Email Processing MCP Server
by Cam10001110101
Verified
- .context
- modules
# Email Processing
## Overview
The email processing pipeline handles the retrieval, analysis, and storage of email data across multiple databases. It implements a robust workflow for managing email content and metadata.
## Core Components
### OutlookConnector
```python
class OutlookConnector:
# Manages Outlook connection
# Retrieves email data from Inbox, Sent Items, and optionally Deleted Items
# Handles authentication
# Supports configurable folder processing
```
### EmailProcessingThread
```python
class EmailProcessingThread:
# Orchestrates processing workflow
# Manages database operations
# Handles error recovery
```
### EmailMetadata
```python
class EmailMetadata:
# Structured email data
# Metadata extraction
# Data validation
```
## Processing Pipeline
### 1. Email Retrieval
- Connect to Outlook
- Fetch email content from configured folders
- Process Inbox and Sent Items by default
- Optionally process Deleted Items when enabled
- Extract attachments
- Clean HTML content
### 2. Content Processing
- Text extraction
- Image processing
- URL extraction
- Metadata compilation
### 3. Analysis
- LLM categorization
- Embedding generation
- Summary creation
- Relationship mapping
### 4. Storage
- MongoDB metadata storage
- ChromaDB embedding storage
- Neo4j relationship creation
## Data Flow
### Input Processing
1. Raw email retrieval
2. Content cleaning
3. Metadata extraction
4. Attachment handling
### Analysis Pipeline
1. Text preprocessing
2. Category generation
3. Embedding creation
4. Summary generation
### Storage Operations
1. Metadata persistence
2. Vector storage
3. Graph creation
4. Status tracking
## Configuration
### Environment Variables
```
MONGODB_URI=mongodb://localhost:27017/MCP
SQLITE_DB_PATH=C:\path\to\emails.db
EMBEDDING_BASE_URL=http://localhost:11434
EMBEDDING_MODEL=nomic-embed-text
COLLECTION_NAME=outlook-emails
PROCESS_DELETED_ITEMS=true|false
```
### Processing Options
- Batch size
- Concurrency
- Retry settings
- Timeout values
## Error Handling
### Recovery Mechanisms
- Connection retries
- Partial updates
- State tracking
- Cleanup operations
### Logging
- Operation status
- Error details
- Performance metrics
- Progress tracking
## Best Practices
### Performance
- Batch processing
- Asynchronous operations
- Resource management
- Connection pooling
### Data Integrity
- Validation checks
- Atomic operations
- Consistency verification
- Error recovery
### Monitoring
- Progress tracking
- Status reporting
- Error notification
- Performance metrics
## Integration Points
### Database Handlers
- MongoDB operations
- ChromaDB interactions
- Neo4j transactions
### User Interface
- Progress updates
- Status reporting
- Error notifications
- Operation control