The page now leads with projection rather than partition and points at the
scopes API; OpenAPI declares the 201/409/404 those routes actually return.
Co-authored-by: Cursor <cursoragent@cursor.com>
Using it as a verb for session design, recall filters, and CLI targeting
collides with the named-session-set feature.
Co-authored-by: Cursor <cursoragent@cursor.com>
- Drop the recall-resolution diagram (restated the Two Arms table)
- Replace the enumerated Rules table with prose; caps and error shapes
now live in the API reference schema descriptions
- Trim backfill/removal internals to observable behavior and note that a
backfilled scope deepens through subsequent dreams
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- Add the seven /scopes routes and their schemas to openapi.json, plus the
scope/kind fields on chat, representation, session-create, and peer-list
schemas; generate the endpoint pages and register a scopes nav group
- Add a Scopes subsection and diagram node to the architecture data model
- Add scope guidance to design patterns: quick-reference rows, an isolation
boundary comparison (workspace / scope / session allowlist), and common
mistakes (scope-per-reader, scopes-as-access-control)
- Replace the "Underneath the Facade" section in scopes.mdx with behavioral
guardrails and pointers to the implementation source
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* feat(cli): fix API key typing
Co-authored-by: Cursor <cursoragent@cursor.com>
* chore: bump honcho-cli to 0.1.4
Ship the masked --setup API key prompt plus the openai-compatible embedding base URL already on main.
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(cli): check for newer version
* docs: nit
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
* docs: make honcho start the documented local path
honcho-cli 0.1.3 can run a personal stack without cloning the repo; point the README, self-hosting, and CLI reference at that, and drop the community installer callouts.
Co-authored-by: Cursor <cursoragent@cursor.com>
* docs: updating with new CLI language
* docs: drop compatibility-guide changes from this PR
Leave that file on main; CLI version cards are updated at release time.
Co-authored-by: Cursor <cursoragent@cursor.com>
* docs: small language changes
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(cli): add honcho start/stop/status for a local Docker stack
* feat(cli): fix status command
* feat(cli): improving how we pull docker images and writing a config,toml
* feat(cli): add honcho start --setup wizard for local stack config
* feat(cli): cleaning up unnecessary func, and error throwing
* feat(cli): minor clean up in stack.py
* feat(cli): read setup wizard defaults from the image config.toml
* feat(cli): cleaning up unused commands
* feat(cli): adding ignored docker-compose.yml
* feat(cli): forward host LLM env into honcho start
* feat(cli): share start/stop progress helpers via output.py and cleaning up language
* chore: update issue templates and docs
* chore: slim bug/quality forms and add integration template
Drop high-friction required fields from bug and quality issue forms.
Add an integration-request form (app stores, plugins, frameworks) labeled
integration, and point contributing docs at it.
* chore: route security mail to support@ and polish docs intake
Use support@honcho.dev for private vulnerability email. List the
documentation template in contributing guides, rename Media prove,
and add public-issue redaction/security redirects on the docs form.
* fix: address render issue in templates and add version field
* feat(docs): Initial draft of new contributing policies
* feat(ci): defer issue-gate closes to a scheduled sweeper
Addresses review feedback on #1041.
The gate now reads GitHub's resolved closing references
(closingIssuesReferences) instead of regex-parsing the pull request body,
so an issue linked through the sidebar Development panel counts, and a
bare `#123` mention no longer does.
It also no longer closes on the pull request event. It labels and
explains; pr-sweeper.yml re-checks every six hours and closes only what is
still failing 72 hours after the notice. That re-check is load-bearing:
linking an issue via the sidebar fires no webhook, so an event-only gate
could never observe a contributor complying that way. The sweeper also
closes drafts from outside the org after 30 days.
The shared check lives in .github/scripts/issue-gate.js so both workflows
run identical logic, with a dependency-free self-check wired into static
analysis. Its one regression guard: author_association CONTRIBUTOR stays
gated, since GitHub assigns it to anyone who has previously committed.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* chore(codeowners): drop the third reviewer from most areas
Discussed with @akattelu. Also reassigns SECURITY.md to @Rajat-Ahuja1997
and strips trailing whitespace from the deployment block.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* docs(v2): port the issue gate policy into the v2 contributing guide
The v2 guide is still published (v2.5.1 in docs.json) but carried no
mention of the issue gate, so a contributor reading it would not learn
that a pull request needs an approved issue until the bot labelled theirs.
Ports the policy, both linking routes, and the gate's place among the
automated checks, keeping the v2 guide's own structure and unwrapped
prose rather than importing the v3 rewrite wholesale.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(ci): count only bot-authored gate notices, share the exemption list
Two review findings on the issue gate, with a common root cause.
MARKER is an invisible HTML comment, so anyone who can comment on a
public repository can paste it. findNotices accepted any comment
containing it, so a third party could post one on someone else's pull
request: runGate posts a notice only when none exists, so the author
would never be told, and runSweep would then measure the 72-hour grace
window from the stranger's timestamp and close them unwarned. Notices now
require bot authorship.
The stale-draft sweep re-listed the gate's exemptions and had lost the
bot case, so a bot's long-lived draft was closable despite checkGate
exempting bots. Both callers now share one exemptReason(pr) rather than
keeping parallel lists that drift.
Not changed: closingIssuesReferences(first: 20) truncation. It needs a
pull request with 21+ closing references where only a later one carries
the label, and the outcome would be a label plus the grace window, not a
close.
Coverage goes 11 -> 20 cases, including the stale-draft close path, which
had none. Both fixes were confirmed to fail their tests when reverted.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Aakash Kattelu <aakash@plasticlabs.ai>
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
* chore: update issue templates and docs
* chore: slim bug/quality forms and add integration template
Drop high-friction required fields from bug and quality issue forms.
Add an integration-request form (app stores, plugins, frameworks) labeled
integration, and point contributing docs at it.
* chore: route security mail to support@ and polish docs intake
Use support@honcho.dev for private vulnerability email. List the
documentation template in contributing guides, rename Media prove,
and add public-issue redaction/security redirects on the docs form.
* fix: address render issue in templates and add version field
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
* fix(llm): forward provider_params.timeout to the OpenAI-compatible embedding client
#832 and #903 added a configurable request timeout for the LLM registry
and the Gemini embedding client respectively, but the OpenAI-compatible
embedding client (src/embedding_client.py) was never wired up. It
constructed AsyncOpenAI with no timeout at all, so a stalled socket
against a slow or contended OpenAI-compatible backend (e.g. a
self-hosted embedding model under load) wedges the deriver worker's
event loop indefinitely — the exact failure #785/#903 describe, just
via a code path #903 didn't cover.
EmbeddingModelConfig now carries provider_params through from
resolve_embedding_model_config, mirroring how resolve_model_config
already does it for ModelConfig, and the OpenAI branch of
_EmbeddingClient.__init__ extracts `timeout` via the existing
request_timeout_from_extra_params helper. Unset stays unset — no
existing behavior changes.
Reproduced and verified against a real self-hosted deployment (local
Ollama backend under load): before this fix, a single stuck embedding
call blocked all deriver queue processing for 20+ minutes with no
error logged, twice in one session.
* fix(embedding): use first-class timeout on embedding model config
provider_params is the LLM per-request escape hatch; embedding timeouts are
client-construction knobs and belong next to max_batch_size. Wire the field
for OpenAI and Gemini, omit the OpenAI kwarg when unset so the SDK default
stays, and keep Gemini's 10-minute floor when unset.
* test(embedding): live coverage for first-class embedding timeout
Exercise EmbeddingModelConfig.timeout on one representative OpenAI and
Gemini model: configured timeout lands on the SDK client, and a near-zero
timeout aborts before the provider answers.
---------
Co-authored-by: Aakash Kattelu <aakash@plasticlabs.ai>
* fix(filter): reject unknown operator dicts on scalar columns
An unrecognized operator dict on a non-JSONB column (e.g.
{"session_id": {"operator": "null"}}) fell through to `column == value`,
binding a dict to a VARCHAR parameter. That compiles, then fails in the
driver at execute time with "cannot adapt type 'dict'" — an unhandled
500 for what is invalid input.
Raise FilterError (422) instead. The guard lives in the shared
_build_field_condition, so every route through apply_filter is covered.
It keys on the actual column type rather than the JSONB_COLUMNS name
list, so dict equality still works on JSONB columns reachable through
Document's raw-key fallback (e.g. source_ids), where the driver adapts
dicts fine.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* chore(filter): name psycopg explicitly in comment
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(filter): handle null operands and non-numeric columns in comparisons
Two defects in _build_comparison_conditions, both reachable from any
route that accepts filters:
1. A null operand hit float(None), raising TypeError where only
ValueError was caught — an unhandled 500. A null operand is a null
check, not a value comparison, so {"ne": null} now compiles to
IS NOT NULL and the other operators reject null with a 422. Equality
against null already produced IS NULL via _build_field_condition.
2. Numeric operators float()-cast on every column type, so a string
inequality on a text column ({"session_id": {"ne": "abc"}}) was
rejected as an invalid number. Coercion is now gated on the column
actually being numeric; text columns compare as text. Numeric columns
still validate, and TypeError is caught alongside ValueError.
Existing ne coverage only exercised the JSONB metadata path, which uses
_safe_numeric_cast and handles strings — the scalar column path was
untested. Adds cases for both.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(filter): fail closed on unrecognized filter shapes
The filter body is arbitrary client JSON with no schema, so validation
was emergent: any shape the DSL didn't recognize surfaced as an
unhandled 500 from somewhere in SQLAlchemy or psycopg. Fixing individual
shapes doesn't converge — a fuzz over the DSL found five more families
beyond the three already fixed here:
{"AND": [None]} TypeError, non-dict in a logical list
{"AND": [[]]} AttributeError on .items()
{"session_id": {"gte": true}} SQLAlchemy ArgumentError
{"embedding": []} NotImplementedError, no python_type
{"session_id": {"ne": {...}}} execute-time "cannot adapt type 'dict'"
Two generic guards instead:
1. Any operand bound to a non-JSONB column must be a scalar, checked
element-wise for `in`. A dict or list bound to a scalar column
compiles cleanly and only fails in the driver at execute time, so it
has to be rejected during construction. JSONB columns are exempt —
a dict there is a containment match.
2. apply_filter fails closed: FilterError propagates, anything else is
logged with logger.exception (filter shape included) and re-raised as
FilterError. Unknown filter failures become 422s while staying fully
visible as errors rather than being swallowed.
Adds two invariant tests over a generated matrix of filter shapes: every
shape either compiles or raises FilterError, and no non-scalar is ever
bound to a scalar column. Both fail without the guards above. They cover
shapes nobody enumerated, so the next unimagined body fails in CI rather
than in production.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* chore: clear the two remaining basedpyright warnings
`uv run basedpyright tests/ src/` reported two warnings in files that
predate this change. The pre-commit hook is file-scoped, so neither was
visible unless the owning file was touched.
- src/vector_store/__init__.py: join the lancedb error message with
explicit `+` instead of adjacent literals (reportImplicitStringConcatenation).
- tests/test_cache_redaction.py: the test covers a private helper
deliberately, so annotate the import (reportPrivateUsage).
No behavior change; whole-tree check is now clean.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(filter): keep numeric operands exact instead of coercing to float
float() rounds any integer past 2**53 and flattens a Decimal, so
{"token_count": {"gt": 9007199254740993}} silently compared against
9007199254740992 — a different row set than the client asked for.
_coerce_numeric passes already-numeric operands through untouched and
only parses strings, trying int() before float() so "5" stays exact
while "5.5" still parses. bool narrows to int: it is an int subclass,
but binding it as a boolean against a numeric column produces SQL
Postgres has no operator for.
Not coerced to the column's own type: int(5.5) would turn
{"token_count": {"lt": 5.5}} into `lt 5`, changing which rows match.
Also fixes a vacuous assertion in test_dict_on_jsonb_column_still_works.
It checked for "internal_metadata" in the whole statement, but that name
is in the SELECT projection either way, so the test passed even when no
WHERE clause was applied. Now asserts on stmt.whereclause and that the
filter payload is actually bound.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(filter): bind boolean columns as booleans, numerics without a cast
Boolean columns were treated as numeric because bool subclasses int, so
`{"is_active": {"ne": true}}` coerced true to 1 and Postgres rejected
`boolean <> integer` at execute time. Confirmed against a live database:
every form except bare equality with a native boolean was a 500.
Boolean columns now get their own branch: native true/false bind as
booleans, and any other operand is a FilterError. SQLAlchemy types the
bind from the operand rather than the column, so "true" renders
`is_active = %(param)s::VARCHAR` and Postgres has no such operator — a
422 is the honest answer. String booleans have never worked, are absent
from the docs (every documented boolean is inside metadata, which is
JSONB containment and unaffected), and produced no Sentry events in 90
days, so nothing can depend on the current behavior.
Also corrects the previous commit. Coercing operands to exact ints made
SQLAlchemy render an ::INTEGER cast, so any value past int4 — not 2**53
— started failing with "integer out of range" where float() had silently
compared as a double. Decimal keeps the value exact and renders no cast,
matching what float() did. The `in` branch never went through coercion
at all, so {"token_count": {"in": [1, 2147483648]}} was a 500 before
this PR too; it now takes the same path.
Verified end to end against the live database, not just at compile time.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(filter): coerce every operand against its column's type in one place
The DSL had two operand paths with different rules. Comparison operators
parsed datetimes and coerced numbers; bare equality bound whatever it was
handed. SQLAlchemy types a bind from the operand rather than the column
and the psycopg dialect renders that type as an explicit cast, so a
mismatch compiled into valid-looking SQL and failed at execute time:
"operator does not exist: timestamp with time zone = character varying".
A matrix of column type x operand type x operator against a live
database found 462 combinations, of which 55 built cleanly and then
failed. The most plausible was a filter someone would write first try:
{"created_at": "2026-01-01"} 500
{"created_at": {"gte": "2026-01-01"}} worked
_coerce_operand now handles every operand, whatever the operator, keyed
on the column's real type: JSONB takes an object, boolean takes only
true/false, datetime parses strings, numeric goes through _coerce_numeric,
text requires a string, and a column with no python_type (pgvector) is
not filterable. eq/ne/gt/in cannot drift apart because they share the
one call; `in` coerces element-wise, since a single element's type
decides the cast rendered for that parameter. The matrix is now clean.
This is a net deletion: the separate datetime, numeric, in-datetime and
boolean branches, plus _require_bindable_operand, all collapse into it.
Two more execute-time failures fixed on the way. `contains` was keyed on
column_name == "h_metadata", so Document's equally-JSONB
internal_metadata fell through to ILIKE and produced `jsonb ~~* text`;
it now keys on the column type. And {"source_ids": "abc"} was
`jsonb = character varying`.
Closed-set columns are validated against the Literal that defines them,
so declaring a new level or sync state updates filter validation with no
change here. {"level": "banana"} was silently matching nothing.
Empty IN is now always applied rather than skipped. Unifying the branches
inherited a guard that had only ever wrapped the datetime path, which
dropped the condition entirely and widened the query to every row —
fail-open on an empty allowlist, which session scoping relies on to fail
closed (see extract_session_allowlist). Caught by an existing test that
asserts returned rows; the fuzz and the type matrix only check for
errors, so neither would have seen it.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(filter): make NOT and ne include rows where the field is unset
NOT (col = v) and col <> v are NULL when col is NULL, so negation
dropped rows whose column is unset — a conclusion with no session is
not "some other session", but was excluded anyway. IS NOT TRUE and
IS DISTINCT FROM behave identically when no NULL is involved.
Adds a row-count test, since this class of bug builds and executes
cleanly, and documents {"ne": null} for excluding unset fields.
* fix(filter): reject session ids that can't name a real session
extract_session_allowlist accepted any non-empty string, so "*" reached
the three consumers of the allowlist — direct IN, the filter DSL, and a
Python membership test — which disagree about it. The DSL reads "*" as
"drop the condition" and matches every session; the others treat it as a
literal name and match none. One /chat request could have some recall
sources unscoped and others scoped to nothing.
Entries are now validated against RESOURCE_NAME_PATTERN, the same pattern
the API requires of session ids, so no session could ever be named "*"
anyway. Wildcards were never part of this endpoint's documented contract
(an id, a list of ids, or {"in": [...]}), and a wildcard alongside a
top-level session_id already 422'd via must_include.
* fix(filter): treat a bare null operand as a null check
Routing every operand through _coerce_operand made bare `None` a type
error rather than a null check, so {"session_id": null} raised FilterError
where it previously built IS NULL: _build_field_condition used to end in
`column == value`, which SQLAlchemy renders as IS NULL. Confirmed 422 on
all five column families (text, numeric, boolean, datetime, JSONB).
Nothing caught it. The docs added in this branch promise
`{"session_id": None}` matches unset rows, the comment in
_build_comparison_conditions claimed the equality path already covered it,
and the DSL-wide invariant test accepts "compiles OR raises FilterError",
so a 422 passed. _coerce_operand's docstring already stated the contract
its caller wasn't honoring — "Callers handle None (a null check) and `*`
(a wildcard) before calling" — so the guard restores that rather than
adding a new rule.
The three null forms now agree: {"col": null} is IS NULL, {"col": {"ne":
null}} is IS NOT NULL, NOT [{"col": null}] is (IS NULL) IS NOT true.
Also from review of #947:
- Log filter keys, not the body. That log line is new in this branch and
operands carry peer/session ids and free-text `contains` values; the
traceback plus the entry shape is what locates a builder bug.
- Assert whereclause in the _where test helper, so a dropped condition
fails instead of returning the whole statement to substring-match.
- Cover the raw-key JSONB path via source_ids, a JSONB column outside
JSONB_COLUMNS reachable through Document's raw-key fallback.
- Drop the orphaned comment left above ENUM_COLUMN_VALUES when
_coerce_operand replaced SCALAR_OPERAND_TYPES.
- Document that a JSONB column takes an object bare or under `contains`
and nothing else. Bare {"metadata": X} is containment, so the `ne` this
branch removed was never its inverse: a row with {"status":"done","x":1}
satisfied both it and {"metadata": {"status":"done"}}. Per-key operators
and NOT cover the real intents.
- Rewrite "Negation and Unset Fields" to lead with the operator rule and a
truth table, forward-linking to Filtering Conclusions instead of using
conclusions ~525 lines before they are introduced. A conclusion's
session_id is the only nullable documented filterable field, verified
across Message/Document/Session/Peer/Workspace.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
* test(embedding): add reproducer for missing encoding_format on openai paths
The openai SDK defaults encoding_format to base64 when it is not passed. OpenAI-compatible providers that don't support base64 embeddings (e.g. OpenRouter with nvidia/nemotron-3-embed-1b:free) return HTTP 200 with empty data, and every embedding call fails with 'No embedding data received'.
* fix(embedding): request float encoding_format on openai embedding calls
The openai SDK defaults encoding_format to base64 when the caller does not pass one. OpenAI-compatible providers that don't support base64 embeddings (e.g. OpenRouter hosting nvidia/nemotron-3-embed-1b:free) answer HTTP 200 with empty embedding data, and every embedding call fails with 'No embedding data received', breaking conclusions, semantic search, and the deriver. Pass encoding_format='float' explicitly on both the single-query and batch call paths.
* test(embedding): cover openai-compatible providers in the live embedding matrix
The existing openai family runs against real OpenAI, which serves base64
embeddings happily, so the matrix passes with or without the #932 fix. Adds an
`openai_compatible_embedding` family (openai transport, third-party base_url)
so the matrix can reach a provider that rejects base64. Empty default_models
keeps it skipped unless LIVE_EMBEDDING_OPENAI_COMPATIBLE_MODELS is set.
Also adds test_live_openai_float_encoding_matches_base64, which pins the other
direction: switching the wire format must not move vectors on real OpenAI.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* fix(embedding): keep an explicit embedding-count check on the openai paths
Passing `encoding_format` disables the openai SDK's own empty-data guard, so a
provider answering 200 with missing embeddings surfaced as `IndexError: list
index out of range` on the single path and `zip() argument 2 is shorter than
argument 1` on the batch path. The latter is also #745's signature, which would
have left it with two unrelated causes.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* docs(live-llm): correct the openai-compatible embedding matrix env docs
The documented default dimensions said 2048 after the family moved to 3072, and
LIVE_EMBEDDING_OPENAI_COMPATIBLE_SEND_DIMENSIONS was missing entirely. Also
points the example and the coverage note at a model that is actually reachable,
and records that OpenRouter load-balances, so the base64 failure is per-attempt.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* feat(embedding): resolve openai encoding_format by mode instead of pinning float
Requesting float unconditionally costs ~3.6x the response bytes of base64 and up
to +83% latency on a 500-item batch, which the default deployment on real OpenAI
pays for nothing: only third-party OpenAI-compatible providers reject base64.
Adds EMBEDDING_MODEL_CONFIG__ENCODING_FORMAT_MODE, mirroring dimensions_mode.
`auto` keeps base64 when no base_url override is set or it points at
api.openai.com, and picks float elsewhere. The format is still always sent
explicitly, since the SDK otherwise injects base64 on its own.
Also corrects the _validate_embedding_count docstring, which said "fewer" where
the guard is an inequality.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* fix(embedding): request base64 embeddings by omission, not by name
The openai SDK decodes a base64 response only when it injected the default
itself; naming any format makes it skip the decode and hand back the raw string,
which then fails the dimension check with "Expected 1536, got 8192". base64 mode
therefore has to omit the kwarg rather than pass it.
The unit fake returned float lists whatever was asked for, so it could not catch
this. It now mirrors the SDK and returns a base64 string for a named base64
request, which fails against the previous commit.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---------
Co-authored-by: Aakash Kattelu <aakash@plasticlabs.ai>
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
* feat(cli): add `honcho session view` transcript command
Adds a read-only transcript view for a session, with three paging modes:
a tail window (`--last N`, the default), server pages (`--page N --size M`),
and the whole conversation (`--all`). `--reverse` selects newest-first in
every mode, `--ids` exposes message IDs, and `-p` scopes to one peer.
JSON mode emits the same shape as `message list`.
The renderer is deliberately literal: content and identifiers go through
`rich.text.Text` rather than Markdown or console markup, so newlines, tag
delimiters like `<thinking>`, and bracketed text survive intact — this is a
debugging surface, so it has to show what was actually stored. Timestamps
are converted to UTC (not just stripped of their offset) and keep
millisecond precision. Nothing is truncated with an ellipsis: a displayed
message ID is always usable with `honcho message get`.
Flags are validated before the client is built, and the session is
constructed directly instead of via the get-or-create `client.session()`,
so an invalid or mistyped invocation never reaches — or creates — anything
server-side. `--size` is bounded locally to the server's 100-item ceiling
rather than surfacing a raw 422, and the "more:" hint echoes back the size
and ordering actually in use so following it lands on the adjacent window.
Also fixes `honcho message list --last N`, which stopped at the first page
of 50: both commands now share the page-walking helper, so the same flag
returns the same window either way.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* docs(cli): regenerate command reference for `session view`
Adds the generated `session view` accordion to the docs snippet and points
the session-debugging workflows at it. Trims the docstring to plain prose —
the RST double-backticks were rendering literally in `--help`, where every
other command uses unmarked flag names — and stops the generator emitting a
trailing blank line that tripped end-of-file-fixer on every regeneration.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* fix(cli): carry invocation scope into the next-page hint
Addresses CodeRabbit review on #1006.
The "more:" hint echoed only `--page`, `--size`, and `--reverse`, so copying
it off a scoped invocation dropped `-w`, `-p`, and `--ids` — landing on a
different workspace or an unfiltered transcript. Hint construction moves into
`_next_page_command` in the command module, which knows the invocation; the
renderer now just prints the string it's handed and no longer needs to know
CLI flag syntax. Only flags passed explicitly are echoed, since anything from
the environment or config resolves the same way on the next run.
Also rejects non-positive `--last` on `honcho message list`, which slice
semantics turned into a silently empty result. `session view` already errored
on it; the two now agree.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* fix(cli): read next-page hint scope from effective flag overrides
Addresses the second CodeRabbit pass on #1006.
`-w`/`-p` parse at group and top level as well as command level, all landing
in `_global_overrides`, so reading the command-level params dropped the scope
from `honcho session -w ws2 view ...`. The hint now reads the effective
overrides via a new `get_flag_overrides()`, which deliberately excludes
environment and config values since those resolve the same way on the next run.
Also shell-quotes the hint's identifiers with `shlex.join`. Note this is
hardening rather than a live injection fix: the API constrains IDs to
`^[a-zA-Z0-9_-]+$`, so an ID carrying a space or metacharacter fails the fetch
before any hint is printed. `validate_resource_id` is looser than the server
though, so quoting is the cheaper invariant to hold locally.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
* docs: adding honcho-memory skill
* fix: skills to point at llm friendly content
* docs: split honcho-mcp skill out of honcho-memory; address PR review
Restructure honcho-memory into a concepts/strategy hub that routes to
per-connection path skills, and add a dedicated honcho-mcp skill holding
the MCP-tool mechanics that previously lived inline.
Addresses review feedback on #784:
- honcho-memory step 2 now leads with fast context reads, with chat as
the slower escalation
- honcho-mcp adds a "Speed: reads vs reasoning" section, describes what
each context call returns, and a reasoning-levels table
- get_representation framed as a contextualized snapshot insertable into
a system prompt
- drop schedule_dream from the tool table (manual escape hatch, not
routine guidance)
- prune queue-status references from honcho-cli; document honcho-mcp in
vibecoding skill registry
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* refactor(skills): move skills to canonical top-level skills/ with .claude symlink
Establish a single source of truth for agent skills. The real files now live in
the top-level skills/ directory (the publishing convention used by Vercel,
Supabase, and Cloudflare, and the tree Honcho's `npx skills add` distributes).
.claude/skills becomes a symlink to ../skills so Claude Code discovery keeps
working off the one tree — eliminating the parallel-copy sync burden.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix: splitting context into references & verifying content is consistent.
* docs: fixing core language
* docs: fixing core language
* fix: language about observe_others
* chore: adding .agents folder for codex
* fix: add instructions.md into the mcp server & delete mcp skill in favor of including it in honcho-memory.
* chore: remove migrate docs (can be found on older versions)
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* docs: document pgvector preinstall for least-privilege DB roles
Honcho issues CREATE EXTENSION IF NOT EXISTS vector before migrations
and again at server startup, both using the DB_CONNECTION_URI role. On
deployments where that role deliberately cannot create extensions
(managed Postgres, Kubernetes operators, NixOS), both statements fail
with a privilege error — IF NOT EXISTS does not save you, because
Postgres checks the privilege before checking for the extension.
Document preinstalling pgvector as a privileged role as the supported
path, and add a troubleshooting entry keyed to the exact error string.
Note that docker compose is unaffected, since the bundled database
service creates the extension via an initdb script.
Refs #614
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* docs: clarify why docker compose avoids the pgvector privilege error
The previous wording pinned the claim entirely on database/init.sql,
which only runs on first boot of an empty data volume. The load-bearing
reason is that the bundled stack connects as the postgres superuser, so
it can create the extension regardless of volume state. Name that first
and keep init.sql as the secondary reason.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
* fix: resolve tiktoken encoding without constructing the embedding client
EmbeddingClient.encoding forced full client construction, which raises
'OpenAI API key is required' even though tiktoken needs no credentials.
The document dedup tie-break (src/crud/document.py) only needs .encoding
for token counting, so any test hitting that path fails in environments
without embedding keys — notably CI for pull requests from forks, where
repo secrets are unavailable (e.g. #908's test-python job failing on
tests/crud/test_document.py::test_duplicate_rejection_reinforces_existing).
Resolve the encoding from the configured model directly, falling back to
cl100k_base, and only reuse the underlying client's encoding when it has
already been constructed.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix: make embedding batch size configurable
Add optional max_batch_size to the embedding model config
(EMBEDDING_MODEL_CONFIG__MAX_BATCH_SIZE) to cap texts per request for
OpenAI-compatible providers with smaller limits than OpenAI's, such as
DashScope text-embedding-v4 (10) and Alibaba Bailian
qwen3.7-text-embedding (20). When unset, native provider defaults are
preserved (OpenAI 2048, Gemini 100).
Fixes#687.
* test(embedding): cover Gemini batching and config fallbacks per review
- Gemini transport now tested for configured batch splitting and the 100
default fallback
- OpenAI unset default (2048, single request) explicitly covered
- env-parsing test now asserts the value survives resolve_embedding_model_config
- docs: 100 is the client's conservative Gemini default, not a native limit
* test(embedding): assert provider batch-size defaults
---------
Co-authored-by: adavyas <adavyasharma@gmail.com>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* fix(llm): support per-request provider timeouts
* fix(llm): convert Gemini timeout to milliseconds
* fix(llm): validate Gemini HTTP options
* test(llm): type Anthropic stream context args
* test(llm): live per-request timeout coverage for all providers
Two live checks per provider: a generous timeout asserted at the SDK
call boundary, and a tight timeout that must abort well under the 600s
client default. Gemini's async transport can be aiohttp, so its tight
timeout surfaces as asyncio.TimeoutError rather than httpx.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* style(tests): drop extra blank line in anthropic backend test
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(llm): validate provider_params.timeout at config load
Move the timeout coercion into src.config as coerce_provider_timeout and
run it from a field validator on ModelOverrideSettings.provider_params, so
a bad value in config.toml/env fails at startup with the exact config path
instead of surfacing per-request as a retried 500. Good values normalize
to float seconds at load. The per-request guard in src.llm.backend now
delegates to the same coercion (wrapping ValueError in ValidationException)
and continues to cover extra_params passed programmatically.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* docs: document provider_params.timeout load-time validation and gotchas
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* refactor(llm): address review nits on timeout plumbing
Apply eisene's review feedback:
- Rename PROVIDER_TIMEOUT_ERROR → PROVIDER_TIMEOUT_ERROR_TEXT
- Move request_timeout_from_extra_params from backend.py (pure
dataclasses) to request_builder.py (request assembly)
- Add comment explaining Gemini's ms timeout conversion
- Generalize _normalize_extra_params with _strip_none_params helper
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Aakash Kattelu <aakash@plasticlabs.ai>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* Structured outputs for dialectic
* cleanup
* rename json_schema_to_pydantic to clarify it's not a general schema converter
* clean up schema DoS guards
* simplification and cleanup of schema conversion
* chore: ruff and pyproject toml
* chore: basedpyright cleanup in test
* fix: some needed unrelated test failures
* test(schema_conversion-and-anthropic-backend): expand test coverage
include table tests
* fix(llm): support combined tool calling and structured output across backends
- OpenAI: parse() 500s on non-strict function tools; route tool-carrying
structured requests through create() with an explicit json_schema
response_format (mirrors the streaming path)
- Anthropic: skip the '{' JSON prefill when tools are present so tool_use
blocks stay reachable; make the schema instruction conditional and rely
on parse + repair
- Gemini: native response_schema + function calling is rejected before
Gemini 3; with tools present, inject a schema instruction into the final
turn instead and rely on parse + repair
- All backends: tool-call turns carry no consumable content, so skip
structured-output parsing on them
Extracted from the dialectic structured-output branch (DEV-1652) so the
transport layer can land independently.
DEV-2035
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* test(live_llm): exercise combined tools + structured output per provider
Two-turn live flow per backend: a forced tool-call turn (structured
parsing must be skipped) followed by a replay turn that must return a
schema-conforming answer with tools still attached. Asserts the
provider-specific request shaping: no parse() for OpenAI (500s on
non-strict tools), no '{' prefill for Anthropic, no native
response_schema for Gemini.
Verified against live OpenAI (gpt-4.1, gpt-5, gpt-5.4, gpt-5.4-mini)
and Gemini (gemini-2.5-flash).
DEV-2035
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* test(unified): dialectic chat with response_format schema under tool use
Adds response_format pass-through to the unified runner's chat query and
a test case that forces the dialectic tool loop (reasoning off + global
enumeration question) while requiring a schema-conforming JSON answer —
end-to-end coverage of the combined tools + structured output transport
path on whichever provider each level is configured with.
Verified locally against a full harness run (json_match assertions pass;
the llm_judge assertion additionally runs in CI where the Anthropic key
is available).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix: some needed unrelated test failures
* ci: add label-triggered live LLM test workflow
Adding the run-live-llm label to a PR (or workflow_dispatch) runs
tests/live_llm/ against real provider APIs — the only place the
--live-llm suite runs in CI. Reuses the unified-tests environment and
its Secrets Manager staging-dotenv resolution for provider keys; runs
on ubuntu-latest (no Fly runner, no Docker — the suite only touches the
LLM backends). Pins LIVE_LLM_ANTHROPIC_45_PLUS_MODELS=claude-sonnet-4-5
since the Anthropic family has no default models and would otherwise
silently collect empty.
Opt-in by design: live model behavior is variable, so this is a signal,
not a required check.
DEV-2035
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* ci: run live LLM tests on main pushes touching the transport
Mirrors unified-tests' push trigger, scoped to paths that can affect
the live suite (src/llm/, config, the tests, deps, and the workflow
itself) so provider API calls aren't spent on unrelated changes.
DEV-2035
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* ci: disable auth in live LLM test environment
The staging dotenv sets AUTH_USE_AUTH=true without a usable JWT secret,
and src/config.py validates the pair at import time — the same reason
unified-tests overrides it. This suite never runs the API server.
DEV-2035
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* test(live_llm): fix gpt-5.4 reasoning_effort and gemini replay-turn flake
- test_live_openai: gpt-5.4 dropped 'minimal' from the reasoning_effort
vocabulary, so the gpt5 caching test 400'd — and the OpenAI backend's
BadRequestError terminal swallowed it into an empty CompletionResult.
Pick the effort per model generation.
- test_live_tools_structured_output: use tool_choice='auto' on the
replay turn, matching the production dialectic loop (which never
forces 'none') — NONE mode is what provoked gemini-2.5-flash's empty
candidates. Drop the temperature pin so retries actually resample,
and treat a repeat tool call as a retryable attempt.
Verified live: full suite green, gemini 4/4 consecutive passes.
DEV-2035
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* ci: fail live LLM run when no staging secret was loaded
If the latest-tag fetch fails and no second tag exists, the fallback
step is skipped rather than failed, and the job would proceed without
provider keys — every test then skips via require_provider_key and the
run goes green. Guard on both fetch outcomes so that path fails loudly.
DEV-2035
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* docs(live-llm-tests-GHA): remove extra comments
* feat(structured-output): enable non-recursive schema references
* docs(structured-outputs): clean up new doc
* test(structured-output): fix caching refs memory leak, add tests
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* feat(conclusions): expose reasoning level + allow filtering by level
The `level` of a conclusion (explicit / deductive / inductive /
contradiction) was filterable server-side but stripped from the
`Conclusion` response and not surfaced in either SDK. This adds it
end-to-end so callers can list explicit-only ("not dreamed on")
conclusions without dropping to raw HTTP.
- api: add `level` to the Conclusion response schema
- python sdk: `ConclusionLevel` type, `level` on Conclusion/response,
`level=` kwarg on ConclusionScope.list() and the async variant
- ts sdk: `ConclusionLevel` type, `level` on Conclusion/response,
`level` option on list()
- tests: assert level is exposed; add level-filter list test
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* refactor(conclusions): use generic filters= on list() instead of level= kwarg
Match the documented SDK convention (peers/sessions/messages all take a
generic `filters` dict passed through to the same dynamic server-side
filter logic) instead of a one-off `level=` kwarg. `level` filtering now
works as `list(filters={"level": "explicit"})` alongside any other
supported filter/operator.
The `level` field on the Conclusion response (added in the previous
commit) is kept — it's still not otherwise returned by the API.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* feat(conclusions): allow filtering by level on query() in py + ts SDKs
The branch's level-filter work exposed `filters=` on `list()` but left
`query()` (semantic search) hardcoding `{observer, observed}`, so callers
could filter the list endpoint by reasoning level but not semantic search —
asymmetric in both SDKs.
- Python: add keyword-only `filters` to `ConclusionScope.query` and
`ConclusionScopeAio.query`, merged over the scope's observer/observed.
- TypeScript: add optional `filters` arg to `ConclusionScope.query`,
mirroring the existing `list()` change.
The server `/conclusions/query` endpoint already honors filters in the body
(verified against production), so this is purely SDK surface parity.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* docs(filters): document filtering conclusions by reasoning level
The using-filters page covered workspaces/peers/sessions/messages but not
conclusions. Add a "Filtering Conclusions" section showing level-based
filtering on both list() and query(), including the common "explicit only"
(exclude dream-derived) case and the in[deductive,inductive] inverse.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* refactor(conclusions): simplify filter merge to a single dict spread
Replace the merged_filters + if-block pattern in list()/query() (py sync,
aio, ts) with a single dict spread that layers the caller's filters over the
scope's observer/observed (and session). No behavior change — same merge
order (caller wins) — just less code.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(conclusions): reject scope-managed keys in SDK conclusion filters
The generic filters= argument on ConclusionScope.list()/query() spread
user-supplied filters last, so a stray observer/observed/session key
silently overrode the scope and returned data from a different peer
pair. Add a fail-loud guard in both the Python and TypeScript SDKs that
rejects scope-managed filter keys with a clear error, directing callers
to peer.conclusions / conclusions_of(target) and the session= parameter.
session_id remains a valid filter on query() (which has no dedicated
session parameter).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
* feat: add model config option for json_object mode
* fix: catch possible validation error from structured output
* fix(llm): harden structured_output_mode json_object path
Follow-up fixes to the json_object structured-output mode for
OpenAI-compatible providers without Structured Outputs support:
- runtime: carry structured_output_mode onto the per-attempt fallback
config (select_model_config_for_attempt dropped it, silently sending
json_schema to a provider that can't parse it)
- backend: return a graceful empty on a contentless json_object
response instead of raising, matching the json_schema path, and
preserve token usage by normalizing the response
- backend: narrow the parse-failure catch to BadRequestError only, so
transient JSONDecodeError/ValidationError propagate to retry/fallback
instead of being swallowed to empty on the first attempt
- config: reject structured_output_mode on non-openai transports
(silent no-op otherwise); trim docs to the deriver, the only
structured-output feature
- backend: validate clean JSON before repair, cache the schema
instruction, and share json_object setup between complete()/stream()
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* refactor(llm): consolidate structured-output repair, drop dead seam
Fold the OpenAI backend's three structured-output repair sites
(LengthFinishReasonError, parsed=None, json_object) into the one shared
_parse_or_repair_structured_content helper, gated by an empty_on_missing
flag: json_object returns a graceful empty on a contentless response so a
loose provider can't crash the call, while json_schema raises so the
retry/fallback chain engages.
Delete the dead execute_structured_output_call seam and its only
collaborators (attempt_structured_output_repair, StructuredOutputFailurePolicy)
— it was never called and its single-shot validate/repair/empty model
conflicts with the retry behavior in honcho_llm_call.
No behavior change. Adds tests covering the json_schema parse fallbacks
(repair, refusal passthrough, no-content raise).
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Peer- and session-scoped JWTs were effectively workspace-scoped: auth() walked the route's declared scope and fell through to a workspace match, so a {w: ws-a, p: alice} token could act on any peer in ws-a.
* feat: peer keys can read sessions they belong to; require workspace on scoped keys
* fix: authorize JWTs by narrowest scope and gate member reads
Follow-up hardening on the narrowest-claim auth fix:
- Scope get_peer_config member-read to the caller's own peer; a session
member could previously read a co-member's per-session config.
- Enforce session membership on POST /peers/{id}/chat: the session_id
arrives in the body (invisible to require_auth), so a peer key could
read any session's injected message history. Check is_peer_in_session
in the handler before the dialectic runs.
- Consolidate the workspace-match check in auth() to a single hoisted
guard so no branch can silently re-open cross-workspace access.
- Normalize empty-string scope claims to None in verify_jwt so a blank
workspace can't satisfy the peer/session token-shape invariant.
- Extract scope_requires_workspace(), shared by verify_jwt and the keys
API so the creation-time guard and verification invariant can't drift.
route requires auth) and CLAUDE.md auth-scoping guidance.
- docs: describe narrow-scope key semantics in the platform reference.
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
* feat: defer embedding messages
* fix: rm gauges
* feat: embed messages immediately on create with reconciler fallback (#766)
Adds embed_messages_now background task so newly created messages are
searchable within seconds instead of waiting up to the reconciler
interval. Three-phase claim/lease → embed → persist never holds a DB
session across the embedding call; the reconciler remains the fallback
for failures and stragglers.
* fix: harden immediate-embed fast path and cover its error branches
Wrap embed_messages_now in a top-level try/except so a failure in the
claim or persist phase degrades to "reconciler will retry" instead of
escaping into the background-task runner; the rows stay pending+leased
and the reconciler heals them.
Add tests for the previously-uncovered branches: external-store-unavailable
persist path, the file-upload endpoint's embed scheduling, and direct unit
tests for the shared compute_chunk_positions / build_message_vector_record
helpers.
Document the semantic-search eventual-consistency window in search.mdx
(keyword matches are immediate; vector matches lag creation by seconds).
* fix: don't hold DB session across vector-store upserts
* fix: align semantic-search function to filter null rows
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
* fix(deriver): Remove connection retry logic and add jitter to polling interval
* chore(docs): Update changelog and document new configurations
* chore: increment version numbers
* feat(db): add connection retry, adaptive deriver polling, and pool metrics
Add resilience and visibility for DB connection handling under transaction-
pooler (Supavisor) saturation, where client-connection limits get exhausted
across many tenants.
- get_db/tracked_db now force an eager pool checkout with bounded exponential
backoff (tenacity), retrying SQLAlchemy TimeoutError + OperationalError so
transient pooler rejections degrade gracefully instead of 500ing. Toggle via
DB_CONNECTION_RETRY_ENABLED (+ delay/backoff knobs); ~10s default budget.
- Deriver polling backs off when idle or erroring (base -> max, x2 each cycle)
and snaps back to base on claimed work, cutting steady-state query load.
Toggle via DERIVER_POLLING_BACKOFF_ENABLED (+ max/multiplier).
- Add scrape-time db_pool_connections Prometheus gauge (checked_out/checked_in/
size/overflow, labeled api|deriver), registered in both the API lifespan and
the deriver metrics server.
- Make SqlalchemyIntegration explicit in both Sentry inits; wrap connection
acquisition in a db.pool.acquire span and capture live pool stats on
retry-exhaustion.
* feat(db): add acquisition counter and in-flight query gauge
Build on the pool-connection metrics with two signals that turn detection
into diagnosis under transaction-pooler saturation:
- db_connection_acquisitions{outcome=ok|retried|exhausted}: counts how often
connection checkout retries through pooler rejection — the alertable early
warning before requests start failing.
- db_queries_in_flight: statements actually executing on the wire (via
SQLAlchemy cursor-execute events, drift-proof across query errors). Pairs
with checked_out: the gap reveals connections held but parked (the "idle in
transaction during an external call" antipattern). Labeled namespace +
instance_type only; gated on METRICS.ENABLED for zero overhead when off.
Add DB-free unit tests for retry outcomes, polling backoff, and in-flight
gauge drift handling.
* fix: address CodeRabbit review on PR #758
- db: roll back the session on a retryable checkout failure before
retrying — a failed autobegin can leave it pending-rollback, making the
next db.connection() raise instead of re-checking-out cleanly. Cheap
Python-side cleanup when no connection was bound.
- metrics: guard DBPoolCollector.collect() so a pool-read/import hiccup
can't raise and abort the whole /metrics scrape (Prometheus drops ALL
metrics if any collector raises) — log and fall back to empty.
* fix(db): lazy retrying session + review fixes for connection backoff
Address Codex/CodeRabbit review on PR #758.
- Replace eager checkout with HonchoAsyncSession: a lazy AsyncSession that
checks out its connection (with retry) on the first DB-touching call, not at
construction. Request handlers doing non-DB work (embedding/file/LLM) before
their first query no longer pin a connection across it, while the API path
still gets checkout retry. Only the checkout is retried — the statement runs
once via super(), so writes are never duplicated. Tracing's set_config moves
into the same lazy acquire hook.
- Roll the session back on a retryable checkout failure before retrying, so a
failed autobegin can't leave it pending-rollback.
- Lower default POOL_TIMEOUT to 5s and validate it stays under the retry budget
for pooled (non-null) POOL_CLASS; update config.toml.example and v2/v3 docs.
- Clamp pool overflow gauge to >= 0 (was negative before the pool fills).
- Remove double-sleep in the deriver idle poll (true backoff cap, not 2x);
make in-flight instrumentation registration idempotent.
- Tests: HonchoAsyncSession lazy/idempotent acquire, statement-runs-once,
tracing, commit/rollback flag reset, get_db no-acquire-at-entry, polling-loop
single-sleep, and the POOL_TIMEOUT/retry-budget validator.
* fix(db): cover all DB-touching session methods; clear flag on close/reset
Address Codex follow-up review on PR #758 (polish, no behavior-critical bug).
- HonchoAsyncSession: wrap get/get_one/stream/stream_scalars/delete in addition
to execute/scalar/scalars/flush/merge/refresh/commit, so the "lazy checkout
with retry on first DB use" guarantee has no holes. connection() stays
unwrapped (acquire_connection_with_retry calls it — wrapping would recurse).
- Reset the acquired flag on close()/reset() too, so a session reused after
close/reset re-acquires (and re-wraps retry) on its next DB use.
- Fix stale comments: connection retry now applies lazily to the request path
via HonchoAsyncSession (config.py), and the FakeSession helper note.
- Tests: close/reset flag reset, and get/delete route through acquisition.
* feat(embedding): add dimensions_mode for OpenAI dimensions= forwarding
Add EMBEDDING_MODEL_CONFIG__DIMENSIONS_MODE (auto|always|never) controlling
whether the dimensions= parameter is forwarded on OpenAI embeddings.create
calls. auto (default) sends it when the operator explicitly set
EMBEDDING_VECTOR_DIMENSIONS and the configured model is not on the
known-rejecting allowlist (currently text-embedding-ada-002).
The provenance check (was VECTOR_DIMENSIONS explicitly set?) lives as
EmbeddingSettings.resolve_send_dimensions() because it needs access to
model_fields_set, which the standalone resolver does not have. The
resolved boolean is passed into _EmbeddingClient at construction time;
the client never inspects mode or provenance.
Also pins cloudevents <2.0 — 2.0.0 reorganized the package and dropped
cloudevents.conversion and cloudevents.http, which src/telemetry/emitter.py
imports. The original `>=1.12.0` constraint allowed the broken 2.0 resolve.
With the pin, the imports resolve cleanly and the basedpyright warning
cascade (37+ warnings about unknown types) disappears.
Drive-by cleanups (all unnecessary cast/ignore comments flagged by
basedpyright after the cloudevents downgrade):
- vector_store/lancedb.py, tests/conftest.py, and
tests/deriver/test_vector_reconciliation.py — drop dead pyright ignores
- sdks/python/src/honcho/http/{async_,}client.py — drop unnecessary
cast(datetime, ...) (parsedate_to_datetime already returns datetime)
- vector_store/turbopuffer.py — cast(Any, rows) for the upsert_rows
TypedDict that the SDK exposes but our row builder doesn't satisfy
- tests/test_datetime_parsing.py — ignore reportArgumentType on the
test that deliberately passes wrong types to assert raises
* feat(models): honor EMBEDDING_VECTOR_DIMENSIONS in pgvector columns
* feat(startup): atomic swap dim-vs-MIGRATED guard for runtime schema validator
Add src/startup/embedding_validator.py that introspects the actual pgvector
column dim at boot and refuses to start if it does not match
EMBEDDING_VECTOR_DIMENSIONS. Runs after the DB pool is up and before the
embedding client is constructed, in both src/main.py (FastAPI lifespan) and
src/deriver/__main__.py.
Implementation details:
- Schema-qualified pg_attribute join through pg_class/pg_namespace respects
DB.SCHEMA rather than relying on search_path
- Bounded retry (3 attempts, 1s backoff) for transient introspection failure,
then fail-closed with "could not validate embedding schema" — uncertainty
is not a green light to serve traffic
- External-store sampler (turbopuffer, lancedb) enumerates workspaces from
the application DB and probes their lazy-created namespaces; current
per-namespace probe is a no-op stub since the SDKs do not expose
uniform dim introspection — full enumeration is left to
`configure_embeddings --report` in Phase 3
Atomic guard swap: deletes the old dim-vs-MIGRATED config validator (which
forbade non-1536 pgvector unless MIGRATED=True) in the same commit as the
new runtime validator. There is no release window where non-1536 pgvector
can start unprotected. The 9 dual-write branches that use VECTOR_STORE.MIGRATED
remain untouched and load-bearing for legacy-tenant backend swaps.
VECTOR_STORE_DIMENSIONS deprecation: drop the "must match" raise; in
propagate_namespace, check model_fields_set and emit logger.warning +
DeprecationWarning (DeprecationWarning alone is filtered by Python's default
config and would not reach operators). Always overwrite with
EMBEDDING.VECTOR_DIMENSIONS regardless.
Test changes:
- tests/test_models_vector_dim.py: Phase 1's VECTOR_STORE_TYPE=lancedb +
MIGRATED=true escape hatches removed; the test now passes on plain
EMBEDDING_VECTOR_DIMENSIONS=768
- tests/llm/test_model_config.py: the two tests asserting the old guards
replaced with tests for the new deprecation + acceptance behavior
- tests/startup/test_embedding_validator.py: 10 new tests — dim assertion
logic (pass/mismatch/missing/unbounded/non-public-schema), fail-closed
retry budget, real-test-DB pass, real-DB ALTER-then-validate, deprecation
warning capture, non-1536 + pgvector + MIGRATED=false at config time
* feat(scripts): add configure_embeddings bootstrap CLI
Adds scripts/configure_embeddings.py alongside the other one-off scripts
(provision_db, migrate_db, generate_jwt_secret, etc.). Invoked as
`uv run python scripts/configure_embeddings.py` — same convention as the
existing scripts in that directory, including the sys.path shim that
lets src.* imports resolve when run directly.
Bootstrap step for self-hosted installs at a non-default
EMBEDDING_VECTOR_DIMENSIONS — runs between `alembic upgrade head` and
starting the API/deriver.
pgvector ALTER safety (single transaction):
- LOCK TABLE {schema}.documents, {schema}.message_embeddings IN ACCESS
EXCLUSIVE MODE — closes the TOCTOU window between population check
and ALTER
- COUNT(*) WHERE embedding IS NOT NULL on both tables; refuse with a
non-zero exit if either is populated (ALTER ... USING NULL would
silently wipe those vectors)
- Snapshot HNSW index DDL from pg_indexes; drop, ALTER, recreate from
the captured DDL so operator-set HNSW params (m, ef_construction)
survive the round trip
External vector stores (turbopuffer, lancedb) are never created or
modified — namespaces are per-workspace and lazy-created on first write.
The --report mode enumerates workspaces and collections from the
application DB, derives the expected namespaces via
get_vector_namespace(), and prints a per-namespace status table.
CLI modes (mutually exclusive):
- (default) interactive: print plan, prompt to confirm
- --dry-run: print plan and exit 0 without touching the DB
- --yes: apply without prompt
- --report: print external-store namespace inventory and exit
Also updates src/startup/embedding_validator.py error-message paths and
docs/v3/contributing/configuration.mdx invocations to point at the new
script location.
Tests cover plan no-op, plan needs-alter, plan raises on missing column,
ALTER + HNSW round-trip, refuse-when-populated (monkeypatched count to
avoid wiring the full workspace/peer/collection/document FK chain just
to land one vector row), and idempotency.
* docs: add changing-embeddings operations page
Document the supported way to change EMBEDDING_VECTOR_DIMENSIONS or
EMBEDDING_MODEL_CONFIG__MODEL on a Honcho deployment: provision a new
deployment at the desired configuration, replay source data out of
band, cut over at the application layer.
The page explains the asymmetry:
- Dimension is machine-enforced as immutable. The startup validator
introspects pg_attribute and crashes the API/deriver on mismatch.
- Model is operator-owned. There is no persistent metadata recording
which model produced each vector, so a same-dim model swap is
silently undetectable — flagged with a Warning callout.
Also documents the truncation edge case (text-embedding-3-large truncated to 1536 with EMBEDDING_VECTOR_DIMENSIONS left at default)
and the DIMENSIONS_MODE=always mitigation, plus a pointer that
storage-backend swap (VECTOR_STORE_MIGRATED + reconciler) is a distinct operation unaffected by this work.
Registers the page in docs/docs.json under the Self-Hosting nav group
and cross-links from configuration.mdx.
* fix(embedding): correct turbopuffer regex + tighten DIMENSIONS_MODE docs
- Turbopuffer attribute type for a vector column is `[N]f32` / `[N]f16` /
`[N]i8`, not `f32_vector(N)` as the earlier probe assumed. The earlier
regex returned None for the real SDK format, so existing Turbopuffer
namespaces would have been reported as "missing" instead of validated
for mismatch. Regex switched to `\[(\d+)\]` which is the
vendor-stable shape. Test cases rewritten to lock the actual format.
- docs/v3/contributing/configuration.mdx had a contradictory pair of
bullets: 223 said explicit 1536 makes `auto` forward dimensions=, 224
said `auto` would skip the parameter because 1536 is the default.
Operators reading both would (rightly) conclude they need `always`
even when `auto` would work. Rewrote both bullets so:
- `auto` is provenance-driven (explicit-set, not non-default-value).
- `always` is positioned as defense-in-depth for config layers that
might strip explicit default-valued envs, not the only path for
same-as-default truncation.
* fix(embedding): address PR #678 review comments
CodeRabbit + Rajat review feedback. All actionable items addressed
except two false-positives (responded on PR).
Bug fixes:
- deriver telemetry leak: validator was called outside try/finally so
shutdown_telemetry() did not run on validation failure. Moved inside.
- _emit_report printed "no effect with pgvector" unconditionally,
including from implicit post-apply calls. Added is_report_mode flag;
only print on explicit --report.
- LanceDB and Turbopuffer probes returned None when the namespace
existed but its schema was malformed (no vector field / unparseable
type string), silently bucketing real corruption as "missing"
(lazy-create) and letting it pass the startup validator. Now raise
VectorStoreError with actionable diagnostics; None remains valid only
for "namespace does not exist."
- Startup validator only sampled message namespaces; added a parallel
Collection-row sample so document namespaces are probed too, with the
same dim assertion. Mirrors the --report path.
Hygiene:
- StartupValidationError now subclasses HonchoException so existing
exception handlers recognize it. ValidationException is @final and
has 422 request-validation semantics that would be misleading here.
- scripts/configure_embeddings.py main() no longer spins up two event
loops. engine.dispose() moved into a try/finally inside _async_main
so cleanup runs in the same loop as the pipeline.
- Replaced hand-rolled retry loop with tenacity.AsyncRetrying; same
fail-closed semantics, less code, before_sleep_log for visibility.
- Added _validate_identifier() defense-in-depth: DB.SCHEMA and HNSW
index names are regex-checked against [A-Za-z_][A-Za-z0-9_]* before
SQL interpolation. Operator config + DB catalog are not user input
under the current threat model, but the constraint is cheap to gate.
Test + docs:
- test_app_settings_accepts_non_1536_with_any_vector_store_configuration
now actually exercises turbopuffer (was missing); supplies a dummy
TURBOPUFFER_API_KEY to satisfy the model_validator.
- changing-embeddings.mdx: hyphenated "out-of-band" per reviewer style.
* fix: modify conftest to fix ci
* fix: ci tests for typescript server
ajspig
Vineeth Voruganti
ajspig
Aakash Kattelu
Joe-Kneeland
Rajat Ahuja
Vansh Sharma
JUNZE
Alexei Vedernikov
Eugene Eisenstein
Aru Sharma
Harish Kukreja
adavyas