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:
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:
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
Security and Compliance
Ensure your document management practices meet security and compliance requirements:
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 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:
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