448 lines
16 KiB
Python
448 lines
16 KiB
Python
import asyncio
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import atexit
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import concurrent.futures
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import json
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import os
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from collections.abc import Callable
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from dataclasses import dataclass
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from pathlib import Path
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from typing import TYPE_CHECKING, Any, Literal
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import pydantic_monty
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from pydantic_monty import CallbackFile, MemoryFile, MontyRepl, OSAccess
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from haiku.rag.config.models import AppConfig
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from haiku.rag.sandbox.dependencies import AnalysisContext
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from haiku.rag.store.models.chunk import SearchResult
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from haiku.rag.store.models.document_item import PICTURE_REF_PREFIX, DocumentItem
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if TYPE_CHECKING:
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from pathlib import PurePosixPath
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@dataclass
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class SandboxResult:
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"""Result of executing code in the sandbox."""
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stdout: str
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stderr: str
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success: bool
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_executor = concurrent.futures.ThreadPoolExecutor(max_workers=1)
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atexit.register(_executor.shutdown, wait=False)
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def _run_async(coro: Any) -> Any:
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"""Run an async coroutine from a sync context (CallbackFile read)."""
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return _executor.submit(asyncio.run, coro).result()
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def _build_toc(
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items: list["DocumentItem"],
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chunk_index: dict[str, list[str]],
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) -> list[dict[str, Any]]:
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"""Build a nested section tree from items in position order.
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Each ``section_header`` with ``heading_level > 0`` becomes a node. Nesting
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follows the explicit levels: a header pops the stack until the top is at
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a strictly shallower level, then becomes a child of that top (or a root).
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``item_range = [position, end_exclusive]`` where ``end_exclusive`` is the
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position of the next header whose level is the same or shallower (i.e.
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the next sibling or ancestor that ends this section), or the total item
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count if no such header exists.
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``chunk_ids`` aggregates the chunks covered by all items in the section's
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``item_range`` (deduped, order preserved). Pass directly to ``cite()`` to
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ground a section-scoped answer without a corpus-wide ``search()`` call.
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Items without a section_header label (or with ``heading_level == 0``) are
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skipped. When all section_headers carry the same level the output is a
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flat sibling list (see docling-project/docling#2121 for an upstream case
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where every PDF section_header is emitted at level=1).
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"""
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# Defensive: every consumer is supposed to pass items in position order,
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# but the end_exclusive lookahead below silently miscomputes section
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# boundaries if it's not — better to sort once than trust the caller.
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items = sorted(items, key=lambda i: i.position)
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headers: list[DocumentItem] = [
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i for i in items if i.label == "section_header" and i.heading_level > 0
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]
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if not headers:
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return []
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total = max((i.position for i in items), default=-1) + 1
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items_by_position: dict[int, DocumentItem] = {i.position: i for i in items}
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ends: list[int] = []
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for idx, h in enumerate(headers):
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end = total
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for j in range(idx + 1, len(headers)):
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if headers[j].heading_level <= h.heading_level:
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end = headers[j].position
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break
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ends.append(end)
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roots: list[dict[str, Any]] = []
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stack: list[tuple[int, dict[str, Any]]] = []
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for h, end in zip(headers, ends, strict=True):
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seen: set[str] = set()
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chunk_ids: list[str] = []
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for pos in range(h.position, end):
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item = items_by_position.get(pos)
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if item is None:
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continue
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for cid in chunk_index.get(item.self_ref, []):
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if cid not in seen:
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seen.add(cid)
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chunk_ids.append(cid)
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node: dict[str, Any] = {
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"self_ref": h.self_ref,
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"level": h.heading_level,
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"title": h.text,
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"page_numbers": list(h.page_numbers),
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"item_range": [h.position, end],
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"chunk_ids": chunk_ids,
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"children": [],
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}
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while stack and stack[-1][0] >= h.heading_level:
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stack.pop()
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(stack[-1][1]["children"] if stack else roots).append(node)
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stack.append((h.heading_level, node))
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return roots
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class Sandbox:
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"""Execute code in a sandboxed Python interpreter.
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Uses pydantic-monty, a minimal secure Python interpreter written in Rust.
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External functions (search, list_documents) are called by Monty code
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using ``await`` and resolved asynchronously on the host. Documents are
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exposed via a virtual filesystem at ``/documents/{id}/``.
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The interpreter uses a REPL session — variables persist across
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``execute()`` calls within the same Sandbox instance.
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sandbox = Sandbox(db_path, config, context)
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result = await sandbox.execute("x = await search('query')")
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result = await sandbox.execute("print(x[0]['content'])") # x persists
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"""
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_db_path: Path
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_config: AppConfig
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_context: AnalysisContext
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_search_results: "list[SearchResult]"
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_doc_items: dict[str, list["DocumentItem"]]
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_doc_chunk_index: dict[str, dict[str, list[str]]]
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_items_jsonl_cache: dict[str, str]
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_toc_json_cache: dict[str, str]
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_repl: MontyRepl | None
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_vfs: OSAccess | None
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def __init__(
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self,
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db_path: Path,
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config: AppConfig,
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context: AnalysisContext,
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):
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self._db_path = db_path
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self._config = config
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self._context = context
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self._search_results = []
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self._doc_items = {}
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self._doc_chunk_index = {}
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self._items_jsonl_cache = {}
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self._toc_json_cache = {}
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self._repl = None
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self._vfs = None
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def _build_external_functions(self) -> dict[str, Any]:
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"""Build async external functions for the Monty interpreter."""
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db_path = self._db_path
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config = self._config
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context = self._context
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async def search(query: str, limit: int = 10) -> list[dict[str, Any]]:
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# Picture bytes are deliberately not attached to in-code search
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# results: the Monty interpreter has no PIL/base64/hashlib, so the
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# agent's Python can't do anything with them. The driving model
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# gets figures through the top-level `search` tool when the
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# question is visual; in-code search is for structural work.
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from haiku.rag.client import HaikuRAG
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async with HaikuRAG(db_path, config=config, read_only=True) as rag:
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results = await rag.search(query, limit=limit, filter=context.filter)
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expanded = await rag.expand_context(results)
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self._search_results.extend(expanded)
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out: list[dict[str, Any]] = []
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for r in expanded:
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picture_refs = [
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ref for ref in r.doc_item_refs if ref.startswith(PICTURE_REF_PREFIX)
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]
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out.append(
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{
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"chunk_id": r.chunk_id,
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"content": r.content,
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"document_id": r.document_id,
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"document_title": r.document_title,
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"document_uri": r.document_uri,
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"score": r.score,
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"page_numbers": r.page_numbers,
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"headings": r.headings,
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"doc_item_refs": r.doc_item_refs,
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"labels": r.labels,
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"picture_refs": picture_refs,
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}
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)
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return out
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async def list_documents() -> list[dict[str, Any]]:
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from haiku.rag.client import HaikuRAG
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async with HaikuRAG(db_path, config=config, read_only=True) as rag:
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docs = await rag.list_documents(filter=context.filter)
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return [
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{
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"id": d.id,
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"title": d.title,
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"uri": d.uri,
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"created_at": str(d.created_at),
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}
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for d in docs
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]
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return {
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"search": search,
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"list_documents": list_documents,
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}
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async def _build_vfs(self) -> OSAccess:
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"""Build the virtual filesystem with document data.
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Mounts per-document directories with:
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- metadata.json: MemoryFile (eager, small)
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- content.txt: CallbackFile (lazy, can be large)
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- items.jsonl: CallbackFile (lazy, bulk-cached)
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- toc.json: CallbackFile (lazy, bulk-cached)
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"""
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from haiku.rag.client import HaikuRAG
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db_path = self._db_path
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config = self._config
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files: list[MemoryFile | CallbackFile] = []
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def _deny_write(_path: "PurePosixPath", _content: str | bytes) -> None:
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raise PermissionError(f"Document files are read-only: {_path}")
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async with HaikuRAG(db_path, config=config, read_only=True) as rag:
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docs = await rag.list_documents(filter=self._context.filter)
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doc_titles = {doc.id: doc.title for doc in docs if doc.id}
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sandbox = self
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def _get_items(did: str) -> list[DocumentItem]:
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"""Fetch items for one doc, cached on the sandbox."""
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cached = sandbox._doc_items.get(did)
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if cached is not None:
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return cached
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async def _fetch() -> list[DocumentItem]:
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from haiku.rag.client import HaikuRAG
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async with HaikuRAG(db_path, config=config, read_only=True) as rag:
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return await rag.document_item_repository.get_all_items(did)
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items = _run_async(_fetch())
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sandbox._doc_items[did] = items
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return items
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def _get_chunk_index(did: str) -> dict[str, list[str]]:
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"""Fetch the self_ref → chunk_ids index for one doc, cached."""
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cached = sandbox._doc_chunk_index.get(did)
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if cached is not None:
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return cached
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async def _fetch() -> dict[str, list[str]]:
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from haiku.rag.client import HaikuRAG
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async with HaikuRAG(db_path, config=config, read_only=True) as rag:
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index = (
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await rag.chunk_repository.get_chunk_ids_by_self_ref_grouped(
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[did]
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)
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)
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return index.get(did, {})
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chunk_index = _run_async(_fetch())
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sandbox._doc_chunk_index[did] = chunk_index
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return chunk_index
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def _make_items_reader(
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did: str,
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) -> Callable[["PurePosixPath"], str]:
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def read_items(_path: "PurePosixPath") -> str:
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cached = sandbox._items_jsonl_cache.get(did)
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if cached is not None:
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return cached
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items = _get_items(did)
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chunk_index = _get_chunk_index(did)
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jsonl = "\n".join(
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json.dumps(
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{
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"self_ref": item.self_ref,
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"label": item.label,
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"text": item.text,
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"page_numbers": item.page_numbers,
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"heading_level": item.heading_level,
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"chunk_ids": chunk_index.get(item.self_ref, []),
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},
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ensure_ascii=False,
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)
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for item in items
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)
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sandbox._items_jsonl_cache[did] = jsonl
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return jsonl
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return read_items
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def _make_toc_reader(
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did: str,
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) -> Callable[["PurePosixPath"], str]:
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def read_toc(_path: "PurePosixPath") -> str:
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cached = sandbox._toc_json_cache.get(did)
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if cached is not None:
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return cached
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items = _get_items(did)
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chunk_index = _get_chunk_index(did)
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toc = json.dumps(
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{
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"doc_id": did,
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"title": doc_titles.get(did),
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"tree": _build_toc(items, chunk_index),
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},
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ensure_ascii=False,
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)
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sandbox._toc_json_cache[did] = toc
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return toc
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return read_toc
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for doc in docs:
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if not doc.id:
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continue
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doc_id: str = doc.id
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doc_dir = f"/documents/{doc_id}"
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metadata = json.dumps(
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{
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"id": doc_id,
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"title": doc.title,
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"uri": doc.uri,
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"created_at": str(doc.created_at),
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},
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ensure_ascii=False,
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)
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files.append(MemoryFile(f"{doc_dir}/metadata.json", metadata))
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def _make_content_reader(
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did: str,
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) -> Callable[["PurePosixPath"], str]:
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def read_content(_path: "PurePosixPath") -> str:
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async def _fetch() -> str:
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from haiku.rag.client import HaikuRAG
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async with HaikuRAG(
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db_path, config=config, read_only=True
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) as rag:
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content = await rag.document_repository.get_content(did)
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return content or ""
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return _run_async(_fetch())
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return read_content
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files.append(
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CallbackFile(
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f"{doc_dir}/content.txt",
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read=_make_content_reader(doc_id),
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write=_deny_write,
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)
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)
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files.append(
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CallbackFile(
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f"{doc_dir}/items.jsonl",
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read=_make_items_reader(doc_id),
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write=_deny_write,
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)
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)
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# HAIKU_RAG_DISABLE_TOC is an evaluation-time toggle for measuring
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# whether toc.json's outline view earns its place in the VFS.
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# Production callers should leave it unset.
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if not os.environ.get("HAIKU_RAG_DISABLE_TOC"):
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files.append(
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CallbackFile(
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f"{doc_dir}/toc.json",
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read=_make_toc_reader(doc_id),
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write=_deny_write,
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)
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)
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return OSAccess(files)
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async def _ensure_initialized(self) -> tuple[MontyRepl, OSAccess]:
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"""Initialize the REPL session and VFS on first use."""
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if self._repl is None:
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self._vfs = await self._build_vfs()
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self._repl = MontyRepl(
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limits={
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"max_duration_secs": self._config.analysis.code_timeout,
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},
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)
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assert self._repl is not None and self._vfs is not None
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return self._repl, self._vfs
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async def execute(self, code: str) -> SandboxResult:
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"""Execute Python code in the Monty REPL.
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Variables persist across calls within the same Sandbox instance.
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"""
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repl, vfs = await self._ensure_initialized()
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external_fns = self._build_external_functions()
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stdout_lines: list[str] = []
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def print_callback(_stream: Literal["stdout"], text: str) -> None:
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stdout_lines.append(text)
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max_chars = self._config.analysis.max_output_chars
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try:
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output = await repl.feed_run_async(
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code,
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external_functions=external_fns,
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print_callback=print_callback,
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os=vfs,
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)
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except (
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pydantic_monty.MontySyntaxError,
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pydantic_monty.MontyRuntimeError,
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) as e:
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stdout = "".join(stdout_lines)
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if len(stdout) > max_chars:
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stdout = stdout[:max_chars] + "\n... (output truncated)"
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return SandboxResult(stdout=stdout, stderr=str(e), success=False)
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stdout = "".join(stdout_lines)
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if output is not None:
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stdout_with_output = f"{stdout}{output}" if stdout else str(output)
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else:
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stdout_with_output = stdout
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if len(stdout_with_output) > max_chars:
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stdout_with_output = (
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stdout_with_output[:max_chars] + "\n... (output truncated)"
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)
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return SandboxResult(stdout=stdout_with_output, stderr="", success=True)
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