- Updated the assertion in `test_dingtalk_completed_input_lines_include_text_and_select_values` to remove unnecessary markdown formatting.
- Added new tests to verify that `_dingtalk_clean_form_content` maintains the order of prompts and completed values in various scenarios.
- Introduced `test_dingtalk_card_markdown_preserves_internal_line_breaks` to ensure internal line breaks are correctly converted to HTML line breaks.
feat(qqofficial): update fallback text handling for non-streaming scenarios
feat(difysvapi): enhance form content processing for interactive fields and actions
test: add unit tests for Lark and QQ Official adapter functionalities
- Added support for select fields in Telegram adapter, including option extraction and callback handling.
- Implemented form action processing for Telegram callbacks, improving user interaction feedback.
- Introduced new helper functions for building keyboards and resolving select button actions in QQ Official adapter.
- Enhanced DifyServiceAPIRunner to handle cumulative streaming responses and improve error handling during workflow resumes.
- Added unit tests for new functionalities in Telegram and QQ Official adapters, ensuring robust behavior for select fields and form actions.
- Implement tests for DingTalk adapter helper functions including form content cleaning, input extraction, and completed input lines.
- Create unit tests for Lark adapter helper functions focusing on input extraction and completed input lines.
- Add tests for WeComBot template card functionalities, including event extraction and payload building for human input.
- Enhance Dify service API runner tests to cover human input forms, including input collection, action handling, and form snapshot extraction.
* 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>
Operators can now set a global default memory limit for all stdio MCP
servers in config.yaml or via environment variable:
config.yaml:
box:
default_memory_mb: 2048 # default: 1536
env:
BOX__DEFAULT_MEMORY_MB=2048
The default is raised from 1024 to 1536 MB — a safer floor for
Node.js V8 + WASM (undici llhttp) under nsjail cgroup limits.
Individual MCP servers can still override via their own box.memory_mb.
Previously the fallback was hardcoded to 1024 MB, causing OOM kills
(return_code=137) on node/npx MCP servers that need more RAM.
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
The previous _TransferredStack approach broke anyio lexical context:
websocket_client/ClientSession use anyio task groups whose cancel scope is
bound to the frame that entered them. Deferring their aclose via a transferred
exit stack left the underlying memory streams closed once initialize() returned,
so the very next request (refresh -> list_tools) failed with Connection closed.
New design:
- Attach on the owner exit stack (same task as the serve loop, lexically intact)
- A cold-starting process makes initialize() fail; signal _ColdStartRetry up to
the outer retry loop, which reuses the live process without consuming retry budget
- _lifecycle_loop_with_retry handles _ColdStartRetry like _TransportReconnect:
preserves process, no fatal budget, backs off 2s and retries
- Two new unit tests: cold-start raises _ColdStartRetry (not fatal) when process
is alive; raises fatal error when process has actually exited
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
A node/npx stdio MCP server (e.g. firecrawl-mcp via npx -y) failed on first
connect with Connection closed / Failed after 4 attempts, even though the
process was fine. An npx cold start downloads+installs the package before the
server can answer the MCP handshake (measured ~27s for a simple official
server; longer for heavier ones). The old code attached the WS and called
session.initialize() the instant the process was started, so the handshake ran
before the process could answer and failed; the outer lifecycle retry then
rebuilt the process, churning it in a loop.
Verified decisively: attaching + initialize() against a mid-cold-start process
times out on attempt 1 (process still installing) but SUCCEEDS at t+0.6s on
attempt 2 once the process is ready. So the fix is to retry the handshake in
place, not to rebuild the process.
Changes (mcp_stdio.initialize):
- Start the managed process ONCE, then loop attach WS -> ClientSession ->
initialize() within the startup_timeout budget, tearing down each failed
attempt cleanly, until the handshake succeeds or the budget elapses. A
successful transport/session is transferred into the owner exit stack via a
small _TransferredStack adapter.
- Bound each attempt with asyncio.wait_for(initialize, _HANDSHAKE_ATTEMPT_TIMEOUT_SEC=10s)
so a cold-starting process fails fast and retries instead of hanging until
the transport drops.
- Stop retrying ONLY when the process has DEFINITIVELY exited: new
_managed_process_has_exited() (checks EXITED status) replaces the previous
not-_managed_process_is_running() test, which false-negatived on a
just-spawned process that had not yet reported RUNNING and made the loop bail
to the outer rebuild path (relay then rejected the early re-attach with HTTP 400).
Adds a unit test that fails the first two handshakes with the process alive and
asserts the loop retries to success while starting the process exactly once.
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
* refactor(mcp): make MCP test reuse the shared Box session instead of a per-test session
Testing an MCP server (config-page "test" button) previously spun up a fresh
isolated mcp-test-<uuid> Box session every time: cold-start the container, run
the dependency bootstrap, probe, then tear the whole session down. That is slow
(tens of seconds) and, on an already-hosted server, wasteful — the server is
already running in the shared session.
Change the test to reuse the shared session / live process:
- _build_box_session_id: transient tests now use mcp-shared, the same Box
session as live servers, so a test reuses the running container (and, for an
existing server, its live managed process) instead of a cold per-test session.
- cleanup_session: a transient test no longer deletes the whole session (which
under the shared model would kill every other MCP server in the container). It
stops only its own process_id, exactly like a live server. Isolation is now at
the process level (distinct process_id per server/test), not the session level.
- test_mcp_server (persisted server): reuse the live connection with a real
list_tools refresh/probe; only fall back to a full start() when there is no
live connection to probe or the refresh fails, instead of an ERROR->start()
rebuild.
Trade-off: a failing test now shares the container with live servers rather than
a throwaway session. Accepted deliberately in favour of near-instant tests;
process-level isolation keeps a test from stopping another server's process.
* chore(deps): pin langbot-plugin 0.4.9 for the nsjail RLIMIT_AS node/npx MCP fix
---------
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.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.
* feat(api): support global API key from config.yaml (api.global_api_key)
Accept a config-defined global API key anywhere a web-UI key is accepted
(X-API-Key / Bearer), with no login session and no DB record. Useful for
automated deployments and AI agents (HTTP API + MCP). Defaults to empty
(disabled); does not require the lbk_ prefix.
- templates/config.yaml: add api.global_api_key with security notes
- service/apikey.py: verify_api_key checks global key first (constant-time)
- docs/API_KEY_AUTH.md: document the global key + security guidance
- tests: cover global-key match, prefix-free, fallback-to-db, disabled
* feat(mcp): expose LangBot management as an MCP server at /mcp
Add an MCP (Model Context Protocol) server so external AI agents can manage a
LangBot instance. Reuses the same API-key auth as the HTTP API (including the
config.yaml global API key).
- pkg/api/mcp/server.py: FastMCP server wrapping the service layer; 21 curated
tools across system/bots/pipelines/models/knowledge/mcp-servers/skills
- pkg/api/mcp/mount.py: ASGI dispatcher fronting Quart; authenticates /mcp
requests with an API key, runs the streamable-HTTP session manager lifespan
- controller/main.py: serve the wrapped ASGI app via hypercorn (was run_task)
- web: new 'MCP' tab in the API integration dialog showing endpoint, auth, and
client config; i18n for 8 locales
- tests/manual/mcp_smoke.py: e2e check (401 unauth, list tools, call tools)
Tool surface is intentionally curated (not all ~25 route groups) to keep the
agent surface small, safe, and maintainable. Extend deliberately.
* feat(skills): add in-repo skills/ as the single source of truth
Migrate the agent skills + QA/e2e test harness from the (now archived)
langbot-app/langbot-skills repo into LangBot/skills/, and add four new skills.
Migrated:
- langbot-plugin-dev, langbot-testing (e2e), langbot-env-setup,
langbot-skills-maintenance, langbot-eba-adapter-dev
- the bin/lbs CLI (src/, test/, scripts/, schemas/, qa-agent-docs/)
New:
- langbot-dev core backend + web development
- langbot-deploy Docker/K8s deployment + config.yaml + global API key
- langbot-mcp-ops operating the LangBot MCP server (/mcp)
- langbot-space-ops operating the Space marketplace MCP server
- src/cli.ts repoRoot(): recognize the skills assets root (skills.index.json +
bin/lbs) so the CLI works when nested inside the LangBot repo
- README.md: unified skill catalog; skills.index.json regenerated
Parity with source verified: bin/lbs validate + node test suite match the
source repo (only the uncommitted .lbpkg build-artifact fixture differs).
* docs(agents): document agent-facing surfaces + API/MCP/skills sync rule
* docs(readme): add 'Built for AI Agents' section across all locales
Highlight MCP server, in-repo skills (single source of truth), AGENTS.md
sync rule, and llms.txt. Cross-link LangBot Space MCP marketplace.
* style(mcp): fix ruff format + prettier lint in MCP server and API panel
* style(web): prettier format MCP i18n locale entries
* docs(skills): note MCP instance control in dev/testing skills
All development-guidance skills now point to the LangBot instance MCP
server (/mcp) and the Space marketplace MCP server, reusing API keys.
Update _normalize_stream_tool_calls to preserve provider_specific_fields
(including thought_signature) from streaming tool call chunks. Also preserve
provider_specific_fields from delta in invoke_llm_stream.
This ensures Gemini's thought_signature is round-tripped correctly:
1. LiteLLM extracts thought_signature from Gemini response
2. It's preserved in Message/ToolCall entities (via SDK changes)
3. _convert_messages includes it in the next request
Also add unit tests for provider_specific_fields round-tripping.
Fixes: langbot-app/LangBot#1899
Ollama's OpenAI-compatible streaming endpoint emits a tool-call delta
carrying an `index` and a `function` payload but never an OpenAI-style
`id`. `_normalize_stream_tool_calls` dropped any tool call without an
`id`, so a tool-only turn yielded neither content nor a tool call: the
stream "completed" with 0 chars, the tool never ran, and the chat
appeared stuck. Models on standard OpenAI APIs (e.g. SiliconFlow) were
unaffected because they always send a `call_...` id.
Synthesize a stable per-index id (`call_<index>`) when the provider
omits one but a function name is present. Providers that do send ids
keep theirs, and parallel id-less calls keep distinct ids.
Adds regression tests for the single and multi id-less tool-call cases.
Fixes#2261
Outbound attachment collection (pipeline wrapper) runs on every turn
regardless of inbound files, but the agent was only told the per-query
outbox path inside the inbound-attachment note in LocalAgentRunner. So on
pure-generation turns (e.g. "generate a QR code"/chart/mermaid where the
user sent no file), the agent never learned the outbox path or the
query_id, wrote the generated file nowhere deliverable, and it was
silently dropped.
Move the outbox instruction into BoxService.get_system_guidance(query_id),
which is injected as a system message on every turn the exec tool is
available. The inbound note keeps its own (now redundant but harmless)
outbox line. Add unit tests asserting the outbox path is present with a
query_id and absent without one.
The agent attachment outbox is written by the sandbox container as root over
the bind-mount, so the LangBot host process (non-root) cannot rmtree those
files — the host-side delete failed silently and stale files were re-collected
on a later turn that reused the same query_id (the query_id counter resets to 0
on every restart).
- BoxService.initialize now purges leftover inbox/outbox after the runtime is
available: host rmtree first, then an in-sandbox 'rm -rf' via exec for any
root-owned survivors.
- _clear_outbox now falls back to exec when the host delete leaves root-owned
files behind, instead of silently failing.
- collect_outbound_attachments clears the outbox unconditionally (even on an
empty collection) so a reused query_id never inherits stale files.
- Tests: startup purge (host-owned + root-owned exec fallback + no-workspace
noop) and empty-collection-still-clears.
* feat(box): bidirectional attachment transfer for sandbox
Materialize inbound attachments into the sandbox workspace so agents can
process user-sent files, and collect agent-produced files from the outbox
to attach them back to the reply.
- box(service): add materialize_inbound_attachments / collect_outbound
attachments. Prefer direct host-filesystem read/write on the bind-mounted
workspace (no size limit), falling back to chunked exec only for
non-shared backends (e2b/remote). Clear per-query inbox/outbox dirs at
turn start to avoid query_id-reuse collisions.
- provider(localagent): inject inbound attachment descriptors into the
sandbox and append a system note telling the agent the inbox/outbox paths.
- pipeline(wrapper): collect outbox files on the final stream chunk and
append them as attachment components to the response chain.
- web(debug-dialog): render File components with a download link when
base64/url is present; add base64/path fields to the File entity.
- tests: cover inbound/outbound, large-file transfer without truncation,
and stale-dir clearing (86 passing).
* feat(box): support voice/file attachment round-trip end-to-end
Extends the bidirectional attachment transfer to audio and arbitrary files
through the real webchat UI, and fixes the model-payload errors that
non-image attachments triggered.
- platform(websocket_adapter): resolve Voice/File component storage keys to
base64 (previously only Image), so audio/documents reach the sandbox inbox.
- web(debug-dialog): accept audio/* and any file in the uploader (was
image-only), classify by mimetype, upload Voice/File via the documents
endpoint, and render non-image staged attachments as a chip.
- provider(litellmchat): drop non-image file parts (file_base64 / file_url)
when building the OpenAI/LiteLLM payload. These come from Voice/File
attachments — including ones replayed from conversation history — and the
agent reads their bytes from the sandbox, not the model. Without this the
provider rejects the request: 'invalid content type=file_base64'.
- provider(localagent): also strip those parts from the current user message
alongside the sandbox-path note (model-facing clarity; the requester is the
real safety net for history).
- tests: cover the requester strip/keep behavior (file dropped, image kept and
reshaped to image_url, mixed history, plain-string content).
* test(box): cover inbound/outbound attachment helpers; fix ruff format
- ruff format localagent.py (CI ruff format --check was failing)
- add unit tests for ResponseWrapper outbound-attachment helpers (wrapper.py 78%->98%)
- add unit tests for LocalAgentRunner._inject_inbound_attachments
- add unit tests for WebSocketAdapter._process_image_components (0%->covered)
Lifts PR patch coverage from 68.97% to ~88% (>75% target).
Load the instance creation timestamp from data/labels/instance_id.json
(backfilling+persisting it for instances created before the field existed),
expose it as constants.instance_create_ts, and include it in the heartbeat
payload so Space can anchor Time-To-Value / onboarding analytics on real
install time rather than first-heartbeat.
Verified: py_compile, ruff, pytest tests/unit_tests/telemetry/ (37 passed).
The pkg/core/migrations system (m001-m043 DBMigration-style config
migrations, MigrationStage, and the core.migration base class) only ever
ran when upgrading from LangBot 3.x. The last 3.x release is over a year
old and is no longer supported, so this dead code is removed entirely:
- delete pkg/core/migrations/ (43 mXXX_*.py + __init__)
- delete pkg/core/migration.py (base class + registry)
- delete pkg/core/stages/migrate.py (MigrationStage)
- drop 'MigrationStage' from boot.py stage_order
- delete tests/unit_tests/core/test_migration.py (tested the removed base class)
* 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>
Counts successful non-WebSocket bot responses (persisted in the metadata
table as survey_bot_response_count, survives restarts) and fires the
bot_response_success_100 survey event once the instance reaches 100
responses. Counting stops after the milestone has been triggered.
Existing first_bot_response_success behavior unchanged. 6 new unit tests.
Per-query events now carry event_type='query' and a features JSON object:
- tool_calls by source (native/plugin/mcp/skill) via ToolManager
- tool_call_rounds, kb usage (count/engine plugins/retrieved entries) via local-agent
- sandbox execs/errors via BoxService
- activated_skills and bound mcp_servers snapshots
New instance_heartbeat event (startup + daily) reports anonymous instance
profile: deploy platform, database/vdb kind, box backend/availability,
adapter type names, and resource counts. Respects space.disable_telemetry.
All collection helpers are defensive and never break the pipeline.
Verified: ruff, 37 telemetry unit tests (13 new), 504 box/provider/pipeline tests.
fdc310