deaf3d7730
- Introduce dynamic variable definitions in AssistantConfig and Assistant models, allowing for flexible prompt customization. - Implement validation for dynamic variable names and types in the schema. - Update backend services and routes to handle dynamic variables in assistant configurations and runtime processing. - Enhance frontend components to support dynamic variable definitions, including a new editor for managing variables. - Add tests to ensure proper functionality and validation of dynamic variables in various scenarios.
130 lines
4.2 KiB
Python
130 lines
4.2 KiB
Python
"""请求/配置数据模型。
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分两层(重要):
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- AssistantConfig:**运行时**配置,含真 key,只在后端内部流转,绝不返回前端。
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由 config_resolver 从 DB 组装(或信令内联传入)。
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- schemas.py 里的 *Request/*Response:面向前端的 DTO(key 打码)。
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"""
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from __future__ import annotations
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from typing import Literal
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from pydantic import BaseModel, Field
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RuntimeMode = Literal["pipeline", "realtime"]
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class RuntimeTool(BaseModel):
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"""Tool data resolved from an assistant binding for one runtime session."""
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id: str
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name: str
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function_name: str
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type: str
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description: str = ""
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definition: dict = Field(default_factory=dict)
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secrets: dict = Field(default_factory=dict)
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class AssistantConfig(BaseModel):
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"""运行时配置:前端可见部分(name/prompt/...) + 服务端注入部分(*_api_key/*_base_url)。"""
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name: str = "未命名助手"
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# prompt|workflow|dify|fastgpt|opencode;决定由哪种「大脑」驱动对话
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type: str = "prompt"
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greeting: str = "您好,我是 AI 视频助手,请问有什么可以帮您?"
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prompt: str = "你是一个有帮助的助手。"
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dynamic_variable_definitions: dict = Field(default_factory=dict)
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dynamic_variables: dict = Field(default_factory=dict)
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secret_dynamic_variables: dict = Field(default_factory=dict)
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conversation_id: str = ""
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runtimeMode: RuntimeMode = "pipeline"
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# 模型/音色选项
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model: str = "" # LLM
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asr: str = "" # STT
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tts_model: str = ""
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voice: str = "" # TTS 音色
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stt_language: str = ""
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tts_speed: float = 1.0
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realtimeModel: str = ""
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realtime_interface_type: str = ""
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realtime_values: dict = {}
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realtime_secrets: dict = {}
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agent_interface_type: str = ""
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agent_values: dict = {}
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agent_secrets: dict = {}
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llm_interface_type: str = "openai-llm"
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stt_interface_type: str = "openai-asr"
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tts_interface_type: str = "openai-tts"
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llm_values: dict = {}
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llm_secrets: dict = {}
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llm_support_image_input: bool = False
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vision_enabled: bool = False
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vision_model_resource_id: str | None = None
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vision_model: str = ""
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vision_llm_interface_type: str = "openai-llm"
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vision_llm_values: dict = {}
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vision_llm_secrets: dict = {}
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vision_llm_support_image_input: bool = False
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vision_llm_api_key: str = ""
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vision_llm_base_url: str = ""
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stt_values: dict = {}
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stt_secrets: dict = {}
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tts_values: dict = {}
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tts_secrets: dict = {}
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enableInterrupt: bool = True
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turnConfig: dict = Field(default_factory=dict)
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# Prompt assistant reusable tools. Execution remains type-specific in the pipeline.
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tools: list[RuntimeTool] = Field(default_factory=list)
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knowledge_base_id: str | None = None
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knowledge_base_name: str = ""
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knowledge_base_description: str = ""
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knowledge_retrieval_config: dict = Field(
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default_factory=lambda: {
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"mode": "automatic",
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"top_n": 5,
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"score_threshold": 0.0,
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}
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)
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# workflow 类型:节点图(nodes/edges)。非 workflow 为空,引擎据此决定是否启用。
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graph: dict = {}
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# 外部托管类型(fastgpt/dify/opencode)的连接信息:context/KB/tools 由对方服务端接管。
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dify_api_url: str = ""
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dify_api_key: str = ""
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fastgpt_api_url: str = ""
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fastgpt_api_key: str = ""
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fastgpt_app_id: str = ""
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# ---- 运行时连接信息(服务端注入,不来自浏览器) ----
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# 由模型资源注入;调试 inline_config 也必须显式提供。
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llm_api_key: str = ""
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llm_base_url: str = ""
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stt_api_key: str = ""
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stt_base_url: str = ""
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tts_api_key: str = ""
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tts_base_url: str = ""
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realtime_api_key: str = ""
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realtime_base_url: str = ""
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class SignalingOffer(BaseModel):
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"""WS 信令里 offer 消息的 payload。
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推荐用 assistant_id(浏览器只传 id,key 在服务端解析);
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inline_config 仅用于调试/无库场景。
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"""
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pc_id: str | None = None
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sdp: str
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type: str
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assistant_id: str | None = None
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inline_config: AssistantConfig | None = None
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vision_enabled: bool = False
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dynamic_variables: dict = Field(default_factory=dict)
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