Configuration

Variable	Default	Applies to	Description
`KB_EMBED_MODEL`	`BAAI/bge-small-zh-v1.5`	Engine	Embedding model; downloaded to the Hugging Face cache on first use
`KB_RERANK_MODEL`	`BAAI/bge-reranker-base`	Engine	Reranking model
`KB_DEVICE`	`auto`	Engine	`auto` uses the GPU whenever a CUDA build of torch finds a working device; `cpu` / `cuda` / `cuda:1` / `mps` force a choice. Any device failure falls back to CPU automatically
`KB_GPU_PROBE`	`1`	Engine	`0` skips the one-off "can this device actually compute?" probe (a tiny matmul) and goes straight to the model load — an escape hatch for unusual environments
`KB_EMBED_BATCH`	GPU 128 / CPU 32 (tiered below 8 GB VRAM)	Engine	Embedding batch size for ingest and search. Raising it does not help on small models — measured throughput is flat from 32 to 256
`KB_RERANK_BATCH`	GPU 64 / CPU 16 (tiered below 8 GB VRAM)	Engine	Reranking batch size
`KB_MODEL_RETRY_SECS`	`120`	Engine	How long a failed model load is remembered before retrying (`0` = every call, negative = never retry)
`HF_ENDPOINT`	none	Engine	Set to `https://hf-mirror.com` on restricted networks
`KB_AUTO_PIP`	`0`	npm package	`1` installs missing Python dependencies at startup (fixed argv; by default only the command is printed). The dynamic plugin host reports but does not install
`KB_RAG_ROOT`	DSH: session workspace `.kb`; MCP: `~/.kb-rag`	MCP	Knowledge base directory; per-call override with `kb_root`
`KB_RAG_PYTHON`	current interpreter	MCP	Interpreter used for the engine, to avoid a bare `python` resolving elsewhere
`KB_ASYNC_THRESHOLD`	`25`	Engine	Pending file count above which `kb_ingest` forks a background job and returns a `job_id` (poll with `kb_status`)
`KB_SQLITE_WAL`	off	Engine	`1` enables SQLite WAL; the default is safer when the `.kb` directory is synchronised
`UNPAYWALL_EMAIL`	built-in placeholder	Engine	Contact address used by `kb_fetch` for Unpaywall queries; set your own

dsh-kb-rag

安裝

說明文件

Configuration