mirror of
https://github.com/agentscope-ai/ReMe.git
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- add neo4j dependency to project requirements - introduce NetworkXFileGraph to replace LocalFileGraph implementation - rename local_file_graph.py to networkx_file_graph.py with updated component registration as 'networkx' - remove pickle persistence logic from NetworkX backend, simplify initialization - update Neo4jFileGraph to return FileLink objects instead of (FileNode, FileLink) tuples from get_inlinks/get_outlinks methods - remove unused AsyncIterator import and adjust method signatures - add BareFileParser for handling binary/attachment files without content parsing - move wikilink resolution utilities to dedicated utility module - refactor memory I/O to use file graph's link resolution methods directly - remove link extraction utilities from schema module, consolidate in utils.wikilink_resolver
217 lines
8.3 KiB
Python
217 lines
8.3 KiB
Python
"""Smart Ingestor — ReAct agent over the Memory File System.
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Per `structure.md` L31-44, the Ingestor is the SSOT engine: the **single
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write entry point** to the markdown vault. Every mutation (create, body
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edit, frontmatter flip, rename, delete, archive) flows through here.
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Mirrors `Summarizer`'s pattern — drives a `ReActAgent` whose toolkit is
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built by `agent_toolkit.build_agent_toolkit`. The agent runs its own
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R-M-W loop: read related files via tools, decide which ones to mutate,
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call the right write tool. Every write tool records into an audit list,
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so the caller gets a deterministic mutation trail regardless of how the
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agent's reasoning unfolded.
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When no LLM is configured, falls back to a direct create from
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`target_path` + `metadata` + `content`. Edits/renames/deletes are not
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available without an LLM.
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"""
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from __future__ import annotations
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import datetime
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import json
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import zoneinfo
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from pathlib import Path
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from agentscope.agent import ReActAgent
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from agentscope.message import Msg
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from agentscope.tool import Toolkit
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from pydantic import BaseModel, Field
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from .runtime_response import _set_answer, _to_jsonable
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from . import memory_io
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from .memory_io import create_file
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from .agent_toolkit import build_agent_toolkit
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from ..component import R
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from ..component.base_step import BaseStep
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from ..enumeration import ComponentEnum
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from ..utils.wikilink_resolver import extract_wikilinks
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class IngestResult(BaseModel):
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"""Audit trail for a single ingest call."""
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applied: list[dict] = Field(default_factory=list, description="Successful ops with paths.")
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rejected: list[dict] = Field(default_factory=list, description="Ops the validator refused.")
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failed: list[dict] = Field(default_factory=list, description="Ops that errored at apply time.")
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skipped: bool = Field(default=False, description="True if the LLM returned a SkipOp.")
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used_llm: bool = Field(default=False, description="False = degraded path (no LLM configured).")
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@property
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def success(self) -> bool:
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"""Skipped or any-applied with no failures = success."""
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if self.skipped:
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return True
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return len(self.applied) > 0 and len(self.failed) == 0
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@R.register("ingestor")
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class Ingestor(BaseStep):
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"""R-M-W ingestor exposed as a single-write-entry MCP tool.
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Inputs (read from RuntimeContext):
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content (str, required): the material being ingested.
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hint (str, optional): caller guidance to the LLM.
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target_path (str, optional): suggested file path; required for
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the degraded path (no LLM).
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metadata (dict, optional): suggested frontmatter; used by
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the degraded path or as a hint to the LLM.
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related_paths (list[str], optional): explicit related files,
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auto-extended with wikilinks parsed from `content`.
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Output (written to context.response.answer):
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IngestResult JSON — applied/failed lists plus used_llm flag and
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the agent's final-message summary.
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"""
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def __init__(
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self,
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toolkit: Toolkit | None = None,
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console_enabled: bool = False,
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timezone: str | None = None,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.toolkit = toolkit
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self.console_enabled = console_enabled
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self.timezone = timezone
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self._protocol = (Path(__file__).parent / "protocol.md").read_text(encoding="utf-8")
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def _now(self) -> datetime.datetime:
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if self.timezone:
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try:
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return datetime.datetime.now(zoneinfo.ZoneInfo(self.timezone))
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except Exception as e:
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self.logger.error(f"Invalid timezone: {self.timezone}, error={e}")
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return datetime.datetime.now()
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def _working_dir(self) -> Path:
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vr = getattr(self.file_store, "working_dir", None)
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return Path(vr).resolve() if vr else Path.cwd().resolve()
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async def execute(self):
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assert self.context is not None
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mode: str = (self.context.get("mode") or "distill").lower()
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if mode == "log":
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await self._delegate_to_sync()
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return
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if mode != "distill":
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self.context.response.success = False
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_set_answer(
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self.context,
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{
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"error": f"unknown mode {mode!r}; expected 'log' or 'distill'",
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},
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)
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return
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content: str = self.context.get("content", "") or ""
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hint: str = self.context.get("hint", "") or ""
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target_path: str = self.context.get("target_path") or ""
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metadata: dict = dict(self.context.get("metadata") or {})
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related_paths: list[str] = list(self.context.get("related_paths") or [])
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assert content, "content is required"
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# Auto-discover wikilink targets in content as a hint for the agent.
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for link in extract_wikilinks(content):
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hit = memory_io.resolve_wikilink(self.file_store, link)["path"]
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if hit and hit not in related_paths:
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related_paths.append(hit)
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as_llm = self._get_component_optional(ComponentEnum.AS_LLM, "default", "model")
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if as_llm is None:
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result = self._degraded(target_path, metadata, content)
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self.context.response.success = result.success
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_set_answer(self.context, result.model_dump())
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return
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working_dir = self._working_dir()
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audit: list[dict] = []
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toolkit = build_agent_toolkit(self.app_context, audit=audit, toolkit=self.toolkit)
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agent = ReActAgent(
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name="reme_ingestor",
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model=self.as_llm,
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sys_prompt=self.prompt_format(
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"system_prompt",
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working_dir=str(working_dir),
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protocol=self._protocol,
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),
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formatter=self.as_llm_formatter,
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toolkit=toolkit,
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)
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agent.set_console_output_enabled(self.console_enabled)
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user_message: str = self.prompt_format(
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"user_message",
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today=self._now().strftime("%Y-%m-%d"),
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working_dir=str(working_dir),
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hint=hint or "(none)",
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target_path=target_path or "(none)",
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metadata=json.dumps(_to_jsonable(metadata), ensure_ascii=False),
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related=json.dumps(related_paths, ensure_ascii=False),
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content=content,
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)
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final_msg: Msg = await agent.reply(
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Msg(name="reme", role="user", content=user_message),
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)
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summary = final_msg.get_text_content() or ""
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result = IngestResult(used_llm=True)
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for entry in audit:
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(result.applied if entry.get("ok") else result.failed).append(entry)
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if not audit and summary.strip().upper().startswith("SKIP"):
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result.skipped = True
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self.context.response.success = result.success
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payload = result.model_dump()
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payload["agent_summary"] = summary
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_set_answer(self.context, payload)
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def _degraded(self, target_path: str, metadata: dict, content: str) -> IngestResult:
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"""Without an LLM, only direct create from explicit target_path is
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supported. Useful for tests and bootstrap scripts."""
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result = IngestResult(used_llm=False)
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if not target_path:
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result.failed.append(
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{
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"op": "create",
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"ok": False,
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"error": "degraded path: target_path is required when no LLM is configured",
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}
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)
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return result
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path = Path(target_path)
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if not path.is_absolute():
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path = self._working_dir() / path
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ok, payload = create_file(
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self.file_store,
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path,
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metadata=metadata,
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content=content,
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)
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bucket = result.applied if ok else result.failed
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bucket.append({"op": "create", "ok": ok, "path": str(path), "result": payload})
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return result
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async def _delegate_to_sync(self) -> None:
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"""Hot-path event-folder upsert — same code path as the standalone
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`sync` step. Lazy-instantiated so we don't pay the construction
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cost on every distill call."""
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from .sync import Sync
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if getattr(self, "_sync_step", None) is None:
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self._sync_step = Sync(app_context=self.app_context)
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await self._sync_step(self.context)
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