Skip to main content

Introduction to Knowledge Bases

Knowledge bases in Vectense Platform provide contextual information to AI models, enabling them to make intelligent decisions based on your organizational data and expertise.

What are Knowledge Bases?​

Knowledge bases are structured collections of information that AI models can search and reference during workflow execution. They transform static documents and data into dynamic, searchable context that enhances AI capabilities.

Core Functions​

  • Information Storage: Organize and store various types of content
  • Intelligent Indexing: Convert content into searchable vector representations
  • Semantic Search: Find relevant information based on meaning and context
  • Context Provision: Supply relevant information to AI models for informed decision-making

Key Benefits​

  • Context-Aware AI: AI responses are informed by your specific organizational knowledge
  • Consistent Information: Ensure AI uses accurate, up-to-date information
  • Knowledge Preservation: Capture and maintain institutional knowledge
  • Improved Accuracy: Reduce AI hallucinations by providing factual context

How Knowledge Bases Work​

The Knowledge Pipeline​

  1. Content Ingestion: Documents and data are uploaded or connected
  2. Text Extraction: Content is extracted from various file formats
  3. Content Chunking: Large documents are split into manageable segments
  4. Vector Embedding: Text is converted to mathematical representations
  5. Indexing: Embeddings are stored in a searchable vector database
  6. Retrieval: Relevant content is found and provided to AI models

Vector Embeddings​

Knowledge bases use vector embeddings to understand content semantically:

  • Mathematical Representation: Text is converted to numerical vectors
  • Semantic Understanding: Similar concepts have similar vector representations
  • Fast Search: Vector similarity enables rapid content retrieval
  • Context Matching: Find relevant content even without exact keyword matches

Knowledge Source Types​

File Bucket​

Direct file uploads through the web interface:

Supported Formats

  • PDF: Portable Document Format files
  • Word: Microsoft Word documents (.docx, .doc)
  • Excel: Spreadsheets (.xlsx, .xls, .csv)
  • Text: Plain text files (.txt)
  • RTF: Rich Text Format files
  • Markdown: Markdown formatted files (.md)

Features

  • Drag and drop file upload
  • Automatic format detection
  • Version tracking and updates
  • File organization and management

Best For

  • Company policies and procedures
  • Product documentation
  • Training materials
  • Reference documents

Local Filesystem​

Connection to file systems and network drives:

Capabilities

  • Directory Monitoring: Watch for file changes in real-time
  • Pattern Matching: Use glob patterns to filter files
  • Recursive Scanning: Process entire directory trees
  • Automatic Updates: Index new and modified files automatically

Configuration Options

  • Source Path: Root directory to monitor
  • File Patterns: Glob patterns (e.g., *.pdf, **/*.md)
  • Update Frequency: How often to check for changes
  • Exclusion Rules: Patterns for files to ignore

Best For

  • Shared network drives
  • Document management systems
  • Version-controlled repositories
  • Large document collections

Web Content​

Crawling and indexing web pages:

Capabilities

  • Website Crawling: Extract content from web pages
  • Depth Control: Set maximum crawling depth
  • Content Extraction: Convert HTML to clean text
  • Link Following: Follow internal links automatically

Configuration Options

  • Start URL: Initial page to begin crawling
  • Crawl Depth: How many levels deep to crawl
  • Max Pages: Maximum number of pages to process
  • Update Schedule: How often to refresh content

Best For

  • Product documentation websites
  • Internal wikis and knowledge bases
  • News and blog content
  • Public information sources

Content Processing​

Text Extraction​

Different file types are processed using specialized extractors:

  • PDF: Extract text while preserving structure
  • Word: Process document content and formatting
  • Excel: Extract data from spreadsheets and tables
  • Web: Convert HTML to clean, structured text

Content Chunking​

Large documents are split into manageable segments:

  • Size Optimization: Chunks sized for optimal AI processing
  • Context Preservation: Maintain document structure and relationships
  • Overlap Strategy: Ensure continuity between chunks
  • Metadata Retention: Preserve source information and structure

Quality Assurance​

Content is validated and enhanced during processing:

  • Format Validation: Ensure content is readable and well-formed
  • Language Detection: Identify content language for appropriate processing
  • Deduplication: Remove or flag duplicate content
  • Error Handling: Gracefully handle corrupted or unreadable files

Search and Retrieval​

Knowledge bases use advanced search techniques:

  • Vector Similarity: Find content with similar meaning
  • Context Matching: Understand query intent and context
  • Relevance Ranking: Order results by relevance and importance
  • Multi-language Support: Search across different languages

Search Process​

  1. Query Processing: User query is converted to vector representation
  2. Similarity Calculation: Compare query vector with content vectors
  3. Result Ranking: Order results by relevance score
  4. Context Assembly: Format results for AI model consumption

Result Optimization​

  • Result Limits: Control number of results returned
  • Quality Filtering: Remove low-quality or irrelevant results
  • Context Windowing: Provide appropriate amount of context
  • Source Attribution: Track content sources for transparency

Integration with AI Models​

Context Injection​

Knowledge bases provide context to AI models during workflow execution:

Automatic Retrieval

  • AI determines what information it needs
  • Knowledge base searches for relevant content
  • Results are provided as context to the AI model
  • AI incorporates knowledge into its response

Manual Context

  • Workflow explicitly queries knowledge base
  • Specific content is retrieved and formatted
  • Context is provided to AI model as input
  • AI uses provided context for informed responses

Context Optimization​

  • Relevance Scoring: Prioritize most relevant content
  • Context Length: Balance comprehensiveness with processing efficiency
  • Source Diversity: Include varied perspectives when appropriate
  • Freshness Weighting: Prefer more recent content when relevant

Performance Considerations​

Indexing Performance​

Factors affecting knowledge base creation and updates:

  • Content Volume: Large document collections take longer to process
  • File Complexity: Complex formats require more processing time
  • Network Speed: Remote sources depend on connection quality
  • System Resources: Available CPU and memory affect processing speed

Search Performance​

Factors affecting query response time:

  • Index Size: Larger knowledge bases may have slower search
  • Query Complexity: Complex queries take more processing time
  • Result Set Size: More results require additional processing
  • Concurrent Usage: Multiple users may affect response times

Optimization Strategies​

  • Incremental Updates: Only process changed content
  • Efficient Indexing: Optimize vector storage and retrieval
  • Content Curation: Remove irrelevant or outdated content
  • Resource Scaling: Adjust system resources based on usage

Security and Privacy​

Data Protection​

  • Encryption: Content encrypted at rest and in transit
  • Access Control: Role-based access to knowledge bases
  • Audit Logging: Track all access and modifications
  • Data Retention: Configurable retention policies

Content Security​

  • Source Validation: Verify content sources and authenticity
  • Malware Scanning: Scan uploaded files for security threats
  • Content Filtering: Remove or flag sensitive information
  • Privacy Controls: Respect document privacy and confidentiality

Compliance​

  • GDPR Compliance: Support for European data protection regulations
  • Industry Standards: Meet sector-specific compliance requirements
  • Data Sovereignty: Keep data within specified geographical boundaries
  • Audit Trails: Comprehensive logging for compliance reporting

Best Practices​

Content Strategy​

  • Quality Over Quantity: Focus on high-quality, relevant content
  • Regular Updates: Keep content current and accurate
  • Clear Organization: Structure content logically and consistently
  • Source Documentation: Maintain clear records of content sources

Performance Management​

  • Monitor Usage: Track knowledge base usage and performance
  • Optimize Regularly: Perform regular maintenance and optimization
  • Capacity Planning: Plan for growth in content and usage
  • Resource Monitoring: Track system resource usage and requirements

Security Management​

  • Regular Audits: Review access permissions and content security
  • Update Procedures: Keep systems updated with security patches
  • Incident Response: Have procedures for security incidents
  • Training: Ensure users understand security best practices

Understanding these fundamentals will help you effectively create and manage knowledge bases. Continue to Add Knowledge to start building your first knowledge base.