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twwu
4c1c41c2ea
Recognize initial templates, generate graphs in the app mode, and replace default nodes atomically after approval. Normalize node ordering in execution revisions to prevent false 409 conflicts, with frontend and backend regression coverage.
93 lines
4.0 KiB
Python
93 lines
4.0 KiB
Python
"""Recognize the unconfigured Chatflow template before replacing its nodes.
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Canvas presentation is irrelevant. Model identity and empty memory role prefixes
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can be filled by the editor without user configuration. Other configuration must
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still match the initial template. Keep the frontend Builder classifier aligned.
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"""
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from core.dify_builder.models import Graph, MutationIntent
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def _initial_data(data: dict) -> dict:
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return {
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key: value
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for key, value in data.items()
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if not key.startswith("_") and key not in {"title", "selected"} and not (key == "desc" and not value)
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}
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def is_initial_chatflow(graph: Graph, *, mode: str) -> bool:
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nodes, edges = graph.get("nodes", []), graph.get("edges", [])
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if mode != "advanced-chat" or len(nodes) != 3 or len(edges) != 2 or any(n.get("parentId") for n in nodes):
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return False
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by_type = {node.get("data", {}).get("type"): node for node in nodes}
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if set(by_type) != {"start", "llm", "answer"}:
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return False
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start, llm, answer = (by_type[kind] for kind in ("start", "llm", "answer"))
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if not all(node.get("id") for node in (start, llm, answer)):
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return False
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for source, target in ((start["id"], llm["id"]), (llm["id"], answer["id"])):
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if not any(
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edge.get("source") == source
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and edge.get("target") == target
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and edge.get("sourceHandle") in (None, "", "source")
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and edge.get("targetHandle") in (None, "", "target")
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for edge in edges
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):
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return False
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llm_data = _initial_data(llm["data"])
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model, memory = llm_data.get("model"), llm_data.get("memory")
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if not isinstance(model, dict) or not isinstance(memory, dict):
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return False
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llm_data["model"] = {key: value for key, value in model.items() if key not in {"provider", "name"}}
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llm_data["memory"] = {
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key: value for key, value in memory.items() if key != "role_prefix" or value != {"user": "", "assistant": ""}
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}
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return (
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_initial_data(start["data"]) == {"type": "start", "variables": []}
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and _initial_data(answer["data"])
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== {"type": "answer", "variables": [], "answer": f"{{{{#{llm['id']}.text#}}}}"}
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and llm_data
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== {
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"type": "llm",
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"model": {"mode": "chat", "completion_params": {"temperature": 0.7}},
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"prompt_template": [{"role": "system", "text": ""}],
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"context": {"enabled": False, "variable_selector": []},
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"vision": {"enabled": False},
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"memory": {
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"window": {"enabled": False, "size": 10},
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"query_prompt_template": "{{#sys.query#}}\n\n{{#sys.files#}}",
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},
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}
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)
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def build_intents_for_draft(graph: Graph, intents: list[MutationIntent], *, mode: str) -> list[MutationIntent]:
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"""Replace initial placeholders; retain already-built nodes on reapproval.
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Delete and create stay in one apply_repair batch. Deleted IDs and their
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incident edges must not suppress generated intents that reuse those IDs.
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"""
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nodes = graph.get("nodes", [])
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replace_initial = is_initial_chatflow(graph, mode=mode) or all(
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node.get("data", {}).get("type") == "start" for node in nodes
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)
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deletes = (
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[MutationIntent(op="delete_node", args={"node_id": node["id"]}) for node in nodes] if replace_initial else []
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)
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deleted_ids = {intent.args["node_id"] for intent in deletes}
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existing_ids = {node["id"] for node in nodes} - deleted_ids
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existing_edges = {
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(edge.get("source"), edge.get("target"))
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for edge in graph.get("edges", [])
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if edge.get("source") not in deleted_ids and edge.get("target") not in deleted_ids
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}
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return deletes + [
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intent
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for intent in intents
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if not (
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(intent.op == "create_node" and intent.args.get("node_id") in existing_ids)
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or (intent.op == "connect" and (intent.args.get("from_node"), intent.args.get("to_node")) in existing_edges)
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)
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]
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