24 lines
1.9 KiB
Markdown
24 lines
1.9 KiB
Markdown
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# Chapter 03 – Knowledge‑Base (VectorDB) Documentation Overview
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This chapter explains how GeneralBots manages knowledge‑base collections, indexing, caching, and semantic search. The implementation uses vector databases for semantic search and highlights the use of the **.gbdrive** package for storage when needed.
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| Document | File | Description |
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|----------|------|-------------|
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| **README** | [README.md](README.md) | High‑level reference for the `.gbkb` package and its core commands (`USE KB`, `CLEAR KB`, `USE WEBSITE`). |
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| **Caching** | [caching.md](caching.md) | Optional in‑memory and persistent SQLite caching to speed up frequent `FIND` queries. |
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| **Context Compaction** | [context-compaction.md](context-compaction.md) | Techniques to keep the LLM context window within limits (summarization, memory pruning, sliding window). |
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| **Indexing** | [indexing.md](indexing.md) | Process of extracting, chunking, embedding, and storing document vectors in the VectorDB. |
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| **Semantic Caching** | [caching.md](caching.md) | Intelligent caching for LLM responses, including semantic similarity matching. |
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| **Semantic Search** | [semantic-search.md](semantic-search.md) | How the `FIND` keyword performs meaning‑based retrieval using the VectorDB. |
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| **Vector Collections** | [vector-collections.md](vector-collections.md) | Definition and management of vector collections, including creation, document addition, and usage in dialogs. |
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## How to Use This Overview
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- **Navigate**: Click the file links to read the full documentation for each topic.
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- **Reference**: Use this table as a quick lookup when developing or extending knowledge‑base functionality.
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- **Update**: When the underlying storage or VectorDB implementation changes, edit the corresponding markdown files and keep this summary in sync.
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---
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*This summary was added to provide a cohesive overview of Chapter 03, aligning terminology with the current architecture (VectorDB, .gbdrive, etc.).*
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