Jaroslav Beneš
1eba860d39
Four places a model can reach for, differing in who writes a record and how it gets in front of the model. **Knowledge** is uploaded by a person and searched by the model. It goes through `services/files.py:prepare` — the same pipeline as a chat attachment — so the same PDF produces the same text whichever way it arrived, and `Document` carries the same content columns as `Attachment` for the same reason. **Notes** are written by the model and edited by you. Too long to inject, so they are searched. **Memory** is short facts, and every one of them goes into every request. That single decision is where the rest of its design comes from: records are capped short, the block has a budget, there is no search tool because the model is already looking at them, and they are not shareable — a record about a person is not content to hand round. **Skills** are saved procedures. Only the name and description are injected; the body is fetched when the model decides one applies, which is what makes a hundred skills affordable. A model may write and revise its own — the safety story is not a gate but a record: every revision is kept, attributed and revertible. A model that has just read a hostile page can save a skill that outlives the conversation, and the honest mitigation is that it is visible and undoable rather than that it was prevented. **The harness** is why any of it gets used. A model handed a tools array ignores it and answers from recall, because nothing in the request suggests otherwise. `services/harness.py` assembles a preamble from what this chat actually has: when to reach for each tool, the memories, the skill index. This is an exception to "system prompts are precedence, not concatenation", and a deliberate one. That rule governs the three *authored* layers and is untouched — exactly one still wins. The harness is a different axis: it describes the machinery rather than the behaviour, nobody authored it, and there is nothing for it to disagree with. It is prepended to whichever authored prompt won, in one system message, since several endpoints reject a second. Supporting changes: - **Sharing**, in one helper. `visible_to()` is the only definition of who can see a library item and every listing and tool goes through it. Sharing grants *reading*; two people editing one note with no history and no merge is worse than copying it. **Administrators do not bypass this** — they bypass permissions elsewhere because an admin can grant themselves those anyway, but reading somebody's private notes is a different act. - **FTS5**, created by `db/migrations.py:ensure_fts` with the triggers an external-content index needs. Idempotent, like the column sync beside it. Terms are ANDed and then ORed: the caller is usually a model writing a whole question, and requiring every word loses the match on one absent term. - **The attach button is a menu** — file, image, a web page, or a document from the library. Attaching a document copies it, because history must not change when a document is edited later. - **A URL fetcher with an SSRF guard.** This server can reach the router, the other services on the box and LLeMbas itself, and the address can come from a model. Private ranges are refused *after resolution* and redirects are followed by hand so every hop is checked. An admin can open it deliberately. - **Model capabilities split** into protocol support and a toggle per built-in tool. Rows predating the split have no `tool_*` keys, and absent counts as on when `tools` is on — otherwise an upgrade silently takes web search away from every model already configured for it. Also fixes the test fixture, which built the schema with `create_all` and so ran against a database without the FTS tables production has; it now runs `sync_schema`, the same path startup takes. 430 tests, ruff clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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A self-hosted web UI for your language models, written in Python.
Talks to anything that speaks the OpenAI API. Themed after Middle-earth.
Lembas is the Elvish waybread — one bite sustains a traveller for a day's march. The capitals hide what it runs on: LLeMbas.
Why this exists
Most self-hosted LLM front-ends are large JavaScript applications with a Python
API bolted underneath. LLeMbas is the other way round: server-rendered
Python, with htmx and a little Alpine for interactivity. There is no
package.json, no bundler, no build step, and nothing is fetched from a CDN at
runtime. Clone it, pip install -e ., run it.
Features
Working now
- Chats — streaming replies, Markdown with server-side syntax highlighting, copy and regenerate, automatic chat titles. Chats are created when you send the first message, so an abandoned one never clutters the sidebar
- System prompts — instance-wide, per-model and per-chat, with the most specific winning outright
- Reasoning display — thinking streams into its own collapsible block (closed by default), labelled with how long it took, and is never replayed as context
- Live Markdown — formatting appears as the model writes, not at the end
- Stop and rewind — cut a reply short and keep what arrived, or edit an earlier message and run the conversation on from there
- Replies keep running in the background — navigate away, open another chat, close the tab; a green dot and a notification tell you when it lands
- Attachments — drag, paste or pick images, PDFs and text files. Images are downscaled and sent to vision models; PDF and text content is extracted and put in the prompt
- Folders — arbitrarily nested, delete a folder without losing the chats inside it
- Web search — offered to the model as a tool it calls when a question needs it. DuckDuckGo out of the box (no account, no key), or point it at your own SearXNG, or Firecrawl. The sources stay in the transcript
- Speech in and out — dictate a message and have replies read aloud, against any OpenAI-compatible audio endpoint (whisper.cpp, Speaches, Kokoro…). Each person picks their own voice
- A library — four places a model can reach for. Knowledge: documents, images and web pages you collect, searched before the web. Notes: longer things it writes down and finds again later. Memory: short facts about you, in front of it on every turn. Skills: saved procedures it can follow, and write. All of it visible and editable by you, and shareable with a group or a person, read-only
- Installable — add it to a phone home screen or a desktop launcher and it runs in its own window
- OpenAI connections — point at OpenAI, LM Studio, vLLM, llama.cpp, llama-swap, Ollama or OpenRouter; models are discovered and cached
- Model settings — searchable, filterable list with a page per model: ordering, pinned models, an instance default and a per-user default, custom names, descriptions and images. Scales to hundreds of models
- Users, groups & permissions — per-group grants that union rather than override, and model access restricted to chosen groups
- Accounts — first account becomes the administrator, argon2 password hashing, revocable server-side sessions, self-service password change, admin-managed accounts
- Admin settings — open or close registration from the UI, stored in the database and effective immediately
- Two themes — Moria (dark) and Shire (light), switchable per user
Planned
Custom tools and MCP servers · agentic execution (local and over SSH) · image generation · OCR for scanned PDFs · semantic search in the library.
See PLAN.md for what is built, what is not, and why.
Quick start
git clone https://git.houmeres.sk/Houmeres/LLeMbas.git
cd LLeMbas
python -m venv .venv && . .venv/bin/activate
pip install -e ".[dev,search]" # `search` adds DuckDuckGo; drop it if unwanted
cp .env.example .env
lembas secret-key # paste the result into LEMBAS_SECRET_KEY
lembas serve # http://127.0.0.1:8080
Open the address and create the first account — it becomes the administrator.
Then go to Admin → Connections and add an endpoint. For a local runner that
is usually http://localhost:1234/v1 with no API key. Press Test & refresh
and its models appear in the chat model picker.
The vendored browser libraries (htmx, Alpine) are committed, so no network access is needed to run. To re-fetch or bump them:
python scripts/fetch_vendor.py --update.
Web search
Admin → Web search. DuckDuckGo needs nothing beyond the search extra
above. SearXNG needs its JSON format enabled — add - json under
search.formats in its settings.yml, or every search fails. Firecrawl needs
an API key.
Search is offered to the model as a tool, so it decides when a question needs looking up. It is only offered to models marked tools under Admin → Models: an endpoint without tool support rejects the whole request rather than ignoring the extra field, so the flag is a real switch and not a hint.
Audio
Admin → Audio. Two endpoints, because they are usually two servers:
| Speaks | Example | |
|---|---|---|
| Dictation | POST /v1/audio/transcriptions |
whisper.cpp's whisper-server, Speaches, faster-whisper-server |
| Read aloud | POST /v1/audio/speech |
Kokoro-FastAPI, OpenAI |
If the speech endpoint also answers GET /v1/audio/voices the voice list is
read from it, and each person can pick their own under Settings → Audio.
Recorded audio is passed straight through and never written to disk.
The microphone needs HTTPS or localhost. Browsers do not grant it over plain HTTP, so a LAN install without TLS will not offer dictation.
The library
Sidebar → Library, and Settings → Memory. Nothing is on by default for a
model: give it the tools it should have under Admin → Models, where
tools decides whether a tool list may be sent at all and the built-in tools are
chosen one by one.
Search is SQLite's FTS5 — keyword matching with BM25 ranking, no embedding service to run and nothing that stops working offline. It will not match a paraphrase, so a line of description on a document is worth writing.
Saving a link makes your server fetch a URL. Addresses on your own machine and network are refused unless an administrator opts in under Admin → Web search, because the address can come from a model and the server can reach things your browser cannot.
Installing as an app
Open it in a browser and use Install (Chromium) or Share → Add to Home Screen (iOS). This also needs HTTPS or localhost — service workers are unavailable over plain HTTP, and without one there is nothing to install.
There is no offline mode beyond a page saying so. Everything is rendered by your server, so a cached conversation would be a snapshot that silently went stale.
Configuration
All variables are prefixed LEMBAS_ and can live in .env. See
.env.example for the annotated list.
| Variable | Default | Purpose |
|---|---|---|
LEMBAS_SECRET_KEY |
generated | Signs sessions and encrypts stored API keys. Set this. A generated key changes every restart, signing everyone out and making stored API keys unreadable. |
LEMBAS_DATA_DIR |
./data |
SQLite database and uploads. |
LEMBAS_HOST / LEMBAS_PORT |
127.0.0.1 / 8080 |
Bind address. |
LEMBAS_ALLOW_SIGNUP |
true |
Whether new users may register themselves — the initial value only. Once set under Admin → General the stored setting wins. The first account is always an admin regardless. |
LEMBAS_DEFAULT_THEME |
moria |
moria (dark) or shire (light). |
LEMBAS_SESSION_TTL |
2592000 |
Session lifetime in seconds. |
LEMBAS_REQUEST_TIMEOUT |
300 |
Seconds to wait on an upstream model. |
Commands
lembas serve # run the server
lembas info # where data lives, what is configured
lembas secret-key # generate a value for LEMBAS_SECRET_KEY
lembas create-admin # create or promote an administrator
How it fits together
Browser ──form POST──▶ FastAPI ──▶ SQLite
▲ │
│ └──httpx──▶ any OpenAI-compatible endpoint
└──── server-sent events ◀───────────────┘ (streamed reply)
Sending a message stores the turn and returns two HTML fragments: the user's
bubble and an empty assistant bubble carrying an sse-connect. That opens a
server-sent event stream which appends tokens as they arrive, then replaces the
whole bubble with the finished, Markdown-rendered version. Rendering and
highlighting happen in Python, so the streamed and final views cannot disagree.
src/lembas/
api/ routes: auth, chats, folders, admin, pages
db/models/ SQLAlchemy schema
security/ password hashing, sessions
services/ llm client, chat orchestration, markdown, crypto, sse
web/ Jinja templates and static assets
assets/ SVG artwork masters
scripts/ artwork generator, vendored-JS fetcher
deploy/ systemd unit and nginx vhost for a real install
Development
pytest # test suite
ruff check . # lint
python scripts/build_artwork.py # regenerate the SVG artwork
python scripts/fetch_vendor.py # verify vendored JS against the lockfile
There is no Alembic. The schema is SQLite-only and synchronised at startup:
missing tables and missing columns are added automatically, so adding a field to
a model needs nothing but a restart. Renames, drops and retypes are still manual
— see CLAUDE.md.
Artwork
The logo, favicon and banner are original vector work, generated by
scripts/build_artwork.py so the mallorn leaf stays
identical across every size it appears at. The wordmark is
Source Serif 4 (SIL OFL 1.1)
converted to outlines — a README banner cannot load a webfont, and <text>
would render in whatever serif the reader happens to have.
Licence
A note on the theme
This is an independent hobby project, themed as an affectionate nod to J.R.R. Tolkien's world. It is not affiliated with, endorsed by, or connected to the Tolkien Estate, Middle-earth Enterprises, or any related rights holder. All artwork here is original.