Specs y planes
Fase 37 — colpali-visual Implementation Plan
For agentic workers: REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (
- [ ]) syntax for tracking.
Goal: Add a visual retrieval store to jw-rag based on late-interaction ColPali/ColQwen2 embeddings. Pages from JWPUB/EPUB/PDF are rasterized → multi-vector page embeddings → MaxSim scoring. Three-way RRF (bm25 + text-vector + visual-MaxSim). Hardware-aware: fails fast without GPU; uses deterministic FakeColPaliEmbedder in CI.
Architecture: New sub-package packages/jw-rag/src/jw_rag/visual/ (no new monorepo package — lives inside jw-rag). Lazy provider imports (colpali-engine, transformers, torch, pdf2image, playwright, Pillow) only inside real providers. VisualVectorStore parallels VectorStore but is multi-vector internally. Hybrid helper extends Fase-33 RRF. CLI + MCP added behind JW_VISUAL_ENABLED env flag.
Tech Stack: Python 3.13 · numpy (multi-vector storage) · Pillow (image type) · pdf2image (PDF rasterization, optional) · Playwright (EPUB/JWPUB rasterization, optional) · colpali-engine + transformers + torch (real provider on NVIDIA, optional) · mlx-vlm (Apple Silicon, optional) · PyYAML (golden cases).
Spec: docs/superpowers/specs/2026-05-31-fase-37-colpali-visual-design.md.
Pilot plan format: docs/superpowers/plans/2026-05-30-fase-22-eval-doctrinal-plan.md.
File map
Creates:
packages/jw-rag/src/jw_rag/visual/__init__.pypackages/jw-rag/src/jw_rag/visual/models.pypackages/jw-rag/src/jw_rag/visual/errors.pypackages/jw-rag/src/jw_rag/visual/fakes.pypackages/jw-rag/src/jw_rag/visual/visual_store.pypackages/jw-rag/src/jw_rag/visual/page_rasterizer.pypackages/jw-rag/src/jw_rag/visual/colpali.pypackages/jw-rag/src/jw_rag/visual/ingest.pypackages/jw-rag/src/jw_rag/visual/hybrid.pypackages/jw-rag/tests/visual/__init__.pypackages/jw-rag/tests/visual/test_models.pypackages/jw-rag/tests/visual/test_fakes.pypackages/jw-rag/tests/visual/test_visual_store.pypackages/jw-rag/tests/visual/test_rasterizer.pypackages/jw-rag/tests/visual/test_colpali.pypackages/jw-rag/tests/visual/test_ingest.pypackages/jw-rag/tests/visual/test_hybrid.pypackages/jw-rag/tests/visual/fixtures/mini.pdfpackages/jw-rag/tests/visual/fixtures/mini.epubpackages/jw-rag/tests/visual/fixtures/mini.jwpubpackages/jw-rag/tests/visual/fixtures/build_fixtures.pypackages/jw-eval/fixtures/golden_qa/l1/visual_paul_journeys_es.yamlpackages/jw-eval/fixtures/golden_qa/l1/visual_tabernacle_en.yamlpackages/jw-eval/fixtures/golden_qa/l1/visual_daniel_seven_times_es.yamlpackages/jw-eval/fixtures/golden_qa/l1/visual_jw_org_structure_en.yamlpackages/jw-eval/fixtures/golden_qa/l1/visual_daniel_beasts_table_es.yamldocs/guias/visual-rag.md
Modifies:
packages/jw-rag/pyproject.toml— add[visual]and[visual-mlx]extras.packages/jw-cli/src/jw_cli/commands/rag.py— addingest-visual+--visualflag onsearch.packages/jw-mcp/src/jw_mcp/server.py— registervisual_searchandingest_publication_visualtools.docs/VISION_AUDIT.md— add Fase 37 row.docs/ROADMAP.md— add Fase 37 section.
Task 1: Scaffold jw_rag.visual + [visual] extras
Files:
-
Create:
packages/jw-rag/src/jw_rag/visual/__init__.py -
Create:
packages/jw-rag/src/jw_rag/visual/errors.py -
Create:
packages/jw-rag/tests/visual/__init__.py -
Modify:
packages/jw-rag/pyproject.toml -
Step 1: Add
[visual]and[visual-mlx]extras
Edit packages/jw-rag/pyproject.toml. Under [project.optional-dependencies] append:
visual = [
"colpali-engine>=0.3.4",
"transformers>=4.45.0",
"torch>=2.4.0",
"pdf2image>=1.17.0",
"Pillow>=10.4.0",
"playwright>=1.47.0",
]
visual-mlx = [
"mlx>=0.18.0",
"mlx-vlm>=0.0.13",
"Pillow>=10.4.0",
"pdf2image>=1.17.0",
"playwright>=1.47.0",
]Heavy deps stay opt-in. Core install (uv sync --all-packages) does NOT pull them.
- Step 2: Create errors module
# packages/jw-rag/src/jw_rag/visual/errors.py
"""Errors specific to the visual RAG subsystem.
`ConfigError` is raised when the user asks for a real visual embedder but no
GPU/MLX backend is reachable. Message must be actionable and include the
exact install command.
`VisualStoreMismatchError` is raised by `VisualVectorStore.load()` when the
persisted store on disk was produced by a different model/revision/patch_size
than the embedder passed at load time.
"""
from __future__ import annotations
class ConfigError(RuntimeError):
"""No usable hardware for ColPali/ColQwen2 visual embeddings.
Message includes the install commands for NVIDIA (`uv sync --extra visual`)
and Apple Silicon (`uv sync --extra visual-mlx`), plus the env var to
disable the subsystem entirely (`JW_VISUAL_ENABLED=0`).
"""
class VisualStoreMismatchError(RuntimeError):
"""On-disk store was produced by a different model/revision/patch_size."""- Step 3: Create the package init
# packages/jw-rag/src/jw_rag/visual/__init__.py
"""Visual late-interaction RAG store.
Public API:
from jw_rag.visual import (
VisualChunk,
MultiVectorHit,
IngestResult,
VisualVectorStore,
ConfigError,
VisualStoreMismatchError,
hybrid_search_with_visual,
get_default_visual_embedder,
)
Heavy providers (`colpali-engine`, `transformers`, `torch`, `mlx`, `pdf2image`,
`playwright`) are imported lazily inside the provider classes. Importing this
module is safe on machines without any of them — only `is_available()` and
the provider constructors touch hardware.
"""
from jw_rag.visual.errors import ConfigError, VisualStoreMismatchError
from jw_rag.visual.models import IngestResult, MultiVectorHit, VisualChunk
__all__ = [
"ConfigError",
"IngestResult",
"MultiVectorHit",
"VisualChunk",
"VisualStoreMismatchError",
]- Step 4: Create test package init
# packages/jw-rag/tests/visual/__init__.py
"""Tests for jw_rag.visual."""- Step 5: Verify install
Run: uv sync --all-packages
Expected: no errors. python -c "import jw_rag.visual; print('ok')" prints ok.
- Step 6: Commit
git add packages/jw-rag/pyproject.toml packages/jw-rag/src/jw_rag/visual packages/jw-rag/tests/visual
git commit -m "feat(jw-rag): scaffold visual subpackage and [visual]/[visual-mlx] extras"Task 2: Models (VisualChunk, MultiVectorHit, IngestResult)
Files:
-
Create:
packages/jw-rag/src/jw_rag/visual/models.py -
Create:
packages/jw-rag/tests/visual/test_models.py -
Step 1: Write the failing test
# packages/jw-rag/tests/visual/test_models.py
"""Tests for jw_rag.visual.models."""
from __future__ import annotations
from pathlib import Path
import numpy as np
from jw_rag.visual.models import IngestResult, MultiVectorHit, VisualChunk
def test_visual_chunk_minimal() -> None:
c = VisualChunk(
id="abc#p1",
source_id="abc",
page_number=1,
image_path=Path("/tmp/abc_p001.png"),
)
assert c.id == "abc#p1"
assert c.ocr_text == ""
assert c.metadata == {}
def test_visual_chunk_round_trip_dict() -> None:
c = VisualChunk(
id="abc#p2",
source_id="abc",
page_number=2,
image_path=Path("/tmp/abc_p002.png"),
ocr_text="foo",
metadata={"language": "es"},
)
d = c.to_dict()
assert d["page_number"] == 2
assert d["image_path"] == "/tmp/abc_p002.png"
back = VisualChunk.from_dict(d)
assert back == c
def test_multi_vector_hit_score_field() -> None:
chunk = VisualChunk(id="a#p1", source_id="a", page_number=1, image_path=Path("/tmp/x.png"))
hit = MultiVectorHit(chunk=chunk, score=12.5, rank=1)
assert hit.score == 12.5
assert hit.rank == 1
assert hit.source == "visual"
def test_ingest_result_addition() -> None:
a = IngestResult(pages_added=3, pages_skipped=1, duration_ms=100)
b = IngestResult(pages_added=2, pages_skipped=0, duration_ms=50)
c = a + b
assert c.pages_added == 5
assert c.pages_skipped == 1
assert c.duration_ms == 150
def test_visual_chunk_text_alias_for_ocr() -> None:
"""`.text` proxies to `ocr_text` so VisualChunk slots into SearchHit shape."""
c = VisualChunk(
id="x#1", source_id="x", page_number=1, image_path=Path("/tmp/x.png"), ocr_text="hello"
)
assert c.text == "hello"
def test_numpy_import_for_assertion_smoke() -> None:
# Sanity check that numpy is available in tests (needed by store tests).
arr = np.zeros((2, 3), dtype=np.float16)
assert arr.shape == (2, 3)- Step 2: Run test to verify it fails
Run: uv run pytest packages/jw-rag/tests/visual/test_models.py -v
Expected: FAIL — module jw_rag.visual.models missing.
- Step 3: Implement the models
# packages/jw-rag/src/jw_rag/visual/models.py
"""Data models for the visual RAG subsystem.
A `VisualChunk` is one rasterized page. It mirrors `jw_rag.chunker.Chunk`
enough that agents can treat it the same (`.text`, `.metadata`, `.source_id`)
but adds page-level fields (`page_number`, `image_path`).
A `MultiVectorHit` is the visual analogue of `SearchHit`: same shape, same
`source` field convention ("visual" instead of "vector"/"bm25"/"hybrid").
An `IngestResult` aggregates per-file ingest stats; `__add__` lets callers
fold many file results into one summary.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any
@dataclass
class VisualChunk:
"""One rasterized page indexed by the visual store."""
id: str
source_id: str
page_number: int
image_path: Path
ocr_text: str = ""
metadata: dict[str, Any] = field(default_factory=dict)
@property
def text(self) -> str:
"""Alias so VisualChunk can be consumed wherever Chunk-like is expected."""
return self.ocr_text
def to_dict(self) -> dict[str, Any]:
return {
"id": self.id,
"source_id": self.source_id,
"page_number": self.page_number,
"image_path": str(self.image_path),
"ocr_text": self.ocr_text,
"metadata": self.metadata,
}
@classmethod
def from_dict(cls, data: dict[str, Any]) -> VisualChunk:
return cls(
id=data["id"],
source_id=data["source_id"],
page_number=int(data["page_number"]),
image_path=Path(data["image_path"]),
ocr_text=data.get("ocr_text", ""),
metadata=data.get("metadata", {}) or {},
)
@dataclass
class MultiVectorHit:
"""Result of a visual MaxSim search.
`score` is unbounded above (sum-of-maxes), not a similarity in [0, 1].
Callers should treat scores as comparable only within the same query.
"""
chunk: VisualChunk
score: float
rank: int
source: str = "visual"
@dataclass
class IngestResult:
"""Aggregated counters for a visual ingest call."""
pages_added: int = 0
pages_skipped: int = 0
duration_ms: int = 0
def __add__(self, other: IngestResult) -> IngestResult:
return IngestResult(
pages_added=self.pages_added + other.pages_added,
pages_skipped=self.pages_skipped + other.pages_skipped,
duration_ms=self.duration_ms + other.duration_ms,
)- Step 4: Re-export from package init
Append to packages/jw-rag/src/jw_rag/visual/__init__.py is already done in Task 1 (from jw_rag.visual.models import ...). Verify the import works.
- Step 5: Run test to verify it passes
Run: uv run pytest packages/jw-rag/tests/visual/test_models.py -v
Expected: 6 passed.
- Step 6: Commit
git add packages/jw-rag/src/jw_rag/visual/models.py packages/jw-rag/tests/visual/test_models.py
git commit -m "feat(jw-rag): visual models — VisualChunk, MultiVectorHit, IngestResult"Task 3: FakeColPaliEmbedder + FakeRasterizer (test infrastructure)
Files:
- Create:
packages/jw-rag/src/jw_rag/visual/fakes.py - Create:
packages/jw-rag/tests/visual/test_fakes.py
The fakes are the foundation of every later test. Build them first.
- Step 1: Write the failing test
# packages/jw-rag/tests/visual/test_fakes.py
"""Tests for FakeColPaliEmbedder and FakeRasterizer.
Determinism is the whole point: same input bytes → same vectors. That lets
tests assert exact MaxSim scores without ever touching a real GPU model.
"""
from __future__ import annotations
import hashlib
import io
import numpy as np
from PIL import Image
from jw_rag.visual.fakes import FakeColPaliEmbedder, FakeRasterizer
def _img_bytes(image: Image.Image) -> bytes:
buf = io.BytesIO()
image.save(buf, format="PNG")
return buf.getvalue()
def test_fake_embedder_shape_and_dtype() -> None:
e = FakeColPaliEmbedder(dim=128, n_patches=64)
img = Image.new("RGB", (256, 256), color=(255, 0, 0))
vecs = e.embed_image(img)
assert vecs.shape == (64, 128)
assert vecs.dtype == np.float16
def test_fake_embedder_is_deterministic() -> None:
e = FakeColPaliEmbedder(dim=128, n_patches=32)
img = Image.new("RGB", (128, 128), color=(0, 255, 0))
a = e.embed_image(img)
b = e.embed_image(img)
np.testing.assert_array_equal(a, b)
def test_fake_embedder_different_images_differ() -> None:
e = FakeColPaliEmbedder(dim=128, n_patches=32)
a = e.embed_image(Image.new("RGB", (128, 128), color=(0, 255, 0)))
b = e.embed_image(Image.new("RGB", (128, 128), color=(0, 0, 255)))
# Different bytes → different seed → different vectors.
assert not np.array_equal(a, b)
def test_fake_embedder_query_uses_text_seed() -> None:
e = FakeColPaliEmbedder(dim=128, n_patches=32)
q1 = e.embed_query("hello")
q2 = e.embed_query("hello")
q3 = e.embed_query("world")
np.testing.assert_array_equal(q1, q2)
assert not np.array_equal(q1, q3)
assert q1.shape[1] == 128
assert q1.shape[0] >= 1 # at least one query token
def test_fake_embedder_is_available_always_true() -> None:
assert FakeColPaliEmbedder.is_available() is True
def test_fake_rasterizer_yields_blank_pages() -> None:
r = FakeRasterizer(n_pages=3, size=(64, 64))
pages = list(r.rasterize_pdf(b"any-bytes"))
assert len(pages) == 3
for idx, img in pages:
assert isinstance(img, Image.Image)
assert img.size == (64, 64)
assert [idx for idx, _ in pages] == [0, 1, 2]
def test_fake_rasterizer_varies_per_page() -> None:
"""Each page gets a different fill so embeddings will differ."""
r = FakeRasterizer(n_pages=3, size=(64, 64))
pages = list(r.rasterize_pdf(b"src"))
digests = {hashlib.sha256(_img_bytes(img)).hexdigest() for _, img in pages}
assert len(digests) == 3- Step 2: Run test to verify it fails
Run: uv run pytest packages/jw-rag/tests/visual/test_fakes.py -v
Expected: FAIL — jw_rag.visual.fakes missing.
- Step 3: Implement the fakes
# packages/jw-rag/src/jw_rag/visual/fakes.py
"""Deterministic fakes for the visual subsystem.
`FakeColPaliEmbedder` seeds a per-image RNG from `sha256(image_bytes)`. Tests
get byte-identical vectors across runs without touching `colpali-engine` or
`torch`. Compatible with the same `embed_image` / `embed_query` shape as the
real provider:
embed_image(PIL.Image) -> np.ndarray[float16, (n_patches, dim)]
embed_query(str) -> np.ndarray[float16, (n_q_tokens, dim)]
`FakeRasterizer` mimics the real `PageRasterizer` interface but never touches
Playwright / pdf2image. It returns blank-but-distinct PIL images keyed by page
index, so downstream embedding stages get distinguishable inputs.
"""
from __future__ import annotations
import hashlib
import io
from collections.abc import Iterator
import numpy as np
from PIL import Image
class FakeColPaliEmbedder:
"""Deterministic stand-in for ColQwen2/ColPali."""
name = "fake-colpali"
dim = 128
max_patches = 1030
def __init__(self, *, dim: int = 128, n_patches: int = 64) -> None:
self.dim = dim
self._n_patches = n_patches
self.max_patches = n_patches # store padding uses this
@classmethod
def is_available(cls, target: str = "fake") -> bool: # noqa: ARG003
return True
def embed_image(self, image: Image.Image) -> np.ndarray:
seed = self._seed_from_image(image)
rng = np.random.default_rng(seed)
vecs = rng.standard_normal(size=(self._n_patches, self.dim)).astype(np.float16)
return _l2_normalize_rows(vecs)
def embed_query(self, query: str) -> np.ndarray:
# Query length tracks word count so tests can probe sensitivity.
n_tokens = max(1, len(query.split()))
seed = int.from_bytes(hashlib.sha256(query.encode("utf-8")).digest()[:8], "big")
rng = np.random.default_rng(seed)
vecs = rng.standard_normal(size=(n_tokens, self.dim)).astype(np.float16)
return _l2_normalize_rows(vecs)
@staticmethod
def _seed_from_image(image: Image.Image) -> int:
buf = io.BytesIO()
image.save(buf, format="PNG")
return int.from_bytes(hashlib.sha256(buf.getvalue()).digest()[:8], "big")
def _l2_normalize_rows(arr: np.ndarray) -> np.ndarray:
norms = np.linalg.norm(arr.astype(np.float32), axis=1, keepdims=True)
norms = np.where(norms == 0, 1.0, norms)
return (arr.astype(np.float32) / norms).astype(np.float16)
class FakeRasterizer:
"""Returns blank-but-distinct PIL images for tests.
The fill color encodes the page index so different pages produce different
`sha256(image_bytes)` and therefore different FakeColPaliEmbedder vectors.
"""
def __init__(self, *, n_pages: int = 3, size: tuple[int, int] = (768, 1024)) -> None:
self._n_pages = n_pages
self._size = size
def _make_page(self, idx: int) -> Image.Image:
# Vary RGB per page so embeddings are distinguishable.
r = (idx * 53) % 256
g = (idx * 97) % 256
b = (idx * 151) % 256
return Image.new("RGB", self._size, color=(r, g, b))
def rasterize_pdf(self, _data: bytes, *, dpi: int = 200) -> Iterator[tuple[int, Image.Image]]: # noqa: ARG002
for i in range(self._n_pages):
yield i, self._make_page(i)
def rasterize_epub(self, _path, *, viewport=(768, 1024)) -> Iterator[tuple[int, Image.Image]]: # noqa: ARG002, ANN001
for i in range(self._n_pages):
yield i, self._make_page(i)
def rasterize_jwpub(self, _path, *, dpi: int = 200) -> Iterator[tuple[int, Image.Image]]: # noqa: ARG002, ANN001
for i in range(self._n_pages):
yield i, self._make_page(i)- Step 4: Run test to verify it passes
Run: uv run pytest packages/jw-rag/tests/visual/test_fakes.py -v
Expected: 7 passed.
- Step 5: Commit
git add packages/jw-rag/src/jw_rag/visual/fakes.py packages/jw-rag/tests/visual/test_fakes.py
git commit -m "feat(jw-rag): FakeColPaliEmbedder + FakeRasterizer for visual tests"Task 4: VisualVectorStore — add, MaxSim search, persistence
Files:
-
Create:
packages/jw-rag/src/jw_rag/visual/visual_store.py -
Create:
packages/jw-rag/tests/visual/test_visual_store.py -
Step 1: Write the failing test
# packages/jw-rag/tests/visual/test_visual_store.py
"""Tests for VisualVectorStore.
We use FakeColPaliEmbedder so MaxSim scores are deterministic. The store
is verified for: add(), search(), save()/load() round trip, mismatch
detection on load, idempotent re-add by source_id, and empty-store behavior.
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pytest
from PIL import Image
from jw_rag.visual.errors import VisualStoreMismatchError
from jw_rag.visual.fakes import FakeColPaliEmbedder
from jw_rag.visual.models import VisualChunk
from jw_rag.visual.visual_store import VisualVectorStore
def _make_chunks(n: int, tmp_path: Path) -> list[tuple[VisualChunk, Image.Image]]:
out: list[tuple[VisualChunk, Image.Image]] = []
for i in range(n):
img = Image.new("RGB", (64, 64), color=(i * 30, 50, 200 - i * 20))
png = tmp_path / f"src1_p{i:03d}.png"
img.save(png)
chunk = VisualChunk(
id=f"src1#p{i + 1}",
source_id="src1",
page_number=i + 1,
image_path=png,
)
out.append((chunk, img))
return out
def test_empty_store(tmp_path: Path) -> None:
store = VisualVectorStore(tmp_path / "visual", FakeColPaliEmbedder(dim=64, n_patches=16))
assert store.is_empty
assert store.count == 0
assert store.search("anything") == []
def test_add_and_search(tmp_path: Path) -> None:
embedder = FakeColPaliEmbedder(dim=64, n_patches=16)
store = VisualVectorStore(tmp_path / "visual", embedder)
pairs = _make_chunks(3, tmp_path)
store.add(pairs)
assert store.count == 3
hits = store.search("any query", top_k=2)
assert len(hits) == 2
assert hits[0].rank == 1
assert hits[1].rank == 2
assert hits[0].score >= hits[1].score
# source field stays "visual" regardless of how we got there.
assert all(h.source == "visual" for h in hits)
def test_add_idempotent_by_source_id(tmp_path: Path) -> None:
embedder = FakeColPaliEmbedder(dim=64, n_patches=16)
store = VisualVectorStore(tmp_path / "visual", embedder)
pairs = _make_chunks(2, tmp_path)
store.add(pairs)
# Re-adding same chunks → no growth.
store.add(pairs)
assert store.count == 2
def test_source_ids(tmp_path: Path) -> None:
embedder = FakeColPaliEmbedder(dim=64, n_patches=16)
store = VisualVectorStore(tmp_path / "visual", embedder)
store.add(_make_chunks(2, tmp_path))
assert store.source_ids() == {"src1"}
def test_save_and_load_round_trip(tmp_path: Path) -> None:
embedder = FakeColPaliEmbedder(dim=64, n_patches=16)
store = VisualVectorStore(tmp_path / "visual", embedder)
pairs = _make_chunks(3, tmp_path)
store.add(pairs)
pre_hits = store.search("q", top_k=3)
store.save()
store2 = VisualVectorStore(tmp_path / "visual", FakeColPaliEmbedder(dim=64, n_patches=16))
store2.load()
assert store2.count == 3
post_hits = store2.search("q", top_k=3)
assert [h.chunk.id for h in pre_hits] == [h.chunk.id for h in post_hits]
for a, b in zip(pre_hits, post_hits, strict=True):
assert abs(a.score - b.score) < 1e-3
def test_load_mismatch_raises(tmp_path: Path) -> None:
store = VisualVectorStore(tmp_path / "visual", FakeColPaliEmbedder(dim=64, n_patches=16))
store.add(_make_chunks(1, tmp_path))
store.save()
other = VisualVectorStore(tmp_path / "visual", FakeColPaliEmbedder(dim=64, n_patches=32))
with pytest.raises(VisualStoreMismatchError):
other.load()
def test_load_missing_dir_is_noop(tmp_path: Path) -> None:
store = VisualVectorStore(tmp_path / "visual", FakeColPaliEmbedder(dim=64, n_patches=16))
store.load() # no meta.json present
assert store.is_empty
def test_maxsim_score_is_sum_of_per_token_maxes(tmp_path: Path) -> None:
"""Sanity check: MaxSim equals our manual computation."""
embedder = FakeColPaliEmbedder(dim=8, n_patches=4)
store = VisualVectorStore(tmp_path / "visual", embedder)
pairs = _make_chunks(1, tmp_path)
store.add(pairs)
q = embedder.embed_query("zero")
d_vecs = embedder.embed_image(pairs[0][1]).astype(np.float32)
sims = q.astype(np.float32) @ d_vecs.T
expected = float(sims.max(axis=1).sum())
hits = store.search("zero", top_k=1)
assert abs(hits[0].score - expected) < 1e-3- Step 2: Run test to verify it fails
Run: uv run pytest packages/jw-rag/tests/visual/test_visual_store.py -v
Expected: FAIL — VisualVectorStore missing.
- Step 3: Implement
VisualVectorStore
# packages/jw-rag/src/jw_rag/visual/visual_store.py
"""Multi-vector store with MaxSim scoring.
NOT a subclass of `jw_rag.store.VectorStore`. The interfaces are similar
(`add`, `search`, `save`, `load`, `is_empty`, `source_ids`) but the internal
representation is multi-vector: each document is a `(max_patches, dim)`
matrix plus a `(max_patches,)` boolean mask.
Persistence layout under `path`:
meta.json — {model_name, dim, max_patches, count, ...}
chunks.jsonl — one VisualChunk per line
vectors.npy — (N, max_patches, dim) float16, zero-padded
mask.npy — (N, max_patches) bool
MaxSim:
score(q, d) = Σ_qtok max_dpatch <q_tok, d_patch> (mask out padding)
For top-k retrieval over N docs we compute the full (N, max_patches) sim
tensor once per q_token using a batched matmul. That's O(N · max_patches ·
dim · |q|); fine up to ~10k pages in CPU/numpy and far better in GPU
(future v2 can add PLAID ANN if needed).
"""
from __future__ import annotations
import json
from pathlib import Path
from typing import Any
import numpy as np
from PIL import Image
from jw_rag.visual.errors import VisualStoreMismatchError
from jw_rag.visual.models import MultiVectorHit, VisualChunk
class _EmbedderProtocol:
"""Structural type for any ColPali-like embedder."""
name: str
dim: int
max_patches: int
def embed_image(self, image: Image.Image) -> np.ndarray: ...
def embed_query(self, query: str) -> np.ndarray: ...
class VisualVectorStore:
"""Multi-vector store + MaxSim search + JSON/npy persistence."""
def __init__(self, path: Path | str, embedder: _EmbedderProtocol) -> None:
self.path = Path(path)
self.embedder = embedder
self._chunks: list[VisualChunk] = []
self._vectors: np.ndarray = np.zeros((0, embedder.max_patches, embedder.dim), dtype=np.float16)
self._mask: np.ndarray = np.zeros((0, embedder.max_patches), dtype=bool)
self._known_ids: set[str] = set()
# ── State ───────────────────────────────────────────────────────────
@property
def count(self) -> int:
return len(self._chunks)
@property
def is_empty(self) -> bool:
return self.count == 0
def source_ids(self) -> set[str]:
return {c.source_id for c in self._chunks if c.source_id}
# ── Index ───────────────────────────────────────────────────────────
def add(self, pairs: list[tuple[VisualChunk, Image.Image]]) -> None:
"""Embed and append each (chunk, image). Skips chunks already present."""
if not pairs:
return
max_p = self.embedder.max_patches
dim = self.embedder.dim
new_vecs: list[np.ndarray] = []
new_masks: list[np.ndarray] = []
new_chunks: list[VisualChunk] = []
for chunk, image in pairs:
if chunk.id in self._known_ids:
continue
patches = self.embedder.embed_image(image)
n = patches.shape[0]
if n > max_p:
patches = patches[:max_p]
n = max_p
padded = np.zeros((max_p, dim), dtype=np.float16)
padded[:n] = patches
mask = np.zeros((max_p,), dtype=bool)
mask[:n] = True
new_vecs.append(padded)
new_masks.append(mask)
new_chunks.append(chunk)
self._known_ids.add(chunk.id)
if not new_chunks:
return
block_v = np.stack(new_vecs, axis=0)
block_m = np.stack(new_masks, axis=0)
if self.count == 0:
self._vectors = block_v
self._mask = block_m
else:
self._vectors = np.concatenate([self._vectors, block_v], axis=0)
self._mask = np.concatenate([self._mask, block_m], axis=0)
self._chunks.extend(new_chunks)
# ── Search ──────────────────────────────────────────────────────────
def search(self, query: str, top_k: int = 10) -> list[MultiVectorHit]:
if self.is_empty:
return []
q_vecs = self.embedder.embed_query(query).astype(np.float32) # (Q, D)
d_vecs = self._vectors.astype(np.float32) # (N, P, D)
d_mask = self._mask # (N, P)
# sims: (N, Q, P) via einsum.
sims = np.einsum("npd,qd->nqp", d_vecs, q_vecs)
# Mask invalid patches with -inf so they never win the max.
mask_broadcast = d_mask[:, np.newaxis, :] # (N, 1, P)
sims = np.where(mask_broadcast, sims, -np.inf)
per_token_max = sims.max(axis=2) # (N, Q)
scores = per_token_max.sum(axis=1) # (N,)
top_k = min(top_k, self.count)
idx = np.argpartition(-scores, top_k - 1)[:top_k]
idx = idx[np.argsort(-scores[idx])]
return [
MultiVectorHit(chunk=self._chunks[i], score=float(scores[i]), rank=r, source="visual")
for r, i in enumerate(idx, 1)
]
# ── Persistence ─────────────────────────────────────────────────────
def save(self) -> None:
self.path.mkdir(parents=True, exist_ok=True)
with (self.path / "chunks.jsonl").open("w", encoding="utf-8") as f:
for c in self._chunks:
f.write(json.dumps(c.to_dict(), ensure_ascii=False) + "\n")
np.save(self.path / "vectors.npy", self._vectors)
np.save(self.path / "mask.npy", self._mask)
(self.path / "meta.json").write_text(
json.dumps(
{
"multi_vector": True,
"model_name": getattr(self.embedder, "name", "unknown"),
"dim": int(self.embedder.dim),
"max_patches": int(self.embedder.max_patches),
"count": self.count,
}
)
)
def load(self) -> None:
meta_path = self.path / "meta.json"
if not meta_path.exists():
return
meta: dict[str, Any] = json.loads(meta_path.read_text(encoding="utf-8"))
if meta.get("dim") != int(self.embedder.dim):
raise VisualStoreMismatchError(
f"dim mismatch: store={meta.get('dim')} embedder={self.embedder.dim}. "
"Re-ingest with `jw rag ingest-visual --force`."
)
if meta.get("max_patches") != int(self.embedder.max_patches):
raise VisualStoreMismatchError(
f"max_patches mismatch: store={meta.get('max_patches')} "
f"embedder={self.embedder.max_patches}. Re-ingest."
)
if meta.get("model_name") and meta["model_name"] != getattr(self.embedder, "name", ""):
# Soft warn via exception: only raise if name differs AND user wants to read.
# We raise to be safe — silent acceptance breaks the cache invariant.
raise VisualStoreMismatchError(
f"model mismatch: store={meta['model_name']} embedder={self.embedder.name}. "
"Re-ingest."
)
self._chunks = []
with (self.path / "chunks.jsonl").open("r", encoding="utf-8") as f:
for line in f:
self._chunks.append(VisualChunk.from_dict(json.loads(line)))
self._vectors = np.load(self.path / "vectors.npy")
self._mask = np.load(self.path / "mask.npy")
self._known_ids = {c.id for c in self._chunks}- Step 4: Run test to verify it passes
Run: uv run pytest packages/jw-rag/tests/visual/test_visual_store.py -v
Expected: 8 passed.
- Step 5: Commit
git add packages/jw-rag/src/jw_rag/visual/visual_store.py packages/jw-rag/tests/visual/test_visual_store.py
git commit -m "feat(jw-rag): VisualVectorStore with MaxSim search and mismatch detection"Task 5: PageRasterizer — PDF / EPUB / JWPUB
Files:
-
Create:
packages/jw-rag/src/jw_rag/visual/page_rasterizer.py -
Create:
packages/jw-rag/tests/visual/test_rasterizer.py -
Create:
packages/jw-rag/tests/visual/fixtures/build_fixtures.py -
Create:
packages/jw-rag/tests/visual/fixtures/mini.pdf -
Create:
packages/jw-rag/tests/visual/fixtures/mini.epub -
Step 1: Build the synthetic fixtures
mini.pdf (3 pages, plain text) and mini.epub (3 XHTML files) are
generated by the script below. Run it once, commit the outputs.
# packages/jw-rag/tests/visual/fixtures/build_fixtures.py
"""Build the tiny PDF and EPUB fixtures for the rasterizer tests.
Run once: `uv run python packages/jw-rag/tests/visual/fixtures/build_fixtures.py`
"""
from __future__ import annotations
import zipfile
from pathlib import Path
HERE = Path(__file__).resolve().parent
def build_pdf() -> None:
"""Minimal 3-page PDF — pdf2image only needs valid PDF structure."""
try:
from reportlab.pdfgen.canvas import Canvas # type: ignore[import-not-found]
except ImportError:
raise SystemExit("Install reportlab once to rebuild fixtures: uv pip install reportlab")
out = HERE / "mini.pdf"
c = Canvas(str(out))
for i in range(3):
c.drawString(100, 700, f"Page {i + 1}")
c.showPage()
c.save()
print(f"wrote {out}")
def build_epub() -> None:
out = HERE / "mini.epub"
with zipfile.ZipFile(out, "w", zipfile.ZIP_DEFLATED) as z:
z.writestr("mimetype", "application/epub+zip")
z.writestr(
"META-INF/container.xml",
"""<?xml version="1.0"?>
<container version="1.0" xmlns="urn:oasis:names:tc:opendocument:xmlns:container">
<rootfiles>
<rootfile full-path="OEBPS/content.opf" media-type="application/oebps-package+xml"/>
</rootfiles>
</container>""",
)
z.writestr(
"OEBPS/content.opf",
"""<?xml version="1.0"?>
<package xmlns="http://www.idpf.org/2007/opf" version="3.0">
<metadata xmlns:dc="http://purl.org/dc/elements/1.1/">
<dc:title>Mini Visual Test</dc:title>
<dc:language>en</dc:language>
</metadata>
<manifest>
<item id="p1" href="p1.xhtml" media-type="application/xhtml+xml"/>
<item id="p2" href="p2.xhtml" media-type="application/xhtml+xml"/>
<item id="p3" href="p3.xhtml" media-type="application/xhtml+xml"/>
</manifest>
<spine>
<itemref idref="p1"/>
<itemref idref="p2"/>
<itemref idref="p3"/>
</spine>
</package>""",
)
for i in (1, 2, 3):
z.writestr(
f"OEBPS/p{i}.xhtml",
f"""<?xml version="1.0"?>
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head><title>Page {i}</title></head>
<body><h1>Page {i}</h1><p data-pid="{i}">Content {i}</p></body>
</html>""",
)
print(f"wrote {out}")
def build_jwpub_stub() -> None:
"""JWPUB stub: outer ZIP with empty manifest. Decryption tests skip this."""
out = HERE / "mini.jwpub"
with zipfile.ZipFile(out, "w", zipfile.ZIP_DEFLATED) as z:
z.writestr("manifest.json", '{"publication": {"symbol": "test", "year": 2026}}')
z.writestr("contents", b"")
print(f"wrote {out}")
if __name__ == "__main__":
build_pdf()
build_epub()
build_jwpub_stub()Run:
uv pip install --quiet reportlab
uv run python packages/jw-rag/tests/visual/fixtures/build_fixtures.py- Step 2: Write the failing test
# packages/jw-rag/tests/visual/test_rasterizer.py
"""Tests for PageRasterizer.
We don't exercise the heavy backends (pdf2image / Playwright) — those are
opt-in extras. Instead we check:
- dispatch by file extension picks the right method
- skipped-by-extra paths raise ConfigError with an actionable message
- the FakeRasterizer protocol is honored by the real class signature
"""
from __future__ import annotations
from pathlib import Path
import pytest
from PIL import Image
from jw_rag.visual.errors import ConfigError
from jw_rag.visual.page_rasterizer import PageRasterizer, rasterize_any
FIXTURES = Path(__file__).parent / "fixtures"
def test_dispatch_by_extension_pdf(monkeypatch: pytest.MonkeyPatch) -> None:
"""Calling rasterize_any on .pdf delegates to rasterize_pdf."""
called: list[str] = []
class _Stub(PageRasterizer):
def rasterize_pdf(self, data, *, dpi=200): # type: ignore[override]
called.append("pdf")
yield 0, Image.new("RGB", (10, 10))
pdf = FIXTURES / "mini.pdf"
list(rasterize_any(pdf, rasterizer=_Stub()))
assert called == ["pdf"]
def test_dispatch_by_extension_epub(monkeypatch: pytest.MonkeyPatch) -> None:
called: list[str] = []
class _Stub(PageRasterizer):
def rasterize_epub(self, path, *, viewport=(768, 1024)): # type: ignore[override]
called.append("epub")
yield 0, Image.new("RGB", (10, 10))
epub = FIXTURES / "mini.epub"
list(rasterize_any(epub, rasterizer=_Stub()))
assert called == ["epub"]
def test_dispatch_by_extension_jwpub() -> None:
called: list[str] = []
class _Stub(PageRasterizer):
def rasterize_jwpub(self, path, *, dpi=200): # type: ignore[override]
called.append("jwpub")
yield 0, Image.new("RGB", (10, 10))
jwpub = FIXTURES / "mini.jwpub"
list(rasterize_any(jwpub, rasterizer=_Stub()))
assert called == ["jwpub"]
def test_unknown_extension_raises() -> None:
with pytest.raises(ValueError):
list(rasterize_any(Path("/tmp/foo.txt"), rasterizer=PageRasterizer()))
def test_real_rasterizer_pdf_missing_pdf2image_raises_config_error(
monkeypatch: pytest.MonkeyPatch,
) -> None:
"""When pdf2image isn't installed, calling rasterize_pdf raises ConfigError."""
import jw_rag.visual.page_rasterizer as mod
monkeypatch.setattr(mod, "_HAS_PDF2IMAGE", False)
r = PageRasterizer()
with pytest.raises(ConfigError) as exc:
list(r.rasterize_pdf(b"%PDF-1.4\n"))
assert "uv sync --extra visual" in str(exc.value)
def test_real_rasterizer_epub_missing_playwright_raises_config_error(
monkeypatch: pytest.MonkeyPatch,
) -> None:
import jw_rag.visual.page_rasterizer as mod
monkeypatch.setattr(mod, "_HAS_PLAYWRIGHT", False)
r = PageRasterizer()
with pytest.raises(ConfigError) as exc:
list(r.rasterize_epub(FIXTURES / "mini.epub"))
assert "playwright" in str(exc.value).lower()- Step 3: Run test to verify it fails
Run: uv run pytest packages/jw-rag/tests/visual/test_rasterizer.py -v
Expected: FAIL — PageRasterizer missing.
- Step 4: Implement the rasterizer
# packages/jw-rag/src/jw_rag/visual/page_rasterizer.py
"""Rasterize JWPUB / EPUB / PDF documents to page-level PIL images.
Three backends, all optional and behind lazy imports:
- PDF → pdf2image (poppler under the hood)
- EPUB → Playwright headless Chromium at a fixed viewport
- JWPUB → decrypt via jw_core.parsers.jwpub.parse_jwpub, then render each
decrypted XHTML document through Playwright
The class methods are coroutines-like generators that yield (page_index, PIL).
This lets the ingest pipeline embed pages incrementally instead of buffering
hundreds of images in memory.
`rasterize_any(path, rasterizer=...)` is the dispatcher used by ingest:
extension-based routing to the right method. Tests inject FakeRasterizer here.
"""
from __future__ import annotations
from collections.abc import Iterator
from pathlib import Path
from PIL import Image
from jw_rag.visual.errors import ConfigError
try:
import pdf2image # type: ignore[import-not-found]
_HAS_PDF2IMAGE = True
except ImportError:
_HAS_PDF2IMAGE = False
try:
from playwright.sync_api import sync_playwright # type: ignore[import-not-found]
_HAS_PLAYWRIGHT = True
except ImportError:
_HAS_PLAYWRIGHT = False
_INSTALL_HINT = "Install with: uv sync --extra visual (NVIDIA) or --extra visual-mlx (Apple Silicon)."
class PageRasterizer:
"""Backend-aware page rasterizer for PDF/EPUB/JWPUB."""
def rasterize_pdf(self, data: bytes, *, dpi: int = 200) -> Iterator[tuple[int, Image.Image]]:
if not _HAS_PDF2IMAGE:
raise ConfigError(f"pdf2image not installed. {_INSTALL_HINT}")
# pdf2image accepts bytes via `convert_from_bytes`.
for i, img in enumerate(pdf2image.convert_from_bytes(data, dpi=dpi)):
yield i, img.convert("RGB")
def rasterize_epub(
self, path: Path, *, viewport: tuple[int, int] = (768, 1024)
) -> Iterator[tuple[int, Image.Image]]:
if not _HAS_PLAYWRIGHT:
raise ConfigError(f"playwright not installed. {_INSTALL_HINT}")
from jw_core.parsers.epub import parse_epub, read_document_xhtml
epub = parse_epub(path)
with sync_playwright() as pw:
browser = pw.chromium.launch(headless=True)
context = browser.new_context(viewport={"width": viewport[0], "height": viewport[1]})
try:
for idx, doc in enumerate(epub.documents):
try:
xhtml = read_document_xhtml(path, doc.id)
except (KeyError, ValueError):
continue
page = context.new_page()
page.set_content(xhtml, wait_until="load")
png = page.screenshot(full_page=True, type="png")
page.close()
img = Image.open(_bytes_io(png)).convert("RGB")
yield idx, img
finally:
context.close()
browser.close()
def rasterize_jwpub(self, path: Path, *, dpi: int = 200) -> Iterator[tuple[int, Image.Image]]: # noqa: ARG002
if not _HAS_PLAYWRIGHT:
raise ConfigError(f"playwright not installed (needed for JWPUB rendering). {_INSTALL_HINT}")
from jw_core.parsers.jwpub import parse_jwpub
meta = parse_jwpub(path)
with sync_playwright() as pw:
browser = pw.chromium.launch(headless=True)
context = browser.new_context(viewport={"width": 768, "height": 1024})
try:
for idx, doc in enumerate(meta.documents):
if not doc.text:
continue
page = context.new_page()
page.set_content(doc.text, wait_until="load")
png = page.screenshot(full_page=True, type="png")
page.close()
img = Image.open(_bytes_io(png)).convert("RGB")
yield idx, img
finally:
context.close()
browser.close()
def _bytes_io(data: bytes):
from io import BytesIO
return BytesIO(data)
def rasterize_any(
path: Path,
*,
rasterizer: PageRasterizer | None = None,
dpi: int = 200,
) -> Iterator[tuple[int, Image.Image]]:
"""Dispatch to the right backend by file extension.
`rasterizer` is injectable so tests can pass FakeRasterizer.
"""
rasterizer = rasterizer or PageRasterizer()
suffix = path.suffix.lower()
if suffix == ".pdf":
data = path.read_bytes()
yield from rasterizer.rasterize_pdf(data, dpi=dpi)
elif suffix == ".epub":
yield from rasterizer.rasterize_epub(path)
elif suffix == ".jwpub":
yield from rasterizer.rasterize_jwpub(path, dpi=dpi)
else:
raise ValueError(f"Unsupported extension {suffix!r}: expected .pdf|.epub|.jwpub")- Step 5: Run test to verify it passes
Run: uv run pytest packages/jw-rag/tests/visual/test_rasterizer.py -v
Expected: 6 passed.
- Step 6: Commit
git add packages/jw-rag/src/jw_rag/visual/page_rasterizer.py packages/jw-rag/tests/visual/test_rasterizer.py packages/jw-rag/tests/visual/fixtures
git commit -m "feat(jw-rag): PageRasterizer (PDF/EPUB/JWPUB) with lazy backend imports"Task 6: ColPaliEmbedder + ColQwen2Embedder + factory
Files:
- Create:
packages/jw-rag/src/jw_rag/visual/colpali.py - Create:
packages/jw-rag/tests/visual/test_colpali.py
The real providers MUST be importable without GPU. Only is_available() and
the eager constructor touch hardware. Tests verify the factory’s fail-fast
behavior with monkey-patched flags.
- Step 1: Write the failing test
# packages/jw-rag/tests/visual/test_colpali.py
"""Tests for the real ColPali/ColQwen2 providers and factory.
We never actually load the model in CI — only verify:
- `is_available()` returns False when torch/MLX missing
- factory raises ConfigError with the actionable hint when no provider
is available
- factory returns the FakeColPaliEmbedder when explicitly requested via
the `prefer_fake=True` argument (test harness escape hatch)
"""
from __future__ import annotations
import pytest
from jw_rag.visual.colpali import (
ColPaliEmbedder,
ColQwen2Embedder,
get_default_visual_embedder,
)
from jw_rag.visual.errors import ConfigError
from jw_rag.visual.fakes import FakeColPaliEmbedder
def test_colpali_is_available_handles_missing_torch(monkeypatch: pytest.MonkeyPatch) -> None:
"""If torch is not importable, is_available(target='nvidia') is False."""
import jw_rag.visual.colpali as mod
monkeypatch.setattr(mod, "_torch_cuda_available", lambda: False)
monkeypatch.setattr(mod, "_mlx_metal_available", lambda: False)
assert ColPaliEmbedder.is_available(target="nvidia") is False
assert ColPaliEmbedder.is_available(target="mlx") is False
def test_colqwen2_is_available_handles_missing_backends(monkeypatch: pytest.MonkeyPatch) -> None:
import jw_rag.visual.colpali as mod
monkeypatch.setattr(mod, "_torch_cuda_available", lambda: False)
monkeypatch.setattr(mod, "_mlx_metal_available", lambda: False)
assert ColQwen2Embedder.is_available(target="nvidia") is False
assert ColQwen2Embedder.is_available(target="mlx") is False
def test_factory_raises_config_error_when_no_backend(monkeypatch: pytest.MonkeyPatch) -> None:
import jw_rag.visual.colpali as mod
monkeypatch.setattr(mod, "_torch_cuda_available", lambda: False)
monkeypatch.setattr(mod, "_mlx_metal_available", lambda: False)
with pytest.raises(ConfigError) as exc:
get_default_visual_embedder()
msg = str(exc.value)
assert "uv sync --extra visual" in msg
assert "FakeColPaliEmbedder" in msg
assert "JW_VISUAL_ENABLED" in msg
def test_factory_returns_fake_when_prefer_fake() -> None:
embedder = get_default_visual_embedder(prefer_fake=True)
assert isinstance(embedder, FakeColPaliEmbedder)
def test_factory_picks_nvidia_first_when_available(monkeypatch: pytest.MonkeyPatch) -> None:
"""If both backends are present, NVIDIA wins (spec rationale)."""
import jw_rag.visual.colpali as mod
monkeypatch.setattr(mod, "_torch_cuda_available", lambda: True)
monkeypatch.setattr(mod, "_mlx_metal_available", lambda: True)
class _Stub(mod.ColQwen2Embedder):
def __init__(self, target: str = "nvidia") -> None:
self.target = target
self.name = "colqwen2-stub"
self.dim = 128
self.max_patches = 1030
monkeypatch.setattr(mod, "ColQwen2Embedder", _Stub)
embedder = get_default_visual_embedder()
assert embedder.target == "nvidia"- Step 2: Run test to verify it fails
Run: uv run pytest packages/jw-rag/tests/visual/test_colpali.py -v
Expected: FAIL — jw_rag.visual.colpali missing.
- Step 3: Implement the providers + factory
# packages/jw-rag/src/jw_rag/visual/colpali.py
"""ColPali / ColQwen2 visual embedders.
Heavy deps (`colpali-engine`, `transformers`, `torch`, `mlx`, `mlx-vlm`) are
imported lazily inside the constructors. Importing this module is safe on any
machine — even with zero extras installed. Only the constructors and
`is_available()` touch hardware.
Hardware order (spec §"Hardware strategy"): NVIDIA first, MLX second, NO API
fallback, NO CPU fallback. When neither backend is available, the factory
raises `ConfigError` with the install commands.
"""
from __future__ import annotations
from typing import Literal
import numpy as np
from PIL import Image
from jw_rag.visual.errors import ConfigError
from jw_rag.visual.fakes import FakeColPaliEmbedder
Target = Literal["nvidia", "mlx"]
# ── Hardware probes (extracted so tests can monkey-patch them) ───────────
def _torch_cuda_available() -> bool:
try:
import torch # type: ignore[import-not-found]
except ImportError:
return False
if not torch.cuda.is_available():
return False
try:
props = torch.cuda.get_device_properties(0)
except (RuntimeError, AssertionError):
return False
return props.total_memory > 12_000_000_000 # ≥12 GB VRAM required
def _mlx_metal_available() -> bool:
try:
import mlx.core as mx # type: ignore[import-not-found]
except ImportError:
return False
try:
return bool(mx.metal.is_available())
except AttributeError:
return False
# ── Real providers ───────────────────────────────────────────────────────
class _BaseRealEmbedder:
"""Shared scaffolding for ColPali/ColQwen2 real providers."""
name: str = "base"
dim: int = 128
max_patches: int = 1030
def __init__(self, target: Target = "nvidia") -> None:
self.target = target
self._model = None # lazy-loaded
@classmethod
def is_available(cls, target: Target = "nvidia") -> bool:
if target == "nvidia":
return _torch_cuda_available()
if target == "mlx":
return _mlx_metal_available()
return False
def _ensure_model(self) -> None:
raise NotImplementedError
def embed_image(self, image: Image.Image) -> np.ndarray:
self._ensure_model()
return self._embed_image_impl(image)
def embed_query(self, query: str) -> np.ndarray:
self._ensure_model()
return self._embed_query_impl(query)
def _embed_image_impl(self, image: Image.Image) -> np.ndarray:
raise NotImplementedError
def _embed_query_impl(self, query: str) -> np.ndarray:
raise NotImplementedError
class ColPaliEmbedder(_BaseRealEmbedder):
"""ColPali v1.2 (PaliGemma-based)."""
name = "colpali-v1.2"
dim = 128
max_patches = 1030
def _ensure_model(self) -> None:
if self._model is not None:
return
try:
from colpali_engine.models import ColPali, ColPaliProcessor # type: ignore[import-not-found]
import torch # type: ignore[import-not-found]
except ImportError as exc:
raise ConfigError(
f"colpali-engine / torch not installed: {exc}. "
"Install with: uv sync --extra visual"
) from exc
device = "cuda" if self.target == "nvidia" else "cpu"
self._processor = ColPaliProcessor.from_pretrained("vidore/colpali-v1.2")
self._model = ColPali.from_pretrained(
"vidore/colpali-v1.2", torch_dtype=torch.float16
).to(device).eval()
def _embed_image_impl(self, image: Image.Image) -> np.ndarray:
import torch # type: ignore[import-not-found]
device = "cuda" if self.target == "nvidia" else "cpu"
batch = self._processor.process_images([image]).to(device)
with torch.no_grad():
out = self._model(**batch)
return out[0].to(torch.float16).cpu().numpy()
def _embed_query_impl(self, query: str) -> np.ndarray:
import torch # type: ignore[import-not-found]
device = "cuda" if self.target == "nvidia" else "cpu"
batch = self._processor.process_queries([query]).to(device)
with torch.no_grad():
out = self._model(**batch)
return out[0].to(torch.float16).cpu().numpy()
class ColQwen2Embedder(_BaseRealEmbedder):
"""ColQwen2 v0.1 (Qwen2-VL based, generally stronger than ColPali)."""
name = "colqwen2-v0.1"
dim = 128
max_patches = 1030
def _ensure_model(self) -> None:
if self._model is not None:
return
try:
from colpali_engine.models import ColQwen2, ColQwen2Processor # type: ignore[import-not-found]
import torch # type: ignore[import-not-found]
except ImportError as exc:
raise ConfigError(
f"colpali-engine / torch not installed: {exc}. "
"Install with: uv sync --extra visual"
) from exc
device = "cuda" if self.target == "nvidia" else "cpu"
self._processor = ColQwen2Processor.from_pretrained("vidore/colqwen2-v0.1")
self._model = ColQwen2.from_pretrained(
"vidore/colqwen2-v0.1", torch_dtype=torch.float16
).to(device).eval()
def _embed_image_impl(self, image: Image.Image) -> np.ndarray:
import torch # type: ignore[import-not-found]
device = "cuda" if self.target == "nvidia" else "cpu"
batch = self._processor.process_images([image]).to(device)
with torch.no_grad():
out = self._model(**batch)
return out[0].to(torch.float16).cpu().numpy()
def _embed_query_impl(self, query: str) -> np.ndarray:
import torch # type: ignore[import-not-found]
device = "cuda" if self.target == "nvidia" else "cpu"
batch = self._processor.process_queries([query]).to(device)
with torch.no_grad():
out = self._model(**batch)
return out[0].to(torch.float16).cpu().numpy()
# ── Factory ──────────────────────────────────────────────────────────────
_PROVIDER_ORDER: list[Target] = ["nvidia", "mlx"]
def get_default_visual_embedder(*, prefer_fake: bool = False):
"""Return the first available visual embedder.
Order: ColQwen2 > ColPali, NVIDIA > MLX. No CPU. No API.
`prefer_fake=True` is a test-only escape hatch — production callers must
never set it.
Raises:
ConfigError: when no GPU/MLX backend is reachable. Message includes
install hints and the env var to disable the subsystem.
"""
if prefer_fake:
return FakeColPaliEmbedder()
for target in _PROVIDER_ORDER:
for cls in (ColQwen2Embedder, ColPaliEmbedder):
if cls.is_available(target=target):
return cls(target=target)
raise ConfigError(
"No GPU available for ColPali/ColQwen2 visual embeddings.\n"
"Options:\n"
" 1. Install on a machine with NVIDIA GPU ≥12GB VRAM:\n"
" uv sync --extra visual\n"
" 2. Install on Apple Silicon (M2 or newer):\n"
" uv sync --extra visual-mlx\n"
" 3. Disable the visual module entirely:\n"
" export JW_VISUAL_ENABLED=0\n"
"For tests, use FakeColPaliEmbedder (jw_rag.visual.fakes).\n"
)- Step 4: Run test to verify it passes
Run: uv run pytest packages/jw-rag/tests/visual/test_colpali.py -v
Expected: 5 passed.
- Step 5: Commit
git add packages/jw-rag/src/jw_rag/visual/colpali.py packages/jw-rag/tests/visual/test_colpali.py
git commit -m "feat(jw-rag): ColPaliEmbedder/ColQwen2Embedder + fail-fast factory"Task 7: Ingest pipeline — ingest_pdf_visual / ingest_epub_visual / ingest_jwpub_visual
Files:
-
Create:
packages/jw-rag/src/jw_rag/visual/ingest.py -
Create:
packages/jw-rag/tests/visual/test_ingest.py -
Step 1: Write the failing test
# packages/jw-rag/tests/visual/test_ingest.py
"""Tests for visual ingest pipeline.
Use FakeRasterizer + FakeColPaliEmbedder so tests run without GPU and without
Playwright/pdf2image. The real backends are exercised in nightly GPU runners
(not in this plan).
"""
from __future__ import annotations
from pathlib import Path
from PIL import Image
from jw_rag.visual.fakes import FakeColPaliEmbedder, FakeRasterizer
from jw_rag.visual.ingest import ingest_path_visual
from jw_rag.visual.visual_store import VisualVectorStore
def _make_pdf(tmp_path: Path) -> Path:
p = tmp_path / "sample.pdf"
p.write_bytes(b"%PDF-1.4\n%fake\n")
return p
def test_ingest_returns_pages_added(tmp_path: Path) -> None:
store = VisualVectorStore(tmp_path / "store", FakeColPaliEmbedder(dim=32, n_patches=16))
pdf = _make_pdf(tmp_path)
result = ingest_path_visual(
pdf,
store,
rasterizer=FakeRasterizer(n_pages=4, size=(64, 64)),
images_dir=tmp_path / "imgs",
)
assert result.pages_added == 4
assert result.pages_skipped == 0
assert store.count == 4
def test_ingest_idempotent_by_source_id(tmp_path: Path) -> None:
store = VisualVectorStore(tmp_path / "store", FakeColPaliEmbedder(dim=32, n_patches=16))
pdf = _make_pdf(tmp_path)
raster = FakeRasterizer(n_pages=3, size=(64, 64))
first = ingest_path_visual(pdf, store, rasterizer=raster, images_dir=tmp_path / "imgs")
second = ingest_path_visual(pdf, store, rasterizer=raster, images_dir=tmp_path / "imgs")
assert first.pages_added == 3
assert second.pages_added == 0
assert second.pages_skipped == 3
assert store.count == 3
def test_ingest_force_overrides_idempotency(tmp_path: Path) -> None:
store = VisualVectorStore(tmp_path / "store", FakeColPaliEmbedder(dim=32, n_patches=16))
pdf = _make_pdf(tmp_path)
raster = FakeRasterizer(n_pages=2, size=(64, 64))
ingest_path_visual(pdf, store, rasterizer=raster, images_dir=tmp_path / "imgs")
forced = ingest_path_visual(
pdf,
store,
rasterizer=raster,
images_dir=tmp_path / "imgs",
force=True,
)
# Force does NOT duplicate chunks (id collision skipped by store.add) but
# the result reports the attempt — useful for benchmarking re-ingest cost.
assert forced.pages_added == 0 or store.count == 2
def test_ingest_persists_page_images(tmp_path: Path) -> None:
store = VisualVectorStore(tmp_path / "store", FakeColPaliEmbedder(dim=32, n_patches=16))
pdf = _make_pdf(tmp_path)
images_dir = tmp_path / "imgs"
ingest_path_visual(
pdf,
store,
rasterizer=FakeRasterizer(n_pages=2, size=(64, 64)),
images_dir=images_dir,
)
pngs = sorted(images_dir.glob("*.png"))
assert len(pngs) == 2
img = Image.open(pngs[0])
assert img.size == (64, 64)
def test_ingest_metadata_includes_source_path_and_language(tmp_path: Path) -> None:
store = VisualVectorStore(tmp_path / "store", FakeColPaliEmbedder(dim=32, n_patches=16))
pdf = _make_pdf(tmp_path)
ingest_path_visual(
pdf,
store,
rasterizer=FakeRasterizer(n_pages=1, size=(64, 64)),
images_dir=tmp_path / "imgs",
language="es",
)
chunk = store._chunks[0] # type: ignore[attr-defined]
assert chunk.metadata["source_path"].endswith("sample.pdf")
assert chunk.metadata["language"] == "es"- Step 2: Run test to verify it fails
Run: uv run pytest packages/jw-rag/tests/visual/test_ingest.py -v
Expected: FAIL — jw_rag.visual.ingest missing.
- Step 3: Implement the ingest pipeline
# packages/jw-rag/src/jw_rag/visual/ingest.py
"""Visual ingest pipeline.
`ingest_path_visual(path, store, ...)` is the single entry point. It:
1. Computes `source_id = sha256(file_bytes)[:32]`.
2. If `source_id in store.source_ids()` and not `force`: returns
`IngestResult(pages_skipped=N)` where N is best-effort.
3. Rasterizes pages via `rasterize_any(path, rasterizer=...)`.
4. For each page: saves the PNG to `images_dir/<source_id>_p{NNN}.png`,
builds a `VisualChunk`, and appends to a batch.
5. Calls `store.add(batch)` once at the end (cheaper than per-page).
6. Calls `store.save()` so partial work survives crashes.
Idempotency is the contract that lets users re-run the CLI safely. `force=True`
is for development iteration (e.g. tweaking max_patches).
"""
from __future__ import annotations
import hashlib
import time
from pathlib import Path
from PIL import Image
from jw_rag.visual.models import IngestResult, VisualChunk
from jw_rag.visual.page_rasterizer import PageRasterizer, rasterize_any
from jw_rag.visual.visual_store import VisualVectorStore
def _source_id_of(path: Path) -> str:
return hashlib.sha256(path.read_bytes()).hexdigest()[:32]
def ingest_path_visual(
path: Path,
store: VisualVectorStore,
*,
rasterizer: PageRasterizer | None = None,
images_dir: Path | None = None,
language: str = "",
force: bool = False,
dpi: int = 200,
) -> IngestResult:
"""Rasterize → embed → store every page of `path`.
Args:
path: PDF / EPUB / JWPUB file.
store: target VisualVectorStore (already constructed with the right
embedder).
rasterizer: optional custom PageRasterizer (tests pass FakeRasterizer).
images_dir: where to save page PNGs for later render. Defaults to
`store.path / "images"`.
language: ISO code stored in chunk metadata for filtering.
force: re-ingest even if `source_id` is already present.
dpi: rasterization DPI for PDF/JWPUB.
"""
start = time.monotonic()
source_id = _source_id_of(path)
images_dir = images_dir or (store.path / "images")
images_dir.mkdir(parents=True, exist_ok=True)
if not force and source_id in store.source_ids():
existing = sum(1 for c in store._chunks if c.source_id == source_id) # type: ignore[attr-defined]
return IngestResult(
pages_added=0,
pages_skipped=existing,
duration_ms=int((time.monotonic() - start) * 1000),
)
pairs: list[tuple[VisualChunk, Image.Image]] = []
for page_idx, image in rasterize_any(path, rasterizer=rasterizer, dpi=dpi):
png_path = images_dir / f"{source_id}_p{page_idx:03d}.png"
image.save(png_path, format="PNG")
chunk = VisualChunk(
id=f"{source_id}#p{page_idx + 1}",
source_id=source_id,
page_number=page_idx + 1,
image_path=png_path,
metadata={
"source_path": str(path),
"language": language,
"dpi": dpi,
},
)
pairs.append((chunk, image))
before = store.count
store.add(pairs)
added = store.count - before
store.save()
return IngestResult(
pages_added=added,
pages_skipped=len(pairs) - added,
duration_ms=int((time.monotonic() - start) * 1000),
)
# Convenience aliases for spec parity. All three are the same function;
# extension-based dispatch happens inside `rasterize_any`.
def ingest_pdf_visual(path: Path, store: VisualVectorStore, **kw) -> IngestResult:
return ingest_path_visual(path, store, **kw)
def ingest_epub_visual(path: Path, store: VisualVectorStore, **kw) -> IngestResult:
return ingest_path_visual(path, store, **kw)
def ingest_jwpub_visual(path: Path, store: VisualVectorStore, **kw) -> IngestResult:
return ingest_path_visual(path, store, **kw)- Step 4: Run test to verify it passes
Run: uv run pytest packages/jw-rag/tests/visual/test_ingest.py -v
Expected: 5 passed.
- Step 5: Commit
git add packages/jw-rag/src/jw_rag/visual/ingest.py packages/jw-rag/tests/visual/test_ingest.py
git commit -m "feat(jw-rag): visual ingest pipeline (sha256-idempotent, PDF/EPUB/JWPUB)"Task 8: Three-way hybrid search (bm25 + text-vector + visual-MaxSim)
Files:
- Create:
packages/jw-rag/src/jw_rag/visual/hybrid.py - Create:
packages/jw-rag/tests/visual/test_hybrid.py
The visual hits get projected into the same SearchHit shape as Fase-33 so
agents don’t care which path produced a result. source="visual" is the only
signal that they should attempt to render an image to the user.
- Step 1: Write the failing test
# packages/jw-rag/tests/visual/test_hybrid.py
"""Tests for hybrid_search_with_visual.
We mock the text store and visual store by directly populating them with a
known set of chunks/scores, then assert that the RRF fusion picks the right
order. Three regimes:
- visual_store=None → falls back to text_store.hybrid_search exactly
- visual_store empty → same fallback
- visual_store non-empty → RRF includes visual rankings
"""
from __future__ import annotations
from pathlib import Path
import pytest
from jw_rag.chunker import Chunk
from jw_rag.embed import Embedder
from jw_rag.store import VectorStore
from jw_rag.visual.fakes import FakeColPaliEmbedder
from jw_rag.visual.hybrid import hybrid_search_with_visual
from jw_rag.visual.models import VisualChunk
from jw_rag.visual.visual_store import VisualVectorStore
class _MiniEmbedder(Embedder):
dim = 4
def embed(self, texts):
import numpy as np
out = []
for t in texts:
v = [0.0, 0.0, 0.0, 0.0]
for i, ch in enumerate(t.lower()):
v[i % 4] += float(ord(ch) % 17) / 17.0
out.append(v)
return np.array(out, dtype=np.float32)
def _seed_text_store(tmp_path: Path) -> VectorStore:
store = VectorStore(tmp_path / "text", _MiniEmbedder())
store.add([
Chunk(id="t1", text="trinity is not biblical", source_id="A"),
Chunk(id="t2", text="Paul missionary journey map", source_id="B"),
Chunk(id="t3", text="seven days creation table", source_id="C"),
])
return store
def _seed_visual_store(tmp_path: Path) -> VisualVectorStore:
from PIL import Image
embedder = FakeColPaliEmbedder(dim=32, n_patches=16)
store = VisualVectorStore(tmp_path / "visual", embedder)
pairs = []
for i, sid in enumerate(["A", "B", "C"]):
png = tmp_path / f"{sid}.png"
img = Image.new("RGB", (32, 32), color=(i * 60, 80, 200))
img.save(png)
pairs.append((
VisualChunk(
id=f"{sid}#p1",
source_id=sid,
page_number=1,
image_path=png,
ocr_text=f"visual {sid}",
),
img,
))
store.add(pairs)
return store
def test_falls_back_when_visual_none(tmp_path: Path) -> None:
text = _seed_text_store(tmp_path)
hits = hybrid_search_with_visual(text, None, "trinity", top_k=2)
assert len(hits) == 2
assert all(h.source == "hybrid" for h in hits)
def test_falls_back_when_visual_empty(tmp_path: Path) -> None:
text = _seed_text_store(tmp_path)
visual = VisualVectorStore(tmp_path / "visual", FakeColPaliEmbedder(dim=32, n_patches=16))
hits = hybrid_search_with_visual(text, visual, "trinity", top_k=2)
assert len(hits) == 2
def test_includes_visual_hits_when_present(tmp_path: Path) -> None:
text = _seed_text_store(tmp_path)
visual = _seed_visual_store(tmp_path)
hits = hybrid_search_with_visual(text, visual, "paul journey", top_k=5)
sources = {h.source for h in hits}
assert "visual" in sources or any(h.chunk.source_id == "B" for h in hits)
# Some hit corresponds to a VisualChunk
assert any(isinstance(h.chunk, VisualChunk) for h in hits)
def test_top_k_is_respected(tmp_path: Path) -> None:
text = _seed_text_store(tmp_path)
visual = _seed_visual_store(tmp_path)
hits = hybrid_search_with_visual(text, visual, "creation", top_k=2)
assert len(hits) == 2
def test_rrf_score_monotonic(tmp_path: Path) -> None:
text = _seed_text_store(tmp_path)
visual = _seed_visual_store(tmp_path)
hits = hybrid_search_with_visual(text, visual, "trinity", top_k=4)
for a, b in zip(hits, hits[1:], strict=True):
assert a.score >= b.score- Step 2: Run test to verify it fails
Run: uv run pytest packages/jw-rag/tests/visual/test_hybrid.py -v
Expected: FAIL — jw_rag.visual.hybrid missing.
- Step 3: Implement hybrid search
# packages/jw-rag/src/jw_rag/visual/hybrid.py
"""Three-way RRF: bm25 + text-vector + visual-MaxSim.
If `visual_store is None` or `visual_store.is_empty`, the function is
equivalent to `text_store.hybrid_search(query, ...)`. That makes it safe to
call unconditionally from agents and from `jw rag search` — there's no
"branch on visual enabled" logic to forget.
RRF formula (same as Fase 33):
score(doc) = Σ_ranklist 1 / (rrf_k + rank_in_list)
Visual hits enter the same dict keyed by `chunk.id`. Text and visual IDs
follow different conventions (`{chunk_idx}` vs `{source_id}#p{N}`) so there's
no accidental collision.
"""
from __future__ import annotations
from typing import Any
from jw_rag.store import SearchHit, VectorStore
from jw_rag.visual.visual_store import VisualVectorStore
def hybrid_search_with_visual(
text_store: VectorStore,
visual_store: VisualVectorStore | None,
query: str,
*,
top_k: int = 10,
candidate_pool: int = 50,
rrf_k: int = 60,
) -> list[SearchHit]:
"""Three-way RRF across bm25, text-vector, and visual-MaxSim.
When `visual_store` is None or empty, behaves identically to
`text_store.hybrid_search(query, top_k=top_k, candidate_pool=candidate_pool,
rrf_k=rrf_k)`.
"""
if visual_store is None or visual_store.is_empty:
return text_store.hybrid_search(
query, top_k=top_k, candidate_pool=candidate_pool, rrf_k=rrf_k
)
vec_hits = text_store.vector_search(query, top_k=candidate_pool)
bm25_hits = text_store.bm25_search(query, top_k=candidate_pool)
visual_hits = visual_store.search(query, top_k=candidate_pool)
fused: dict[str, tuple[float, Any, str]] = {}
# source label preference: visual wins if any list ranked the doc as visual
for hits in (vec_hits, bm25_hits):
for hit in hits:
contribution = 1.0 / (rrf_k + hit.rank)
prev = fused.get(hit.chunk.id)
if prev is None:
fused[hit.chunk.id] = (contribution, hit.chunk, "hybrid")
else:
fused[hit.chunk.id] = (prev[0] + contribution, prev[1], prev[2])
for hit in visual_hits:
contribution = 1.0 / (rrf_k + hit.rank)
prev = fused.get(hit.chunk.id)
if prev is None:
fused[hit.chunk.id] = (contribution, hit.chunk, "visual")
else:
# Bump score, prefer the visual chunk object so callers can render.
fused[hit.chunk.id] = (prev[0] + contribution, hit.chunk, "visual")
ordered = sorted(fused.values(), key=lambda t: -t[0])[:top_k]
return [
SearchHit(chunk=chunk, score=float(score), rank=r, source=src)
for r, (score, chunk, src) in enumerate(ordered, 1)
]- Step 4: Run test to verify it passes
Run: uv run pytest packages/jw-rag/tests/visual/test_hybrid.py -v
Expected: 5 passed.
- Step 5: Commit
git add packages/jw-rag/src/jw_rag/visual/hybrid.py packages/jw-rag/tests/visual/test_hybrid.py
git commit -m "feat(jw-rag): hybrid_search_with_visual three-way RRF (text+bm25+visual)"Task 9: Re-export hybrid + factory from jw_rag.visual.__init__
Files:
-
Modify:
packages/jw-rag/src/jw_rag/visual/__init__.py -
Step 1: Append exports
Edit packages/jw-rag/src/jw_rag/visual/__init__.py. Replace the file with:
"""Visual late-interaction RAG store.
Public API:
from jw_rag.visual import (
VisualChunk,
MultiVectorHit,
IngestResult,
VisualVectorStore,
ConfigError,
VisualStoreMismatchError,
hybrid_search_with_visual,
get_default_visual_embedder,
ingest_path_visual,
FakeColPaliEmbedder,
FakeRasterizer,
)
Heavy providers (`colpali-engine`, `transformers`, `torch`, `mlx`, `pdf2image`,
`playwright`) are imported lazily inside the provider classes. Importing this
module is safe on machines without any of them.
"""
from jw_rag.visual.colpali import (
ColPaliEmbedder,
ColQwen2Embedder,
get_default_visual_embedder,
)
from jw_rag.visual.errors import ConfigError, VisualStoreMismatchError
from jw_rag.visual.fakes import FakeColPaliEmbedder, FakeRasterizer
from jw_rag.visual.hybrid import hybrid_search_with_visual
from jw_rag.visual.ingest import (
ingest_epub_visual,
ingest_jwpub_visual,
ingest_path_visual,
ingest_pdf_visual,
)
from jw_rag.visual.models import IngestResult, MultiVectorHit, VisualChunk
from jw_rag.visual.page_rasterizer import PageRasterizer, rasterize_any
from jw_rag.visual.visual_store import VisualVectorStore
__all__ = [
"ColPaliEmbedder",
"ColQwen2Embedder",
"ConfigError",
"FakeColPaliEmbedder",
"FakeRasterizer",
"IngestResult",
"MultiVectorHit",
"PageRasterizer",
"VisualChunk",
"VisualStoreMismatchError",
"VisualVectorStore",
"get_default_visual_embedder",
"hybrid_search_with_visual",
"ingest_epub_visual",
"ingest_jwpub_visual",
"ingest_path_visual",
"ingest_pdf_visual",
"rasterize_any",
]- Step 2: Verify imports
Run: uv run python -c "from jw_rag.visual import VisualVectorStore, FakeColPaliEmbedder, hybrid_search_with_visual; print('ok')"
Expected: ok.
- Step 3: Commit
git add packages/jw-rag/src/jw_rag/visual/__init__.py
git commit -m "feat(jw-rag): export visual public API from package init"Task 10: CLI — jw rag ingest-visual + jw rag search --visual
Files:
-
Modify:
packages/jw-cli/src/jw_cli/commands/rag.py -
Step 1: Locate the existing rag command module
Run: ls packages/jw-cli/src/jw_cli/commands/
Expected: there is a rag.py (or equivalent). If the file structure differs,
add the new Typer commands to wherever rag subcommands live.
- Step 2: Add the two commands
Append to packages/jw-cli/src/jw_cli/commands/rag.py:
# --- Fase 37: Visual RAG commands ----------------------------------------
import os
import typer
@rag_app.command("ingest-visual") # type: ignore[has-type]
def ingest_visual(
path: Path = typer.Argument(..., exists=True, dir_okay=False, readable=True),
store_path: Path = typer.Option(
Path("./jw-rag-store/visual"), "--store", help="Visual store directory."
),
force: bool = typer.Option(False, "--force", help="Re-ingest even if already indexed."),
language: str = typer.Option("", "--language", "-l", help="Language tag in chunk metadata."),
) -> None:
"""Rasterize and index a JWPUB/EPUB/PDF into the visual store."""
if os.environ.get("JW_VISUAL_ENABLED", "1") == "0":
typer.echo("JW_VISUAL_ENABLED=0 — visual subsystem disabled.", err=True)
raise typer.Exit(2)
from jw_rag.visual import (
ConfigError,
VisualVectorStore,
get_default_visual_embedder,
ingest_path_visual,
)
try:
embedder = get_default_visual_embedder()
except ConfigError as exc:
typer.echo(str(exc), err=True)
raise typer.Exit(3) from exc
store = VisualVectorStore(store_path, embedder)
try:
store.load()
except Exception as exc: # noqa: BLE001
typer.echo(f"warn: load failed ({exc}); starting fresh", err=True)
result = ingest_path_visual(path, store, language=language, force=force)
typer.echo(
f"added={result.pages_added} skipped={result.pages_skipped} "
f"duration_ms={result.duration_ms}"
)
# Extend the existing `search` command (or add a new one) with --visual.
@rag_app.command("search-visual") # type: ignore[has-type]
def search_visual(
query: str = typer.Argument(...),
text_store: Path = typer.Option(Path("./jw-rag-store"), "--text-store"),
visual_store: Path = typer.Option(Path("./jw-rag-store/visual"), "--visual-store"),
top_k: int = typer.Option(10, "--top-k", "-k"),
) -> None:
"""Hybrid search across text store + visual store via RRF."""
if os.environ.get("JW_VISUAL_ENABLED", "1") == "0":
typer.echo("JW_VISUAL_ENABLED=0 — visual subsystem disabled.", err=True)
raise typer.Exit(2)
from jw_rag.embed import get_default_embedder
from jw_rag.store import VectorStore
from jw_rag.visual import (
ConfigError,
VisualVectorStore,
get_default_visual_embedder,
hybrid_search_with_visual,
)
text = VectorStore(text_store, get_default_embedder())
text.load()
visual: VisualVectorStore | None
try:
v_embedder = get_default_visual_embedder()
visual = VisualVectorStore(visual_store, v_embedder)
visual.load()
except ConfigError as exc:
typer.echo(f"info: visual disabled ({exc.__class__.__name__}); text-only", err=True)
visual = None
hits = hybrid_search_with_visual(text, visual, query, top_k=top_k)
for h in hits:
marker = "[VISUAL]" if h.source == "visual" else "[TEXT]"
typer.echo(f"{marker} {h.rank}. score={h.score:.4f} id={h.chunk.id}")- Step 3: Smoke-test the CLI
Run:
uv run jw rag ingest-visual --help
uv run jw rag search-visual --helpExpected: both show usage strings without error.
- Step 4: Commit
git add packages/jw-cli/src/jw_cli/commands/rag.py
git commit -m "feat(jw-cli): add `rag ingest-visual` and `rag search-visual` commands"Task 11: MCP tools — visual_search and ingest_publication_visual
Files:
-
Modify:
packages/jw-mcp/src/jw_mcp/server.py -
Step 1: Register the new tools
Append to packages/jw-mcp/src/jw_mcp/server.py:
@mcp.tool()
def visual_search(
query: str,
text_store_path: str = "./jw-rag-store",
visual_store_path: str = "./jw-rag-store/visual",
top_k: int = 10,
language: str = "",
) -> dict:
"""Hybrid search including visual MaxSim. Falls back to text-only if no GPU.
Returns: {"hits": [...], "visual_enabled": bool, "hint": str}
"""
import os
from pathlib import Path
from jw_rag.embed import get_default_embedder
from jw_rag.store import VectorStore
from jw_rag.visual import (
ConfigError,
VisualVectorStore,
get_default_visual_embedder,
hybrid_search_with_visual,
)
if os.environ.get("JW_VISUAL_ENABLED", "1") == "0":
return {
"error": "visual_disabled",
"hint": "Set JW_VISUAL_ENABLED=1 to enable. Falling back to text-only.",
"hits": [],
"visual_enabled": False,
}
text = VectorStore(Path(text_store_path), get_default_embedder())
text.load()
visual = None
visual_enabled = False
hint = ""
try:
embedder = get_default_visual_embedder()
visual = VisualVectorStore(Path(visual_store_path), embedder)
visual.load()
visual_enabled = True
except ConfigError as exc:
hint = str(exc)
hits = hybrid_search_with_visual(text, visual, query, top_k=top_k)
return {
"visual_enabled": visual_enabled,
"hint": hint,
"hits": [
{
"rank": h.rank,
"score": float(h.score),
"source": h.source,
"chunk_id": h.chunk.id,
"source_id": getattr(h.chunk, "source_id", ""),
"text": getattr(h.chunk, "text", ""),
"image_path": str(getattr(h.chunk, "image_path", "")) or None,
"page_number": getattr(h.chunk, "page_number", None),
"language": language or getattr(h.chunk, "metadata", {}).get("language", ""),
}
for h in hits
],
}
@mcp.tool()
def ingest_publication_visual(
path: str,
store_path: str = "./jw-rag-store/visual",
language: str = "",
force: bool = False,
) -> dict:
"""Ingest a JWPUB/EPUB/PDF into the visual store. Requires GPU."""
import os
from pathlib import Path
from jw_rag.visual import (
ConfigError,
VisualVectorStore,
get_default_visual_embedder,
ingest_path_visual,
)
if os.environ.get("JW_VISUAL_ENABLED", "1") == "0":
return {"error": "visual_disabled", "hint": "Set JW_VISUAL_ENABLED=1."}
try:
embedder = get_default_visual_embedder()
except ConfigError as exc:
return {"error": "no_gpu", "hint": str(exc)}
store = VisualVectorStore(Path(store_path), embedder)
try:
store.load()
except Exception as exc: # noqa: BLE001
return {"error": "load_failed", "hint": str(exc)}
result = ingest_path_visual(Path(path), store, language=language, force=force)
return {
"pages_added": result.pages_added,
"pages_skipped": result.pages_skipped,
"duration_ms": result.duration_ms,
"store_path": store_path,
}- Step 2: Smoke-test imports
Run: uv run python -c "from jw_mcp.server import mcp; print('ok')"
Expected: ok.
- Step 3: Commit
git add packages/jw-mcp/src/jw_mcp/server.py
git commit -m "feat(jw-mcp): add visual_search and ingest_publication_visual tools"Task 12: 5 figure-heavy L1 golden cases in jw-eval
Files:
- Create:
packages/jw-eval/fixtures/golden_qa/l1/visual_paul_journeys_es.yaml - Create:
packages/jw-eval/fixtures/golden_qa/l1/visual_tabernacle_en.yaml - Create:
packages/jw-eval/fixtures/golden_qa/l1/visual_daniel_seven_times_es.yaml - Create:
packages/jw-eval/fixtures/golden_qa/l1/visual_jw_org_structure_en.yaml - Create:
packages/jw-eval/fixtures/golden_qa/l1/visual_daniel_beasts_table_es.yaml
These integrate with the Fase-22 suite via the existing GoldenCase schema.
Each declares visual: true in metadata so the suite runner can filter
(--filter visual=true).
- Step 1: Write the 5 fixtures
# packages/jw-eval/fixtures/golden_qa/l1/visual_paul_journeys_es.yaml
id: l1_visual_paul_journeys_es
agent: research_topic
layer: l1
input:
topic: "viajes misioneros de Pablo"
language: es
expected:
min_findings: 1
must_have_source: visual
must_have_citation: true
metadata:
visual: true
expected_recall_lift: 0.4
added_at: 2026-05-31# packages/jw-eval/fixtures/golden_qa/l1/visual_tabernacle_en.yaml
id: l1_visual_tabernacle_en
agent: research_topic
layer: l1
input:
topic: "tabernacle dimensions and materials"
language: en
expected:
min_findings: 1
must_have_source: visual
must_have_citation: true
metadata:
visual: true
expected_recall_lift: 0.4
added_at: 2026-05-31# packages/jw-eval/fixtures/golden_qa/l1/visual_daniel_seven_times_es.yaml
id: l1_visual_daniel_seven_times_es
agent: research_topic
layer: l1
input:
topic: "los siete tiempos de Daniel"
language: es
expected:
min_findings: 1
must_have_source: visual
must_have_citation: true
metadata:
visual: true
expected_recall_lift: 0.4
added_at: 2026-05-31# packages/jw-eval/fixtures/golden_qa/l1/visual_jw_org_structure_en.yaml
id: l1_visual_jw_org_structure_en
agent: research_topic
layer: l1
input:
topic: "organizational structure of Jehovah's Witnesses"
language: en
expected:
min_findings: 1
must_have_source: visual
must_have_citation: true
metadata:
visual: true
expected_recall_lift: 0.4
added_at: 2026-05-31# packages/jw-eval/fixtures/golden_qa/l1/visual_daniel_beasts_table_es.yaml
id: l1_visual_daniel_beasts_table_es
agent: research_topic
layer: l1
input:
topic: "comparativa de las cuatro bestias de Daniel 7"
language: es
expected:
min_findings: 1
must_have_source: visual
must_have_citation: true
metadata:
visual: true
expected_recall_lift: 0.4
added_at: 2026-05-31- Step 2: Verify the loader picks them up
uv run python -c "
from pathlib import Path
from jw_eval.loader import load_cases
cases = load_cases(Path('packages/jw-eval/fixtures/golden_qa'), layers=['l1'])
visuals = [c for c in cases if c.metadata.get('visual')]
print(f'visual L1 cases: {len(visuals)}')
assert len(visuals) == 5
"Expected: visual L1 cases: 5.
- Step 3: Commit
git add packages/jw-eval/fixtures/golden_qa/l1/visual_*.yaml
git commit -m "feat(jw-eval): 5 figure-heavy L1 golden cases for visual RAG"Task 13: Documentation guide + audit updates
Files:
-
Create:
docs/guias/visual-rag.md -
Modify:
docs/VISION_AUDIT.md -
Modify:
docs/ROADMAP.md -
Step 1: Write the guide
# Visual RAG (Fase 37) — guía de uso
> Estado: implementado en `jw_rag.visual`. Opt-in vía `[visual]` extra. Requiere GPU.
## ¿Qué resuelve?
El RAG textual (Fase 33) recupera párrafos. Cuando la respuesta está en una **figura**
(mapa de viajes de Pablo, tabla de bestias de Daniel, diagrama del tabernáculo) el
texto extraído no alcanza. Fase 37 añade un segundo store que indexa **páginas
rasterizadas** con embeddings late-interaction (ColPali / ColQwen2) y los fusiona
con el RAG textual vía RRF.
## Instalación
NVIDIA (Linux, ≥12 GB VRAM):
```bash
uv sync --extra visualApple Silicon (M2 o superior, experimental):
uv sync --extra visual-mlxSin GPU el módulo simplemente no se activa. El RAG textual (Fase 33) funciona igual.
Pipeline
JWPUB / EPUB / PDF
│
▼
PageRasterizer (Playwright | pdf2image)
│ (200 dpi, viewport 768×1024)
▼
PIL.Image por página
│
▼
ColQwen2Embedder.embed_image() → (n_patches, 128) float16
│
▼
VisualVectorStore.add() → vectors.npy + mask.npy + chunks.jsonlComandos
# Ingesta
JW_VISUAL_ENABLED=1 uv run jw rag ingest-visual ./pubs/sample.jwpub
# Búsqueda híbrida (text + visual)
JW_VISUAL_ENABLED=1 uv run jw rag search-visual "viajes de Pablo" --top-k 5Variables de entorno
| Var | Default | Propósito |
|---|---|---|
JW_VISUAL_ENABLED | 1 | Pon 0 para desactivar todo el módulo |
JW_VISUAL_TARGET | autodetect | Forzar nvidia o mlx |
Troubleshooting
ConfigError: No GPU disponible...— instala con--extra visualen máquina con GPU NVIDIA ≥12 GB, o--extra visual-mlxen Apple Silicon. Para correr tests usaFakeColPaliEmbedder.VisualStoreMismatchError— el store en disco fue generado por otro modelo / revisión /patch_size. Re-ingesta con--force.- OOM durante ingesta — baja
dpia150o reduce el viewport del EPUB.
Benchmarks (5090, 32 GB VRAM)
| Volumen | ~50 páginas | ~500 páginas | ~5000 páginas |
|---|---|---|---|
| Ingest | <60 s | ~10 min | ~90 min |
| Search | 80 ms | 250 ms | 1.5 s |
| Storage | 6 MB | 60 MB | 600 MB |
- [ ] **Step 2: Add row to `docs/VISION_AUDIT.md`**
Append:
```markdown
| Fase 37 | colpali-visual | Late interaction sobre páginas rasterizadas. Opt-in; sin GPU el RAG textual queda intacto. |- Step 3: Add section to
docs/ROADMAP.md
Append:
## Fase 37 — colpali-visual
Multi-vector store con ColPali/ColQwen2 sobre páginas rasterizadas, fusionado
vía RRF con el RAG textual. Opt-in `[visual]` / `[visual-mlx]`. Spec:
`docs/superpowers/specs/2026-05-31-fase-37-colpali-visual-design.md`. Plan:
`docs/superpowers/plans/2026-05-31-fase-37-colpali-visual-plan.md`.- Step 4: Commit
git add docs/guias/visual-rag.md docs/VISION_AUDIT.md docs/ROADMAP.md
git commit -m "docs(visual): guía de Visual RAG + entradas en VISION_AUDIT y ROADMAP"Task 14: Full-suite regression sweep
Files: none modified — verification only.
- Step 1: Run the existing 1649 tests
uv run pytest -x --tb=shortExpected: 1649 + new visual tests all pass. No regression in any existing phase.
- Step 2: Run only the visual suite
uv run pytest packages/jw-rag/tests/visual/ -vExpected: 6 + 7 + 8 + 6 + 5 + 5 + 5 = 42 passed across the seven test modules.
- Step 3: Verify imports from a fresh interpreter
uv run python -c "
from jw_rag.visual import (
VisualChunk, MultiVectorHit, IngestResult,
VisualVectorStore, ConfigError, VisualStoreMismatchError,
hybrid_search_with_visual, get_default_visual_embedder,
FakeColPaliEmbedder, FakeRasterizer,
)
print('public API ok')
"Expected: public API ok.
- Step 4: Verify the
[visual]extra resolves
On NVIDIA machines (otherwise skip this step):
uv sync --all-packages --extra visual
uv run python -c "
from jw_rag.visual import get_default_visual_embedder
e = get_default_visual_embedder()
print(type(e).__name__, e.target)
"Expected: ColQwen2Embedder nvidia (or ColPaliEmbedder nvidia as fallback).
- Step 5: Verify fail-fast on CPU-only machine
JW_VISUAL_ENABLED=1 uv run python -c "
from jw_rag.visual import get_default_visual_embedder, ConfigError
try:
get_default_visual_embedder()
except ConfigError as e:
print('expected ConfigError:', str(e)[:80])
"Expected: expected ConfigError: No GPU disponible....
- Step 6: Commit (final sweep — only if anything changed)
If anything had to be fixed in steps 1-5, commit those fixes. Otherwise no commit.
Self-review
Plan covers every spec deliverable:
- Scaffold +
[visual]/[visual-mlx]extras → Task 1 ✓ - Models (
VisualChunk,MultiVectorHit,IngestResult) → Task 2 ✓ FakeColPaliEmbedder+FakeRasterizer→ Task 3 ✓ (built early so all downstream tests don’t touch hardware)VisualVectorStorewith MaxSim, save/load, mismatch detection → Task 4 ✓PageRasterizerfor PDF/EPUB/JWPUB with lazy imports → Task 5 ✓- Real
ColPaliEmbedder/ColQwen2Embedder+ fail-fast factory → Task 6 ✓ ingest_path_visualidempotent by sha256 → Task 7 ✓hybrid_search_with_visualthree-way RRF → Task 8 ✓- Public API re-export → Task 9 ✓
- CLI:
jw rag ingest-visual+jw rag search-visual→ Task 10 ✓ - MCP:
visual_search+ingest_publication_visualtools → Task 11 ✓ - 5 figure-heavy L1 golden cases → Task 12 ✓
- Guía + VISION_AUDIT + ROADMAP → Task 13 ✓
- Full regression sweep → Task 14 ✓
Spec acceptance criteria checked:
- Recall@10 ≥+40% on 5 golden queries → fixtures present (Task 12), measurable
once GPU runner is available. Plan documents the target in fixture metadata
(
expected_recall_lift: 0.4). - Fail-fast
ConfigErrorwith install hint → Task 6 covers it, Task 14 verifies. - Zero impact on public CI → All tests use
FakeColPaliEmbedder/FakeRasterizer; heavy deps stay in[visual]extras. - Idempotent by sha256 → Task 7 test
test_ingest_idempotent_by_source_id. - Hybrid graceful → Task 8
test_falls_back_when_visual_noneandtest_falls_back_when_visual_empty. VisualStoreMismatchErroron model swap → Task 4test_load_mismatch_raises.
Boundaries respected:
VisualVectorStoredoes NOT subclassVectorStore— composition only.jw_rag.visualimports do not pullcolpali-engine/torch/playwright/pdf2imageat import time. Verified by the test in Task 9 which imports on a clean interpreter.- No CPU path. No API path.
_PROVIDER_ORDER = ["nvidia", "mlx"]only. - Heavy deps live in
[visual]and[visual-mlx]extras ofjw-rag, not in any package’s requireddependencies.
Test count: 42 new tests across 7 modules. 14 TDD tasks (matches the 14-18 target). Each task: failing test first, implement, passing test, commit. Existing 1649 tests must stay green (Task 14 verifies).
Execution choice
Execute via superpowers:subagent-driven-development: each task is self-contained (failing test → minimal code → passing test → commit), the file map is explicit, and there are no hidden cross-task dependencies beyond the natural order (fakes before store before ingest before hybrid). A single agent or worker per task is the most efficient path. If parallelism is desired, Tasks 5 (rasterizer) and 6 (real providers) can run concurrently once Task 3 (fakes) and Task 4 (store) are merged.
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