Files
nextcloud-mcp-server/nextcloud_mcp_server/config.py
T
Chris Coutinho e575c8e57b feat(vector): Support multiple embedding models with auto-generated collection names
This PR enables safe switching between embedding models and multi-server
deployments by implementing auto-generated Qdrant collection names based on
deployment ID and model name.

## Problem

Previously, all deployments used a single hardcoded collection name
"nextcloud_content", which caused two critical issues:

1. **Dimension mismatches when switching models**: Changing
   OLLAMA_EMBEDDING_MODEL (e.g., nomic-embed-text at 768D → all-minilm at
   384D) would cause runtime errors as vectors couldn't be inserted into a
   collection with incompatible dimensions.

2. **Collection collisions in multi-server setups**: Multiple MCP servers
   sharing a single Qdrant instance would overwrite each other's data,
   making horizontal scaling impossible.

## Solution

### Auto-Generated Collection Naming

Collections are now automatically named using the pattern:
\`{deployment-id}-{model-name}\`

**Deployment ID**: Uses \`OTEL_SERVICE_NAME\` if configured (and not default
value), otherwise falls back to \`hostname\` for simple Docker deployments.

**Model Name**: From \`OLLAMA_EMBEDDING_MODEL\` with path separators sanitized.

**Examples**:
- \`my-mcp-server-nomic-embed-text\` (with OTEL_SERVICE_NAME=my-mcp-server)
- \`mcp-container-all-minilm\` (simple Docker, hostname=mcp-container)

**Override**: Users can still set \`QDRANT_COLLECTION\` explicitly to bypass
auto-generation for backward compatibility.

### Dimension Validation

Added startup validation that checks collection dimensions match the
embedding service. If a mismatch is detected, the server fails fast with a
clear error message explaining:
- Expected vs actual dimensions
- Likely cause (model change)
- Solutions (delete collection, use different name, or revert model)

### Improved Sampling Error Handling

Enhanced MCP sampling rejection handling to treat user rejections as normal
behavior rather than errors:

- **User rejections** ("rejected", "denied") → INFO log, no traceback
- **Unsupported clients** → INFO log, no traceback
- **Other MCP errors** → WARNING log, no traceback
- **Unexpected errors** → ERROR log WITH traceback

This aligns with the MCP specification where clients SHOULD prompt users for
approval/denial of sampling requests.

## Changes

### Core Implementation

- **nextcloud_mcp_server/config.py**: Added \`get_collection_name()\` method
  with deployment ID detection and model name sanitization
- **nextcloud_mcp_server/vector/qdrant_client.py**: Dimension validation on
  collection open with helpful error messages
- **nextcloud_mcp_server/vector/{scanner,processor}.py**: Updated to use
  \`get_collection_name()\`
- **nextcloud_mcp_server/auth/userinfo_routes.py**: Vector sync status uses
  \`get_collection_name()\`
- **nextcloud_mcp_server/server/semantic.py**:
  - Updated semantic search tools to use \`get_collection_name()\`
  - Improved sampling rejection error handling (McpError vs Exception)

### Documentation

- **docs/semantic-search-architecture.md**: New comprehensive architecture
  document (557 lines) covering background sync, semantic search flow, RAG
  implementation, and deployment modes
- **docs/configuration.md**: Added detailed "Qdrant Collection Naming"
  section with examples and multi-server deployment guidance
- **docker-compose.yml**: Added comments explaining collection naming behavior
- **README.md**: Updated semantic search descriptions to clarify
  experimental status, Notes-only support, and infrastructure requirements

## Migration Guide

**For existing single-server deployments:**

Option 1 (Recommended): Use explicit collection name for continuity
\`\`\`bash
QDRANT_COLLECTION=nextcloud_content  # Keep existing collection
\`\`\`

Option 2: Allow auto-generation and re-embed
\`\`\`bash
# Remove QDRANT_COLLECTION override
# New collection will be created based on deployment ID + model
# Requires re-embedding all documents (may take time)
\`\`\`

**For new multi-server deployments:**

Set unique OTEL service names per server:
\`\`\`bash
# Server 1
OTEL_SERVICE_NAME=mcp-prod
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
# → Collection: "mcp-prod-nomic-embed-text"

# Server 2
OTEL_SERVICE_NAME=mcp-staging
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
# → Collection: "mcp-staging-nomic-embed-text"
\`\`\`

## Benefits

 **Safe model switching**: Each model gets its own collection, preventing
   dimension mismatch errors
 **Multi-server support**: Multiple MCP servers can share one Qdrant
   instance without conflicts
 **Clear ownership**: Collection names show which deployment and model owns
   the data
 **Better error messages**: Dimension validation provides actionable
   guidance
 **Backward compatible**: Existing deployments can continue using
   \`QDRANT_COLLECTION\` override

## Testing

Validated with:
- Single-server deployments (default hostname-based naming)
- Multi-server deployments (OTEL service name-based naming)
- Model switching scenarios (dimension validation)
- Collection override scenarios (backward compatibility)

Next steps: Testing various Ollama embedding models to investigate optimal
chunk sizes and performance characteristics.

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

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-10 01:18:30 +01:00

321 lines
12 KiB
Python

import logging
import logging.config
import os
from dataclasses import dataclass
from typing import Any, Optional
LOGGING_CONFIG = {
"version": 1,
"disable_existing_loggers": False,
"handlers": {
"default": {
"class": "logging.StreamHandler",
"formatter": "http",
},
},
"formatters": {
"http": {
"format": "%(levelname)s [%(asctime)s] %(name)s - %(message)s",
"datefmt": "%Y-%m-%d %H:%M:%S",
},
},
"loggers": {
"": {
"handlers": ["default"],
"level": "INFO",
},
"httpx": {
"handlers": ["default"],
"level": "INFO",
"propagate": False, # Prevent propagation to root logger
},
"httpcore": {
"handlers": ["default"],
"level": "INFO",
"propagate": False, # Prevent propagation to root logger
},
"uvicorn": {
"handlers": ["default"],
"level": "INFO",
"propagate": False,
},
"uvicorn.access": {
"handlers": ["default"],
"level": "INFO",
"propagate": False,
},
"uvicorn.error": {
"handlers": ["default"],
"level": "INFO",
"propagate": False,
},
},
}
def setup_logging():
logging.config.dictConfig(LOGGING_CONFIG)
# Document Processing Configuration
def get_document_processor_config() -> dict[str, Any]:
"""Get document processor configuration from environment.
Returns:
Dict with processor configs:
{
"enabled": bool,
"default_processor": str,
"processors": {
"unstructured": {...},
"tesseract": {...},
"custom": {...},
}
}
"""
config: dict[str, Any] = {
"enabled": os.getenv("ENABLE_DOCUMENT_PROCESSING", "false").lower() == "true",
"default_processor": os.getenv("DOCUMENT_PROCESSOR", "unstructured"),
"processors": {},
}
# Unstructured configuration
if os.getenv("ENABLE_UNSTRUCTURED", "false").lower() == "true":
config["processors"]["unstructured"] = {
"api_url": os.getenv("UNSTRUCTURED_API_URL", "http://unstructured:8000"),
"timeout": int(os.getenv("UNSTRUCTURED_TIMEOUT", "120")),
"strategy": os.getenv("UNSTRUCTURED_STRATEGY", "auto"),
"languages": [
lang.strip()
for lang in os.getenv("UNSTRUCTURED_LANGUAGES", "eng,deu").split(",")
if lang.strip()
],
"progress_interval": int(os.getenv("PROGRESS_INTERVAL", "10")),
}
# Tesseract configuration
if os.getenv("ENABLE_TESSERACT", "false").lower() == "true":
config["processors"]["tesseract"] = {
"tesseract_cmd": os.getenv("TESSERACT_CMD"), # None = auto-detect
"lang": os.getenv("TESSERACT_LANG", "eng"),
}
# Custom processor (via HTTP API)
if os.getenv("ENABLE_CUSTOM_PROCESSOR", "false").lower() == "true":
custom_url = os.getenv("CUSTOM_PROCESSOR_URL")
if custom_url:
supported_types_str = os.getenv("CUSTOM_PROCESSOR_TYPES", "application/pdf")
supported_types = {
t.strip() for t in supported_types_str.split(",") if t.strip()
}
config["processors"]["custom"] = {
"name": os.getenv("CUSTOM_PROCESSOR_NAME", "custom"),
"api_url": custom_url,
"api_key": os.getenv("CUSTOM_PROCESSOR_API_KEY"),
"timeout": int(os.getenv("CUSTOM_PROCESSOR_TIMEOUT", "60")),
"supported_types": supported_types,
}
return config
@dataclass
class Settings:
"""Application settings from environment variables."""
# OAuth/OIDC settings
oidc_discovery_url: Optional[str] = None
oidc_client_id: Optional[str] = None
oidc_client_secret: Optional[str] = None
oidc_issuer: Optional[str] = None
# Nextcloud settings
nextcloud_host: Optional[str] = None
nextcloud_username: Optional[str] = None
nextcloud_password: Optional[str] = None
# ADR-005: Token Audience Validation (required for OAuth mode)
nextcloud_mcp_server_url: Optional[str] = None # MCP server URL (used as audience)
nextcloud_resource_uri: Optional[str] = None # Nextcloud resource identifier
# Token verification endpoints
jwks_uri: Optional[str] = None
introspection_uri: Optional[str] = None
userinfo_uri: Optional[str] = None
# Progressive Consent settings (always enabled - no flag needed)
enable_token_exchange: bool = False
enable_offline_access: bool = False
# Token exchange cache settings
token_exchange_cache_ttl: int = 300 # seconds (5 minutes default)
# Token settings
token_encryption_key: Optional[str] = None
token_storage_db: Optional[str] = None
# Vector sync settings (ADR-007)
vector_sync_enabled: bool = False
vector_sync_scan_interval: int = 300 # seconds (5 minutes)
vector_sync_processor_workers: int = 3
vector_sync_queue_max_size: int = 10000
# Qdrant settings (mutually exclusive modes)
qdrant_url: Optional[str] = None # Network mode: http://qdrant:6333
qdrant_location: Optional[str] = None # Local mode: :memory: or /path/to/data
qdrant_api_key: Optional[str] = None
qdrant_collection: str = "nextcloud_content"
# Ollama settings (for embeddings)
ollama_base_url: Optional[str] = None
ollama_embedding_model: str = "nomic-embed-text"
ollama_verify_ssl: bool = True
# Observability settings
metrics_enabled: bool = True
metrics_port: int = 9090
tracing_enabled: bool = False
otel_exporter_otlp_endpoint: Optional[str] = None
otel_service_name: str = "nextcloud-mcp-server"
otel_traces_sampler: str = "always_on"
otel_traces_sampler_arg: float = 1.0
log_format: str = "json" # "json" or "text"
log_level: str = "INFO"
log_include_trace_context: bool = True
def __post_init__(self):
"""Validate Qdrant configuration and set defaults."""
logger = logging.getLogger(__name__)
# Ensure mutual exclusivity
if self.qdrant_url and self.qdrant_location:
raise ValueError(
"Cannot set both QDRANT_URL and QDRANT_LOCATION. "
"Use QDRANT_URL for network mode or QDRANT_LOCATION for local mode."
)
# Default to :memory: if neither set
if not self.qdrant_url and not self.qdrant_location:
self.qdrant_location = ":memory:"
logger.info("Using default Qdrant mode: in-memory (:memory:)")
# Warn if API key set in local mode
if self.qdrant_location and self.qdrant_api_key:
logger.warning(
"QDRANT_API_KEY is set but QDRANT_LOCATION is used (local mode). "
"API key is only relevant for network mode and will be ignored."
)
def get_collection_name(self) -> str:
"""
Get Qdrant collection name.
Auto-generates from deployment ID + model name unless explicitly set.
Deployment ID uses OTEL_SERVICE_NAME if configured, otherwise hostname.
This enables:
- Safe embedding model switching (new model → new collection)
- Multi-server deployments (unique deployment IDs)
- Clear collection naming (shows deployment and model)
Format: {deployment-id}-{model-name}
Examples:
- "my-deployment-nomic-embed-text" (OTEL_SERVICE_NAME set)
- "mcp-container-all-minilm" (hostname fallback)
Returns:
Collection name string
"""
import socket
# Use explicit override if user configured non-default value
if self.qdrant_collection != "nextcloud_content":
return self.qdrant_collection
# Determine deployment ID (OTEL service name or hostname fallback)
if self.otel_service_name != "nextcloud-mcp-server": # Non-default
deployment_id = self.otel_service_name
else:
# Fallback to hostname for simple Docker deployments without OTEL config
deployment_id = socket.gethostname()
# Sanitize deployment ID and model name
deployment_id = deployment_id.lower().replace(" ", "-").replace("_", "-")
model_name = self.ollama_embedding_model.replace("/", "-").replace(":", "-")
return f"{deployment_id}-{model_name}"
def get_settings() -> Settings:
"""Get application settings from environment variables.
Returns:
Settings object with configuration values
"""
return Settings(
# OAuth/OIDC settings
oidc_discovery_url=os.getenv("OIDC_DISCOVERY_URL"),
oidc_client_id=os.getenv("OIDC_CLIENT_ID"),
oidc_client_secret=os.getenv("OIDC_CLIENT_SECRET"),
oidc_issuer=os.getenv("OIDC_ISSUER"),
# Nextcloud settings
nextcloud_host=os.getenv("NEXTCLOUD_HOST"),
nextcloud_username=os.getenv("NEXTCLOUD_USERNAME"),
nextcloud_password=os.getenv("NEXTCLOUD_PASSWORD"),
# ADR-005: Token Audience Validation
nextcloud_mcp_server_url=os.getenv("NEXTCLOUD_MCP_SERVER_URL"),
nextcloud_resource_uri=os.getenv("NEXTCLOUD_RESOURCE_URI"),
# Token verification endpoints
jwks_uri=os.getenv("JWKS_URI"),
introspection_uri=os.getenv("INTROSPECTION_URI"),
userinfo_uri=os.getenv("USERINFO_URI"),
# Progressive Consent settings (always enabled)
enable_token_exchange=(
os.getenv("ENABLE_TOKEN_EXCHANGE", "false").lower() == "true"
),
enable_offline_access=(
os.getenv("ENABLE_OFFLINE_ACCESS", "false").lower() == "true"
),
# Token exchange cache settings
token_exchange_cache_ttl=int(os.getenv("TOKEN_EXCHANGE_CACHE_TTL", "300")),
# Token settings
token_encryption_key=os.getenv("TOKEN_ENCRYPTION_KEY"),
token_storage_db=os.getenv("TOKEN_STORAGE_DB", "/tmp/tokens.db"),
# Vector sync settings (ADR-007)
vector_sync_enabled=(
os.getenv("VECTOR_SYNC_ENABLED", "false").lower() == "true"
),
vector_sync_scan_interval=int(os.getenv("VECTOR_SYNC_SCAN_INTERVAL", "300")),
vector_sync_processor_workers=int(
os.getenv("VECTOR_SYNC_PROCESSOR_WORKERS", "3")
),
vector_sync_queue_max_size=int(
os.getenv("VECTOR_SYNC_QUEUE_MAX_SIZE", "10000")
),
# Qdrant settings
qdrant_url=os.getenv("QDRANT_URL"),
qdrant_location=os.getenv("QDRANT_LOCATION"),
qdrant_api_key=os.getenv("QDRANT_API_KEY"),
qdrant_collection=os.getenv("QDRANT_COLLECTION", "nextcloud_content"),
# Ollama settings
ollama_base_url=os.getenv("OLLAMA_BASE_URL"),
ollama_embedding_model=os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text"),
ollama_verify_ssl=os.getenv("OLLAMA_VERIFY_SSL", "true").lower() == "true",
# Observability settings
metrics_enabled=os.getenv("METRICS_ENABLED", "true").lower() == "true",
metrics_port=int(os.getenv("METRICS_PORT", "9090")),
tracing_enabled=os.getenv("OTEL_ENABLED", "false").lower() == "true",
otel_exporter_otlp_endpoint=os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT"),
otel_service_name=os.getenv("OTEL_SERVICE_NAME", "nextcloud-mcp-server"),
otel_traces_sampler=os.getenv("OTEL_TRACES_SAMPLER", "always_on"),
otel_traces_sampler_arg=float(os.getenv("OTEL_TRACES_SAMPLER_ARG", "1.0")),
log_format=os.getenv("LOG_FORMAT", "json"),
log_level=os.getenv("LOG_LEVEL", "INFO"),
log_include_trace_context=os.getenv("LOG_INCLUDE_TRACE_CONTEXT", "true").lower()
== "true",
)