mirror of
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feat(models): add provider model scanning (#2106)
* feat(models): add provider model scanning * fix: double close button * feat: update plugin module * fix(monitoring): WeChat Work feedback recording bugs (#2108) * fix(monitoring): fix WeChat Work feedback recording bugs - Fix feedback events silently dropped when stream session expires: dispatch feedback handlers regardless of session availability - Fix IntegrityError on repeated feedback (like→dislike) for same message: implement UPSERT logic in record_feedback() - Fix cancel feedback (type=3) not removing records: add delete logic - Fix inaccurate_reasons validation error: convert int reason codes to strings before creating FeedbackEvent (Pydantic expects List[str]) - Fix feedback timestamps 8 hours off in frontend: use parseUTCTimestamp instead of new Date() for UTC timestamp parsing - Fix StreamSessionManager.cleanup missing _feedback_index cleanup Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix(monitoring): apply ruff format to wecom feedback files Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: 6mvp6 <13727783693@163.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> * feat: add feat for receive files in wecombot * fix: ruff error * fix: always show sidebar plus buttons on touch/mobile devices (#2115) Agent-Logs-Url: https://github.com/langbot-app/LangBot/sessions/e27a4886-fbad-4a7a-8558-67a387852753 Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com> * fix: SPA fallback for all frontend routes, not just /home/* After migrating from Next.js to Vite SPA, routes like /auth/space/callback returned 404 because the static file server only had SPA fallback for /home/*. Now all non-API routes fall back to index.html for React Router to handle. * style: ruff format main.py * feat: add marketplace link when no parser available for file upload Links to /home/market?category=Parser, same pattern as knowledge engine selector. * fix: lint error * fix(user): allow password login and password change for Space accounts with local password set Previously, Space accounts were unconditionally blocked from password login and password change based on account_type. Now the check verifies whether the user actually has a local password set, allowing Space users who have set a local password to authenticate and change it normally. * feat: add edition field to telemetry payload Sends constants.edition (community/saas) with each telemetry event so Space can distinguish between community and SaaS instances. * style: ruff format telemetry.py * fix(dingtalk): use voice recognition text instead of raw audio binary When DingTalk sends a voice message to the bot, the callback JSON contains a 'recognition' field with the speech-to-text result (powered by Qwen). Previously, LangBot only extracted the 'downloadCode' to download the raw audio binary and passed it as 'file_base64' to LLM APIs, which caused 400 errors since most models don't support this content type. This patch: - Extracts the 'recognition' field from DingTalk audio message content - Uses it as plain text input to the LLM instead of raw audio - Falls back to audio binary only when no recognition text is available - Fixes duplicate text issue for audio messages with recognition Fixes voice messages returning 'Request failed' on all LLM models. * feat: integrate Alembic for database migrations Replace manual if-sqlite/if-postgres branching with Alembic: - Add alembic dependency - Create programmatic alembic env (no CLI/alembic.ini needed) - Support async engines via run_sync passthrough - render_as_batch=True for SQLite ALTER TABLE compatibility - Auto-stamp baseline on first run (existing DB at version 25) - Run alembic upgrade head after legacy migrations - Include sample migration showing schema + data migration patterns - Add alembic dir to package-data for distribution * ci: add migration test workflow for SQLite and PostgreSQL Tests alembic upgrade on both databases: - Stamp baseline on existing schema - Upgrade to head - Idempotent re-upgrade - Fresh DB upgrade from scratch * feat: add autogenerate support and CLI entrypoint for alembic - autogenerate: compare ORM models vs DB schema to generate migrations - CLI: python -m langbot.pkg.persistence.alembic_runner <command> - autogenerate, upgrade, stamp, current - Reads data/config.yaml for DB connection * fix: add filereader for dingtalk,lark (#2122) * fix: add filereader for dingtalk * feat: add lark * feat: update uv.lock * chore: update version to 4.9.6 in pyproject.toml, __init__.py, and uv.lock * fix: update langbot-plugin version to 0.3.8 * fix: update langbot-plugin version to 0.3.8 * docs: update database migration instructions in AGENTS.md * fix(dashscopeapi): fix null value check in reasoning content processing logic (#2128) * fix(n8n-runner): fix output_key not applied when n8n returns plain JSON (#2119) * fix: bump dependencies to resolve Dependabot security alerts (#2130) * fix: bump dependencies to resolve Dependabot security alerts Python: - aiohttp: >=3.11.18 → >=3.13.4 (duplicate Host headers, header injection, redirect leak, multipart DoS) - cryptography: >=44.0.3 → >=46.0.7 (buffer overflow with non-contiguous buffers) - pillow: >=11.2.1 → >=12.2.0 (FITS GZIP decompression bomb, HIGH) - langchain-text-splitters: >=0.0.1 → >=1.1.2 (SSRF redirect bypass) - langchain-core: add >=1.2.28 (incomplete f-string validation) - langsmith: add >=0.7.31 (streaming token redaction bypass) - python-multipart: add >=0.0.26 (multipart DoS) - Mako: add >=1.3.11 (path traversal) - pytest: >=8.4.1 → >=9.0.3 (tmpdir handling) - uv: >=0.7.11 → >=0.11.6 (arbitrary file deletion) JavaScript (web/): - vite: ^8.0.3 → ^8.0.5 (fs.deny bypass, WebSocket file read, path traversal, HIGH) - axios: ^1.13.5 → ^1.15.0 (cloud metadata exfiltration) - lodash: ^4.17.23 → ^4.18.0 (code injection via _.template, prototype pollution, HIGH) * fix: update pnpm-lock.yaml for bumped dependencies * feat(ci): add i18n key consistency check for frontend locales (#2133) * feat(ci): add i18n key consistency check workflow Agent-Logs-Url: https://github.com/langbot-app/LangBot/sessions/c7bf50da-189b-49a5-9671-dbe8e70ff9d0 Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com> * feat(ci): replace eval with line-by-line parser, add permissions block Agent-Logs-Url: https://github.com/langbot-app/LangBot/sessions/c7bf50da-189b-49a5-9671-dbe8e70ff9d0 Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com> * feat(models): add provider model scanning * feat(models): add 'select all' functionality and enrich model abilities * fix:ruff * fix:ruff --------- Co-authored-by: WangCham <651122857@qq.com> Co-authored-by: 6mvp6 <119733319+6mvp6@users.noreply.github.com> Co-authored-by: 6mvp6 <13727783693@163.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: Guanchao Wang <wangcham233@gmail.com> Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com> Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com> Co-authored-by: RockChinQ <rockchinq@gmail.com> Co-authored-by: haiyangbg <zhouhaiyangaa@gmail.com> Co-authored-by: Rock Chin <1010553892@qq.com> Co-authored-by: Amadeus <115918672+AmadeusKurisu1@users.noreply.github.com> Co-authored-by: hzhhong <hung.z.h916@gmail.com> Co-authored-by: fdc310 <2213070223@qq.com>
This commit is contained in:
sheetung
@@ -43,3 +43,12 @@ class ModelProvidersRouterGroup(group.RouterGroup):
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return self.success()
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except ValueError as e:
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return self.http_status(400, -1, str(e))
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@self.route('/<provider_uuid>/scan-models', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
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async def _(provider_uuid: str) -> str:
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try:
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model_type = quart.request.args.get('type')
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result = await self.ap.provider_service.scan_provider_models(provider_uuid, model_type)
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return self.success(data=result)
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except ValueError as e:
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return self.http_status(400, -1, str(e))
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@@ -1,6 +1,7 @@
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from __future__ import annotations
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import uuid
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import traceback
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import sqlalchemy
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@@ -164,3 +165,66 @@ class ModelProviderService:
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.values(api_keys=[api_key])
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)
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await self.ap.model_mgr.reload_provider('00000000-0000-0000-0000-000000000000')
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async def scan_provider_models(self, provider_uuid: str, model_type: str | None = None) -> dict:
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provider = await self.get_provider(provider_uuid)
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if provider is None:
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raise ValueError('provider not found')
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runtime_provider = await self.ap.model_mgr.load_provider(provider)
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try:
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scan_result = await runtime_provider.requester.scan_models(
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runtime_provider.token_mgr.get_token() if runtime_provider.token_mgr.tokens else None
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)
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except NotImplementedError:
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raise ValueError('current provider does not support model scanning')
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except Exception as exc:
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self.ap.logger.warning(
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f'Failed to scan models for provider {provider_uuid}: {exc}\n{traceback.format_exc()}'
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)
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raise ValueError(str(exc)) from exc
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if isinstance(scan_result, dict):
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scanned_models = scan_result.get('models', [])
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debug_info = scan_result.get('debug')
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else:
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scanned_models = scan_result
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debug_info = None
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llm_models = await self.ap.llm_model_service.get_llm_models_by_provider(provider_uuid)
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embedding_models = await self.ap.embedding_models_service.get_embedding_models_by_provider(provider_uuid)
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existing_llm_names = {model['name'] for model in llm_models}
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existing_embedding_names = {model['name'] for model in embedding_models}
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filtered_models = []
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for model in scanned_models:
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scanned_type = model.get('type', 'llm')
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if model_type and scanned_type != model_type:
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continue
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model_name = model.get('name') or model.get('id')
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if not model_name:
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continue
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filtered_models.append(
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{
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'id': model.get('id', model_name),
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'name': model_name,
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'type': scanned_type,
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'abilities': model.get('abilities', []),
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'display_name': model.get('display_name'),
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'description': model.get('description'),
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'context_length': model.get('context_length'),
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'owned_by': model.get('owned_by'),
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'input_modalities': model.get('input_modalities', []),
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'output_modalities': model.get('output_modalities', []),
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'already_added': (
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model_name in existing_embedding_names
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if scanned_type == 'embedding'
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else model_name in existing_llm_names
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),
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}
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)
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return {'models': filtered_models, 'debug': debug_info}
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@@ -227,7 +227,8 @@ class ModelManager:
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raise provider_errors.RequesterNotFoundError(provider_entity.requester)
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requester_inst = self.requester_dict[provider_entity.requester](
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ap=self.ap, config={'base_url': provider_entity.base_url}
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ap=self.ap,
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config={'base_url': provider_entity.base_url},
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)
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await requester_inst.initialize()
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@@ -303,6 +303,14 @@ class ProviderAPIRequester(metaclass=abc.ABCMeta):
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async def initialize(self):
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pass
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async def scan_models(self, api_key: str | None = None) -> dict[str, typing.Any] | list[dict[str, typing.Any]]:
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"""Scan models supported by the provider.
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The default implementation does not support scanning. Requesters that
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can enumerate remote models should override this method.
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"""
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raise NotImplementedError('This provider does not support model scanning')
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@abc.abstractmethod
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async def invoke_llm(
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self,
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@@ -31,6 +31,192 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
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http_client=httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']),
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)
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def _mask_api_key(self, api_key: str | None) -> str:
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if not api_key:
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return ''
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if len(api_key) <= 8:
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return '****'
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return f'{api_key[:4]}...{api_key[-4:]}'
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def _infer_model_type(self, model_id: str) -> str:
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normalized_model_id = (model_id or '').lower()
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embedding_keywords = (
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'embedding',
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'embed',
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'bge-',
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'e5-',
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'm3e',
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'gte-',
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'multilingual-e5',
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'text-embedding',
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)
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return 'embedding' if any(keyword in normalized_model_id for keyword in embedding_keywords) else 'llm'
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def _infer_model_abilities(self, item: dict[str, typing.Any], model_id: str) -> list[str]:
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normalized_model_id = (model_id or '').lower()
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abilities: set[str] = set()
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def _flatten(value: typing.Any) -> list[str]:
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if value is None:
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return []
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if isinstance(value, str):
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return [value.lower()]
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if isinstance(value, dict):
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flattened: list[str] = []
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for nested_value in value.values():
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flattened.extend(_flatten(nested_value))
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return flattened
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if isinstance(value, (list, tuple, set)):
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flattened: list[str] = []
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for nested_value in value:
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flattened.extend(_flatten(nested_value))
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return flattened
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return [str(value).lower()]
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capability_tokens = _flatten(item.get('capabilities'))
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capability_tokens.extend(_flatten(item.get('modalities')))
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capability_tokens.extend(_flatten(item.get('input_modalities')))
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capability_tokens.extend(_flatten(item.get('output_modalities')))
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capability_tokens.extend(_flatten(item.get('supported_generation_methods')))
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capability_tokens.extend(_flatten(item.get('supported_parameters')))
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capability_tokens.extend(_flatten(item.get('architecture')))
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combined_tokens = capability_tokens + [normalized_model_id]
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vision_keywords = (
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'vision',
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'image',
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'file',
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'video',
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'multimodal',
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'vl',
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'ocr',
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'omni',
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)
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function_call_keywords = (
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'function',
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'tool',
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'tools',
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'tool_choice',
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'tool_call',
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'tool-use',
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'tool_use',
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)
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if any(any(keyword in token for keyword in vision_keywords) for token in combined_tokens):
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abilities.add('vision')
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if any(any(keyword in token for keyword in function_call_keywords) for token in combined_tokens):
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abilities.add('func_call')
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return sorted(abilities)
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def _normalize_modalities(self, value: typing.Any) -> list[str]:
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normalized: list[str] = []
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def _collect(item: typing.Any):
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if item is None:
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return
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if isinstance(item, str):
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for part in item.replace('->', ',').replace('+', ',').split(','):
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token = part.strip().lower()
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if token and token not in normalized:
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normalized.append(token)
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return
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if isinstance(item, dict):
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for nested in item.values():
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_collect(nested)
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return
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if isinstance(item, (list, tuple, set)):
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for nested in item:
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_collect(nested)
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return
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_collect(value)
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return normalized
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def _extract_scan_metadata(self, item: dict[str, typing.Any], model_id: str) -> dict[str, typing.Any]:
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display_name = item.get('name')
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if not isinstance(display_name, str) or not display_name.strip() or display_name == model_id:
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display_name = ''
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description = item.get('description')
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if not isinstance(description, str) or not description.strip():
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description = ''
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context_length = item.get('context_length')
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if context_length is None and isinstance(item.get('top_provider'), dict):
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context_length = item['top_provider'].get('context_length')
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if not isinstance(context_length, int):
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try:
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context_length = int(context_length) if context_length is not None else None
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except (TypeError, ValueError):
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context_length = None
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input_modalities = self._normalize_modalities(item.get('input_modalities'))
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output_modalities = self._normalize_modalities(item.get('output_modalities'))
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if isinstance(item.get('architecture'), dict):
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if not input_modalities:
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input_modalities = self._normalize_modalities(item['architecture'].get('input_modalities'))
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if not output_modalities:
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output_modalities = self._normalize_modalities(item['architecture'].get('output_modalities'))
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owned_by = item.get('owned_by')
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if not isinstance(owned_by, str) or not owned_by.strip():
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owned_by = ''
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return {
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'display_name': display_name or None,
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'description': description or None,
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'context_length': context_length,
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'owned_by': owned_by or None,
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'input_modalities': input_modalities,
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'output_modalities': output_modalities,
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}
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async def scan_models(self, api_key: str | None = None) -> dict[str, typing.Any]:
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headers = {}
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if api_key:
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headers['Authorization'] = f'Bearer {api_key}'
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models_url = f'{self.requester_cfg["base_url"].rstrip("/")}/models'
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async with httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']) as client:
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response = await client.get(models_url, headers=headers)
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response.raise_for_status()
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payload = response.json()
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models = []
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for item in payload.get('data', []):
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model_id = item.get('id')
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if not model_id:
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continue
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models.append(
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{
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'id': model_id,
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'name': model_id,
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'type': self._infer_model_type(model_id),
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'abilities': self._infer_model_abilities(item, model_id),
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**self._extract_scan_metadata(item, model_id),
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}
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)
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models.sort(key=lambda item: (item['type'] != 'llm', item['name'].lower()))
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return {
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'models': models,
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'debug': {
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'request': {
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'method': 'GET',
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'url': models_url,
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'headers': {
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'Authorization': f'Bearer {self._mask_api_key(api_key)}' if api_key else '',
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},
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},
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'response': payload,
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},
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}
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async def _req(
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self,
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args: dict,
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@@ -1,6 +1,7 @@
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from __future__ import annotations
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import typing
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import httpx
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from . import chatcmpl
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@@ -20,6 +21,68 @@ class GeminiChatCompletions(chatcmpl.OpenAIChatCompletions):
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'timeout': 120,
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}
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async def scan_models(self, api_key: str | None = None) -> dict[str, typing.Any]:
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models_url = 'https://generativelanguage.googleapis.com/v1beta/models'
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params = {'key': api_key} if api_key else {}
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all_models: list[dict[str, typing.Any]] = []
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next_page_token = ''
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last_payload: dict[str, typing.Any] = {}
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async with httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']) as client:
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while True:
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request_params = dict(params)
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if next_page_token:
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request_params['pageToken'] = next_page_token
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response = await client.get(models_url, params=request_params)
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response.raise_for_status()
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payload = response.json()
|
||||
last_payload = payload
|
||||
|
||||
for item in payload.get('models', []):
|
||||
model_name = item.get('name', '')
|
||||
model_id = model_name.replace('models/', '', 1)
|
||||
if not model_id:
|
||||
continue
|
||||
|
||||
supported_methods = item.get('supportedGenerationMethods', []) or []
|
||||
if 'embedContent' in supported_methods and 'generateContent' not in supported_methods:
|
||||
model_type = 'embedding'
|
||||
else:
|
||||
model_type = 'llm'
|
||||
|
||||
all_models.append(
|
||||
{
|
||||
'id': model_id,
|
||||
'name': model_id,
|
||||
'type': model_type,
|
||||
'abilities': self._infer_model_abilities(item, model_id),
|
||||
'display_name': item.get('displayName') or None,
|
||||
'description': item.get('description') or None,
|
||||
'context_length': item.get('inputTokenLimit'),
|
||||
'input_modalities': self._normalize_modalities(item.get('inputModalities')),
|
||||
'output_modalities': self._normalize_modalities(item.get('outputModalities')),
|
||||
}
|
||||
)
|
||||
|
||||
next_page_token = payload.get('nextPageToken', '')
|
||||
if not next_page_token:
|
||||
break
|
||||
|
||||
all_models.sort(key=lambda item: (item['type'] != 'llm', item['name'].lower()))
|
||||
return {
|
||||
'models': all_models,
|
||||
'debug': {
|
||||
'request': {
|
||||
'method': 'GET',
|
||||
'url': models_url,
|
||||
'query': {'key': self._mask_api_key(api_key)} if api_key else {},
|
||||
},
|
||||
'response': last_payload,
|
||||
},
|
||||
}
|
||||
|
||||
async def _closure_stream(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
|
||||
@@ -31,6 +31,175 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
|
||||
http_client=httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']),
|
||||
)
|
||||
|
||||
def _mask_api_key(self, api_key: str | None) -> str:
|
||||
if not api_key:
|
||||
return ''
|
||||
if len(api_key) <= 8:
|
||||
return '****'
|
||||
return f'{api_key[:4]}...{api_key[-4:]}'
|
||||
|
||||
def _infer_model_type(self, model_id: str) -> str:
|
||||
normalized_model_id = (model_id or '').lower()
|
||||
embedding_keywords = (
|
||||
'embedding',
|
||||
'embed',
|
||||
'bge-',
|
||||
'e5-',
|
||||
'm3e',
|
||||
'gte-',
|
||||
'multilingual-e5',
|
||||
'text-embedding',
|
||||
)
|
||||
return 'embedding' if any(keyword in normalized_model_id for keyword in embedding_keywords) else 'llm'
|
||||
|
||||
def _infer_model_abilities(self, item: dict[str, typing.Any], model_id: str) -> list[str]:
|
||||
normalized_model_id = (model_id or '').lower()
|
||||
abilities: set[str] = set()
|
||||
|
||||
def _flatten(value: typing.Any) -> list[str]:
|
||||
if value is None:
|
||||
return []
|
||||
if isinstance(value, str):
|
||||
return [value.lower()]
|
||||
if isinstance(value, dict):
|
||||
flattened: list[str] = []
|
||||
for nested_value in value.values():
|
||||
flattened.extend(_flatten(nested_value))
|
||||
return flattened
|
||||
if isinstance(value, (list, tuple, set)):
|
||||
flattened: list[str] = []
|
||||
for nested_value in value:
|
||||
flattened.extend(_flatten(nested_value))
|
||||
return flattened
|
||||
return [str(value).lower()]
|
||||
|
||||
capability_tokens = _flatten(item.get('capabilities'))
|
||||
capability_tokens.extend(_flatten(item.get('modalities')))
|
||||
capability_tokens.extend(_flatten(item.get('input_modalities')))
|
||||
capability_tokens.extend(_flatten(item.get('output_modalities')))
|
||||
capability_tokens.extend(_flatten(item.get('supported_generation_methods')))
|
||||
capability_tokens.extend(_flatten(item.get('supported_parameters')))
|
||||
capability_tokens.extend(_flatten(item.get('architecture')))
|
||||
|
||||
combined_tokens = capability_tokens + [normalized_model_id]
|
||||
|
||||
vision_keywords = ('vision', 'image', 'file', 'video', 'multimodal', 'vl', 'ocr', 'omni')
|
||||
function_call_keywords = ('function', 'tool', 'tools', 'tool_choice', 'tool_call', 'tool-use', 'tool_use')
|
||||
|
||||
if any(any(keyword in token for keyword in vision_keywords) for token in combined_tokens):
|
||||
abilities.add('vision')
|
||||
|
||||
if any(any(keyword in token for keyword in function_call_keywords) for token in combined_tokens):
|
||||
abilities.add('func_call')
|
||||
|
||||
return sorted(abilities)
|
||||
|
||||
def _normalize_modalities(self, value: typing.Any) -> list[str]:
|
||||
normalized: list[str] = []
|
||||
|
||||
def _collect(item: typing.Any):
|
||||
if item is None:
|
||||
return
|
||||
if isinstance(item, str):
|
||||
for part in item.replace('->', ',').replace('+', ',').split(','):
|
||||
token = part.strip().lower()
|
||||
if token and token not in normalized:
|
||||
normalized.append(token)
|
||||
return
|
||||
if isinstance(item, dict):
|
||||
for nested in item.values():
|
||||
_collect(nested)
|
||||
return
|
||||
if isinstance(item, (list, tuple, set)):
|
||||
for nested in item:
|
||||
_collect(nested)
|
||||
return
|
||||
|
||||
_collect(value)
|
||||
return normalized
|
||||
|
||||
def _extract_scan_metadata(self, item: dict[str, typing.Any], model_id: str) -> dict[str, typing.Any]:
|
||||
display_name = item.get('name')
|
||||
if not isinstance(display_name, str) or not display_name.strip() or display_name == model_id:
|
||||
display_name = ''
|
||||
|
||||
description = item.get('description')
|
||||
if not isinstance(description, str) or not description.strip():
|
||||
description = ''
|
||||
|
||||
context_length = item.get('context_length')
|
||||
if context_length is None and isinstance(item.get('top_provider'), dict):
|
||||
context_length = item['top_provider'].get('context_length')
|
||||
|
||||
if not isinstance(context_length, int):
|
||||
try:
|
||||
context_length = int(context_length) if context_length is not None else None
|
||||
except (TypeError, ValueError):
|
||||
context_length = None
|
||||
|
||||
input_modalities = self._normalize_modalities(item.get('input_modalities'))
|
||||
output_modalities = self._normalize_modalities(item.get('output_modalities'))
|
||||
|
||||
if isinstance(item.get('architecture'), dict):
|
||||
if not input_modalities:
|
||||
input_modalities = self._normalize_modalities(item['architecture'].get('input_modalities'))
|
||||
if not output_modalities:
|
||||
output_modalities = self._normalize_modalities(item['architecture'].get('output_modalities'))
|
||||
|
||||
owned_by = item.get('owned_by')
|
||||
if not isinstance(owned_by, str) or not owned_by.strip():
|
||||
owned_by = ''
|
||||
|
||||
return {
|
||||
'display_name': display_name or None,
|
||||
'description': description or None,
|
||||
'context_length': context_length,
|
||||
'owned_by': owned_by or None,
|
||||
'input_modalities': input_modalities,
|
||||
'output_modalities': output_modalities,
|
||||
}
|
||||
|
||||
async def scan_models(self, api_key: str | None = None) -> dict[str, typing.Any]:
|
||||
headers = {}
|
||||
if api_key:
|
||||
headers['Authorization'] = f'Bearer {api_key}'
|
||||
|
||||
models_url = f'{self.requester_cfg["base_url"].rstrip("/")}/models'
|
||||
async with httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']) as client:
|
||||
response = await client.get(models_url, headers=headers)
|
||||
response.raise_for_status()
|
||||
payload = response.json()
|
||||
|
||||
models = []
|
||||
for item in payload.get('data', []):
|
||||
model_id = item.get('id')
|
||||
if not model_id:
|
||||
continue
|
||||
models.append(
|
||||
{
|
||||
'id': model_id,
|
||||
'name': model_id,
|
||||
'type': self._infer_model_type(model_id),
|
||||
'abilities': self._infer_model_abilities(item, model_id),
|
||||
**self._extract_scan_metadata(item, model_id),
|
||||
}
|
||||
)
|
||||
|
||||
models.sort(key=lambda item: (item['type'] != 'llm', item['name'].lower()))
|
||||
return {
|
||||
'models': models,
|
||||
'debug': {
|
||||
'request': {
|
||||
'method': 'GET',
|
||||
'url': models_url,
|
||||
'headers': {
|
||||
'Authorization': f'Bearer {self._mask_api_key(api_key)}' if api_key else '',
|
||||
},
|
||||
},
|
||||
'response': payload,
|
||||
},
|
||||
}
|
||||
|
||||
async def _req(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
|
||||
@@ -8,6 +8,7 @@ import uuid
|
||||
import json
|
||||
|
||||
import ollama
|
||||
import httpx
|
||||
|
||||
from .. import errors, requester
|
||||
import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
|
||||
@@ -31,6 +32,60 @@ class OllamaChatCompletions(requester.ProviderAPIRequester):
|
||||
os.environ['OLLAMA_HOST'] = self.requester_cfg['base_url']
|
||||
self.client = ollama.AsyncClient(timeout=self.requester_cfg['timeout'])
|
||||
|
||||
def _infer_model_type(self, model_id: str) -> str:
|
||||
normalized_model_id = (model_id or '').lower()
|
||||
embedding_keywords = ('embedding', 'embed', 'bge-', 'e5-', 'm3e', 'gte-', 'text-embedding')
|
||||
return 'embedding' if any(keyword in normalized_model_id for keyword in embedding_keywords) else 'llm'
|
||||
|
||||
def _infer_model_abilities(self, item: dict[str, typing.Any], model_id: str) -> list[str]:
|
||||
normalized_model_id = (model_id or '').lower()
|
||||
abilities: set[str] = set()
|
||||
details = item.get('details', {}) or {}
|
||||
families = details.get('families', []) or []
|
||||
tokens = [normalized_model_id, str(details.get('family', '')).lower()]
|
||||
tokens.extend(str(family).lower() for family in families)
|
||||
|
||||
if any(keyword in token for token in tokens for keyword in ('vision', 'vl', 'omni', 'llava', 'ocr')):
|
||||
abilities.add('vision')
|
||||
if any(keyword in token for token in tokens for keyword in ('tool', 'function')):
|
||||
abilities.add('func_call')
|
||||
return sorted(abilities)
|
||||
|
||||
async def scan_models(self, api_key: str | None = None) -> dict[str, typing.Any]:
|
||||
del api_key
|
||||
models_url = f'{self.requester_cfg["base_url"].rstrip("/")}/api/tags'
|
||||
|
||||
async with httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']) as client:
|
||||
response = await client.get(models_url)
|
||||
response.raise_for_status()
|
||||
payload = response.json()
|
||||
|
||||
models: list[dict[str, typing.Any]] = []
|
||||
for item in payload.get('models', []):
|
||||
model_id = item.get('model') or item.get('name')
|
||||
if not model_id:
|
||||
continue
|
||||
models.append(
|
||||
{
|
||||
'id': model_id,
|
||||
'name': item.get('name', model_id),
|
||||
'type': self._infer_model_type(model_id),
|
||||
'abilities': self._infer_model_abilities(item, model_id),
|
||||
}
|
||||
)
|
||||
|
||||
models.sort(key=lambda item: (item['type'] != 'llm', item['name'].lower()))
|
||||
return {
|
||||
'models': models,
|
||||
'debug': {
|
||||
'request': {
|
||||
'method': 'GET',
|
||||
'url': models_url,
|
||||
},
|
||||
'response': payload,
|
||||
},
|
||||
}
|
||||
|
||||
async def _req(
|
||||
self,
|
||||
args: dict,
|
||||
|
||||
@@ -15,3 +15,11 @@ class OpenRouterChatCompletions(modelscopechatcmpl.ModelScopeChatCompletions):
|
||||
'base_url': 'https://openrouter.ai/api/v1',
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
async def scan_models(self, api_key: str | None = None) -> dict[str, typing.Any]:
|
||||
original_base_url = self.requester_cfg.get('base_url', '')
|
||||
self.requester_cfg['base_url'] = 'https://openrouter.ai/api/v1'
|
||||
try:
|
||||
return await super().scan_models(api_key)
|
||||
finally:
|
||||
self.requester_cfg['base_url'] = original_base_url
|
||||
|
||||
Reference in New Issue
Block a user