mirror of
https://github.com/dograh-hq/dograh.git
synced 2026-06-13 08:15:21 +02:00
feat: refactor node spec and add mcp tools (#244)
* refactor: carve out extraction panel * refactor: create spec versions for node types * refactor: create a GenericNode and remove custom nodes * feat: add python and typescript sdk * add dograh sdk * fix: fetch draft workflow definition over published one * fix: fix routes of SDKs to use code gen * chore: remove doclink dependency to reduce image size * chore: format files * chore: bump pipecat * feat: let mcp fetch archived workflows on demand * chore: fix tests * feat: add sdk documentation * chore: change banner and add badge
This commit is contained in:
Abhishek
GitHub
parent
0a61ef295f
commit
00a1a22b74
162 changed files with 14355 additions and 3554 deletions
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@ -1,5 +1,5 @@
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from enum import Enum
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from typing import List, Optional
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from typing import Annotated, List, Literal, Optional, Union
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from pydantic import BaseModel, Field, ValidationError, model_validator
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@ -42,17 +42,48 @@ class RetryConfigDTO(BaseModel):
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retry_delay_seconds: int = 5
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class NodeDataDTO(BaseModel):
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# ─────────────────────────────────────────────────────────────────────────
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# Per-type node data classes.
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#
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# Shared fields are factored out as Pydantic mixins; per-type classes
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# inherit only the mixins they need so mistyped fields raise at validation
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# time and downstream consumers get accurate types. `is_start` / `is_end`
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# live on every variant so the WorkflowGraph can identify boundary nodes
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# without dispatching on type.
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# ─────────────────────────────────────────────────────────────────────────
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class _NodeDataBase(BaseModel):
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name: str = Field(..., min_length=1)
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prompt: Optional[str] = Field(default=None)
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is_static: bool = False
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is_start: bool = False
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is_end: bool = False
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class _PromptedNodeDataMixin(BaseModel):
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prompt: Optional[str] = Field(default=None)
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is_static: bool = False
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allow_interrupt: bool = False
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add_global_prompt: bool = True
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class _ExtractionNodeDataMixin(BaseModel):
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extraction_enabled: bool = False
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extraction_prompt: Optional[str] = None
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extraction_variables: Optional[list[ExtractionVariableDTO]] = None
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add_global_prompt: bool = True
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class _ToolDocumentRefsMixin(BaseModel):
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tool_uuids: Optional[List[str]] = None
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document_uuids: Optional[List[str]] = None
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class StartCallNodeData(
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_NodeDataBase,
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_PromptedNodeDataMixin,
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_ExtractionNodeDataMixin,
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_ToolDocumentRefsMixin,
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):
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is_start: bool = True
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greeting: Optional[str] = None
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greeting_type: Optional[str] = None # 'text' or 'audio'
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greeting_recording_id: Optional[str] = None
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@ -61,14 +92,38 @@ class NodeDataDTO(BaseModel):
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detect_voicemail: bool = False
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delayed_start: bool = False
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delayed_start_duration: Optional[float] = None
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# Pre-call fetch (start node only)
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pre_call_fetch_enabled: bool = False
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pre_call_fetch_url: Optional[str] = None
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pre_call_fetch_credential_uuid: Optional[str] = None
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tool_uuids: Optional[List[str]] = None
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document_uuids: Optional[List[str]] = None
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class AgentNodeData(
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_NodeDataBase,
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_PromptedNodeDataMixin,
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_ExtractionNodeDataMixin,
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_ToolDocumentRefsMixin,
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):
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pass
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class EndCallNodeData(
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_NodeDataBase,
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_PromptedNodeDataMixin,
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_ExtractionNodeDataMixin,
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):
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is_end: bool = True
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class GlobalNodeData(_NodeDataBase, _PromptedNodeDataMixin):
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pass
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class TriggerNodeData(_NodeDataBase):
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trigger_path: Optional[str] = None
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# Webhook node specific fields
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enabled: bool = True
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class WebhookNodeData(_NodeDataBase):
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enabled: bool = True
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http_method: Optional[str] = None
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endpoint_url: Optional[str] = None
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@ -76,30 +131,129 @@ class NodeDataDTO(BaseModel):
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custom_headers: Optional[list[CustomHeaderDTO]] = None
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payload_template: Optional[dict] = None
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retry_config: Optional[RetryConfigDTO] = None
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# QA node specific fields
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class QANodeData(_NodeDataBase):
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qa_enabled: bool = True
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qa_system_prompt: Optional[str] = None
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qa_use_workflow_llm: bool = True
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qa_provider: Optional[str] = None
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qa_model: Optional[str] = None
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qa_api_key: Optional[str] = None
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qa_endpoint: Optional[str] = None
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qa_system_prompt: Optional[str] = None
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qa_min_call_duration: int = 15
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qa_voicemail_calls: bool = False
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qa_sample_rate: int = 100
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class RFNodeDTO(BaseModel):
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# Union of every per-type data class — useful as a type annotation on
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# consumers that handle any node data without dispatching on type. Cannot
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# be called as a constructor; use the per-type class directly.
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NodeDataDTO = Union[
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StartCallNodeData,
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AgentNodeData,
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EndCallNodeData,
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GlobalNodeData,
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TriggerNodeData,
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WebhookNodeData,
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QANodeData,
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]
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# ─────────────────────────────────────────────────────────────────────────
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# Per-type RF nodes.
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#
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# RFNodeDTO is a discriminated Union over `type`. Pydantic dispatches to
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# the right variant when validating wire JSON. Direct instantiation must
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# use the concrete per-type class (StartCallRFNode, AgentRFNode, ...).
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# ─────────────────────────────────────────────────────────────────────────
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class _RFNodeBase(BaseModel):
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id: str
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type: NodeType = Field(default=NodeType.agentNode)
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position: Position
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data: NodeDataDTO
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def _require_prompt(data, type_label: str) -> None:
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prompt = getattr(data, "prompt", None)
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if not prompt or len(prompt.strip()) == 0:
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raise ValueError(f"Prompt is required for {type_label} nodes")
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class StartCallRFNode(_RFNodeBase):
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type: Literal["startCall"] = "startCall"
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data: StartCallNodeData
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@model_validator(mode="after")
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def _validate_prompt_required(self):
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"""Require prompt for all node types except trigger, webhook, and qa."""
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if self.type not in (NodeType.trigger, NodeType.webhook, NodeType.qa):
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if not self.data.prompt or len(self.data.prompt.strip()) == 0:
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raise ValueError("Prompt is required for non-trigger nodes")
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def _validate(self):
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_require_prompt(self.data, "start")
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return self
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class AgentRFNode(_RFNodeBase):
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type: Literal["agentNode"] = "agentNode"
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data: AgentNodeData
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@model_validator(mode="after")
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def _validate(self):
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_require_prompt(self.data, "agent")
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return self
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class EndCallRFNode(_RFNodeBase):
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type: Literal["endCall"] = "endCall"
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data: EndCallNodeData
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@model_validator(mode="after")
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def _validate(self):
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_require_prompt(self.data, "end")
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return self
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class GlobalRFNode(_RFNodeBase):
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type: Literal["globalNode"] = "globalNode"
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data: GlobalNodeData
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@model_validator(mode="after")
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def _validate(self):
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_require_prompt(self.data, "global")
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return self
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class TriggerRFNode(_RFNodeBase):
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type: Literal["trigger"] = "trigger"
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data: TriggerNodeData
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class WebhookRFNode(_RFNodeBase):
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type: Literal["webhook"] = "webhook"
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data: WebhookNodeData
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class QARFNode(_RFNodeBase):
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type: Literal["qa"] = "qa"
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data: QANodeData
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RFNodeDTO = Annotated[
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Union[
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StartCallRFNode,
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AgentRFNode,
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EndCallRFNode,
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GlobalRFNode,
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TriggerRFNode,
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WebhookRFNode,
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QARFNode,
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],
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Field(discriminator="type"),
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]
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# ─────────────────────────────────────────────────────────────────────────
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# Edges
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# ─────────────────────────────────────────────────────────────────────────
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class EdgeDataDTO(BaseModel):
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label: str = Field(..., min_length=1)
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condition: str = Field(..., min_length=1)
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@ -144,3 +298,60 @@ class ReactFlowDTO(BaseModel):
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)
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return self
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# Node type → per-type data class. Keeps sanitize_workflow_definition in
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# step with RFNodeDTO's discriminated union.
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_NODE_DATA_CLASSES: dict[str, type[BaseModel]] = {
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NodeType.startNode.value: StartCallNodeData,
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NodeType.agentNode.value: AgentNodeData,
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NodeType.endNode.value: EndCallNodeData,
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NodeType.globalNode.value: GlobalNodeData,
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NodeType.trigger.value: TriggerNodeData,
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NodeType.webhook.value: WebhookNodeData,
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NodeType.qa.value: QANodeData,
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}
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def sanitize_workflow_definition(definition: dict | None) -> dict | None:
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"""Strip unknown fields from each node.data and edge.data so UI-only
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runtime state (`invalid`, `validationMessage`, etc.) doesn't leak into
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persisted workflow JSON.
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Only `.data` is filtered — top-level keys on nodes/edges/definition
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(viewport, ReactFlow-computed width/height, etc.) are preserved as-is.
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This is a stripper, not a validator: it doesn't enforce required fields
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or run model_validators, so partial drafts save cleanly.
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"""
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if not definition:
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return definition
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out = dict(definition)
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raw_nodes = out.get("nodes")
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if isinstance(raw_nodes, list):
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out["nodes"] = [_sanitize_node(n) for n in raw_nodes]
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raw_edges = out.get("edges")
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if isinstance(raw_edges, list):
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out["edges"] = [_sanitize_edge(e) for e in raw_edges]
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return out
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def _sanitize_node(node):
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if not isinstance(node, dict):
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return node
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data_cls = _NODE_DATA_CLASSES.get(node.get("type"))
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raw_data = node.get("data")
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if not data_cls or not isinstance(raw_data, dict):
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return node
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allowed = data_cls.model_fields.keys()
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return {**node, "data": {k: v for k, v in raw_data.items() if k in allowed}}
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def _sanitize_edge(edge):
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if not isinstance(edge, dict):
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return edge
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raw_data = edge.get("data")
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if not isinstance(raw_data, dict):
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return edge
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allowed = EdgeDataDTO.model_fields.keys()
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return {**edge, "data": {k: v for k, v in raw_data.items() if k in allowed}}
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