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Chapter 03 – Knowledge‑Base (VectorDB) Documentation Overview
This chapter explains how GeneralBots manages knowledge‑base collections, indexing, caching, and semantic search. The implementation now references a generic VectorDB (instead of a specific Qdrant instance) and highlights the use of the .gbdrive package for storage when needed.
| Document | File | Description |
|---|---|---|
| README | README.md | High‑level reference for the .gbkb package and its core commands (ADD_KB, SET_KB, ADD_WEBSITE). |
| Caching | caching.md | Optional in‑memory and persistent SQLite caching to speed up frequent FIND queries. |
| Context Compaction | context-compaction.md | Techniques to keep the LLM context window within limits (summarization, memory pruning, sliding window). |
| Indexing | indexing.md | Process of extracting, chunking, embedding, and storing document vectors in the VectorDB. |
| VectorDB Integration | qdrant.md | (Renamed) Details the VectorDB connection, collection mapping, and operations. References to Qdrant have been generalized to VectorDB. |
| Semantic Search | semantic-search.md | How the FIND keyword performs meaning‑based retrieval using the VectorDB. |
| Vector Collections | vector-collections.md | Definition and management of vector collections, including creation, document addition, and usage in dialogs. |
How to Use This Overview
- Navigate: Click the file links to read the full documentation for each topic.
- Reference: Use this table as a quick lookup when developing or extending knowledge‑base functionality.
- Update: When the underlying storage or VectorDB implementation changes, edit the corresponding markdown files and keep this summary in sync.
This summary was added to provide a cohesive overview of Chapter 03, aligning terminology with the current architecture (VectorDB, .gbdrive, etc.).