feat: update requesters and improve provider selection UI

- Added `litellm_provider` field to various requesters' YAML configurations.
- Removed obsolete Python requester files for OpenRouter, PPIO, QHAIGC, ShengSuanYun, SiliconFlow, Space, TokenPony, VolcArk, and Xai.
- Introduced new requesters for Tencent and Together AI with corresponding YAML configurations and SVG icons.
- Enhanced the ProviderForm component to include a searchable dropdown for selecting providers, improving user experience.
- Updated localization files to include search provider text for both English and Chinese.

pyproject.toml

@@ -77,6 +77,7 @@ dependencies = [
     "pymilvus>=2.6.4",
     "pgvector>=0.4.1",
     "botocore>=1.42.39",
+    "litellm>=1.0.0",
 ]
 keywords = [
     "bot",

src/langbot/pkg/api/http/controller/groups/provider/models.py

@@ -97,3 +97,51 @@ class EmbeddingModelsRouterGroup(group.RouterGroup):
             await self.ap.embedding_models_service.test_embedding_model(model_uuid, json_data)
 
             return self.success()
+
+
+@group.group_class('models/rerank', '/api/v1/provider/models/rerank')
+class RerankModelsRouterGroup(group.RouterGroup):
+    async def initialize(self) -> None:
+        @self.route('', methods=['GET', 'POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _() -> str:
+            if quart.request.method == 'GET':
+                provider_uuid = quart.request.args.get('provider_uuid')
+                if provider_uuid:
+                    return self.success(
+                        data={
+                            'models': await self.ap.rerank_models_service.get_rerank_models_by_provider(provider_uuid)
+                        }
+                    )
+                return self.success(data={'models': await self.ap.rerank_models_service.get_rerank_models()})
+            elif quart.request.method == 'POST':
+                json_data = await quart.request.json
+                model_uuid = await self.ap.rerank_models_service.create_rerank_model(json_data)
+                return self.success(data={'uuid': model_uuid})
+
+        @self.route('/<model_uuid>', methods=['GET', 'PUT', 'DELETE'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _(model_uuid: str) -> str:
+            if quart.request.method == 'GET':
+                model = await self.ap.rerank_models_service.get_rerank_model(model_uuid)
+
+                if model is None:
+                    return self.http_status(404, -1, 'model not found')
+
+                return self.success(data={'model': model})
+            elif quart.request.method == 'PUT':
+                json_data = await quart.request.json
+
+                await self.ap.rerank_models_service.update_rerank_model(model_uuid, json_data)
+
+                return self.success()
+            elif quart.request.method == 'DELETE':
+                await self.ap.rerank_models_service.delete_rerank_model(model_uuid)
+
+                return self.success()
+
+        @self.route('/<model_uuid>/test', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _(model_uuid: str) -> str:
+            json_data = await quart.request.json
+
+            await self.ap.rerank_models_service.test_rerank_model(model_uuid, json_data)
+
+            return self.success()

src/langbot/pkg/api/http/controller/groups/provider/providers.py

@@ -15,6 +15,7 @@ class ModelProvidersRouterGroup(group.RouterGroup):
                     counts = await self.ap.provider_service.get_provider_model_counts(provider['uuid'])
                     provider['llm_count'] = counts['llm_count']
                     provider['embedding_count'] = counts['embedding_count']
+                    provider['rerank_count'] = counts['rerank_count']
                 return self.success(data={'providers': providers})
             elif quart.request.method == 'POST':
                 json_data = await quart.request.json
@@ -32,6 +33,7 @@ class ModelProvidersRouterGroup(group.RouterGroup):
                 counts = await self.ap.provider_service.get_provider_model_counts(provider_uuid)
                 provider['llm_count'] = counts['llm_count']
                 provider['embedding_count'] = counts['embedding_count']
+                provider['rerank_count'] = counts['rerank_count']
                 return self.success(data={'provider': provider})
             elif quart.request.method == 'PUT':
                 json_data = await quart.request.json

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

@@ -367,3 +367,162 @@ class EmbeddingModelsService:
             input_text=['Hello, world!'],
             extra_args={},
         )
+
+
+class RerankModelsService:
+    ap: app.Application
+
+    def __init__(self, ap: app.Application) -> None:
+        self.ap = ap
+
+    async def get_rerank_models(self) -> list[dict]:
+        """Get all rerank models with provider info"""
+        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.RerankModel))
+        models = result.all()
+
+        providers_result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.ModelProvider)
+        )
+        providers = {p.uuid: p for p in providers_result.all()}
+
+        models_list = []
+        for model in models:
+            model_dict = self.ap.persistence_mgr.serialize_model(persistence_model.RerankModel, model)
+            provider = providers.get(model.provider_uuid)
+            if provider:
+                provider_dict = self.ap.persistence_mgr.serialize_model(persistence_model.ModelProvider, provider)
+                model_dict['provider'] = _parse_provider_api_keys(provider_dict)
+            models_list.append(model_dict)
+
+        return models_list
+
+    async def get_rerank_models_by_provider(self, provider_uuid: str) -> list[dict]:
+        """Get rerank models by provider UUID"""
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.RerankModel).where(
+                persistence_model.RerankModel.provider_uuid == provider_uuid
+            )
+        )
+        models = result.all()
+        return [self.ap.persistence_mgr.serialize_model(persistence_model.RerankModel, m) for m in models]
+
+    async def create_rerank_model(self, model_data: dict, preserve_uuid: bool = False) -> str:
+        """Create a new rerank model"""
+        if not preserve_uuid:
+            model_data['uuid'] = str(uuid.uuid4())
+
+        if 'provider' in model_data:
+            provider_data = model_data.pop('provider')
+            if provider_data.get('uuid'):
+                model_data['provider_uuid'] = provider_data['uuid']
+            else:
+                provider_uuid = await self.ap.provider_service.find_or_create_provider(
+                    requester=provider_data.get('requester', ''),
+                    base_url=provider_data.get('base_url', ''),
+                    api_keys=provider_data.get('api_keys', []),
+                )
+                model_data['provider_uuid'] = provider_uuid
+
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.insert(persistence_model.RerankModel).values(**model_data)
+        )
+
+        runtime_provider = self.ap.model_mgr.provider_dict.get(model_data['provider_uuid'])
+        if runtime_provider is None:
+            raise Exception('provider not found')
+
+        runtime_rerank_model = await self.ap.model_mgr.load_rerank_model_with_provider(
+            persistence_model.RerankModel(**model_data),
+            runtime_provider,
+        )
+        self.ap.model_mgr.rerank_models.append(runtime_rerank_model)
+
+        return model_data['uuid']
+
+    async def get_rerank_model(self, model_uuid: str) -> dict | None:
+        """Get a single rerank model with provider info"""
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.RerankModel).where(persistence_model.RerankModel.uuid == model_uuid)
+        )
+        model = result.first()
+        if model is None:
+            return None
+
+        model_dict = self.ap.persistence_mgr.serialize_model(persistence_model.RerankModel, model)
+
+        provider_result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.ModelProvider).where(
+                persistence_model.ModelProvider.uuid == model.provider_uuid
+            )
+        )
+        provider = provider_result.first()
+        if provider:
+            provider_dict = self.ap.persistence_mgr.serialize_model(persistence_model.ModelProvider, provider)
+            model_dict['provider'] = _parse_provider_api_keys(provider_dict)
+
+        return model_dict
+
+    async def update_rerank_model(self, model_uuid: str, model_data: dict) -> None:
+        """Update an existing rerank model"""
+        if 'uuid' in model_data:
+            del model_data['uuid']
+
+        if 'provider' in model_data:
+            provider_data = model_data.pop('provider')
+            if provider_data.get('uuid'):
+                model_data['provider_uuid'] = provider_data['uuid']
+            else:
+                provider_uuid = await self.ap.provider_service.find_or_create_provider(
+                    requester=provider_data.get('requester', ''),
+                    base_url=provider_data.get('base_url', ''),
+                    api_keys=provider_data.get('api_keys', []),
+                )
+                model_data['provider_uuid'] = provider_uuid
+
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.update(persistence_model.RerankModel)
+            .where(persistence_model.RerankModel.uuid == model_uuid)
+            .values(**model_data)
+        )
+
+        await self.ap.model_mgr.remove_rerank_model(model_uuid)
+
+        runtime_provider = self.ap.model_mgr.provider_dict.get(model_data['provider_uuid'])
+        if runtime_provider is None:
+            raise Exception('provider not found')
+
+        runtime_rerank_model = await self.ap.model_mgr.load_rerank_model_with_provider(
+            persistence_model.RerankModel(**model_data),
+            runtime_provider,
+        )
+        self.ap.model_mgr.rerank_models.append(runtime_rerank_model)
+
+    async def delete_rerank_model(self, model_uuid: str) -> None:
+        """Delete a rerank model"""
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.delete(persistence_model.RerankModel).where(persistence_model.RerankModel.uuid == model_uuid)
+        )
+        await self.ap.model_mgr.remove_rerank_model(model_uuid)
+
+    async def test_rerank_model(self, model_uuid: str, model_data: dict) -> None:
+        """Test a rerank model"""
+        runtime_rerank_model: model_requester.RuntimeRerankModel | None = None
+
+        if model_uuid != '_':
+            for model in self.ap.model_mgr.rerank_models:
+                if model.model_entity.uuid == model_uuid:
+                    runtime_rerank_model = model
+                    break
+            if runtime_rerank_model is None:
+                raise Exception('model not found')
+        else:
+            runtime_rerank_model = await self.ap.model_mgr.init_temporary_runtime_rerank_model(model_data)
+
+        await runtime_rerank_model.provider.invoke_rerank(
+            model=runtime_rerank_model,
+            query='What is artificial intelligence?',
+            documents=[
+                'Artificial intelligence is a branch of computer science.',
+                'The weather is nice today.',
+            ],
+        )

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

@@ -98,6 +98,14 @@ class ModelProviderService:
         if embedding_result.first() is not None:
             raise ValueError('Cannot delete provider: Embedding models still reference it')
 
+        rerank_result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.RerankModel).where(
+                persistence_model.RerankModel.provider_uuid == provider_uuid
+            )
+        )
+        if rerank_result.first() is not None:
+            raise ValueError('Cannot delete provider: Rerank models still reference it')
+
         await self.ap.persistence_mgr.execute_async(
             sqlalchemy.delete(persistence_model.ModelProvider).where(
                 persistence_model.ModelProvider.uuid == provider_uuid
@@ -122,7 +130,14 @@ class ModelProviderService:
         )
         embedding_count = embedding_result.scalar() or 0
 
-        return {'llm_count': llm_count, 'embedding_count': embedding_count}
+        rerank_result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(sqlalchemy.func.count())
+            .select_from(persistence_model.RerankModel)
+            .where(persistence_model.RerankModel.provider_uuid == provider_uuid)
+        )
+        rerank_count = rerank_result.scalar() or 0
+
+        return {'llm_count': llm_count, 'embedding_count': embedding_count, 'rerank_count': rerank_count}
 
     async def find_or_create_provider(self, requester: str, base_url: str, api_keys: list) -> str:
         """Find existing provider or create new one"""

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

@@ -179,7 +179,7 @@ class SpaceService:
         space_url = space_config['url']
 
         session = httpclient.get_session()
-        async with session.get(f'{space_url}/api/v1/models') as response:
+        async with session.get(f'{space_url}/api/v1/models', params={'page_size': 100}) as response:
             if response.status != 200:
                 raise ValueError(f'Failed to get models: {await response.text()}')
             data = await response.json()

src/langbot/pkg/core/app.py

@@ -133,6 +133,8 @@ class Application:
 
     embedding_models_service: model_service.EmbeddingModelsService = None
 
+    rerank_models_service: model_service.RerankModelsService = None
+
     provider_service: provider_service.ModelProviderService = None
 
     pipeline_service: pipeline_service.PipelineService = None

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

@@ -61,6 +61,9 @@ class BuildAppStage(stage.BootingStage):
         embedding_models_service_inst = model_service.EmbeddingModelsService(ap)
         ap.embedding_models_service = embedding_models_service_inst
 
+        rerank_models_service_inst = model_service.RerankModelsService(ap)
+        ap.rerank_models_service = rerank_models_service_inst
+
         provider_service_inst = provider_service.ModelProviderService(ap)
         ap.provider_service = provider_service_inst
 

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

@@ -59,3 +59,22 @@ class EmbeddingModel(Base):
         server_default=sqlalchemy.func.now(),
         onupdate=sqlalchemy.func.now(),
     )
+
+
+class RerankModel(Base):
+    """Rerank model"""
+
+    __tablename__ = 'rerank_models'
+
+    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+    name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    provider_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    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())
+    updated_at = sqlalchemy.Column(
+        sqlalchemy.DateTime,
+        nullable=False,
+        server_default=sqlalchemy.func.now(),
+        onupdate=sqlalchemy.func.now(),
+    )

src/langbot/pkg/persistence/alembic/versions/0003_add_rerank_models.py

@@ -0,0 +1,35 @@
+"""add rerank_models table
+
+Revision ID: 0003_add_rerank_models
+Revises: 0002_sample
+Create Date: 2026-04-19
+"""
+
+import sqlalchemy as sa
+from alembic import op
+
+revision = '0003_add_rerank_models'
+down_revision = '0002_sample'
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Check if table already exists (may have been created by create_all())
+    conn = op.get_bind()
+    inspector = sa.inspect(conn)
+    if 'rerank_models' not in inspector.get_table_names():
+        op.create_table(
+            'rerank_models',
+            sa.Column('uuid', sa.String(255), primary_key=True, unique=True),
+            sa.Column('name', sa.String(255), nullable=False),
+            sa.Column('provider_uuid', sa.String(255), nullable=False),
+            sa.Column('extra_args', sa.JSON, nullable=False, server_default='{}'),
+            sa.Column('prefered_ranking', sa.Integer, nullable=False, server_default='0'),
+            sa.Column('created_at', sa.DateTime, nullable=False, server_default=sa.func.now()),
+            sa.Column('updated_at', sa.DateTime, nullable=False, server_default=sa.func.now()),
+        )
+
+
+def downgrade() -> None:
+    op.drop_table('rerank_models')

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

@@ -4,12 +4,12 @@ import sqlalchemy
 import traceback
 
 from . import requester
+from .requesters import litellmchat
 from ...core import app
 from ...discover import engine
 from . import token
 from ...entity.persistence import model as persistence_model
 from ...entity.errors import provider as provider_errors
-from async_lru import alru_cache
 
 
 class ModelManager:
@@ -24,6 +24,8 @@ class ModelManager:
 
     embedding_models: list[requester.RuntimeEmbeddingModel]
 
+    rerank_models: list[requester.RuntimeRerankModel]
+
     requester_components: list[engine.Component]
 
     requester_dict: dict[str, type[requester.ProviderAPIRequester]]
@@ -32,6 +34,7 @@ class ModelManager:
         self.ap = ap
         self.llm_models = []
         self.embedding_models = []
+        self.rerank_models = []
         self.requester_components = []
         self.requester_dict = {}
 
@@ -40,6 +43,13 @@ class ModelManager:
 
         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)
+            if component.spec.get('litellm_provider'):
+                self.ap.logger.debug(
+                    f'Skipping Python class loading for {component.metadata.name} '
+                    f'(uses litellm_provider={component.spec.get("litellm_provider")})'
+                )
+                continue
             requester_dict[component.metadata.name] = component.get_python_component_class()
 
         self.requester_dict = requester_dict
@@ -64,8 +74,7 @@ class ModelManager:
 
         self.llm_models = []
         self.embedding_models = []
-
+        self.rerank_models = []
-        # Load all providers first
         self.provider_dict = {}
         providers_result = await self.ap.persistence_mgr.execute_async(
             sqlalchemy.select(persistence_model.ModelProvider)
@@ -110,6 +119,22 @@ class ModelManager:
             except Exception as e:
                 self.ap.logger.error(f'Failed to load model {embedding_model.uuid}: {e}\n{traceback.format_exc()}')
 
+        # Load rerank models
+        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.RerankModel))
+        rerank_models = result.all()
+        for rerank_model in rerank_models:
+            try:
+                provider = self.provider_dict.get(rerank_model.provider_uuid)
+                if provider is None:
+                    self.ap.logger.warning(
+                        f'Provider {rerank_model.provider_uuid} not found for model {rerank_model.uuid}'
+                    )
+                    continue
+                runtime_rerank_model = await self.load_rerank_model_with_provider(rerank_model, provider)
+                self.rerank_models.append(runtime_rerank_model)
+            except Exception as e:
+                self.ap.logger.error(f'Failed to load model {rerank_model.uuid}: {e}\n{traceback.format_exc()}')
+
     async def sync_new_models_from_space(self):
         """Sync models from Space"""
         space_model_provider = await self.ap.persistence_mgr.execute_async(
@@ -212,6 +237,26 @@ class ModelManager:
 
         return runtime_embedding_model
 
+    async def init_temporary_runtime_rerank_model(
+        self,
+        model_info: dict,
+    ) -> requester.RuntimeRerankModel:
+        """Initialize runtime rerank model from dict (for testing)"""
+        provider_info = model_info.get('provider', {})
+        runtime_provider = await self.load_provider(provider_info)
+
+        runtime_rerank_model = requester.RuntimeRerankModel(
+            model_entity=persistence_model.RerankModel(
+                uuid=model_info.get('uuid', ''),
+                name=model_info.get('name', ''),
+                provider_uuid='',
+                extra_args=model_info.get('extra_args', {}),
+            ),
+            provider=runtime_provider,
+        )
+
+        return runtime_rerank_model
+
     async def load_provider(
         self, provider_info: persistence_model.ModelProvider | sqlalchemy.Row | dict
     ) -> requester.RuntimeProvider:
@@ -223,13 +268,34 @@ class ModelManager:
         else:
             provider_entity = provider_info
 
-        if provider_entity.requester not in self.requester_dict:
+        # Get requester manifest to check for litellm_provider
-            raise provider_errors.RequesterNotFoundError(provider_entity.requester)
+        requester_manifest = self.get_available_requester_manifest_by_name(provider_entity.requester)
+
+        # Build config from base_url
+        config = {'base_url': provider_entity.base_url}
+
+        # Check if requester manifest specifies litellm_provider
+        if requester_manifest and requester_manifest.spec.get('litellm_provider'):
+            # Use unified LiteLLMRequester with provider prefix
+            # Map litellm_provider (YAML spec) to custom_llm_provider (config)
+            config['custom_llm_provider'] = requester_manifest.spec['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 [])
@@ -269,6 +335,9 @@ class ModelManager:
         for model in self.embedding_models:
             if model.provider.provider_entity.uuid == provider_uuid:
                 model.provider = new_runtime_provider
+        for model in self.rerank_models:
+            if model.provider.provider_entity.uuid == provider_uuid:
+                model.provider = new_runtime_provider
 
         # update ref in provider dict
         self.provider_dict[provider_uuid] = new_runtime_provider
@@ -305,6 +374,22 @@ class ModelManager:
 
         return runtime_embedding_model
 
+    async def load_rerank_model_with_provider(
+        self,
+        model_info: persistence_model.RerankModel | sqlalchemy.Row,
+        provider: requester.RuntimeProvider,
+    ) -> requester.RuntimeRerankModel:
+        """Load rerank model with provider info"""
+        if isinstance(model_info, sqlalchemy.Row):
+            model_info = persistence_model.RerankModel(**model_info._mapping)
+
+        runtime_rerank_model = requester.RuntimeRerankModel(
+            model_entity=model_info,
+            provider=provider,
+        )
+
+        return runtime_rerank_model
+
     async def load_llm_model(self, model_info: dict):
         """Load LLM model from dict (with provider info)"""
         provider_info = model_info.get('provider', {})
@@ -352,7 +437,6 @@ class ModelManager:
 
         await self.load_embedding_model_with_provider(model_entity, provider_entity)
 
-    @alru_cache(ttl=60 * 5)
     async def get_model_by_uuid(self, uuid: str) -> requester.RuntimeLLMModel:
         """Get LLM model by uuid"""
         for model in self.llm_models:
@@ -360,7 +444,6 @@ class ModelManager:
                 return model
         raise ValueError(f'LLM model {uuid} not found')
 
-    @alru_cache(ttl=60 * 5)
     async def get_embedding_model_by_uuid(self, uuid: str) -> requester.RuntimeEmbeddingModel:
         """Get embedding model by uuid"""
         for model in self.embedding_models:
@@ -368,6 +451,13 @@ class ModelManager:
                 return model
         raise ValueError(f'Embedding model {uuid} not found')
 
+    async def get_rerank_model_by_uuid(self, uuid: str) -> requester.RuntimeRerankModel:
+        """Get rerank model by uuid"""
+        for model in self.rerank_models:
+            if model.model_entity.uuid == uuid:
+                return model
+        raise ValueError(f'Rerank model {uuid} not found')
+
     async def remove_llm_model(self, model_uuid: str):
         """Remove LLM model"""
         for model in self.llm_models:
@@ -382,6 +472,13 @@ class ModelManager:
                 self.embedding_models.remove(model)
                 return
 
+    async def remove_rerank_model(self, model_uuid: str):
+        """Remove rerank model"""
+        for model in self.rerank_models:
+            if model.model_entity.uuid == model_uuid:
+                self.rerank_models.remove(model)
+                return
+
     def get_available_requesters_info(self, model_type: str) -> list[dict]:
         """Get all available requesters"""
         if model_type != '':

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)
+                    input_tokens = usage_info.get('prompt_tokens', 0)
-                    output_tokens = usage_info.get('output_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)
@@ -247,6 +255,40 @@ class RuntimeProvider:
             except Exception as monitor_err:
                 self.requester.ap.logger.error(f'[Monitoring] Failed to record embedding call: {monitor_err}')
 
+    async def invoke_rerank(
+        self,
+        model: RuntimeRerankModel,
+        query: str,
+        documents: typing.List[str],
+        extra_args: dict[str, typing.Any] = {},
+    ) -> typing.List[dict]:
+        """Bridge method for invoking rerank with monitoring"""
+        start_time = time.time()
+        status = 'success'
+
+        try:
+            result = await self.requester.invoke_rerank(
+                model=model,
+                query=query,
+                documents=documents,
+                extra_args=extra_args,
+            )
+            return result
+
+        except Exception:
+            status = 'error'
+            raise
+        finally:
+            duration_ms = int((time.time() - start_time) * 1000)
+
+            try:
+                self.requester.ap.logger.debug(
+                    f'[Rerank] model={model.model_entity.name} docs={len(documents)} '
+                    f'duration={duration_ms}ms status={status}'
+                )
+            except Exception as monitor_err:
+                self.requester.ap.logger.error(f'[Monitoring] Failed to record rerank call: {monitor_err}')
+
 
 class RuntimeLLMModel:
     """运行时模型"""
@@ -284,6 +326,24 @@ class RuntimeEmbeddingModel:
         self.provider = provider
 
 
+class RuntimeRerankModel:
+    """运行时 Rerank 模型"""
+
+    model_entity: persistence_model.RerankModel
+    """模型数据"""
+
+    provider: RuntimeProvider
+    """提供商实例"""
+
+    def __init__(
+        self,
+        model_entity: persistence_model.RerankModel,
+        provider: RuntimeProvider,
+    ):
+        self.model_entity = model_entity
+        self.provider = provider
+
+
 class ProviderAPIRequester(metaclass=abc.ABCMeta):
     """Provider API请求器"""
 
@@ -376,3 +436,23 @@ class ProviderAPIRequester(metaclass=abc.ABCMeta):
             或者 tuple[typing.List[typing.List[float]], dict]: 返回 (embedding 向量, usage_info)
         """
         pass
+
+    async def invoke_rerank(
+        self,
+        model: RuntimeRerankModel,
+        query: str,
+        documents: typing.List[str],
+        extra_args: dict[str, typing.Any] = {},
+    ) -> typing.List[dict]:
+        """调用 Rerank API
+
+        Args:
+            model (RuntimeRerankModel): 使用的模型信息
+            query (str): 查询文本
+            documents (typing.List[str]): 待重排序的文档列表
+            extra_args (dict[str, typing.Any], optional): 额外的参数. Defaults to {}.
+
+        Returns:
+            typing.List[dict]: [{"index": int, "relevance_score": float}, ...]
+        """
+        raise NotImplementedError('This requester does not support rerank')

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:
@@ -25,6 +26,7 @@ spec:
   support_type:
   - llm
   - text-embedding
+  - rerank
   provider_category: maas
 execution:
   python:

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:
@@ -24,6 +25,8 @@ spec:
     default: 120
   support_type:
   - llm
+  - text-embedding
+  - rerank
   provider_category: manufacturer
 execution:
   python:

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 +1,30 @@
+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
+  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:
@@ -24,6 +25,8 @@ spec:
     default: 120
   support_type:
   - llm
+  - text-embedding
+  - rerank
   provider_category: maas
 execution:
   python:

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

@@ -1,617 +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='',
-            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}')

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:
@@ -25,6 +26,7 @@ spec:
   support_type:
   - llm
   - text-embedding
+  - rerank
   provider_category: manufacturer
 execution:
   python:

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

@@ -1,7 +1,8 @@
-<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 128 128" id="Chroma--Streamline-Svg-Logos" height="128" width="128">
-  <rect width="24" height="24" rx="5" fill="#7B68EE"/>
+  <desc>
-  <circle cx="12" cy="12" r="6" fill="#FF6B35"/>
+    Chroma Streamline Icon: https://streamlinehq.com
-  <circle cx="12" cy="12" r="3" fill="#7B68EE"/>
+  </desc>
-  <path d="M12 6V18" stroke="#FFF" stroke-width="1.5" stroke-linecap="round"/>
+  <path fill="#ffde2d" d="M84.88839999999999 104.10666666666665c23.0732 0 41.77773333333333 -17.956266666666664 41.77773333333333 -40.10653333333333 0 -22.150266666666667 -18.70453333333333 -40.10653333333333 -41.77773333333333 -40.10653333333333 -23.0732 0 -41.77773333333333 17.956266666666664 -41.77773333333333 40.10653333333333 0 22.150266666666667 18.70453333333333 40.10653333333333 41.77773333333333 40.10653333333333Z" stroke-width="1.3333"></path>
-  <path d="M6 12H18" stroke="#FFF" stroke-width="1.5" stroke-linecap="round"/>
+  <path fill="#327eff" d="M43.111066666666666 104.10666666666665c23.0732 0 41.77773333333333 -17.956266666666664 41.77773333333333 -40.10653333333333 0 -22.150266666666667 -18.70453333333333 -40.10653333333333 -41.77773333333333 -40.10653333333333C20.037866666666666 23.8936 1.3333333333333333 41.849866666666664 1.3333333333333333 64.00013333333334 1.3333333333333333 86.15039999999999 20.037866666666666 104.10666666666665 43.111066666666666 104.10666666666665Z" stroke-width="1.3333"></path>
+  <path fill="#ff6446" d="M84.88866666666667 64.00013333333334c0 22.150399999999998 -18.704666666666665 40.10626666666666 -41.778 40.10626666666666V64.00013333333334h41.778Zm-41.778 0c0 -22.150266666666667 18.70453333333333 -40.10653333333333 41.778 -40.10653333333333v40.10653333333333H43.11066666666666Z" stroke-width="1.3333"></path>
 </svg>

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

@@ -0,0 +1 @@
+<svg height="1em" style="flex:none;line-height:1" viewBox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><title>Cohere</title><path clip-rule="evenodd" d="M8.128 14.099c.592 0 1.77-.033 3.398-.703 1.897-.781 5.672-2.2 8.395-3.656 1.905-1.018 2.74-2.366 2.74-4.18A4.56 4.56 0 0018.1 1H7.549A6.55 6.55 0 001 7.55c0 3.617 2.745 6.549 7.128 6.549z" fill="#39594D" fill-rule="evenodd"></path><path clip-rule="evenodd" d="M9.912 18.61a4.387 4.387 0 012.705-4.052l3.323-1.38c3.361-1.394 7.06 1.076 7.06 4.715a5.104 5.104 0 01-5.105 5.104l-3.597-.001a4.386 4.386 0 01-4.386-4.387z" fill="#D18EE2" fill-rule="evenodd"></path><path d="M4.776 14.962A3.775 3.775 0 001 18.738v.489a3.776 3.776 0 007.551 0v-.49a3.775 3.775 0 00-3.775-3.775z" fill="#FF7759"></path></svg>

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

@@ -0,0 +1,32 @@
+apiVersion: v1
+kind: LLMAPIRequester
+metadata:
+  name: cohere-rerank
+  label:
+    en_US: Cohere
+    zh_Hans: Cohere
+  icon: cohere.svg
+spec:
+  litellm_provider: cohere
+  config:
+  - name: base_url
+    label:
+      en_US: Base URL
+      zh_Hans: 基础 URL
+    type: string
+    required: true
+    default: https://api.cohere.com/v2
+  - name: timeout
+    label:
+      en_US: Timeout
+      zh_Hans: 超时时间
+    type: integer
+    required: true
+    default: 120
+  support_type:
+  - rerank
+  provider_category: manufacturer
+execution:
+  python:
+    path: ./chatcmpl.py
+    attr: OpenAIChatCompletions

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:
@@ -24,6 +25,8 @@ spec:
     default: 120
   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:
@@ -24,6 +25,8 @@ spec:
     default: 120
   support_type:
   - llm
+  - text-embedding
+  - rerank
   provider_category: manufacturer
 execution:
   python:

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 +1,30 @@
+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
+  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:
@@ -24,6 +25,8 @@ spec:
     default: 120
   support_type:
   - llm
+  - text-embedding
+  - rerank
   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:
@@ -25,6 +26,7 @@ spec:
   support_type:
   - llm
   - text-embedding
+  - rerank
   provider_category: maas
 execution:
   python:

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 +1,30 @@
+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
+  support_type:
+  - llm
+  - text-embedding
+  - rerank
+  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 +1,30 @@
+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
+  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:

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

@@ -0,0 +1 @@
+<svg fill="currentColor" fill-rule="evenodd" height="1em" style="flex:none;line-height:1" viewBox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><title>Jina</title><path d="M6.608 21.416a4.608 4.608 0 100-9.217 4.608 4.608 0 000 9.217zM20.894 2.015c.614 0 1.106.492 1.106 1.106v9.002c0 5.13-4.148 9.309-9.217 9.37v-9.355l-.03-9.032c0-.614.491-1.106 1.106-1.106h7.158l-.123.015z"></path></svg>

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

@@ -0,0 +1,32 @@
+apiVersion: v1
+kind: LLMAPIRequester
+metadata:
+  name: jina-rerank
+  label:
+    en_US: Jina
+    zh_Hans: Jina
+  icon: jina.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.jina.ai/v1
+  - name: timeout
+    label:
+      en_US: Timeout
+      zh_Hans: 超时时间
+    type: integer
+    required: true
+    default: 120
+  support_type:
+  - rerank
+  provider_category: manufacturer
+execution:
+  python:
+    path: ./chatcmpl.py
+    attr: OpenAIChatCompletions

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

@@ -0,0 +1,407 @@
+"""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."""
+
+    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
+            return f'{provider}/{model_name}'
+        # If no custom provider, assume model_name already includes prefix or is OpenAI-compatible
+        return model_name
+
+    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 ''
+
+    def _extract_usage(self, response) -> dict:
+        """Extract usage info from LiteLLM response."""
+        usage = response.usage
+        return {
+            'prompt_tokens': usage.prompt_tokens or 0,
+            'completion_tokens': usage.completion_tokens or 0,
+            'total_tokens': usage.total_tokens or 0,
+        }
+
+    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
+
+        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'
+
+        try:
+            response = await acompletion(**args)
+            async for chunk in response:
+                # Check for usage chunk (final chunk with stream_options include_usage)
+                if hasattr(chunk, 'usage') and chunk.usage and (not hasattr(chunk, 'choices') or not chunk.choices):
+                    usage_info = {
+                        'prompt_tokens': chunk.usage.prompt_tokens or 0,
+                        'completion_tokens': chunk.usage.completion_tokens or 0,
+                        'total_tokens': chunk.usage.total_tokens or 0,
+                    }
+                    if query:
+                        if query.variables is None:
+                            query.variables = {}
+                        query.variables['_stream_usage'] = usage_info
+                    continue
+
+                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
+
+                if chunk_idx == 0 and not delta_content and not delta.get('tool_calls'):
+                    chunk_idx += 1
+                    continue
+
+                chunk_data = {
+                    'role': role,
+                    'content': delta_content if delta_content else None,
+                    'tool_calls': delta.get('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)
+
+            embeddings = [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()
+
+        args = {
+            'model': model_name,
+            'query': query,
+            'documents': documents,
+            'api_key': api_key,
+            'top_n': min(len(documents), 64),
+        }
+        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 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 Exception as e:
+            self._handle_litellm_error(e)
+
+    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
+
+                # Infer model type
+                normalized_id = (model_id or '').lower()
+                embedding_keywords = ('embedding', 'embed', 'bge-', 'e5-', 'm3e', 'gte-', 'text-embedding')
+                model_type = 'embedding' if any(kw in normalized_id for kw in embedding_keywords) else 'llm'
+
+                models.append(
+                    {
+                        'id': model_id,
+                        'name': model_id,
+                        'type': model_type,
+                    }
+                )
+
+            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 +1,64 @@
+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: ''
+  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:

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 +1,30 @@
+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
+  support_type:
+  - llm
+  - text-embedding
+  - rerank
+  provider_category: manufacturer

src/langbot/pkg/provider/modelmgr/requesters/minimax.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="#4F46E5"/>
+  <text x="30" y="32" font-family="Arial, sans-serif" font-size="12" font-weight="bold" fill="white" text-anchor="middle">MiniMax</text>
+</svg>

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

@@ -0,0 +1,30 @@
+apiVersion: v1
+kind: LLMAPIRequester
+metadata:
+  name: minimax-chat-completions
+  label:
+    en_US: MiniMax
+    zh_Hans: MiniMax
+  icon: minimax.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.minimax.chat/v1
+  - name: timeout
+    label:
+      en_US: Timeout
+      zh_Hans: 超时时间
+    type: integer
+    required: true
+    default: 120
+  support_type:
+  - llm
+  - text-embedding
+  - rerank
+  provider_category: manufacturer

src/langbot/pkg/provider/modelmgr/requesters/mistral.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="#FF6B35"/>
+  <text x="30" y="28" font-family="Arial, sans-serif" font-size="10" font-weight="bold" fill="white" text-anchor="middle">Mistral</text>
+  <text x="30" y="40" font-family="Arial, sans-serif" font-size="8" fill="white" text-anchor="middle">AI</text>
+</svg>

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

@@ -0,0 +1,30 @@
+apiVersion: v1
+kind: LLMAPIRequester
+metadata:
+  name: mistral-chat-completions
+  label:
+    en_US: Mistral AI
+    zh_Hans: Mistral AI
+  icon: mistral.svg
+spec:
+  litellm_provider: mistral
+  config:
+  - name: base_url
+    label:
+      en_US: Base URL
+      zh_Hans: 基础 URL
+    type: string
+    required: true
+    default: https://api.mistral.ai/v1
+  - name: timeout
+    label:
+      en_US: Timeout
+      zh_Hans: 超时时间
+    type: integer
+    required: true
+    default: 120
+  support_type:
+  - llm
+  - text-embedding
+  - rerank
+  provider_category: manufacturer

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

@@ -1,561 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import typing
-
-import openai
-import openai.types.chat.chat_completion as chat_completion
-import httpx
-
-from .. import entities, 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 ModelScopeChatCompletions(requester.ProviderAPIRequester):
-    """ModelScope ChatCompletion API 请求器"""
-
-    client: openai.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'https://api-inference.modelscope.cn/v1',
-        'timeout': 120,
-    }
-
-    async def initialize(self):
-        self.client = openai.AsyncClient(
-            api_key='',
-            base_url=self.requester_cfg['base_url'],
-            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,
-        query: pipeline_query.Query,
-        args: dict,
-        extra_body: dict = {},
-        remove_think: bool = False,
-    ) -> list[dict[str, typing.Any]]:
-        args['stream'] = True
-
-        chunk = None
-
-        pending_content = ''
-
-        tool_calls = []
-
-        resp_gen: openai.AsyncStream = await self.client.chat.completions.create(**args, extra_body=extra_body)
-
-        chunk_idx = 0
-        thinking_started = False
-        thinking_ended = False
-        tool_id = ''
-        tool_name = ''
-        message_delta = {}
-        async for chunk in resp_gen:
-            if not chunk or not chunk.id or not chunk.choices or not chunk.choices[0] or not chunk.choices[0].delta:
-                continue
-
-            delta = chunk.choices[0].delta.model_dump() if hasattr(chunk.choices[0], 'delta') else {}
-            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
-                    pending_content += '<think>\n' + reasoning_content
-                else:
-                    # 继续输出 reasoning_content
-                    pending_content += reasoning_content
-            elif thinking_started and not thinking_ended and delta.get('content'):
-                # reasoning_content 结束，normal content 开始，添加 </think> 结束标签
-                thinking_ended = True
-                pending_content += '\n</think>\n' + delta.get('content')
-
-            if delta.get('content') is not None:
-                pending_content += delta.get('content')
-
-            if delta.get('tool_calls') is not None:
-                for tool_call in delta.get('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['function']['arguments'] is None:
-                        continue
-                    tool_call['id'] = tool_id
-                    tool_call['name'] = tool_name
-                    for tc in tool_calls:
-                        if tc['index'] == tool_call['index']:
-                            tc['function']['arguments'] += tool_call['function']['arguments']
-                            break
-                    else:
-                        tool_calls.append(tool_call)
-
-            if chunk.choices[0].finish_reason is not None:
-                break
-        message_delta['content'] = pending_content
-        message_delta['role'] = 'assistant'
-
-        message_delta['tool_calls'] = tool_calls if tool_calls else None
-        return [message_delta]
-
-    async def _make_msg(
-        self,
-        chat_completion: list[dict[str, typing.Any]],
-    ) -> provider_message.Message:
-        chatcmpl_message = chat_completion[0]
-
-        # 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'
-
-        message = provider_message.Message(**chatcmpl_message)
-
-        return message
-
-    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(query, args, extra_body=extra_args, remove_think=remove_think)
-
-        # 处理请求结果
-        message = await self._make_msg(resp)
-
-        # ModelScope uses streaming, usage info not available
-        usage_info = {}
-
-        return message, usage_info
-
-    async def _req_stream(
-        self,
-        args: dict,
-        extra_body: dict = {},
-    ) -> chat_completion.ChatCompletion:
-        async for chunk in await self.client.chat.completions.create(**args, extra_body=extra_body):
-            yield chunk
-
-    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
-
-        # 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
-        # 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
-            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
-
-    async def invoke_llm(
-        self,
-        query: pipeline_query.Query,
-        model: entities.LLMModelInfo,
-        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:
-        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:
-            return await self._closure(
-                query=query,
-                req_messages=req_messages,
-                use_model=model,
-                use_funcs=funcs,
-                extra_args=extra_args,
-                remove_think=remove_think,
-            )
-        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_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}')

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

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: 魔搭社区
   icon: modelscope.svg
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:
@@ -31,6 +32,8 @@ spec:
     default: 120
   support_type:
   - llm
+  - text-embedding
+  - rerank
   provider_category: maas
 execution:
   python:

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

@@ -1,67 +0,0 @@
-from __future__ import annotations
-
-import typing
-
-
-from . import chatcmpl
-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 MoonshotChatCompletions(chatcmpl.OpenAIChatCompletions):
-    """Moonshot ChatCompletion API 请求器"""
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'https://api.moonshot.cn/v1',
-        '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']])
-
-        # 删除空的，不知道干嘛的，直接删了。
-        # messages = [m for m in messages if m["content"].strip() != "" and ('tool_calls' not in m or not m['tool_calls'])]
-
-        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

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

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: 月之暗面
   icon: moonshot.png
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:
@@ -24,6 +25,8 @@ spec:
     default: 120
   support_type:
   - llm
+  - text-embedding
+  - rerank
   provider_category: manufacturer
 execution:
   python:

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

@@ -1,17 +0,0 @@
-from __future__ import annotations
-
-import typing
-import openai
-
-from . import chatcmpl
-
-
-class NewAPIChatCompletions(chatcmpl.OpenAIChatCompletions):
-    """New API ChatCompletion API 请求器"""
-
-    client: openai.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'http://localhost:3000/v1',
-        'timeout': 120,
-    }

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

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: New API
   icon: newapi.png
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:

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

@@ -1,314 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import os
-import typing
-from typing import Union, Mapping, Any, AsyncIterator
-import uuid
-import json
-
-import ollama
-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
-
-REQUESTER_NAME: str = 'ollama-chat'
-
-
-class OllamaChatCompletions(requester.ProviderAPIRequester):
-    """Ollama平台 ChatCompletion API请求器"""
-
-    client: ollama.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'http://127.0.0.1:11434',
-        'timeout': 120,
-    }
-
-    async def initialize(self):
-        os.environ['OLLAMA_HOST'] = self.requester_cfg['base_url']
-        self.client = ollama.AsyncClient(timeout=self.requester_cfg['timeout'])
-
-    def _infer_model_type(self, model_id: str) -> str:
-        normalized_model_id = (model_id or '').lower()
-        embedding_keywords = ('embedding', 'embed', 'bge-', 'e5-', 'm3e', 'gte-', 'text-embedding')
-        return 'embedding' if any(keyword in normalized_model_id for keyword in embedding_keywords) else 'llm'
-
-    def _infer_model_abilities(self, item: dict[str, typing.Any], model_id: str) -> list[str]:
-        normalized_model_id = (model_id or '').lower()
-        abilities: set[str] = set()
-        details = item.get('details', {}) or {}
-        families = details.get('families', []) or []
-        tokens = [normalized_model_id, str(details.get('family', '')).lower()]
-        tokens.extend(str(family).lower() for family in families)
-
-        if any(keyword in token for token in tokens for keyword in ('vision', 'vl', 'omni', 'llava', 'ocr')):
-            abilities.add('vision')
-        if any(keyword in token for token in tokens for keyword in ('tool', 'function')):
-            abilities.add('func_call')
-        return sorted(abilities)
-
-    async def scan_models(self, api_key: str | None = None) -> dict[str, typing.Any]:
-        del api_key
-        models_url = f'{self.requester_cfg["base_url"].rstrip("/")}/api/tags'
-
-        async with httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']) as client:
-            response = await client.get(models_url)
-            response.raise_for_status()
-            payload = response.json()
-
-        models: list[dict[str, typing.Any]] = []
-        for item in payload.get('models', []):
-            model_id = item.get('model') or item.get('name')
-            if not model_id:
-                continue
-            models.append(
-                {
-                    'id': model_id,
-                    'name': item.get('name', model_id),
-                    'type': self._infer_model_type(model_id),
-                    'abilities': self._infer_model_abilities(item, model_id),
-                }
-            )
-
-        models.sort(key=lambda item: (item['type'] != 'llm', item['name'].lower()))
-        return {
-            'models': models,
-            'debug': {
-                'request': {
-                    'method': 'GET',
-                    'url': models_url,
-                },
-                'response': payload,
-            },
-        }
-
-    async def _req(
-        self,
-        args: dict,
-    ) -> Union[Mapping[str, Any], AsyncIterator[Mapping[str, Any]]]:
-        return await self.client.chat(**args)
-
-    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,
-    ) -> provider_message.Message:
-        args = extra_args.copy()
-        args['model'] = use_model.model_entity.name
-
-        messages: list[dict] = req_messages.copy()
-        for msg in messages:
-            if 'content' in msg and isinstance(msg['content'], list):
-                text_content: list = []
-                image_urls: list = []
-                for me in msg['content']:
-                    if me['type'] == 'text':
-                        text_content.append(me['text'])
-                    elif me['type'] == 'image_base64':
-                        image_urls.append(me['image_base64'])
-
-                msg['content'] = '\n'.join(text_content)
-                msg['images'] = [url.split(',')[1] for url in image_urls]
-            if 'tool_calls' in msg:  # LangBot 内部以 str 存储 tool_calls 的参数，这里需要转换为 dict
-                for tool_call in msg['tool_calls']:
-                    tool_call['function']['arguments'] = json.loads(tool_call['function']['arguments'])
-        args['messages'] = messages
-
-        args['tools'] = []
-        if use_funcs:
-            tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
-            if tools:
-                args['tools'] = tools
-
-        resp = await self._req(args)
-        message: provider_message.Message = await self._make_msg(resp)
-        return message
-
-    async def _make_msg(self, chat_completions: ollama.ChatResponse) -> provider_message.Message:
-        message: ollama.Message = chat_completions.message
-        if message is None:
-            raise ValueError("chat_completions must contain a 'message' field")
-
-        ret_msg: provider_message.Message = None
-
-        if message.content is not None:
-            ret_msg = provider_message.Message(role='assistant', content=message.content)
-        if message.tool_calls is not None and len(message.tool_calls) > 0:
-            tool_calls: list[provider_message.ToolCall] = []
-
-            for tool_call in message.tool_calls:
-                tool_calls.append(
-                    provider_message.ToolCall(
-                        id=uuid.uuid4().hex,
-                        type='function',
-                        function=provider_message.FunctionCall(
-                            name=tool_call.function.name,
-                            arguments=json.dumps(tool_call.function.arguments),
-                        ),
-                    )
-                )
-            ret_msg.tool_calls = tool_calls
-
-        return ret_msg
-
-    async def _prepare_messages(
-        self,
-        messages: typing.List[provider_message.Message],
-    ) -> list[dict]:
-        """Prepare messages for Ollama API request."""
-        req_messages: list = []
-        for m in messages:
-            msg_dict: dict = m.dict(exclude_none=True)
-            content: Any = msg_dict.get('content')
-            if isinstance(content, list):
-                if all(isinstance(part, dict) and part.get('type') == 'text' for part in content):
-                    msg_dict['content'] = '\n'.join(part['text'] for part in content)
-            req_messages.append(msg_dict)
-        return req_messages
-
-    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:
-        req_messages = await self._prepare_messages(messages)
-        try:
-            return await self._closure(
-                query=query,
-                req_messages=req_messages,
-                use_model=model,
-                use_funcs=funcs,
-                extra_args=extra_args,
-                remove_think=remove_think,
-            )
-        except asyncio.TimeoutError:
-            raise errors.RequesterError('请求超时')
-
-    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 = await self._prepare_messages(messages)
-
-        try:
-            args = extra_args.copy()
-            args['model'] = model.model_entity.name
-
-            # Process messages for Ollama format
-            msgs: list[dict] = req_messages.copy()
-            for msg in msgs:
-                if 'content' in msg and isinstance(msg['content'], list):
-                    text_content: list = []
-                    image_urls: list = []
-                    for me in msg['content']:
-                        if me['type'] == 'text':
-                            text_content.append(me['text'])
-                        elif me['type'] == 'image_base64':
-                            image_urls.append(me['image_base64'])
-                    msg['content'] = '\n'.join(text_content)
-                    msg['images'] = [url.split(',')[1] for url in image_urls]
-                if 'tool_calls' in msg:
-                    for tool_call in msg['tool_calls']:
-                        tool_call['function']['arguments'] = json.loads(tool_call['function']['arguments'])
-            args['messages'] = msgs
-
-            args['tools'] = []
-            if funcs:
-                tools = await self.ap.tool_mgr.generate_tools_for_openai(funcs)
-                if tools:
-                    args['tools'] = tools
-
-            args['stream'] = True
-
-            chunk_idx = 0
-            thinking_started = False
-            thinking_ended = False
-            role = 'assistant'
-
-            async for chunk in await self.client.chat(**args):
-                message: ollama.Message = chunk.message
-                done = chunk.done
-
-                delta_content = message.content or ''
-                reasoning_content = getattr(message, 'thinking', '') or ''
-
-                # Handle reasoning/thinking content
-                if reasoning_content:
-                    if remove_think:
-                        chunk_idx += 1
-                        continue
-
-                    if not thinking_started:
-                        thinking_started = True
-                        delta_content = '<think>\n' + reasoning_content
-                    else:
-                        delta_content = reasoning_content
-                elif thinking_started and not thinking_ended and delta_content:
-                    thinking_ended = True
-                    delta_content = '\n</think>\n' + delta_content
-
-                # Handle tool calls
-                tool_calls_data = None
-                if message.tool_calls:
-                    tool_calls_data = []
-                    for tc in message.tool_calls:
-                        tool_calls_data.append(
-                            {
-                                'id': uuid.uuid4().hex,
-                                'type': 'function',
-                                'function': {
-                                    'name': tc.function.name,
-                                    'arguments': json.dumps(tc.function.arguments),
-                                },
-                            }
-                        )
-
-                # Skip empty first chunk
-                if chunk_idx == 0 and not delta_content and not reasoning_content and not tool_calls_data:
-                    chunk_idx += 1
-                    continue
-
-                chunk_data = {
-                    'role': role,
-                    'content': delta_content if delta_content else None,
-                    'tool_calls': tool_calls_data,
-                    'is_final': bool(done),
-                }
-                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 asyncio.TimeoutError:
-            raise errors.RequesterError('请求超时')
-
-    async def invoke_embedding(
-        self,
-        model: requester.RuntimeEmbeddingModel,
-        input_text: list[str],
-        extra_args: dict[str, typing.Any] = {},
-    ) -> list[list[float]]:
-        return (
-            await self.client.embed(
-                model=model.model_entity.name,
-                input=input_text,
-                **extra_args,
-            )
-        ).embeddings

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

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: Ollama
   icon: ollama.svg
 spec:
+  litellm_provider: ollama
   config:
   - name: base_url
     label:

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

@@ -1,25 +0,0 @@
-from __future__ import annotations
-
-import typing
-import openai
-
-from . import modelscopechatcmpl
-
-
-class OpenRouterChatCompletions(modelscopechatcmpl.ModelScopeChatCompletions):
-    """OpenRouter ChatCompletion API 请求器"""
-
-    client: openai.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'https://openrouter.ai/api/v1',
-        'timeout': 120,
-    }
-
-    async def scan_models(self, api_key: str | None = None) -> dict[str, typing.Any]:
-        original_base_url = self.requester_cfg.get('base_url', '')
-        self.requester_cfg['base_url'] = 'https://openrouter.ai/api/v1'
-        try:
-            return await super().scan_models(api_key)
-        finally:
-            self.requester_cfg['base_url'] = original_base_url

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

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: OpenRouter
   icon: openrouter.svg
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:
@@ -25,6 +26,7 @@ spec:
   support_type:
   - llm
   - text-embedding
+  - rerank
   provider_category: maas
 execution:
   python:

src/langbot/pkg/provider/modelmgr/requesters/ppiochatcmpl.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 PPIOChatCompletions(chatcmpl.OpenAIChatCompletions):
-    """欧派云 ChatCompletion API 请求器"""
-
-    client: openai.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'https://api.ppinfra.com/v3/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/ppiochatcmpl.yaml

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: 派欧云
   icon: ppio.svg
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:

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

@@ -1,17 +0,0 @@
-from __future__ import annotations
-
-import openai
-import typing
-
-from . import chatcmpl
-
-
-class QHAIGCChatCompletions(chatcmpl.OpenAIChatCompletions):
-    """启航 AI ChatCompletion API 请求器"""
-
-    client: openai.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'https://api.qhaigc.com/v1',
-        'timeout': 120,
-    }

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

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: 启航 AI
   icon: qhaigc.png
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:

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

@@ -1,8 +1,17 @@
-<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+<svg id="_图层_1" data-name="图层 1" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 334.84 76.22">
-  <rect width="24" height="24" rx="5" fill="#1E3A5F"/>
+  <defs>
-  <path d="M6 12C6 8.68629 8.68629 6 12 6C15.3137 6 18 8.68629 18 12" stroke="#4FC3F7" stroke-width="2" stroke-linecap="round"/>
+    <style>
-  <path d="M18 12C18 15.3137 15.3137 18 12 18C8.68629 18 6 15.3137 6 12" stroke="#81D4FA" stroke-width="2" stroke-linecap="round"/>
+      .cls-1 {
-  <circle cx="12" cy="12" r="2" fill="#4FC3F7"/>
+        fill: currentColor;
-  <circle cx="6" cy="12" r="1.5" fill="#81D4FA"/>
+      }
-  <circle cx="18" cy="12" r="1.5" fill="#4FC3F7"/>
+    </style>
-</svg>
+  </defs>
+  <path class="cls-1" d="M308.56,23.63c-5.04,0-9.73,1.43-13.73,3.88V1.08l-12.56,4.61v70h12.56v-3.35c4,2.46,8.71,3.88,13.73,3.88,14.49,0,26.29-11.79,26.29-26.29s-11.79-26.29-26.29-26.29h0ZM308.56,63.88c-6.87,0-12.57-4.98-13.73-11.51v-4.91c1.16-6.54,6.88-11.51,13.73-11.51,7.7,0,13.96,6.26,13.96,13.96s-6.26,13.96-13.96,13.96Z"></path>
+  <path class="cls-1" d="M255.54,5.69v21.83c-4-2.46-8.71-3.88-13.73-3.88-14.49,0-26.29,11.79-26.29,26.29s11.79,26.29,26.29,26.29c5.04,0,9.73-1.43,13.73-3.88v3.35h12.56V1.08l-12.56,4.61ZM241.81,63.88c-7.7,0-13.96-6.26-13.96-13.96s6.26-13.96,13.96-13.96c6.87,0,12.57,4.98,13.73,11.51v4.91c-1.16,6.54-6.88,11.51-13.73,11.51Z"></path>
+  <polygon class="cls-1" points="195.35 52.2 186.65 61.17 200.64 75.62 209.32 75.62 218.01 75.62 195.35 52.2"></polygon>
+  <path class="cls-1" d="M167.14,4.59c.65,3.99.68,8.04.03,12.15-.03.17.16.3.31.21,3.82-2.21,7.82-3.69,12.01-4.33.12-.02.19-.13.17-.23-.68-4.13-.61-8.18-.03-12.16.02-.17-.16-.3-.31-.2-4.01,2.31-8.01,3.81-12.01,4.34-.12.01-.19.12-.17.23h0Z"></path>
+  <path class="cls-1" d="M198.75,24.09l-19.07,19.72v-25.57c-4.49.67-8.7,2.11-12.56,4.57v52.83h12.56v-13.87l3.78-3.9.02.02,8.68-8.97-.02-.02,23.98-24.8h-17.37Z"></path>
+  <path class="cls-1" d="M145.03,57.86c-2.56,4.45-7.17,7.2-12.13,7.2-5.96,0-11.3-3.96-13.32-9.85h38.87l.08-.42c.29-1.5.42-3.06.42-4.65,0-14.37-11.69-26.06-26.06-26.06s-26.06,11.69-26.06,26.06,11.69,26.06,26.06,26.06c9.63,0,18.43-5.28,22.98-13.77l.26-.49-11.1-4.08h-.01ZM132.88,35.19h.03c5.96,0,11.3,3.96,13.32,9.85h-26.67c2.02-5.89,7.36-9.85,13.32-9.85Z"></path>
+  <path class="cls-1" d="M75.92,65.07c-5.96,0-11.29-3.96-13.32-9.85h38.87l.08-.42c.29-1.5.42-3.06.42-4.65,0-14.37-11.69-26.06-26.06-26.06s-26.06,11.69-26.06,26.06,11.69,26.06,26.06,26.06c9.63,0,18.43-5.28,22.98-13.77l.26-.49h0l-11.1-4.08c-2.56,4.45-7.17,7.2-12.13,7.2h-.01ZM75.92,35.19h.03c5.96,0,11.29,3.96,13.32,9.85h-26.67c2.03-5.89,7.36-9.85,13.32-9.85Z"></path>
+  <path class="cls-1" d="M30.43,45.58l-10.2-1.91c-3.03-.56-4.98-2.25-4.98-4.33,0-1.5,1.61-4.35,7.68-4.35,5.53,0,9.36,3.5,10.25,6.26l10.9-4-.14-.42c-1.17-3.54-3.5-6.58-6.94-9.04-3.49-2.49-8.04-3.69-13.88-3.69s-10.98,1.5-14.78,4.34c-3.88,2.91-5.84,6.76-5.84,11.46,0,7.98,4.72,12.77,14.42,14.64l9.9,1.81c3.05.61,4.94,2.27,4.94,4.33,0,2.61-3.58,4.44-8.7,4.44-5.79,0-9.9-3.72-11.85-7.14L0,62.1l.14.39c1.3,3.8,3.89,7.07,7.7,9.71,3.78,2.6,8.65,3.95,14.51,3.98l.25.03c6.87,0,12.55-1.57,16.43-4.53,3.98-3.05,6-6.99,6-11.74,0-3.73-1.14-6.7-3.6-9.33-2.27-2.42-5.98-4.11-10.98-5.02h-.02Z"></path>
+</svg>

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

@@ -1,32 +0,0 @@
-from __future__ import annotations
-
-import openai
-import typing
-
-from . import chatcmpl
-import openai.types.chat.chat_completion as chat_completion
-
-
-class ShengSuanYunChatCompletions(chatcmpl.OpenAIChatCompletions):
-    """胜算云(ModelSpot.AI) ChatCompletion API 请求器"""
-
-    client: openai.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'https://router.shengsuanyun.com/api/v1',
-        'timeout': 120,
-    }
-
-    async def _req(
-        self,
-        args: dict,
-        extra_body: dict = {},
-    ) -> chat_completion.ChatCompletion:
-        return await self.client.chat.completions.create(
-            **args,
-            extra_body=extra_body,
-            extra_headers={
-                'HTTP-Referer': 'https://langbot.app',
-                'X-Title': 'LangBot',
-            },
-        )

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

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: 胜算云
   icon: shengsuanyun.svg
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:

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

@@ -1,17 +0,0 @@
-from __future__ import annotations
-
-import typing
-import openai
-
-from . import chatcmpl
-
-
-class SiliconFlowChatCompletions(chatcmpl.OpenAIChatCompletions):
-    """SiliconFlow ChatCompletion API 请求器"""
-
-    client: openai.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'https://api.siliconflow.cn/v1',
-        'timeout': 120,
-    }

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

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: 硅基流动
   icon: siliconflow.svg
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:
@@ -25,6 +26,7 @@ spec:
   support_type:
   - llm
   - text-embedding
+  - rerank
   provider_category: maas
 execution:
   python:

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

@@ -1,17 +0,0 @@
-from __future__ import annotations
-
-import typing
-import openai
-
-from . import chatcmpl
-
-
-class LangBotSpaceChatCompletions(chatcmpl.OpenAIChatCompletions):
-    """LangBot Space ChatCompletion API 请求器"""
-
-    client: openai.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'https://api.langbot.cloud/v1',
-        'timeout': 120,
-    }

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

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: Space
   icon: space.webp
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:

src/langbot/pkg/provider/modelmgr/requesters/tencent.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="#0052D9"/>
+  <text x="30" y="28" font-family="Arial, sans-serif" font-size="10" font-weight="bold" fill="white" text-anchor="middle">Tencent</text>
+  <text x="30" y="40" font-family="Arial, sans-serif" font-size="8" fill="white" text-anchor="middle">Hunyuan</text>
+</svg>

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

@@ -0,0 +1,30 @@
+apiVersion: v1
+kind: LLMAPIRequester
+metadata:
+  name: tencent-chat-completions
+  label:
+    en_US: Tencent Hunyuan
+    zh_Hans: 腾讯混元
+  icon: tencent.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://hunyuan.tencentcloudapi.com/v1
+  - name: timeout
+    label:
+      en_US: Timeout
+      zh_Hans: 超时时间
+    type: integer
+    required: true
+    default: 120
+  support_type:
+  - llm
+  - text-embedding
+  - rerank
+  provider_category: manufacturer

src/langbot/pkg/provider/modelmgr/requesters/together.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="#8B5CF6"/>
+  <text x="30" y="28" font-family="Arial, sans-serif" font-size="10" font-weight="bold" fill="white" text-anchor="middle">Together</text>
+  <text x="30" y="40" font-family="Arial, sans-serif" font-size="8" fill="white" text-anchor="middle">AI</text>
+</svg>

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

@@ -0,0 +1,30 @@
+apiVersion: v1
+kind: LLMAPIRequester
+metadata:
+  name: together-chat-completions
+  label:
+    en_US: Together AI
+    zh_Hans: Together AI
+  icon: together.svg
+spec:
+  litellm_provider: together_ai
+  config:
+  - name: base_url
+    label:
+      en_US: Base URL
+      zh_Hans: 基础 URL
+    type: string
+    required: true
+    default: https://api.together.xyz/v1
+  - name: timeout
+    label:
+      en_US: Timeout
+      zh_Hans: 超时时间
+    type: integer
+    required: true
+    default: 120
+  support_type:
+  - llm
+  - text-embedding
+  - rerank
+  provider_category: manufacturer

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

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: 小马算力
   icon: tokenpony.svg
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:

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

@@ -1,17 +0,0 @@
-from __future__ import annotations
-
-import typing
-import openai
-
-from . import chatcmpl
-
-
-class TokenPonyChatCompletions(chatcmpl.OpenAIChatCompletions):
-    """TokenPony ChatCompletion API 请求器"""
-
-    client: openai.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'https://api.tokenpony.cn/v1',
-        'timeout': 120,
-    }

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

@@ -1,17 +0,0 @@
-from __future__ import annotations
-
-import typing
-import openai
-
-from . import chatcmpl
-
-
-class VolcArkChatCompletions(chatcmpl.OpenAIChatCompletions):
-    """火山方舟大模型平台 ChatCompletion API 请求器"""
-
-    client: openai.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'https://ark.cn-beijing.volces.com/api/v3',
-        'timeout': 120,
-    }

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

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: 火山方舟
   icon: volcark.svg
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:
@@ -24,6 +25,8 @@ spec:
     default: 120
   support_type:
   - llm
+  - text-embedding
+  - rerank
   provider_category: maas
 execution:
   python:

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

@@ -0,0 +1 @@
+<svg height="1em" style="flex:none;line-height:1" viewBox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><title>Voyage</title><path d="M5.407 0v.066a.974.974 0 00-.048.245c-.011.11-.016.208-.016.295 0 .339.043.715.128 1.13.097.405.274.912.531 1.524l7.125 16.366L20.011 3.39c.161-.404.333-.846.515-1.327.182-.48.273-.966.273-1.458a1.406 1.406 0 00-.096-.54V0H24v.066c-.204.207-.45.578-.74 1.114-.29.535-.606 1.195-.949 1.982L13.095 24h-1.287L3.075 3.965c-.204-.47-.418-.923-.644-1.36-.214-.437-.418-.83-.61-1.18-.194-.36-.365-.66-.515-.9A5.666 5.666 0 001 .064V0h4.407z" fill="#012E33"></path></svg>

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

@@ -0,0 +1,32 @@
+apiVersion: v1
+kind: LLMAPIRequester
+metadata:
+  name: voyageai-rerank
+  label:
+    en_US: Voyage AI
+    zh_Hans: Voyage AI
+  icon: voyageai.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.voyageai.com/v1
+  - name: timeout
+    label:
+      en_US: Timeout
+      zh_Hans: 超时时间
+    type: integer
+    required: true
+    default: 120
+  support_type:
+  - rerank
+  provider_category: manufacturer
+execution:
+  python:
+    path: ./chatcmpl.py
+    attr: OpenAIChatCompletions

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

@@ -1,17 +0,0 @@
-from __future__ import annotations
-
-import typing
-import openai
-
-from . import chatcmpl
-
-
-class XaiChatCompletions(chatcmpl.OpenAIChatCompletions):
-    """xAI ChatCompletion API 请求器"""
-
-    client: openai.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'https://api.x.ai/v1',
-        'timeout': 120,
-    }

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

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: xAI
   icon: xai.svg
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:
@@ -24,6 +25,8 @@ spec:
     default: 120
   support_type:
   - llm
+  - text-embedding
+  - rerank
   provider_category: manufacturer
 execution:
   python:

src/langbot/pkg/provider/modelmgr/requesters/yi.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="#10B981"/>
+  <text x="30" y="28" font-family="Arial, sans-serif" font-size="10" font-weight="bold" fill="white" text-anchor="middle">01.AI</text>
+  <text x="30" y="40" font-family="Arial, sans-serif" font-size="8" fill="white" text-anchor="middle">Yi</text>
+</svg>

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

@@ -0,0 +1,30 @@
+apiVersion: v1
+kind: LLMAPIRequester
+metadata:
+  name: yi-chat-completions
+  label:
+    en_US: 01.AI Yi
+    zh_Hans: 零一万物
+  icon: yi.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.lingyiwanwu.com/v1
+  - name: timeout
+    label:
+      en_US: Timeout
+      zh_Hans: 超时时间
+    type: integer
+    required: true
+    default: 120
+  support_type:
+  - llm
+  - text-embedding
+  - rerank
+  provider_category: manufacturer

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

@@ -1,17 +0,0 @@
-from __future__ import annotations
-
-import typing
-import openai
-
-from . import chatcmpl
-
-
-class ZhipuAIChatCompletions(chatcmpl.OpenAIChatCompletions):
-    """智谱AI ChatCompletion API 请求器"""
-
-    client: openai.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'https://open.bigmodel.cn/api/paas/v4',
-        'timeout': 120,
-    }

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

@@ -7,6 +7,7 @@ metadata:
     zh_Hans: 智谱 AI
   icon: zhipuai.svg
 spec:
+  litellm_provider: openai
   config:
   - name: base_url
     label:
@@ -24,6 +25,8 @@ spec:
     default: 120
   support_type:
   - llm
+  - text-embedding
+  - rerank
   provider_category: manufacturer
 execution:
   python:

src/langbot/pkg/provider/runners/localagent.py

@@ -172,6 +172,45 @@ class LocalAgentRunner(runner.RequestRunner):
                 if result:
                     all_results.extend(result)
 
+            # Rerank step: re-score results using a rerank model if configured
+            local_agent_config = query.pipeline_config.get('ai', {}).get('local-agent', {})
+            rerank_model_uuid = local_agent_config.get('rerank-model', '')
+            if rerank_model_uuid == '__none__':
+                rerank_model_uuid = ''
+            self.ap.logger.info(
+                f'Rerank config: model_uuid={rerank_model_uuid!r}, '
+                f'results={len(all_results)}, '
+                f'local_agent_keys={list(local_agent_config.keys())}'
+            )
+            if all_results and rerank_model_uuid:
+                try:
+                    rerank_model = await self.ap.model_mgr.get_rerank_model_by_uuid(rerank_model_uuid)
+                    rerank_top_k = int(local_agent_config.get('rerank-top-k', 5))
+
+                    doc_texts = []
+                    for entry in all_results:
+                        text = ' '.join(c.text for c in entry.content if c.type == 'text' and c.text)
+                        doc_texts.append(text)
+
+                    doc_texts_capped = doc_texts[:64]
+                    scores = await rerank_model.provider.invoke_rerank(
+                        model=rerank_model,
+                        query=user_message_text,
+                        documents=doc_texts_capped,
+                    )
+
+                    scored = sorted(scores, key=lambda x: x.get('relevance_score', 0), reverse=True)
+                    top_indices = [s['index'] for s in scored[:rerank_top_k] if s['index'] < len(all_results)]
+                    all_results = [all_results[i] for i in top_indices]
+
+                    self.ap.logger.info(
+                        f'Rerank complete: {len(doc_texts)} docs reranked -> top {len(all_results)} kept (top_k={rerank_top_k})'
+                    )
+                except ValueError:
+                    self.ap.logger.warning(f'Rerank model {rerank_model_uuid} not found, skipping rerank')
+                except Exception as e:
+                    self.ap.logger.warning(f'Rerank failed, using original order: {e}')
+
             final_user_message_text = ''
 
             if all_results:

src/langbot/pkg/provider/tools/toolmgr.py

@@ -57,41 +57,6 @@ class ToolManager:
 
         return tools
 
-    async def generate_tools_for_anthropic(self, use_funcs: list[resource_tool.LLMTool]) -> list:
-        """为anthropic生成函数列表
-
-        e.g.
-
-        [
-          {
-            "name": "get_stock_price",
-            "description": "Get the current stock price for a given ticker symbol.",
-            "input_schema": {
-              "type": "object",
-              "properties": {
-                "ticker": {
-                  "type": "string",
-                  "description": "The stock ticker symbol, e.g. AAPL for Apple Inc."
-                }
-              },
-              "required": ["ticker"]
-            }
-          }
-        ]
-        """
-
-        tools = []
-
-        for function in use_funcs:
-            function_schema = {
-                'name': function.name,
-                'description': function.description,
-                'input_schema': function.parameters,
-            }
-            tools.append(function_schema)
-
-        return tools
-
     async def execute_func_call(self, name: str, parameters: dict, query: pipeline_query.Query) -> typing.Any:
         """执行函数调用"""
 

src/langbot/templates/default-pipeline-config.json

@@ -52,7 +52,9 @@
                     "content": "You are a helpful assistant."
                 }
             ],
-            "knowledge-bases": []
+            "knowledge-bases": [],
+            "rerank-model": "",
+            "rerank-top-k": 5
         },
         "dify-service-api": {
             "base-url": "https://api.dify.ai/v1",

src/langbot/templates/metadata/pipeline/ai.yaml

@@ -104,6 +104,34 @@ stages:
           field: __system.is_wizard
           operator: neq
           value: true
+      - name: rerank-model
+        label:
+          en_US: Rerank Model
+          zh_Hans: 重排序模型
+        description:
+          en_US: Optional rerank model to improve retrieval quality by re-scoring retrieved chunks
+          zh_Hans: 可选的重排序模型，通过重新评分检索结果来提升检索质量
+        type: rerank-model-selector
+        required: false
+        default: ''
+        show_if:
+          field: knowledge-bases
+          operator: neq
+          value: []
+      - name: rerank-top-k
+        label:
+          en_US: Rerank Top K
+          zh_Hans: 重排序保留数量
+        description:
+          en_US: Number of top results to keep after reranking
+          zh_Hans: 重排序后保留的最相关结果数量
+        type: integer
+        required: false
+        default: 5
+        show_if:
+          field: rerank-model
+          operator: neq
+          value: ''
   - name: dify-service-api
     label:
       en_US: Dify Service API

tests/unit_tests/provider/__init__.py

@@ -0,0 +1 @@
+"""Provider requester tests"""

tests/unit_tests/provider/test_litellmchat.py

@@ -0,0 +1,633 @@
+"""
+Tests for LiteLLMRequester - unified requester for chat, embedding, and rerank.
+
+These tests verify:
+- Parameter building and LiteLLM API calls
+- Response processing and usage extraction
+- Error handling and exception translation
+- Model name building with provider prefix
+"""
+
+import pytest
+from unittest.mock import Mock, AsyncMock, patch
+
+import litellm
+
+from langbot.pkg.provider.modelmgr.requesters import litellmchat
+from langbot.pkg.provider.modelmgr import errors
+
+
+class MockRuntimeModel:
+    """Mock RuntimeLLMModel for testing"""
+
+    def __init__(self, model_name: str = 'gpt-4o', api_key: str = 'test-key'):
+        self.model_entity = Mock()
+        self.model_entity.name = model_name
+        self.model_entity.extra_args = {}
+        self.provider = Mock()
+        self.provider.token_mgr = Mock()
+        self.provider.token_mgr.get_token = Mock(return_value=api_key)
+
+
+class MockRuntimeEmbeddingModel:
+    """Mock RuntimeEmbeddingModel for testing"""
+
+    def __init__(self, model_name: str = 'text-embedding-3-small', api_key: str = 'test-key'):
+        self.model_entity = Mock()
+        self.model_entity.name = model_name
+        self.model_entity.extra_args = {}
+        self.provider = Mock()
+        self.provider.token_mgr = Mock()
+        self.provider.token_mgr.get_token = Mock(return_value=api_key)
+
+
+class MockRuntimeRerankModel:
+    """Mock RuntimeRerankModel for testing"""
+
+    def __init__(self, model_name: str = 'cohere/rerank-english-v3.0', api_key: str = 'test-key'):
+        self.model_entity = Mock()
+        self.model_entity.name = model_name
+        self.model_entity.extra_args = {}
+        self.provider = Mock()
+        self.provider.token_mgr = Mock()
+        self.provider.token_mgr.get_token = Mock(return_value=api_key)
+
+
+class TestBuildLiteLLMModelName:
+    """Test _build_litellm_model_name method"""
+
+    def test_no_provider_prefix(self):
+        """Test model name without provider prefix"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={'custom_llm_provider': ''})
+        result = requester._build_litellm_model_name('gpt-4o')
+        assert result == 'gpt-4o'
+
+    def test_with_provider_prefix(self):
+        """Test model name with provider prefix"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={'custom_llm_provider': 'openai'})
+        result = requester._build_litellm_model_name('gpt-4o')
+        assert result == 'openai/gpt-4o'
+
+    def test_override_provider(self):
+        """Test override provider via parameter"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={'custom_llm_provider': 'openai'})
+        result = requester._build_litellm_model_name('claude-3', custom_llm_provider='anthropic')
+        assert result == 'anthropic/claude-3'
+
+
+class TestExtractUsage:
+    """Test _extract_usage method"""
+
+    def test_extract_usage_with_data(self):
+        """Test extraction with valid usage data"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        response = Mock()
+        response.usage = Mock()
+        response.usage.prompt_tokens = 100
+        response.usage.completion_tokens = 50
+        response.usage.total_tokens = 150
+
+        result = requester._extract_usage(response)
+
+        assert result['prompt_tokens'] == 100
+        assert result['completion_tokens'] == 50
+        assert result['total_tokens'] == 150
+
+    def test_extract_usage_with_zero_values(self):
+        """Test extraction when values are 0"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        response = Mock()
+        response.usage = Mock()
+        response.usage.prompt_tokens = 0
+        response.usage.completion_tokens = 0
+        response.usage.total_tokens = 0
+
+        result = requester._extract_usage(response)
+
+        assert result['prompt_tokens'] == 0
+        assert result['completion_tokens'] == 0
+
+
+class TestProcessThinkingContent:
+    """Test _process_thinking_content method"""
+
+    def test_no_thinking_markers(self):
+        """Test content without thinking markers"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        result = requester._process_thinking_content('Hello world', None, remove_think=True)
+        assert result == 'Hello world'
+
+    def test_remove_thinking_markers(self):
+        """Test removing thinking markers when remove_think=True"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        content = 'CRETIRE_REASONING_BEGINkLet me think...CRETIRE_REASONING_ENDk The answer is 42.'
+        result = requester._process_thinking_content(content, None, remove_think=True)
+        assert result == 'The answer is 42.'
+
+    def test_preserve_thinking_markers(self):
+        """Test preserving thinking markers when remove_think=False"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        content = 'CRETIRE_REASONING_BEGINkLet me think...CRETIRE_REASONING_ENDk The answer is 42.'
+        result = requester._process_thinking_content(content, None, remove_think=False)
+        assert 'CRETIRE_REASONING_BEGINk' in result
+        assert 'The answer is 42.' in result
+
+    def test_empty_content(self):
+        """Test empty content"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        result = requester._process_thinking_content('', None, remove_think=True)
+        assert result == ''
+
+
+class TestBuildCommonArgs:
+    """Test _build_common_args method"""
+
+    def test_build_args_with_all_params(self):
+        """Test building args with all config params"""
+        requester = litellmchat.LiteLLMRequester(
+            ap=Mock(),
+            config={
+                'base_url': 'https://api.openai.com/v1',
+                'timeout': 60,
+                'drop_params': True,
+                'num_retries': 3,
+                'api_version': '2024-01-01',
+            },
+        )
+
+        args = {}
+        requester._build_common_args(args)
+
+        assert args['api_base'] == 'https://api.openai.com/v1'
+        assert args['timeout'] == 60
+        assert args['drop_params'] == True
+        assert args['num_retries'] == 3
+        assert args['api_version'] == '2024-01-01'
+
+    def test_build_args_without_retry_params(self):
+        """Test building args without retry params for embedding/rerank"""
+        requester = litellmchat.LiteLLMRequester(
+            ap=Mock(),
+            config={
+                'base_url': 'https://api.openai.com/v1',
+                'timeout': 60,
+                'num_retries': 3,
+            },
+        )
+
+        args = {}
+        requester._build_common_args(args, include_retry_params=False)
+
+        assert args['api_base'] == 'https://api.openai.com/v1'
+        assert args['timeout'] == 60
+        assert 'num_retries' not in args
+
+
+class TestHandleLiteLLMError:
+    """Test _handle_litellm_error method"""
+
+    def test_bad_request_error(self):
+        """Test BadRequestError translation"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        # Create proper LiteLLM exception with required args
+        error = litellm.BadRequestError(message='test error', model='gpt-4o', llm_provider='openai')
+
+        with pytest.raises(errors.RequesterError) as exc_info:
+            requester._handle_litellm_error(error)
+
+        assert '请求参数错误' in str(exc_info.value)
+
+    def test_authentication_error(self):
+        """Test AuthenticationError translation"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        error = litellm.AuthenticationError(message='invalid key', model='gpt-4o', llm_provider='openai')
+
+        with pytest.raises(errors.RequesterError) as exc_info:
+            requester._handle_litellm_error(error)
+
+        assert 'API key 无效' in str(exc_info.value)
+
+    def test_rate_limit_error(self):
+        """Test RateLimitError translation"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        error = litellm.RateLimitError(message='rate limited', model='gpt-4o', llm_provider='openai')
+
+        with pytest.raises(errors.RequesterError) as exc_info:
+            requester._handle_litellm_error(error)
+
+        assert '请求过于频繁' in str(exc_info.value)
+
+    def test_timeout_error(self):
+        """Test Timeout translation"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        error = litellm.Timeout(message='timeout', model='gpt-4o', llm_provider='openai')
+
+        with pytest.raises(errors.RequesterError) as exc_info:
+            requester._handle_litellm_error(error)
+
+        assert '请求超时' in str(exc_info.value)
+
+    def test_context_window_error(self):
+        """Test ContextWindowExceededError translation"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        error = litellm.ContextWindowExceededError(message='context too long', model='gpt-4o', llm_provider='openai')
+
+        with pytest.raises(errors.RequesterError) as exc_info:
+            requester._handle_litellm_error(error)
+
+        assert '上下文长度超限' in str(exc_info.value)
+
+    def test_unknown_error(self):
+        """Test unknown error translation"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        with pytest.raises(errors.RequesterError) as exc_info:
+            requester._handle_litellm_error(Exception('unknown'))
+
+        assert '未知错误' in str(exc_info.value)
+
+
+class TestInvokeLLM:
+    """Test invoke_llm method"""
+
+    @pytest.mark.asyncio
+    async def test_invoke_llm_basic(self):
+        """Test basic LLM invocation"""
+        mock_ap = Mock()
+        mock_ap.tool_mgr = Mock()
+        mock_ap.tool_mgr.generate_tools_for_openai = AsyncMock(return_value=None)
+
+        requester = litellmchat.LiteLLMRequester(
+            ap=mock_ap,
+            config={
+                'base_url': 'https://api.openai.com/v1',
+                'timeout': 60,
+            },
+        )
+
+        model = MockRuntimeModel('gpt-4o', 'test-api-key')
+
+        # Mock LiteLLM response
+        mock_response = Mock()
+        mock_response.choices = [Mock()]
+        mock_response.choices[0].message = Mock()
+        mock_response.choices[0].message.model_dump = Mock(
+            return_value={
+                'role': 'assistant',
+                'content': 'Hello! How can I help you?',
+            }
+        )
+        mock_response.usage = Mock()
+        mock_response.usage.prompt_tokens = 10
+        mock_response.usage.completion_tokens = 20
+        mock_response.usage.total_tokens = 30
+
+        import langbot_plugin.api.entities.builtin.provider.message as provider_message
+
+        messages = [provider_message.Message(role='user', content='Hello')]
+
+        # Patch acompletion at the import location
+        with patch.object(litellmchat, 'acompletion', new_callable=AsyncMock, return_value=mock_response):
+            result_msg, usage = await requester.invoke_llm(
+                query=None,
+                model=model,
+                messages=messages,
+            )
+
+            assert result_msg.role == 'assistant'
+            assert result_msg.content == 'Hello! How can I help you?'
+            assert usage['prompt_tokens'] == 10
+            assert usage['completion_tokens'] == 20
+
+    @pytest.mark.asyncio
+    async def test_invoke_llm_with_tools(self):
+        """Test LLM invocation with function calling"""
+        mock_ap = Mock()
+        mock_ap.tool_mgr = Mock()
+        mock_ap.tool_mgr.generate_tools_for_openai = AsyncMock(
+            return_value=[{'type': 'function', 'function': {'name': 'get_weather'}}]
+        )
+
+        requester = litellmchat.LiteLLMRequester(ap=mock_ap, config={})
+
+        model = MockRuntimeModel('gpt-4o', 'test-api-key')
+
+        mock_response = Mock()
+        mock_response.choices = [Mock()]
+        mock_response.choices[0].message = Mock()
+        mock_response.choices[0].message.model_dump = Mock(
+            return_value={
+                'role': 'assistant',
+                'content': None,
+                'tool_calls': [
+                    {'id': 'call_123', 'type': 'function', 'function': {'name': 'get_weather', 'arguments': '{}'}}
+                ],
+            }
+        )
+        mock_response.usage = Mock()
+        mock_response.usage.prompt_tokens = 15
+        mock_response.usage.completion_tokens = 10
+        mock_response.usage.total_tokens = 25
+
+        import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
+        import langbot_plugin.api.entities.builtin.provider.message as provider_message
+
+        messages = [provider_message.Message(role='user', content='What is the weather?')]
+        # Create proper LLMTool with all required fields
+        funcs = [Mock(spec=resource_tool.LLMTool)]
+        funcs[0].name = 'get_weather'
+        funcs[0].description = 'Get weather'
+
+        with patch.object(litellmchat, 'acompletion', new_callable=AsyncMock, return_value=mock_response):
+            result_msg, usage = await requester.invoke_llm(
+                query=None,
+                model=model,
+                messages=messages,
+                funcs=funcs,
+            )
+
+            assert result_msg.tool_calls is not None
+
+    @pytest.mark.asyncio
+    async def test_invoke_llm_error_handling(self):
+        """Test LLM invocation error handling"""
+        mock_ap = Mock()
+        mock_ap.tool_mgr = Mock()
+        mock_ap.tool_mgr.generate_tools_for_openai = AsyncMock(return_value=None)
+
+        requester = litellmchat.LiteLLMRequester(ap=mock_ap, config={})
+
+        model = MockRuntimeModel('gpt-4o', 'test-api-key')
+
+        import langbot_plugin.api.entities.builtin.provider.message as provider_message
+
+        messages = [provider_message.Message(role='user', content='Hello')]
+
+        error = litellm.AuthenticationError(message='invalid key', model='gpt-4o', llm_provider='openai')
+
+        with patch.object(litellmchat, 'acompletion', new_callable=AsyncMock, side_effect=error):
+            with pytest.raises(errors.RequesterError) as exc_info:
+                await requester.invoke_llm(
+                    query=None,
+                    model=model,
+                    messages=messages,
+                )
+
+            assert 'API key 无效' in str(exc_info.value)
+
+
+class TestInvokeEmbedding:
+    """Test invoke_embedding method"""
+
+    @pytest.mark.asyncio
+    async def test_invoke_embedding_basic(self):
+        """Test basic embedding invocation"""
+        requester = litellmchat.LiteLLMRequester(
+            ap=Mock(),
+            config={
+                'base_url': 'https://api.openai.com/v1',
+            },
+        )
+
+        model = MockRuntimeEmbeddingModel('text-embedding-3-small', 'test-api-key')
+
+        # Mock LiteLLM embedding response
+        mock_response = Mock()
+        mock_response.data = [
+            Mock(embedding=[0.1, 0.2, 0.3]),
+            Mock(embedding=[0.4, 0.5, 0.6]),
+        ]
+        mock_response.usage = Mock()
+        mock_response.usage.prompt_tokens = 20
+        mock_response.usage.completion_tokens = 0
+        mock_response.usage.total_tokens = 20
+
+        with patch.object(litellmchat, 'aembedding', new_callable=AsyncMock, return_value=mock_response):
+            embeddings, usage = await requester.invoke_embedding(
+                model=model,
+                input_text=['Hello', 'World'],
+            )
+
+            assert len(embeddings) == 2
+            assert embeddings[0] == [0.1, 0.2, 0.3]
+            assert embeddings[1] == [0.4, 0.5, 0.6]
+            assert usage['prompt_tokens'] == 20
+
+
+class TestInvokeRerank:
+    """Test invoke_rerank method"""
+
+    @pytest.mark.asyncio
+    async def test_invoke_rerank_basic(self):
+        """Test basic rerank invocation"""
+        requester = litellmchat.LiteLLMRequester(
+            ap=Mock(),
+            config={
+                'base_url': 'https://api.cohere.ai',
+            },
+        )
+
+        model = MockRuntimeRerankModel('rerank-english-v3.0', 'test-api-key')
+
+        # Mock LiteLLM rerank response
+        mock_response = Mock()
+        mock_response.results = [
+            {'index': 0, 'relevance_score': 0.95},
+            {'index': 1, 'relevance_score': 0.3},
+            {'index': 2, 'relevance_score': 0.8},
+        ]
+
+        with patch.object(litellmchat, 'arerank', new_callable=AsyncMock, return_value=mock_response):
+            results = await requester.invoke_rerank(
+                model=model,
+                query='What is the capital of France?',
+                documents=['Paris is the capital.', 'London is a city.', 'France is in Europe.'],
+            )
+
+            assert len(results) == 3
+            # Scores should be normalized
+            assert results[0]['index'] == 0
+            assert results[0]['relevance_score'] >= 0 and results[0]['relevance_score'] <= 1
+
+    @pytest.mark.asyncio
+    async def test_invoke_rerank_normalization(self):
+        """Test rerank score normalization"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        model = MockRuntimeRerankModel('rerank-english-v3.0', 'test-api-key')
+
+        # Mock response with varying scores
+        mock_response = Mock()
+        mock_response.results = [
+            {'index': 0, 'relevance_score': 0.9},
+            {'index': 1, 'relevance_score': 0.1},
+        ]
+
+        with patch.object(litellmchat, 'arerank', new_callable=AsyncMock, return_value=mock_response):
+            results = await requester.invoke_rerank(
+                model=model,
+                query='test query',
+                documents=['doc1', 'doc2'],
+            )
+
+            # After normalization: 0.9 -> 1.0, 0.1 -> 0.0
+            assert results[0]['relevance_score'] == 1.0
+            assert results[1]['relevance_score'] == 0.0
+
+    @pytest.mark.asyncio
+    async def test_invoke_rerank_single_document(self):
+        """Test rerank with single document (no normalization needed)"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        model = MockRuntimeRerankModel('rerank-english-v3.0', 'test-api-key')
+
+        mock_response = Mock()
+        mock_response.results = [
+            {'index': 0, 'relevance_score': 0.5},
+        ]
+
+        with patch.object(litellmchat, 'arerank', new_callable=AsyncMock, return_value=mock_response):
+            results = await requester.invoke_rerank(
+                model=model,
+                query='test query',
+                documents=['doc1'],
+            )
+
+            assert len(results) == 1
+            # Single score stays as is (min==max, no normalization)
+            assert results[0]['relevance_score'] == 0.5
+
+
+class TestConvertMessages:
+    """Test _convert_messages method"""
+
+    def test_convert_simple_message(self):
+        """Test converting simple text message"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        import langbot_plugin.api.entities.builtin.provider.message as provider_message
+
+        messages = [provider_message.Message(role='user', content='Hello')]
+        result = requester._convert_messages(messages)
+
+        assert len(result) == 1
+        assert result[0]['role'] == 'user'
+        assert result[0]['content'] == 'Hello'
+
+    def test_convert_message_with_image_base64(self):
+        """Test converting message with image_base64 content"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        import langbot_plugin.api.entities.builtin.provider.message as provider_message
+
+        messages = [
+            provider_message.Message(
+                role='user',
+                content=[
+                    {'type': 'text', 'text': 'What is in this image?'},
+                    {'type': 'image_base64', 'image_base64': 'data:image/png;base64,abc123'},
+                ],
+            )
+        ]
+        result = requester._convert_messages(messages)
+
+        assert len(result) == 1
+        content = result[0]['content']
+        assert isinstance(content, list)
+        # Check image_base64 converted to image_url
+        image_part = [p for p in content if p.get('type') == 'image_url'][0]
+        assert 'image_url' in image_part
+        assert image_part['image_url']['url'] == 'data:image/png;base64,abc123'
+
+    def test_convert_message_with_multiple_text_parts(self):
+        """Test converting message with multiple text parts (LiteLLM handles this)"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        import langbot_plugin.api.entities.builtin.provider.message as provider_message
+
+        messages = [
+            provider_message.Message(
+                role='user',
+                content=[
+                    {'type': 'text', 'text': 'Hello'},
+                    {'type': 'text', 'text': 'World'},
+                ],
+            )
+        ]
+        result = requester._convert_messages(messages)
+
+        assert len(result) == 1
+        # LiteLLM handles multiple text parts, we pass them through
+        assert isinstance(result[0]['content'], list)
+
+
+class TestScanModels:
+    """Test scan_models method"""
+
+    @pytest.mark.asyncio
+    async def test_scan_models_basic(self):
+        """Test basic model scanning"""
+        requester = litellmchat.LiteLLMRequester(
+            ap=Mock(),
+            config={
+                'base_url': 'https://api.openai.com/v1',
+                'timeout': 60,
+            },
+        )
+
+        # Mock httpx response
+        mock_response = Mock()
+        mock_response.json = Mock(
+            return_value={
+                'data': [
+                    {'id': 'gpt-4o'},
+                    {'id': 'text-embedding-3-small'},
+                    {'id': 'gpt-3.5-turbo'},
+                ]
+            }
+        )
+        mock_response.raise_for_status = Mock()
+
+        with patch('httpx.AsyncClient') as mock_client:
+            mock_client.return_value.__aenter__ = AsyncMock(return_value=Mock())
+            mock_client.return_value.__aenter__.return_value.get = AsyncMock(return_value=mock_response)
+
+            result = await requester.scan_models(api_key='test-key')
+
+            assert 'models' in result
+            assert len(result['models']) == 3
+            # Check LLM models are first
+            assert result['models'][0]['type'] == 'llm'
+            # Check embedding model is detected
+            embedding_models = [m for m in result['models'] if m['type'] == 'embedding']
+            assert len(embedding_models) == 1
+
+    @pytest.mark.asyncio
+    async def test_scan_models_no_base_url(self):
+        """Test scan_models without base_url raises error"""
+        requester = litellmchat.LiteLLMRequester(
+            ap=Mock(),
+            config={
+                'base_url': '',
+            },
+        )
+
+        with pytest.raises(errors.RequesterError) as exc_info:
+            await requester.scan_models()
+
+        assert 'Base URL required' in str(exc_info.value)
+
+
+if __name__ == '__main__':
+    pytest.main([__file__, '-v'])

web/src/app/home/components/dynamic-form/DynamicFormComponent.tsx

@@ -240,6 +240,9 @@ export default function DynamicFormComponent({
           case 'embedding-model-selector':
             fieldSchema = z.string();
             break;
+          case 'rerank-model-selector':
+            fieldSchema = z.string();
+            break;
           case 'knowledge-base-selector':
             fieldSchema = z.string();
             break;

web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx

@@ -23,6 +23,7 @@ import {
   Bot,
   KnowledgeBase,
   EmbeddingModel,
+  RerankModel,
   PluginTool,
 } from '@/app/infra/entities/api';
 import { toast } from 'sonner';
@@ -74,6 +75,7 @@ export default function DynamicFormItemComponent({
 }) {
   const [llmModels, setLlmModels] = useState<LLMModel[]>([]);
   const [embeddingModels, setEmbeddingModels] = useState<EmbeddingModel[]>([]);
+  const [rerankModels, setRerankModels] = useState<RerankModel[]>([]);
   const [knowledgeBases, setKnowledgeBases] = useState<KnowledgeBase[]>([]);
   const [bots, setBots] = useState<Bot[]>([]);
   const [tools, setTools] = useState<PluginTool[]>([]);
@@ -180,6 +182,19 @@ export default function DynamicFormItemComponent({
     }
   }, [config.type]);
 
+  useEffect(() => {
+    if (config.type === DynamicFormItemType.RERANK_MODEL_SELECTOR) {
+      httpClient
+        .getProviderRerankModels()
+        .then((resp) => {
+          setRerankModels(resp.models);
+        })
+        .catch((err) => {
+          toast.error('Failed to load rerank models: ' + err.msg);
+        });
+    }
+  }, [config.type]);
+
   useEffect(() => {
     if (config.type === DynamicFormItemType.MODEL_FALLBACK_SELECTOR) {
       fetchLlmModels();
@@ -585,6 +600,45 @@ export default function DynamicFormItemComponent({
         </div>
       );
 
+    case DynamicFormItemType.RERANK_MODEL_SELECTOR:
+      const groupedRerankModels = rerankModels.reduce(
+        (acc, model) => {
+          const providerName = model.provider?.name || 'Unknown';
+          if (!acc[providerName]) acc[providerName] = [];
+          acc[providerName].push(model);
+          return acc;
+        },
+        {} as Record<string, RerankModel[]>,
+      );
+
+      return (
+        <div className="max-w-md">
+          <Select
+            value={field.value || '__none__'}
+            onValueChange={(v) => field.onChange(v === '__none__' ? '' : v)}
+          >
+            <SelectTrigger className="bg-[#ffffff] dark:bg-[#2a2a2e]">
+              <SelectValue placeholder={t('models.rerank')} />
+            </SelectTrigger>
+            <SelectContent>
+              <SelectItem value="__none__">{t('common.none')}</SelectItem>
+              {Object.entries(groupedRerankModels).map(
+                ([providerName, models]) => (
+                  <SelectGroup key={providerName}>
+                    <SelectLabel>{providerName}</SelectLabel>
+                    {models.map((model) => (
+                      <SelectItem key={model.uuid} value={model.uuid}>
+                        {model.name}
+                      </SelectItem>
+                    ))}
+                  </SelectGroup>
+                ),
+              )}
+            </SelectContent>
+          </Select>
+        </div>
+      );
+
     case DynamicFormItemType.MODEL_FALLBACK_SELECTOR: {
       // Separate space models from regular models
       const fbSpaceModels = llmModels.filter(

web/src/app/home/components/models-dialog/ModelsDialog.tsx

@@ -147,15 +147,17 @@ export default function ModelsDialog({
       setLoadingProviders((prev) => new Set(prev).add(providerUuid));
     }
     try {
-      const [llmResp, embeddingResp] = await Promise.all([
+      const [llmResp, embeddingResp, rerankResp] = await Promise.all([
         httpClient.getProviderLLMModels(providerUuid),
         httpClient.getProviderEmbeddingModels(providerUuid),
+        httpClient.getProviderRerankModels(providerUuid),
       ]);
       setProviderModels((prev) => ({
         ...prev,
         [providerUuid]: {
           llm: llmResp.models,
           embedding: embeddingResp.models,
+          rerank: rerankResp.models,
         },
       }));
     } catch (err) {
@@ -247,12 +249,18 @@ export default function ModelsDialog({
           abilities,
           extra_args: extraArgsObj,
         } as never);
-      } else {
+      } else if (modelType === 'embedding') {
         await httpClient.createProviderEmbeddingModel({
           name,
           provider_uuid: providerUuid,
           extra_args: extraArgsObj,
         } as never);
+      } else {
+        await httpClient.createProviderRerankModel({
+          name,
+          provider_uuid: providerUuid,
+          extra_args: extraArgsObj,
+        } as never);
       }
       setAddModelPopoverOpen(null);
       loadProviderModels(providerUuid, true);
@@ -341,12 +349,18 @@ export default function ModelsDialog({
           abilities,
           extra_args: extraArgsObj,
         } as never);
-      } else {
+      } else if (modelType === 'embedding') {
         await httpClient.updateProviderEmbeddingModel(modelId, {
           name,
           provider_uuid: providerUuid,
           extra_args: extraArgsObj,
         } as never);
+      } else {
+        await httpClient.updateProviderRerankModel(modelId, {
+          name,
+          provider_uuid: providerUuid,
+          extra_args: extraArgsObj,
+        } as never);
       }
       setEditModelPopoverOpen(null);
       loadProviderModels(providerUuid, true);
@@ -366,8 +380,10 @@ export default function ModelsDialog({
     try {
       if (modelType === 'llm') {
         await httpClient.deleteProviderLLMModel(modelId);
-      } else {
+      } else if (modelType === 'embedding') {
         await httpClient.deleteProviderEmbeddingModel(modelId);
+      } else {
+        await httpClient.deleteProviderRerankModel(modelId);
       }
       toast.success(t('models.deleteSuccess'));
       loadProviderModels(providerUuid, true);
@@ -407,7 +423,7 @@ export default function ModelsDialog({
           abilities,
           extra_args: extraArgsObj,
         } as never);
-      } else {
+      } else if (modelType === 'embedding') {
         await httpClient.testEmbeddingModel('_', {
           uuid: '',
           name,
@@ -415,6 +431,14 @@ export default function ModelsDialog({
           provider: providerData,
           extra_args: extraArgsObj,
         } as never);
+      } else {
+        await httpClient.testRerankModel('_', {
+          uuid: '',
+          name,
+          provider_uuid: '',
+          provider: providerData,
+          extra_args: extraArgsObj,
+        } as never);
       }
       const duration = Date.now() - startTime;
       setTestResult({ success: true, duration });

web/src/app/home/components/models-dialog/component/provider-form/ProviderForm.tsx

@@ -1,4 +1,4 @@
-import { useEffect, useState } from 'react';
+import { useEffect, useState, useRef, useCallback } from 'react';
 import { httpClient } from '@/app/infra/http/HttpClient';
 
 import { zodResolver } from '@hookform/resolvers/zod';
@@ -16,19 +16,12 @@ import {
   FormMessage,
 } from '@/components/ui/form';
 import { Input } from '@/components/ui/input';
-import {
-  Select,
-  SelectContent,
-  SelectGroup,
-  SelectItem,
-  SelectLabel,
-  SelectTrigger,
-  SelectValue,
-} from '@/components/ui/select';
 import { DialogFooter } from '@/components/ui/dialog';
 import { toast } from 'sonner';
 import { extractI18nObject } from '@/i18n/I18nProvider';
 import { CustomApiError } from '@/app/infra/entities/common';
+import { cn } from '@/lib/utils';
+import { Check, ChevronDown, Search } from 'lucide-react';
 
 const getFormSchema = (t: (key: string) => string) =>
   z.object({
@@ -71,6 +64,10 @@ export default function ProviderForm({
       description: string;
     }[]
   >([]);
+  const [searchQuery, setSearchQuery] = useState('');
+  const [isOpen, setIsOpen] = useState(false);
+  const dropdownRef = useRef<HTMLDivElement>(null);
+  const searchInputRef = useRef<HTMLInputElement>(null);
 
   useEffect(() => {
     loadRequesters();
@@ -79,6 +76,54 @@ export default function ProviderForm({
     }
   }, [providerId]);
 
+  // Close dropdown when clicking outside
+  useEffect(() => {
+    function handleClickOutside(event: MouseEvent) {
+      if (
+        dropdownRef.current &&
+        !dropdownRef.current.contains(event.target as Node)
+      ) {
+        setIsOpen(false);
+        setSearchQuery('');
+      }
+    }
+    document.addEventListener('mousedown', handleClickOutside);
+    return () => document.removeEventListener('mousedown', handleClickOutside);
+  }, []);
+
+  // Focus search input when dropdown opens
+  useEffect(() => {
+    if (isOpen && searchInputRef.current) {
+      searchInputRef.current.focus();
+    }
+  }, [isOpen]);
+
+  // Filter requesters based on search query
+  const filteredRequesters = requesterList.filter(
+    (r) =>
+      r.label.toLowerCase().includes(searchQuery.toLowerCase()) ||
+      r.value.toLowerCase().includes(searchQuery.toLowerCase()),
+  );
+
+  // Group filtered requesters by category
+  const groupedRequesters = {
+    builtin: filteredRequesters.filter((r) => r.category === 'builtin'),
+    manufacturer: filteredRequesters.filter(
+      (r) => r.category === 'manufacturer',
+    ),
+    maas: filteredRequesters.filter((r) => r.category === 'maas'),
+    'self-hosted': filteredRequesters.filter(
+      (r) => r.category === 'self-hosted',
+    ),
+  };
+
+  const categoryLabels: Record<string, string> = {
+    builtin: t('models.builtin'),
+    manufacturer: t('models.modelManufacturer'),
+    maas: t('models.aggregationPlatform'),
+    'self-hosted': t('models.selfDeployed'),
+  };
+
   async function loadRequesters() {
     const resp = await httpClient.getProviderRequesters();
     setRequesterList(
@@ -165,17 +210,16 @@ export default function ProviderForm({
                   {t('models.requester')}
                   <span className="text-red-500">*</span>
                 </FormLabel>
-                <Select
+                <div ref={dropdownRef} className="relative">
-                  onValueChange={(v) => {
+                  {/* Trigger button */}
-                    field.onChange(v);
+                  <button
-                    const req = requesterList.find((r) => r.value === v);
+                    type="button"
-                    if (req && (!providerId || !form.getValues('base_url'))) {
+                    onClick={() => setIsOpen(!isOpen)}
-                      form.setValue('base_url', req.defaultUrl);
+                    className={cn(
-                    }
+                      'flex h-10 w-full items-center justify-between rounded-md border border-input bg-background px-3 py-2 text-sm ring-offset-background placeholder:text-muted-foreground focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50',
-                  }}
+                      isOpen && 'ring-2 ring-ring ring-offset-2',
-                  value={field.value}
+                    )}
-                >
+                  >
-                  <SelectTrigger className="bg-background">
                     {selectedRequester ? (
                       <div className="flex items-center gap-2">
                         <img
@@ -188,90 +232,102 @@ export default function ProviderForm({
                         <span>{selectedRequester.label}</span>
                       </div>
                     ) : (
-                      <SelectValue placeholder={t('models.selectRequester')} />
+                      <span className="text-muted-foreground">
+                        {t('models.selectRequester')}
+                      </span>
                     )}
-                  </SelectTrigger>
+                    <ChevronDown
-                  <SelectContent>
+                      className={cn(
-                    <SelectGroup>
+                        'h-4 w-4 opacity-50 transition-transform',
-                      <SelectLabel>{t('models.builtin')}</SelectLabel>
+                        isOpen && 'rotate-180',
-                      {requesterList
+                      )}
-                        .filter((r) => r.category === 'builtin')
+                    />
-                        .map((r) => (
+                  </button>
-                          <SelectItem key={r.value} value={r.value}>
+
-                            <div className="flex items-center gap-2">
+                  {/* Dropdown */}
-                              <img
+                  {isOpen && (
-                                src={httpClient.getProviderRequesterIconURL(
+                    <div className="absolute z-50 mt-1 w-full rounded-md border bg-popover text-popover-foreground shadow-md animate-in fade-in-0 zoom-in-95">
-                                  r.value,
+                      {/* Search input */}
-                                )}
+                      <div className="flex items-center border-b px-3">
-                                alt={r.label}
+                        <Search className="mr-2 h-4 w-4 shrink-0 opacity-50" />
-                                className="h-5 w-5 rounded"
+                        <input
-                              />
+                          ref={searchInputRef}
-                              <span>{r.label}</span>
+                          type="text"
-                            </div>
+                          placeholder={
-                          </SelectItem>
+                            t('models.searchProviders') || 'Search providers...'
-                        ))}
+                          }
-                    </SelectGroup>
+                          value={searchQuery}
-                    <SelectGroup>
+                          onChange={(e) => setSearchQuery(e.target.value)}
-                      <SelectLabel>{t('models.modelManufacturer')}</SelectLabel>
+                          className="flex h-10 w-full rounded-md bg-transparent py-3 text-sm outline-none placeholder:text-muted-foreground"
-                      {requesterList
+                        />
-                        .filter((r) => r.category === 'manufacturer')
+                      </div>
-                        .map((r) => (
+
-                          <SelectItem key={r.value} value={r.value}>
+                      {/* Options list */}
-                            <div className="flex items-center gap-2">
+                      <div className="max-h-[300px] overflow-y-auto p-1">
-                              <img
+                        {Object.entries(groupedRequesters).map(
-                                src={httpClient.getProviderRequesterIconURL(
+                          ([category, items]) => {
-                                  r.value,
+                            if (items.length === 0) return null;
-                                )}
+                            return (
-                                alt={r.label}
+                              <div key={category}>
-                                className="h-5 w-5 rounded"
+                                <div className="py-1.5 px-2 text-xs font-semibold text-muted-foreground">
-                              />
+                                  {categoryLabels[category]}
-                              <span>{r.label}</span>
+                                </div>
-                            </div>
+                                {items.map((r) => (
-                          </SelectItem>
+                                  <button
-                        ))}
+                                    key={r.value}
-                    </SelectGroup>
+                                    type="button"
-                    <SelectGroup>
+                                    onClick={() => {
-                      <SelectLabel>
+                                      field.onChange(r.value);
-                        {t('models.aggregationPlatform')}
+                                      const req = requesterList.find(
-                      </SelectLabel>
+                                        (req) => req.value === r.value,
-                      {requesterList
+                                      );
-                        .filter((r) => r.category === 'maas')
+                                      if (
-                        .map((r) => (
+                                        req &&
-                          <SelectItem key={r.value} value={r.value}>
+                                        (!providerId ||
-                            <div className="flex items-center gap-2">
+                                          !form.getValues('base_url'))
-                              <img
+                                      ) {
-                                src={httpClient.getProviderRequesterIconURL(
+                                        form.setValue(
-                                  r.value,
+                                          'base_url',
-                                )}
+                                          req.defaultUrl,
-                                alt={r.label}
+                                        );
-                                className="h-5 w-5 rounded"
+                                      }
-                              />
+                                      setIsOpen(false);
-                              <span>{r.label}</span>
+                                      setSearchQuery('');
-                            </div>
+                                    }}
-                          </SelectItem>
+                                    className={cn(
-                        ))}
+                                      'flex w-full items-center gap-2 rounded-sm px-2 py-1.5 text-sm outline-none hover:bg-accent hover:text-accent-foreground cursor-pointer',
-                    </SelectGroup>
+                                      field.value === r.value &&
-                    <SelectGroup>
+                                        'bg-accent text-accent-foreground',
-                      <SelectLabel>{t('models.selfDeployed')}</SelectLabel>
+                                    )}
-                      {requesterList
+                                  >
-                        .filter((r) => r.category === 'self-hosted')
+                                    <img
-                        .map((r) => (
+                                      src={httpClient.getProviderRequesterIconURL(
-                          <SelectItem key={r.value} value={r.value}>
+                                        r.value,
-                            <div className="flex items-center gap-2">
+                                      )}
-                              <img
+                                      alt={r.label}
-                                src={httpClient.getProviderRequesterIconURL(
+                                      className="h-5 w-5 rounded"
-                                  r.value,
+                                    />
-                                )}
+                                    <span className="flex-1 text-left">
-                                alt={r.label}
+                                      {r.label}
-                                className="h-5 w-5 rounded"
+                                    </span>
-                              />
+                                    {field.value === r.value && (
-                              <span>{r.label}</span>
+                                      <Check className="h-4 w-4" />
-                            </div>
+                                    )}
-                          </SelectItem>
+                                  </button>
-                        ))}
+                                ))}
-                    </SelectGroup>
+                              </div>
-                  </SelectContent>
+                            );
-                </Select>
+                          },
+                        )}
+                        {filteredRequesters.length === 0 && (
+                          <div className="py-6 text-center text-sm text-muted-foreground">
+                            No results found.
+                          </div>
+                        )}
+                      </div>
+                    </div>
+                  )}
+                </div>
                 <FormMessage />
                 {selectedRequester?.description && (
                   <p className="text-sm text-muted-foreground">