            }}
+                        }} catch (error) {{
+                            alert('Error loading chunk: ' + error.message);
+                        }} finally {{
+                            delete this.chunkLoading[resultKey];
+                        }}
                     }}
                 }}
             }}

nextcloud_mcp_server/auth/viz_routes.py

@@ -12,8 +12,10 @@ All processing happens server-side following ADR-012:
 
 import logging
 import time
+from pathlib import Path
 
 import numpy as np
+from jinja2 import Environment, FileSystemLoader
 from starlette.authentication import requires
 from starlette.requests import Request
 from starlette.responses import HTMLResponse, JSONResponse
@@ -28,6 +30,10 @@ from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
 
 logger = logging.getLogger(__name__)
 
+# Setup Jinja2 environment for templates
+_template_dir = Path(__file__).parent / "templates"
+_jinja_env = Environment(loader=FileSystemLoader(_template_dir))
+
 
 @requires("authenticated", redirect="oauth_login")
 async def vector_visualization_html(request: Request) -> HTMLResponse:
@@ -63,252 +69,9 @@ async def vector_visualization_html(request: Request) -> HTMLResponse:
         else "unknown"
     )
 
-    html_content = f"""
-        <style>
-            .viz-card {{
-                background: white;
-                border-radius: 8px;
-                padding: 20px;
-                margin-bottom: 20px;
-                box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-            }}
-            .viz-controls {{
-                margin-bottom: 20px;
-            }}
-            .viz-control-row {{
-                display: grid;
-                grid-template-columns: 2fr 1fr auto;
-                gap: 12px;
-                margin-bottom: 12px;
-                align-items: end;
-            }}
-            .viz-control-group {{
-                margin-bottom: 15px;
-            }}
-            .viz-control-group label {{
-                display: block;
-                margin-bottom: 5px;
-                font-weight: 500;
-                color: #333;
-            }}
-            .viz-control-group input[type="text"],
-            .viz-control-group input[type="number"],
-            .viz-control-group select {{
-                width: 100%;
-                padding: 8px 12px;
-                border: 1px solid #ddd;
-                border-radius: 4px;
-                font-size: 14px;
-            }}
-            .viz-control-group input[type="range"] {{
-                width: 100%;
-            }}
-            .viz-control-group select[multiple] {{
-                min-height: 100px;
-            }}
-            .viz-weight-display {{
-                display: inline-block;
-                min-width: 40px;
-                text-align: right;
-                color: #666;
-            }}
-            .viz-btn {{
-                background: #0066cc;
-                color: white;
-                border: none;
-                padding: 10px 20px;
-                border-radius: 4px;
-                cursor: pointer;
-                font-size: 14px;
-                font-weight: 500;
-            }}
-            .viz-btn:hover {{
-                background: #0052a3;
-            }}
-            .viz-btn-secondary {{
-                background: #6c757d;
-                color: white;
-                border: none;
-                padding: 6px 12px;
-                border-radius: 4px;
-                cursor: pointer;
-                font-size: 13px;
-                margin-bottom: 12px;
-            }}
-            .viz-btn-secondary:hover {{
-                background: #5a6268;
-            }}
-            #viz-plot-container {{
-                width: 100%;
-                height: 600px;
-                position: relative;
-            }}
-            #viz-plot {{
-                width: 100%;
-                height: 100%;
-            }}
-            .viz-loading {{
-                text-align: center;
-                padding: 40px;
-                color: #666;
-            }}
-            .viz-loading-overlay {{
-                position: absolute;
-                inset: 0;
-                display: flex;
-                align-items: center;
-                justify-content: center;
-                background: white;
-                color: #666;
-            }}
-            .viz-no-results {{
-                text-align: center;
-                padding: 40px;
-                color: #666;
-                font-style: italic;
-            }}
-            .viz-advanced-section {{
-                margin-top: 16px;
-                padding: 16px;
-                background: #f8f9fa;
-                border-radius: 4px;
-                border: 1px solid #dee2e6;
-            }}
-            .viz-advanced-grid {{
-                display: grid;
-                grid-template-columns: 1fr 1fr;
-                gap: 20px;
-            }}
-            .viz-info-box {{
-                background: #e3f2fd;
-                border-left: 4px solid #2196f3;
-                padding: 12px;
-                margin-bottom: 20px;
-                font-size: 14px;
-            }}
-        </style>
-
-        <div x-data="vizApp()">
-            <div class="viz-card">
-                <h2>Vector Visualization</h2>
-                <div class="viz-info-box">
-                    Testing search algorithms on your indexed documents. User: <strong>{username}</strong>
-                </div>
-
-                <form @submit.prevent="executeSearch">
-                    <div class="viz-controls">
-                        <!-- Main Controls -->
-                        <div class="viz-control-group">
-                            <label>Search Query</label>
-                            <input type="text" x-model="query" placeholder="Enter search query..." required />
-                        </div>
-
-                        <div class="viz-control-row">
-                            <div class="viz-control-group" style="margin-bottom: 0;">
-                                <label>Algorithm</label>
-                                <select x-model="algorithm">
-                                    <option value="semantic">Semantic (Dense Vectors)</option>
-                                    <option value="bm25_hybrid" selected>BM25 Hybrid (Dense + Sparse RRF)</option>
-                                </select>
-                            </div>
-
-                            <div style="display: flex; align-items: flex-end;">
-                                <button type="submit" class="viz-btn" style="width: 100%;">Search & Visualize</button>
-                            </div>
-
-                            <div style="display: flex; align-items: flex-end;">
-                                <button type="button" class="viz-btn-secondary" @click="showAdvanced = !showAdvanced" style="white-space: nowrap;">
-                                    <span x-text="showAdvanced ? 'Hide Advanced' : 'Advanced'"></span>
-                                </button>
-                            </div>
-                        </div>
-
-                        <!-- Advanced Options (Collapsible) -->
-                        <div class="viz-advanced-section" x-show="showAdvanced" x-transition.opacity.duration.200ms>
-                            <h3 style="margin-top: 0; margin-bottom: 16px; font-size: 16px;">Advanced Options</h3>
-
-                            <div class="viz-advanced-grid">
-                                <div class="viz-control-group">
-                                    <label>Document Types</label>
-                                    <select x-model="docTypes" multiple>
-                                        <option value="">All Types (cross-app search)</option>
-                                        <option value="note">Notes</option>
-                                        <option value="file">Files</option>
-                                        <option value="calendar">Calendar Events</option>
-                                        <option value="contact">Contacts</option>
-                                        <option value="deck">Deck Cards</option>
-                                    </select>
-                                    <small style="color: #666; display: block; margin-top: 4px;">
-                                        Hold Ctrl/Cmd to select multiple
-                                    </small>
-                                </div>
-
-                                <div>
-                                    <div class="viz-control-group">
-                                        <label>Score Threshold (Semantic/Hybrid)</label>
-                                        <input type="number" x-model.number="scoreThreshold" min="0" max="1" step="0.1" />
-                                    </div>
-
-                                    <div class="viz-control-group">
-                                        <label>Result Limit</label>
-                                        <input type="number" x-model.number="limit" min="1" max="100" />
-                                    </div>
-                                </div>
-                            </div>
-
-                            <!-- Info: BM25 Hybrid uses native RRF fusion (no manual weights) -->
-                            <div x-show="algorithm === 'bm25_hybrid'" style="margin-top: 16px; padding: 12px; background: #e9ecef; border-radius: 4px;">
-                                <p style="margin: 0; font-size: 14px; color: #666;">
-                                    <strong>BM25 Hybrid Search:</strong> Uses Qdrant's native Reciprocal Rank Fusion (RRF)
-                                    to automatically combine dense semantic vectors with sparse BM25 keyword vectors.
-                                    No manual weight tuning required.
-                                </p>
-                            </div>
-                        </div>
-                    </div>
-                </form>
-            </div>
-
-            <div class="viz-card">
-                <div id="viz-plot-container">
-                    <div x-show="loading" class="viz-loading-overlay" x-transition.opacity.duration.200ms>
-                        Executing search and computing PCA projection...
-                    </div>
-                    <div id="viz-plot" x-show="!loading" x-transition.opacity.duration.200ms></div>
-                </div>
-            </div>
-
-            <div class="viz-card">
-                <h3>Search Results (<span x-text="loading ? '...' : results.length"></span>)</h3>
-
-                <div x-show="loading" class="viz-loading" x-transition.opacity.duration.200ms>
-                    Loading results...
-                </div>
-
-                <div x-show="!loading && results.length === 0" class="viz-no-results" x-transition.opacity.duration.200ms>
-                    No results found. Try a different query or adjust your search parameters.
-                </div>
-
-                <template x-if="!loading && results.length > 0">
-                    <div x-transition.opacity.duration.200ms>
-                        <template x-for="result in results" :key="result.id">
-                            <div style="padding: 12px; border-bottom: 1px solid #eee;">
-                                <a :href="getNextcloudUrl(result)" target="_blank" style="font-weight: 500; color: #0066cc; text-decoration: none;">
-                                    <span x-text="result.title"></span>
-                                </a>
-                                <div style="font-size: 14px; color: #666; margin-top: 4px;" x-text="result.excerpt"></div>
-                                <div style="font-size: 12px; color: #999; margin-top: 4px;">
-                                    Score: <span x-text="result.score.toFixed(3)"></span> |
-                                    Type: <span x-text="result.doc_type"></span>
-                                </div>
-                            </div>
-                        </template>
-                    </div>
-                </template>
-            </div>
-        </div>
-    """
-
+    # Load and render template
+    template = _jinja_env.get_template("vector_viz.html")
+    html_content = template.render(username=username)
     return HTMLResponse(content=html_content)
 
 
@@ -352,6 +115,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
     algorithm = request.query_params.get("algorithm", "bm25_hybrid")
     limit = int(request.query_params.get("limit", "50"))
     score_threshold = float(request.query_params.get("score_threshold", "0.0"))
+    fusion = request.query_params.get("fusion", "rrf")  # Default to RRF
 
     # Parse doc_types (comma-separated list, None = all types)
     doc_types_param = request.query_params.get("doc_types", "")
@@ -359,7 +123,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
 
     logger.info(
         f"Viz search: user={username}, query='{query}', "
-        f"algorithm={algorithm}, limit={limit}, doc_types={doc_types}"
+        f"algorithm={algorithm}, fusion={fusion}, limit={limit}, doc_types={doc_types}"
     )
 
     try:
@@ -377,7 +141,9 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             if algorithm == "semantic":
                 search_algo = SemanticSearchAlgorithm(score_threshold=score_threshold)
             elif algorithm == "bm25_hybrid":
-                search_algo = BM25HybridSearchAlgorithm(score_threshold=score_threshold)
+                search_algo = BM25HybridSearchAlgorithm(
+                    score_threshold=score_threshold, fusion=fusion
+                )
             else:
                 return JSONResponse(
                     {"success": False, "error": f"Unknown algorithm: {algorithm}"},
@@ -552,6 +318,8 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
                 "title": r.title,
                 "excerpt": r.excerpt,
                 "score": r.score,
+                "chunk_start_offset": r.chunk_start_offset,
+                "chunk_end_offset": r.chunk_end_offset,
             }
             for r in search_results
         ]
@@ -594,3 +362,125 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             {"success": False, "error": str(e)},
             status_code=500,
         )
+
+
+@requires("authenticated", redirect="oauth_login")
+async def chunk_context_endpoint(request: Request) -> JSONResponse:
+    """Fetch chunk text with surrounding context for visualization.
+
+    This endpoint retrieves the matched chunk along with surrounding text
+    to provide context for the search result. Used by the viz pane to
+    display chunks inline.
+
+    Query parameters:
+        doc_type: Document type (e.g., "note")
+        doc_id: Document ID
+        start: Chunk start offset (character position)
+        end: Chunk end offset (character position)
+        context: Characters of context before/after (default: 500)
+
+    Returns:
+        JSON with chunk_text, before_context, after_context, and flags
+    """
+    try:
+        # Get query parameters
+        doc_type = request.query_params.get("doc_type")
+        doc_id = request.query_params.get("doc_id")
+        start_str = request.query_params.get("start")
+        end_str = request.query_params.get("end")
+        context_chars = int(request.query_params.get("context", "500"))
+
+        # Validate required parameters
+        if not all([doc_type, doc_id, start_str, end_str]):
+            return JSONResponse(
+                {
+                    "success": False,
+                    "error": "Missing required parameters: doc_type, doc_id, start, end",
+                },
+                status_code=400,
+            )
+
+        start = int(start_str)
+        end = int(end_str)
+
+        # Currently only support notes
+        if doc_type != "note":
+            return JSONResponse(
+                {"success": False, "error": f"Unsupported doc_type: {doc_type}"},
+                status_code=400,
+            )
+
+        # Get authenticated HTTP client and fetch note
+        from nextcloud_mcp_server.auth.userinfo_routes import (
+            _get_authenticated_client_for_userinfo,
+        )
+        from nextcloud_mcp_server.client.notes import NotesClient
+
+        # Get username from request auth
+        username = (
+            request.user.display_name
+            if hasattr(request.user, "display_name")
+            else "unknown"
+        )
+
+        # Create notes client with authenticated HTTP client
+        http_client = await _get_authenticated_client_for_userinfo(request)
+        notes_client = NotesClient(http_client, username)
+
+        # Fetch full note content
+        note = await notes_client.get_note(int(doc_id))
+        full_content = f"{note['title']}\n\n{note['content']}"
+
+        # Validate offsets
+        if start < 0 or end > len(full_content) or start >= end:
+            return JSONResponse(
+                {
+                    "success": False,
+                    "error": f"Invalid offsets: start={start}, end={end}, content_length={len(full_content)}",
+                },
+                status_code=400,
+            )
+
+        # Extract chunk
+        chunk_text = full_content[start:end]
+
+        # Extract context before and after
+        before_start = max(0, start - context_chars)
+        before_context = full_content[before_start:start]
+
+        after_end = min(len(full_content), end + context_chars)
+        after_context = full_content[end:after_end]
+
+        # Determine if there's more content
+        has_more_before = before_start > 0
+        has_more_after = after_end < len(full_content)
+
+        logger.info(
+            f"Fetched chunk context for {doc_type}_{doc_id}: "
+            f"chunk_len={len(chunk_text)}, before_len={len(before_context)}, "
+            f"after_len={len(after_context)}"
+        )
+
+        return JSONResponse(
+            {
+                "success": True,
+                "chunk_text": chunk_text,
+                "before_context": before_context,
+                "after_context": after_context,
+                "has_more_before": has_more_before,
+                "has_more_after": has_more_after,
+            }
+        )
+
+    except ValueError as e:
+        logger.error(f"Invalid parameter format: {e}")
+        return JSONResponse(
+            {"success": False, "error": f"Invalid parameter format: {e}"},
+            status_code=400,
+        )
+    except Exception as e:
+        logger.error(f"Chunk context error: {e}", exc_info=True)
+        return JSONResponse(
+            {"success": False, "error": str(e)},
+            status_code=500,
+        )

nextcloud_mcp_server/embedding/service.py

@@ -1,57 +1,30 @@
-"""Embedding service with provider detection."""
+"""Embedding service with provider detection.
+
+DEPRECATED: This module is maintained for backward compatibility.
+New code should use nextcloud_mcp_server.providers.get_provider() directly.
+"""
 
 import logging
-import os
 
-from .base import EmbeddingProvider
+from nextcloud_mcp_server.providers import get_provider
+
 from .bm25_provider import BM25SparseEmbeddingProvider
-from .ollama_provider import OllamaEmbeddingProvider
-from .simple_provider import SimpleEmbeddingProvider
 
 logger = logging.getLogger(__name__)
 
 
 class EmbeddingService:
-    """Unified embedding service with automatic provider detection."""
+    """
+    Unified embedding service with automatic provider detection.
+
+    DEPRECATED: This class wraps the new unified provider infrastructure
+    for backward compatibility. New code should use
+    nextcloud_mcp_server.providers.get_provider() directly.
+    """
 
     def __init__(self):
         """Initialize embedding service with auto-detected provider."""
-        self.provider = self._detect_provider()
-
-    def _detect_provider(self) -> EmbeddingProvider:
-        """
-        Auto-detect available embedding provider.
-
-        Checks environment variables in order:
-        1. OLLAMA_BASE_URL - Use Ollama provider (production)
-        2. OPENAI_API_KEY - Use OpenAI provider (future)
-        3. Fallback to SimpleEmbeddingProvider (testing/development)
-
-        Returns:
-            Configured embedding provider
-        """
-        # Ollama provider (production)
-        ollama_url = os.getenv("OLLAMA_BASE_URL")
-        if ollama_url:
-            logger.info(f"Using Ollama embedding provider: {ollama_url}")
-            return OllamaEmbeddingProvider(
-                base_url=ollama_url,
-                model=os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text"),
-                verify_ssl=os.getenv("OLLAMA_VERIFY_SSL", "true").lower() == "true",
-            )
-
-        # OpenAI provider (future implementation)
-        # openai_key = os.getenv("OPENAI_API_KEY")
-        # if openai_key:
-        #     return OpenAIEmbeddingProvider(api_key=openai_key)
-
-        # Fallback to simple provider for development/testing
-        logger.warning(
-            "No embedding provider configured (OLLAMA_BASE_URL or OPENAI_API_KEY not set). "
-            "Using SimpleEmbeddingProvider for testing/development. "
-            "For production, configure an external embedding service."
-        )
-        return SimpleEmbeddingProvider(dimension=384)
+        self.provider = get_provider()
 
     async def embed(self, text: str) -> list[float]:
         """

nextcloud_mcp_server/models/semantic.py

@@ -19,9 +19,22 @@ class SemanticSearchResult(BaseModel):
         default="", description="Document category (notes) or location (calendar)"
     )
     excerpt: str = Field(description="Excerpt from matching chunk")
-    score: float = Field(description="Semantic similarity score (0-1)")
+    score: float = Field(
+        description=(
+            "Relevance score (≥ 0.0, higher is better). "
+            "Score range depends on fusion method: "
+            "RRF produces scores in [0.0, 1.0], "
+            "DBSF can exceed 1.0 (sum of normalized scores from multiple systems)"
+        )
+    )
     chunk_index: int = Field(description="Index of matching chunk in document")
     total_chunks: int = Field(description="Total number of chunks in document")
+    chunk_start_offset: Optional[int] = Field(
+        default=None, description="Character position where chunk starts in document"
+    )
+    chunk_end_offset: Optional[int] = Field(
+        default=None, description="Character position where chunk ends in document"
+    )
 
 
 class SemanticSearchResponse(BaseResponse):

nextcloud_mcp_server/providers/__init__.py

@@ -0,0 +1,18 @@
+"""Unified provider infrastructure for embeddings and text generation."""
+
+from .anthropic import AnthropicProvider
+from .base import Provider
+from .bedrock import BedrockProvider
+from .ollama import OllamaProvider
+from .registry import get_provider, reset_provider
+from .simple import SimpleProvider
+
+__all__ = [
+    "Provider",
+    "OllamaProvider",
+    "AnthropicProvider",
+    "SimpleProvider",
+    "BedrockProvider",
+    "get_provider",
+    "reset_provider",
+]

nextcloud_mcp_server/providers/anthropic.py

@@ -0,0 +1,97 @@
+"""Unified Anthropic provider for text generation."""
+
+import logging
+
+from anthropic import AsyncAnthropic
+
+from .base import Provider
+
+logger = logging.getLogger(__name__)
+
+
+class AnthropicProvider(Provider):
+    """
+    Anthropic provider for text generation.
+
+    Supports Claude models via the Anthropic API.
+    Note: Anthropic doesn't provide embedding models, only text generation.
+    """
+
+    def __init__(self, api_key: str, model: str = "claude-3-5-sonnet-20241022"):
+        """
+        Initialize Anthropic provider.
+
+        Args:
+            api_key: Anthropic API key
+            model: Model name (e.g., "claude-3-5-sonnet-20241022")
+        """
+        self.client = AsyncAnthropic(api_key=api_key)
+        self.model = model
+
+        logger.info(f"Initialized Anthropic provider (model={model})")
+
+    @property
+    def supports_embeddings(self) -> bool:
+        """Whether this provider supports embedding generation."""
+        return False
+
+    @property
+    def supports_generation(self) -> bool:
+        """Whether this provider supports text generation."""
+        return True
+
+    async def embed(self, text: str) -> list[float]:
+        """
+        Generate embedding vector for text.
+
+        Raises:
+            NotImplementedError: Anthropic doesn't provide embedding models
+        """
+        raise NotImplementedError(
+            "Embedding not supported by Anthropic - use Ollama or Bedrock for embeddings"
+        )
+
+    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
+        """
+        Generate embeddings for multiple texts.
+
+        Raises:
+            NotImplementedError: Anthropic doesn't provide embedding models
+        """
+        raise NotImplementedError(
+            "Embedding not supported by Anthropic - use Ollama or Bedrock for embeddings"
+        )
+
+    def get_dimension(self) -> int:
+        """
+        Get embedding dimension.
+
+        Raises:
+            NotImplementedError: Anthropic doesn't provide embedding models
+        """
+        raise NotImplementedError(
+            "Embedding not supported by Anthropic - use Ollama or Bedrock for embeddings"
+        )
+
+    async def generate(self, prompt: str, max_tokens: int = 500) -> str:
+        """
+        Generate text using Anthropic API.
+
+        Args:
+            prompt: The prompt to generate from
+            max_tokens: Maximum tokens to generate
+
+        Returns:
+            Generated text
+        """
+        message = await self.client.messages.create(
+            model=self.model,
+            max_tokens=max_tokens,
+            temperature=0.7,
+            messages=[{"role": "user", "content": prompt}],
+        )
+        return message.content[0].text
+
+    async def close(self) -> None:
+        """Close the client (no-op for Anthropic SDK)."""
+        pass

nextcloud_mcp_server/providers/base.py

@@ -0,0 +1,91 @@
+"""Unified provider interface for embeddings and text generation."""
+
+from abc import ABC, abstractmethod
+
+
+class Provider(ABC):
+    """
+    Unified base class for LLM providers.
+
+    Providers can support embeddings, text generation, or both.
+    Use capability properties to determine what features are available.
+    """
+
+    @property
+    @abstractmethod
+    def supports_embeddings(self) -> bool:
+        """Whether this provider supports embedding generation."""
+        pass
+
+    @property
+    @abstractmethod
+    def supports_generation(self) -> bool:
+        """Whether this provider supports text generation."""
+        pass
+
+    @abstractmethod
+    async def embed(self, text: str) -> list[float]:
+        """
+        Generate embedding vector for text.
+
+        Args:
+            text: Input text to embed
+
+        Returns:
+            Vector embedding as list of floats
+
+        Raises:
+            NotImplementedError: If provider doesn't support embeddings
+        """
+        pass
+
+    @abstractmethod
+    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
+        """
+        Generate embeddings for multiple texts (optimized).
+
+        Args:
+            texts: List of texts to embed
+
+        Returns:
+            List of vector embeddings
+
+        Raises:
+            NotImplementedError: If provider doesn't support embeddings
+        """
+        pass
+
+    @abstractmethod
+    def get_dimension(self) -> int:
+        """
+        Get embedding dimension for this provider.
+
+        Returns:
+            Vector dimension (e.g., 768 for nomic-embed-text)
+
+        Raises:
+            NotImplementedError: If provider doesn't support embeddings
+        """
+        pass
+
+    @abstractmethod
+    async def generate(self, prompt: str, max_tokens: int = 500) -> str:
+        """
+        Generate text from a prompt.
+
+        Args:
+            prompt: The prompt to generate from
+            max_tokens: Maximum tokens to generate
+
+        Returns:
+            Generated text
+
+        Raises:
+            NotImplementedError: If provider doesn't support generation
+        """
+        pass
+
+    @abstractmethod
+    async def close(self) -> None:
+        """Close the provider and release resources."""
+        pass

nextcloud_mcp_server/providers/bedrock.py

@@ -0,0 +1,397 @@
+"""Amazon Bedrock provider for embeddings and text generation."""
+
+import json
+import logging
+from typing import Any
+
+try:
+    import boto3
+    from botocore.exceptions import BotoCoreError, ClientError
+
+    BOTO3_AVAILABLE = True
+except ImportError:
+    BOTO3_AVAILABLE = False
+
+from .base import Provider
+
+logger = logging.getLogger(__name__)
+
+
+class BedrockProvider(Provider):
+    """
+    Amazon Bedrock provider supporting both embeddings and text generation.
+
+    Uses AWS Bedrock Runtime API with boto3. Supports various model families:
+    - Embeddings: amazon.titan-embed-text-v1, amazon.titan-embed-text-v2, cohere.embed-*
+    - Text Generation: anthropic.claude-*, meta.llama3-*, amazon.titan-text-*, mistral.*, etc.
+
+    Requires AWS credentials configured via:
+    - Environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION)
+    - AWS credentials file (~/.aws/credentials)
+    - IAM role (when running on AWS)
+    """
+
+    def __init__(
+        self,
+        region_name: str | None = None,
+        embedding_model: str | None = None,
+        generation_model: str | None = None,
+        aws_access_key_id: str | None = None,
+        aws_secret_access_key: str | None = None,
+    ):
+        """
+        Initialize Bedrock provider.
+
+        Args:
+            region_name: AWS region (e.g., "us-east-1"). Defaults to AWS_REGION env var.
+            embedding_model: Model ID for embeddings (e.g., "amazon.titan-embed-text-v2:0").
+                None disables embeddings.
+            generation_model: Model ID for text generation (e.g., "anthropic.claude-3-sonnet-20240229-v1:0").
+                None disables generation.
+            aws_access_key_id: AWS access key (optional, uses default credential chain if not provided)
+            aws_secret_access_key: AWS secret key (optional, uses default credential chain if not provided)
+
+        Raises:
+            ImportError: If boto3 is not installed
+        """
+        if not BOTO3_AVAILABLE:
+            raise ImportError(
+                "boto3 is required for Bedrock provider. Install with: pip install boto3"
+            )
+
+        self.embedding_model = embedding_model
+        self.generation_model = generation_model
+        self._dimension: int | None = None  # Detected dynamically
+
+        # Initialize bedrock-runtime client
+        client_kwargs: dict[str, Any] = {}
+        if region_name:
+            client_kwargs["region_name"] = region_name
+        if aws_access_key_id:
+            client_kwargs["aws_access_key_id"] = aws_access_key_id
+        if aws_secret_access_key:
+            client_kwargs["aws_secret_access_key"] = aws_secret_access_key
+
+        self.client = boto3.client("bedrock-runtime", **client_kwargs)
+
+        logger.info(
+            f"Initialized Bedrock provider in region {region_name or 'default'} "
+            f"(embedding_model={embedding_model}, generation_model={generation_model})"
+        )
+
+    @property
+    def supports_embeddings(self) -> bool:
+        """Whether this provider supports embedding generation."""
+        return self.embedding_model is not None
+
+    @property
+    def supports_generation(self) -> bool:
+        """Whether this provider supports text generation."""
+        return self.generation_model is not None
+
+    def _create_embedding_request(self, text: str) -> dict[str, Any]:
+        """
+        Create model-specific embedding request payload.
+
+        Args:
+            text: Input text to embed
+
+        Returns:
+            Request payload dict for the embedding model
+        """
+        if not self.embedding_model:
+            raise NotImplementedError(
+                "Embedding not supported - no embedding_model configured"
+            )
+
+        # Titan Embed models
+        if self.embedding_model.startswith("amazon.titan-embed"):
+            return {"inputText": text}
+
+        # Cohere Embed models
+        elif self.embedding_model.startswith("cohere.embed"):
+            return {"texts": [text], "input_type": "search_document"}
+
+        # Unknown model - try Titan format as default
+        else:
+            logger.warning(
+                f"Unknown embedding model format for {self.embedding_model}, "
+                "using Titan format as default"
+            )
+            return {"inputText": text}
+
+    def _parse_embedding_response(self, response: dict[str, Any]) -> list[float]:
+        """
+        Parse model-specific embedding response.
+
+        Args:
+            response: Raw response from Bedrock
+
+        Returns:
+            Embedding vector as list of floats
+        """
+        # Titan Embed models
+        if self.embedding_model and self.embedding_model.startswith(
+            "amazon.titan-embed"
+        ):
+            return response["embedding"]
+
+        # Cohere Embed models
+        elif self.embedding_model and self.embedding_model.startswith("cohere.embed"):
+            return response["embeddings"][0]
+
+        # Unknown model - try Titan format as default
+        else:
+            logger.warning(
+                f"Unknown embedding response format for {self.embedding_model}, "
+                "trying Titan format"
+            )
+            return response.get("embedding", response.get("embeddings", [None])[0])
+
+    async def embed(self, text: str) -> list[float]:
+        """
+        Generate embedding vector for text.
+
+        Args:
+            text: Input text to embed
+
+        Returns:
+            Vector embedding as list of floats
+
+        Raises:
+            NotImplementedError: If embeddings not enabled (no embedding_model)
+            ClientError: If Bedrock API call fails
+        """
+        if not self.supports_embeddings:
+            raise NotImplementedError(
+                "Embedding not supported - no embedding_model configured"
+            )
+
+        try:
+            request_body = self._create_embedding_request(text)
+
+            response = self.client.invoke_model(
+                modelId=self.embedding_model,
+                body=json.dumps(request_body),
+                accept="application/json",
+                contentType="application/json",
+            )
+
+            response_body = json.loads(response["body"].read())
+            embedding = self._parse_embedding_response(response_body)
+
+            return embedding
+
+        except (BotoCoreError, ClientError) as e:
+            logger.error(f"Bedrock embedding error: {e}")
+            raise
+
+    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
+        """
+        Generate embeddings for multiple texts.
+
+        Note: Current implementation sends requests sequentially.
+        Future optimization could use asyncio for concurrent requests.
+
+        Args:
+            texts: List of texts to embed
+
+        Returns:
+            List of vector embeddings
+
+        Raises:
+            NotImplementedError: If embeddings not enabled (no embedding_model)
+            ClientError: If Bedrock API call fails
+        """
+        if not self.supports_embeddings:
+            raise NotImplementedError(
+                "Embedding not supported - no embedding_model configured"
+            )
+
+        embeddings = []
+        for text in texts:
+            embedding = await self.embed(text)
+            embeddings.append(embedding)
+        return embeddings
+
+    async def _detect_dimension(self):
+        """
+        Detect embedding dimension by generating a test embedding.
+        """
+        if self._dimension is None and self.supports_embeddings:
+            logger.debug(
+                f"Detecting embedding dimension for model {self.embedding_model}..."
+            )
+            test_embedding = await self.embed("test")
+            self._dimension = len(test_embedding)
+            logger.info(
+                f"Detected embedding dimension: {self._dimension} "
+                f"for model {self.embedding_model}"
+            )
+
+    def get_dimension(self) -> int:
+        """
+        Get embedding dimension.
+
+        Returns:
+            Vector dimension for the configured embedding model
+
+        Raises:
+            NotImplementedError: If embeddings not enabled (no embedding_model)
+            RuntimeError: If dimension not detected yet (call _detect_dimension first)
+        """
+        if not self.supports_embeddings:
+            raise NotImplementedError(
+                "Embedding not supported - no embedding_model configured"
+            )
+
+        if self._dimension is None:
+            raise RuntimeError(
+                f"Embedding dimension not detected yet for model {self.embedding_model}. "
+                "Call _detect_dimension() first or generate an embedding."
+            )
+        return self._dimension
+
+    def _create_generation_request(
+        self, prompt: str, max_tokens: int
+    ) -> dict[str, Any]:
+        """
+        Create model-specific text generation request payload.
+
+        Args:
+            prompt: The prompt to generate from
+            max_tokens: Maximum tokens to generate
+
+        Returns:
+            Request payload dict for the generation model
+        """
+        if not self.generation_model:
+            raise NotImplementedError(
+                "Text generation not supported - no generation_model configured"
+            )
+
+        # Anthropic Claude models
+        if self.generation_model.startswith("anthropic.claude"):
+            return {
+                "anthropic_version": "bedrock-2023-05-31",
+                "max_tokens": max_tokens,
+                "temperature": 0.7,
+                "messages": [{"role": "user", "content": prompt}],
+            }
+
+        # Meta Llama models
+        elif self.generation_model.startswith("meta.llama"):
+            return {"prompt": prompt, "max_gen_len": max_tokens, "temperature": 0.7}
+
+        # Amazon Titan Text models
+        elif self.generation_model.startswith("amazon.titan-text"):
+            return {
+                "inputText": prompt,
+                "textGenerationConfig": {
+                    "maxTokenCount": max_tokens,
+                    "temperature": 0.7,
+                },
+            }
+
+        # Mistral models
+        elif self.generation_model.startswith("mistral"):
+            return {"prompt": prompt, "max_tokens": max_tokens, "temperature": 0.7}
+
+        # Unknown model - try Claude format as default
+        else:
+            logger.warning(
+                f"Unknown generation model format for {self.generation_model}, "
+                "using Claude format as default"
+            )
+            return {
+                "anthropic_version": "bedrock-2023-05-31",
+                "max_tokens": max_tokens,
+                "temperature": 0.7,
+                "messages": [{"role": "user", "content": prompt}],
+            }
+
+    def _parse_generation_response(self, response: dict[str, Any]) -> str:
+        """
+        Parse model-specific text generation response.
+
+        Args:
+            response: Raw response from Bedrock
+
+        Returns:
+            Generated text
+        """
+        # Anthropic Claude models
+        if self.generation_model and self.generation_model.startswith(
+            "anthropic.claude"
+        ):
+            return response["content"][0]["text"]
+
+        # Meta Llama models
+        elif self.generation_model and self.generation_model.startswith("meta.llama"):
+            return response["generation"]
+
+        # Amazon Titan Text models
+        elif self.generation_model and self.generation_model.startswith(
+            "amazon.titan-text"
+        ):
+            return response["results"][0]["outputText"]
+
+        # Mistral models
+        elif self.generation_model and self.generation_model.startswith("mistral"):
+            return response["outputs"][0]["text"]
+
+        # Unknown model - try common response fields
+        else:
+            logger.warning(
+                f"Unknown generation response format for {self.generation_model}, "
+                "trying common fields"
+            )
+            # Try common response field names
+            for field in ["text", "generation", "outputText", "completion"]:
+                if field in response:
+                    return response[field]
+            # Last resort: return JSON string
+            return json.dumps(response)
+
+    async def generate(self, prompt: str, max_tokens: int = 500) -> str:
+        """
+        Generate text from a prompt.
+
+        Args:
+            prompt: The prompt to generate from
+            max_tokens: Maximum tokens to generate
+
+        Returns:
+            Generated text
+
+        Raises:
+            NotImplementedError: If generation not enabled (no generation_model)
+            ClientError: If Bedrock API call fails
+        """
+        if not self.supports_generation:
+            raise NotImplementedError(
+                "Text generation not supported - no generation_model configured"
+            )
+
+        try:
+            request_body = self._create_generation_request(prompt, max_tokens)
+
+            response = self.client.invoke_model(
+                modelId=self.generation_model,
+                body=json.dumps(request_body),
+                accept="application/json",
+                contentType="application/json",
+            )
+
+            response_body = json.loads(response["body"].read())
+            text = self._parse_generation_response(response_body)
+
+            return text
+
+        except (BotoCoreError, ClientError) as e:
+            logger.error(f"Bedrock generation error: {e}")
+            raise
+
+    async def close(self) -> None:
+        """Close the client (no-op for boto3 clients)."""
+        pass

nextcloud_mcp_server/providers/ollama.py

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

nextcloud_mcp_server/providers/registry.py

@@ -0,0 +1,126 @@
+"""Provider registry and factory for auto-detection and instantiation."""
+
+import logging
+import os
+
+from .base import Provider
+from .bedrock import BedrockProvider
+from .ollama import OllamaProvider
+from .simple import SimpleProvider
+
+logger = logging.getLogger(__name__)
+
+
+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. Ollama (OLLAMA_BASE_URL)
+    3. Simple (fallback for testing/development)
+    """
+
+    @staticmethod
+    def create_provider() -> Provider:
+        """
+        Auto-detect and create provider based on environment variables.
+
+        Priority order:
+        1. Bedrock - if AWS_REGION or BEDROCK_EMBEDDING_MODEL is set
+        2. Ollama - if OLLAMA_BASE_URL is set
+        3. 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")
+
+            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")
+
+        if aws_region or bedrock_embedding_model or bedrock_generation_model:
+            logger.info(
+                f"Using Bedrock provider: region={aws_region}, "
+                f"embedding_model={bedrock_embedding_model}, "
+                f"generation_model={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"),
+            )
+
+        # 2. Check for Ollama
+        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"
+
+            logger.info(
+                f"Using Ollama provider: {ollama_url}, "
+                f"embedding_model={embedding_model}, "
+                f"generation_model={generation_model}"
+            )
+            return OllamaProvider(
+                base_url=ollama_url,
+                embedding_model=embedding_model,
+                generation_model=generation_model,
+                verify_ssl=verify_ssl,
+            )
+
+        # 3. Fallback to Simple provider for development/testing
+        dimension = int(os.getenv("SIMPLE_EMBEDDING_DIMENSION", "384"))
+        logger.warning(
+            "No provider configured (AWS_REGION, OLLAMA_BASE_URL not set). "
+            "Using SimpleProvider for testing/development. "
+            "For production, configure Bedrock or Ollama."
+        )
+        return SimpleProvider(dimension=dimension)
+
+
+# Singleton instance
+_provider: Provider | None = None
+
+
+def get_provider() -> Provider:
+    """
+    Get singleton provider instance.
+
+    Returns:
+        Global Provider instance (auto-detected on first call)
+    """
+    global _provider
+    if _provider is None:
+        _provider = ProviderRegistry.create_provider()
+    return _provider
+
+
+def reset_provider():
+    """
+    Reset singleton provider instance.
+
+    Useful for testing or reconfiguration.
+    """
+    global _provider
+    _provider = None

nextcloud_mcp_server/providers/simple.py

@@ -0,0 +1,149 @@
+"""Simple in-process embedding provider for testing.
+
+This provider uses a basic TF-IDF-like approach with feature hashing to generate
+deterministic embeddings without requiring external services. Suitable for testing
+but not for production use.
+"""
+
+import hashlib
+import math
+import re
+from collections import Counter
+
+from .base import Provider
+
+
+class SimpleProvider(Provider):
+    """Simple deterministic embedding provider using feature hashing.
+
+    This implementation:
+    - Tokenizes text into words
+    - Uses feature hashing to map words to fixed-size vectors
+    - Applies TF-IDF-like weighting
+    - Normalizes vectors to unit length
+
+    Not suitable for production but good for testing semantic search infrastructure.
+    Only supports embeddings, not text generation.
+    """
+
+    def __init__(self, dimension: int = 384):
+        """Initialize simple embedding provider.
+
+        Args:
+            dimension: Embedding dimension (default: 384)
+        """
+        self.dimension = dimension
+
+    @property
+    def supports_embeddings(self) -> bool:
+        """Whether this provider supports embedding generation."""
+        return True
+
+    @property
+    def supports_generation(self) -> bool:
+        """Whether this provider supports text generation."""
+        return False
+
+    def _tokenize(self, text: str) -> list[str]:
+        """Tokenize text into lowercase words.
+
+        Args:
+            text: Input text
+
+        Returns:
+            List of lowercase word tokens
+        """
+        # Simple word tokenization
+        text = text.lower()
+        words = re.findall(r"\b\w+\b", text)
+        return words
+
+    def _hash_word(self, word: str) -> int:
+        """Hash word to dimension index.
+
+        Args:
+            word: Word to hash
+
+        Returns:
+            Index in range [0, dimension)
+        """
+        hash_bytes = hashlib.md5(word.encode()).digest()
+        hash_int = int.from_bytes(hash_bytes[:4], byteorder="big")
+        return hash_int % self.dimension
+
+    def _embed_single(self, text: str) -> list[float]:
+        """Generate embedding for single text.
+
+        Args:
+            text: Input text
+
+        Returns:
+            Normalized embedding vector
+        """
+        tokens = self._tokenize(text)
+        if not tokens:
+            return [0.0] * self.dimension
+
+        # Count term frequencies
+        term_freq = Counter(tokens)
+
+        # Initialize vector
+        vector = [0.0] * self.dimension
+
+        # Apply TF weighting with feature hashing
+        for word, count in term_freq.items():
+            idx = self._hash_word(word)
+            # Simple TF weighting: log(1 + count)
+            vector[idx] += math.log1p(count)
+
+        # Normalize to unit length
+        norm = math.sqrt(sum(x * x for x in vector))
+        if norm > 0:
+            vector = [x / norm for x in vector]
+
+        return vector
+
+    async def embed(self, text: str) -> list[float]:
+        """Generate embedding vector for text.
+
+        Args:
+            text: Input text to embed
+
+        Returns:
+            Vector embedding as list of floats
+        """
+        return self._embed_single(text)
+
+    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
+        """Generate embeddings for multiple texts.
+
+        Args:
+            texts: List of texts to embed
+
+        Returns:
+            List of vector embeddings
+        """
+        return [self._embed_single(text) for text in texts]
+
+    def get_dimension(self) -> int:
+        """Get embedding dimension.
+
+        Returns:
+            Vector dimension
+        """
+        return self.dimension
+
+    async def generate(self, prompt: str, max_tokens: int = 500) -> str:
+        """
+        Generate text from a prompt.
+
+        Raises:
+            NotImplementedError: Simple provider doesn't support text generation
+        """
+        raise NotImplementedError(
+            "Text generation not supported by Simple provider - use Ollama, Anthropic, or Bedrock"
+        )
+
+    async def close(self) -> None:
+        """Close the provider (no-op for simple provider)."""
+        pass

nextcloud_mcp_server/search/algorithms.py

@@ -127,8 +127,12 @@ class SearchResult:
         doc_type: Document type (note, file, calendar, contact, etc.)
         title: Document title
         excerpt: Content excerpt showing match context
-        score: Relevance score (0.0-1.0, higher is better)
+        score: Relevance score (≥ 0.0, higher is better)
+            - RRF fusion: scores in [0.0, 1.0]
+            - DBSF fusion: scores can exceed 1.0 (sum of normalized scores)
         metadata: Additional algorithm-specific metadata
+        chunk_start_offset: Character position where chunk starts (None if not available)
+        chunk_end_offset: Character position where chunk ends (None if not available)
     """
 
     id: int
@@ -137,11 +141,20 @@ class SearchResult:
     excerpt: str
     score: float
     metadata: dict[str, Any] | None = None
+    chunk_start_offset: int | None = None
+    chunk_end_offset: int | None = None
 
     def __post_init__(self):
-        """Validate score is in valid range."""
-        if not 0.0 <= self.score <= 1.0:
-            raise ValueError(f"Score must be between 0.0 and 1.0, got {self.score}")
+        """Validate score is non-negative.
+
+        Note: Different fusion methods produce different score ranges:
+        - RRF (Reciprocal Rank Fusion): Bounded to [0.0, 1.0]
+        - DBSF (Distribution-Based Score Fusion): Unbounded (can exceed 1.0)
+          DBSF sums normalized scores from multiple systems, so scores can be
+          1.5, 2.0, etc. when multiple systems agree a document is highly relevant.
+        """
+        if self.score < 0.0:
+            raise ValueError(f"Score must be non-negative, got {self.score}")
 
 
 class SearchAlgorithm(ABC):

nextcloud_mcp_server/search/bm25_hybrid.py

@@ -28,15 +28,27 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
     eliminating the need for application-layer result merging.
     """
 
-    def __init__(self, score_threshold: float = 0.0):
+    def __init__(self, score_threshold: float = 0.0, fusion: str = "rrf"):
         """
         Initialize BM25 hybrid search algorithm.
 
         Args:
-            score_threshold: Minimum RRF score (0-1, default: 0.0 to allow RRF scoring)
-                           Note: RRF produces normalized scores, so threshold is typically lower
+            score_threshold: Minimum fusion score (0-1, default: 0.0 to allow fusion scoring)
+                           Note: Both RRF and DBSF produce normalized scores
+            fusion: Fusion algorithm to use: "rrf" (Reciprocal Rank Fusion, default)
+                   or "dbsf" (Distribution-Based Score Fusion)
+
+        Raises:
+            ValueError: If fusion is not "rrf" or "dbsf"
         """
+        if fusion not in ("rrf", "dbsf"):
+            raise ValueError(
+                f"Invalid fusion algorithm '{fusion}'. Must be 'rrf' or 'dbsf'"
+            )
+
         self.score_threshold = score_threshold
+        self.fusion = models.Fusion.RRF if fusion == "rrf" else models.Fusion.DBSF
+        self.fusion_name = fusion
 
     @property
     def name(self) -> str:
@@ -78,7 +90,8 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
 
         logger.info(
             f"BM25 hybrid search: query='{query}', user={user_id}, "
-            f"limit={limit}, score_threshold={score_threshold}, doc_type={doc_type}"
+            f"limit={limit}, score_threshold={score_threshold}, doc_type={doc_type}, "
+            f"fusion={self.fusion_name}"
         )
 
         # Generate dense embedding for semantic search
@@ -139,8 +152,8 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
                         filter=query_filter,
                     ),
                 ],
-                # RRF fusion query (no additional query needed, just fusion)
-                query=models.FusionQuery(fusion=models.Fusion.RRF),
+                # Fusion query (RRF or DBSF based on initialization)
+                query=models.FusionQuery(fusion=self.fusion),
                 limit=limit * 2,  # Get extra for deduplication
                 score_threshold=score_threshold,
                 with_payload=True,
@@ -152,14 +165,16 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
             raise
 
         logger.info(
-            f"Qdrant RRF fusion returned {len(search_response.points)} results "
+            f"Qdrant {self.fusion_name.upper()} fusion returned {len(search_response.points)} results "
             f"(before deduplication)"
         )
 
         if search_response.points:
-            # Log top 3 RRF scores to help with threshold tuning
+            # Log top 3 fusion scores to help with threshold tuning
             top_scores = [p.score for p in search_response.points[:3]]
-            logger.debug(f"Top 3 RRF fusion scores: {top_scores}")
+            logger.debug(
+                f"Top 3 {self.fusion_name.upper()} fusion scores: {top_scores}"
+            )
 
         # Deduplicate by (doc_id, doc_type) - multiple chunks per document
         seen_docs = set()
@@ -183,12 +198,14 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
                     doc_type=doc_type,
                     title=result.payload.get("title", "Untitled"),
                     excerpt=result.payload.get("excerpt", ""),
-                    score=result.score,  # RRF fusion score
+                    score=result.score,  # Fusion score (RRF or DBSF)
                     metadata={
                         "chunk_index": result.payload.get("chunk_index"),
                         "total_chunks": result.payload.get("total_chunks"),
-                        "search_method": "bm25_hybrid_rrf",
+                        "search_method": f"bm25_hybrid_{self.fusion_name}",
                     },
+                    chunk_start_offset=result.payload.get("chunk_start_offset"),
+                    chunk_end_offset=result.payload.get("chunk_end_offset"),
                 )
             )
 

nextcloud_mcp_server/search/semantic.py

@@ -150,6 +150,8 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
                         "chunk_index": result.payload.get("chunk_index"),
                         "total_chunks": result.payload.get("total_chunks"),
                     },
+                    chunk_start_offset=result.payload.get("chunk_start_offset"),
+                    chunk_end_offset=result.payload.get("chunk_end_offset"),
                 )
             )
 

nextcloud_mcp_server/server/semantic.py

@@ -42,6 +42,7 @@ def configure_semantic_tools(mcp: FastMCP):
         limit: int = 10,
         doc_types: list[str] | None = None,
         score_threshold: float = 0.0,
+        fusion: str = "rrf",
     ) -> SemanticSearchResponse:
         """
         Search Nextcloud content using BM25 hybrid search with cross-app support.
@@ -50,7 +51,7 @@ def configure_semantic_tools(mcp: FastMCP):
         - Dense semantic vectors: For conceptual similarity and natural language queries
         - BM25 sparse vectors: For precise keyword matching, acronyms, and specific terms
 
-        Results are automatically fused using Reciprocal Rank Fusion (RRF) in the
+        Results are automatically fused using the selected fusion algorithm in the
         database for optimal relevance. This provides the best of both semantic
         understanding and keyword precision.
 
@@ -61,10 +62,13 @@ def configure_semantic_tools(mcp: FastMCP):
             query: Natural language or keyword search query
             limit: Maximum number of results to return (default: 10)
             doc_types: Document types to search (e.g., ["note", "file"]). None = search all indexed types (default)
-            score_threshold: Minimum RRF fusion score (0-1, default: 0.0 for RRF scoring)
+            score_threshold: Minimum fusion score (0-1, default: 0.0)
+            fusion: Fusion algorithm: "rrf" (Reciprocal Rank Fusion, default) or "dbsf" (Distribution-Based Score Fusion)
+                   RRF: Good general-purpose fusion using reciprocal ranks
+                   DBSF: Uses distribution-based normalization, may better balance different score ranges
 
         Returns:
-            SemanticSearchResponse with matching documents ranked by RRF fusion scores
+            SemanticSearchResponse with matching documents ranked by fusion scores
         """
         from nextcloud_mcp_server.config import get_settings
 
@@ -74,7 +78,7 @@ def configure_semantic_tools(mcp: FastMCP):
 
         logger.info(
             f"BM25 hybrid search: query='{query}', user={username}, "
-            f"limit={limit}, score_threshold={score_threshold}"
+            f"limit={limit}, score_threshold={score_threshold}, fusion={fusion}"
         )
 
         # Check that vector sync is enabled
@@ -87,8 +91,10 @@ def configure_semantic_tools(mcp: FastMCP):
             )
 
         try:
-            # Create BM25 hybrid search algorithm
-            search_algo = BM25HybridSearchAlgorithm(score_threshold=score_threshold)
+            # Create BM25 hybrid search algorithm with specified fusion
+            search_algo = BM25HybridSearchAlgorithm(
+                score_threshold=score_threshold, fusion=fusion
+            )
 
             # Execute search across requested document types
             # If doc_types is None, search all indexed types (cross-app search)
@@ -152,6 +158,8 @@ def configure_semantic_tools(mcp: FastMCP):
                         total_chunks=r.metadata.get("total_chunks", 1)
                         if r.metadata
                         else 1,
+                        chunk_start_offset=r.chunk_start_offset,
+                        chunk_end_offset=r.chunk_end_offset,
                     )
                 )
 
@@ -161,7 +169,7 @@ def configure_semantic_tools(mcp: FastMCP):
                 results=results,
                 query=query,
                 total_found=len(results),
-                search_method="bm25_hybrid",
+                search_method=f"bm25_hybrid_{fusion}",
             )
 
         except ValueError as e:
@@ -193,6 +201,7 @@ def configure_semantic_tools(mcp: FastMCP):
         limit: int = 5,
         score_threshold: float = 0.7,
         max_answer_tokens: int = 500,
+        fusion: str = "rrf",
     ) -> SamplingSearchResponse:
         """
         Semantic search with LLM-generated answer using MCP sampling.
@@ -217,6 +226,7 @@ def configure_semantic_tools(mcp: FastMCP):
             limit: Maximum number of documents to retrieve (default: 5)
             score_threshold: Minimum similarity score 0-1 (default: 0.7)
             max_answer_tokens: Maximum tokens for generated answer (default: 500)
+            fusion: Fusion algorithm: "rrf" (Reciprocal Rank Fusion, default) or "dbsf" (Distribution-Based Score Fusion)
 
         Returns:
             SamplingSearchResponse containing:
@@ -256,6 +266,7 @@ def configure_semantic_tools(mcp: FastMCP):
             ctx=ctx,
             limit=limit,
             score_threshold=score_threshold,
+            fusion=fusion,
         )
 
         # 2. Handle no results case - don't waste a sampling call

nextcloud_mcp_server/vector/document_chunker.py

@@ -1,10 +1,21 @@
 """Document chunking for large texts."""
 
 import logging
+import re
+from dataclasses import dataclass
 
 logger = logging.getLogger(__name__)
 
 
+@dataclass
+class ChunkWithPosition:
+    """A text chunk with its character position in the original document."""
+
+    text: str
+    start_offset: int  # Character position where chunk starts
+    end_offset: int  # Character position where chunk ends (exclusive)
+
+
 class DocumentChunker:
     """Chunk large documents for optimal embedding."""
 
@@ -19,33 +30,66 @@ class DocumentChunker:
         self.chunk_size = chunk_size
         self.overlap = overlap
 
-    def chunk_text(self, content: str) -> list[str]:
+    def chunk_text(self, content: str) -> list[ChunkWithPosition]:
         """
-        Split text into overlapping chunks.
+        Split text into overlapping chunks with position tracking.
 
         Uses simple word-based chunking with configurable overlap to preserve
-        context across chunk boundaries.
+        context across chunk boundaries. Tracks character positions for each chunk.
 
         Args:
             content: Text content to chunk
 
         Returns:
-            List of text chunks (may be single item if content is small)
+            List of chunks with their character positions in the original content
         """
-        # Simple word-based chunking
-        words = content.split()
+        # Use regex to find all words and their positions
+        # This preserves the original spacing and allows accurate position tracking
+        word_pattern = re.compile(r"\S+")
+        word_matches = list(word_pattern.finditer(content))
 
-        if len(words) <= self.chunk_size:
-            return [content]
+        if len(word_matches) <= self.chunk_size:
+            # Single chunk - use entire content
+            return [
+                ChunkWithPosition(text=content, start_offset=0, end_offset=len(content))
+            ]
 
         chunks = []
-        start = 0
+        start_idx = 0
 
-        while start < len(words):
-            end = start + self.chunk_size
-            chunk_words = words[start:end]
-            chunks.append(" ".join(chunk_words))
-            start = end - self.overlap
+        while start_idx < len(word_matches):
+            end_idx = min(start_idx + self.chunk_size, len(word_matches))
 
-        logger.debug(f"Chunked document into {len(chunks)} chunks ({len(words)} words)")
+            # Get the first and last word positions
+            first_word = word_matches[start_idx]
+            last_word = word_matches[end_idx - 1]
+
+            # Extract chunk using character positions
+            start_offset = first_word.start()
+            end_offset = last_word.end()
+            chunk_text = content[start_offset:end_offset]
+
+            chunks.append(
+                ChunkWithPosition(
+                    text=chunk_text, start_offset=start_offset, end_offset=end_offset
+                )
+            )
+
+            # If we've reached the end, break
+            if end_idx >= len(word_matches):
+                break
+
+            # Move to next chunk with overlap
+            next_start_idx = end_idx - self.overlap
+
+            # Safety check: ensure we're making forward progress
+            # If we're not advancing (overlap >= chunk processed), break to prevent infinite loop
+            if next_start_idx <= start_idx:
+                break
+
+            start_idx = next_start_idx
+
+        logger.debug(
+            f"Chunked document into {len(chunks)} chunks ({len(word_matches)} words)"
+        )
         return chunks

nextcloud_mcp_server/vector/processor.py

@@ -233,13 +233,16 @@ async def _index_document(
     )
     chunks = chunker.chunk_text(content)
 
+    # Extract chunk texts for embedding
+    chunk_texts = [chunk.text for chunk in chunks]
+
     # Generate dense embeddings (I/O bound - external API call)
     embedding_service = get_embedding_service()
-    dense_embeddings = await embedding_service.embed_batch(chunks)
+    dense_embeddings = await embedding_service.embed_batch(chunk_texts)
 
     # Generate sparse embeddings (BM25 for keyword matching)
     bm25_service = get_bm25_service()
-    sparse_embeddings = bm25_service.encode_batch(chunks)
+    sparse_embeddings = bm25_service.encode_batch(chunk_texts)
 
     # Prepare Qdrant points
     indexed_at = int(time.time())
@@ -265,12 +268,15 @@ async def _index_document(
                     "doc_id": doc_task.doc_id,
                     "doc_type": doc_task.doc_type,
                     "title": title,
-                    "excerpt": chunk[:200],
+                    "excerpt": chunk.text[:200],
                     "indexed_at": indexed_at,
                     "modified_at": doc_task.modified_at,
                     "etag": etag,
                     "chunk_index": i,
                     "total_chunks": len(chunks),
+                    "chunk_start_offset": chunk.start_offset,
+                    "chunk_end_offset": chunk.end_offset,
+                    "metadata_version": 2,  # v2 includes position metadata
                 },
             )
         )

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "nextcloud-mcp-server"
-version = "0.38.0"
+version = "0.41.0"
 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"}
@@ -12,7 +12,7 @@ keywords = ["nextcloud", "mcp", "model-context-protocol", "llm", "ai", "claude",
 dependencies = [
     "mcp[cli] (>=1.21,<1.22)",
     "httpx (>=0.28.1,<0.29.0)",
-    "pillow (>=10.3.0,<12.0.0)",  # Compatible with fastembed
+    "pillow (>=10.3.0,<12.0.0)", # Compatible with fastembed
     "icalendar (>=6.0.0,<7.0.0)",
     "pythonvcard4>=0.2.0",
     "pydantic>=2.11.4",
@@ -22,7 +22,9 @@ dependencies = [
     "aiosqlite>=0.20.0", # Async SQLite for refresh token storage
     "authlib>=1.6.5",
     "qdrant-client>=1.7.0",
-    "fastembed>=0.4.2",  # BM25 sparse vector embeddings for hybrid search
+    "fastembed>=0.4.2", # BM25 sparse vector embeddings for hybrid search
+    "anthropic>=0.42.0", # For RAG evaluation with Anthropic LLMs
+    "boto3>=1.35.0", # For Amazon Bedrock provider (optional)
     # Observability dependencies
     "prometheus-client>=0.21.0", # Prometheus metrics
     "opentelemetry-api>=1.28.2", # OpenTelemetry API
@@ -32,6 +34,7 @@ dependencies = [
     "opentelemetry-instrumentation-logging>=0.49b2", # Logging integration
     "opentelemetry-exporter-otlp-proto-grpc>=1.28.2", # OTLP gRPC exporter
     "python-json-logger>=3.2.0", # Structured JSON logging
+    "jinja2>=3.1.6",
 ]
 classifiers = [
     "Development Status :: 4 - Beta",
@@ -103,7 +106,6 @@ module-root = ""
 
 [dependency-groups]
 dev = [
-    "anthropic>=0.42.0",  # For RAG evaluation with Anthropic LLMs
     "commitizen>=4.8.2",
     "datasets>=3.3.0",  # For BeIR nfcorpus dataset loading
     "ipython>=9.2.0",

tests/rag_evaluation/llm_providers.py

@@ -1,99 +1,20 @@
 """LLM provider abstraction for RAG evaluation.
 
-Supports Ollama (local) and Anthropic (cloud) providers for both ground truth
+DEPRECATED: This module is maintained for backward compatibility with RAG evaluation tests.
+New code should use nextcloud_mcp_server.providers directly.
+
+Supports Ollama (local), Anthropic (cloud), and Bedrock (AWS) providers for both ground truth
 generation and evaluation.
 """
 
 import os
-from typing import Protocol
 
-import httpx
-from anthropic import AsyncAnthropic
-
-
-class LLMProvider(Protocol):
-    """Protocol for LLM providers."""
-
-    async def generate(self, prompt: str, max_tokens: int = 500) -> str:
-        """Generate text from a prompt.
-
-        Args:
-            prompt: The prompt to generate from
-            max_tokens: Maximum tokens to generate
-
-        Returns:
-            Generated text
-        """
-        ...
-
-    async def close(self) -> None:
-        """Close the provider and release resources."""
-        ...
-
-
-class OllamaProvider:
-    """Ollama provider for local LLM inference."""
-
-    def __init__(self, base_url: str, model: str):
-        """Initialize Ollama provider.
-
-        Args:
-            base_url: Ollama API base URL (e.g., http://localhost:11434)
-            model: Model name (e.g., llama3.1:8b)
-        """
-        self.base_url = base_url.rstrip("/")
-        self.model = model
-        self.client = httpx.AsyncClient(timeout=600.0)  # 10 min timeout for generation
-
-    async def generate(self, prompt: str, max_tokens: int = 500) -> str:
-        """Generate text using Ollama API."""
-        response = await self.client.post(
-            f"{self.base_url}/api/generate",
-            json={
-                "model": self.model,
-                "prompt": prompt,
-                "stream": False,
-                "options": {
-                    "num_predict": max_tokens,
-                    "temperature": 0.7,
-                },
-            },
-        )
-        response.raise_for_status()
-        data = response.json()
-        return data["response"]
-
-    async def close(self):
-        """Close the HTTP client."""
-        await self.client.aclose()
-
-
-class AnthropicProvider:
-    """Anthropic provider for cloud LLM inference."""
-
-    def __init__(self, api_key: str, model: str):
-        """Initialize Anthropic provider.
-
-        Args:
-            api_key: Anthropic API key
-            model: Model name (e.g., claude-3-5-sonnet-20241022)
-        """
-        self.client = AsyncAnthropic(api_key=api_key)
-        self.model = model
-
-    async def generate(self, prompt: str, max_tokens: int = 500) -> str:
-        """Generate text using Anthropic API."""
-        message = await self.client.messages.create(
-            model=self.model,
-            max_tokens=max_tokens,
-            temperature=0.7,
-            messages=[{"role": "user", "content": prompt}],
-        )
-        return message.content[0].text
-
-    async def close(self):
-        """Close the client (no-op for Anthropic)."""
-        pass
+from nextcloud_mcp_server.providers import (
+    AnthropicProvider,
+    BedrockProvider,
+    OllamaProvider,
+    Provider,
+)
 
 
 def create_llm_provider(
@@ -102,18 +23,24 @@ def create_llm_provider(
     ollama_model: str | None = None,
     anthropic_api_key: str | None = None,
     anthropic_model: str | None = None,
-) -> LLMProvider:
+    bedrock_region: str | None = None,
+    bedrock_model: str | None = None,
+) -> Provider:
     """Create an LLM provider from environment variables or arguments.
 
     Args:
-        provider: Provider type ('ollama' or 'anthropic'). Defaults to RAG_EVAL_PROVIDER env var or 'ollama'
+        provider: Provider type ('ollama', 'anthropic', or 'bedrock').
+            Defaults to RAG_EVAL_PROVIDER env var or 'ollama'
         ollama_base_url: Ollama base URL. Defaults to RAG_EVAL_OLLAMA_BASE_URL or 'http://localhost:11434'
-        ollama_model: Ollama model. Defaults to RAG_EVAL_OLLAMA_MODEL or 'llama3.1:8b'
+        ollama_model: Ollama model. Defaults to RAG_EVAL_OLLAMA_MODEL or 'llama3.2:1b'
         anthropic_api_key: Anthropic API key. Defaults to RAG_EVAL_ANTHROPIC_API_KEY env var
         anthropic_model: Anthropic model. Defaults to RAG_EVAL_ANTHROPIC_MODEL or 'claude-3-5-sonnet-20241022'
+        bedrock_region: AWS region. Defaults to RAG_EVAL_BEDROCK_REGION or AWS_REGION env var
+        bedrock_model: Bedrock model ID. Defaults to RAG_EVAL_BEDROCK_MODEL or
+            'anthropic.claude-3-sonnet-20240229-v1:0'
 
     Returns:
-        LLMProvider instance
+        Provider instance
 
     Raises:
         ValueError: If provider is invalid or required credentials are missing
@@ -130,7 +57,9 @@ def create_llm_provider(
             or "http://localhost:11434"
         )
         model = ollama_model or os.environ.get("RAG_EVAL_OLLAMA_MODEL", "llama3.2:1b")
-        return OllamaProvider(base_url=base_url, model=model)
+        return OllamaProvider(
+            base_url=base_url, embedding_model=None, generation_model=model
+        )
 
     elif provider == "anthropic":
         api_key = anthropic_api_key or os.environ.get("RAG_EVAL_ANTHROPIC_API_KEY")
@@ -143,7 +72,18 @@ def create_llm_provider(
         )
         return AnthropicProvider(api_key=api_key, model=model)
 
+    elif provider == "bedrock":
+        region = bedrock_region or os.environ.get(
+            "RAG_EVAL_BEDROCK_REGION", os.environ.get("AWS_REGION", "us-east-1")
+        )
+        model = bedrock_model or os.environ.get(
+            "RAG_EVAL_BEDROCK_MODEL", "anthropic.claude-3-sonnet-20240229-v1:0"
+        )
+        return BedrockProvider(
+            region_name=region, embedding_model=None, generation_model=model
+        )
+
     else:
         raise ValueError(
-            f"Invalid provider: {provider}. Must be 'ollama' or 'anthropic'."
+            f"Invalid provider: {provider}. Must be 'ollama', 'anthropic', or 'bedrock'."
         )

tests/unit/providers/__init__.py

@@ -0,0 +1 @@
+"""Unit tests for provider infrastructure."""

tests/unit/providers/test_bedrock.py

@@ -0,0 +1,280 @@
+"""Unit tests for Bedrock provider."""
+
+import json
+from unittest.mock import MagicMock
+
+import pytest
+
+from nextcloud_mcp_server.providers.bedrock import BOTO3_AVAILABLE, BedrockProvider
+
+
+@pytest.fixture
+def mock_bedrock_client(mocker):
+    """Mock boto3 bedrock-runtime client."""
+    if not BOTO3_AVAILABLE:
+        pytest.skip("boto3 not installed")
+
+    mock_client = MagicMock()
+    mocker.patch("boto3.client", return_value=mock_client)
+    return mock_client
+
+
+@pytest.mark.unit
+async def test_bedrock_embedding_titan(mock_bedrock_client):
+    """Test Bedrock embedding with Titan model."""
+    # Mock response
+    mock_response = {
+        "body": MagicMock(
+            read=MagicMock(
+                return_value=json.dumps({"embedding": [0.1, 0.2, 0.3]}).encode()
+            )
+        )
+    }
+    mock_bedrock_client.invoke_model.return_value = mock_response
+
+    # Create provider
+    provider = BedrockProvider(
+        region_name="us-east-1",
+        embedding_model="amazon.titan-embed-text-v2:0",
+        generation_model=None,
+    )
+
+    # Test embedding
+    embedding = await provider.embed("test text")
+
+    assert embedding == [0.1, 0.2, 0.3]
+    mock_bedrock_client.invoke_model.assert_called_once()
+    call_args = mock_bedrock_client.invoke_model.call_args
+
+    assert call_args.kwargs["modelId"] == "amazon.titan-embed-text-v2:0"
+    body = json.loads(call_args.kwargs["body"])
+    assert body == {"inputText": "test text"}
+
+
+@pytest.mark.unit
+async def test_bedrock_embedding_batch(mock_bedrock_client):
+    """Test Bedrock batch embedding."""
+    # Mock response
+    mock_response = {
+        "body": MagicMock(
+            read=MagicMock(
+                return_value=json.dumps({"embedding": [0.1, 0.2, 0.3]}).encode()
+            )
+        )
+    }
+    mock_bedrock_client.invoke_model.return_value = mock_response
+
+    # Create provider
+    provider = BedrockProvider(
+        region_name="us-east-1",
+        embedding_model="amazon.titan-embed-text-v2:0",
+        generation_model=None,
+    )
+
+    # Test batch embedding
+    embeddings = await provider.embed_batch(["text1", "text2"])
+
+    assert len(embeddings) == 2
+    assert embeddings[0] == [0.1, 0.2, 0.3]
+    assert embeddings[1] == [0.1, 0.2, 0.3]
+    assert mock_bedrock_client.invoke_model.call_count == 2
+
+
+@pytest.mark.unit
+async def test_bedrock_generation_claude(mock_bedrock_client):
+    """Test Bedrock text generation with Claude model."""
+    # Mock response
+    mock_response = {
+        "body": MagicMock(
+            read=MagicMock(
+                return_value=json.dumps(
+                    {"content": [{"text": "Generated response"}]}
+                ).encode()
+            )
+        )
+    }
+    mock_bedrock_client.invoke_model.return_value = mock_response
+
+    # Create provider
+    provider = BedrockProvider(
+        region_name="us-east-1",
+        embedding_model=None,
+        generation_model="anthropic.claude-3-sonnet-20240229-v1:0",
+    )
+
+    # Test generation
+    text = await provider.generate("test prompt", max_tokens=100)
+
+    assert text == "Generated response"
+    mock_bedrock_client.invoke_model.assert_called_once()
+    call_args = mock_bedrock_client.invoke_model.call_args
+
+    assert call_args.kwargs["modelId"] == "anthropic.claude-3-sonnet-20240229-v1:0"
+    body = json.loads(call_args.kwargs["body"])
+    assert body["messages"][0]["content"] == "test prompt"
+    assert body["max_tokens"] == 100
+
+
+@pytest.mark.unit
+async def test_bedrock_generation_llama(mock_bedrock_client):
+    """Test Bedrock text generation with Llama model."""
+    # Mock response
+    mock_response = {
+        "body": MagicMock(
+            read=MagicMock(
+                return_value=json.dumps({"generation": "Llama response"}).encode()
+            )
+        )
+    }
+    mock_bedrock_client.invoke_model.return_value = mock_response
+
+    # Create provider
+    provider = BedrockProvider(
+        region_name="us-east-1",
+        embedding_model=None,
+        generation_model="meta.llama3-8b-instruct-v1:0",
+    )
+
+    # Test generation
+    text = await provider.generate("test prompt")
+
+    assert text == "Llama response"
+    body = json.loads(mock_bedrock_client.invoke_model.call_args.kwargs["body"])
+    assert body["prompt"] == "test prompt"
+    assert "max_gen_len" in body
+
+
+@pytest.mark.unit
+async def test_bedrock_both_capabilities(mock_bedrock_client):
+    """Test Bedrock with both embedding and generation models."""
+    # Mock responses
+    embed_response = {
+        "body": MagicMock(
+            read=MagicMock(return_value=json.dumps({"embedding": [0.1, 0.2]}).encode())
+        )
+    }
+    gen_response = {
+        "body": MagicMock(
+            read=MagicMock(
+                return_value=json.dumps({"content": [{"text": "Response"}]}).encode()
+            )
+        )
+    }
+
+    # Mock to return different responses based on modelId
+    def mock_invoke(modelId, body, **kwargs):
+        if "embed" in modelId:
+            return embed_response
+        else:
+            return gen_response
+
+    mock_bedrock_client.invoke_model.side_effect = mock_invoke
+
+    # Create provider with both models
+    provider = BedrockProvider(
+        region_name="us-east-1",
+        embedding_model="amazon.titan-embed-text-v2:0",
+        generation_model="anthropic.claude-3-sonnet-20240229-v1:0",
+    )
+
+    assert provider.supports_embeddings is True
+    assert provider.supports_generation is True
+
+    # Test both capabilities
+    embedding = await provider.embed("test")
+    assert embedding == [0.1, 0.2]
+
+    text = await provider.generate("test")
+    assert text == "Response"
+
+
+@pytest.mark.unit
+async def test_bedrock_no_embeddings():
+    """Test Bedrock provider with no embedding model raises error."""
+    provider = BedrockProvider(
+        region_name="us-east-1",
+        embedding_model=None,
+        generation_model="anthropic.claude-3-sonnet-20240229-v1:0",
+    )
+
+    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_bedrock_no_generation():
+    """Test Bedrock provider with no generation model raises error."""
+    provider = BedrockProvider(
+        region_name="us-east-1",
+        embedding_model="amazon.titan-embed-text-v2:0",
+        generation_model=None,
+    )
+
+    assert provider.supports_generation is False
+
+    with pytest.raises(NotImplementedError, match="no generation_model configured"):
+        await provider.generate("test")
+
+
+@pytest.mark.unit
+async def test_bedrock_dimension_detection(mock_bedrock_client):
+    """Test dimension detection for Bedrock embeddings."""
+    # Mock response with specific dimension
+    mock_response = {
+        "body": MagicMock(
+            read=MagicMock(
+                return_value=json.dumps(
+                    {"embedding": [0.1] * 1536}  # 1536-dim embedding
+                ).encode()
+            )
+        )
+    }
+    mock_bedrock_client.invoke_model.return_value = mock_response
+
+    provider = BedrockProvider(
+        region_name="us-east-1",
+        embedding_model="amazon.titan-embed-text-v2:0",
+    )
+
+    # Dimension not detected yet
+    with pytest.raises(RuntimeError, match="not detected yet"):
+        provider.get_dimension()
+
+    # Detect dimension
+    await provider._detect_dimension()
+
+    # Now dimension should be available
+    assert provider.get_dimension() == 1536
+
+
+@pytest.mark.unit
+async def test_bedrock_cohere_embedding(mock_bedrock_client):
+    """Test Bedrock with Cohere embedding model."""
+    # Mock response
+    mock_response = {
+        "body": MagicMock(
+            read=MagicMock(
+                return_value=json.dumps({"embeddings": [[0.1, 0.2, 0.3]]}).encode()
+            )
+        )
+    }
+    mock_bedrock_client.invoke_model.return_value = mock_response
+
+    provider = BedrockProvider(
+        region_name="us-east-1",
+        embedding_model="cohere.embed-english-v3",
+    )
+
+    embedding = await provider.embed("test text")
+
+    assert embedding == [0.1, 0.2, 0.3]
+    body = json.loads(mock_bedrock_client.invoke_model.call_args.kwargs["body"])
+    assert body == {"texts": ["test text"], "input_type": "search_document"}

tests/unit/search/__init__.py

@@ -0,0 +1 @@
+"""Unit tests for search algorithms."""

tests/unit/search/test_bm25_hybrid.py

@@ -0,0 +1,54 @@
+"""Unit tests for BM25 hybrid search algorithm."""
+
+import pytest
+from qdrant_client import models
+
+from nextcloud_mcp_server.search.bm25_hybrid import BM25HybridSearchAlgorithm
+
+
+@pytest.mark.unit
+def test_bm25_hybrid_initialization_default():
+    """Test BM25HybridSearchAlgorithm initializes with default RRF fusion."""
+    algo = BM25HybridSearchAlgorithm()
+
+    assert algo.score_threshold == 0.0
+    assert algo.fusion == models.Fusion.RRF
+    assert algo.fusion_name == "rrf"
+    assert algo.name == "bm25_hybrid"
+
+
+@pytest.mark.unit
+def test_bm25_hybrid_initialization_with_rrf():
+    """Test BM25HybridSearchAlgorithm initializes with explicit RRF fusion."""
+    algo = BM25HybridSearchAlgorithm(score_threshold=0.5, fusion="rrf")
+
+    assert algo.score_threshold == 0.5
+    assert algo.fusion == models.Fusion.RRF
+    assert algo.fusion_name == "rrf"
+
+
+@pytest.mark.unit
+def test_bm25_hybrid_initialization_with_dbsf():
+    """Test BM25HybridSearchAlgorithm initializes with DBSF fusion."""
+    algo = BM25HybridSearchAlgorithm(score_threshold=0.7, fusion="dbsf")
+
+    assert algo.score_threshold == 0.7
+    assert algo.fusion == models.Fusion.DBSF
+    assert algo.fusion_name == "dbsf"
+
+
+@pytest.mark.unit
+def test_bm25_hybrid_invalid_fusion_raises_error():
+    """Test BM25HybridSearchAlgorithm raises ValueError for invalid fusion."""
+    with pytest.raises(ValueError) as exc_info:
+        BM25HybridSearchAlgorithm(fusion="invalid")
+
+    assert "Invalid fusion algorithm 'invalid'" in str(exc_info.value)
+    assert "Must be 'rrf' or 'dbsf'" in str(exc_info.value)
+
+
+@pytest.mark.unit
+def test_bm25_hybrid_requires_vector_db():
+    """Test BM25HybridSearchAlgorithm reports it requires vector database."""
+    algo = BM25HybridSearchAlgorithm()
+    assert algo.requires_vector_db is True

tests/unit/search/test_search_result.py

@@ -0,0 +1,135 @@
+"""Unit tests for SearchResult validation."""
+
+import pytest
+
+from nextcloud_mcp_server.search.algorithms import SearchResult
+
+
+@pytest.mark.unit
+def test_search_result_rrf_score_in_range():
+    """Test SearchResult accepts RRF scores in [0.0, 1.0] range."""
+    result = SearchResult(
+        id=1,
+        doc_type="note",
+        title="Test Note",
+        excerpt="Test excerpt",
+        score=0.85,
+    )
+
+    assert result.score == 0.85
+
+
+@pytest.mark.unit
+def test_search_result_rrf_score_at_lower_bound():
+    """Test SearchResult accepts RRF score at lower bound (0.0)."""
+    result = SearchResult(
+        id=1,
+        doc_type="note",
+        title="Test Note",
+        excerpt="Test excerpt",
+        score=0.0,
+    )
+
+    assert result.score == 0.0
+
+
+@pytest.mark.unit
+def test_search_result_rrf_score_at_upper_bound():
+    """Test SearchResult accepts RRF score at upper bound (1.0)."""
+    result = SearchResult(
+        id=1,
+        doc_type="note",
+        title="Test Note",
+        excerpt="Test excerpt",
+        score=1.0,
+    )
+
+    assert result.score == 1.0
+
+
+@pytest.mark.unit
+def test_search_result_dbsf_score_above_one():
+    """Test SearchResult accepts DBSF scores > 1.0.
+
+    DBSF (Distribution-Based Score Fusion) sums normalized scores from multiple
+    systems (dense semantic + sparse BM25), so scores can exceed 1.0 when both
+    systems strongly agree a document is relevant.
+    """
+    # Typical DBSF score when both systems agree
+    result = SearchResult(
+        id=1,
+        doc_type="note",
+        title="Highly Relevant Note",
+        excerpt="Contains keywords and is semantically similar",
+        score=1.55,
+    )
+
+    assert result.score == 1.55
+
+
+@pytest.mark.unit
+def test_search_result_dbsf_score_edge_case():
+    """Test SearchResult accepts DBSF maximum theoretical score (2.0).
+
+    Maximum DBSF score with 2 systems: 1.0 (dense) + 1.0 (sparse) = 2.0
+    """
+    result = SearchResult(
+        id=1,
+        doc_type="note",
+        title="Perfect Match",
+        excerpt="Perfect semantic and keyword match",
+        score=2.0,
+    )
+
+    assert result.score == 2.0
+
+
+@pytest.mark.unit
+def test_search_result_negative_score_raises_error():
+    """Test SearchResult rejects negative scores."""
+    with pytest.raises(ValueError) as exc_info:
+        SearchResult(
+            id=1,
+            doc_type="note",
+            title="Test Note",
+            excerpt="Test excerpt",
+            score=-0.1,
+        )
+
+    assert "Score must be non-negative" in str(exc_info.value)
+    assert "got -0.1" in str(exc_info.value)
+
+
+@pytest.mark.unit
+def test_search_result_with_metadata():
+    """Test SearchResult with optional metadata field."""
+    result = SearchResult(
+        id=1,
+        doc_type="note",
+        title="Test Note",
+        excerpt="Test excerpt",
+        score=1.25,
+        metadata={"fusion_method": "dbsf", "dense_score": 0.8, "sparse_score": 0.45},
+    )
+
+    assert result.score == 1.25
+    assert result.metadata["fusion_method"] == "dbsf"
+    assert result.metadata["dense_score"] == 0.8
+    assert result.metadata["sparse_score"] == 0.45
+
+
+@pytest.mark.unit
+def test_search_result_with_chunk_offsets():
+    """Test SearchResult with chunk offset information."""
+    result = SearchResult(
+        id=1,
+        doc_type="note",
+        title="Test Note",
+        excerpt="matching chunk text",
+        score=0.9,
+        chunk_start_offset=100,
+        chunk_end_offset=500,
+    )
+
+    assert result.chunk_start_offset == 100
+    assert result.chunk_end_offset == 500

tests/unit/test_document_chunker.py

@@ -0,0 +1,190 @@
+"""Unit tests for DocumentChunker with position tracking."""
+
+from nextcloud_mcp_server.vector.document_chunker import (
+    ChunkWithPosition,
+    DocumentChunker,
+)
+
+
+class TestDocumentChunkerPositions:
+    """Test suite for DocumentChunker position tracking functionality."""
+
+    def test_single_chunk_simple_text(self):
+        """Test that single-chunk documents return correct positions."""
+        chunker = DocumentChunker(chunk_size=512, overlap=50)
+        content = "This is a short document."
+
+        chunks = chunker.chunk_text(content)
+
+        assert len(chunks) == 1
+        assert isinstance(chunks[0], ChunkWithPosition)
+        assert chunks[0].text == content
+        assert chunks[0].start_offset == 0
+        assert chunks[0].end_offset == len(content)
+
+    def test_multiple_chunks_positions(self):
+        """Test that multi-chunk documents have correct positions."""
+        chunker = DocumentChunker(chunk_size=10, overlap=2)  # Small chunks for testing
+        # Create content with exactly 30 words
+        words = [f"word{i:02d}" for i in range(30)]
+        content = " ".join(words)
+
+        chunks = chunker.chunk_text(content)
+
+        # Verify we got multiple chunks (30 words, 10 per chunk, 2 overlap = 4 chunks)
+        assert len(chunks) == 4
+
+        # Verify all chunks are ChunkWithPosition
+        for chunk in chunks:
+            assert isinstance(chunk, ChunkWithPosition)
+
+        # Verify first chunk starts at 0
+        assert chunks[0].start_offset == 0
+
+        # Verify last chunk ends at content length
+        assert chunks[-1].end_offset == len(content)
+
+        # Verify chunks are contiguous or overlap (no gaps)
+        for i in range(len(chunks) - 1):
+            # Next chunk should start at or before current chunk ends
+            assert chunks[i + 1].start_offset <= chunks[i].end_offset
+
+        # Verify we can reconstruct the content using positions
+        for chunk in chunks:
+            extracted = content[chunk.start_offset : chunk.end_offset]
+            assert extracted == chunk.text
+
+    def test_chunk_positions_with_whitespace(self):
+        """Test position tracking with various whitespace."""
+        chunker = DocumentChunker(chunk_size=5, overlap=1)
+        content = "word1  word2\n\nword3\tword4    word5 word6"
+
+        chunks = chunker.chunk_text(content)
+
+        # Verify positions correctly handle whitespace
+        for chunk in chunks:
+            extracted = content[chunk.start_offset : chunk.end_offset]
+            assert extracted == chunk.text
+            # Verify no leading/trailing whitespace unless in original
+            if chunk != chunks[0] and chunk != chunks[-1]:
+                # Middle chunks should be extracted correctly
+                assert len(chunk.text.strip()) > 0
+
+    def test_empty_content(self):
+        """Test that empty content returns empty chunk."""
+        chunker = DocumentChunker(chunk_size=512, overlap=50)
+        content = ""
+
+        chunks = chunker.chunk_text(content)
+
+        assert len(chunks) == 1
+        assert chunks[0].text == ""
+        assert chunks[0].start_offset == 0
+        assert chunks[0].end_offset == 0
+
+    def test_chunk_overlap_positions(self):
+        """Test that overlapping chunks have correct positions."""
+        chunker = DocumentChunker(chunk_size=10, overlap=3)
+        words = [f"word{i:02d}" for i in range(25)]
+        content = " ".join(words)
+
+        chunks = chunker.chunk_text(content)
+
+        # Verify overlap exists
+        for i in range(len(chunks) - 1):
+            current_chunk = chunks[i]
+            next_chunk = chunks[i + 1]
+
+            # Next chunk should start before current ends (overlap)
+            # This happens because we move back by overlap words
+            # The actual character overlap depends on word lengths
+            assert next_chunk.start_offset >= 0
+            assert current_chunk.end_offset <= len(content)
+
+    def test_unicode_content_positions(self):
+        """Test position tracking with Unicode characters."""
+        chunker = DocumentChunker(chunk_size=10, overlap=2)
+        content = "Hello 世界 こんにちは мир Привет שלום مرحبا 你好"
+
+        chunks = chunker.chunk_text(content)
+
+        # Verify all chunks extract correctly
+        for chunk in chunks:
+            extracted = content[chunk.start_offset : chunk.end_offset]
+            assert extracted == chunk.text
+
+        # Verify full coverage
+        if len(chunks) == 1:
+            assert chunks[0].start_offset == 0
+            assert chunks[0].end_offset == len(content)
+
+    def test_single_word_chunks(self):
+        """Test position tracking with single-word chunks."""
+        chunker = DocumentChunker(chunk_size=1, overlap=0)
+        content = "one two three"
+
+        chunks = chunker.chunk_text(content)
+
+        assert len(chunks) == 3
+        assert chunks[0].text == "one"
+        assert chunks[1].text == "two"
+        assert chunks[2].text == "three"
+
+        # Verify positions
+        assert content[chunks[0].start_offset : chunks[0].end_offset] == "one"
+        assert content[chunks[1].start_offset : chunks[1].end_offset] == "two"
+        assert content[chunks[2].start_offset : chunks[2].end_offset] == "three"
+
+    def test_realistic_note_content(self):
+        """Test with realistic note content similar to Nextcloud Notes."""
+        chunker = DocumentChunker(chunk_size=50, overlap=10)
+        content = """My Project Notes
+
+This is a note about my project. It contains several paragraphs of text
+that should be chunked appropriately for embedding.
+
+## Key Points
+
+- First important point with some details
+- Second point that needs to be remembered
+- Third point for future reference
+
+The document continues with more content here. We want to make sure that
+the chunking preserves context across boundaries while maintaining proper
+position tracking for each chunk.
+
+This allows us to highlight the exact chunk that matched a search query,
+which builds trust in the RAG system."""
+
+        chunks = chunker.chunk_text(content)
+
+        # Should have multiple chunks
+        assert len(chunks) > 1
+
+        # Verify all chunks
+        for chunk in chunks:
+            assert isinstance(chunk, ChunkWithPosition)
+            # Verify extraction
+            extracted = content[chunk.start_offset : chunk.end_offset]
+            assert extracted == chunk.text
+            # Verify positions are valid
+            assert chunk.start_offset >= 0
+            assert chunk.end_offset <= len(content)
+            assert chunk.start_offset < chunk.end_offset
+
+    def test_chunk_boundaries(self):
+        """Test that chunk boundaries are word-aligned."""
+        chunker = DocumentChunker(chunk_size=10, overlap=2)
+        words = [f"word{i:02d}" for i in range(30)]
+        content = " ".join(words)
+
+        chunks = chunker.chunk_text(content)
+
+        for chunk in chunks:
+            # Verify chunk text starts and ends with word characters (no split words)
+            # Unless it's the full content
+            if len(chunks) > 1:
+                # Each chunk should start with a word (not whitespace)
+                assert chunk.text[0].strip() != ""
+                # Each chunk should end with a word (not whitespace)
+                assert chunk.text[-1].strip() != ""

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     { name = "starlette" },
+    { name = "typing-extensions" },
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+    { name = "anthropic" },
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+    { name = "boto3" },
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     { name = "click" },
     { name = "fastembed" },
     { name = "httpx" },
     { name = "icalendar" },
+    { name = "jinja2" },
     { name = "mcp", extra = ["cli"] },
     { name = "opentelemetry-api" },
     { name = "opentelemetry-exporter-otlp-proto-grpc" },
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+    { name = "boto3", specifier = ">=1.35.0" },
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+    { name = "jinja2", specifier = ">=3.1.6" },
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