LLeMbas

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2 Commits

Include renames

Author	SHA1	Message	Date
Jaroslav BenešandClaude Opus 5	20bb569b00	Finding a thing that does not use your words Three pieces, and the first one is that they are all optional. Extraction stops being constants. Upload size, image edge, JPEG quality, PDF pages, extracted characters, orphan age and the text-extension list are settings now, read through a process-level snapshot rather than a session -- `prepare` and everything under it are called from routes, tool runners and the startup sweep, and several of those have no session in hand. Two things deliberately stayed constants: the decompression-bomb guard, which is a guard and not a preference, and ORPHAN_AGE, which would have been evaluated at import if it stayed in the signature and pinned the shipped 24 hours whatever anybody set. An embedding model is picked from the models an administrator flagged for it, and one that has since lost its flag is named rather than dropped from the picker: a setting that vanishes is one nobody can tell from a setting never made. Nothing here is required. Choosing none means no chunk rows, no requests, and retrieval.search returning exactly what fts.search_ids returns in exactly that order -- asserted, because it is what makes this safe to land on an instance that never asked for it. The two rankings are fused by reciprocal rank fusion: ranks and not scores, because bm25 is a corpus-dependent negative and cosine is 0..1, and normalising them onto one scale means picking a constant nobody can tune without a labelled set they do not have. RRF's one constant is famously insensitive and degrades to whichever list is non-empty -- which is what turns "no embedding model" into a branch that does not exist. A record scores as its best chunk rather than its average, or a long document about something else outranks a short one that says the thing. Width and model are stored beside every vector and a mismatch is skipped, because vectors from two spaces score against each other perfectly happily and mean nothing -- a search that works and is wrong is the worst failure this can have, and a model change now leaves stale rows ignored rather than trusted. Indexing is fired and forgotten, and how a change is noticed is a session event rather than a call in each of the ten library writers. That is a departure from this codebase's taste for explicit seams, for the reason tool_label is a Jinja global: a step every writer has to remember is one that gets forgotten, and here forgetting is silent -- the record saves, keyword search still finds it, and only its recall goes stale. Chunks are embedded before anything is deleted, so a failure leaves the old index rather than half a new one. Also: `embeddings` joins the model capabilities, and the three tool flags that had shipped with no checkbox -- canvas, scheduling and helpers -- have one. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-08-06 16:15:21 +02:00
Jaroslav BenešandClaude Opus 4.8	1eba860d39	Knowledge, notes, memory and skills, and a harness to make them used 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>	2026-07-21 19:43:57 +02:00

Author

SHA1

Message

Date

Jaroslav BenešandClaude Opus 5

20bb569b00

Finding a thing that does not use your words

Three pieces, and the first one is that they are all optional.

Extraction stops being constants. Upload size, image edge, JPEG quality, PDF
pages, extracted characters, orphan age and the text-extension list are settings
now, read through a process-level snapshot rather than a session -- `prepare` and
everything under it are called from routes, tool runners and the startup sweep,
and several of those have no session in hand. Two things deliberately stayed
constants: the decompression-bomb guard, which is a guard and not a preference,
and ORPHAN_AGE, which would have been evaluated at import if it stayed in the
signature and pinned the shipped 24 hours whatever anybody set.

An embedding model is picked from the models an administrator flagged for it, and
one that has since lost its flag is *named* rather than dropped from the picker:
a setting that vanishes is one nobody can tell from a setting never made. Nothing
here is required. Choosing none means no chunk rows, no requests, and
retrieval.search returning exactly what fts.search_ids returns in exactly that
order -- asserted, because it is what makes this safe to land on an instance that
never asked for it.

The two rankings are fused by reciprocal rank fusion: ranks and not scores,
because bm25 is a corpus-dependent negative and cosine is 0..1, and normalising
them onto one scale means picking a constant nobody can tune without a labelled
set they do not have. RRF's one constant is famously insensitive and degrades to
whichever list is non-empty -- which is what turns "no embedding model" into a
branch that does not exist.

A record scores as its best chunk rather than its average, or a long document
about something else outranks a short one that says the thing. Width and model
are stored beside every vector and a mismatch is skipped, because vectors from
two spaces score against each other perfectly happily and mean nothing -- a
search that works and is wrong is the worst failure this can have, and a model
change now leaves stale rows ignored rather than trusted.

Indexing is fired and forgotten, and how a change is noticed is a session event
rather than a call in each of the ten library writers. That is a departure from
this codebase's taste for explicit seams, for the reason tool_label is a Jinja
global: a step every writer has to remember is one that gets forgotten, and here
forgetting is silent -- the record saves, keyword search still finds it, and only
its recall goes stale. Chunks are embedded before anything is deleted, so a
failure leaves the old index rather than half a new one.

Also: `embeddings` joins the model capabilities, and the three tool flags that
had shipped with no checkbox -- canvas, scheduling and helpers -- have one.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

2026-08-06 16:15:21 +02:00

Jaroslav BenešandClaude Opus 4.8

1eba860d39

Knowledge, notes, memory and skills, and a harness to make them used

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>

2026-07-21 19:43:57 +02:00