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1
.github/pull_request_template.md
vendored
1
.github/pull_request_template.md
vendored
@@ -21,6 +21,7 @@
|
||||
*请在方括号间写`x`以打勾 / Please tick the box with `x`*
|
||||
|
||||
- [ ] 阅读仓库[贡献指引](https://github.com/langbot-app/LangBot/blob/master/CONTRIBUTING.md)了吗？ / Have you read the [contribution guide](https://github.com/langbot-app/LangBot/blob/master/CONTRIBUTING.md)?
|
||||
- [ ] 我已签署或将在机器人提示后签署 [CLA](https://github.com/langbot-app/LangBot/blob/master/CLA.md)。 / I have signed, or will sign when prompted by the bot, the [CLA](https://github.com/langbot-app/LangBot/blob/master/CLA.md).
|
||||
- [ ] 与项目所有者沟通过了吗？ / Have you communicated with the project maintainer?
|
||||
- [ ] 我确定已自行测试所作的更改，确保功能符合预期。 / I have tested the changes and ensured they work as expected.
|
||||
|
||||
|
||||
41
.github/workflows/cla.yml
vendored
Normal file
41
.github/workflows/cla.yml
vendored
Normal file
@@ -0,0 +1,41 @@
|
||||
name: "CLA Assistant"
|
||||
on:
|
||||
issue_comment:
|
||||
types: [created]
|
||||
pull_request_target:
|
||||
types: [opened, closed, synchronize, reopened]
|
||||
|
||||
permissions:
|
||||
actions: write # re-run the failed CLA check after signing
|
||||
contents: read # signatures are stored in the remote langbot-app/cla repo
|
||||
pull-requests: write # post guidance comments, lock PR after merge
|
||||
statuses: write # set the commit status
|
||||
|
||||
jobs:
|
||||
CLAAssistant:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: "CLA Assistant"
|
||||
if: (github.event.comment.body == 'recheck' || github.event.comment.body == 'I have read the CLA Document and I hereby sign the CLA') || github.event_name == 'pull_request_target'
|
||||
# Upstream repo was archived in 2026-03; pin to the v2.6.1 commit SHA.
|
||||
uses: contributor-assistant/github-action@ca4a40a7d1004f18d9960b404b97e5f30a505a08 # v2.6.1
|
||||
env:
|
||||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
# repo-scope PAT with write access to langbot-app/cla
|
||||
PERSONAL_ACCESS_TOKEN: ${{ secrets.CLA_PAT }}
|
||||
with:
|
||||
path-to-document: 'https://github.com/langbot-app/LangBot/blob/master/CLA.md'
|
||||
remote-organization-name: 'langbot-app'
|
||||
remote-repository-name: 'cla'
|
||||
path-to-signatures: 'signatures/version1/cla.json'
|
||||
branch: 'main'
|
||||
allowlist: 'dependabot[bot],github-actions[bot],devin-ai-integration[bot],Copilot,renovate[bot],bot*'
|
||||
custom-notsigned-prcomment: |
|
||||
Thank you for your contribution! :heart: Before we can merge this pull request, we need you to sign the [LangBot Contributor License Agreement (CLA)](https://github.com/langbot-app/LangBot/blob/master/CLA.md). You keep full copyright of your code — the CLA grants us a license to use and distribute your contribution. Signing takes 10 seconds and covers all repositories in this organization, permanently.
|
||||
|
||||
感谢您的贡献！合并前请阅读并签署[贡献者许可协议（CLA）](https://github.com/langbot-app/LangBot/blob/master/CLA.md)。您保留代码的全部版权，签署仅需回复下方指定内容，一次签署对本组织全部仓库永久有效。
|
||||
custom-allsigned-prcomment: 'All contributors have signed the CLA. :white_check_mark: 所有贡献者均已签署 CLA。'
|
||||
lock-pullrequest-aftermerge: true
|
||||
# SECURITY: this workflow runs on pull_request_target (it holds secrets and has
|
||||
# write access to the base repository). NEVER add an actions/checkout step that
|
||||
# checks out the PR's code here.
|
||||
@@ -125,6 +125,14 @@ uv run python -m langbot.pkg.persistence.alembic_runner autogenerate "descriptio
|
||||
|
||||
Review and edit the generated script before committing. Migrations execute automatically on startup. `autogenerate` detects schema changes (add/drop columns, tables, type changes) but **data migrations** (e.g. mutating JSON field contents) must be hand-written into the generated script. `env.py` sets `render_as_batch=True`, so SQLite's ALTER TABLE limits are handled automatically — no need to branch per database. More in the wiki ["开发配置"](https://docs.langbot.app/zh/develop/dev-config#数据库迁移).
|
||||
|
||||
When writing a migration, follow these rules:
|
||||
|
||||
- **Revision id ≤ 32 characters.** PostgreSQL stores `alembic_version.version_num` as `varchar(32)`; a longer id raises `StringDataRightTruncationError` at runtime. Prefer short, descriptive ids like `0005_add_llm_context_length`.
|
||||
- **Guard every operation against missing tables/columns.** Fresh installs build the schema via `create_all()` and then stamp the Alembic baseline, so a migration may run against a table that already has the change — or, in tests, against an empty database. Check `inspector.get_table_names()` / `inspector.get_columns(...)` before `add_column` / `drop_column`, mirroring the existing migrations.
|
||||
- **Keep a single linear head.** Chain `down_revision` to the current head; do not create branches. Run the migration tests after adding one: `uv run pytest tests/integration/persistence/ -q` (the PostgreSQL test needs a running PG via `TEST_POSTGRES_URL`).
|
||||
|
||||
> **Legacy migration system (deprecated — do not extend).** The old 3.x migration system under `src/langbot/pkg/persistence/migrations/` (`DBMigration` subclasses in `dbmXXX_*.py`, run from `pkg/persistence/mgr.py`) is **frozen**. Do **not** add new `dbmXXX_*.py` files. The chain is capped at `required_database_version = 25` (`pkg/utils/constants.py`); those files only exist to upgrade pre-existing 3.x databases up to the Alembic baseline and are kept read-only. All new schema changes go through Alembic.
|
||||
|
||||
## Some Principles
|
||||
|
||||
- Keep it simple, stupid.
|
||||
|
||||
107
CLA.md
Normal file
107
CLA.md
Normal file
@@ -0,0 +1,107 @@
|
||||
# LangBot Individual Contributor License Agreement (v1.0)
|
||||
|
||||
Thank you for your interest in contributing to LangBot (the "Project"), stewarded by Beijing Langbo Intelligent Technology Co., Ltd. (北京浪波智能科技有限公司) ("We" or "Us").
|
||||
|
||||
This Individual Contributor License Agreement ("Agreement") documents the rights granted by contributors to Us. By signing this Agreement (see Section 9), You accept and agree to the following terms and conditions for Your present and future Contributions submitted to the Project. Except for the licenses granted herein to Us and recipients of software distributed by Us, You reserve all right, title, and interest in and to Your Contributions.
|
||||
|
||||
## 1. Definitions
|
||||
|
||||
"You" (or "Your") shall mean the copyright owner or legal entity authorized by the copyright owner that is making this Agreement with Us.
|
||||
|
||||
"Contribution" shall mean any original work of authorship, including any modifications or additions to an existing work, that is intentionally submitted by You to Us for inclusion in, or documentation of, any of the products or repositories owned or managed by Us (the "Work"). For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to Us or our representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, Us for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by You as "Not a Contribution."
|
||||
|
||||
## 2. Grant of Copyright License
|
||||
|
||||
Subject to the terms and conditions of this Agreement, You hereby grant to Us and to recipients of software distributed by Us a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare derivative works of, publicly display, publicly perform, sublicense, and distribute Your Contributions and such derivative works. For clarity, this includes the right for Us to distribute Your Contributions, alone or as part of the Work, under the terms of any license, including without limitation open source licenses and commercial or proprietary licenses.
|
||||
|
||||
## 3. Grant of Patent License
|
||||
|
||||
Subject to the terms and conditions of this Agreement, You hereby grant to Us and to recipients of software distributed by Us a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by You that are necessarily infringed by Your Contribution(s) alone or by combination of Your Contribution(s) with the Work to which such Contribution(s) was submitted. If any entity institutes patent litigation against You or any other entity (including a cross-claim or counterclaim in a lawsuit) alleging that Your Contribution, or the Work to which You have contributed, constitutes direct or contributory patent infringement, then any patent licenses granted to that entity under this Agreement for that Contribution or Work shall terminate as of the date such litigation is filed.
|
||||
|
||||
## 4. Authority; Employer
|
||||
|
||||
You represent that You are legally entitled to grant the above licenses. If Your employer(s) has rights to intellectual property that You create that includes Your Contributions, You represent that You have received permission to make Contributions on behalf of that employer, that Your employer has waived such rights for Your Contributions to Us, or that Your employer has executed a separate Corporate Contributor License Agreement with Us.
|
||||
|
||||
## 5. Original Creation; Disclosure
|
||||
|
||||
You represent that each of Your Contributions is Your original creation (see Section 7 for submissions on behalf of others). You represent that Your Contribution submissions include complete details of any third-party license or other restriction (including, but not limited to, related patents and trademarks) of which You are personally aware and which are associated with any part of Your Contributions.
|
||||
|
||||
## 6. No Obligation of Support; Disclaimer
|
||||
|
||||
You are not expected to provide support for Your Contributions, except to the extent You desire to provide support. You may provide support for free, for a fee, or not at all. Unless required by applicable law or agreed to in writing, You provide Your Contributions on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE.
|
||||
|
||||
## 7. Third-Party Works
|
||||
|
||||
Should You wish to submit work that is not Your original creation, You may submit it to Us separately from any Contribution, identifying the complete details of its source and of any license or other restriction (including, but not limited to, related patents, trademarks, and license agreements) of which You are personally aware, and conspicuously marking the work as "Submitted on behalf of a third-party: [named here]".
|
||||
|
||||
## 8. Notification
|
||||
|
||||
You agree to notify Us of any facts or circumstances of which You become aware that would make these representations inaccurate in any respect.
|
||||
|
||||
## 9. Electronic Signature
|
||||
|
||||
This Agreement is accepted and signed electronically: posting a comment containing the exact phrase designated by Us (currently "I have read the CLA Document and I hereby sign the CLA") from Your GitHub account on a pull request in the Project's repositories constitutes Your binding electronic signature to this Agreement. You represent that the GitHub account used to sign belongs to You and that You are of legal age to form a binding contract. Your signature covers Your present and future Contributions to all repositories owned or managed by Us, until and unless You notify Us in writing that You withdraw from this Agreement for future Contributions (licenses already granted are irrevocable).
|
||||
|
||||
## 10. Our Commitment
|
||||
|
||||
We commit that the Project's main repository will continue to make an open source version of the Work publicly available.
|
||||
|
||||
## 11. Miscellaneous
|
||||
|
||||
This Agreement is the entire agreement between You and Us regarding Your Contributions and supersedes any prior agreements on this subject. If any provision is held unenforceable, the remaining provisions remain in effect. This Agreement is executed in English; the Chinese translation below is provided for reference only, and the English version shall prevail in case of any discrepancy.
|
||||
|
||||
---
|
||||
|
||||
# LangBot 个人贡献者许可协议（v1.0）中文参考译文
|
||||
|
||||
> 本译文仅供参考，如与英文版有任何歧义，以英文版为准。
|
||||
|
||||
感谢您有意为 LangBot（下称"本项目"）作出贡献。本项目由北京浪波智能科技有限公司（下称"我方"）运营管理。
|
||||
|
||||
本《个人贡献者许可协议》（下称"本协议"）旨在记录贡献者授予我方的各项权利。您一经签署本协议（见第 9 条），即接受并同意以下条款与条件，适用于您向本项目提交的现在及未来的全部贡献。除本协议授予我方及我方分发软件之接收者的许可外，您保留对您的贡献的全部权利、所有权和利益。
|
||||
|
||||
## 1. 定义
|
||||
|
||||
"您"指与我方订立本协议的版权所有人，或经版权所有人授权的法律实体。
|
||||
|
||||
"贡献"指您有意提交给我方、用于纳入我方拥有或管理的任何产品或代码仓库（下称"作品"）或其文档的任何原创作品，包括对既有作品的修改或增补。就本定义而言，"提交"指以任何电子、口头或书面形式向我方或我方代表发送的通信，包括但不限于在由我方或代表我方管理的电子邮件列表、源代码管理系统和问题跟踪系统中，为讨论和改进作品而进行的通信；但您以显著方式标注或以书面形式声明为"非贡献"（Not a Contribution）的通信除外。
|
||||
|
||||
## 2. 版权许可的授予
|
||||
|
||||
在遵守本协议条款与条件的前提下，您特此授予我方及我方分发软件之接收者一项永久的、全球范围的、非独占的、免费的、免版税的、不可撤销的版权许可，以复制您的贡献、基于其创作衍生作品、公开展示、公开表演、再许可以及分发您的贡献及上述衍生作品。为明确起见，上述许可包括我方有权以任何许可条款（包括但不限于开源许可证以及商业或专有许可证）单独或作为作品的一部分分发您的贡献。
|
||||
|
||||
## 3. 专利许可的授予
|
||||
|
||||
在遵守本协议条款与条件的前提下，您特此授予我方及我方分发软件之接收者一项永久的、全球范围的、非独占的、免费的、免版税的、不可撤销的（本条所述情形除外）专利许可，以制造、委托制造、使用、许诺销售、销售、进口及以其他方式转让作品；该许可仅适用于您可许可的、且因您的贡献本身或您的贡献与其所提交之作品的结合而必然受到侵犯的专利权利要求。如任何实体对您或任何其他实体提起专利诉讼（包括诉讼中的交叉请求或反诉），主张您的贡献或您所贡献的作品构成直接或帮助性专利侵权，则依据本协议就该贡献或作品授予该实体的任何专利许可，自该诉讼提起之日起终止。
|
||||
|
||||
## 4. 权利能力与雇主
|
||||
|
||||
您声明您在法律上有权授予上述许可。如您的雇主对您创作的、包含您的贡献在内的知识产权享有权利，您声明：您已获得该雇主代表其作出贡献的许可，或该雇主已就您向我方的贡献放弃上述权利，或该雇主已与我方另行签署《企业贡献者许可协议》。
|
||||
|
||||
## 5. 原创性声明与披露义务
|
||||
|
||||
您声明您的每项贡献均为您的原创作品（代表第三方提交的情形见第 7 条）。您声明您提交的贡献中已完整披露您本人知悉的、与您的贡献任何部分相关的任何第三方许可或其他限制（包括但不限于相关专利和商标）的全部细节。
|
||||
|
||||
## 6. 无支持义务；免责声明
|
||||
|
||||
您无义务为您的贡献提供支持，除非您自愿提供。您可以免费提供支持、收费提供支持或不提供支持。除非适用法律要求或另有书面约定，您的贡献按"现状"（AS IS）提供，不附带任何明示或默示的保证或条件，包括但不限于关于权属、不侵权、适销性或特定用途适用性的任何保证或条件。
|
||||
|
||||
## 7. 第三方作品
|
||||
|
||||
如您希望提交非您原创的作品，您可以将其与任何贡献分开单独提交给我方，并完整说明其来源以及您本人知悉的任何许可或其他限制（包括但不限于相关专利、商标和许可协议）的全部细节，同时以显著方式将该作品标注为"代表第三方提交：[此处注明第三方名称]"。
|
||||
|
||||
## 8. 通知义务
|
||||
|
||||
如您知悉任何事实或情况将导致上述声明在任何方面不准确，您同意通知我方。
|
||||
|
||||
## 9. 电子签署
|
||||
|
||||
本协议以电子方式接受并签署：您通过您的 GitHub 账号，在本项目代码仓库的拉取请求（pull request）中发表包含我方指定语句（现为 "I have read the CLA Document and I hereby sign the CLA"）的评论，即构成您对本协议具有约束力的电子签名。您声明用于签署的 GitHub 账号归您本人所有，且您已达到订立有约束力合同的法定年龄。您的签署覆盖您对我方拥有或管理的全部代码仓库的现在及未来的贡献，直至您以书面形式通知我方就未来贡献退出本协议为止（已授予的许可不可撤销）。
|
||||
|
||||
## 10. 我方承诺
|
||||
|
||||
我方承诺本项目主仓库将持续公开提供作品的开源版本。
|
||||
|
||||
## 11. 其他
|
||||
|
||||
本协议构成您与我方之间就您的贡献达成的完整协议，并取代双方先前就此主题达成的任何协议。如本协议任何条款被认定为不可执行，其余条款仍然有效。本协议以英文签署，中文译文仅供参考，如有歧义以英文版为准。
|
||||
@@ -14,6 +14,12 @@
|
||||
- 在 PR 和 Commit Message 中请使用全英文
|
||||
- 对于中文用户，issue 中可以使用中文
|
||||
|
||||
### 贡献者许可协议（CLA）
|
||||
|
||||
为了保护项目和每一位贡献者，我们要求所有代码贡献者签署[贡献者许可协议（CLA）](./CLA.md)。这是 Apache、Google、Grafana 等主流开源项目的标准做法：您保留自己代码的全部版权，仅授予项目使用、分发您贡献的许可。
|
||||
|
||||
签署只需 10 秒：首次提交 PR 时，机器人会自动评论提示，按提示回复一句话即完成签署，此后对本组织所有仓库永久有效。历史贡献不受影响。
|
||||
|
||||
<hr/>
|
||||
|
||||
## Guidelines
|
||||
@@ -29,3 +35,9 @@
|
||||
|
||||
- Use English in PRs and Commit Messages
|
||||
- For English users, you can use English in issues
|
||||
|
||||
### Contributor License Agreement (CLA)
|
||||
|
||||
To protect the project and every contributor, we require all code contributors to sign our [Contributor License Agreement](./CLA.md). This is standard practice in major open source projects such as Apache, Google, and Grafana: you keep full copyright of your code — the CLA only grants us a license to use and distribute your contribution.
|
||||
|
||||
Signing takes 10 seconds: when you open your first PR, a bot will guide you to reply with a single comment. One signature covers all repositories in this organization, permanently. Past contributions are not affected.
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "langbot"
|
||||
version = "4.10.1"
|
||||
version = "4.10.2"
|
||||
description = "Production-grade platform for building agentic IM bots"
|
||||
readme = "README.md"
|
||||
license-files = ["LICENSE"]
|
||||
@@ -70,7 +70,7 @@ dependencies = [
|
||||
"chromadb>=1.0.0,<2.0.0",
|
||||
"qdrant-client (>=1.15.1,<2.0.0)",
|
||||
"pyseekdb==1.1.0.post3",
|
||||
"langbot-plugin==0.4.2",
|
||||
"langbot-plugin==0.4.4",
|
||||
"asyncpg>=0.30.0",
|
||||
"line-bot-sdk>=3.19.0",
|
||||
"matrix-nio>=0.25.2",
|
||||
@@ -79,6 +79,7 @@ dependencies = [
|
||||
"pymilvus>=2.6.4",
|
||||
"pgvector>=0.4.1",
|
||||
"botocore>=1.42.39",
|
||||
"litellm>=1.0.0",
|
||||
]
|
||||
keywords = [
|
||||
"bot",
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
"""LangBot - Production-grade platform for building agentic IM bots"""
|
||||
|
||||
__version__ = '4.10.1'
|
||||
__version__ = '4.10.2'
|
||||
|
||||
@@ -46,6 +46,30 @@ class MonitoringRouterGroup(group.RouterGroup):
|
||||
|
||||
return self.success(data=metrics)
|
||||
|
||||
@self.route('/token-statistics', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
|
||||
async def get_token_statistics() -> str:
|
||||
"""Get detailed token usage statistics (summary, per-model, timeseries)."""
|
||||
bot_ids = quart.request.args.getlist('botId')
|
||||
pipeline_ids = quart.request.args.getlist('pipelineId')
|
||||
start_time_str = quart.request.args.get('startTime')
|
||||
end_time_str = quart.request.args.get('endTime')
|
||||
bucket = quart.request.args.get('bucket', 'hour')
|
||||
if bucket not in ('hour', 'day'):
|
||||
bucket = 'hour'
|
||||
|
||||
start_time = parse_iso_datetime(start_time_str)
|
||||
end_time = parse_iso_datetime(end_time_str)
|
||||
|
||||
stats = await self.ap.monitoring_service.get_token_statistics(
|
||||
bot_ids=bot_ids if bot_ids else None,
|
||||
pipeline_ids=pipeline_ids if pipeline_ids else None,
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
bucket=bucket,
|
||||
)
|
||||
|
||||
return self.success(data=stats)
|
||||
|
||||
@self.route('/messages', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
|
||||
async def get_messages() -> str:
|
||||
"""Get message logs"""
|
||||
|
||||
@@ -271,6 +271,20 @@ class PluginsRouterGroup(group.RouterGroup):
|
||||
readme = await self.ap.plugin_connector.get_plugin_readme(author, plugin_name, language=language)
|
||||
return self.success(data={'readme': readme})
|
||||
|
||||
@self.route(
|
||||
'/<author>/<plugin_name>/logs',
|
||||
methods=['GET'],
|
||||
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
|
||||
)
|
||||
async def _(author: str, plugin_name: str) -> quart.Response:
|
||||
try:
|
||||
limit = int(quart.request.args.get('limit', 200))
|
||||
except (TypeError, ValueError):
|
||||
limit = 200
|
||||
level = quart.request.args.get('level') or None
|
||||
logs = await self.ap.plugin_connector.get_plugin_logs(author, plugin_name, limit=limit, level=level)
|
||||
return self.success(data={'logs': logs})
|
||||
|
||||
@self.route(
|
||||
'/<author>/<plugin_name>/icon',
|
||||
methods=['GET'],
|
||||
|
||||
@@ -31,6 +31,18 @@ class SystemRouterGroup(group.RouterGroup):
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# ``system.outbound_ips`` may be a comma-separated string instead of
|
||||
# a list when injected via the SYSTEM__OUTBOUND_IPS env var into a
|
||||
# pre-existing data/config.yaml that lacks the key (env overrides
|
||||
# only coerce to list when the key already holds one).
|
||||
outbound_ips = self.ap.instance_config.data.get('system', {}).get('outbound_ips', [])
|
||||
if isinstance(outbound_ips, str):
|
||||
outbound_ips = [ip.strip() for ip in outbound_ips.split(',') if ip.strip()]
|
||||
elif isinstance(outbound_ips, list):
|
||||
outbound_ips = [str(ip).strip() for ip in outbound_ips if str(ip).strip()]
|
||||
else:
|
||||
outbound_ips = []
|
||||
|
||||
return self.success(
|
||||
data={
|
||||
'version': constants.semantic_version,
|
||||
@@ -49,6 +61,7 @@ class SystemRouterGroup(group.RouterGroup):
|
||||
'disable_models_service', False
|
||||
),
|
||||
'limitation': self.ap.instance_config.data.get('system', {}).get('limitation', {}),
|
||||
'outbound_ips': outbound_ips,
|
||||
'wizard_status': wizard_status,
|
||||
'wizard_progress': wizard_progress,
|
||||
}
|
||||
|
||||
@@ -34,6 +34,46 @@ def _runtime_model_data(model_uuid: str, model_data: dict) -> dict:
|
||||
return {**model_data, 'uuid': model_uuid}
|
||||
|
||||
|
||||
async def _validate_provider_supports(ap: app.Application, provider_uuid: str, model_type: str) -> None:
|
||||
"""Validate that the provider's requester declares support for ``model_type``.
|
||||
|
||||
``model_type`` is one of the manifest ``support_type`` values:
|
||||
'llm', 'text-embedding', 'rerank'. Raises ValueError when the requester
|
||||
manifest does not list the requested type. This is a server-side guard so
|
||||
a model cannot be attached to a provider that does not support it, even if
|
||||
the frontend tab restriction is bypassed.
|
||||
"""
|
||||
model_mgr = getattr(ap, 'model_mgr', None)
|
||||
if model_mgr is None:
|
||||
return
|
||||
|
||||
provider_dict = getattr(model_mgr, 'provider_dict', None)
|
||||
if not provider_dict:
|
||||
return
|
||||
runtime_provider = provider_dict.get(provider_uuid)
|
||||
if runtime_provider is None:
|
||||
return
|
||||
|
||||
requester_name = getattr(getattr(runtime_provider, 'provider_entity', None), 'requester', None)
|
||||
if not requester_name:
|
||||
return
|
||||
|
||||
get_manifest = getattr(model_mgr, 'get_available_requester_manifest_by_name', None)
|
||||
if not callable(get_manifest):
|
||||
return
|
||||
manifest = get_manifest(requester_name)
|
||||
if manifest is None:
|
||||
return
|
||||
|
||||
spec = getattr(manifest, 'spec', None) or {}
|
||||
support_type = spec.get('support_type') if isinstance(spec, dict) else None
|
||||
# When a manifest omits support_type, do not block (backward compatible).
|
||||
if not support_type:
|
||||
return
|
||||
if model_type not in support_type:
|
||||
raise ValueError(f'Provider requester "{requester_name}" does not support {model_type} models')
|
||||
|
||||
|
||||
class LLMModelsService:
|
||||
ap: app.Application
|
||||
|
||||
@@ -96,6 +136,8 @@ class LLMModelsService:
|
||||
)
|
||||
model_data['provider_uuid'] = provider_uuid
|
||||
|
||||
await _validate_provider_supports(self.ap, model_data['provider_uuid'], 'llm')
|
||||
|
||||
await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_model.LLMModel).values(**model_data))
|
||||
|
||||
runtime_provider = self.ap.model_mgr.provider_dict.get(model_data['provider_uuid'])
|
||||
@@ -274,6 +316,8 @@ class EmbeddingModelsService:
|
||||
)
|
||||
model_data['provider_uuid'] = provider_uuid
|
||||
|
||||
await _validate_provider_supports(self.ap, model_data['provider_uuid'], 'text-embedding')
|
||||
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.insert(persistence_model.EmbeddingModel).values(**model_data)
|
||||
)
|
||||
@@ -434,6 +478,8 @@ class RerankModelsService:
|
||||
)
|
||||
model_data['provider_uuid'] = provider_uuid
|
||||
|
||||
await _validate_provider_supports(self.ap, model_data['provider_uuid'], 'rerank')
|
||||
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.insert(persistence_model.RerankModel).values(**model_data)
|
||||
)
|
||||
|
||||
@@ -472,6 +472,179 @@ class MonitoringService:
|
||||
'active_sessions': active_sessions,
|
||||
}
|
||||
|
||||
async def get_token_statistics(
|
||||
self,
|
||||
bot_ids: list[str] | None = None,
|
||||
pipeline_ids: list[str] | None = None,
|
||||
start_time: datetime.datetime | None = None,
|
||||
end_time: datetime.datetime | None = None,
|
||||
bucket: str = 'hour',
|
||||
) -> dict:
|
||||
"""Get detailed token usage statistics for production observability.
|
||||
|
||||
Returns:
|
||||
- summary: aggregate token counters and call/latency stats over the window
|
||||
- by_model: per-model token + call breakdown (sorted by total tokens desc)
|
||||
- timeseries: token usage bucketed by `bucket` ('hour' or 'day')
|
||||
|
||||
Only successful LLM calls are counted toward token totals; error calls are
|
||||
reported separately so a spike in failures is visible without polluting
|
||||
token accounting.
|
||||
"""
|
||||
LLMCall = persistence_monitoring.MonitoringLLMCall
|
||||
|
||||
conditions = []
|
||||
if bot_ids:
|
||||
conditions.append(LLMCall.bot_id.in_(bot_ids))
|
||||
if pipeline_ids:
|
||||
conditions.append(LLMCall.pipeline_id.in_(pipeline_ids))
|
||||
if start_time:
|
||||
conditions.append(LLMCall.timestamp >= start_time)
|
||||
if end_time:
|
||||
conditions.append(LLMCall.timestamp <= end_time)
|
||||
|
||||
def _apply(query):
|
||||
if conditions:
|
||||
query = query.where(sqlalchemy.and_(*conditions))
|
||||
return query
|
||||
|
||||
# ---- Summary aggregates ----
|
||||
summary_query = _apply(
|
||||
sqlalchemy.select(
|
||||
sqlalchemy.func.count(LLMCall.id),
|
||||
sqlalchemy.func.coalesce(sqlalchemy.func.sum(LLMCall.input_tokens), 0),
|
||||
sqlalchemy.func.coalesce(sqlalchemy.func.sum(LLMCall.output_tokens), 0),
|
||||
sqlalchemy.func.coalesce(sqlalchemy.func.sum(LLMCall.total_tokens), 0),
|
||||
sqlalchemy.func.coalesce(sqlalchemy.func.sum(LLMCall.duration), 0),
|
||||
sqlalchemy.func.coalesce(sqlalchemy.func.sum(LLMCall.cost), 0.0),
|
||||
sqlalchemy.func.sum(sqlalchemy.case((LLMCall.status == 'success', 1), else_=0)),
|
||||
sqlalchemy.func.sum(sqlalchemy.case((LLMCall.status == 'error', 1), else_=0)),
|
||||
# Count of successful calls that nonetheless recorded zero tokens —
|
||||
# a data-quality signal that usage reporting may be broken upstream.
|
||||
sqlalchemy.func.sum(
|
||||
sqlalchemy.case(
|
||||
(sqlalchemy.and_(LLMCall.status == 'success', LLMCall.total_tokens == 0), 1),
|
||||
else_=0,
|
||||
)
|
||||
),
|
||||
)
|
||||
)
|
||||
summary_result = await self.ap.persistence_mgr.execute_async(summary_query)
|
||||
row = summary_result.first()
|
||||
(
|
||||
total_calls,
|
||||
total_input_tokens,
|
||||
total_output_tokens,
|
||||
total_tokens,
|
||||
total_duration,
|
||||
total_cost,
|
||||
success_calls,
|
||||
error_calls,
|
||||
zero_token_success_calls,
|
||||
) = row if row else (0, 0, 0, 0, 0, 0.0, 0, 0, 0)
|
||||
|
||||
total_calls = total_calls or 0
|
||||
success_calls = success_calls or 0
|
||||
error_calls = error_calls or 0
|
||||
zero_token_success_calls = zero_token_success_calls or 0
|
||||
|
||||
summary = {
|
||||
'total_calls': total_calls,
|
||||
'success_calls': success_calls,
|
||||
'error_calls': error_calls,
|
||||
'total_input_tokens': int(total_input_tokens or 0),
|
||||
'total_output_tokens': int(total_output_tokens or 0),
|
||||
'total_tokens': int(total_tokens or 0),
|
||||
'total_cost': round(float(total_cost or 0.0), 6),
|
||||
'avg_tokens_per_call': int((total_tokens or 0) / total_calls) if total_calls > 0 else 0,
|
||||
'avg_duration_ms': int((total_duration or 0) / total_calls) if total_calls > 0 else 0,
|
||||
'avg_tokens_per_second': round((total_output_tokens or 0) / (total_duration / 1000), 2)
|
||||
if total_duration and total_duration > 0
|
||||
else 0,
|
||||
'zero_token_success_calls': zero_token_success_calls,
|
||||
}
|
||||
|
||||
# ---- Per-model breakdown ----
|
||||
by_model_query = _apply(
|
||||
sqlalchemy.select(
|
||||
LLMCall.model_name,
|
||||
sqlalchemy.func.count(LLMCall.id),
|
||||
sqlalchemy.func.coalesce(sqlalchemy.func.sum(LLMCall.input_tokens), 0),
|
||||
sqlalchemy.func.coalesce(sqlalchemy.func.sum(LLMCall.output_tokens), 0),
|
||||
sqlalchemy.func.coalesce(sqlalchemy.func.sum(LLMCall.total_tokens), 0),
|
||||
sqlalchemy.func.coalesce(sqlalchemy.func.sum(LLMCall.duration), 0),
|
||||
sqlalchemy.func.coalesce(sqlalchemy.func.sum(LLMCall.cost), 0.0),
|
||||
sqlalchemy.func.sum(sqlalchemy.case((LLMCall.status == 'error', 1), else_=0)),
|
||||
).group_by(LLMCall.model_name)
|
||||
)
|
||||
by_model_result = await self.ap.persistence_mgr.execute_async(by_model_query)
|
||||
by_model = []
|
||||
for mrow in by_model_result.all():
|
||||
(
|
||||
model_name,
|
||||
m_calls,
|
||||
m_in,
|
||||
m_out,
|
||||
m_total,
|
||||
m_duration,
|
||||
m_cost,
|
||||
m_errors,
|
||||
) = mrow
|
||||
m_calls = m_calls or 0
|
||||
by_model.append(
|
||||
{
|
||||
'model_name': model_name,
|
||||
'calls': m_calls,
|
||||
'error_calls': m_errors or 0,
|
||||
'input_tokens': int(m_in or 0),
|
||||
'output_tokens': int(m_out or 0),
|
||||
'total_tokens': int(m_total or 0),
|
||||
'cost': round(float(m_cost or 0.0), 6),
|
||||
'avg_tokens_per_call': int((m_total or 0) / m_calls) if m_calls > 0 else 0,
|
||||
'avg_duration_ms': int((m_duration or 0) / m_calls) if m_calls > 0 else 0,
|
||||
}
|
||||
)
|
||||
by_model.sort(key=lambda x: x['total_tokens'], reverse=True)
|
||||
|
||||
# ---- Time-bucketed series ----
|
||||
# Use a DB-agnostic bucketing approach: fetch (timestamp, tokens) rows and
|
||||
# aggregate in Python. The window is bounded by the time filter, so this is
|
||||
# cheap for typical dashboard ranges (hours/days).
|
||||
series_query = _apply(
|
||||
sqlalchemy.select(
|
||||
LLMCall.timestamp,
|
||||
LLMCall.input_tokens,
|
||||
LLMCall.output_tokens,
|
||||
LLMCall.total_tokens,
|
||||
).order_by(LLMCall.timestamp.asc())
|
||||
)
|
||||
series_result = await self.ap.persistence_mgr.execute_async(series_query)
|
||||
|
||||
bucket_fmt = '%Y-%m-%d %H:00' if bucket == 'hour' else '%Y-%m-%d'
|
||||
buckets: dict[str, dict] = {}
|
||||
for srow in series_result.all():
|
||||
ts, s_in, s_out, s_total = srow
|
||||
if ts is None:
|
||||
continue
|
||||
key = ts.strftime(bucket_fmt)
|
||||
b = buckets.setdefault(
|
||||
key,
|
||||
{'bucket': key, 'input_tokens': 0, 'output_tokens': 0, 'total_tokens': 0, 'calls': 0},
|
||||
)
|
||||
b['input_tokens'] += int(s_in or 0)
|
||||
b['output_tokens'] += int(s_out or 0)
|
||||
b['total_tokens'] += int(s_total or 0)
|
||||
b['calls'] += 1
|
||||
|
||||
timeseries = [buckets[k] for k in sorted(buckets.keys())]
|
||||
|
||||
return {
|
||||
'summary': summary,
|
||||
'by_model': by_model,
|
||||
'timeseries': timeseries,
|
||||
'bucket': bucket,
|
||||
}
|
||||
|
||||
async def get_messages(
|
||||
self,
|
||||
bot_ids: list[str] | None = None,
|
||||
|
||||
@@ -82,7 +82,7 @@ class UserService:
|
||||
payload = {
|
||||
'user': user_email,
|
||||
'iss': 'LangBot-' + constants.edition,
|
||||
'exp': datetime.datetime.now() + datetime.timedelta(seconds=jwt_expire),
|
||||
'exp': datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(seconds=jwt_expire),
|
||||
}
|
||||
|
||||
return jwt.encode(payload, jwt_secret, algorithm='HS256')
|
||||
|
||||
@@ -12,6 +12,7 @@ import pydantic
|
||||
|
||||
from langbot_plugin.box.client import BoxRuntimeClient
|
||||
from .connector import BoxRuntimeConnector, _get_box_config
|
||||
from ..telemetry import features as telemetry_features
|
||||
from langbot_plugin.box.errors import BoxError, BoxValidationError
|
||||
from langbot_plugin.box.models import (
|
||||
BUILTIN_PROFILES,
|
||||
@@ -218,6 +219,7 @@ class BoxService:
|
||||
f'query_id={query.query_id} '
|
||||
f'summary={json.dumps(self._summarize_result(result), ensure_ascii=False)}'
|
||||
)
|
||||
telemetry_features.increment(query, 'sandbox', 'execs')
|
||||
return self._serialize_result(result)
|
||||
|
||||
def resolve_box_session_id(self, query: pipeline_query.Query) -> str:
|
||||
@@ -785,6 +787,7 @@ class BoxService:
|
||||
# ── Observability ─────────────────────────────────────────────────
|
||||
|
||||
def _record_error(self, exc: Exception, query: pipeline_query.Query):
|
||||
telemetry_features.increment(query, 'sandbox', 'errors')
|
||||
self._recent_errors.append(
|
||||
{
|
||||
'timestamp': _dt.datetime.now(_UTC).isoformat(),
|
||||
|
||||
@@ -200,6 +200,17 @@ class Application:
|
||||
scopes=[core_entities.LifecycleControlScope.APPLICATION],
|
||||
)
|
||||
|
||||
# Telemetry instance heartbeat (startup + daily); respects
|
||||
# space.disable_telemetry via TelemetryManager.send().
|
||||
if self.telemetry is not None:
|
||||
from ..telemetry import heartbeat as telemetry_heartbeat
|
||||
|
||||
self.task_mgr.create_task(
|
||||
telemetry_heartbeat.heartbeat_loop(self),
|
||||
name='telemetry-heartbeat',
|
||||
scopes=[core_entities.LifecycleControlScope.APPLICATION],
|
||||
)
|
||||
|
||||
# Start monitoring data cleanup task if enabled
|
||||
monitoring_cfg = self.instance_config.data.get('monitoring', {})
|
||||
auto_cleanup_cfg = monitoring_cfg.get('auto_cleanup', {})
|
||||
|
||||
@@ -16,7 +16,6 @@ importutil.import_modules_in_pkg(stages)
|
||||
|
||||
stage_order = [
|
||||
'LoadConfigStage',
|
||||
'MigrationStage',
|
||||
'GenKeysStage',
|
||||
'SetupLoggerStage',
|
||||
'BuildAppStage',
|
||||
|
||||
@@ -42,6 +42,7 @@ required_deps = {
|
||||
'telegramify_markdown': 'telegramify-markdown',
|
||||
'slack_sdk': 'slack_sdk',
|
||||
'asyncpg': 'asyncpg',
|
||||
'litellm': 'litellm',
|
||||
}
|
||||
|
||||
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
import logging
|
||||
import logging.handlers
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
|
||||
@@ -20,6 +21,66 @@ log_colors_config = {
|
||||
LOG_FILE_MAX_BYTES = 10 * 1024 * 1024 # 10MB per file
|
||||
LOG_FILE_BACKUP_COUNT = 5 # Keep 5 backup files (total ~50MB max)
|
||||
|
||||
LOG_DIR = 'data/logs'
|
||||
|
||||
|
||||
class DailyGroupedRotatingFileHandler(logging.handlers.RotatingFileHandler):
|
||||
"""File handler that writes to ``data/logs/langbot-YYYY-MM-DD.log``.
|
||||
|
||||
It combines two rotation triggers:
|
||||
|
||||
* **Size** — within a single day the file is rotated once it exceeds
|
||||
``maxBytes``, producing numbered backups (``langbot-DATE.log.1`` etc.),
|
||||
exactly like :class:`~logging.handlers.RotatingFileHandler`.
|
||||
* **Date** — when the local date changes, logging switches to a fresh
|
||||
``langbot-<new date>.log`` file. This happens even within a single
|
||||
long-running process, so a bot started on day N keeps writing to that
|
||||
day's file and rolls over to day N+1's file at midnight, instead of
|
||||
appending every subsequent day's logs to the start-day file.
|
||||
|
||||
The on-disk naming stays compatible with the log-retention cleanup in
|
||||
``api/http/service/maintenance.py`` (``LOG_FILE_PATTERN``).
|
||||
"""
|
||||
|
||||
def __init__(self, log_dir: str, max_bytes: int, backup_count: int, encoding: str = 'utf-8'):
|
||||
self.log_dir = log_dir
|
||||
self._current_date = self._today()
|
||||
super().__init__(
|
||||
self._build_path(self._current_date),
|
||||
maxBytes=max_bytes,
|
||||
backupCount=backup_count,
|
||||
encoding=encoding,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _today() -> str:
|
||||
return time.strftime('%Y-%m-%d', time.localtime())
|
||||
|
||||
def _build_path(self, date_str: str) -> str:
|
||||
return os.path.join(self.log_dir, 'langbot-%s.log' % date_str)
|
||||
|
||||
def shouldRollover(self, record):
|
||||
# Roll over when the day changes, regardless of file size.
|
||||
if self._today() != self._current_date:
|
||||
return True
|
||||
return super().shouldRollover(record)
|
||||
|
||||
def doRollover(self):
|
||||
today = self._today()
|
||||
if today != self._current_date:
|
||||
# Date changed: point the handler at the new day's file.
|
||||
# This is a date switch, not a size-based numbered rotation.
|
||||
if self.stream:
|
||||
self.stream.close()
|
||||
self.stream = None
|
||||
self._current_date = today
|
||||
self.baseFilename = os.path.abspath(self._build_path(today))
|
||||
if not self.delay:
|
||||
self.stream = self._open()
|
||||
else:
|
||||
# Same day, file exceeded maxBytes: numbered rotation.
|
||||
super().doRollover()
|
||||
|
||||
|
||||
async def init_logging(extra_handlers: list[logging.Handler] = None) -> logging.Logger:
|
||||
# Remove all existing loggers
|
||||
@@ -31,8 +92,6 @@ async def init_logging(extra_handlers: list[logging.Handler] = None) -> logging.
|
||||
if constants.debug_mode:
|
||||
level = logging.DEBUG
|
||||
|
||||
log_file_name = 'data/logs/langbot-%s.log' % time.strftime('%Y-%m-%d', time.localtime())
|
||||
|
||||
qcg_logger = logging.getLogger('langbot')
|
||||
|
||||
qcg_logger.setLevel(level)
|
||||
@@ -48,12 +107,13 @@ async def init_logging(extra_handlers: list[logging.Handler] = None) -> logging.
|
||||
# stream_handler.setFormatter(color_formatter)
|
||||
stream_handler.stream = open(sys.stdout.fileno(), mode='w', encoding='utf-8', buffering=1)
|
||||
|
||||
# Use RotatingFileHandler to prevent unbounded log file growth
|
||||
rotating_file_handler = logging.handlers.RotatingFileHandler(
|
||||
log_file_name,
|
||||
# Rotate by size within a day and switch files when the date changes,
|
||||
# so long-running processes still produce a log file for the current day.
|
||||
rotating_file_handler = DailyGroupedRotatingFileHandler(
|
||||
LOG_DIR,
|
||||
max_bytes=LOG_FILE_MAX_BYTES,
|
||||
backup_count=LOG_FILE_BACKUP_COUNT,
|
||||
encoding='utf-8',
|
||||
maxBytes=LOG_FILE_MAX_BYTES,
|
||||
backupCount=LOG_FILE_BACKUP_COUNT,
|
||||
)
|
||||
|
||||
log_handlers: list[logging.Handler] = [
|
||||
|
||||
@@ -1,45 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import abc
|
||||
import typing
|
||||
|
||||
from . import app
|
||||
|
||||
|
||||
preregistered_migrations: list[typing.Type[Migration]] = []
|
||||
"""Currently not supported for extension"""
|
||||
|
||||
|
||||
def migration_class(name: str, number: int):
|
||||
"""Register a migration"""
|
||||
|
||||
def decorator(cls: typing.Type[Migration]) -> typing.Type[Migration]:
|
||||
cls.name = name
|
||||
cls.number = number
|
||||
preregistered_migrations.append(cls)
|
||||
return cls
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
class Migration(abc.ABC):
|
||||
"""A version migration"""
|
||||
|
||||
name: str
|
||||
|
||||
number: int
|
||||
|
||||
ap: app.Application
|
||||
|
||||
def __init__(self, ap: app.Application):
|
||||
self.ap = ap
|
||||
|
||||
@abc.abstractmethod
|
||||
async def need_migrate(self) -> bool:
|
||||
"""Determine if the current environment needs to run this migration"""
|
||||
pass
|
||||
|
||||
@abc.abstractmethod
|
||||
async def run(self):
|
||||
"""Run migration"""
|
||||
pass
|
||||
@@ -1,24 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('sensitive-word-migration', 1)
|
||||
class SensitiveWordMigration(migration.Migration):
|
||||
"""敏感词迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return os.path.exists('data/config/sensitive-words.json') and not os.path.exists(
|
||||
'data/metadata/sensitive-words.json'
|
||||
)
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
# 移动文件
|
||||
os.rename('data/config/sensitive-words.json', 'data/metadata/sensitive-words.json')
|
||||
|
||||
# 重新加载配置
|
||||
await self.ap.sensitive_meta.load_config()
|
||||
@@ -1,44 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('openai-config-migration', 2)
|
||||
class OpenAIConfigMigration(migration.Migration):
|
||||
"""OpenAI配置迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'openai-config' in self.ap.provider_cfg.data
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
old_openai_config = self.ap.provider_cfg.data['openai-config'].copy()
|
||||
|
||||
if 'keys' not in self.ap.provider_cfg.data:
|
||||
self.ap.provider_cfg.data['keys'] = {}
|
||||
|
||||
if 'openai' not in self.ap.provider_cfg.data['keys']:
|
||||
self.ap.provider_cfg.data['keys']['openai'] = []
|
||||
|
||||
self.ap.provider_cfg.data['keys']['openai'] = old_openai_config['api-keys']
|
||||
|
||||
self.ap.provider_cfg.data['model'] = old_openai_config['chat-completions-params']['model']
|
||||
|
||||
del old_openai_config['chat-completions-params']['model']
|
||||
|
||||
if 'requester' not in self.ap.provider_cfg.data:
|
||||
self.ap.provider_cfg.data['requester'] = {}
|
||||
|
||||
if 'openai-chat-completions' not in self.ap.provider_cfg.data['requester']:
|
||||
self.ap.provider_cfg.data['requester']['openai-chat-completions'] = {}
|
||||
|
||||
self.ap.provider_cfg.data['requester']['openai-chat-completions'] = {
|
||||
'base-url': old_openai_config['base_url'],
|
||||
'args': old_openai_config['chat-completions-params'],
|
||||
'timeout': old_openai_config['request-timeout'],
|
||||
}
|
||||
|
||||
del self.ap.provider_cfg.data['openai-config']
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,29 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('anthropic-requester-config-completion', 3)
|
||||
class AnthropicRequesterConfigCompletionMigration(migration.Migration):
|
||||
"""OpenAI配置迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return (
|
||||
'anthropic-messages' not in self.ap.provider_cfg.data['requester']
|
||||
or 'anthropic' not in self.ap.provider_cfg.data['keys']
|
||||
)
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
if 'anthropic-messages' not in self.ap.provider_cfg.data['requester']:
|
||||
self.ap.provider_cfg.data['requester']['anthropic-messages'] = {
|
||||
'base-url': 'https://api.anthropic.com',
|
||||
'args': {'max_tokens': 1024},
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
if 'anthropic' not in self.ap.provider_cfg.data['keys']:
|
||||
self.ap.provider_cfg.data['keys']['anthropic'] = []
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,29 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('moonshot-config-completion', 4)
|
||||
class MoonshotConfigCompletionMigration(migration.Migration):
|
||||
"""OpenAI配置迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return (
|
||||
'moonshot-chat-completions' not in self.ap.provider_cfg.data['requester']
|
||||
or 'moonshot' not in self.ap.provider_cfg.data['keys']
|
||||
)
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
if 'moonshot-chat-completions' not in self.ap.provider_cfg.data['requester']:
|
||||
self.ap.provider_cfg.data['requester']['moonshot-chat-completions'] = {
|
||||
'base-url': 'https://api.moonshot.cn/v1',
|
||||
'args': {},
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
if 'moonshot' not in self.ap.provider_cfg.data['keys']:
|
||||
self.ap.provider_cfg.data['keys']['moonshot'] = []
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,29 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('deepseek-config-completion', 5)
|
||||
class DeepseekConfigCompletionMigration(migration.Migration):
|
||||
"""OpenAI配置迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return (
|
||||
'deepseek-chat-completions' not in self.ap.provider_cfg.data['requester']
|
||||
or 'deepseek' not in self.ap.provider_cfg.data['keys']
|
||||
)
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
if 'deepseek-chat-completions' not in self.ap.provider_cfg.data['requester']:
|
||||
self.ap.provider_cfg.data['requester']['deepseek-chat-completions'] = {
|
||||
'base-url': 'https://api.deepseek.com',
|
||||
'args': {},
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
if 'deepseek' not in self.ap.provider_cfg.data['keys']:
|
||||
self.ap.provider_cfg.data['keys']['deepseek'] = []
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,19 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('vision-config', 6)
|
||||
class VisionConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'enable-vision' not in self.ap.provider_cfg.data
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
if 'enable-vision' not in self.ap.provider_cfg.data:
|
||||
self.ap.provider_cfg.data['enable-vision'] = False
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,20 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('qcg-center-url-config', 7)
|
||||
class QCGCenterURLConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'qcg-center-url' not in self.ap.system_cfg.data
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
|
||||
if 'qcg-center-url' not in self.ap.system_cfg.data:
|
||||
self.ap.system_cfg.data['qcg-center-url'] = 'https://api.qchatgpt.rockchin.top/api/v2'
|
||||
|
||||
await self.ap.system_cfg.dump_config()
|
||||
@@ -1,25 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('ad-fixwin-cfg-migration', 8)
|
||||
class AdFixwinConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return isinstance(self.ap.pipeline_cfg.data['rate-limit']['fixwin']['default'], int)
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
|
||||
for session_name in self.ap.pipeline_cfg.data['rate-limit']['fixwin']:
|
||||
temp_dict = {
|
||||
'window-size': 60,
|
||||
'limit': self.ap.pipeline_cfg.data['rate-limit']['fixwin'][session_name],
|
||||
}
|
||||
|
||||
self.ap.pipeline_cfg.data['rate-limit']['fixwin'][session_name] = temp_dict
|
||||
|
||||
await self.ap.pipeline_cfg.dump_config()
|
||||
@@ -1,22 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('msg-truncator-cfg-migration', 9)
|
||||
class MsgTruncatorConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'msg-truncate' not in self.ap.pipeline_cfg.data
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
|
||||
self.ap.pipeline_cfg.data['msg-truncate'] = {
|
||||
'method': 'round',
|
||||
'round': {'max-round': 10},
|
||||
}
|
||||
|
||||
await self.ap.pipeline_cfg.dump_config()
|
||||
@@ -1,23 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('ollama-requester-config', 10)
|
||||
class MsgTruncatorConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'ollama-chat' not in self.ap.provider_cfg.data['requester']
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
|
||||
self.ap.provider_cfg.data['requester']['ollama-chat'] = {
|
||||
'base-url': 'http://127.0.0.1:11434',
|
||||
'args': {},
|
||||
'timeout': 600,
|
||||
}
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,19 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('command-prefix-config', 11)
|
||||
class CommandPrefixConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'command-prefix' not in self.ap.command_cfg.data
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
|
||||
self.ap.command_cfg.data['command-prefix'] = ['!', '！']
|
||||
|
||||
await self.ap.command_cfg.dump_config()
|
||||
@@ -1,19 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('runner-config', 12)
|
||||
class RunnerConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'runner' not in self.ap.provider_cfg.data
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
|
||||
self.ap.provider_cfg.data['runner'] = 'local-agent'
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,29 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('http-api-config', 13)
|
||||
class HttpApiConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'http-api' not in self.ap.system_cfg.data or 'persistence' not in self.ap.system_cfg.data
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
|
||||
self.ap.system_cfg.data['http-api'] = {
|
||||
'enable': True,
|
||||
'host': '0.0.0.0',
|
||||
'port': 5300,
|
||||
'jwt-expire': 604800,
|
||||
}
|
||||
|
||||
self.ap.system_cfg.data['persistence'] = {
|
||||
'sqlite': {'path': 'data/persistence.db'},
|
||||
'use': 'sqlite',
|
||||
}
|
||||
|
||||
await self.ap.system_cfg.dump_config()
|
||||
@@ -1,22 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('force-delay-config', 14)
|
||||
class ForceDelayConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return isinstance(self.ap.platform_cfg.data['force-delay'], list)
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
|
||||
self.ap.platform_cfg.data['force-delay'] = {
|
||||
'min': self.ap.platform_cfg.data['force-delay'][0],
|
||||
'max': self.ap.platform_cfg.data['force-delay'][1],
|
||||
}
|
||||
|
||||
await self.ap.platform_cfg.dump_config()
|
||||
@@ -1,27 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('gitee-ai-config', 15)
|
||||
class GiteeAIConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return (
|
||||
'gitee-ai-chat-completions' not in self.ap.provider_cfg.data['requester']
|
||||
or 'gitee-ai' not in self.ap.provider_cfg.data['keys']
|
||||
)
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.provider_cfg.data['requester']['gitee-ai-chat-completions'] = {
|
||||
'base-url': 'https://ai.gitee.com/v1',
|
||||
'args': {},
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
self.ap.provider_cfg.data['keys']['gitee-ai'] = ['XXXXX']
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,23 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('dify-service-api-config', 16)
|
||||
class DifyServiceAPICfgMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'dify-service-api' not in self.ap.provider_cfg.data
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.provider_cfg.data['dify-service-api'] = {
|
||||
'base-url': 'https://api.dify.ai/v1',
|
||||
'app-type': 'chat',
|
||||
'chat': {'api-key': 'app-1234567890'},
|
||||
'workflow': {'api-key': 'app-1234567890', 'output-key': 'summary'},
|
||||
}
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,27 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('dify-api-timeout-params', 17)
|
||||
class DifyAPITimeoutParamsMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return (
|
||||
'timeout' not in self.ap.provider_cfg.data['dify-service-api']['chat']
|
||||
or 'timeout' not in self.ap.provider_cfg.data['dify-service-api']['workflow']
|
||||
or 'agent' not in self.ap.provider_cfg.data['dify-service-api']
|
||||
)
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.provider_cfg.data['dify-service-api']['chat']['timeout'] = 120
|
||||
self.ap.provider_cfg.data['dify-service-api']['workflow']['timeout'] = 120
|
||||
self.ap.provider_cfg.data['dify-service-api']['agent'] = {
|
||||
'api-key': 'app-1234567890',
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,23 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('xai-config', 18)
|
||||
class XaiConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'xai-chat-completions' not in self.ap.provider_cfg.data['requester']
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.provider_cfg.data['requester']['xai-chat-completions'] = {
|
||||
'base-url': 'https://api.x.ai/v1',
|
||||
'args': {},
|
||||
'timeout': 120,
|
||||
}
|
||||
self.ap.provider_cfg.data['keys']['xai'] = ['xai-1234567890']
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,23 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('zhipuai-config', 19)
|
||||
class ZhipuaiConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'zhipuai-chat-completions' not in self.ap.provider_cfg.data['requester']
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.provider_cfg.data['requester']['zhipuai-chat-completions'] = {
|
||||
'base-url': 'https://open.bigmodel.cn/api/paas/v4',
|
||||
'args': {},
|
||||
'timeout': 120,
|
||||
}
|
||||
self.ap.provider_cfg.data['keys']['zhipuai'] = ['xxxxxxx']
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,36 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('wecom-config', 20)
|
||||
class WecomConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
# for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
# if adapter['adapter'] == 'wecom':
|
||||
# return False
|
||||
|
||||
# return True
|
||||
return False
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.platform_cfg.data['platform-adapters'].append(
|
||||
{
|
||||
'adapter': 'wecom',
|
||||
'enable': False,
|
||||
'host': '0.0.0.0',
|
||||
'port': 2290,
|
||||
'corpid': '',
|
||||
'secret': '',
|
||||
'token': '',
|
||||
'EncodingAESKey': '',
|
||||
'contacts_secret': '',
|
||||
}
|
||||
)
|
||||
|
||||
await self.ap.platform_cfg.dump_config()
|
||||
@@ -1,35 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('lark-config', 21)
|
||||
class LarkConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
# for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
# if adapter['adapter'] == 'lark':
|
||||
# return False
|
||||
|
||||
# return True
|
||||
return False
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.platform_cfg.data['platform-adapters'].append(
|
||||
{
|
||||
'adapter': 'lark',
|
||||
'enable': False,
|
||||
'app_id': 'cli_abcdefgh',
|
||||
'app_secret': 'XXXXXXXXXX',
|
||||
'bot_name': 'LangBot',
|
||||
'enable-webhook': False,
|
||||
'port': 2285,
|
||||
'encrypt-key': 'xxxxxxxxx',
|
||||
}
|
||||
)
|
||||
|
||||
await self.ap.platform_cfg.dump_config()
|
||||
@@ -1,23 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('lmstudio-config', 22)
|
||||
class LmStudioConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
return 'lmstudio-chat-completions' not in self.ap.provider_cfg.data['requester']
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.provider_cfg.data['requester']['lmstudio-chat-completions'] = {
|
||||
'base-url': 'http://127.0.0.1:1234/v1',
|
||||
'args': {},
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,25 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('siliconflow-config', 23)
|
||||
class SiliconFlowConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
return 'siliconflow-chat-completions' not in self.ap.provider_cfg.data['requester']
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.provider_cfg.data['keys']['siliconflow'] = ['xxxxxxx']
|
||||
|
||||
self.ap.provider_cfg.data['requester']['siliconflow-chat-completions'] = {
|
||||
'base-url': 'https://api.siliconflow.cn/v1',
|
||||
'args': {},
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,31 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('discord-config', 24)
|
||||
class DiscordConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
# for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
# if adapter['adapter'] == 'discord':
|
||||
# return False
|
||||
|
||||
# return True
|
||||
return False
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.platform_cfg.data['platform-adapters'].append(
|
||||
{
|
||||
'adapter': 'discord',
|
||||
'enable': False,
|
||||
'client_id': '1234567890',
|
||||
'token': 'XXXXXXXXXX',
|
||||
}
|
||||
)
|
||||
|
||||
await self.ap.platform_cfg.dump_config()
|
||||
@@ -1,35 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('gewechat-config', 25)
|
||||
class GewechatConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
# for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
# if adapter['adapter'] == 'gewechat':
|
||||
# return False
|
||||
|
||||
# return True
|
||||
return False
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.platform_cfg.data['platform-adapters'].append(
|
||||
{
|
||||
'adapter': 'gewechat',
|
||||
'enable': False,
|
||||
'gewechat_url': 'http://your-gewechat-server:2531',
|
||||
'gewechat_file_url': 'http://your-gewechat-server:2532',
|
||||
'port': 2286,
|
||||
'callback_url': 'http://your-callback-url:2286/gewechat/callback',
|
||||
'app_id': '',
|
||||
'token': '',
|
||||
}
|
||||
)
|
||||
|
||||
await self.ap.platform_cfg.dump_config()
|
||||
@@ -1,33 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('qqofficial-config', 26)
|
||||
class QQOfficialConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
# for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
# if adapter['adapter'] == 'qqofficial':
|
||||
# return False
|
||||
|
||||
# return True
|
||||
return False
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.platform_cfg.data['platform-adapters'].append(
|
||||
{
|
||||
'adapter': 'qqofficial',
|
||||
'enable': False,
|
||||
'appid': '',
|
||||
'secret': '',
|
||||
'port': 2284,
|
||||
'token': '',
|
||||
}
|
||||
)
|
||||
|
||||
await self.ap.platform_cfg.dump_config()
|
||||
@@ -1,35 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('wx-official-account-config', 27)
|
||||
class WXOfficialAccountConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
# for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
# if adapter['adapter'] == 'officialaccount':
|
||||
# return False
|
||||
|
||||
# return True
|
||||
return False
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.platform_cfg.data['platform-adapters'].append(
|
||||
{
|
||||
'adapter': 'officialaccount',
|
||||
'enable': False,
|
||||
'token': '',
|
||||
'EncodingAESKey': '',
|
||||
'AppID': '',
|
||||
'AppSecret': '',
|
||||
'host': '0.0.0.0',
|
||||
'port': 2287,
|
||||
}
|
||||
)
|
||||
|
||||
await self.ap.platform_cfg.dump_config()
|
||||
@@ -1,25 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('bailian-requester-config', 28)
|
||||
class BailianRequesterConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
return 'bailian-chat-completions' not in self.ap.provider_cfg.data['requester']
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.provider_cfg.data['keys']['bailian'] = ['sk-xxxxxxx']
|
||||
|
||||
self.ap.provider_cfg.data['requester']['bailian-chat-completions'] = {
|
||||
'base-url': 'https://dashscope.aliyuncs.com/compatible-mode/v1',
|
||||
'args': {},
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,27 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('dashscope-app-api-config', 29)
|
||||
class DashscopeAppAPICfgMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'dashscope-app-api' not in self.ap.provider_cfg.data
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.provider_cfg.data['dashscope-app-api'] = {
|
||||
'app-type': 'agent',
|
||||
'api-key': 'sk-1234567890',
|
||||
'agent': {'app-id': 'Your_app_id', 'references_quote': '参考资料来自:'},
|
||||
'workflow': {
|
||||
'app-id': 'Your_app_id',
|
||||
'references_quote': '参考资料来自:',
|
||||
'biz_params': {'city': '北京', 'date': '2023-08-10'},
|
||||
},
|
||||
}
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,31 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('lark-config-cmpl', 30)
|
||||
class LarkConfigCmplMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
if adapter['adapter'] == 'lark':
|
||||
if 'enable-webhook' not in adapter:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
if adapter['adapter'] == 'lark':
|
||||
if 'enable-webhook' not in adapter:
|
||||
adapter['enable-webhook'] = False
|
||||
if 'port' not in adapter:
|
||||
adapter['port'] = 2285
|
||||
if 'encrypt-key' not in adapter:
|
||||
adapter['encrypt-key'] = 'xxxxxxxxx'
|
||||
|
||||
await self.ap.platform_cfg.dump_config()
|
||||
@@ -1,33 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('dingtalk-config', 31)
|
||||
class DingTalkConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
# for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
# if adapter['adapter'] == 'dingtalk':
|
||||
# return False
|
||||
|
||||
# return True
|
||||
return False
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.platform_cfg.data['platform-adapters'].append(
|
||||
{
|
||||
'adapter': 'dingtalk',
|
||||
'enable': False,
|
||||
'client_id': '',
|
||||
'client_secret': '',
|
||||
'robot_code': '',
|
||||
'robot_name': '',
|
||||
}
|
||||
)
|
||||
|
||||
await self.ap.platform_cfg.dump_config()
|
||||
@@ -1,25 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('volcark-requester-config', 32)
|
||||
class VolcArkRequesterConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
return 'volcark-chat-completions' not in self.ap.provider_cfg.data['requester']
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.provider_cfg.data['keys']['volcark'] = ['xxxxxxxx']
|
||||
|
||||
self.ap.provider_cfg.data['requester']['volcark-chat-completions'] = {
|
||||
'base-url': 'https://ark.cn-beijing.volces.com/api/v3',
|
||||
'args': {},
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,24 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('dify-thinking-config', 33)
|
||||
class DifyThinkingConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
if 'options' not in self.ap.provider_cfg.data['dify-service-api']:
|
||||
return True
|
||||
|
||||
if 'convert-thinking-tips' not in self.ap.provider_cfg.data['dify-service-api']['options']:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.provider_cfg.data['dify-service-api']['options'] = {'convert-thinking-tips': 'plain'}
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,29 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from urllib.parse import urlparse
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('gewechat-file-url-config', 34)
|
||||
class GewechatFileUrlConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
if adapter['adapter'] == 'gewechat':
|
||||
if 'gewechat_file_url' not in adapter:
|
||||
return True
|
||||
return False
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
if adapter['adapter'] == 'gewechat':
|
||||
if 'gewechat_file_url' not in adapter:
|
||||
parsed_url = urlparse(adapter['gewechat_url'])
|
||||
adapter['gewechat_file_url'] = f'{parsed_url.scheme}://{parsed_url.hostname}:2532'
|
||||
|
||||
await self.ap.platform_cfg.dump_config()
|
||||
@@ -1,26 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('wxoa-mode', 35)
|
||||
class WxoaModeMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
if adapter['adapter'] == 'officialaccount':
|
||||
if 'Mode' not in adapter:
|
||||
return True
|
||||
return False
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
if adapter['adapter'] == 'officialaccount':
|
||||
if 'Mode' not in adapter:
|
||||
adapter['Mode'] = 'drop'
|
||||
|
||||
await self.ap.platform_cfg.dump_config()
|
||||
@@ -1,26 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('wxoa-loading-message', 36)
|
||||
class WxoaLoadingMessageMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
if adapter['adapter'] == 'officialaccount':
|
||||
if 'LoadingMessage' not in adapter:
|
||||
return True
|
||||
return False
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
if adapter['adapter'] == 'officialaccount':
|
||||
if 'LoadingMessage' not in adapter:
|
||||
adapter['LoadingMessage'] = 'AI正在思考中，请发送任意内容获取回复。'
|
||||
|
||||
await self.ap.platform_cfg.dump_config()
|
||||
@@ -1,18 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('mcp-config', 37)
|
||||
class MCPConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'mcp' not in self.ap.provider_cfg.data
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.provider_cfg.data['mcp'] = {'servers': []}
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,25 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('tg-dingtalk-markdown', 38)
|
||||
class TgDingtalkMarkdownMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
|
||||
for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
if adapter['adapter'] in ['dingtalk', 'telegram']:
|
||||
if 'markdown_card' not in adapter:
|
||||
return True
|
||||
return False
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
for adapter in self.ap.platform_cfg.data['platform-adapters']:
|
||||
if adapter['adapter'] in ['dingtalk', 'telegram']:
|
||||
if 'markdown_card' not in adapter:
|
||||
adapter['markdown_card'] = False
|
||||
await self.ap.platform_cfg.dump_config()
|
||||
@@ -1,29 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('modelscope-config-completion', 39)
|
||||
class ModelScopeConfigCompletionMigration(migration.Migration):
|
||||
"""ModelScope配置迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return (
|
||||
'modelscope-chat-completions' not in self.ap.provider_cfg.data['requester']
|
||||
or 'modelscope' not in self.ap.provider_cfg.data['keys']
|
||||
)
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
if 'modelscope-chat-completions' not in self.ap.provider_cfg.data['requester']:
|
||||
self.ap.provider_cfg.data['requester']['modelscope-chat-completions'] = {
|
||||
'base-url': 'https://api-inference.modelscope.cn/v1',
|
||||
'args': {},
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
if 'modelscope' not in self.ap.provider_cfg.data['keys']:
|
||||
self.ap.provider_cfg.data['keys']['modelscope'] = []
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,29 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('ppio-config', 40)
|
||||
class PPIOConfigMigration(migration.Migration):
|
||||
"""PPIO配置迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return (
|
||||
'ppio-chat-completions' not in self.ap.provider_cfg.data['requester']
|
||||
or 'ppio' not in self.ap.provider_cfg.data['keys']
|
||||
)
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
if 'ppio-chat-completions' not in self.ap.provider_cfg.data['requester']:
|
||||
self.ap.provider_cfg.data['requester']['ppio-chat-completions'] = {
|
||||
'base-url': 'https://api.ppinfra.com/v3/openai',
|
||||
'args': {},
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
if 'ppio' not in self.ap.provider_cfg.data['keys']:
|
||||
self.ap.provider_cfg.data['keys']['ppio'] = []
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,17 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('dingtalk_card_auto_layout', 41)
|
||||
class DingTalkCardAutoLayoutMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return True
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.platform_cfg.data['platform-adapters']['app']['dingtalk']['card_auto_layout'] = False
|
||||
await self.ap.platform_cfg.dump_config()
|
||||
@@ -1,27 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('weknora-api-config', 42)
|
||||
class WeKnoraAPICfgMigration(migration.Migration):
|
||||
"""WeKnora API 配置迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'weknora-api' not in self.ap.provider_cfg.data
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.provider_cfg.data['weknora-api'] = {
|
||||
'base-url': 'http://localhost:8080/api/v1',
|
||||
'app-type': 'agent',
|
||||
'api-key': '',
|
||||
'agent-id': 'builtin-smart-reasoning',
|
||||
'knowledge-base-ids': [],
|
||||
'web-search-enabled': False,
|
||||
'timeout': 120,
|
||||
'base-prompt': '请回答用户的问题。',
|
||||
}
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -1,30 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('deerflow-api-config', 43)
|
||||
class DeerFlowAPICfgMigration(migration.Migration):
|
||||
"""DeerFlow API 配置迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'deerflow-api' not in self.ap.provider_cfg.data
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
self.ap.provider_cfg.data['deerflow-api'] = {
|
||||
'api-base': 'http://127.0.0.1:2026',
|
||||
'api-key': '',
|
||||
'auth-header': '',
|
||||
'assistant-id': 'lead_agent',
|
||||
'model-name': '',
|
||||
'thinking-enabled': False,
|
||||
'plan-mode': False,
|
||||
'subagent-enabled': False,
|
||||
'max-concurrent-subagents': 3,
|
||||
'timeout': 300,
|
||||
'recursion-limit': 1000,
|
||||
}
|
||||
|
||||
await self.ap.provider_cfg.dump_config()
|
||||
@@ -202,6 +202,16 @@ class LoadConfigStage(stage.BootingStage):
|
||||
constants.instance_id = new_id
|
||||
constants.edition = ap.instance_config.data.get('system', {}).get('edition', 'community')
|
||||
|
||||
# Instance creation timestamp: sourced from data/labels/instance_id.json.
|
||||
# Instances created before this field existed (or supplied via
|
||||
# system.instance_id) won't have it, so backfill with the current time
|
||||
# and persist it via the dump below — from then on it stays stable.
|
||||
instance_create_ts = ap.instance_id.data.get('instance_create_ts', 0)
|
||||
if not isinstance(instance_create_ts, int) or instance_create_ts <= 0:
|
||||
instance_create_ts = int(time.time())
|
||||
ap.instance_id.data['instance_create_ts'] = instance_create_ts
|
||||
constants.instance_create_ts = instance_create_ts
|
||||
|
||||
print(f'LangBot instance id: {constants.instance_id}')
|
||||
print(f'LangBot edition: {constants.edition}')
|
||||
|
||||
|
||||
@@ -1,43 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
|
||||
from .. import stage, app
|
||||
from .. import migration
|
||||
from ...utils import importutil
|
||||
from .. import migrations
|
||||
|
||||
importutil.import_modules_in_pkg(migrations)
|
||||
|
||||
|
||||
@stage.stage_class('MigrationStage')
|
||||
class MigrationStage(stage.BootingStage):
|
||||
"""Migration stage
|
||||
|
||||
These migrations are legacy, only performed in version 3.x
|
||||
"""
|
||||
|
||||
async def run(self, ap: app.Application):
|
||||
"""Run migration"""
|
||||
|
||||
if any(
|
||||
[
|
||||
ap.command_cfg is None,
|
||||
ap.pipeline_cfg is None,
|
||||
ap.platform_cfg is None,
|
||||
ap.provider_cfg is None,
|
||||
ap.system_cfg is None,
|
||||
]
|
||||
): # only run migration when version is 3.x
|
||||
return
|
||||
|
||||
migrations = migration.preregistered_migrations
|
||||
|
||||
# Sort by migration number
|
||||
migrations.sort(key=lambda x: x.number)
|
||||
|
||||
for migration_cls in migrations:
|
||||
migration_instance = migration_cls(ap)
|
||||
|
||||
if await migration_instance.need_migrate():
|
||||
await migration_instance.run()
|
||||
print(f'Migration {migration_instance.name} executed')
|
||||
@@ -31,6 +31,7 @@ class LLMModel(Base):
|
||||
name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
|
||||
provider_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
|
||||
abilities = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default=[])
|
||||
context_length = sqlalchemy.Column(sqlalchemy.Integer, nullable=True)
|
||||
extra_args = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
|
||||
prefered_ranking = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, default=0)
|
||||
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
|
||||
|
||||
@@ -0,0 +1,39 @@
|
||||
"""add llm model context length
|
||||
|
||||
Revision ID: 0005_add_llm_context_length
|
||||
Revises: 0004_add_mcp_readme
|
||||
Create Date: 2026-06-07
|
||||
"""
|
||||
|
||||
import sqlalchemy as sa
|
||||
from alembic import op
|
||||
|
||||
revision = '0005_add_llm_context_length'
|
||||
down_revision = '0004_add_mcp_readme'
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
# Add ``context_length`` to llm_models if the table exists and the column is
|
||||
# missing. The table may have been created by create_all() with the column
|
||||
# already present on fresh installs, so guard against duplicate-add; it may
|
||||
# also be absent entirely (e.g. migrating a truly empty DB), so guard against
|
||||
# a missing table too.
|
||||
conn = op.get_bind()
|
||||
inspector = sa.inspect(conn)
|
||||
if 'llm_models' not in inspector.get_table_names():
|
||||
return
|
||||
columns = {column['name'] for column in inspector.get_columns('llm_models')}
|
||||
if 'context_length' not in columns:
|
||||
op.add_column('llm_models', sa.Column('context_length', sa.Integer(), nullable=True))
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
inspector = sa.inspect(conn)
|
||||
if 'llm_models' not in inspector.get_table_names():
|
||||
return
|
||||
columns = {column['name'] for column in inspector.get_columns('llm_models')}
|
||||
if 'context_length' in columns:
|
||||
op.drop_column('llm_models', 'context_length')
|
||||
36
src/langbot/pkg/persistence/migrations/README.md
Normal file
36
src/langbot/pkg/persistence/migrations/README.md
Normal file
@@ -0,0 +1,36 @@
|
||||
# Legacy migrations (DEPRECATED — do not add new files here)
|
||||
|
||||
This directory holds the **legacy 3.x database migration system**
|
||||
(`DBMigration` subclasses in `dbmXXX_*.py`, registered via
|
||||
`@migration.migration_class(N)` and run from `pkg/persistence/mgr.py`).
|
||||
|
||||
**This system is frozen. Do not add new `dbmXXX_*.py` migrations.**
|
||||
|
||||
The chain is capped at version 25 (`required_database_version = 25` in
|
||||
`pkg/utils/constants.py`). These files exist only to upgrade pre-existing
|
||||
3.x databases up to the Alembic baseline (`0001_baseline`). Removing them
|
||||
would break in-place upgrades from old installations, so they are kept
|
||||
read-only.
|
||||
|
||||
## All new schema changes use Alembic
|
||||
|
||||
Migrations now live in `pkg/persistence/alembic/versions/`. To create one:
|
||||
|
||||
```bash
|
||||
uv run python -m langbot.pkg.persistence.alembic_runner autogenerate "description of your change"
|
||||
```
|
||||
|
||||
(requires `data/config.yaml` to exist). Review and edit the generated
|
||||
script before committing — Alembic migrations run automatically on startup
|
||||
and must be idempotent and guard against missing tables (the test suite
|
||||
runs them against empty databases).
|
||||
|
||||
### Rules for Alembic revision ids
|
||||
|
||||
- Keep the revision id **≤ 32 characters** — PostgreSQL stores
|
||||
`alembic_version.version_num` as `varchar(32)` and will raise
|
||||
`StringDataRightTruncationError` on overflow.
|
||||
- Guard every `op` call against a missing table / missing column
|
||||
(`inspector.get_table_names()` / `inspector.get_columns()`); fresh
|
||||
installs create the schema via `create_all()` and stamp the baseline,
|
||||
so migrations may run against tables that already match or do not exist.
|
||||
@@ -109,7 +109,7 @@ class PreProcessor(stage.PipelineStage):
|
||||
if llm_model:
|
||||
query.use_llm_model_uuid = llm_model.model_entity.uuid
|
||||
|
||||
if llm_model.model_entity.abilities.__contains__('func_call'):
|
||||
if 'func_call' in (llm_model.model_entity.abilities or []):
|
||||
# Get bound plugins and MCP servers for filtering tools
|
||||
bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
|
||||
bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None)
|
||||
@@ -159,11 +159,7 @@ class PreProcessor(stage.PipelineStage):
|
||||
|
||||
# Check if this model supports vision, if not, remove all images
|
||||
# TODO this checking should be performed in runner, and in this stage, the image should be reserved
|
||||
if (
|
||||
selected_runner == 'local-agent'
|
||||
and llm_model
|
||||
and not llm_model.model_entity.abilities.__contains__('vision')
|
||||
):
|
||||
if selected_runner == 'local-agent' and llm_model and 'vision' not in (llm_model.model_entity.abilities or []):
|
||||
for msg in query.messages:
|
||||
if isinstance(msg.content, list):
|
||||
for me in msg.content:
|
||||
@@ -181,7 +177,7 @@ class PreProcessor(stage.PipelineStage):
|
||||
plain_text += me.text
|
||||
elif isinstance(me, platform_message.Image):
|
||||
if selected_runner != 'local-agent' or (
|
||||
llm_model and llm_model.model_entity.abilities.__contains__('vision')
|
||||
llm_model and 'vision' in (llm_model.model_entity.abilities or [])
|
||||
):
|
||||
if me.base64 is not None:
|
||||
content_list.append(provider_message.ContentElement.from_image_base64(me.base64))
|
||||
@@ -202,7 +198,7 @@ class PreProcessor(stage.PipelineStage):
|
||||
content_list.append(provider_message.ContentElement.from_text(msg.text))
|
||||
elif isinstance(msg, platform_message.Image):
|
||||
if selected_runner != 'local-agent' or (
|
||||
llm_model and llm_model.model_entity.abilities.__contains__('vision')
|
||||
llm_model and 'vision' in (llm_model.model_entity.abilities or [])
|
||||
):
|
||||
if msg.base64 is not None:
|
||||
content_list.append(provider_message.ContentElement.from_image_base64(msg.base64))
|
||||
|
||||
@@ -13,6 +13,7 @@ from ....provider import runner as runner_module
|
||||
|
||||
import langbot_plugin.api.entities.events as events
|
||||
from ....utils import importutil, constants, runner as runner_utils
|
||||
from ....telemetry import features as telemetry_features
|
||||
from ....provider import runners
|
||||
import langbot_plugin.api.entities.builtin.provider.session as provider_session
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
@@ -201,7 +202,12 @@ class ChatMessageHandler(handler.MessageHandler):
|
||||
runner_name, runner, query.pipeline_config
|
||||
)
|
||||
|
||||
# Feature usage collected during query processing (tool calls,
|
||||
# knowledge base usage, sandbox executions, activated skills, ...)
|
||||
features = telemetry_features.collect_features(query)
|
||||
|
||||
payload = {
|
||||
'event_type': 'query',
|
||||
'query_id': query.query_id,
|
||||
'adapter': adapter_name,
|
||||
'runner': runner_name,
|
||||
@@ -212,6 +218,7 @@ class ChatMessageHandler(handler.MessageHandler):
|
||||
'instance_id': constants.instance_id,
|
||||
'edition': constants.edition,
|
||||
'pipeline_plugins': pipeline_plugins,
|
||||
'features': features,
|
||||
'error': locals().get('error_info', None),
|
||||
'timestamp': datetime.utcnow().isoformat(),
|
||||
}
|
||||
@@ -219,10 +226,12 @@ class ChatMessageHandler(handler.MessageHandler):
|
||||
# Send telemetry asynchronously and do not block pipeline via app's telemetry manager
|
||||
await self.ap.telemetry.start_send_task(payload)
|
||||
|
||||
# Trigger survey event on first successful non-WebSocket response
|
||||
# Trigger survey events on successful non-WebSocket responses
|
||||
if not locals().get('error_info') and adapter_name and 'WebSocket' not in adapter_name:
|
||||
if self.ap.survey:
|
||||
await self.ap.survey.trigger_event('first_bot_response_success')
|
||||
# Counts toward the bot_response_success_100 milestone event
|
||||
await self.ap.survey.record_bot_response_success()
|
||||
except Exception as ex:
|
||||
# Ensure telemetry issues do not affect normal flow
|
||||
self.ap.logger.warning(f'Failed to send telemetry: {ex}')
|
||||
|
||||
@@ -31,6 +31,18 @@ spec:
|
||||
type: webhook-url
|
||||
required: false
|
||||
default: ""
|
||||
- name: __system.outbound_ips
|
||||
label:
|
||||
en_US: IP Whitelist
|
||||
zh_Hans: IP 白名单
|
||||
zh_Hant: IP 白名單
|
||||
description:
|
||||
en_US: Add these outbound IPs of the LangBot server to the IP whitelist in the "Basic Configuration" of the WeChat Official Account platform
|
||||
zh_Hans: 请将这些 LangBot 服务器的出网 IP 添加到微信公众平台「基本配置」中的 IP 白名单
|
||||
zh_Hant: 請將這些 LangBot 伺服器的出網 IP 加入微信公眾平台「基本配置」中的 IP 白名單
|
||||
type: array[string]
|
||||
required: false
|
||||
default: []
|
||||
- name: token
|
||||
label:
|
||||
en_US: Token
|
||||
|
||||
@@ -19,6 +19,18 @@ spec:
|
||||
en: https://link.langbot.app/en/platforms/qqofficial
|
||||
ja: https://link.langbot.app/ja/platforms/qqofficial
|
||||
config:
|
||||
- name: __system.outbound_ips
|
||||
label:
|
||||
en_US: IP Whitelist
|
||||
zh_Hans: IP 白名单
|
||||
zh_Hant: IP 白名單
|
||||
description:
|
||||
en_US: Add these outbound IPs of the LangBot server to the IP whitelist in the development settings of the QQ Open Platform
|
||||
zh_Hans: 请将这些 LangBot 服务器的出网 IP 添加到 QQ 开放平台开发设置中的 IP 白名单
|
||||
zh_Hant: 請將這些 LangBot 伺服器的出網 IP 加入 QQ 開放平台開發設定中的 IP 白名單
|
||||
type: array[string]
|
||||
required: false
|
||||
default: []
|
||||
- name: appid
|
||||
label:
|
||||
en_US: App ID
|
||||
|
||||
@@ -32,6 +32,18 @@ spec:
|
||||
type: webhook-url
|
||||
required: false
|
||||
default: ""
|
||||
- name: __system.outbound_ips
|
||||
label:
|
||||
en_US: Trusted IPs
|
||||
zh_Hans: 企业可信 IP
|
||||
zh_Hant: 企業可信 IP
|
||||
description:
|
||||
en_US: Add these outbound IPs of the LangBot server to the "Trusted Enterprise IPs" of your app in the WeCom admin console
|
||||
zh_Hans: 请将这些 LangBot 服务器的出网 IP 添加到企业微信管理后台应用详情页的「企业可信 IP」中
|
||||
zh_Hant: 請將這些 LangBot 伺服器的出網 IP 加入企業微信管理後台應用詳情頁的「企業可信 IP」中
|
||||
type: array[string]
|
||||
required: false
|
||||
default: []
|
||||
- name: corpid
|
||||
label:
|
||||
en_US: Corpid
|
||||
|
||||
@@ -75,6 +75,18 @@ spec:
|
||||
field: enable-webhook
|
||||
operator: eq
|
||||
value: true
|
||||
- name: __system.outbound_ips
|
||||
label:
|
||||
en_US: Trusted IPs
|
||||
zh_Hans: 企业可信 IP
|
||||
zh_Hant: 企業可信 IP
|
||||
description:
|
||||
en_US: Add these outbound IPs of the LangBot server to the "Trusted Enterprise IPs" of the bot configuration in the WeCom admin console
|
||||
zh_Hans: 请将这些 LangBot 服务器的出网 IP 添加到企业微信管理后台智能机器人配置的「企业可信 IP」中
|
||||
zh_Hant: 請將這些 LangBot 伺服器的出網 IP 加入企業微信管理後台智慧機器人設定的「企業可信 IP」中
|
||||
type: array[string]
|
||||
required: false
|
||||
default: []
|
||||
- name: Secret
|
||||
label:
|
||||
en_US: Secret
|
||||
|
||||
@@ -31,6 +31,18 @@ spec:
|
||||
type: webhook-url
|
||||
required: false
|
||||
default: ""
|
||||
- name: __system.outbound_ips
|
||||
label:
|
||||
en_US: Trusted IPs
|
||||
zh_Hans: 企业可信 IP
|
||||
zh_Hant: 企業可信 IP
|
||||
description:
|
||||
en_US: Add these outbound IPs of the LangBot server to the "Trusted Enterprise IPs" of WeChat Customer Service in the WeCom admin console
|
||||
zh_Hans: 请将这些 LangBot 服务器的出网 IP 添加到企业微信管理后台微信客服的「企业可信 IP」中
|
||||
zh_Hant: 請將這些 LangBot 伺服器的出網 IP 加入企業微信管理後台微信客服的「企業可信 IP」中
|
||||
type: array[string]
|
||||
required: false
|
||||
default: []
|
||||
- name: corpid
|
||||
label:
|
||||
en_US: Corpid
|
||||
|
||||
@@ -689,6 +689,16 @@ class PluginRuntimeConnector(ManagedRuntimeConnector):
|
||||
async def get_plugin_readme(self, plugin_author: str, plugin_name: str, language: str = 'en') -> str:
|
||||
return await self.handler.get_plugin_readme(plugin_author, plugin_name, language)
|
||||
|
||||
async def get_plugin_logs(
|
||||
self,
|
||||
plugin_author: str,
|
||||
plugin_name: str,
|
||||
limit: int = 200,
|
||||
level: str | None = None,
|
||||
) -> list[dict[str, Any]]:
|
||||
# Not cached: logs are live and change constantly.
|
||||
return await self.handler.get_plugin_logs(plugin_author, plugin_name, limit, level)
|
||||
|
||||
@alru_cache(ttl=5 * 60)
|
||||
async def get_plugin_assets(self, plugin_author: str, plugin_name: str, filepath: str) -> dict[str, Any]:
|
||||
return await self.handler.get_plugin_assets(plugin_author, plugin_name, filepath)
|
||||
|
||||
@@ -953,6 +953,31 @@ class RuntimeConnectionHandler(handler.Handler):
|
||||
|
||||
return readme_bytes.decode('utf-8')
|
||||
|
||||
async def get_plugin_logs(
|
||||
self,
|
||||
plugin_author: str,
|
||||
plugin_name: str,
|
||||
limit: int = 200,
|
||||
level: str | None = None,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Get recent log lines captured from the plugin's stderr."""
|
||||
try:
|
||||
result = await self.call_action(
|
||||
LangBotToRuntimeAction.GET_PLUGIN_LOGS,
|
||||
{
|
||||
'plugin_author': plugin_author,
|
||||
'plugin_name': plugin_name,
|
||||
'limit': limit,
|
||||
'level': level,
|
||||
},
|
||||
timeout=20,
|
||||
)
|
||||
except Exception:
|
||||
traceback.print_exc()
|
||||
return []
|
||||
|
||||
return result.get('logs', [])
|
||||
|
||||
async def get_plugin_assets(self, plugin_author: str, plugin_name: str, filepath: str) -> dict[str, Any]:
|
||||
"""Get plugin assets"""
|
||||
result = await self.call_action(
|
||||
|
||||
@@ -37,11 +37,41 @@ class ModelManager:
|
||||
self.requester_components = []
|
||||
self.requester_dict = {}
|
||||
|
||||
@staticmethod
|
||||
def _get_litellm_provider_from_manifest(component: engine.Component | None) -> str | None:
|
||||
if component is None:
|
||||
return None
|
||||
|
||||
spec = getattr(component, 'spec', None) or {}
|
||||
litellm_provider = None
|
||||
|
||||
if isinstance(spec, dict):
|
||||
litellm_provider = spec.get('litellm_provider')
|
||||
else:
|
||||
getter = getattr(spec, 'get', None)
|
||||
if callable(getter):
|
||||
try:
|
||||
litellm_provider = getter('litellm_provider')
|
||||
except Exception:
|
||||
litellm_provider = None
|
||||
|
||||
if isinstance(litellm_provider, str) and litellm_provider:
|
||||
return litellm_provider
|
||||
return None
|
||||
|
||||
async def initialize(self):
|
||||
self.requester_components = self.ap.discover.get_components_by_kind('LLMAPIRequester')
|
||||
|
||||
requester_dict: dict[str, type[requester.ProviderAPIRequester]] = {}
|
||||
for component in self.requester_components:
|
||||
# Skip components that use litellm_provider (they will use litellmchat.py instead)
|
||||
litellm_provider = self._get_litellm_provider_from_manifest(component)
|
||||
if litellm_provider:
|
||||
self.ap.logger.debug(
|
||||
f'Skipping Python class loading for {component.metadata.name} '
|
||||
f'(uses litellm_provider={litellm_provider})'
|
||||
)
|
||||
continue
|
||||
requester_dict[component.metadata.name] = component.get_python_component_class()
|
||||
|
||||
self.requester_dict = requester_dict
|
||||
@@ -236,6 +266,7 @@ class ModelManager:
|
||||
name=model_info.get('name', ''),
|
||||
provider_uuid='',
|
||||
abilities=model_info.get('abilities', []),
|
||||
context_length=model_info.get('context_length'),
|
||||
extra_args=model_info.get('extra_args', {}),
|
||||
),
|
||||
provider=runtime_provider,
|
||||
@@ -294,13 +325,37 @@ class ModelManager:
|
||||
else:
|
||||
provider_entity = provider_info
|
||||
|
||||
if provider_entity.requester not in self.requester_dict:
|
||||
raise provider_errors.RequesterNotFoundError(provider_entity.requester)
|
||||
# Get requester manifest to check for litellm_provider
|
||||
requester_manifest = self.get_available_requester_manifest_by_name(provider_entity.requester)
|
||||
litellm_provider = self._get_litellm_provider_from_manifest(requester_manifest)
|
||||
|
||||
# Build config from base_url
|
||||
config = {'base_url': provider_entity.base_url}
|
||||
|
||||
# Check if requester manifest specifies litellm_provider
|
||||
if litellm_provider:
|
||||
from .requesters import litellmchat
|
||||
|
||||
# Use unified LiteLLMRequester with provider prefix
|
||||
# Map litellm_provider (YAML spec) to custom_llm_provider (config)
|
||||
config['custom_llm_provider'] = litellm_provider
|
||||
requester_inst = litellmchat.LiteLLMRequester(
|
||||
ap=self.ap,
|
||||
config=config,
|
||||
)
|
||||
self.ap.logger.debug(
|
||||
f'Using LiteLLMRequester for {provider_entity.requester} '
|
||||
f'with custom_llm_provider={config["custom_llm_provider"]}'
|
||||
)
|
||||
else:
|
||||
# Use original requester class (for backward compatibility)
|
||||
if provider_entity.requester not in self.requester_dict:
|
||||
raise provider_errors.RequesterNotFoundError(provider_entity.requester)
|
||||
requester_inst = self.requester_dict[provider_entity.requester](
|
||||
ap=self.ap,
|
||||
config=config,
|
||||
)
|
||||
|
||||
requester_inst = self.requester_dict[provider_entity.requester](
|
||||
ap=self.ap,
|
||||
config={'base_url': provider_entity.base_url},
|
||||
)
|
||||
await requester_inst.initialize()
|
||||
|
||||
token_mgr = token.TokenManager(name=provider_entity.uuid, tokens=provider_entity.api_keys or [])
|
||||
@@ -406,6 +461,7 @@ class ModelManager:
|
||||
name=model_info.get('name', ''),
|
||||
provider_uuid=model_info.get('provider_uuid', ''),
|
||||
abilities=model_info.get('abilities', []),
|
||||
context_length=model_info.get('context_length'),
|
||||
extra_args=model_info.get('extra_args', {}),
|
||||
)
|
||||
|
||||
|
||||
@@ -67,8 +67,8 @@ class RuntimeProvider:
|
||||
if isinstance(result, tuple):
|
||||
msg, usage_info = result
|
||||
if usage_info:
|
||||
input_tokens = usage_info.get('input_tokens', 0)
|
||||
output_tokens = usage_info.get('output_tokens', 0)
|
||||
input_tokens = usage_info.get('prompt_tokens', 0)
|
||||
output_tokens = usage_info.get('completion_tokens', 0)
|
||||
return msg
|
||||
else:
|
||||
return result
|
||||
@@ -128,7 +128,6 @@ class RuntimeProvider:
|
||||
start_time = time.time()
|
||||
status = 'success'
|
||||
error_message = None
|
||||
# Note: Stream doesn't easily provide token counts, set to 0
|
||||
input_tokens = 0
|
||||
output_tokens = 0
|
||||
|
||||
@@ -143,6 +142,15 @@ class RuntimeProvider:
|
||||
remove_think=remove_think,
|
||||
):
|
||||
yield chunk
|
||||
# Extract usage from stream if available (stored by LiteLLM requester)
|
||||
if query:
|
||||
if query.variables is None:
|
||||
query.variables = {}
|
||||
if '_stream_usage' in query.variables:
|
||||
usage_info = query.variables['_stream_usage']
|
||||
input_tokens = usage_info.get('prompt_tokens', 0)
|
||||
output_tokens = usage_info.get('completion_tokens', 0)
|
||||
del query.variables['_stream_usage']
|
||||
except Exception as e:
|
||||
status = 'error'
|
||||
error_message = str(e)
|
||||
|
||||
@@ -1,17 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import openai
|
||||
|
||||
from . import chatcmpl
|
||||
|
||||
|
||||
class AI302ChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
"""302.AI ChatCompletion API 请求器"""
|
||||
|
||||
client: openai.AsyncClient
|
||||
|
||||
default_config: dict[str, typing.Any] = {
|
||||
'base_url': 'https://api.302.ai/v1',
|
||||
'timeout': 120,
|
||||
}
|
||||
@@ -7,6 +7,7 @@ metadata:
|
||||
zh_Hans: 302.AI
|
||||
icon: 302ai.png
|
||||
spec:
|
||||
litellm_provider: openai
|
||||
config:
|
||||
- name: base_url
|
||||
label:
|
||||
@@ -22,6 +23,7 @@ spec:
|
||||
type: integer
|
||||
required: true
|
||||
default: 120
|
||||
alias: "302ai 302.AI 302 ai 中转 中转站 aggregator gpt claude gemini"
|
||||
support_type:
|
||||
- llm
|
||||
- text-embedding
|
||||
|
||||
@@ -1,370 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import json
|
||||
import platform
|
||||
import socket
|
||||
import anthropic
|
||||
import httpx
|
||||
|
||||
from .. import errors, requester
|
||||
|
||||
from ....utils import image
|
||||
import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
|
||||
|
||||
class AnthropicMessages(requester.ProviderAPIRequester):
|
||||
"""Anthropic Messages API 请求器"""
|
||||
|
||||
client: anthropic.AsyncAnthropic
|
||||
|
||||
default_config: dict[str, typing.Any] = {
|
||||
'base_url': 'https://api.anthropic.com',
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
async def initialize(self):
|
||||
# 兼容 Windows 缺失 TCP_KEEPINTVL 和 TCP_KEEPCNT 的问题
|
||||
if platform.system() == 'Windows':
|
||||
if not hasattr(socket, 'TCP_KEEPINTVL'):
|
||||
socket.TCP_KEEPINTVL = 0
|
||||
if not hasattr(socket, 'TCP_KEEPCNT'):
|
||||
socket.TCP_KEEPCNT = 0
|
||||
httpx_client = anthropic._base_client.AsyncHttpxClientWrapper(
|
||||
base_url=self.requester_cfg['base_url'],
|
||||
# cast to a valid type because mypy doesn't understand our type narrowing
|
||||
timeout=typing.cast(httpx.Timeout, self.requester_cfg['timeout']),
|
||||
limits=anthropic._constants.DEFAULT_CONNECTION_LIMITS,
|
||||
follow_redirects=True,
|
||||
trust_env=True,
|
||||
)
|
||||
|
||||
self.client = anthropic.AsyncAnthropic(
|
||||
api_key='',
|
||||
http_client=httpx_client,
|
||||
base_url=self.requester_cfg['base_url'],
|
||||
)
|
||||
|
||||
async def invoke_llm(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
model: requester.RuntimeLLMModel,
|
||||
messages: typing.List[provider_message.Message],
|
||||
funcs: typing.List[resource_tool.LLMTool] = None,
|
||||
extra_args: dict[str, typing.Any] = {},
|
||||
remove_think: bool = False,
|
||||
) -> provider_message.Message:
|
||||
self.client.api_key = model.provider.token_mgr.get_token()
|
||||
|
||||
args = extra_args.copy()
|
||||
args['model'] = model.model_entity.name
|
||||
|
||||
# 处理消息
|
||||
|
||||
# system
|
||||
system_role_message = None
|
||||
|
||||
for i, m in enumerate(messages):
|
||||
if m.role == 'system':
|
||||
system_role_message = m
|
||||
|
||||
break
|
||||
|
||||
if system_role_message:
|
||||
messages.pop(i)
|
||||
|
||||
if isinstance(system_role_message, provider_message.Message) and isinstance(system_role_message.content, str):
|
||||
args['system'] = system_role_message.content
|
||||
|
||||
req_messages = []
|
||||
|
||||
for m in messages:
|
||||
if m.role == 'tool':
|
||||
tool_call_id = m.tool_call_id
|
||||
|
||||
req_messages.append(
|
||||
{
|
||||
'role': 'user',
|
||||
'content': [
|
||||
{
|
||||
'type': 'tool_result',
|
||||
'tool_use_id': tool_call_id,
|
||||
'is_error': False,
|
||||
'content': [{'type': 'text', 'text': m.content}],
|
||||
}
|
||||
],
|
||||
}
|
||||
)
|
||||
|
||||
continue
|
||||
|
||||
msg_dict = m.dict(exclude_none=True)
|
||||
|
||||
if isinstance(m.content, str) and m.content.strip() != '':
|
||||
msg_dict['content'] = [{'type': 'text', 'text': m.content}]
|
||||
elif isinstance(m.content, list):
|
||||
for i, ce in enumerate(m.content):
|
||||
if ce.type == 'image_base64':
|
||||
image_b64, image_format = await image.extract_b64_and_format(ce.image_base64)
|
||||
|
||||
alter_image_ele = {
|
||||
'type': 'image',
|
||||
'source': {
|
||||
'type': 'base64',
|
||||
'media_type': f'image/{image_format}',
|
||||
'data': image_b64,
|
||||
},
|
||||
}
|
||||
msg_dict['content'][i] = alter_image_ele
|
||||
|
||||
if m.tool_calls:
|
||||
for tool_call in m.tool_calls:
|
||||
msg_dict['content'].append(
|
||||
{
|
||||
'type': 'tool_use',
|
||||
'id': tool_call.id,
|
||||
'name': tool_call.function.name,
|
||||
'input': json.loads(tool_call.function.arguments),
|
||||
}
|
||||
)
|
||||
|
||||
del msg_dict['tool_calls']
|
||||
|
||||
req_messages.append(msg_dict)
|
||||
|
||||
args['messages'] = req_messages
|
||||
|
||||
if 'thinking' in args:
|
||||
args['thinking'] = {'type': 'enabled', 'budget_tokens': 10000}
|
||||
|
||||
if funcs:
|
||||
tools = await self.ap.tool_mgr.generate_tools_for_anthropic(funcs)
|
||||
|
||||
if tools:
|
||||
args['tools'] = tools
|
||||
|
||||
try:
|
||||
resp = await self.client.messages.create(**args)
|
||||
|
||||
args = {
|
||||
'content': '',
|
||||
'role': resp.role,
|
||||
}
|
||||
assert type(resp) is anthropic.types.message.Message
|
||||
|
||||
for block in resp.content:
|
||||
if not remove_think and block.type == 'thinking':
|
||||
args['content'] = '<think>\n' + block.thinking + '\n</think>\n' + args['content']
|
||||
elif block.type == 'text':
|
||||
args['content'] += block.text
|
||||
elif block.type == 'tool_use':
|
||||
assert type(block) is anthropic.types.tool_use_block.ToolUseBlock
|
||||
tool_call = provider_message.ToolCall(
|
||||
id=block.id,
|
||||
type='function',
|
||||
function=provider_message.FunctionCall(name=block.name, arguments=json.dumps(block.input)),
|
||||
)
|
||||
if 'tool_calls' not in args:
|
||||
args['tool_calls'] = []
|
||||
args['tool_calls'].append(tool_call)
|
||||
|
||||
return provider_message.Message(**args)
|
||||
except anthropic.AuthenticationError as e:
|
||||
raise errors.RequesterError(f'api-key 无效: {e.message}')
|
||||
except anthropic.BadRequestError as e:
|
||||
raise errors.RequesterError(str(e.message))
|
||||
except anthropic.NotFoundError as e:
|
||||
if 'model: ' in str(e):
|
||||
raise errors.RequesterError(f'模型无效: {e.message}')
|
||||
else:
|
||||
raise errors.RequesterError(f'请求地址无效: {e.message}')
|
||||
|
||||
async def invoke_llm_stream(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
model: requester.RuntimeLLMModel,
|
||||
messages: typing.List[provider_message.Message],
|
||||
funcs: typing.List[resource_tool.LLMTool] = None,
|
||||
extra_args: dict[str, typing.Any] = {},
|
||||
remove_think: bool = False,
|
||||
) -> provider_message.Message:
|
||||
self.client.api_key = model.provider.token_mgr.get_token()
|
||||
|
||||
args = extra_args.copy()
|
||||
args['model'] = model.model_entity.name
|
||||
args['stream'] = True
|
||||
|
||||
# 处理消息
|
||||
|
||||
# system
|
||||
system_role_message = None
|
||||
|
||||
for i, m in enumerate(messages):
|
||||
if m.role == 'system':
|
||||
system_role_message = m
|
||||
|
||||
break
|
||||
|
||||
if system_role_message:
|
||||
messages.pop(i)
|
||||
|
||||
if isinstance(system_role_message, provider_message.Message) and isinstance(system_role_message.content, str):
|
||||
args['system'] = system_role_message.content
|
||||
|
||||
req_messages = []
|
||||
|
||||
for m in messages:
|
||||
if m.role == 'tool':
|
||||
tool_call_id = m.tool_call_id
|
||||
|
||||
req_messages.append(
|
||||
{
|
||||
'role': 'user',
|
||||
'content': [
|
||||
{
|
||||
'type': 'tool_result',
|
||||
'tool_use_id': tool_call_id,
|
||||
'is_error': False, # 暂时直接写false
|
||||
'content': [
|
||||
{'type': 'text', 'text': m.content}
|
||||
], # 这里要是list包裹，应该是多个返回的情况？type类型好像也可以填其他的，暂时只写text
|
||||
}
|
||||
],
|
||||
}
|
||||
)
|
||||
|
||||
continue
|
||||
|
||||
msg_dict = m.dict(exclude_none=True)
|
||||
|
||||
if isinstance(m.content, str) and m.content.strip() != '':
|
||||
msg_dict['content'] = [{'type': 'text', 'text': m.content}]
|
||||
elif isinstance(m.content, list):
|
||||
for i, ce in enumerate(m.content):
|
||||
if ce.type == 'image_base64':
|
||||
image_b64, image_format = await image.extract_b64_and_format(ce.image_base64)
|
||||
|
||||
alter_image_ele = {
|
||||
'type': 'image',
|
||||
'source': {
|
||||
'type': 'base64',
|
||||
'media_type': f'image/{image_format}',
|
||||
'data': image_b64,
|
||||
},
|
||||
}
|
||||
msg_dict['content'][i] = alter_image_ele
|
||||
if isinstance(msg_dict['content'], str) and msg_dict['content'] == '':
|
||||
msg_dict['content'] = [] # 这里不知道为什么会莫名有个空导致content为字符
|
||||
if m.tool_calls:
|
||||
for tool_call in m.tool_calls:
|
||||
msg_dict['content'].append(
|
||||
{
|
||||
'type': 'tool_use',
|
||||
'id': tool_call.id,
|
||||
'name': tool_call.function.name,
|
||||
'input': json.loads(tool_call.function.arguments),
|
||||
}
|
||||
)
|
||||
|
||||
del msg_dict['tool_calls']
|
||||
|
||||
req_messages.append(msg_dict)
|
||||
if 'thinking' in args:
|
||||
args['thinking'] = {'type': 'enabled', 'budget_tokens': 10000}
|
||||
|
||||
args['messages'] = req_messages
|
||||
|
||||
if funcs:
|
||||
tools = await self.ap.tool_mgr.generate_tools_for_anthropic(funcs)
|
||||
|
||||
if tools:
|
||||
args['tools'] = tools
|
||||
|
||||
try:
|
||||
role = 'assistant' # 默认角色
|
||||
# chunk_idx = 0
|
||||
think_started = False
|
||||
think_ended = False
|
||||
finish_reason = False
|
||||
tool_name = ''
|
||||
tool_id = ''
|
||||
async for chunk in await self.client.messages.create(**args):
|
||||
content = ''
|
||||
tool_call = {'id': None, 'function': {'name': None, 'arguments': None}, 'type': 'function'}
|
||||
if isinstance(
|
||||
chunk, anthropic.types.raw_content_block_start_event.RawContentBlockStartEvent
|
||||
): # 记录开始
|
||||
if chunk.content_block.type == 'tool_use':
|
||||
if chunk.content_block.name is not None:
|
||||
tool_name = chunk.content_block.name
|
||||
if chunk.content_block.id is not None:
|
||||
tool_id = chunk.content_block.id
|
||||
|
||||
tool_call['function']['name'] = tool_name
|
||||
tool_call['function']['arguments'] = ''
|
||||
tool_call['id'] = tool_id
|
||||
|
||||
if not remove_think:
|
||||
if chunk.content_block.type == 'thinking' and not remove_think:
|
||||
think_started = True
|
||||
elif chunk.content_block.type == 'text' and chunk.index != 0 and not remove_think:
|
||||
think_ended = True
|
||||
continue
|
||||
elif isinstance(chunk, anthropic.types.raw_content_block_delta_event.RawContentBlockDeltaEvent):
|
||||
if chunk.delta.type == 'thinking_delta':
|
||||
if think_started:
|
||||
think_started = False
|
||||
content = '<think>\n' + chunk.delta.thinking
|
||||
elif remove_think:
|
||||
continue
|
||||
else:
|
||||
content = chunk.delta.thinking
|
||||
elif chunk.delta.type == 'text_delta':
|
||||
if think_ended:
|
||||
think_ended = False
|
||||
content = '\n</think>\n' + chunk.delta.text
|
||||
else:
|
||||
content = chunk.delta.text
|
||||
elif chunk.delta.type == 'input_json_delta':
|
||||
tool_call['function']['arguments'] = chunk.delta.partial_json
|
||||
tool_call['function']['name'] = tool_name
|
||||
tool_call['id'] = tool_id
|
||||
elif isinstance(chunk, anthropic.types.raw_content_block_stop_event.RawContentBlockStopEvent):
|
||||
continue # 记录raw_content_block结束的
|
||||
|
||||
elif isinstance(chunk, anthropic.types.raw_message_delta_event.RawMessageDeltaEvent):
|
||||
if chunk.delta.stop_reason == 'end_turn':
|
||||
finish_reason = True
|
||||
elif isinstance(chunk, anthropic.types.raw_message_stop_event.RawMessageStopEvent):
|
||||
continue # 这个好像是完全结束
|
||||
else:
|
||||
# print(chunk)
|
||||
self.ap.logger.debug(f'anthropic chunk: {chunk}')
|
||||
continue
|
||||
|
||||
args = {
|
||||
'content': content,
|
||||
'role': role,
|
||||
'is_final': finish_reason,
|
||||
'tool_calls': None if tool_call['id'] is None else [tool_call],
|
||||
}
|
||||
# if chunk_idx == 0:
|
||||
# chunk_idx += 1
|
||||
# continue
|
||||
|
||||
# assert type(chunk) is anthropic.types.message.Chunk
|
||||
|
||||
yield provider_message.MessageChunk(**args)
|
||||
|
||||
# return llm_entities.Message(**args)
|
||||
except anthropic.AuthenticationError as e:
|
||||
raise errors.RequesterError(f'api-key 无效: {e.message}')
|
||||
except anthropic.BadRequestError as e:
|
||||
raise errors.RequesterError(str(e.message))
|
||||
except anthropic.NotFoundError as e:
|
||||
if 'model: ' in str(e):
|
||||
raise errors.RequesterError(f'模型无效: {e.message}')
|
||||
else:
|
||||
raise errors.RequesterError(f'请求地址无效: {e.message}')
|
||||
@@ -7,6 +7,7 @@ metadata:
|
||||
zh_Hans: Anthropic
|
||||
icon: anthropic.svg
|
||||
spec:
|
||||
litellm_provider: anthropic
|
||||
config:
|
||||
- name: base_url
|
||||
label:
|
||||
@@ -22,6 +23,7 @@ spec:
|
||||
type: integer
|
||||
required: true
|
||||
default: 120
|
||||
alias: "anthropic Anthropic 克劳德 claude Claude Opus Sonnet Haiku 安thropic"
|
||||
support_type:
|
||||
- llm
|
||||
provider_category: manufacturer
|
||||
|
||||
5
src/langbot/pkg/provider/modelmgr/requesters/baidu.svg
Normal file
5
src/langbot/pkg/provider/modelmgr/requesters/baidu.svg
Normal file
@@ -0,0 +1,5 @@
|
||||
<svg width="60" height="50" viewBox="0 0 60 50" xmlns="http://www.w3.org/2000/svg">
|
||||
<rect width="60" height="50" rx="8" fill="#2932E1"/>
|
||||
<text x="30" y="28" font-family="Arial, sans-serif" font-size="10" font-weight="bold" fill="white" text-anchor="middle">Baidu</text>
|
||||
<text x="30" y="40" font-family="Arial, sans-serif" font-size="8" fill="white" text-anchor="middle">ERNIE</text>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 396 B |
@@ -0,0 +1,31 @@
|
||||
apiVersion: v1
|
||||
kind: LLMAPIRequester
|
||||
metadata:
|
||||
name: baidu-chat-completions
|
||||
label:
|
||||
en_US: Baidu ERNIE
|
||||
zh_Hans: 百度文心一言
|
||||
icon: baidu.svg
|
||||
spec:
|
||||
litellm_provider: openai
|
||||
config:
|
||||
- name: base_url
|
||||
label:
|
||||
en_US: Base URL
|
||||
zh_Hans: 基础 URL
|
||||
type: string
|
||||
required: true
|
||||
default: https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop
|
||||
- name: timeout
|
||||
label:
|
||||
en_US: Timeout
|
||||
zh_Hans: 超时时间
|
||||
type: integer
|
||||
required: true
|
||||
default: 120
|
||||
alias: "baidu Baidu 百度 千帆 qianfan wenxin 文心 文心一言 ernie ERNIE bce embedding bce-reranker"
|
||||
support_type:
|
||||
- llm
|
||||
- text-embedding
|
||||
- rerank
|
||||
provider_category: manufacturer
|
||||
@@ -1,242 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import dashscope
|
||||
import openai
|
||||
|
||||
from . import modelscopechatcmpl
|
||||
from .. import requester
|
||||
import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
|
||||
|
||||
class BailianChatCompletions(modelscopechatcmpl.ModelScopeChatCompletions):
|
||||
"""阿里云百炼大模型平台 ChatCompletion API 请求器"""
|
||||
|
||||
client: openai.AsyncClient
|
||||
|
||||
default_config: dict[str, typing.Any] = {
|
||||
'base_url': 'https://dashscope.aliyuncs.com/compatible-mode/v1',
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
async def _closure_stream(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
req_messages: list[dict],
|
||||
use_model: requester.RuntimeLLMModel,
|
||||
use_funcs: list[resource_tool.LLMTool] = None,
|
||||
extra_args: dict[str, typing.Any] = {},
|
||||
remove_think: bool = False,
|
||||
) -> provider_message.Message | typing.AsyncGenerator[provider_message.MessageChunk, None]:
|
||||
self.client.api_key = use_model.provider.token_mgr.get_token()
|
||||
|
||||
args = {}
|
||||
args['model'] = use_model.model_entity.name
|
||||
|
||||
if use_funcs:
|
||||
tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
|
||||
|
||||
if tools:
|
||||
args['tools'] = tools
|
||||
|
||||
# 设置此次请求中的messages
|
||||
messages = req_messages.copy()
|
||||
|
||||
is_use_dashscope_call = False # 是否使用阿里原生库调用
|
||||
is_enable_multi_model = True # 是否支持多轮对话
|
||||
use_time_num = 0 # 模型已调用次数，防止存在多文件时重复调用
|
||||
use_time_ids = [] # 已调用的ID列表
|
||||
message_id = 0 # 记录消息序号
|
||||
|
||||
for msg in messages:
|
||||
# print(msg)
|
||||
if 'content' in msg and isinstance(msg['content'], list):
|
||||
for me in msg['content']:
|
||||
if me['type'] == 'image_base64':
|
||||
me['image_url'] = {'url': me['image_base64']}
|
||||
me['type'] = 'image_url'
|
||||
del me['image_base64']
|
||||
elif me['type'] == 'file_url' and '.' in me.get('file_name', ''):
|
||||
# 1. 视频文件推理
|
||||
# https://bailian.console.aliyun.com/?tab=doc#/doc/?type=model&url=2845871
|
||||
file_type = me.get('file_name').lower().split('.')[-1]
|
||||
if file_type in ['mp4', 'avi', 'mkv', 'mov', 'flv', 'wmv']:
|
||||
me['type'] = 'video_url'
|
||||
me['video_url'] = {'url': me['file_url']}
|
||||
del me['file_url']
|
||||
del me['file_name']
|
||||
use_time_num += 1
|
||||
use_time_ids.append(message_id)
|
||||
is_enable_multi_model = False
|
||||
# 2. 语音文件识别, 无法通过openai的audio字段传递，暂时不支持
|
||||
# https://bailian.console.aliyun.com/?tab=doc#/doc/?type=model&url=2979031
|
||||
elif file_type in [
|
||||
'aac',
|
||||
'amr',
|
||||
'aiff',
|
||||
'flac',
|
||||
'm4a',
|
||||
'mp3',
|
||||
'mpeg',
|
||||
'ogg',
|
||||
'opus',
|
||||
'wav',
|
||||
'webm',
|
||||
'wma',
|
||||
]:
|
||||
me['audio'] = me['file_url']
|
||||
me['type'] = 'audio'
|
||||
del me['file_url']
|
||||
del me['type']
|
||||
del me['file_name']
|
||||
is_use_dashscope_call = True
|
||||
use_time_num += 1
|
||||
use_time_ids.append(message_id)
|
||||
is_enable_multi_model = False
|
||||
message_id += 1
|
||||
|
||||
# 使用列表推导式，保留不在 use_time_ids[:-1] 中的元素，仅保留最后一个多媒体消息
|
||||
if not is_enable_multi_model and use_time_num > 1:
|
||||
messages = [msg for idx, msg in enumerate(messages) if idx not in use_time_ids[:-1]]
|
||||
|
||||
if not is_enable_multi_model:
|
||||
messages = [msg for msg in messages if 'resp_message_id' not in msg]
|
||||
|
||||
args['messages'] = messages
|
||||
args['stream'] = True
|
||||
|
||||
# 流式处理状态
|
||||
# tool_calls_map: dict[str, provider_message.ToolCall] = {}
|
||||
chunk_idx = 0
|
||||
thinking_started = False
|
||||
thinking_ended = False
|
||||
role = 'assistant' # 默认角色
|
||||
|
||||
if is_use_dashscope_call:
|
||||
response = dashscope.MultiModalConversation.call(
|
||||
# 若没有配置环境变量，请用百炼API Key将下行替换为：api_key = "sk-xxx"
|
||||
api_key=use_model.provider.token_mgr.get_token(),
|
||||
model=use_model.model_entity.name,
|
||||
messages=messages,
|
||||
result_format='message',
|
||||
asr_options={
|
||||
# "language": "zh", # 可选，若已知音频的语种，可通过该参数指定待识别语种，以提升识别准确率
|
||||
'enable_lid': True,
|
||||
'enable_itn': False,
|
||||
},
|
||||
stream=True,
|
||||
)
|
||||
content_length_list = []
|
||||
previous_length = 0 # 记录上一次的内容长度
|
||||
for res in response:
|
||||
chunk = res['output']
|
||||
# 解析 chunk 数据
|
||||
if hasattr(chunk, 'choices') and chunk.choices:
|
||||
choice = chunk.choices[0]
|
||||
delta_content = choice['message'].content[0]['text']
|
||||
finish_reason = choice['finish_reason']
|
||||
content_length_list.append(len(delta_content))
|
||||
else:
|
||||
delta_content = ''
|
||||
finish_reason = None
|
||||
|
||||
# 跳过空的第一个 chunk（只有 role 没有内容）
|
||||
if chunk_idx == 0 and not delta_content:
|
||||
chunk_idx += 1
|
||||
continue
|
||||
|
||||
# 检查 content_length_list 是否有足够的数据
|
||||
if len(content_length_list) >= 2:
|
||||
now_content = delta_content[previous_length : content_length_list[-1]]
|
||||
previous_length = content_length_list[-1] # 更新上一次的长度
|
||||
else:
|
||||
now_content = delta_content # 第一次循环时直接使用 delta_content
|
||||
previous_length = len(delta_content) # 更新上一次的长度
|
||||
|
||||
# 构建 MessageChunk - 只包含增量内容
|
||||
chunk_data = {
|
||||
'role': role,
|
||||
'content': now_content if now_content else None,
|
||||
'is_final': bool(finish_reason) and finish_reason != 'null',
|
||||
}
|
||||
|
||||
# 移除 None 值
|
||||
chunk_data = {k: v for k, v in chunk_data.items() if v is not None}
|
||||
yield provider_message.MessageChunk(**chunk_data)
|
||||
chunk_idx += 1
|
||||
else:
|
||||
async for chunk in self._req_stream(args, extra_body=extra_args):
|
||||
# 解析 chunk 数据
|
||||
if hasattr(chunk, 'choices') and chunk.choices:
|
||||
choice = chunk.choices[0]
|
||||
delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
|
||||
finish_reason = getattr(choice, 'finish_reason', None)
|
||||
else:
|
||||
delta = {}
|
||||
finish_reason = None
|
||||
|
||||
# 从第一个 chunk 获取 role，后续使用这个 role
|
||||
if 'role' in delta and delta['role']:
|
||||
role = delta['role']
|
||||
|
||||
# 获取增量内容
|
||||
delta_content = delta.get('content', '')
|
||||
reasoning_content = delta.get('reasoning_content', '')
|
||||
|
||||
# 处理 reasoning_content
|
||||
if reasoning_content:
|
||||
# accumulated_reasoning += reasoning_content
|
||||
# 如果设置了 remove_think，跳过 reasoning_content
|
||||
if remove_think:
|
||||
chunk_idx += 1
|
||||
continue
|
||||
|
||||
# 第一次出现 reasoning_content，添加 <think> 开始标签
|
||||
if not thinking_started:
|
||||
thinking_started = True
|
||||
delta_content = '<think>\n' + reasoning_content
|
||||
else:
|
||||
# 继续输出 reasoning_content
|
||||
delta_content = reasoning_content
|
||||
elif thinking_started and not thinking_ended and delta_content:
|
||||
# reasoning_content 结束，normal content 开始，添加 </think> 结束标签
|
||||
thinking_ended = True
|
||||
delta_content = '\n</think>\n' + delta_content
|
||||
|
||||
# 处理工具调用增量
|
||||
if delta.get('tool_calls'):
|
||||
for tool_call in delta['tool_calls']:
|
||||
if tool_call['id'] != '':
|
||||
tool_id = tool_call['id']
|
||||
if tool_call['function']['name'] is not None:
|
||||
tool_name = tool_call['function']['name']
|
||||
|
||||
if tool_call['type'] is None:
|
||||
tool_call['type'] = 'function'
|
||||
tool_call['id'] = tool_id
|
||||
tool_call['function']['name'] = tool_name
|
||||
tool_call['function']['arguments'] = (
|
||||
'' if tool_call['function']['arguments'] is None else tool_call['function']['arguments']
|
||||
)
|
||||
|
||||
# 跳过空的第一个 chunk（只有 role 没有内容）
|
||||
if chunk_idx == 0 and not delta_content and not reasoning_content and not delta.get('tool_calls'):
|
||||
chunk_idx += 1
|
||||
continue
|
||||
|
||||
# 构建 MessageChunk - 只包含增量内容
|
||||
chunk_data = {
|
||||
'role': role,
|
||||
'content': delta_content if delta_content else None,
|
||||
'tool_calls': delta.get('tool_calls'),
|
||||
'is_final': bool(finish_reason),
|
||||
}
|
||||
|
||||
# 移除 None 值
|
||||
chunk_data = {k: v for k, v in chunk_data.items() if v is not None}
|
||||
|
||||
yield provider_message.MessageChunk(**chunk_data)
|
||||
chunk_idx += 1
|
||||
# return
|
||||
@@ -7,6 +7,7 @@ metadata:
|
||||
zh_Hans: 阿里云百炼
|
||||
icon: bailian.png
|
||||
spec:
|
||||
litellm_provider: openai
|
||||
config:
|
||||
- name: base_url
|
||||
label:
|
||||
@@ -22,8 +23,10 @@ spec:
|
||||
type: integer
|
||||
required: true
|
||||
default: 120
|
||||
alias: "bailian 百炼 阿里 阿里云 aliyun alibaba dashscope 通义 通义千问 qwen Qwen tongyi gte-rerank text-embedding-v"
|
||||
support_type:
|
||||
- llm
|
||||
- text-embedding
|
||||
- rerank
|
||||
provider_category: maas
|
||||
execution:
|
||||
|
||||
@@ -1,702 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import typing
|
||||
|
||||
import openai
|
||||
import openai.types.chat.chat_completion as chat_completion_module
|
||||
import httpx
|
||||
|
||||
from .. import errors, requester
|
||||
import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
|
||||
|
||||
class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
"""OpenAI ChatCompletion API 请求器"""
|
||||
|
||||
client: openai.AsyncClient
|
||||
|
||||
default_config: dict[str, typing.Any] = {
|
||||
'base_url': 'https://api.openai.com/v1',
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
async def initialize(self):
|
||||
self.client = openai.AsyncClient(
|
||||
api_key=self.init_api_key,
|
||||
base_url=self.requester_cfg['base_url'].replace(' ', ''),
|
||||
timeout=self.requester_cfg['timeout'],
|
||||
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,
|
||||
args: dict,
|
||||
extra_body: dict = {},
|
||||
) -> chat_completion_module.ChatCompletion:
|
||||
return await self.client.chat.completions.create(**args, extra_body=extra_body)
|
||||
|
||||
async def _req_stream(
|
||||
self,
|
||||
args: dict,
|
||||
extra_body: dict = {},
|
||||
):
|
||||
async for chunk in await self.client.chat.completions.create(**args, extra_body=extra_body):
|
||||
yield chunk
|
||||
|
||||
async def _make_msg(
|
||||
self,
|
||||
chat_completion: chat_completion_module.ChatCompletion,
|
||||
remove_think: bool = False,
|
||||
) -> provider_message.Message:
|
||||
if not isinstance(chat_completion, chat_completion_module.ChatCompletion):
|
||||
raise TypeError(f'Expected ChatCompletion, got {type(chat_completion).__name__}: {chat_completion[:16]}')
|
||||
|
||||
chatcmpl_message = chat_completion.choices[0].message.model_dump()
|
||||
|
||||
# 确保 role 字段存在且不为 None
|
||||
if 'role' not in chatcmpl_message or chatcmpl_message['role'] is None:
|
||||
chatcmpl_message['role'] = 'assistant'
|
||||
|
||||
# 处理思维链
|
||||
content = chatcmpl_message.get('content', '')
|
||||
reasoning_content = chatcmpl_message.get('reasoning_content', None)
|
||||
|
||||
processed_content, _ = await self._process_thinking_content(
|
||||
content=content, reasoning_content=reasoning_content, remove_think=remove_think
|
||||
)
|
||||
|
||||
chatcmpl_message['content'] = processed_content
|
||||
|
||||
# 移除 reasoning_content 字段，避免传递给 Message
|
||||
if 'reasoning_content' in chatcmpl_message:
|
||||
del chatcmpl_message['reasoning_content']
|
||||
|
||||
message = provider_message.Message(**chatcmpl_message)
|
||||
|
||||
return message
|
||||
|
||||
async def _process_thinking_content(
|
||||
self,
|
||||
content: str,
|
||||
reasoning_content: str = None,
|
||||
remove_think: bool = False,
|
||||
) -> tuple[str, str]:
|
||||
"""处理思维链内容
|
||||
|
||||
Args:
|
||||
content: 原始内容
|
||||
reasoning_content: reasoning_content 字段内容
|
||||
remove_think: 是否移除思维链
|
||||
|
||||
Returns:
|
||||
(处理后的内容, 提取的思维链内容)
|
||||
"""
|
||||
thinking_content = ''
|
||||
|
||||
# 1. 从 reasoning_content 提取思维链
|
||||
if reasoning_content:
|
||||
thinking_content = reasoning_content
|
||||
|
||||
# 2. 从 content 中提取 <think> 标签内容
|
||||
if content and '<think>' in content and '</think>' in content:
|
||||
import re
|
||||
|
||||
think_pattern = r'<think>(.*?)</think>'
|
||||
think_matches = re.findall(think_pattern, content, re.DOTALL)
|
||||
if think_matches:
|
||||
# 如果已有 reasoning_content，则追加
|
||||
if thinking_content:
|
||||
thinking_content += '\n' + '\n'.join(think_matches)
|
||||
else:
|
||||
thinking_content = '\n'.join(think_matches)
|
||||
# 移除 content 中的 <think> 标签
|
||||
content = re.sub(think_pattern, '', content, flags=re.DOTALL).strip()
|
||||
|
||||
# 3. 根据 remove_think 参数决定是否保留思维链
|
||||
if remove_think:
|
||||
return content, ''
|
||||
else:
|
||||
# 如果有思维链内容，将其以 <think> 格式添加到 content 开头
|
||||
if thinking_content:
|
||||
content = f'<think>\n{thinking_content}\n</think>\n{content}'.strip()
|
||||
return content, thinking_content
|
||||
|
||||
async def _closure_stream(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
req_messages: list[dict],
|
||||
use_model: requester.RuntimeLLMModel,
|
||||
use_funcs: list[resource_tool.LLMTool] = None,
|
||||
extra_args: dict[str, typing.Any] = {},
|
||||
remove_think: bool = False,
|
||||
) -> provider_message.MessageChunk:
|
||||
self.client.api_key = use_model.provider.token_mgr.get_token()
|
||||
|
||||
args = {}
|
||||
args['model'] = use_model.model_entity.name
|
||||
|
||||
if use_funcs:
|
||||
tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
|
||||
if tools:
|
||||
args['tools'] = tools
|
||||
|
||||
# 设置此次请求中的messages
|
||||
messages = req_messages.copy()
|
||||
|
||||
# 检查vision
|
||||
for msg in messages:
|
||||
if 'content' in msg and isinstance(msg['content'], list):
|
||||
for me in msg['content']:
|
||||
if me['type'] == 'image_base64':
|
||||
me['image_url'] = {'url': me['image_base64']}
|
||||
me['type'] = 'image_url'
|
||||
del me['image_base64']
|
||||
|
||||
args['messages'] = messages
|
||||
args['stream'] = True
|
||||
|
||||
# 流式处理状态
|
||||
# tool_calls_map: dict[str, provider_message.ToolCall] = {}
|
||||
chunk_idx = 0
|
||||
thinking_started = False
|
||||
thinking_ended = False
|
||||
role = 'assistant' # 默认角色
|
||||
tool_id = ''
|
||||
tool_name = ''
|
||||
# accumulated_reasoning = '' # 仅用于判断何时结束思维链
|
||||
|
||||
async for chunk in self._req_stream(args, extra_body=extra_args):
|
||||
# 解析 chunk 数据
|
||||
|
||||
if hasattr(chunk, 'choices') and chunk.choices:
|
||||
choice = chunk.choices[0]
|
||||
delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
|
||||
|
||||
finish_reason = getattr(choice, 'finish_reason', None)
|
||||
else:
|
||||
delta = {}
|
||||
finish_reason = None
|
||||
# 从第一个 chunk 获取 role，后续使用这个 role
|
||||
if 'role' in delta and delta['role']:
|
||||
role = delta['role']
|
||||
|
||||
# 获取增量内容
|
||||
delta_content = delta.get('content', '')
|
||||
reasoning_content = delta.get('reasoning_content', '')
|
||||
|
||||
# 处理 reasoning_content
|
||||
if reasoning_content:
|
||||
# accumulated_reasoning += reasoning_content
|
||||
# 如果设置了 remove_think，跳过 reasoning_content
|
||||
if remove_think:
|
||||
chunk_idx += 1
|
||||
continue
|
||||
|
||||
# 第一次出现 reasoning_content，添加 <think> 开始标签
|
||||
if not thinking_started:
|
||||
thinking_started = True
|
||||
delta_content = '<think>\n' + reasoning_content
|
||||
else:
|
||||
# 继续输出 reasoning_content
|
||||
delta_content = reasoning_content
|
||||
elif thinking_started and not thinking_ended and delta_content:
|
||||
# reasoning_content 结束，normal content 开始，添加 </think> 结束标签
|
||||
thinking_ended = True
|
||||
delta_content = '\n</think>\n' + delta_content
|
||||
|
||||
# 处理 content 中已有的 <think> 标签（如果需要移除）
|
||||
# if delta_content and remove_think and '<think>' in delta_content:
|
||||
# import re
|
||||
#
|
||||
# # 移除 <think> 标签及其内容
|
||||
# delta_content = re.sub(r'<think>.*?</think>', '', delta_content, flags=re.DOTALL)
|
||||
|
||||
# 处理工具调用增量
|
||||
# delta_tool_calls = None
|
||||
if delta.get('tool_calls'):
|
||||
for tool_call in delta['tool_calls']:
|
||||
if tool_call['id'] and tool_call['function']['name']:
|
||||
tool_id = tool_call['id']
|
||||
tool_name = tool_call['function']['name']
|
||||
else:
|
||||
tool_call['id'] = tool_id
|
||||
tool_call['function']['name'] = tool_name
|
||||
if tool_call['type'] is None:
|
||||
tool_call['type'] = 'function'
|
||||
|
||||
# 跳过空的第一个 chunk（只有 role 没有内容）
|
||||
if chunk_idx == 0 and not delta_content and not reasoning_content and not delta.get('tool_calls'):
|
||||
chunk_idx += 1
|
||||
continue
|
||||
# 构建 MessageChunk - 只包含增量内容
|
||||
chunk_data = {
|
||||
'role': role,
|
||||
'content': delta_content if delta_content else None,
|
||||
'tool_calls': delta.get('tool_calls'),
|
||||
'is_final': bool(finish_reason),
|
||||
}
|
||||
|
||||
# 移除 None 值
|
||||
chunk_data = {k: v for k, v in chunk_data.items() if v is not None}
|
||||
|
||||
yield provider_message.MessageChunk(**chunk_data)
|
||||
chunk_idx += 1
|
||||
|
||||
async def _closure(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
req_messages: list[dict],
|
||||
use_model: requester.RuntimeLLMModel,
|
||||
use_funcs: list[resource_tool.LLMTool] = None,
|
||||
extra_args: dict[str, typing.Any] = {},
|
||||
remove_think: bool = False,
|
||||
) -> tuple[provider_message.Message, dict]:
|
||||
self.client.api_key = use_model.provider.token_mgr.get_token()
|
||||
|
||||
args = {}
|
||||
args['model'] = use_model.model_entity.name
|
||||
|
||||
if use_funcs:
|
||||
tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
|
||||
|
||||
if tools:
|
||||
args['tools'] = tools
|
||||
|
||||
# 设置此次请求中的messages
|
||||
messages = req_messages.copy()
|
||||
|
||||
# 检查vision
|
||||
for msg in messages:
|
||||
if 'content' in msg and isinstance(msg['content'], list):
|
||||
for me in msg['content']:
|
||||
if me['type'] == 'image_base64':
|
||||
me['image_url'] = {'url': me['image_base64']}
|
||||
me['type'] = 'image_url'
|
||||
del me['image_base64']
|
||||
|
||||
args['messages'] = messages
|
||||
|
||||
# 发送请求
|
||||
|
||||
resp = await self._req(args, extra_body=extra_args)
|
||||
# 处理请求结果
|
||||
message = await self._make_msg(resp, remove_think)
|
||||
|
||||
# Extract token usage from response
|
||||
usage_info = {}
|
||||
if hasattr(resp, 'usage') and resp.usage:
|
||||
usage_info['input_tokens'] = resp.usage.prompt_tokens or 0
|
||||
usage_info['output_tokens'] = resp.usage.completion_tokens or 0
|
||||
usage_info['total_tokens'] = resp.usage.total_tokens or 0
|
||||
|
||||
return message, usage_info
|
||||
|
||||
async def invoke_llm(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
model: requester.RuntimeLLMModel,
|
||||
messages: typing.List[provider_message.Message],
|
||||
funcs: typing.List[resource_tool.LLMTool] = None,
|
||||
extra_args: dict[str, typing.Any] = {},
|
||||
remove_think: bool = False,
|
||||
) -> tuple[provider_message.Message, dict]:
|
||||
"""Invoke LLM and return message with usage info"""
|
||||
req_messages = [] # req_messages 仅用于类内，外部同步由 query.messages 进行
|
||||
for m in messages:
|
||||
msg_dict = m.dict(exclude_none=True)
|
||||
content = msg_dict.get('content')
|
||||
if isinstance(content, list):
|
||||
# 检查 content 列表中是否每个部分都是文本
|
||||
if all(isinstance(part, dict) and part.get('type') == 'text' for part in content):
|
||||
# 将所有文本部分合并为一个字符串
|
||||
msg_dict['content'] = '\n'.join(part['text'] for part in content)
|
||||
req_messages.append(msg_dict)
|
||||
|
||||
try:
|
||||
msg, usage_info = await self._closure(
|
||||
query=query,
|
||||
req_messages=req_messages,
|
||||
use_model=model,
|
||||
use_funcs=funcs,
|
||||
extra_args=extra_args,
|
||||
remove_think=remove_think,
|
||||
)
|
||||
return msg, usage_info
|
||||
except asyncio.TimeoutError:
|
||||
raise errors.RequesterError('请求超时')
|
||||
except openai.BadRequestError as e:
|
||||
error_message = str(e.message) if hasattr(e, 'message') else str(e)
|
||||
if 'context_length_exceeded' in str(e):
|
||||
raise errors.RequesterError(f'上文过长，请重置会话: {error_message}')
|
||||
else:
|
||||
raise errors.RequesterError(f'请求参数错误: {error_message}')
|
||||
except openai.AuthenticationError as e:
|
||||
error_message = str(e.message) if hasattr(e, 'message') else str(e)
|
||||
raise errors.RequesterError(f'无效的 api-key: {error_message}')
|
||||
except openai.NotFoundError as e:
|
||||
error_message = str(e.message) if hasattr(e, 'message') else str(e)
|
||||
raise errors.RequesterError(f'请求路径错误: {error_message}')
|
||||
except openai.RateLimitError as e:
|
||||
error_message = str(e.message) if hasattr(e, 'message') else str(e)
|
||||
raise errors.RequesterError(f'请求过于频繁或余额不足: {error_message}')
|
||||
except openai.APIConnectionError as e:
|
||||
error_message = f'连接错误: {str(e)}'
|
||||
raise errors.RequesterError(error_message)
|
||||
except openai.APIError as e:
|
||||
error_message = str(e.message) if hasattr(e, 'message') else str(e)
|
||||
raise errors.RequesterError(f'请求错误: {error_message}')
|
||||
|
||||
async def invoke_embedding(
|
||||
self,
|
||||
model: requester.RuntimeEmbeddingModel,
|
||||
input_text: list[str],
|
||||
extra_args: dict[str, typing.Any] = {},
|
||||
) -> tuple[list[list[float]], dict]:
|
||||
"""调用 Embedding API, returns (embeddings, usage_info)"""
|
||||
self.client.api_key = model.provider.token_mgr.get_token()
|
||||
|
||||
args = {
|
||||
'model': model.model_entity.name,
|
||||
'input': input_text,
|
||||
}
|
||||
|
||||
if model.model_entity.extra_args:
|
||||
args.update(model.model_entity.extra_args)
|
||||
|
||||
args.update(extra_args)
|
||||
|
||||
try:
|
||||
resp = await self.client.embeddings.create(**args)
|
||||
|
||||
# Extract usage info
|
||||
usage_info = {}
|
||||
if hasattr(resp, 'usage') and resp.usage:
|
||||
usage_info['prompt_tokens'] = resp.usage.prompt_tokens or 0
|
||||
usage_info['total_tokens'] = resp.usage.total_tokens or 0
|
||||
|
||||
return [d.embedding for d in resp.data], usage_info
|
||||
except asyncio.TimeoutError:
|
||||
raise errors.RequesterError('请求超时')
|
||||
except openai.BadRequestError as e:
|
||||
raise errors.RequesterError(f'请求参数错误: {e.message}')
|
||||
|
||||
async def invoke_llm_stream(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
model: requester.RuntimeLLMModel,
|
||||
messages: typing.List[provider_message.Message],
|
||||
funcs: typing.List[resource_tool.LLMTool] = None,
|
||||
extra_args: dict[str, typing.Any] = {},
|
||||
remove_think: bool = False,
|
||||
) -> provider_message.MessageChunk:
|
||||
req_messages = [] # req_messages 仅用于类内，外部同步由 query.messages 进行
|
||||
for m in messages:
|
||||
msg_dict = m.dict(exclude_none=True)
|
||||
content = msg_dict.get('content')
|
||||
if isinstance(content, list):
|
||||
# 检查 content 列表中是否每个部分都是文本
|
||||
if all(isinstance(part, dict) and part.get('type') == 'text' for part in content):
|
||||
# 将所有文本部分合并为一个字符串
|
||||
msg_dict['content'] = '\n'.join(part['text'] for part in content)
|
||||
req_messages.append(msg_dict)
|
||||
|
||||
try:
|
||||
async for item in self._closure_stream(
|
||||
query=query,
|
||||
req_messages=req_messages,
|
||||
use_model=model,
|
||||
use_funcs=funcs,
|
||||
extra_args=extra_args,
|
||||
remove_think=remove_think,
|
||||
):
|
||||
yield item
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
raise errors.RequesterError('请求超时')
|
||||
except openai.BadRequestError as e:
|
||||
if 'context_length_exceeded' in e.message:
|
||||
raise errors.RequesterError(f'上文过长，请重置会话: {e.message}')
|
||||
else:
|
||||
raise errors.RequesterError(f'请求参数错误: {e.message}')
|
||||
except openai.AuthenticationError as e:
|
||||
raise errors.RequesterError(f'无效的 api-key: {e.message}')
|
||||
except openai.NotFoundError as e:
|
||||
raise errors.RequesterError(f'请求路径错误: {e.message}')
|
||||
except openai.RateLimitError as e:
|
||||
raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
|
||||
except openai.APIError as e:
|
||||
raise errors.RequesterError(f'请求错误: {e.message}')
|
||||
|
||||
async def invoke_rerank(
|
||||
self,
|
||||
model: requester.RuntimeRerankModel,
|
||||
query: str,
|
||||
documents: typing.List[str],
|
||||
extra_args: dict[str, typing.Any] = {},
|
||||
) -> typing.List[dict]:
|
||||
"""Standard /rerank endpoint (Jina/Cohere/SiliconFlow/Voyage/DashScope compatible)
|
||||
|
||||
Supports extra_args from model.extra_args:
|
||||
- rerank_url: full URL override (e.g. "https://dashscope.aliyuncs.com/compatible-api/v1/reranks")
|
||||
- rerank_path: path override appended to base_url (e.g. "reranks" instead of default "rerank")
|
||||
- Any other fields are merged into the request payload.
|
||||
"""
|
||||
api_key = model.provider.token_mgr.get_token()
|
||||
base_url = self.requester_cfg.get('base_url', '').rstrip('/')
|
||||
timeout = self.requester_cfg.get('timeout', 120)
|
||||
|
||||
merged_args = {}
|
||||
if model.model_entity.extra_args:
|
||||
merged_args.update(model.model_entity.extra_args)
|
||||
if extra_args:
|
||||
merged_args.update(extra_args)
|
||||
|
||||
rerank_url = merged_args.pop('rerank_url', None)
|
||||
rerank_path = merged_args.pop('rerank_path', 'rerank')
|
||||
if not rerank_url:
|
||||
rerank_url = f'{base_url}/{rerank_path}'
|
||||
|
||||
headers = {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': f'Bearer {api_key}',
|
||||
}
|
||||
|
||||
payload = {
|
||||
'model': model.model_entity.name,
|
||||
'query': query,
|
||||
'documents': documents[:64],
|
||||
'top_n': min(len(documents), 64),
|
||||
}
|
||||
|
||||
if merged_args:
|
||||
payload.update(merged_args)
|
||||
|
||||
try:
|
||||
async with httpx.AsyncClient(trust_env=True, timeout=timeout) as client:
|
||||
resp = await client.post(rerank_url, headers=headers, json=payload)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
|
||||
results = self._parse_rerank_response(data)
|
||||
|
||||
if results:
|
||||
scores = [r.get('relevance_score', 0.0) for r in results]
|
||||
min_score = min(scores)
|
||||
max_score = max(scores)
|
||||
if max_score - min_score > 1e-6:
|
||||
for r in results:
|
||||
r['relevance_score'] = (r['relevance_score'] - min_score) / (max_score - min_score)
|
||||
|
||||
return results
|
||||
except httpx.HTTPStatusError as e:
|
||||
raise errors.RequesterError(f'Rerank request failed: {e.response.status_code} - {e.response.text}')
|
||||
except httpx.TimeoutException:
|
||||
raise errors.RequesterError('Rerank request timed out')
|
||||
except Exception as e:
|
||||
raise errors.RequesterError(f'Rerank request error: {str(e)}')
|
||||
|
||||
@staticmethod
|
||||
def _parse_rerank_response(data: dict) -> typing.List[dict]:
|
||||
"""Parse rerank response from various providers.
|
||||
|
||||
Handles:
|
||||
- Jina/Cohere/SiliconFlow: {"results": [{"index", "relevance_score"}]}
|
||||
- Voyage AI: {"data": [{"index", "relevance_score"}]}
|
||||
- DashScope: {"output": {"results": [{"index", "relevance_score"}]}}
|
||||
"""
|
||||
if 'results' in data:
|
||||
return data['results']
|
||||
if 'data' in data:
|
||||
return data['data']
|
||||
if 'output' in data and isinstance(data['output'], dict):
|
||||
return data['output'].get('results', [])
|
||||
return []
|
||||
@@ -7,6 +7,7 @@ metadata:
|
||||
zh_Hans: OpenAI
|
||||
icon: openai.svg
|
||||
spec:
|
||||
litellm_provider: openai
|
||||
config:
|
||||
- name: base_url
|
||||
label:
|
||||
@@ -22,10 +23,10 @@ spec:
|
||||
type: integer
|
||||
required: true
|
||||
default: 120
|
||||
alias: "openai OpenAI 欧派 gpt GPT ChatGPT chatgpt o1 o3 o4 text-embedding 通用 openai兼容 compatible"
|
||||
support_type:
|
||||
- llm
|
||||
- text-embedding
|
||||
- rerank
|
||||
provider_category: manufacturer
|
||||
execution:
|
||||
python:
|
||||
|
||||
@@ -12,6 +12,7 @@ metadata:
|
||||
icon: chroma.svg
|
||||
spec:
|
||||
config: []
|
||||
alias: "chroma Chroma 向量 vector embedding 嵌入 chromadb"
|
||||
support_type:
|
||||
- text-embedding
|
||||
provider_category: builtin
|
||||
|
||||
@@ -7,6 +7,7 @@ metadata:
|
||||
zh_Hans: Cohere
|
||||
icon: cohere.svg
|
||||
spec:
|
||||
litellm_provider: cohere
|
||||
config:
|
||||
- name: base_url
|
||||
label:
|
||||
@@ -22,6 +23,7 @@ spec:
|
||||
type: integer
|
||||
required: true
|
||||
default: 120
|
||||
alias: "cohere Cohere rerank 重排 reranker rerank-english rerank-multilingual command"
|
||||
support_type:
|
||||
- rerank
|
||||
provider_category: manufacturer
|
||||
|
||||
@@ -1,17 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import openai
|
||||
|
||||
from . import chatcmpl
|
||||
|
||||
|
||||
class CompShareChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
"""CompShare ChatCompletion API 请求器"""
|
||||
|
||||
client: openai.AsyncClient
|
||||
|
||||
default_config: dict[str, typing.Any] = {
|
||||
'base_url': 'https://api.modelverse.cn/v1',
|
||||
'timeout': 120,
|
||||
}
|
||||
@@ -7,6 +7,7 @@ metadata:
|
||||
zh_Hans: 优云智算
|
||||
icon: compshare.png
|
||||
spec:
|
||||
litellm_provider: openai
|
||||
config:
|
||||
- name: base_url
|
||||
label:
|
||||
@@ -22,8 +23,11 @@ spec:
|
||||
type: integer
|
||||
required: true
|
||||
default: 120
|
||||
alias: "compshare 优刻得 ucloud UCloud 算力 共享算力 GPU"
|
||||
support_type:
|
||||
- llm
|
||||
- text-embedding
|
||||
- rerank
|
||||
provider_category: maas
|
||||
execution:
|
||||
python:
|
||||
|
||||
@@ -1,67 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
|
||||
from . import chatcmpl
|
||||
from .. import errors, requester
|
||||
import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
|
||||
|
||||
class DeepseekChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
"""Deepseek ChatCompletion API 请求器"""
|
||||
|
||||
default_config: dict[str, typing.Any] = {
|
||||
'base_url': 'https://api.deepseek.com',
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
async def _closure(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
req_messages: list[dict],
|
||||
use_model: requester.RuntimeLLMModel,
|
||||
use_funcs: list[resource_tool.LLMTool] = None,
|
||||
extra_args: dict[str, typing.Any] = {},
|
||||
remove_think: bool = False,
|
||||
) -> tuple[provider_message.Message, dict]:
|
||||
self.client.api_key = use_model.provider.token_mgr.get_token()
|
||||
|
||||
args = {}
|
||||
args['model'] = use_model.model_entity.name
|
||||
|
||||
if use_funcs:
|
||||
tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
|
||||
|
||||
if tools:
|
||||
args['tools'] = tools
|
||||
|
||||
# 设置此次请求中的messages
|
||||
messages = req_messages
|
||||
|
||||
# deepseek 不支持多模态，把content都转换成纯文字
|
||||
for m in messages:
|
||||
if 'content' in m and isinstance(m['content'], list):
|
||||
m['content'] = ' '.join([c['text'] for c in m['content'] if 'text' in c])
|
||||
|
||||
args['messages'] = messages
|
||||
|
||||
# 发送请求
|
||||
resp = await self._req(args, extra_body=extra_args)
|
||||
|
||||
# print(resp)
|
||||
|
||||
if resp is None:
|
||||
raise errors.RequesterError('接口返回为空，请确定模型提供商服务是否正常')
|
||||
# 处理请求结果
|
||||
message = await self._make_msg(resp, remove_think)
|
||||
|
||||
# Extract token usage from response
|
||||
usage_info = {}
|
||||
if hasattr(resp, 'usage') and resp.usage:
|
||||
usage_info['input_tokens'] = resp.usage.prompt_tokens or 0
|
||||
usage_info['output_tokens'] = resp.usage.completion_tokens or 0
|
||||
usage_info['total_tokens'] = resp.usage.total_tokens or 0
|
||||
|
||||
return message, usage_info
|
||||
@@ -7,6 +7,7 @@ metadata:
|
||||
zh_Hans: DeepSeek
|
||||
icon: deepseek.svg
|
||||
spec:
|
||||
litellm_provider: deepseek
|
||||
config:
|
||||
- name: base_url
|
||||
label:
|
||||
@@ -22,6 +23,7 @@ spec:
|
||||
type: integer
|
||||
required: true
|
||||
default: 120
|
||||
alias: "deepseek DeepSeek 深度求索 深度 求索 dpsk v3 r1 deepseek-chat deepseek-reasoner"
|
||||
support_type:
|
||||
- llm
|
||||
provider_category: manufacturer
|
||||
|
||||
4
src/langbot/pkg/provider/modelmgr/requesters/doubao.svg
Normal file
4
src/langbot/pkg/provider/modelmgr/requesters/doubao.svg
Normal file
@@ -0,0 +1,4 @@
|
||||
<svg width="60" height="50" viewBox="0 0 60 50" xmlns="http://www.w3.org/2000/svg">
|
||||
<rect width="60" height="50" rx="8" fill="#3B82F6"/>
|
||||
<text x="30" y="32" font-family="Arial, sans-serif" font-size="12" font-weight="bold" fill="white" text-anchor="middle">豆包</text>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 282 B |
@@ -0,0 +1,31 @@
|
||||
apiVersion: v1
|
||||
kind: LLMAPIRequester
|
||||
metadata:
|
||||
name: doubao-chat-completions
|
||||
label:
|
||||
en_US: ByteDance Doubao
|
||||
zh_Hans: 字节豆包
|
||||
icon: doubao.svg
|
||||
spec:
|
||||
litellm_provider: openai
|
||||
config:
|
||||
- name: base_url
|
||||
label:
|
||||
en_US: Base URL
|
||||
zh_Hans: 基础 URL
|
||||
type: string
|
||||
required: true
|
||||
default: https://ark.cn-beijing.volces.com/api/v3
|
||||
- name: timeout
|
||||
label:
|
||||
en_US: Timeout
|
||||
zh_Hans: 超时时间
|
||||
type: integer
|
||||
required: true
|
||||
default: 120
|
||||
alias: "doubao 豆包 字节 字节跳动 bytedance volcengine 火山 火山引擎 ark 方舟 seed"
|
||||
support_type:
|
||||
- llm
|
||||
- text-embedding
|
||||
- rerank
|
||||
provider_category: manufacturer
|
||||
@@ -1,205 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import httpx
|
||||
|
||||
from . import chatcmpl
|
||||
|
||||
import uuid
|
||||
|
||||
from .. import requester
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
|
||||
|
||||
|
||||
class GeminiChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
"""Google Gemini API 请求器"""
|
||||
|
||||
default_config: dict[str, typing.Any] = {
|
||||
'base_url': 'https://generativelanguage.googleapis.com/v1beta/openai',
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
async def scan_models(self, api_key: str | None = None) -> dict[str, typing.Any]:
|
||||
models_url = 'https://generativelanguage.googleapis.com/v1beta/models'
|
||||
params = {'key': api_key} if api_key else {}
|
||||
|
||||
all_models: list[dict[str, typing.Any]] = []
|
||||
next_page_token = ''
|
||||
last_payload: dict[str, typing.Any] = {}
|
||||
|
||||
async with httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']) as client:
|
||||
while True:
|
||||
request_params = dict(params)
|
||||
if next_page_token:
|
||||
request_params['pageToken'] = next_page_token
|
||||
|
||||
response = await client.get(models_url, params=request_params)
|
||||
response.raise_for_status()
|
||||
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,
|
||||
req_messages: list[dict],
|
||||
use_model: requester.RuntimeLLMModel,
|
||||
use_funcs: list[resource_tool.LLMTool] = None,
|
||||
extra_args: dict[str, typing.Any] = {},
|
||||
remove_think: bool = False,
|
||||
) -> provider_message.MessageChunk:
|
||||
self.client.api_key = use_model.provider.token_mgr.get_token()
|
||||
|
||||
args = {}
|
||||
args['model'] = use_model.model_entity.name
|
||||
|
||||
if use_funcs:
|
||||
tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
|
||||
if tools:
|
||||
args['tools'] = tools
|
||||
|
||||
# 设置此次请求中的messages
|
||||
messages = req_messages.copy()
|
||||
|
||||
# 检查vision
|
||||
for msg in messages:
|
||||
if 'content' in msg and isinstance(msg['content'], list):
|
||||
for me in msg['content']:
|
||||
if me['type'] == 'image_base64':
|
||||
me['image_url'] = {'url': me['image_base64']}
|
||||
me['type'] = 'image_url'
|
||||
del me['image_base64']
|
||||
|
||||
args['messages'] = messages
|
||||
args['stream'] = True
|
||||
|
||||
# 流式处理状态
|
||||
# tool_calls_map: dict[str, provider_message.ToolCall] = {}
|
||||
chunk_idx = 0
|
||||
thinking_started = False
|
||||
thinking_ended = False
|
||||
role = 'assistant' # 默认角色
|
||||
tool_id = ''
|
||||
tool_name = ''
|
||||
# accumulated_reasoning = '' # 仅用于判断何时结束思维链
|
||||
|
||||
async for chunk in self._req_stream(args, extra_body=extra_args):
|
||||
# 解析 chunk 数据
|
||||
|
||||
if hasattr(chunk, 'choices') and chunk.choices:
|
||||
choice = chunk.choices[0]
|
||||
delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
|
||||
|
||||
finish_reason = getattr(choice, 'finish_reason', None)
|
||||
else:
|
||||
delta = {}
|
||||
finish_reason = None
|
||||
# 从第一个 chunk 获取 role，后续使用这个 role
|
||||
if 'role' in delta and delta['role']:
|
||||
role = delta['role']
|
||||
|
||||
# 获取增量内容
|
||||
delta_content = delta.get('content', '')
|
||||
reasoning_content = delta.get('reasoning_content', '')
|
||||
|
||||
# 处理 reasoning_content
|
||||
if reasoning_content:
|
||||
# accumulated_reasoning += reasoning_content
|
||||
# 如果设置了 remove_think，跳过 reasoning_content
|
||||
if remove_think:
|
||||
chunk_idx += 1
|
||||
continue
|
||||
|
||||
# 第一次出现 reasoning_content，添加 <think> 开始标签
|
||||
if not thinking_started:
|
||||
thinking_started = True
|
||||
delta_content = '<think>\n' + reasoning_content
|
||||
else:
|
||||
# 继续输出 reasoning_content
|
||||
delta_content = reasoning_content
|
||||
elif thinking_started and not thinking_ended and delta_content:
|
||||
# reasoning_content 结束，normal content 开始，添加 </think> 结束标签
|
||||
thinking_ended = True
|
||||
delta_content = '\n</think>\n' + delta_content
|
||||
|
||||
# 处理 content 中已有的 <think> 标签（如果需要移除）
|
||||
# if delta_content and remove_think and '<think>' in delta_content:
|
||||
# import re
|
||||
#
|
||||
# # 移除 <think> 标签及其内容
|
||||
# delta_content = re.sub(r'<think>.*?</think>', '', delta_content, flags=re.DOTALL)
|
||||
|
||||
# 处理工具调用增量
|
||||
# delta_tool_calls = None
|
||||
if delta.get('tool_calls'):
|
||||
for tool_call in delta['tool_calls']:
|
||||
if tool_call['id'] == '' and tool_id == '':
|
||||
tool_id = str(uuid.uuid4())
|
||||
if tool_call['function']['name']:
|
||||
tool_name = tool_call['function']['name']
|
||||
tool_call['id'] = tool_id
|
||||
tool_call['function']['name'] = tool_name
|
||||
if tool_call['type'] is None:
|
||||
tool_call['type'] = 'function'
|
||||
|
||||
# 跳过空的第一个 chunk（只有 role 没有内容）
|
||||
if chunk_idx == 0 and not delta_content and not reasoning_content and not delta.get('tool_calls'):
|
||||
chunk_idx += 1
|
||||
continue
|
||||
# 构建 MessageChunk - 只包含增量内容
|
||||
chunk_data = {
|
||||
'role': role,
|
||||
'content': delta_content if delta_content else None,
|
||||
'tool_calls': delta.get('tool_calls'),
|
||||
'is_final': bool(finish_reason),
|
||||
}
|
||||
|
||||
# 移除 None 值
|
||||
chunk_data = {k: v for k, v in chunk_data.items() if v is not None}
|
||||
|
||||
yield provider_message.MessageChunk(**chunk_data)
|
||||
chunk_idx += 1
|
||||
@@ -7,6 +7,7 @@ metadata:
|
||||
zh_Hans: Google Gemini
|
||||
icon: gemini.svg
|
||||
spec:
|
||||
litellm_provider: gemini
|
||||
config:
|
||||
- name: base_url
|
||||
label:
|
||||
@@ -22,8 +23,10 @@ spec:
|
||||
type: integer
|
||||
required: true
|
||||
default: 120
|
||||
alias: "gemini Gemini 谷歌 google Google 双子座 bard flash pro text-embedding-004"
|
||||
support_type:
|
||||
- llm
|
||||
- text-embedding
|
||||
provider_category: manufacturer
|
||||
execution:
|
||||
python:
|
||||
|
||||
@@ -1,15 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
|
||||
import typing
|
||||
|
||||
from . import ppiochatcmpl
|
||||
|
||||
|
||||
class GiteeAIChatCompletions(ppiochatcmpl.PPIOChatCompletions):
|
||||
"""Gitee AI ChatCompletions API 请求器"""
|
||||
|
||||
default_config: dict[str, typing.Any] = {
|
||||
'base_url': 'https://ai.gitee.com/v1',
|
||||
'timeout': 120,
|
||||
}
|
||||
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Reference in New Issue
Block a user