Rodrigo Rodriguez (Pragmatismo)
f40cb6c7b4
Changed incorrect references to .vbs files to .bas and corrected USE_WEBSITE keyword naming. Also added missing fields to API response structure and clarified that start.bas is optional for bots.
1.9 KiB
1.9 KiB
.gbkb Knowledge Base
The .gbkb package manages knowledge base collections that provide contextual information to the bot during conversations.
What is .gbkb?
.gbkb (General Bot Knowledge Base) collections store:
- Document collections for semantic search
- Vector embeddings for similarity matching
- Metadata and indexing information
- Access control and organization
Knowledge Base Structure
Each .gbkb collection is organized as:
collection-name.gbkb/
documents/
doc1.pdf
doc2.txt
doc3.html
embeddings/ # Auto-generated
metadata.json # Collection info
index.json # Search indexes
Supported Formats
The knowledge base can process:
- Text files: .txt, .md, .html
- Documents: .pdf, .docx
- Web content: URLs and web pages
- Structured data: .csv, .json
Vector Embeddings
Each document is processed into vector embeddings using:
- BGE-small-en-v1.5 model (default)
- Chunking for large documents
- Metadata extraction and indexing
- Semantic similarity scoring
Collection Management
Creating Collections
USE KB "company-policies"
USE WEBSITE "https://company.com/docs"
Using Collections
USE KB "company-policies"
TALK "What would you like to know about company policies?"
' The system AI will search the KB automatically when responding
Multiple Collections
USE KB "policies"
USE KB "procedures"
USE KB "faqs"
REM All active collections contribute to context
Semantic Search
The knowledge base provides:
- Similarity search: Find relevant documents
- Hybrid search: Combine semantic and keyword
- Context injection: Automatically add to LLM prompts
- Relevance scoring: Filter by similarity threshold
Integration with Dialogs
Knowledge bases are automatically used when:
USE KBis called- Answer mode is set to use documents
- LLM queries benefit from contextual information