merge main
JzoNg
1 year ago
commit
684896d100
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from typing import Optional
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from pydantic import BaseModel, Field
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class BaiduOBSStorageConfig(BaseModel):
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"""
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Configuration settings for Baidu Object Storage Service (OBS)
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"""
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BAIDU_OBS_BUCKET_NAME: Optional[str] = Field(
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description="Name of the Baidu OBS bucket to store and retrieve objects (e.g., 'my-obs-bucket')",
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default=None,
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)
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BAIDU_OBS_ACCESS_KEY: Optional[str] = Field(
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description="Access Key ID for authenticating with Baidu OBS",
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default=None,
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)
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BAIDU_OBS_SECRET_KEY: Optional[str] = Field(
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description="Secret Access Key for authenticating with Baidu OBS",
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default=None,
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)
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BAIDU_OBS_ENDPOINT: Optional[str] = Field(
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description="URL of the Baidu OSS endpoint for your chosen region (e.g., 'https://.bj.bcebos.com')",
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default=None,
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)
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from typing import Optional
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from pydantic import BaseModel, Field
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class SupabaseStorageConfig(BaseModel):
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"""
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Configuration settings for Supabase Object Storage Service
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"""
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SUPABASE_BUCKET_NAME: Optional[str] = Field(
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description="Name of the Supabase bucket to store and retrieve objects (e.g., 'dify-bucket')",
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default=None,
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)
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SUPABASE_API_KEY: Optional[str] = Field(
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description="API KEY for authenticating with Supabase",
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default=None,
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)
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SUPABASE_URL: Optional[str] = Field(
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description="URL of the Supabase",
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default=None,
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)
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@ -0,0 +1,45 @@
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from typing import Optional
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from pydantic import Field, NonNegativeInt, PositiveInt
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from pydantic_settings import BaseSettings
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class BaiduVectorDBConfig(BaseSettings):
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"""
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Configuration settings for Baidu Vector Database
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"""
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BAIDU_VECTOR_DB_ENDPOINT: Optional[str] = Field(
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description="URL of the Baidu Vector Database service (e.g., 'http://vdb.bj.baidubce.com')",
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default=None,
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)
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BAIDU_VECTOR_DB_CONNECTION_TIMEOUT_MS: PositiveInt = Field(
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description="Timeout in milliseconds for Baidu Vector Database operations (default is 30000 milliseconds)",
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default=30000,
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)
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BAIDU_VECTOR_DB_ACCOUNT: Optional[str] = Field(
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description="Account for authenticating with the Baidu Vector Database",
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default=None,
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)
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BAIDU_VECTOR_DB_API_KEY: Optional[str] = Field(
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description="API key for authenticating with the Baidu Vector Database service",
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default=None,
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)
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BAIDU_VECTOR_DB_DATABASE: Optional[str] = Field(
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description="Name of the specific Baidu Vector Database to connect to",
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default=None,
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)
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BAIDU_VECTOR_DB_SHARD: PositiveInt = Field(
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description="Number of shards for the Baidu Vector Database (default is 1)",
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default=1,
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)
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BAIDU_VECTOR_DB_REPLICAS: NonNegativeInt = Field(
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description="Number of replicas for the Baidu Vector Database (default is 3)",
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default=3,
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)
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from typing import Optional
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from pydantic import BaseModel, Field
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class VikingDBConfig(BaseModel):
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"""
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Configuration for connecting to Volcengine VikingDB.
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Refer to the following documentation for details on obtaining credentials:
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https://www.volcengine.com/docs/6291/65568
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"""
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VIKINGDB_ACCESS_KEY: Optional[str] = Field(
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default=None, description="The Access Key provided by Volcengine VikingDB for API authentication."
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)
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VIKINGDB_SECRET_KEY: Optional[str] = Field(
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default=None, description="The Secret Key provided by Volcengine VikingDB for API authentication."
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)
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VIKINGDB_REGION: Optional[str] = Field(
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default="cn-shanghai",
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description="The region of the Volcengine VikingDB service.(e.g., 'cn-shanghai', 'cn-beijing').",
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)
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VIKINGDB_HOST: Optional[str] = Field(
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default="api-vikingdb.mlp.cn-shanghai.volces.com",
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description="The host of the Volcengine VikingDB service.(e.g., 'api-vikingdb.volces.com', \
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'api-vikingdb.mlp.cn-shanghai.volces.com')",
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)
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VIKINGDB_SCHEME: Optional[str] = Field(
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default="http",
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description="The scheme of the Volcengine VikingDB service.(e.g., 'http', 'https').",
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)
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VIKINGDB_CONNECTION_TIMEOUT: Optional[int] = Field(
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default=30, description="The connection timeout of the Volcengine VikingDB service."
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)
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VIKINGDB_SOCKET_TIMEOUT: Optional[int] = Field(
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default=30, description="The socket timeout of the Volcengine VikingDB service."
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)
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from flask import request
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from flask_login import current_user
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from flask_restful import Resource, marshal, reqparse
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from werkzeug.exceptions import Forbidden, InternalServerError, NotFound
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import services
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from controllers.console import api
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from controllers.console.datasets.error import DatasetNameDuplicateError
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from controllers.console.setup import setup_required
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from controllers.console.wraps import account_initialization_required
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from fields.dataset_fields import dataset_detail_fields
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from libs.login import login_required
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from services.dataset_service import DatasetService
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from services.external_knowledge_service import ExternalDatasetService
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from services.hit_testing_service import HitTestingService
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from services.knowledge_service import ExternalDatasetTestService
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def _validate_name(name):
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if not name or len(name) < 1 or len(name) > 100:
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raise ValueError("Name must be between 1 to 100 characters.")
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return name
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def _validate_description_length(description):
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if description and len(description) > 400:
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raise ValueError("Description cannot exceed 400 characters.")
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return description
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class ExternalApiTemplateListApi(Resource):
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@setup_required
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@login_required
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@account_initialization_required
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def get(self):
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page = request.args.get("page", default=1, type=int)
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limit = request.args.get("limit", default=20, type=int)
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search = request.args.get("keyword", default=None, type=str)
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external_knowledge_apis, total = ExternalDatasetService.get_external_knowledge_apis(
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page, limit, current_user.current_tenant_id, search
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)
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response = {
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"data": [item.to_dict() for item in external_knowledge_apis],
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"has_more": len(external_knowledge_apis) == limit,
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"limit": limit,
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"total": total,
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"page": page,
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}
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return response, 200
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@setup_required
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@login_required
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@account_initialization_required
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def post(self):
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parser = reqparse.RequestParser()
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parser.add_argument(
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"name",
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nullable=False,
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required=True,
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help="Name is required. Name must be between 1 to 100 characters.",
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type=_validate_name,
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)
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parser.add_argument(
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"settings",
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type=dict,
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location="json",
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nullable=False,
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required=True,
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)
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args = parser.parse_args()
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ExternalDatasetService.validate_api_list(args["settings"])
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# The role of the current user in the ta table must be admin, owner, or editor, or dataset_operator
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if not current_user.is_dataset_editor:
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raise Forbidden()
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try:
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external_knowledge_api = ExternalDatasetService.create_external_knowledge_api(
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tenant_id=current_user.current_tenant_id, user_id=current_user.id, args=args
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)
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except services.errors.dataset.DatasetNameDuplicateError:
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raise DatasetNameDuplicateError()
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return external_knowledge_api.to_dict(), 201
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class ExternalApiTemplateApi(Resource):
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@setup_required
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@login_required
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@account_initialization_required
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def get(self, external_knowledge_api_id):
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external_knowledge_api_id = str(external_knowledge_api_id)
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external_knowledge_api = ExternalDatasetService.get_external_knowledge_api(external_knowledge_api_id)
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if external_knowledge_api is None:
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raise NotFound("API template not found.")
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return external_knowledge_api.to_dict(), 200
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@setup_required
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@login_required
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@account_initialization_required
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def patch(self, external_knowledge_api_id):
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external_knowledge_api_id = str(external_knowledge_api_id)
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parser = reqparse.RequestParser()
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parser.add_argument(
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"name",
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nullable=False,
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required=True,
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help="type is required. Name must be between 1 to 100 characters.",
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type=_validate_name,
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)
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parser.add_argument(
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"settings",
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type=dict,
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location="json",
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nullable=False,
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required=True,
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)
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args = parser.parse_args()
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ExternalDatasetService.validate_api_list(args["settings"])
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external_knowledge_api = ExternalDatasetService.update_external_knowledge_api(
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tenant_id=current_user.current_tenant_id,
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user_id=current_user.id,
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external_knowledge_api_id=external_knowledge_api_id,
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args=args,
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)
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return external_knowledge_api.to_dict(), 200
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@setup_required
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@login_required
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@account_initialization_required
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def delete(self, external_knowledge_api_id):
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external_knowledge_api_id = str(external_knowledge_api_id)
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# The role of the current user in the ta table must be admin, owner, or editor
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if not current_user.is_editor or current_user.is_dataset_operator:
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raise Forbidden()
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ExternalDatasetService.delete_external_knowledge_api(current_user.current_tenant_id, external_knowledge_api_id)
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return {"result": "success"}, 200
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class ExternalApiUseCheckApi(Resource):
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@setup_required
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@login_required
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@account_initialization_required
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def get(self, external_knowledge_api_id):
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external_knowledge_api_id = str(external_knowledge_api_id)
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external_knowledge_api_is_using, count = ExternalDatasetService.external_knowledge_api_use_check(
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external_knowledge_api_id
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)
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return {"is_using": external_knowledge_api_is_using, "count": count}, 200
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class ExternalDatasetCreateApi(Resource):
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@setup_required
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@login_required
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@account_initialization_required
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def post(self):
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# The role of the current user in the ta table must be admin, owner, or editor
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if not current_user.is_editor:
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raise Forbidden()
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parser = reqparse.RequestParser()
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parser.add_argument("external_knowledge_api_id", type=str, required=True, nullable=False, location="json")
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parser.add_argument("external_knowledge_id", type=str, required=True, nullable=False, location="json")
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parser.add_argument(
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"name",
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nullable=False,
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required=True,
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help="name is required. Name must be between 1 to 100 characters.",
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type=_validate_name,
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)
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parser.add_argument("description", type=str, required=False, nullable=True, location="json")
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parser.add_argument("external_retrieval_model", type=dict, required=False, location="json")
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args = parser.parse_args()
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# The role of the current user in the ta table must be admin, owner, or editor, or dataset_operator
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if not current_user.is_dataset_editor:
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raise Forbidden()
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try:
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dataset = ExternalDatasetService.create_external_dataset(
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tenant_id=current_user.current_tenant_id,
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user_id=current_user.id,
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args=args,
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)
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except services.errors.dataset.DatasetNameDuplicateError:
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raise DatasetNameDuplicateError()
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return marshal(dataset, dataset_detail_fields), 201
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class ExternalKnowledgeHitTestingApi(Resource):
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@setup_required
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@login_required
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@account_initialization_required
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def post(self, dataset_id):
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dataset_id_str = str(dataset_id)
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dataset = DatasetService.get_dataset(dataset_id_str)
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if dataset is None:
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raise NotFound("Dataset not found.")
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try:
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DatasetService.check_dataset_permission(dataset, current_user)
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except services.errors.account.NoPermissionError as e:
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raise Forbidden(str(e))
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parser = reqparse.RequestParser()
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parser.add_argument("query", type=str, location="json")
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parser.add_argument("external_retrieval_model", type=dict, required=False, location="json")
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args = parser.parse_args()
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HitTestingService.hit_testing_args_check(args)
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try:
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response = HitTestingService.external_retrieve(
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dataset=dataset,
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query=args["query"],
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account=current_user,
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external_retrieval_model=args["external_retrieval_model"],
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)
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return response
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except Exception as e:
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raise InternalServerError(str(e))
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class BedrockRetrievalApi(Resource):
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# this api is only for internal testing
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def post(self):
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parser = reqparse.RequestParser()
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parser.add_argument("retrieval_setting", nullable=False, required=True, type=dict, location="json")
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parser.add_argument(
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"query",
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nullable=False,
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required=True,
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type=str,
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)
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parser.add_argument("knowledge_id", nullable=False, required=True, type=str)
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args = parser.parse_args()
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# Call the knowledge retrieval service
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result = ExternalDatasetTestService.knowledge_retrieval(
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args["retrieval_setting"], args["query"], args["knowledge_id"]
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)
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return result, 200
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api.add_resource(ExternalKnowledgeHitTestingApi, "/datasets/<uuid:dataset_id>/external-hit-testing")
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api.add_resource(ExternalDatasetCreateApi, "/datasets/external")
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api.add_resource(ExternalApiTemplateListApi, "/datasets/external-knowledge-api")
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api.add_resource(ExternalApiTemplateApi, "/datasets/external-knowledge-api/<uuid:external_knowledge_api_id>")
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api.add_resource(ExternalApiUseCheckApi, "/datasets/external-knowledge-api/<uuid:external_knowledge_api_id>/use-check")
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# this api is only for internal test
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api.add_resource(BedrockRetrievalApi, "/test/retrieval")
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@ -0,0 +1,7 @@
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from libs.exception import BaseHTTPException
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class UnsupportedFileTypeError(BaseHTTPException):
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error_code = "unsupported_file_type"
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description = "File type not allowed."
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code = 415
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@ -1,2 +1,2 @@
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class VariableError(Exception):
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class VariableError(ValueError):
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pass
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@ -0,0 +1,173 @@
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## Predefined Model Integration
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After completing the vendor integration, the next step is to integrate the models from the vendor.
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|
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First, we need to determine the type of model to be integrated and create the corresponding model type `module` under the respective vendor's directory.
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|
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Currently supported model types are:
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|
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- `llm` Text Generation Model
|
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- `text_embedding` Text Embedding Model
|
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- `rerank` Rerank Model
|
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- `speech2text` Speech-to-Text
|
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- `tts` Text-to-Speech
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- `moderation` Moderation
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|
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Continuing with `Anthropic` as an example, `Anthropic` only supports LLM, so create a `module` named `llm` under `model_providers.anthropic`.
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|
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For predefined models, we first need to create a YAML file named after the model under the `llm` `module`, such as `claude-2.1.yaml`.
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|
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### Prepare Model YAML
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|
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```yaml
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model: claude-2.1 # Model identifier
|
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# Display name of the model, which can be set to en_US English or zh_Hans Chinese. If zh_Hans is not set, it will default to en_US.
|
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# This can also be omitted, in which case the model identifier will be used as the label
|
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label:
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en_US: claude-2.1
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model_type: llm # Model type, claude-2.1 is an LLM
|
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features: # Supported features, agent-thought supports Agent reasoning, vision supports image understanding
|
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- agent-thought
|
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model_properties: # Model properties
|
||||
mode: chat # LLM mode, complete for text completion models, chat for conversation models
|
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context_size: 200000 # Maximum context size
|
||||
parameter_rules: # Parameter rules for the model call; only LLM requires this
|
||||
- name: temperature # Parameter variable name
|
||||
# Five default configuration templates are provided: temperature/top_p/max_tokens/presence_penalty/frequency_penalty
|
||||
# The template variable name can be set directly in use_template, which will use the default configuration in entities.defaults.PARAMETER_RULE_TEMPLATE
|
||||
# Additional configuration parameters will override the default configuration if set
|
||||
use_template: temperature
|
||||
- name: top_p
|
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use_template: top_p
|
||||
- name: top_k
|
||||
label: # Display name of the parameter
|
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zh_Hans: 取样数量
|
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en_US: Top k
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type: int # Parameter type, supports float/int/string/boolean
|
||||
help: # Help information, describing the parameter's function
|
||||
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
|
||||
en_US: Only sample from the top K options for each subsequent token.
|
||||
required: false # Whether the parameter is mandatory; can be omitted
|
||||
- name: max_tokens_to_sample
|
||||
use_template: max_tokens
|
||||
default: 4096 # Default value of the parameter
|
||||
min: 1 # Minimum value of the parameter, applicable to float/int only
|
||||
max: 4096 # Maximum value of the parameter, applicable to float/int only
|
||||
pricing: # Pricing information
|
||||
input: '8.00' # Input unit price, i.e., prompt price
|
||||
output: '24.00' # Output unit price, i.e., response content price
|
||||
unit: '0.000001' # Price unit, meaning the above prices are per 100K
|
||||
currency: USD # Price currency
|
||||
```
|
||||
|
||||
It is recommended to prepare all model configurations before starting the implementation of the model code.
|
||||
|
||||
You can also refer to the YAML configuration information under the corresponding model type directories of other vendors in the `model_providers` directory. For the complete YAML rules, refer to: [Schema](schema.md#aimodelentity).
|
||||
|
||||
### Implement the Model Call Code
|
||||
|
||||
Next, create a Python file named `llm.py` under the `llm` `module` to write the implementation code.
|
||||
|
||||
Create an Anthropic LLM class named `AnthropicLargeLanguageModel` (or any other name), inheriting from the `__base.large_language_model.LargeLanguageModel` base class, and implement the following methods:
|
||||
|
||||
- LLM Call
|
||||
|
||||
Implement the core method for calling the LLM, supporting both streaming and synchronous responses.
|
||||
|
||||
```python
|
||||
def _invoke(self, model: str, credentials: dict,
|
||||
prompt_messages: list[PromptMessage], model_parameters: dict,
|
||||
tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
|
||||
stream: bool = True, user: Optional[str] = None) \
|
||||
-> Union[LLMResult, Generator]:
|
||||
"""
|
||||
Invoke large language model
|
||||
|
||||
:param model: model name
|
||||
:param credentials: model credentials
|
||||
:param prompt_messages: prompt messages
|
||||
:param model_parameters: model parameters
|
||||
:param tools: tools for tool calling
|
||||
:param stop: stop words
|
||||
:param stream: is stream response
|
||||
:param user: unique user id
|
||||
:return: full response or stream response chunk generator result
|
||||
"""
|
||||
```
|
||||
|
||||
Ensure to use two functions for returning data, one for synchronous returns and the other for streaming returns, because Python identifies functions containing the `yield` keyword as generator functions, fixing the return type to `Generator`. Thus, synchronous and streaming returns need to be implemented separately, as shown below (note that the example uses simplified parameters, for actual implementation follow the above parameter list):
|
||||
|
||||
```python
|
||||
def _invoke(self, stream: bool, **kwargs) \
|
||||
-> Union[LLMResult, Generator]:
|
||||
if stream:
|
||||
return self._handle_stream_response(**kwargs)
|
||||
return self._handle_sync_response(**kwargs)
|
||||
|
||||
def _handle_stream_response(self, **kwargs) -> Generator:
|
||||
for chunk in response:
|
||||
yield chunk
|
||||
def _handle_sync_response(self, **kwargs) -> LLMResult:
|
||||
return LLMResult(**response)
|
||||
```
|
||||
|
||||
- Pre-compute Input Tokens
|
||||
|
||||
If the model does not provide an interface to precompute tokens, return 0 directly.
|
||||
|
||||
```python
|
||||
def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
|
||||
tools: Optional[list[PromptMessageTool]] = None) -> int:
|
||||
"""
|
||||
Get number of tokens for given prompt messages
|
||||
|
||||
:param model: model name
|
||||
:param credentials: model credentials
|
||||
:param prompt_messages: prompt messages
|
||||
:param tools: tools for tool calling
|
||||
:return:
|
||||
"""
|
||||
```
|
||||
|
||||
- Validate Model Credentials
|
||||
|
||||
Similar to vendor credential validation, but specific to a single model.
|
||||
|
||||
```python
|
||||
def validate_credentials(self, model: str, credentials: dict) -> None:
|
||||
"""
|
||||
Validate model credentials
|
||||
|
||||
:param model: model name
|
||||
:param credentials: model credentials
|
||||
:return:
|
||||
"""
|
||||
```
|
||||
|
||||
- Map Invoke Errors
|
||||
|
||||
When a model call fails, map it to a specific `InvokeError` type as required by Runtime, allowing Dify to handle different errors accordingly.
|
||||
|
||||
Runtime Errors:
|
||||
|
||||
- `InvokeConnectionError` Connection error
|
||||
|
||||
- `InvokeServerUnavailableError` Service provider unavailable
|
||||
- `InvokeRateLimitError` Rate limit reached
|
||||
- `InvokeAuthorizationError` Authorization failed
|
||||
- `InvokeBadRequestError` Parameter error
|
||||
|
||||
```python
|
||||
@property
|
||||
def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
|
||||
"""
|
||||
Map model invoke error to unified error
|
||||
The key is the error type thrown to the caller
|
||||
The value is the error type thrown by the model,
|
||||
which needs to be converted into a unified error type for the caller.
|
||||
|
||||
:return: Invoke error mapping
|
||||
"""
|
||||
```
|
||||
|
||||
For interface method explanations, see: [Interfaces](./interfaces.md). For detailed implementation, refer to: [llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py).
|
||||
@ -0,0 +1,26 @@
|
||||
model: ai21.jamba-1-5-large-v1:0
|
||||
label:
|
||||
en_US: Jamba 1.5 Large
|
||||
model_type: llm
|
||||
model_properties:
|
||||
mode: completion
|
||||
context_size: 256000
|
||||
parameter_rules:
|
||||
- name: temperature
|
||||
use_template: temperature
|
||||
default: 1
|
||||
min: 0.0
|
||||
max: 2.0
|
||||
- name: top_p
|
||||
use_template: top_p
|
||||
- name: max_gen_len
|
||||
use_template: max_tokens
|
||||
required: true
|
||||
default: 4096
|
||||
min: 1
|
||||
max: 4096
|
||||
pricing:
|
||||
input: '0.002'
|
||||
output: '0.008'
|
||||
unit: '0.001'
|
||||
currency: USD
|
||||
@ -0,0 +1,26 @@
|
||||
model: ai21.jamba-1-5-mini-v1:0
|
||||
label:
|
||||
en_US: Jamba 1.5 Mini
|
||||
model_type: llm
|
||||
model_properties:
|
||||
mode: completion
|
||||
context_size: 256000
|
||||
parameter_rules:
|
||||
- name: temperature
|
||||
use_template: temperature
|
||||
default: 1
|
||||
min: 0.0
|
||||
max: 2.0
|
||||
- name: top_p
|
||||
use_template: top_p
|
||||
- name: max_gen_len
|
||||
use_template: max_tokens
|
||||
required: true
|
||||
default: 4096
|
||||
min: 1
|
||||
max: 4096
|
||||
pricing:
|
||||
input: '0.0002'
|
||||
output: '0.0004'
|
||||
unit: '0.001'
|
||||
currency: USD
|
||||
@ -0,0 +1,29 @@
|
||||
model: us.meta.llama3-2-11b-instruct-v1:0
|
||||
label:
|
||||
en_US: US Meta Llama 3.2 11B Instruct
|
||||
model_type: llm
|
||||
features:
|
||||
- vision
|
||||
- tool-call
|
||||
model_properties:
|
||||
mode: completion
|
||||
context_size: 128000
|
||||
parameter_rules:
|
||||
- name: temperature
|
||||
use_template: temperature
|
||||
default: 0.5
|
||||
min: 0.0
|
||||
max: 1
|
||||
- name: top_p
|
||||
use_template: top_p
|
||||
- name: max_gen_len
|
||||
use_template: max_tokens
|
||||
required: true
|
||||
default: 512
|
||||
min: 1
|
||||
max: 2048
|
||||
pricing:
|
||||
input: '0.00035'
|
||||
output: '0.00035'
|
||||
unit: '0.001'
|
||||
currency: USD
|
||||
@ -0,0 +1,26 @@
|
||||
model: us.meta.llama3-2-1b-instruct-v1:0
|
||||
label:
|
||||
en_US: US Meta Llama 3.2 1B Instruct
|
||||
model_type: llm
|
||||
model_properties:
|
||||
mode: completion
|
||||
context_size: 128000
|
||||
parameter_rules:
|
||||
- name: temperature
|
||||
use_template: temperature
|
||||
default: 0.5
|
||||
min: 0.0
|
||||
max: 1
|
||||
- name: top_p
|
||||
use_template: top_p
|
||||
- name: max_gen_len
|
||||
use_template: max_tokens
|
||||
required: true
|
||||
default: 512
|
||||
min: 1
|
||||
max: 2048
|
||||
pricing:
|
||||
input: '0.0001'
|
||||
output: '0.0001'
|
||||
unit: '0.001'
|
||||
currency: USD
|
||||
@ -0,0 +1,26 @@
|
||||
model: us.meta.llama3-2-3b-instruct-v1:0
|
||||
label:
|
||||
en_US: US Meta Llama 3.2 3B Instruct
|
||||
model_type: llm
|
||||
model_properties:
|
||||
mode: completion
|
||||
context_size: 128000
|
||||
parameter_rules:
|
||||
- name: temperature
|
||||
use_template: temperature
|
||||
default: 0.5
|
||||
min: 0.0
|
||||
max: 1
|
||||
- name: top_p
|
||||
use_template: top_p
|
||||
- name: max_gen_len
|
||||
use_template: max_tokens
|
||||
required: true
|
||||
default: 512
|
||||
min: 1
|
||||
max: 2048
|
||||
pricing:
|
||||
input: '0.00015'
|
||||
output: '0.00015'
|
||||
unit: '0.001'
|
||||
currency: USD
|
||||
@ -0,0 +1,31 @@
|
||||
model: us.meta.llama3-2-90b-instruct-v1:0
|
||||
label:
|
||||
en_US: US Meta Llama 3.2 90B Instruct
|
||||
model_type: llm
|
||||
features:
|
||||
- tool-call
|
||||
model_properties:
|
||||
mode: completion
|
||||
context_size: 128000
|
||||
parameter_rules:
|
||||
- name: temperature
|
||||
use_template: temperature
|
||||
default: 0.5
|
||||
min: 0.0
|
||||
max: 1
|
||||
- name: top_p
|
||||
use_template: top_p
|
||||
default: 0.9
|
||||
min: 0
|
||||
max: 1
|
||||
- name: max_gen_len
|
||||
use_template: max_tokens
|
||||
required: true
|
||||
default: 512
|
||||
min: 1
|
||||
max: 2048
|
||||
pricing:
|
||||
input: '0.002'
|
||||
output: '0.002'
|
||||
unit: '0.001'
|
||||
currency: USD
|
||||
@ -0,0 +1,15 @@
|
||||
- gemini-1.5-pro
|
||||
- gemini-1.5-pro-latest
|
||||
- gemini-1.5-pro-001
|
||||
- gemini-1.5-pro-002
|
||||
- gemini-1.5-pro-exp-0801
|
||||
- gemini-1.5-pro-exp-0827
|
||||
- gemini-1.5-flash
|
||||
- gemini-1.5-flash-latest
|
||||
- gemini-1.5-flash-001
|
||||
- gemini-1.5-flash-002
|
||||
- gemini-1.5-flash-exp-0827
|
||||
- gemini-1.5-flash-8b-exp-0827
|
||||
- gemini-1.5-flash-8b-exp-0924
|
||||
- gemini-pro
|
||||
- gemini-pro-vision
|
||||
@ -0,0 +1,25 @@
|
||||
model: llama-guard-3-8b
|
||||
label:
|
||||
zh_Hans: Llama-Guard-3-8B
|
||||
en_US: Llama-Guard-3-8B
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 8192
|
||||
parameter_rules:
|
||||
- name: temperature
|
||||
use_template: temperature
|
||||
- name: top_p
|
||||
use_template: top_p
|
||||
- name: max_tokens
|
||||
use_template: max_tokens
|
||||
default: 512
|
||||
min: 1
|
||||
max: 8192
|
||||
pricing:
|
||||
input: '0.20'
|
||||
output: '0.20'
|
||||
unit: '0.000001'
|
||||
currency: USD
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue