Document Management in AI Knowledge
Learn how to upload, process, and organize documents for your knowledge bases
Effective document management is crucial for building high-quality knowledge bases. This guide covers how to upload, process, organize, and maintain documents in AI Knowledge to ensure optimal retrieval performance.
Supported Document Types
AI Knowledge supports a wide range of document formats:
| Category | Formats | Notes |
|---|---|---|
| Text Documents | PDF, DOCX, DOC, RTF, TXT | Full text extraction with formatting preservation where possible |
| Presentations | PPTX, PPT, KEY | Extracts text, slide structure, and notes |
| Spreadsheets | XLSX, XLS, CSV, TSV | Processes tabular data with cell relationships |
| Web Content | HTML, MHT, XML | Preserves content structure and extracts relevant text |
| Images | PNG, JPG, TIFF, GIF | OCR for text extraction from images |
| MSG, EML | Extracts message content, metadata, and attachments | |
| Archives | ZIP, RAR, TAR | Automatically extracts and processes contained files |
| Markdown | MD, MARKDOWN | Preserves structure and formatting |
| Code | Various source code files | Maintains code structure and comments |
Document Upload Methods
Upload files directly through the web interface:
- Select individual files or entire folders
- Drag and drop multiple files
- Monitor upload progress
- Receive immediate processing feedback
Best for:
- Small to medium document collections
- Initial knowledge base setup
- Ad-hoc document additions
- Documents stored locally
Upload files directly through the web interface:
- Select individual files or entire folders
- Drag and drop multiple files
- Monitor upload progress
- Receive immediate processing feedback
Best for:
- Small to medium document collections
- Initial knowledge base setup
- Ad-hoc document additions
- Documents stored locally
Import large collections of documents in batch:
- Upload zip archives of documents
- Import from cloud storage (S3, GCS, Azure)
- Process document collections
- Schedule large ingestion jobs
Best for:
- Large document volumes
- Initial migration of existing repositories
- Periodic batch updates
- System-to-system transfers
Connect directly to external document sources:
- SharePoint and OneDrive integration
- Google Drive connector
- Confluence and Notion import
- CMS system integration
Best for:
- Keeping knowledge bases synchronized with live sources
- Accessing documents in existing repositories
- Maintaining document version alignment
- Simplifying ongoing maintenance
Programmatically add documents via API:
- REST API endpoints for document management
- Batch or individual document processing
- Automated document workflows
- Custom integration with existing systems
Best for:
- Automated document workflows
- Custom integrations
- Dynamic document generation
- Programmatic knowledge base maintenance
Document Processing Pipeline
Upload & Initial Validation
Documents are transferred to the system and validated.
This stage includes:
- Format verification
- Size and content checking
- Security scanning
- Corruption detection
- Initial metadata extraction
- File decompression (if applicable)
Text Extraction
Content is extracted from various document formats.
Techniques include:
- PDF text layer extraction
- OCR for images and scanned documents
- Document structure parsing
- Table and chart content extraction
- Formatting preservation
- Header/footer identification
Document Enrichment
Additional information and structure are added.
Enrichment includes:
- Metadata enhancement
- Language detection
- Entity identification
- Topic classification
- Summarization
- Structure annotation
- Content typing
Chunking
Documents are divided into retrievable segments.
Chunking strategies include:
- Semantic chunking (based on meaning)
- Fixed-size chunking (token count)
- Structure-based chunking (sections)
- Paragraph-level chunking
- Sliding window approaches
- Hierarchical chunking
Embedding Generation
Vector representations are created for chunks.
This process includes:
- Embedding model application
- Vector generation for each chunk
- Multi-vector approaches (where applicable)
- Embedding verification
- Quality assessment
- Optimization for retrieval
Indexing
Chunks and embeddings are organized for efficient retrieval.
Indexing includes:
- Vector database storage
- Metadata indexing
- Full-text search indexing
- Relationship mapping
- Access control implementation
- Query optimization structures
Quality Verification
Processing results are checked for quality and completeness.
Verification includes:
- Content extraction validation
- Chunking quality assessment
- Embedding consistency checks
- Missing content detection
- Error logging and reporting
- Sample query testing
Document Management Interface
The document management interface in AI Knowledge provides comprehensive tools for organizing and maintaining your document collection:
The main document view provides:
- Comprehensive document listing
- Sorting and filtering options
- Status indicators
- Batch operations
- Search functionality
- Version history access
Key features:
- Preview documents directly in the interface
- Check processing status and health
- View document metadata
- Manage document tags and categories
- Track document usage statistics
The main document view provides:
- Comprehensive document listing
- Sorting and filtering options
- Status indicators
- Batch operations
- Search functionality
- Version history access
Key features:
- Preview documents directly in the interface
- Check processing status and health
- View document metadata
- Manage document tags and categories
- Track document usage statistics
The document addition interface offers:
- Multiple upload methods
- Batch processing options
- Import wizards for external sources
- Pre-processing configuration
- Metadata assignment during upload
- Folder structure preservation
The detailed document view shows:
- Complete document information
- Processing history
- Generated chunks
- Extracted metadata
- Relationship mapping
- Usage analytics
- Manual override options
Perform actions on multiple documents at once:
- Bulk tagging and categorization
- Batch processing or reprocessing
- Mass deletion or archiving
- Export operations
- Permission updates
- Status changes
Document Organization
Effective document organization improves retrieval quality and knowledge base maintenance:
Categories & Collections
Categories & Collections
Organize documents into logical groupings:
- Create hierarchical category structures
- Establish document collections for specific purposes
- Group related documents together
- Maintain organizational schemes across knowledge bases
Benefits:
- Improved document findability
- Better organizational context
- Enhanced filtering capabilities
- Clearer knowledge structure
Tagging System
Tagging System
Apply flexible tags to documents:
- Create custom tag vocabularies
- Use consistent tagging schemes
- Apply multiple tags to documents
- Tag at both document and chunk levels
Benefits:
- Multi-dimensional organization
- Enhanced search filtering
- Cross-cutting categorization
- Improved retrieval relevance
Metadata Management
Metadata Management
Enrich documents with descriptive information:
- Define custom metadata fields
- Extract metadata automatically
- Maintain consistent schemes
- Use metadata for advanced filtering
Common metadata includes:
- Author and creation information
- Content type and format
- Validity dates and version info
- Source systems and references
- Status and review information
Relationship Mapping
Relationship Mapping
Define connections between documents:
- Create parent-child relationships
- Establish document references
- Map prerequisites and dependencies
- Connect related information
Benefits:
- Enhanced context understanding
- Better navigation between documents
- Improved comprehensive answers
- Support for complex information needs
Document Processing Settings
Customize how documents are processed to optimize for your specific knowledge base needs:
Configure how content is extracted from documents:
- OCR Settings:
- OCR engine selection
- Language optimization
- Image preprocessing
- Confidence thresholds
- Structure Handling:
- Table extraction methods
- Header/footer treatment
- Layout preservation
- Image handling
- Content Filtering:
- Element inclusion/exclusion
- Content type prioritization
- Noise reduction
- Redundancy handling
Configure how content is extracted from documents:
- OCR Settings:
- OCR engine selection
- Language optimization
- Image preprocessing
- Confidence thresholds
- Structure Handling:
- Table extraction methods
- Header/footer treatment
- Layout preservation
- Image handling
- Content Filtering:
- Element inclusion/exclusion
- Content type prioritization
- Noise reduction
- Redundancy handling
Define how documents are divided into retrieval units:
- Chunking Strategy:
- Semantic vs. fixed-size
- Chunk size parameters
- Overlap settings
- Structure preservation
- Special Handling:
- Table chunking methods
- List processing
- Code block treatment
- Short document handling
- Hierarchical Options:
- Parent-child chunk relationships
- Multi-level chunking
- Context preservation
- Navigation structures
Configure vector representations:
- Embedding Model:
- Model selection
- Dimension settings
- Specialized models for content types
- Multi-lingual support
- Vector Optimization:
- Normalization methods
- Dimensionality treatments
- Clustering approaches
- Quality thresholds
- Advanced Techniques:
- Multi-vector representations
- Hybrid embedding strategies
- Document-level embeddings
- Specialized embedding pipelines
Optimize how content is indexed for retrieval:
- Vector Index:
- Index type and algorithm
- Distance metrics
- Performance optimization
- Update strategies
- Metadata Indexing:
- Field indexing configuration
- Search boost settings
- Filter optimization
- Sort capabilities
- Advanced Options:
- Hybrid indexes
- Query routing
- Caching strategies
- Query optimization structures
Document Maintenance
Keep your knowledge base current and optimized with these document maintenance practices:
Regular Content Updates
Keep information current and accurate.
Maintenance activities:
- Schedule regular document reviews
- Update outdated information
- Add new versions of documents
- Remove obsolete content
- Track document freshness
Version Management
Track document changes over time.
Key capabilities:
- Maintain version history
- Compare document versions
- Restore previous versions
- Track change audit trail
- Manage version relevance
Content Health Monitoring
Proactively identify and address issues.
Monitoring areas:
- Processing error detection
- Broken document identification
- Chunking quality analysis
- Embedding anomalies
- Retrieval performance issues
Reprocessing & Optimization
Refresh processing to improve quality.
Optimization activities:
- Reprocess with improved settings
- Apply new chunking strategies
- Update to better embedding models
- Enhance metadata and structure
- Optimize based on performance analytics
Automated Document Processing
Set up automated workflows for efficient document management:
Scheduled Imports
Scheduled Imports
Automatically import documents on a regular basis:
- Configure recurring import jobs
- Set source locations and credentials
- Define processing parameters
- Schedule optimal import times
- Configure notification preferences
Use cases:
- Regular knowledge base updates
- Synchronization with document repositories
- Periodic report processing
- Automated content refreshes
Watch Folders
Watch Folders
Monitor specific locations for new documents:
- Set up folder monitoring for local or network locations
- Configure cloud storage monitoring
- Define instant processing triggers
- Set up filtering rules
- Configure error handling
Benefits:
- Real-time knowledge updates
- Reduced manual intervention
- Streamlined document workflows
- Consistent processing application
Document Processing Pipelines
Document Processing Pipelines
Create customized document workflows:
- Define multi-stage processing
- Set up conditional processing paths
- Configure enrichment steps
- Implement validation checkpoints
- Create custom post-processing
Advanced capabilities:
- Document classification and routing
- Conditional metadata application
- Multi-format conversions
- Specialized content extraction
- Custom data integration
Integrations & Webhooks
Integrations & Webhooks
Connect document processing to external systems:
- Configure webhook notifications for events
- Set up bidirectional system integrations
- Implement custom API workflows
- Create event-driven processing
- Enable cross-system synchronization
Integration types:
- Content management systems
- Document repositories
- Workflow systems
- Enterprise applications
- Custom business systems
Best Practices for Document Management
Consistent Organization
Establish and maintain a logical, consistent document organization scheme
Quality Over Quantity
Focus on high-quality, authoritative documents rather than sheer volume
Rich Metadata
Add comprehensive metadata to enhance context and retrieval
Optimal Chunking
Tune chunking strategies to preserve context and meaning
Regular Maintenance
Schedule routine updates, reviews, and optimizations
Automated Workflows
Implement automation for consistent, efficient processing
Versioning Strategy
Maintain clear version control for evolving documents
Performance Monitoring
Track and optimize document retrieval effectiveness
Troubleshooting Document Issues
Upload failures
Upload failures
If documents fail to upload:
- Check file format compatibility
- Verify file isn’t corrupted or password-protected
- Ensure file size is within system limits
- Check network connectivity and stability
- Verify upload permissions
- Examine client-side browser issues
Resolution steps:
- Convert to a standard format
- Use smaller batch sizes
- Try alternative upload methods
- Check system logs for detailed errors
Processing errors
Processing errors
When documents upload but fail during processing:
- Review document structure and complexity
- Check for unsupported elements or formatting
- Verify text extraction capability for the format
- Examine system resource availability
- Check for timeout issues with large documents
- Review processing logs for specific error messages
Resolution steps:
- Simplify complex documents
- Pre-process problematic files
- Adjust extraction settings
- Split very large documents
- Use alternative processing approaches
Content quality issues
Content quality issues
If extracted content has quality problems:
- Check original document formatting and structure
- Review OCR settings for scanned documents
- Examine table and image extraction results
- Verify language support for the content
- Check for unusual characters or formatting
- Review chunking results for context preservation
Resolution steps:
- Improve original document quality
- Adjust OCR and extraction settings
- Modify chunking parameters
- Add manual metadata to compensate
- Consider document preprocessing
Retrieval relevance problems
Retrieval relevance problems
When document retrieval isn’t meeting expectations:
- Review document relevance to query needs
- Check chunking strategy appropriateness
- Examine embedding model suitability
- Verify index configuration
- Assess query processing effectiveness
- Evaluate content quality and coverage
Resolution steps:
- Adjust chunking strategy
- Try different embedding models
- Enhance metadata for better context
- Implement hybrid search approaches
- Add missing content
- Fine-tune retrieval parameters
Security and Compliance
Ensure your document management practices meet security and compliance requirements:
Access Controls
Access Controls
Control who can access and manage documents:
- Document-level permissions
- Role-based access control
- Group-based permissions
- Temporary access grants
- Inherited vs. explicit permissions
Implementation options:
- Apply permissions during upload
- Inherit from knowledge base settings
- Set up custom access rules
- Implement approval workflows
- Configure visibility restrictions
Data Privacy
Data Privacy
Protect sensitive information in documents:
- PII detection and handling
- Automated redaction capabilities
- Data classification implementation
- Privacy policy enforcement
- Consent management
Privacy features:
- Sensitive information detection
- Configurable redaction rules
- Audit trails for privacy actions
- Policy-based information handling
- Restricted processing options
Compliance Support
Compliance Support
Meet regulatory and organizational requirements:
- Retention policy implementation
- Legal hold capabilities
- Compliance tagging and tracking
- Regulatory metadata
- Audit log maintenance
Compliance tools:
- Document lifecycle management
- Approval and certification workflows
- Chain of custody tracking
- Evidence preservation
- Compliance reporting
Security Measures
Security Measures
Protect document content and processing:
- Encryption for documents at rest
- Secure processing environments
- Malware scanning and prevention
- Data loss prevention integration
- Secure deletion capabilities
Security implementation:
- End-to-end encryption
- Secure temporary storage
- Isolated processing environments
- Authentication requirements
- Security event monitoring
Document Analytics
Gain insights into your document collection and usage:
Understand your document content:
- Document type distribution
- Content age analysis
- Topic clustering and trends
- Language and terminology patterns
- Content complexity metrics
- Duplication identification
Use insights to:
- Identify knowledge gaps
- Prioritize content updates
- Optimize document organization
- Plan maintenance activities
Understand your document content:
- Document type distribution
- Content age analysis
- Topic clustering and trends
- Language and terminology patterns
- Content complexity metrics
- Duplication identification
Use insights to:
- Identify knowledge gaps
- Prioritize content updates
- Optimize document organization
- Plan maintenance activities
Track how documents are being used:
- Retrieval frequency per document
- Most used document sections
- Query patterns leading to documents
- User access patterns
- Time-based usage trends
- Document utility metrics
Use insights to:
- Identify high-value content
- Focus optimization efforts
- Improve popular documents
- Archive unused content
Measure document effectiveness:
- Retrieval accuracy metrics
- Relevance scoring
- User feedback correlation
- Processing efficiency
- Error rate tracking
- Quality metrics over time
Use insights to:
- Optimize processing settings
- Improve document quality
- Enhance retrieval parameters
- Address problematic content
Track the overall health of your document collection:
- Processing error detection
- Missing content identification
- Outdated document tracking
- Embedding quality assessment
- Chunking effectiveness
- System performance impact
Use insights to:
- Address technical issues
- Plan maintenance activities
- Prioritize reprocessing efforts
- Ensure system reliability
Advanced Document Processing Features
Document Transformation
Convert documents between formats and structures for optimal processing.
Options include format conversion, structure normalization, template application, and content standardization.
Content Enrichment
Enhance documents with additional information and context.
Features include entity extraction, topic classification, sentiment analysis, and relationship mapping.
Multi-Language Support
Process and retrieve from documents in multiple languages.
Capabilities include language detection, multi-lingual embeddings, translation integration, and language-specific processing.
Document Summarization
Automatically generate summaries of document content.
Options include executive summaries, section summaries, key point extraction, and customizable summary lengths.
Content Deduplication
Identify and manage duplicate or similar content.
Features include similarity detection, content comparison, redundancy management, and optimized storage.
Intelligent Redaction
Automatically identify and protect sensitive information.
Capabilities include PII detection, configurable redaction rules, entity-based protection, and compliance support.
Integration with External Systems
Connect your document management with other enterprise systems:
Document Management Systems
Document Management Systems
Integrate with existing document repositories:
- SharePoint and OneDrive connections
- Google Workspace integration
- Box and Dropbox connectors
- Enterprise DMS connectors
- ECM system integration
Integration capabilities:
- Bidirectional synchronization
- Metadata mapping
- Permission alignment
- Version synchronization
- Change detection and updates
Content Creation Tools
Content Creation Tools
Connect with tools where documents are created:
- Microsoft Office integration
- Google Docs/Sheets connectors
- Adobe Creative Cloud connection
- CMS system integration
- Email platform connectors
Integration features:
- Direct publishing to knowledge bases
- Creation-time metadata capture
- Version control alignment
- Workflow integration
- Collaborative authoring support
Enterprise Applications
Enterprise Applications
Connect with key business systems:
- CRM integration (Salesforce, Dynamics)
- ERP system connections
- ITSM platforms (ServiceNow, Jira)
- HR systems integration
- Industry-specific application connectors
Integration capabilities:
- Document context enrichment
- Cross-system knowledge alignment
- Business process integration
- Metadata synchronization
- Workflow orchestration
Custom Integrations
Custom Integrations
Build specialized connections for unique needs:
- REST API for document operations
- Webhook support for events
- Custom connector development
- Scripting and automation
- ETL pipeline integration
Development options:
- API documentation and SDKs
- Integration templates
- Event-driven architecture
- Authentication mechanisms
- Data transformation tools
Document Visualization
Understand your document collection through visual analytics:
Visualize document relationships and topics:
- Topic clustering visualization
- Document similarity mapping
- Knowledge domain visualization
- Content coverage analysis
- Gap identification
Benefits:
- Understand knowledge distribution
- Identify related content
- Discover connection patterns
- Plan content development
Visualize document relationships and topics:
- Topic clustering visualization
- Document similarity mapping
- Knowledge domain visualization
- Content coverage analysis
- Gap identification
Benefits:
- Understand knowledge distribution
- Identify related content
- Discover connection patterns
- Plan content development
Visualize internal document organization:
- Section and hierarchy visualization
- Chunk boundary representation
- Embedded content mapping
- Reference visualization
- Content type distribution
Benefits:
- Understand document composition
- Evaluate chunking effectiveness
- Identify structural issues
- Optimize content extraction
Visualize how documents are being utilized:
- Heat maps of content usage
- Temporal access patterns
- User engagement flow
- Query-document mapping
- Relevance visualization
Benefits:
- Identify high-value content
- Track user engagement
- Optimize popular documents
- Understand access patterns
Visualize technical metrics and health:
- Processing status dashboards
- Error rate visualization
- Performance trends
- Quality metrics tracking
- Comparative effectiveness
Benefits:
- Monitor system health
- Identify problem areas
- Track optimization impacts
- Prioritize maintenance
Next Steps
Now that you understand document management in AI Knowledge, explore these related topics: