getbased-rag 0.7.0

getbased-rag — standalone RAG knowledge server (formerly the Electron-bundled Lens)

getbased-rag

Installing for the first time? The getbased-agent-stack meta-package bundles this server with the MCP that Claude Code / Hermes / OpenClaw talk to, plus getbased-dashboard for a browser UI. One command and you're up.

A standalone RAG knowledge server — the backend that used to ship inside the getbased Electron desktop app, now just Python. Point any client (the getbased PWA's External server lens backend, the dashboard, or your own) at it.

• Stack: FastAPI + Uvicorn · Qdrant (embedded local mode) · sentence-transformers / ONNX Runtime
• Default port: 8322, loopback only
• Auth: Bearer token, auto-generated on first start
• Stores: every library is its own Qdrant collection, pinned to its own embedding model at creation

---

Install

Requires Python ≥ 3.10.

pipx install "getbased-rag[full]"

Or from source:

git clone https://github.com/elkimek/getbased-agents.git
cd getbased-agents
uv sync --all-packages --all-extras

---

Run

lens serve

First start auto-generates an API key at the data dir (see below), prints the bind address, and lazy-loads the embedding model on the first query (~90 MB download for MiniLM).

Copy the API key out when you need to configure a client:

lens key

Smoke test:

curl -s http://127.0.0.1:8322/health
curl -s -H "Authorization: Bearer $(lens key)" http://127.0.0.1:8322/info | jq

Ingest a file or directory from the CLI:

lens ingest ~/Documents/research
lens stats

Or over HTTP (what the dashboard + PWA use):

curl -H "Authorization: Bearer $(lens key)" \
  -F "files=@paper.pdf" -F "files=@notes.md" \
  http://127.0.0.1:8322/ingest

---

Per-library embedding models

Every library is pinned to one embedding model at creation time — Qdrant collections are dimension-locked, so you can't swap models on an existing library without re-ingesting. Call GET /models for the curated list (MiniLM-L6-v2 · BGE-small/base/large-en · BGE-M3) with dims and download sizes, then pass embedding_model on create:

curl -H "Authorization: Bearer $(lens key)" \
  -H "Content-Type: application/json" \
  -d '{"name":"Research","embedding_model":"BAAI/bge-m3"}' \
  http://127.0.0.1:8322/libraries

Libraries on the same model share one embedder instance in memory. Two libraries both on BGE-M3 use ~2 GB total, not 4.

---

Streaming ingest progress

The HTTP POST /ingest endpoint speaks two content types:

• Default (no Accept): single-shot JSON summary after the run completes
• Accept: application/x-ndjson: newline-delimited JSON progress stream — one start event (with total chunks), per-batch embed events every ~5 chunks (with current source + index), terminal result or error event

The dashboard uses the streaming path for its bottom-right pill (chunks/sec rate, cancel button, per-filename status). Cancellation works by client disconnect: aborting the fetch causes the server's worker thread to exit at the next batch boundary with cancelled: true in the result. Partial-commit — whatever was embedded before the cancel stays.

---

Wiring into the getbased PWA

In the PWA: Settings → AI → Knowledge Base → External server

Field	Value
URL	http://127.0.0.1:8322
API key	output of lens key

Click Save, then Test connection. rag_ready: false is expected before you ingest anything.

Agent access (Claude Code, Hermes, OpenClaw, etc.)

Pair this server with getbased-mcp to expose knowledge_search, knowledge_list_libraries, knowledge_activate_library, and knowledge_stats as MCP tools. Typical setup: run both the lens server and getbased-mcp on the same VM, point MCP's LENS_URL at http://localhost:8322.

Browser UI

Install getbased-dashboard for a web UI on top of this server — library management, drag-drop ingest with live progress pill, search preview, MCP config generator.

---

Configuration

Every setting is an environment variable. Defaults in parentheses.

Variable	Purpose
LENS_HOST (127.0.0.1)	Bind interface. Change to 0.0.0.0 only if you really want LAN access
LENS_PORT (8322)	TCP port
LENS_DATA_DIR (platform default)	Where Qdrant DB, API key, and model cache live
LENS_EMBEDDING_MODEL (sentence-transformers/all-MiniLM-L6-v2)	Default model for new libraries (overridable per library)
LENS_SIMILARITY_FLOOR (0.55)	Minimum cosine score for a returned chunk
LENS_ONNX_PROVIDER (auto)	cuda | rocm | openvino | coreml | cpu
LENS_RERANKER (false)	Enable reranking of top candidates
LENS_MAX_INGEST_BYTES (268435456 — 256 MB)	Cap on a single ingest upload's total size
LENS_CHUNK_MAX_SIZE (800)	Max chunk size in characters
LENS_CORS_ORIGINS (empty)	Comma-separated extra CORS origins to allow, in addition to the PWA + loopback defaults

Default data dir:

• Linux: $XDG_DATA_HOME/getbased/lens or ~/.local/share/getbased/lens
• macOS: ~/Library/Application Support/getbased/lens
• Windows: %APPDATA%\getbased\lens

A legacy ~/.getbased/lens is honored if it already exists, so pre-v1.21 installs don't lose their data.

GPU acceleration

Install the matching onnxruntime-* wheel (e.g. onnxruntime-gpu for CUDA), then:

LENS_ONNX_PROVIDER=cuda lens serve

---

HTTP API

All endpoints except /, /health require Authorization: Bearer <key>.

Method	Path	Purpose
GET	/health	Liveness + rag_ready + chunk count. Public
GET	/info	Embedder engine/model/dim, active library, similarity floor. For UI engine badges
GET	/models	Curated model picker list (id, label, dim, size_mb) plus the server's default
POST	/query	{ query, top_k } → top-k chunks from the active library, encoded with that library's model
POST	/ingest	Multipart upload; accepts streaming NDJSON progress when Accept: application/x-ndjson
GET	/stats	Per-source chunk counts for the active library
DELETE	/sources/{source}	Drop one source from the active library
DELETE	/sources	Clear the active library's chunks (library stays)
GET	/libraries	List libraries + active id. Each row includes chunks, lastIngestAt, embedding_model
POST	/libraries	{ name, embedding_model? } → create. 409 on duplicate name (case-insensitive)
POST	/libraries/{id}/activate	Set active
PATCH	/libraries/{id}	Rename
DELETE	/libraries/{id}	Delete (drops Qdrant collection)

---

Security notes

• Default bind is 127.0.0.1 — queries never leak to the LAN unless you explicitly set LENS_HOST=0.0.0.0.
• The API key file is mode 0600 and never exposed over HTTP. Use lens key locally to read it.
• Bearer comparison uses secrets.compare_digest — constant-time, no timing-leak class of bug.
• Upload paths are basename-sanitised server-side (so ../../etc/passwd can't escape the ingest temp dir).
• Zip uploads are zip-slip-guarded — each archive entry must resolve inside its own per-zip subdirectory AND inside the overall ingest root.
• If you expose the server to a LAN or the internet, front it with a reverse proxy that terminates TLS and rate-limits.

---

CLI

lens serve            Start the HTTP server (default)
lens ingest <path>    Index files into the active library
lens stats            List indexed sources + chunk counts
lens delete <source>  Drop chunks belonging to one source
lens clear            Wipe the active library
lens info             Show config + API key
lens key              Print the API key (creates one if missing)

---

License

GPL-3.0-only.

---

Lineage

This repo is the Python portion lifted out of getbased after the Electron desktop app was retired. The PWA's external-server lens backend speaks this same HTTP contract unchanged.