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Source code for airflow.providers.common.ai.operators.llm_branch

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"""LLM-driven branching operator."""

from __future__ import annotations

import json
from collections.abc import Iterable, Sequence
from enum import Enum
from typing import TYPE_CHECKING, Any

from airflow.providers.common.ai.operators.llm import LLMOperator
from airflow.providers.common.ai.utils.logging import log_run_summary
from airflow.providers.standard.operators.branch import BranchMixIn

if TYPE_CHECKING:
    from airflow.sdk import Context


[docs] class LLMBranchOperator(LLMOperator, BranchMixIn): """ Ask an LLM to choose which downstream task(s) to execute. Downstream task IDs are discovered automatically from the DAG topology and presented to the LLM as a constrained enum via pydantic-ai structured output. No text parsing or manual validation is needed. :param prompt: The prompt to send to the LLM. :param llm_conn_id: Connection ID for the LLM provider. :param model_id: Model identifier (e.g. ``"openai:gpt-5"``). Overrides the model stored in the connection's extra field. :param system_prompt: System-level instructions for the LLM agent. :param allow_multiple_branches: When ``False`` (default) the LLM returns a single task ID. When ``True`` the LLM may return one or more task IDs. :param agent_params: Additional keyword arguments passed to the pydantic-ai ``Agent`` constructor (e.g. ``retries``, ``model_settings``, ``tools``). Human-in-the-Loop approval parameters are inherited from :class:`~airflow.providers.common.ai.operators.llm.LLMOperator` (``require_approval``, ``approval_timeout``, ``allow_modifications``). The task pauses after the LLM chooses the branch(es) and only skips the unselected downstream tasks once a reviewer approves. The review form lists the valid downstream task IDs; with ``allow_modifications=True`` the editable choice is rendered as a dropdown of those IDs (single-branch mode) or a multi-select of them (``allow_multiple_branches=True``), and the reviewed branch(es) are validated against the downstream task IDs before branching. """
[docs] inherits_from_skipmixin = True
[docs] template_fields: Sequence[str] = LLMOperator.template_fields
def __init__( self, *, allow_multiple_branches: bool = False, **kwargs: Any, ) -> None: kwargs.pop("output_type", None) super().__init__(**kwargs)
[docs] self.allow_multiple_branches = allow_multiple_branches
[docs] def execute(self, context: Context) -> str | Iterable[str] | None: if self.require_approval: self.validate_approval_prompt() # type: ignore[misc] if not self.downstream_task_ids: raise ValueError( f"{self.task_id!r} has no downstream tasks. " "LLMBranchOperator requires at least one downstream task to branch into." ) downstream_tasks_enum = Enum( # type: ignore[misc] "DownstreamTasks", {task_id: task_id for task_id in self.downstream_task_ids}, ) output_type = list[downstream_tasks_enum] if self.allow_multiple_branches else downstream_tasks_enum agent = self.llm_hook.create_agent( output_type=output_type, instructions=self.system_prompt, **self.agent_params, ) result = agent.run_sync(self.prompt, usage_limits=self.usage_limits) log_run_summary(self.log, result) output = result.output branches: str | list[str] if isinstance(output, list): branches = [item.value for item in output] elif isinstance(output, Enum): branches = output.value else: branches = str(output) if not branches: raise ValueError( f"LLM selected no branches for {self.task_id!r}, which would skip every downstream task." ) if self.require_approval: choices = sorted(self.downstream_task_ids) chosen = branches if isinstance(branches, str) else json.dumps(branches) body = ( f"Valid branches: {', '.join(f'`{c}`' for c in choices)}\n\n" f"```\nPrompt: {self.prompt}\n\nChosen branch(es): {chosen}\n```" ) modification_schema = ( {"type": "array", "items": {"type": "string", "enum": choices}, "examples": choices} if self.allow_multiple_branches else {"type": "string", "enum": choices} ) self.defer_for_approval( # type: ignore[misc] context, branches, body=body, modification_schema=modification_schema ) return self.do_branch(context, branches)
[docs] def execute_complete(self, context: Context, generated_output: str, event: dict[str, Any]) -> Any: """Resume after human review, validating the reviewed choice before branching.""" output = super().execute_complete(context, generated_output, event) branches = self._parse_reviewed_branches(output) selected = {branches} if isinstance(branches, str) else set(branches) invalid = selected - self.downstream_task_ids if invalid: raise ValueError( f"Reviewed branch(es) {sorted(invalid)} are not downstream tasks of " f"{self.task_id!r}. Valid choices: {sorted(self.downstream_task_ids)}." ) return self.do_branch(context, branches)
def _parse_reviewed_branches(self, output: str) -> str | list[str]: if not self.allow_multiple_branches: return output try: branches = json.loads(output) except json.JSONDecodeError as e: raise ValueError( f"Reviewed output {output!r} is not valid JSON. With " f"allow_multiple_branches=True the reviewed output must be a " f'JSON list of task IDs, e.g. ["task_a", "task_b"].' ) from e if not isinstance(branches, list) or not all(isinstance(b, str) for b in branches): raise ValueError( f"Reviewed output {output!r} must be a JSON list of task ID strings, " f'e.g. ["task_a", "task_b"].' ) if not branches: raise ValueError( "Reviewed output selects no branches, which would skip every downstream " "task. Select at least one task ID, or reject the review instead." ) return branches

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