chore(release): bump version to 4.10.2

Load the instance creation timestamp from data/labels/instance_id.json
(backfilling+persisting it for instances created before the field existed),
expose it as constants.instance_create_ts, and include it in the heartbeat
payload so Space can anchor Time-To-Value / onboarding analytics on real
install time rather than first-heartbeat.

Verified: py_compile, ruff, pytest tests/unit_tests/telemetry/ (37 passed).

AGENTS.md

@@ -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.

pyproject.toml

@@ -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==0.4.3",
+    "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",

src/langbot/__init__.py

@@ -1,3 +1,3 @@
 """LangBot - Production-grade platform for building agentic IM bots"""
 
-__version__ = '4.10.1'
+__version__ = '4.10.2'

src/langbot/pkg/api/http/controller/groups/monitoring.py

@@ -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"""

src/langbot/pkg/api/http/controller/groups/plugins.py

@@ -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'],

src/langbot/pkg/api/http/service/model.py

@@ -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)
         )

src/langbot/pkg/api/http/service/monitoring.py

@@ -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,

src/langbot/pkg/api/http/service/user.py

@@ -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')

src/langbot/pkg/core/boot.py

@@ -16,7 +16,6 @@ importutil.import_modules_in_pkg(stages)
 
 stage_order = [
     'LoadConfigStage',
-    'MigrationStage',
     'GenKeysStage',
     'SetupLoggerStage',
     'BuildAppStage',

src/langbot/pkg/core/bootutils/deps.py

@@ -42,6 +42,7 @@ required_deps = {
     'telegramify_markdown': 'telegramify-markdown',
     'slack_sdk': 'slack_sdk',
     'asyncpg': 'asyncpg',
+    'litellm': 'litellm',
 }
 
 

src/langbot/pkg/core/migration.py

@@ -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

src/langbot/pkg/core/migrations/m001_sensitive_word_migration.py

@@ -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()

src/langbot/pkg/core/migrations/m002_openai_config_migration.py

@@ -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()

src/langbot/pkg/core/migrations/m003_anthropic_requester_cfg_completion.py

@@ -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()

src/langbot/pkg/core/migrations/m004_moonshot_cfg_completion.py

@@ -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()

src/langbot/pkg/core/migrations/m005_deepseek_cfg_completion.py

@@ -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()

src/langbot/pkg/core/migrations/m006_vision_config.py

@@ -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()

src/langbot/pkg/core/migrations/m007_qcg_center_url.py

@@ -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()

src/langbot/pkg/core/migrations/m008_ad_fixwin_config_migrate.py

@@ -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()

src/langbot/pkg/core/migrations/m009_msg_truncator_cfg.py

@@ -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()

src/langbot/pkg/core/migrations/m010_ollama_requester_config.py

@@ -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()

src/langbot/pkg/core/migrations/m011_command_prefix_config.py

@@ -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()

src/langbot/pkg/core/migrations/m012_runner_config.py

@@ -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()

src/langbot/pkg/core/migrations/m013_http_api_config.py

@@ -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()

src/langbot/pkg/core/migrations/m014_force_delay_config.py

@@ -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()

src/langbot/pkg/core/migrations/m015_gitee_ai_config.py

@@ -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()

src/langbot/pkg/core/migrations/m016_dify_service_api.py

@@ -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()

src/langbot/pkg/core/migrations/m017_dify_api_timeout_params.py

@@ -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()

src/langbot/pkg/core/migrations/m018_xai_config.py

@@ -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()

src/langbot/pkg/core/migrations/m019_zhipuai_config.py

@@ -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()

src/langbot/pkg/core/migrations/m020_wecom_config.py

@@ -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()

src/langbot/pkg/core/migrations/m021_lark_config.py

@@ -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()

src/langbot/pkg/core/migrations/m022_lmstudio_config.py

@@ -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()

src/langbot/pkg/core/migrations/m023_siliconflow_config.py

@@ -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()

src/langbot/pkg/core/migrations/m024_discord_config.py

@@ -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()

src/langbot/pkg/core/migrations/m025_gewechat_config.py

@@ -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()

src/langbot/pkg/core/migrations/m026_qqofficial_config.py

@@ -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()

src/langbot/pkg/core/migrations/m027_wx_official_account_config.py

@@ -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()

src/langbot/pkg/core/migrations/m028_aliyun_requester_config.py

@@ -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()

src/langbot/pkg/core/migrations/m029_dashscope_app_api_config.py

@@ -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()

src/langbot/pkg/core/migrations/m030_lark_config_cmpl.py

@@ -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()

src/langbot/pkg/core/migrations/m031_dingtalk_config.py

@@ -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()

src/langbot/pkg/core/migrations/m032_volcark_config.py

@@ -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()

src/langbot/pkg/core/migrations/m033_dify_thinking_config.py

@@ -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()

src/langbot/pkg/core/migrations/m034_gewechat_file_url_config.py

@@ -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()

src/langbot/pkg/core/migrations/m035_wxoa_mode.py

@@ -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()

src/langbot/pkg/core/migrations/m036_wxoa_loading_message.py

@@ -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()

src/langbot/pkg/core/migrations/m037_mcp_config.py

@@ -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()

src/langbot/pkg/core/migrations/m038_tg_dingtalk_markdown.py

@@ -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()

src/langbot/pkg/core/migrations/m039_modelscope_cfg_completion.py

@@ -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()

src/langbot/pkg/core/migrations/m040_ppio_config.py

@@ -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()

src/langbot/pkg/core/migrations/m041_dingtalk_card_autolayout_config.py

@@ -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()

src/langbot/pkg/core/migrations/m042_weknora_api.py

@@ -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()

src/langbot/pkg/core/migrations/m043_deerflow_api.py

@@ -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()

src/langbot/pkg/core/stages/load_config.py

@@ -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}')
 

src/langbot/pkg/core/stages/migrate.py

@@ -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')

src/langbot/pkg/entity/persistence/model.py

@@ -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())

src/langbot/pkg/persistence/alembic/versions/0005_add_llm_context_length.py

@@ -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')

src/langbot/pkg/persistence/migrations/README.md

@@ -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.

src/langbot/pkg/pipeline/preproc/preproc.py

@@ -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))

src/langbot/pkg/plugin/connector.py

@@ -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)

src/langbot/pkg/plugin/handler.py

@@ -514,35 +514,6 @@ class RuntimeConnectionHandler(handler.Handler):
             except Exception as e:
                 return _make_rag_error_response(e, 'EmbeddingError', embedding_model_uuid=embedding_model_uuid)
 
-        @self.action(PluginToRuntimeAction.INVOKE_RERANK)
-        async def invoke_rerank(data: dict[str, Any]) -> handler.ActionResponse:
-            rerank_model_uuid = data['rerank_model_uuid']
-            query = data['query']
-            documents = data['documents']
-            top_k = data.get('top_k')
-            extra_args = data.get('extra_args', {})
-
-            try:
-                rerank_model = await self.ap.model_mgr.get_rerank_model_by_uuid(rerank_model_uuid)
-            except ValueError:
-                return handler.ActionResponse.error(
-                    message=f'Rerank model with rerank_model_uuid {rerank_model_uuid} not found',
-                )
-
-            try:
-                scores = await rerank_model.provider.invoke_rerank(
-                    model=rerank_model,
-                    query=query,
-                    documents=documents[:64],
-                    extra_args=extra_args,
-                )
-                scored = sorted(scores, key=lambda x: x.get('relevance_score', 0), reverse=True)
-                if top_k is not None:
-                    scored = scored[: int(top_k)]
-                return handler.ActionResponse.success(data={'results': scored})
-            except Exception as e:
-                return _make_rag_error_response(e, 'RerankError', rerank_model_uuid=rerank_model_uuid)
-
         @self.action(PluginToRuntimeAction.VECTOR_UPSERT)
         async def vector_upsert(data: dict[str, Any]) -> handler.ActionResponse:
             collection_id = data['collection_id']
@@ -982,6 +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(

src/langbot/pkg/provider/modelmgr/modelmgr.py

@@ -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', {}),
         )
 

src/langbot/pkg/provider/modelmgr/requester.py

@@ -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)

src/langbot/pkg/provider/modelmgr/requesters/302aichatcmpl.py

@@ -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,
-    }

src/langbot/pkg/provider/modelmgr/requesters/302aichatcmpl.yaml

@@ -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

src/langbot/pkg/provider/modelmgr/requesters/anthropicmsgs.py

@@ -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}')

src/langbot/pkg/provider/modelmgr/requesters/anthropicmsgs.yaml

@@ -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

src/langbot/pkg/provider/modelmgr/requesters/baidu.svg

@@ -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>

src/langbot/pkg/provider/modelmgr/requesters/baiduchatcmpl.yaml

@@ -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

src/langbot/pkg/provider/modelmgr/requesters/bailianchatcmpl.py

@@ -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

src/langbot/pkg/provider/modelmgr/requesters/bailianchatcmpl.yaml

@@ -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:

src/langbot/pkg/provider/modelmgr/requesters/chatcmpl.py

@@ -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 []

src/langbot/pkg/provider/modelmgr/requesters/chatcmpl.yaml

@@ -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:

src/langbot/pkg/provider/modelmgr/requesters/chromaembed.yaml

@@ -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

src/langbot/pkg/provider/modelmgr/requesters/coherererank.yaml

@@ -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

src/langbot/pkg/provider/modelmgr/requesters/compsharechatcmpl.py

@@ -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,
-    }

src/langbot/pkg/provider/modelmgr/requesters/compsharechatcmpl.yaml

@@ -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:

src/langbot/pkg/provider/modelmgr/requesters/deepseekchatcmpl.py

@@ -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

src/langbot/pkg/provider/modelmgr/requesters/deepseekchatcmpl.yaml

@@ -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

src/langbot/pkg/provider/modelmgr/requesters/doubao.svg

@@ -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>

src/langbot/pkg/provider/modelmgr/requesters/doubaochatcmpl.yaml

@@ -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

src/langbot/pkg/provider/modelmgr/requesters/geminichatcmpl.py

@@ -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

src/langbot/pkg/provider/modelmgr/requesters/geminichatcmpl.yaml

@@ -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:

src/langbot/pkg/provider/modelmgr/requesters/giteeaichatcmpl.py

@@ -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,
-    }

src/langbot/pkg/provider/modelmgr/requesters/giteeaichatcmpl.yaml

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: Gitee AI
   icon: giteeai.svg
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:
@@ -22,6 +23,7 @@ spec:
     type: integer
     required: true
     default: 120
+  alias: "gitee Gitee 码云 gitee-ai gitee ai serverless bge embedding rerank"
   support_type:
   - llm
   - text-embedding

src/langbot/pkg/provider/modelmgr/requesters/groq.svg

@@ -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="#F97316"/>
+  <text x="30" y="32" font-family="Arial, sans-serif" font-size="14" font-weight="bold" fill="white" text-anchor="middle">Groq</text>
+</svg>

src/langbot/pkg/provider/modelmgr/requesters/groqchatcmpl.yaml

@@ -0,0 +1,29 @@
+apiVersion: v1
+kind: LLMAPIRequester
+metadata:
+  name: groq-chat-completions
+  label:
+    en_US: Groq
+    zh_Hans: Groq
+  icon: groq.svg
+spec:
+  litellm_provider: groq
+  config:
+  - name: base_url
+    label:
+      en_US: Base URL
+      zh_Hans: 基础 URL
+    type: string
+    required: true
+    default: https://api.groq.com/openai/v1
+  - name: timeout
+    label:
+      en_US: Timeout
+      zh_Hans: 超时时间
+    type: integer
+    required: true
+    default: 120
+  alias: "groq Groq 高速 llama mixtral 推理加速 lpu"
+  support_type:
+  - llm
+  provider_category: manufacturer

src/langbot/pkg/provider/modelmgr/requesters/iflytek.svg

@@ -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="#0066FF"/>
+  <text x="30" y="28" font-family="Arial, sans-serif" font-size="10" font-weight="bold" fill="white" text-anchor="middle">iFlytek</text>
+  <text x="30" y="40" font-family="Arial, sans-serif" font-size="8" fill="white" text-anchor="middle">Spark</text>
+</svg>

src/langbot/pkg/provider/modelmgr/requesters/iflytekchatcmpl.yaml

@@ -0,0 +1,31 @@
+apiVersion: v1
+kind: LLMAPIRequester
+metadata:
+  name: iflytek-chat-completions
+  label:
+    en_US: iFlytek Spark
+    zh_Hans: 讯飞星火
+  icon: iflytek.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://spark-api-open.xf-yun.com/v1
+  - name: timeout
+    label:
+      en_US: Timeout
+      zh_Hans: 超时时间
+    type: integer
+    required: true
+    default: 120
+  alias: "iflytek 讯飞 科大讯飞 星火 spark xinghuo xunfei 讯飞星火"
+  support_type:
+  - llm
+  - text-embedding
+  - rerank
+  provider_category: manufacturer

src/langbot/pkg/provider/modelmgr/requesters/jiekouaichatcmpl.py

@@ -1,208 +0,0 @@
-from __future__ import annotations
-
-import openai
-import typing
-
-from . import chatcmpl
-from .. import requester
-import openai.types.chat.chat_completion as chat_completion
-import re
-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 JieKouAIChatCompletions(chatcmpl.OpenAIChatCompletions):
-    """接口 AI ChatCompletion API 请求器"""
-
-    client: openai.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'https://api.jiekou.ai/openai',
-        'timeout': 120,
-    }
-
-    is_think: bool = False
-
-    async def _make_msg(
-        self,
-        chat_completion: chat_completion.ChatCompletion,
-        remove_think: bool,
-    ) -> provider_message.Message:
-        chatcmpl_message = chat_completion.choices[0].message.model_dump()
-        # print(chatcmpl_message.keys(), chatcmpl_message.values())
-
-        # 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'
-
-        reasoning_content = chatcmpl_message['reasoning_content'] if 'reasoning_content' in chatcmpl_message else None
-
-        # deepseek的reasoner模型
-        chatcmpl_message['content'] = await self._process_thinking_content(
-            chatcmpl_message['content'], reasoning_content, remove_think
-        )
-
-        # 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
-        """
-        if remove_think:
-            content = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL)
-        else:
-            if reasoning_content is not None:
-                content = '<think>\n' + reasoning_content + '\n</think>\n' + content
-        return content
-
-    async def _make_msg_chunk(
-        self,
-        delta: dict[str, typing.Any],
-        idx: int,
-    ) -> provider_message.MessageChunk:
-        # 处理流式chunk和完整响应的差异
-        # print(chat_completion.choices[0])
-
-        # 确保 role 字段存在且不为 None
-        if 'role' not in delta or delta['role'] is None:
-            delta['role'] = 'assistant'
-
-        reasoning_content = delta['reasoning_content'] if 'reasoning_content' in delta else None
-
-        delta['content'] = '' if delta['content'] is None else delta['content']
-        # print(reasoning_content)
-
-        # deepseek的reasoner模型
-
-        if reasoning_content is not None:
-            delta['content'] += reasoning_content
-
-        message = provider_message.MessageChunk(**delta)
-
-        return message
-
-    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()
-
-        # 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
-
-        # tool_calls_map: dict[str, provider_message.ToolCall] = {}
-        chunk_idx = 0
-        thinking_started = False
-        thinking_ended = False
-        role = 'assistant'  # 默认角色
-        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', '')
-
-            if remove_think:
-                if delta['content'] is not None:
-                    if '<think>' in delta['content'] and not thinking_started and not thinking_ended:
-                        thinking_started = True
-                        continue
-                    elif delta['content'] == r'</think>' and not thinking_ended:
-                        thinking_ended = True
-                        continue
-                    elif thinking_ended and delta['content'] == '\n\n' and thinking_started:
-                        thinking_started = False
-                        continue
-                    elif thinking_started and not thinking_ended:
-                        continue
-
-            # 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']
-
-                    if tool_call['id'] is None:
-                        tool_call['id'] = tool_id
-                    if tool_call['function']['name'] is None:
-                        tool_call['function']['name'] = tool_name
-                    if tool_call['function']['arguments'] is None:
-                        tool_call['function']['arguments'] = ''
-                    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 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

src/langbot/pkg/provider/modelmgr/requesters/jiekouaichatcmpl.yaml

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: 接口 AI
   icon: jiekouai.png
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:
@@ -29,9 +30,11 @@ spec:
     type: int
     required: true
     default: 120
+  alias: "jiekouai 接口AI 接口 jiekou ai 中转 中转站 aggregator"
   support_type:
   - llm
   - text-embedding
+  - rerank
   provider_category: maas
 execution:
   python:

src/langbot/pkg/provider/modelmgr/requesters/jinarerank.yaml

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: Jina
   icon: jina.svg
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:
@@ -22,6 +23,7 @@ spec:
     type: integer
     required: true
     default: 120
+  alias: "jina Jina jina-ai jinaai rerank 重排 reranker jina-reranker embedding"
   support_type:
   - rerank
   provider_category: manufacturer

src/langbot/pkg/provider/modelmgr/requesters/litellmchat.py

@@ -0,0 +1,745 @@
+"""LiteLLM unified requester for chat, embedding, and rerank."""
+
+from __future__ import annotations
+
+import typing
+
+import litellm
+from litellm import acompletion, aembedding, arerank
+
+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 LiteLLMRequester(requester.ProviderAPIRequester):
+    """LiteLLM unified API requester supporting chat, embedding, and rerank."""
+
+    _EMBEDDING_MODEL_HINTS = ('embedding', 'embed', 'bge-', 'e5-', 'm3e', 'gte-', 'text-embedding')
+    _RERANK_MODEL_HINTS = ('rerank', 're-rank', 're_rank')
+
+    default_config: dict[str, typing.Any] = {
+        'base_url': '',
+        'timeout': 120,
+        'custom_llm_provider': '',
+        'drop_params': False,
+        'num_retries': 0,
+        'api_version': '',
+    }
+
+    async def initialize(self):
+        """Initialize LiteLLM client settings."""
+        # LiteLLM doesn't require explicit client initialization
+        # Configuration is passed per-request via litellm params
+        pass
+
+    def _build_litellm_model_name(self, model_name: str, custom_llm_provider: str | None = None) -> str:
+        """Build LiteLLM model name with provider prefix if needed."""
+        provider = custom_llm_provider or self.requester_cfg.get('custom_llm_provider', '')
+        if provider:
+            # LiteLLM format: provider/model_name
+            if model_name.startswith(f'{provider}/'):
+                return model_name
+            return f'{provider}/{model_name}'
+        # If no custom provider, assume model_name already includes prefix or is OpenAI-compatible
+        return model_name
+
+    def _get_custom_llm_provider(self) -> str | None:
+        return self.requester_cfg.get('custom_llm_provider') or None
+
+    def _safe_litellm_bool_helper(self, helper_name: str, model_name: str) -> bool:
+        """Call a LiteLLM boolean capability helper without letting metadata gaps fail requests."""
+        helper = getattr(litellm, helper_name, None)
+        if not callable(helper):
+            return False
+
+        provider = self._get_custom_llm_provider()
+        candidates: list[tuple[str, str | None]] = [(model_name, provider)]
+        litellm_model_name = self._build_litellm_model_name(model_name)
+        if litellm_model_name != model_name:
+            candidates.append((litellm_model_name, None))
+        for metadata_provider in self._metadata_provider_candidates(model_name):
+            candidates.append((f'{metadata_provider}/{model_name}', None))
+
+        tried_candidates: set[tuple[str, str | None]] = set()
+        for candidate_model, candidate_provider in candidates:
+            candidate_key = (candidate_model, candidate_provider)
+            if candidate_key in tried_candidates:
+                continue
+            tried_candidates.add(candidate_key)
+            try:
+                if bool(helper(model=candidate_model, custom_llm_provider=candidate_provider)):
+                    return True
+            except Exception:
+                continue
+        return False
+
+    @staticmethod
+    def _positive_int(value: typing.Any) -> int | None:
+        if isinstance(value, bool):
+            return None
+        if isinstance(value, int) and value > 0:
+            return value
+        if isinstance(value, str) and value.isdigit():
+            parsed_value = int(value)
+            if parsed_value > 0:
+                return parsed_value
+        return None
+
+    def _context_length_from_scan_payload(self, model_payload: dict[str, typing.Any] | None) -> int | None:
+        if not model_payload:
+            return None
+
+        for field_name in ('context_length', 'context_window', 'max_context_length'):
+            context_length = self._positive_int(model_payload.get(field_name))
+            if context_length is not None:
+                return context_length
+        return None
+
+    def _context_length_from_litellm_model_info(self, model_info: typing.Any) -> int | None:
+        if isinstance(model_info, dict):
+            return self._positive_int(model_info.get('max_input_tokens'))
+        return self._positive_int(getattr(model_info, 'max_input_tokens', None))
+
+    def _metadata_provider_candidates(self, model_name: str) -> list[str]:
+        normalized_model_name = (model_name or '').lower()
+        candidates = []
+        if normalized_model_name.startswith(('moonshot-', 'kimi-')):
+            candidates.append('moonshot')
+        if normalized_model_name.startswith('deepseek-'):
+            candidates.append('deepseek')
+
+        base_url = self.requester_cfg.get('base_url', '').lower()
+        if 'moonshot' in base_url:
+            candidates.append('moonshot')
+        if 'deepseek' in base_url:
+            candidates.append('deepseek')
+
+        deduped_candidates = []
+        for candidate in candidates:
+            if candidate not in deduped_candidates:
+                deduped_candidates.append(candidate)
+        return deduped_candidates
+
+    def _known_context_length_fallback(self, model_name: str) -> int | None:
+        normalized_model_name = (model_name or '').lower()
+        if normalized_model_name.startswith('deepseek-v4-'):
+            return 1_000_000
+        if normalized_model_name.startswith(('kimi-k2.5', 'kimi-k2.6')):
+            return 256 * 1024
+        if normalized_model_name.startswith('moonshot-v1-8k'):
+            return 8 * 1024
+        if normalized_model_name.startswith('moonshot-v1-32k'):
+            return 32 * 1024
+        if normalized_model_name.startswith('moonshot-v1-128k') or normalized_model_name == 'moonshot-v1-auto':
+            return 128 * 1024
+        return None
+
+    def _safe_context_length(self, model_name: str) -> int | None:
+        helper = getattr(litellm, 'get_model_info', None)
+        if not callable(helper):
+            return self._known_context_length_fallback(model_name)
+
+        candidates = [model_name]
+        litellm_model_name = self._build_litellm_model_name(model_name)
+        if litellm_model_name != model_name:
+            candidates.append(litellm_model_name)
+        for provider in self._metadata_provider_candidates(model_name):
+            candidates.append(f'{provider}/{model_name}')
+
+        tried_candidates = []
+        for candidate in candidates:
+            if candidate in tried_candidates:
+                continue
+            tried_candidates.append(candidate)
+            try:
+                model_info = helper(candidate)
+            except Exception:
+                continue
+            context_length = self._context_length_from_litellm_model_info(model_info)
+            if context_length is not None:
+                return context_length
+        return self._known_context_length_fallback(model_name)
+
+    def _supports_function_calling(self, model_name: str) -> bool:
+        return self._safe_litellm_bool_helper('supports_function_calling', model_name)
+
+    def _supports_vision(self, model_name: str) -> bool:
+        return self._safe_litellm_bool_helper('supports_vision', model_name)
+
+    def _infer_model_type(self, model_id: str) -> str:
+        normalized_id = (model_id or '').lower()
+        if any(kw in normalized_id for kw in self._RERANK_MODEL_HINTS):
+            return 'rerank'
+        if any(kw in normalized_id for kw in self._EMBEDDING_MODEL_HINTS):
+            return 'embedding'
+        return 'llm'
+
+    def _enrich_scanned_model(
+        self,
+        model_id: str,
+        model_payload: dict[str, typing.Any] | None = None,
+    ) -> dict[str, typing.Any]:
+        model_type = self._infer_model_type(model_id)
+        scanned_model: dict[str, typing.Any] = {
+            'id': model_id,
+            'name': model_id,
+            'type': model_type,
+        }
+
+        if model_type == 'llm':
+            abilities = []
+            if self._supports_function_calling(model_id):
+                abilities.append('func_call')
+            supports_provider_reported_vision = bool(
+                model_payload
+                and (model_payload.get('supports_image_in') is True or model_payload.get('supports_vision') is True)
+            )
+            if supports_provider_reported_vision or self._supports_vision(model_id):
+                abilities.append('vision')
+            scanned_model['abilities'] = abilities
+
+            context_length = self._context_length_from_scan_payload(model_payload)
+            if context_length is None:
+                context_length = self._safe_context_length(model_id)
+            if context_length is not None:
+                scanned_model['context_length'] = context_length
+
+        return scanned_model
+
+    def _convert_messages(self, messages: typing.List[provider_message.Message]) -> list[dict]:
+        """Convert LangBot messages to LiteLLM/OpenAI format."""
+        req_messages = []
+        for m in messages:
+            msg_dict = m.dict(exclude_none=True)
+            content = msg_dict.get('content')
+
+            if isinstance(content, list):
+                for part in content:
+                    if isinstance(part, dict) and part.get('type') == 'image_base64':
+                        part['image_url'] = {'url': part['image_base64']}
+                        part['type'] = 'image_url'
+                        del part['image_base64']
+
+            req_messages.append(msg_dict)
+
+        return req_messages
+
+    def _process_thinking_content(self, content: str, reasoning_content: str | None, remove_think: bool) -> str:
+        """Process thinking/reasoning content.
+
+        Args:
+            content: The main content from response
+            reasoning_content: Separate reasoning content from model
+            remove_think: If True, remove thinking markers; if False, preserve them
+
+        Returns:
+            Processed content string
+        """
+        # Extract and handle thinking tags
+        if content and 'CRETIRE_REASONING_BEGINk' in content and 'CRETIRE_REASONING_ENDk' in content:
+            import re
+
+            think_pattern = r'CRETIRE_REASONING_BEGINk(.*?)CRETIRE_REASONING_ENDk'
+
+            if remove_think:
+                # Remove thinking tags and their content from output
+                content = re.sub(think_pattern, '', content, flags=re.DOTALL).strip()
+            # else: preserve thinking content as-is
+
+        # Handle separate reasoning_content field
+        # Currently we don't include reasoning_content in user-facing output regardless of remove_think
+        # because it's typically internal model reasoning, not user-visible thinking
+        return content or ''
+
+    @staticmethod
+    def _normalize_usage(usage: typing.Any) -> dict:
+        """Normalize a LiteLLM/OpenAI usage object into a plain token dict.
+
+        Handles several real-world shapes returned by different upstreams:
+        - object with ``prompt_tokens`` / ``completion_tokens`` / ``total_tokens`` attrs
+        - dict with the same keys
+        - missing ``total_tokens`` (derived from prompt + completion)
+        - ``None`` / partially-populated usage (defaults to 0)
+        """
+        if usage is None:
+            return {'prompt_tokens': 0, 'completion_tokens': 0, 'total_tokens': 0}
+
+        def _get(key: str) -> typing.Any:
+            if isinstance(usage, dict):
+                return usage.get(key)
+            return getattr(usage, key, None)
+
+        prompt_tokens = _get('prompt_tokens') or 0
+        completion_tokens = _get('completion_tokens') or 0
+        total_tokens = _get('total_tokens') or 0
+
+        # Some providers omit total_tokens in streaming usage; derive it.
+        if not total_tokens:
+            total_tokens = prompt_tokens + completion_tokens
+
+        return {
+            'prompt_tokens': int(prompt_tokens),
+            'completion_tokens': int(completion_tokens),
+            'total_tokens': int(total_tokens),
+        }
+
+    def _extract_usage(self, response) -> dict:
+        """Extract usage info from a non-streaming LiteLLM response."""
+        return self._normalize_usage(getattr(response, 'usage', None))
+
+    @staticmethod
+    def _as_dict(value: typing.Any) -> dict:
+        if value is None:
+            return {}
+        if isinstance(value, dict):
+            return value
+        if hasattr(value, 'model_dump'):
+            return value.model_dump()
+        return {}
+
+    def _normalize_stream_tool_calls(
+        self,
+        raw_tool_calls: typing.Any,
+        tool_call_state: dict[int, dict[str, str]],
+    ) -> list[dict] | None:
+        """Fill OpenAI-style streaming tool-call deltas so MessageChunk can validate them."""
+        if not raw_tool_calls:
+            return None
+
+        normalized = []
+        for fallback_index, raw_tool_call in enumerate(raw_tool_calls):
+            tool_call = self._as_dict(raw_tool_call)
+            index = tool_call.get('index')
+            if not isinstance(index, int):
+                index = fallback_index
+
+            state = tool_call_state.setdefault(index, {'id': '', 'type': 'function', 'name': ''})
+            if tool_call.get('id'):
+                state['id'] = tool_call['id']
+            if tool_call.get('type'):
+                state['type'] = tool_call['type']
+
+            function = self._as_dict(tool_call.get('function'))
+            if function.get('name'):
+                state['name'] = function['name']
+
+            arguments = function.get('arguments')
+            if arguments is None:
+                arguments = ''
+            elif not isinstance(arguments, str):
+                arguments = str(arguments)
+
+            if not state['id'] or not state['name']:
+                continue
+
+            normalized.append(
+                {
+                    'id': state['id'],
+                    'type': state['type'] or 'function',
+                    'function': {
+                        'name': state['name'],
+                        'arguments': arguments,
+                    },
+                }
+            )
+
+        return normalized or None
+
+    def _build_common_args(self, args: dict, include_retry_params: bool = True) -> dict:
+        """Apply common requester config to args dict."""
+        if self.requester_cfg.get('base_url'):
+            args['api_base'] = self.requester_cfg['base_url']
+        if self.requester_cfg.get('timeout'):
+            args['timeout'] = self.requester_cfg['timeout']
+        if include_retry_params:
+            if self.requester_cfg.get('drop_params'):
+                args['drop_params'] = self.requester_cfg['drop_params']
+            if self.requester_cfg.get('num_retries'):
+                args['num_retries'] = self.requester_cfg['num_retries']
+            if self.requester_cfg.get('api_version'):
+                args['api_version'] = self.requester_cfg['api_version']
+        return args
+
+    def _handle_litellm_error(self, e: Exception) -> None:
+        """Convert LiteLLM exceptions to RequesterError. Never returns, always raises."""
+        # Check more specific exceptions first (they inherit from base exceptions)
+        if isinstance(e, litellm.ContextWindowExceededError):
+            raise errors.RequesterError(f'上下文长度超限: {str(e)}')
+        if isinstance(e, litellm.BadRequestError):
+            raise errors.RequesterError(f'请求参数错误: {str(e)}')
+        if isinstance(e, litellm.AuthenticationError):
+            raise errors.RequesterError(f'API key 无效: {str(e)}')
+        if isinstance(e, litellm.NotFoundError):
+            raise errors.RequesterError(f'模型或路径无效: {str(e)}')
+        if isinstance(e, litellm.RateLimitError):
+            raise errors.RequesterError(f'请求过于频繁或余额不足: {str(e)}')
+        if isinstance(e, litellm.Timeout):
+            raise errors.RequesterError(f'请求超时: {str(e)}')
+        if isinstance(e, litellm.APIConnectionError):
+            raise errors.RequesterError(f'连接错误: {str(e)}')
+        if isinstance(e, litellm.APIError):
+            raise errors.RequesterError(f'API 错误: {str(e)}')
+        raise errors.RequesterError(f'未知错误: {str(e)}')
+
+    async def _build_completion_args(
+        self,
+        model: requester.RuntimeLLMModel,
+        messages: typing.List[provider_message.Message],
+        funcs: typing.List[resource_tool.LLMTool] = None,
+        extra_args: dict[str, typing.Any] = {},
+        stream: bool = False,
+    ) -> dict:
+        """Build common completion arguments for invoke_llm and invoke_llm_stream."""
+        req_messages = self._convert_messages(messages)
+        model_name = self._build_litellm_model_name(model.model_entity.name)
+        api_key = model.provider.token_mgr.get_token()
+
+        args = {
+            'model': model_name,
+            'messages': req_messages,
+            'api_key': api_key,
+        }
+        if stream:
+            args['stream'] = True
+            args['stream_options'] = {'include_usage': True}
+        self._build_common_args(args)
+
+        # Apply model-level extra_args first, then call-level extra_args
+        if model.model_entity.extra_args:
+            args.update(model.model_entity.extra_args)
+        args.update(extra_args)
+
+        if funcs:
+            tools = await self.ap.tool_mgr.generate_tools_for_openai(funcs)
+            if tools:
+                args['tools'] = tools
+                args.setdefault('tool_choice', 'auto')
+
+        return args
+
+    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."""
+        args = await self._build_completion_args(model, messages, funcs, extra_args, stream=False)
+
+        try:
+            response = await acompletion(**args)
+
+            message_data = response.choices[0].message.model_dump()
+            if 'role' not in message_data or message_data['role'] is None:
+                message_data['role'] = 'assistant'
+
+            content = message_data.get('content', '')
+            reasoning_content = message_data.get('reasoning_content', None)
+            message_data['content'] = self._process_thinking_content(content, reasoning_content, remove_think)
+
+            if 'reasoning_content' in message_data:
+                del message_data['reasoning_content']
+
+            message = provider_message.Message(**message_data)
+            usage_info = self._extract_usage(response)
+
+            return message, usage_info
+
+        except Exception as e:
+            self._handle_litellm_error(e)
+
+    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:
+        """Invoke LLM streaming and yield chunks."""
+        args = await self._build_completion_args(model, messages, funcs, extra_args, stream=True)
+
+        chunk_idx = 0
+        role = 'assistant'
+        tool_call_state: dict[int, dict[str, str]] = {}
+
+        try:
+            response = await acompletion(**args)
+            async for chunk in response:
+                # Capture usage whenever a chunk carries it.
+                #
+                # Important: many OpenAI-compatible gateways (e.g. new-api) and
+                # providers send the final usage payload in a chunk that STILL
+                # contains a (empty-delta) choice, not an empty `choices` list.
+                # The previous implementation only captured usage when `choices`
+                # was empty, so streamed calls always recorded 0 tokens.
+                # We therefore capture usage independently of `choices`, and then
+                # fall through to also process any content this chunk may carry.
+                if getattr(chunk, 'usage', None):
+                    usage_info = self._normalize_usage(chunk.usage)
+                    if query is not None:
+                        if query.variables is None:
+                            query.variables = {}
+                        query.variables['_stream_usage'] = usage_info
+
+                if not hasattr(chunk, 'choices') or not chunk.choices:
+                    continue
+
+                choice = chunk.choices[0]
+                delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
+                finish_reason = getattr(choice, 'finish_reason', None)
+
+                if 'role' in delta and delta['role']:
+                    role = delta['role']
+
+                delta_content = delta.get('content', '')
+                reasoning_content = delta.get('reasoning_content', '')
+
+                # Handle reasoning_content based on remove_think flag
+                if reasoning_content:
+                    if remove_think:
+                        # Skip reasoning content when remove_think is True
+                        chunk_idx += 1
+                        continue
+                    else:
+                        # Use reasoning_content as the displayed content
+                        delta_content = reasoning_content
+
+                tool_calls = self._normalize_stream_tool_calls(delta.get('tool_calls'), tool_call_state)
+
+                if chunk_idx == 0 and not delta_content and not tool_calls:
+                    chunk_idx += 1
+                    continue
+
+                chunk_data = {
+                    'role': role,
+                    'content': delta_content if delta_content else None,
+                    'tool_calls': tool_calls,
+                    'is_final': bool(finish_reason),
+                }
+
+                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
+
+        except Exception as e:
+            self._handle_litellm_error(e)
+
+    async def invoke_embedding(
+        self,
+        model: requester.RuntimeEmbeddingModel,
+        input_text: list[str],
+        extra_args: dict[str, typing.Any] = {},
+    ) -> tuple[list[list[float]], dict]:
+        """Invoke embedding and return vectors with usage info."""
+        model_name = self._build_litellm_model_name(model.model_entity.name)
+        api_key = model.provider.token_mgr.get_token()
+
+        args = {
+            'model': model_name,
+            'input': input_text,
+            'api_key': api_key,
+        }
+        self._build_common_args(args, include_retry_params=False)
+
+        if model.model_entity.extra_args:
+            args.update(model.model_entity.extra_args)
+
+        args.update(extra_args)
+
+        try:
+            response = await aembedding(**args)
+
+            # LiteLLM returns response.data entries either as objects with an
+            # `.embedding` attribute or as plain dicts (many OpenAI-compatible
+            # gateways, e.g. new-api, yield dict-shaped entries). Handle both.
+            embeddings = [d['embedding'] if isinstance(d, dict) else d.embedding for d in response.data]
+            usage_info = self._extract_usage(response)
+
+            return embeddings, usage_info
+
+        except Exception as e:
+            self._handle_litellm_error(e)
+
+    async def invoke_rerank(
+        self,
+        model: requester.RuntimeRerankModel,
+        query: str,
+        documents: typing.List[str],
+        extra_args: dict[str, typing.Any] = {},
+    ) -> typing.List[dict]:
+        """Invoke rerank and return relevance scores."""
+        model_name = self._build_litellm_model_name(model.model_entity.name)
+        api_key = model.provider.token_mgr.get_token()
+
+        top_n = min(len(documents), 64)
+
+        provider = self._get_custom_llm_provider()
+
+        try:
+            # LiteLLM's rerank API does not support the `openai` provider
+            # (litellm/rerank_api/main.py raises "Unsupported provider: openai").
+            # OpenAI-compatible gateways (newapi / one-api / vLLM / Xinference, etc.)
+            # expose the standard Jina/Cohere-style POST /v1/rerank endpoint, so
+            # call it directly over HTTP for openai-compatible (or unspecified) providers.
+            if provider in (None, '', 'openai'):
+                results = await self._invoke_rerank_openai_compatible(
+                    model_name=model.model_entity.name,
+                    query=query,
+                    documents=documents,
+                    api_key=api_key,
+                    top_n=top_n,
+                    extra_args={**(model.model_entity.extra_args or {}), **extra_args},
+                )
+            else:
+                args = {
+                    'model': model_name,
+                    'query': query,
+                    'documents': documents,
+                    'api_key': api_key,
+                    'top_n': top_n,
+                }
+                self._build_common_args(args, include_retry_params=False)
+
+                if model.model_entity.extra_args:
+                    args.update(model.model_entity.extra_args)
+
+                args.update(extra_args)
+
+                response = await arerank(**args)
+
+                results = []
+                for r in response.results:
+                    results.append(
+                        {
+                            'index': r.get('index', 0),
+                            'relevance_score': r.get('relevance_score', 0.0),
+                        }
+                    )
+
+            if results:
+                scores = [r['relevance_score'] 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 errors.RequesterError:
+            raise
+        except Exception as e:
+            self._handle_litellm_error(e)
+
+    async def _invoke_rerank_openai_compatible(
+        self,
+        model_name: str,
+        query: str,
+        documents: typing.List[str],
+        api_key: str,
+        top_n: int,
+        extra_args: dict[str, typing.Any] = {},
+    ) -> typing.List[dict]:
+        """Call the standard Jina/Cohere-style POST /v1/rerank endpoint over HTTP.
+
+        Used for OpenAI-compatible gateways where litellm.arerank rejects the
+        `openai` provider. Returns the same shape as the litellm path:
+        a list of {'index': int, 'relevance_score': float}.
+        """
+        import httpx
+
+        base_url = (self.requester_cfg.get('base_url') or '').rstrip('/')
+        if not base_url:
+            raise errors.RequesterError('Base URL required for rerank')
+
+        timeout = self.requester_cfg.get('timeout', 120)
+
+        headers = {'Content-Type': 'application/json'}
+        if api_key:
+            headers['Authorization'] = f'Bearer {api_key}'
+
+        payload: dict[str, typing.Any] = {
+            'model': model_name,
+            'query': query,
+            'documents': documents,
+            'top_n': top_n,
+        }
+        if extra_args:
+            payload.update(extra_args)
+
+        rerank_url = f'{base_url}/rerank'
+
+        try:
+            async with httpx.AsyncClient(timeout=timeout) as client:
+                resp = await client.post(rerank_url, headers=headers, json=payload)
+                resp.raise_for_status()
+                data = resp.json()
+        except httpx.HTTPStatusError as e:
+            body = ''
+            try:
+                body = e.response.text
+            except Exception:
+                pass
+            raise errors.RequesterError(f'rerank 请求失败 (HTTP {e.response.status_code}): {body or str(e)}')
+        except httpx.HTTPError as e:
+            raise errors.RequesterError(f'rerank 连接错误: {str(e)}')
+
+        raw_results = data.get('results', []) if isinstance(data, dict) else []
+        results = []
+        for r in raw_results:
+            results.append(
+                {
+                    'index': r.get('index', 0),
+                    'relevance_score': r.get('relevance_score', r.get('score', 0.0)) or 0.0,
+                }
+            )
+
+        return results
+
+    async def scan_models(self, api_key: str | None = None) -> dict[str, typing.Any]:
+        """Scan models supported by the provider."""
+        import httpx
+
+        base_url = self.requester_cfg.get('base_url', '').rstrip('/')
+        timeout = self.requester_cfg.get('timeout', 120)
+
+        if not base_url:
+            raise errors.RequesterError('Base URL required for model scanning')
+
+        headers = {}
+        if api_key:
+            headers['Authorization'] = f'Bearer {api_key}'
+
+        models_url = f'{base_url}/models'
+
+        try:
+            async with httpx.AsyncClient(trust_env=True, timeout=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(self._enrich_scanned_model(model_id, item))
+
+            models.sort(key=lambda x: (x['type'] != 'llm', x['name'].lower()))
+
+            return {'models': models}
+
+        except httpx.HTTPStatusError as e:
+            raise errors.RequesterError(f'Model scan failed: {e.response.status_code}')
+        except httpx.TimeoutException:
+            raise errors.RequesterError('Model scan timeout')
+        except Exception as e:
+            raise errors.RequesterError(f'Model scan error: {str(e)}')

src/langbot/pkg/provider/modelmgr/requesters/litellmchat.yaml

@@ -0,0 +1,65 @@
+apiVersion: v1
+kind: LLMAPIRequester
+metadata:
+  name: litellm-chat
+  label:
+    en_US: LiteLLM (Unified)
+    zh_Hans: LiteLLM (统一请求器)
+  icon: litellm.svg
+spec:
+  config:
+  - name: base_url
+    label:
+      en_US: Base URL
+      zh_Hans: 基础 URL
+    type: string
+    required: false
+    default: ''
+  - name: timeout
+    label:
+      en_US: Timeout
+      zh_Hans: 超时时间
+    type: integer
+    required: true
+    default: 120
+  - name: custom_llm_provider
+    label:
+      en_US: Custom Provider
+      zh_Hans: 自定义 Provider
+    type: string
+    required: false
+    default: ''
+    description:
+      en_US: Force provider type (e.g., anthropic, openai, gemini)
+      zh_Hans: 强制指定 provider 类型（如 anthropic, openai, gemini）
+  - name: drop_params
+    label:
+      en_US: Drop Unsupported Params
+      zh_Hans: 丢弃不支持参数
+    type: boolean
+    required: false
+    default: false
+  - name: num_retries
+    label:
+      en_US: Number of Retries
+      zh_Hans: 重试次数
+    type: integer
+    required: false
+    default: 0
+  - name: api_version
+    label:
+      en_US: API Version
+      zh_Hans: API 版本
+    type: string
+    required: false
+    default: ''
+  alias: "litellm LiteLLM 通用 universal 万能 兼容 compatible proxy 代理 中转"
+  support_type:
+  - llm
+  - text-embedding
+  - rerank
+  provider_category: unified
+execution:
+  python:
+    path: ./litellmchat.py
+    attr: LiteLLMRequester

src/langbot/pkg/provider/modelmgr/requesters/lmstudiochatcmpl.py

@@ -1,17 +0,0 @@
-from __future__ import annotations
-
-import typing
-import openai
-
-from . import chatcmpl
-
-
-class LmStudioChatCompletions(chatcmpl.OpenAIChatCompletions):
-    """LMStudio ChatCompletion API 请求器"""
-
-    client: openai.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'http://127.0.0.1:1234/v1',
-        'timeout': 120,
-    }

src/langbot/pkg/provider/modelmgr/requesters/lmstudiochatcmpl.yaml

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: LM Studio
   icon: lmstudio.webp
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:
@@ -22,6 +23,7 @@ spec:
     type: integer
     required: true
     default: 120
+  alias: "lmstudio LM Studio lm-studio 本地 local 本地部署 self-hosted gguf"
   support_type:
   - llm
   - text-embedding

src/langbot/pkg/provider/modelmgr/requesters/mimo.svg

@@ -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="#FF6700"/>
+  <text x="30" y="32" font-family="Arial, sans-serif" font-size="18" font-weight="bold" fill="white" text-anchor="middle">MiMo</text>
+</svg>

src/langbot/pkg/provider/modelmgr/requesters/mimochatcmpl.yaml

@@ -0,0 +1,31 @@
+apiVersion: v1
+kind: LLMAPIRequester
+metadata:
+  name: mimo-chat-completions
+  label:
+    en_US: Xiaomi MiMo
+    zh_Hans: 小米 MiMo
+  icon: mimo.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://api.xiaomimimo.com/v1
+  - name: timeout
+    label:
+      en_US: Timeout
+      zh_Hans: 超时时间
+    type: integer
+    required: true
+    default: 120
+  alias: "mimo MiMo 小米 xiaomi 小米大模型 xiaomi-mimo"
+  support_type:
+  - llm
+  - text-embedding
+  - rerank
+  provider_category: manufacturer