* fix(cloud): provision login workspace just in time
* fix(oauth): send callback URI during code exchange
* fix(oauth): preserve callback URI through browser exchange
* fix(oauth): negotiate redirect-bound codes
---------
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
* fix(cloud): launch new accounts through Space
* style: format Cloud entry URL
* fix(cloud): wait for launch workspace projection
---------
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
* fix(monitoring): restore SQLite token statistics
Allow the monitored SQLite strftime bucket expression through the tenant SQL guard and preserve structured backend errors in the token dashboard. Add persistence and frontend regressions.\n\nVerified-by: independent-review
* test(runtime): accept reconcile timeout in capacity stub
Keep the PostgreSQL capacity probe aligned with the runtime handler contract and assert the bounded reconcile timeout.
---------
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
* feat: report independent instance and workspace identities
* test: include workspace in OAuth callback fixture
* ci: pin production cloud adapter to Space release
* fix: preserve authenticated Workspace telemetry attribution
* ci: pin production cloud adapter to final Space release
---------
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
* fix(cloud): show owner model balance and enforce single owner
* fix(migrations): create owner index idempotently
---------
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
Skip the unsupported valkey-glide dependency on Windows while preserving automatic installation on supported platforms. Keep missing-client runtime and test paths safe, and update the Valkey integration documentation.
* feat(vector): add Valkey Search vector database backend
Add a new opt-in VectorDatabase backend backed by the Valkey Search module
(valkey/valkey-bundle), accessed via the official valkey-glide client's native
ft command namespace.
- Implements the full VectorDatabase ABC: VECTOR, FULL_TEXT and HYBRID search,
all 8 metadata filter operators, and pagination with exact totals.
- HYBRID uses filter-then-KNN (no app-side weighted fusion); vector_weight is
accepted for interface parity but NOT honored (docstring + one-time warning +
docs caveat).
- Lazy connect so a down Valkey never blocks boot; mandatory
client_name=langbot_vector_client; optional auth + TLS (never logged).
- Registered via a single elif branch in vector/mgr.py; disabled by default
(vdb.use stays chroma) for toC compatibility.
- Adds valkey-glide>=2.4.1,<3.0.0; no protobuf/pydantic downgrade; no ORM
change so no Alembic migration.
- Unit tests (fast lane, no server) + slow-gated integration tests
(TEST_VALKEY_URL, valkey/valkey-bundle:9.1.0) + integration doc.
* fix(vector): paginate Valkey Search deletes and guard delete_by_filter
Address self-review follow-ups for the Valkey Search VDB backend:
- _search_keys now paginates through the full result set in batches of
_DELETE_SCAN_BATCH instead of capping at a single hard-coded 10000-key
page, so delete_by_file_id / delete_by_filter fully remove files and
filters that match more than one page of chunks (no orphaned vectors).
- Add unit regression tests for the delete_by_filter mass-deletion guard:
a filter referencing only non-indexed fields must skip and return 0
(never fall back to match-all), and a supported filter still deletes
matching keys.
* refactor(vector): harden Valkey Search backend and add adversarial tests
Address the self-review NICE-TO-HAVE items for the Valkey Search VDB backend:
- Guard the username-without-password credential edge (skip auth + warn
instead of building ServerCredentials(password=None, ...), which glide
rejects).
- Add an async close() teardown that closes the glide client and resets
cached state (re-init is safe via the existing None guard).
- Hoist 'import json' to module top (was imported inside three methods).
- Document the FT TAG literal-brace limitation in _escape_tag (fails closed,
never widens).
Tests:
- Add an adversarial-input integration test proving crafted file_id /
query_text cannot break out of or widen a query (fail-closed on braces).
- Add unit tests for close() and the credential-build guard.
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
* fix(vector): make Valkey Search file_id TAG support arbitrary characters
Valkey Search's FT TAG query parser cannot handle '{', '}' or '*' even when
backslash-escaped, so a file_id containing those characters previously
produced an unparseable query (it failed closed / raised). Percent-encode
exactly those FT-unsafe characters (plus '%' for reversibility) in the
file_id TAG value, applied identically at write time and query time, so an
arbitrary file_id round-trips. For normal UUID/hash ids this is a no-op and
the stored value is unchanged; the original file_id is always preserved
verbatim in metadata_json.
Strengthen the adversarial integration test to assert a brace/star-bearing
file_id matches and deletes exactly its own row (no widening, no raise), and
add unit tests for _encode_file_id and the filter encoding.
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
* refactor(vector): address Valkey Search review feedback
- Add configurable request_timeout (default 5000ms; glide default 250ms is
too low for KNN); expose in config.yaml + docs table
- Validate embedding dimension consistency in add_embeddings (fail fast on
mixed lengths to avoid silent KNN corruption)
- Use ft.info (O(1)) instead of ft.list (O(n)) for index existence checks in
the query hot path; also closes the check-then-create TOCTOU window
- Pipeline HSETs via a non-atomic Batch instead of N sequential awaits
- Extract shared _iter_reply_docs to deduplicate reply parsing between
_reply_to_chroma and list_by_filter
- Parenthesize multi-condition pre-filters before the => KNN clause
- Fail closed when a username is configured without a password
- Catch only RequestError on ft.dropindex (let connection/auth errors surface)
- Bound the delete_collection SCAN loop with a safety cap
- Add VectorDatabase.close() (no-op default) + VectorDBManager.shutdown()
- Simplify _MATCH_ALL literal; normalize typing to builtin generics
* fix(vector/valkey_search): address round-2 review feedback
- Serialize lazy client creation with an asyncio.Lock (double-checked) so
concurrent first-use callers don't construct and leak duplicate clients.
- Make the filter operator chain exhaustive: raise on an unhandled op rather
than silently dropping the condition (which could widen delete_by_filter).
- Cast numeric range (///) values to float, failing closed on
non-numeric input and pre-empting a future NUMERIC-field injection surface.
* refactor(vector): remove shutdown/close from base ABC per maintainer feedback Per maintainer request, interface changes to VectorDatabase ABC and VectorDBManager should be in a separate PR with implementation across all backends. The ValkeySearchVectorDatabase.close() method remains but does not override an ABC method.
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
* docs(test): list valkey_search in vdb coverage exclusions Add valkey_search to the documented vector/vdbs/ coverage-exclusion list, matching the existing chroma/milvus/pgvector/qdrant/seekdb entries. These adapters require a live database instance and are covered by env-gated integration tests instead of unit tests.
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
---------
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
The PostgreSQL migration test had the same hardcoded 0005 head
assertion as the SQLite one; resolve the actual head from the Alembic
ScriptDirectory so 0006 (and future migrations) don't break it.
CI follow-up to the local/remote MCP work:
- Apply ruff format to provider/tools/loaders/mcp.py and the 0006
normalize-remote-mode migration (Lint job failed on formatting).
- test_migrations.py hardcoded the head revision as 0005_*, which broke
once 0006 landed. Resolve the actual head from the Alembic
ScriptDirectory so future migrations don't require editing the test.
* refactor(provider): use LiteLLM as unified LLM requester backend
- Replace 23+ individual requester implementations with unified litellmchat.py
- Add litellm_provider field to 27 YAML manifests for provider routing
- Delete redundant requester subclasses
- Add unit tests for LiteLLMRequester (29 tests)
- Fix num_retries parameter name (was max_retries)
- Fix exception handling order for subclass exceptions
LiteLLM provides unified API for 100+ providers, eliminating need for
provider-specific requesters.
* fix: ruff format provider.py
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* refactor(provider): simplify LiteLLM requester usage handling
- Remove unused Anthropic-specific tool schema generation
- Share completion argument construction between normal and streaming calls
- Use LiteLLM/OpenAI native usage fields for monitoring
- Collect stream token usage from LiteLLM stream_options
- Update LiteLLM requester tests for unified usage fields
* restore: restore deleted provider requester files
Restore individual provider requester implementations that were
removed in de61b5d3. These files coexist with the unified
litellmchat.py backend.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* feat: update requesters and improve provider selection UI
- Added `litellm_provider` field to various requesters' YAML configurations.
- Removed obsolete Python requester files for OpenRouter, PPIO, QHAIGC, ShengSuanYun, SiliconFlow, Space, TokenPony, VolcArk, and Xai.
- Introduced new requesters for Tencent and Together AI with corresponding YAML configurations and SVG icons.
- Enhanced the ProviderForm component to include a searchable dropdown for selecting providers, improving user experience.
- Updated localization files to include search provider text for both English and Chinese.
* fix(provider): align litellm rebase with master
* fix(provider): capture streaming token usage; add token observability
The LiteLLM streaming requester only captured usage when a chunk had an
empty `choices` list. Many OpenAI-compatible gateways (e.g. new-api) and
providers send the final usage payload in a chunk that still carries an
empty-delta choice, so streamed calls always recorded 0 tokens in the
monitoring logs/dashboard (non-streaming worked).
- Capture stream usage whenever a chunk carries it, regardless of choices
- Add robust _normalize_usage (dict/obj shapes, derive missing total_tokens)
- Register litellm in bootutils/deps.py (was in pyproject only)
- Add MonitoringService.get_token_statistics + /monitoring/token-statistics
endpoint: summary, per-model breakdown, token timeseries, and a
zero-token-success data-quality signal
- Add TokenMonitoring dashboard tab (summary tiles, stacked token chart,
per-model table) + i18n (en/zh)
- Regression tests for stream usage capture and usage normalization
Verified end-to-end against a real OpenAI-compatible endpoint with
gpt-5.5 and claude-opus-4-8: tokens now recorded non-zero for both
streaming and non-streaming paths.
* refactor(provider): simplify litellm capabilities
* style: simplify wrapped expressions
* feat(models): persist context metadata
* fix(provider): handle dict embeddings and openai-compatible rerank in LiteLLMRequester
- invoke_embedding: support both object- and dict-shaped response.data
entries (OpenAI-compatible gateways like new-api return dicts)
- invoke_rerank: litellm.arerank rejects the 'openai' provider, so for
openai-compatible (or unspecified) providers call the standard
Jina/Cohere-style POST /v1/rerank endpoint directly over HTTP
- accept both 'relevance_score' and 'score' fields in rerank results
- add unit tests for the openai-compatible HTTP rerank path
* feat(provider): enforce requester support_type when adding models
- frontend: AddModelPopover only shows model-type tabs (llm/embedding/
rerank) that the provider's requester declares in its manifest
support_type; ModelsDialog fetches requester manifests and maps
requester -> support_type, passed down through ProviderCard
- backend: add _validate_provider_supports guard in create_llm_model /
create_embedding_model / create_rerank_model so a model cannot be
attached to a provider whose requester does not support that type,
even if the frontend restriction is bypassed (manifests without
support_type are allowed for backward compatibility)
- manifests: correct support_type for providers that do not offer all
three model types:
- llm only: anthropic, deepseek, groq, moonshot, openrouter, xai
- llm + text-embedding: openai, gemini, mistral
- add rerank to new-api (verified working via /v1/rerank)
- set llm + text-embedding + rerank for aggregator/unknown gateways
* feat(provider): add searchable alias to requester manifests
- add a free-text 'alias' field to every requester manifest spec,
containing the vendor's English/Chinese names, pinyin, common
nicknames and flagship model-series names (e.g. moonshot -> kimi,
月之暗面; zhipu -> glm, 智谱清言)
- frontend: ProviderForm requester search now also matches against
alias (substring/contains), so searching 'kimi' surfaces Moonshot,
'硅基' surfaces SiliconFlow, etc.
- also fix support_type: openrouter (relay) supports embedding+rerank;
LangBot Space gains rerank (coming soon)
* fix(provider): make support_type guard defensive against incomplete model_mgr
- _validate_provider_supports now uses getattr to gracefully skip when
model_mgr / provider_dict / manifest lookup is unavailable, instead of
raising AttributeError (fixes unit tests that mock ap.model_mgr as a
bare SimpleNamespace)
- add TestValidateProviderSupports covering: allow supported type,
reject unsupported type, allow when support_type missing, allow when
provider unknown, degrade safely when model_mgr is incomplete
* fix(persistence): guard 0004 migration against missing llm_models table
The 0004_add_llm_model_context_length migration called
inspector.get_columns('llm_models') unconditionally, raising
NoSuchTableError when the table does not exist (e.g. migrating a
fresh/empty DB, as exercised by the integration tests where
create_all() registers no tables because the ORM models are not
imported). Every other migration guards with a table-existence check
first; add the same guard here for both upgrade and downgrade.
Also restore the test head assertion to 0004 (it had been lowered to
0003 to mask this failure).
* Merge branch 'master' into feat/litellm
Resolve conflicts:
- uv.lock: regenerated via 'uv lock' to reconcile litellm/fastuuid
(ours) with openai bump (master).
- Alembic migrations: master added 0004_add_mcp_readme while this
branch added 0004_add_llm_model_context_length, both as children of
0003 (would create multiple heads). Re-chain the litellm migration as
0005_add_llm_model_context_length with down_revision=0004_add_mcp_readme
for a single linear head. Update test head assertion accordingly.
* fix(persistence): shorten migration revision id to fit varchar(32)
PostgreSQL stores alembic_version.version_num as varchar(32).
'0005_add_llm_model_context_length' (33 chars) overflowed it, raising
StringDataRightTruncationError in the PG migration tests. Rename the
revision (and file) to '0005_add_llm_context_length' (27 chars) and
update the head assertions in both SQLite and PostgreSQL migration
tests.
---------
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Co-authored-by: fdc310 <2213070223@qq.com>
Co-authored-by: RockChinQ <rockchinq@gmail.com>
Tynwink