ducoterra
99c48cbe06
Phase 02 (story: import documents):
- fence-aware markdown chunker (heading sections, 200-char overlap,
heading anchor on every chunk, 1200-char hard cap, fence blocks
kept atomic and split under the cap)
- LLMClient over aipi (LiteLLM) reusing the openai client's httpx
transport to send a clean {model, input} payload — the openai SDK
injects encoding_format, which aipi's openai_like group rejects;
token-budget batching + halving retry for the endpoint's
~1024-token per-request input cap
- two-phase per-file upsert importer: sha256 delta (unchanged skip),
atomic commit, A9 exclusion walk, per-source prune, per-file error
tolerance (rollback + log + continue, non-zero CLI exit), adaptive
re-chunk at half target for URL-dense files the endpoint rejects
- scripts/import_docs CLI (repeatable --source, --prune, --limit,
defaults ~/Homelab + ~/Deployments)
- GET /api/docs with per-doc chunk counts; Sources page wired to the
real endpoint (stat cards, full-width a11y table, designed empty
state, DOM-built rows — no innerHTML)
- tests: 63 passed (chunker/llm/importer units, docs API + importer
integration), story E2E 3/3 (real endpoints, in-thread import);
app/ coverage 98%
- real KB imported: 672 docs / 8969 chunks in ~3m, idempotent
re-run (672 unchanged, 0 batches)
- harness: .agent/validate.sh now gates through uv (pytest +
coverage >90% + ruff + pyright) instead of system python3
248 lines
8.4 KiB
Python
248 lines
8.4 KiB
Python
"""Knowledge-base importer (PLAN §5 / §9 / §11).
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Walks ``*.md`` files (A9 exclusion list), diffs by sha256 against
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``documents.content_hash`` and, for every new or changed file, runs the
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two-phase upsert:
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1. upsert the document row and replace its chunk rows (embeddings NULL)
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2. embed the new chunks in batches and attach the vectors
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3. commit — one transaction per file, so a failed embedding leaves the
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database untouched and the file is simply retried on the next run
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``prune=True`` deletes documents (of the imported sources only) whose files
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no longer exist. Per-file logging uses the verbs
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``added | updated | unchanged | pruned`` plus a summary line (PLAN §9).
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"""
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from __future__ import annotations
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import hashlib
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import logging
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from dataclasses import dataclass
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from datetime import UTC, datetime
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from pathlib import Path
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from typing import Protocol
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from sqlalchemy import select
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from sqlalchemy.orm import Session
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from app.config import Settings
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from app.db import SessionLocal
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from app.models import Chunk, Document
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from app.rag.chunker import chunk_markdown, extract_title
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from app.rag.llm import EmbeddingError
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logger = logging.getLogger("app.importer")
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#: Non-content directories never imported (PLAN anchor A9).
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EXCLUDED_DIRS: frozenset[str] = frozenset(
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{".venv", "node_modules", ".git", "__pycache__", ".pytest_cache", "dist", "build"}
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)
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class Embedder(Protocol):
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"""Everything the importer needs from the LLM client (duck-typed for tests)."""
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settings: Settings
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embed_batches: int
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async def embed(self, texts: list[str]) -> list[list[float]]: ...
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@dataclass
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class ImportSummary:
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"""Counts for one import run (also printed by the CLI)."""
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files: int = 0
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added: int = 0
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updated: int = 0
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unchanged: int = 0
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pruned: int = 0
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errors: int = 0
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chunks: int = 0
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embed_batches: int = 0
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def log(self) -> None:
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logger.info(
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"import: summary files=%d added=%d updated=%d unchanged=%d pruned=%d "
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"errors=%d chunks=%d embed_batches=%d",
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self.files,
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self.added,
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self.updated,
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self.unchanged,
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self.pruned,
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self.errors,
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self.chunks,
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self.embed_batches,
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)
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def iter_markdown_files(root: Path, excluded: frozenset[str] = EXCLUDED_DIRS) -> list[Path]:
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"""All ``*.md`` files under *root* (sorted), skipping excluded dirs (A9)."""
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if not root.is_dir():
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return []
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files: list[Path] = []
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for path in sorted(root.rglob("*.md")):
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rel = path.relative_to(root)
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if any(part in excluded for part in rel.parts[:-1]):
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continue
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files.append(path)
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return files
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async def import_sources(
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sources: list[Path],
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llm: Embedder,
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*,
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prune: bool = False,
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limit: int | None = None,
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session: Session | None = None,
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) -> ImportSummary:
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"""Import every ``*.md`` under *sources* (see module docstring).
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``session`` may be supplied (tests); a private one is opened and closed
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otherwise. ``limit`` caps the number of files processed (debug only) and
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disables pruning, since an incomplete walk must not drive deletions.
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"""
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if limit is not None and limit <= 0:
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raise ValueError("limit must be >= 1")
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summary = ImportSummary()
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owns_session = session is None
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if session is None:
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session = SessionLocal()
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seen: set[tuple[str, str]] = set()
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source_names: set[str] = set()
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try:
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for root in sources:
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if not root.is_dir():
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logger.warning("import: source dir not found, skipping: %s", root)
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continue
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if limit is not None and summary.files >= limit:
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break
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source = root.name
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source_names.add(source)
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for path in iter_markdown_files(root):
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if limit is not None and summary.files >= limit:
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break
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rel = path.relative_to(root).as_posix()
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seen.add((source, rel))
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summary.files += 1
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try:
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await _index_file(
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session, source=source, rel=rel, full_path=path, llm=llm,
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summary=summary,
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)
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except EmbeddingError as e:
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# A pathological file (e.g. content the embedding endpoint
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# refuses) must not abort the whole KB: roll back its
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# uncommitted rows, log loudly, and keep going. The next
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# run retries it.
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session.rollback()
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summary.errors += 1
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logger.error("import: error source=%s path=%s — %s", source, rel, e)
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if prune:
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if limit is not None:
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logger.warning("import: --prune ignored because --limit was given")
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else:
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summary.pruned = _prune(session, source_names, seen)
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summary.embed_batches = llm.embed_batches
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summary.log()
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return summary
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finally:
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if owns_session:
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session.close()
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async def _index_file(
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session: Session,
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*,
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source: str,
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rel: str,
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full_path: Path,
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llm: Embedder,
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summary: ImportSummary,
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) -> None:
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"""Upsert one file: doc row + chunk rows + embeddings, one transaction."""
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settings = llm.settings
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content = full_path.read_text(encoding="utf-8", errors="replace")
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digest = hashlib.sha256(content.encode("utf-8")).hexdigest()
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doc = session.scalar(select(Document).where(Document.source == source, Document.path == rel))
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if doc is not None and doc.content_hash == digest:
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summary.unchanged += 1
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logger.info("import: unchanged source=%s path=%s", source, rel)
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return
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verb = "updated" if doc is not None else "added"
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title = extract_title(content, fallback=full_path.stem)
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if doc is None:
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doc = Document(
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source=source,
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path=rel,
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full_path=str(full_path),
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title=title,
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content=content,
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content_hash=digest,
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indexed_at=datetime.now(UTC),
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)
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session.add(doc)
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else:
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doc.full_path = str(full_path)
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doc.title = title
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doc.content = content
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doc.content_hash = digest
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doc.indexed_at = datetime.now(UTC)
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session.flush() # guarantees doc.id even for brand-new rows
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# Phase 1+2 — chunk, replace the chunk rows (embeddings NULL), embed,
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# and commit the whole file atomically. If the endpoint rejects a chunk
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# as over its input token cap (URL-dense paragraphs tokenize at ~1.1
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# chars/token), halve this file's chunk target and retry — the global
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# policy stays intact for the rest of the KB.
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target = max(400, settings.chunk_target_chars)
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while True:
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chunks_text = chunk_markdown(content, target, settings.chunk_overlap_chars)
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doc.chunks = [
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Chunk(document_id=doc.id, position=i, content=c) for i, c in enumerate(chunks_text)
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]
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session.flush() # delete-orphan cascade drops the previous rows
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if not doc.chunks:
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break
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try:
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vectors = await llm.embed([c.content for c in doc.chunks])
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for row, vec in zip(doc.chunks, vectors, strict=True):
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row.embedding = vec
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break
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except EmbeddingError as e:
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if "token cap" not in str(e) or target <= 400:
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raise
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logger.info(
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"import: re-chunking at %d chars after endpoint token cap: %s",
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target // 2,
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rel,
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)
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target //= 2
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session.commit()
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if verb == "added":
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summary.added += 1
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else:
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summary.updated += 1
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summary.chunks += len(chunks_text)
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logger.info("import: %s source=%s path=%s chunks=%d", verb, source, rel, len(chunks_text))
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def _prune(session: Session, source_names: set[str], seen: set[tuple[str, str]]) -> int:
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"""Delete documents of *source_names* whose file is no longer in *seen*."""
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if not source_names:
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return 0
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pruned = 0
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docs = session.scalars(select(Document).where(Document.source.in_(source_names))).all()
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for doc in docs:
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if (doc.source, doc.path) not in seen:
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session.delete(doc)
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pruned += 1
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logger.info("import: pruned source=%s path=%s", doc.source, doc.path)
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if pruned:
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session.commit()
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return pruned
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