update some search in copali

apps/multimodal/vision-based-pdf-multi-vector/leann_multi_vector.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import sys
+import concurrent.futures
 from pathlib import Path
 
 import numpy as np
@@ -45,6 +46,7 @@ class LeannMultiVector:
             "is_recompute": is_recompute,
         }
         self._labels_meta: list[dict] = []
+        self._docid_to_indices: dict[int, list[int]] | None = None
 
     def _meta_dict(self) -> dict:
         return {
@@ -80,6 +82,10 @@ class LeannMultiVector:
         index_path_obj = Path(self.index_path)
         return index_path_obj.parent / f"{index_path_obj.name}.meta.json"
 
+    def _embeddings_path(self) -> Path:
+        index_path_obj = Path(self.index_path)
+        return index_path_obj.parent / f"{index_path_obj.name}.emb.npy"
+
     def create_index(self) -> None:
         if not self._pending_items:
             return
@@ -121,6 +127,9 @@ class LeannMultiVector:
         with open(self._labels_path(), "w", encoding="utf-8") as f:
             _json.dump(labels_meta, f)
 
+        # Persist embeddings for exact reranking
+        np.save(self._embeddings_path(), embeddings_np)
+
         self._labels_meta = labels_meta
 
     def _load_labels_meta_if_needed(self) -> None:
@@ -133,6 +142,19 @@ class LeannMultiVector:
             with open(labels_path, encoding="utf-8") as f:
                 self._labels_meta = _json.load(f)
 
+    def _build_docid_to_indices_if_needed(self) -> None:
+        if self._docid_to_indices is not None:
+            return
+        self._load_labels_meta_if_needed()
+        mapping: dict[int, list[int]] = {}
+        for idx, meta in enumerate(self._labels_meta):
+            try:
+                doc_id = int(meta["doc_id"])  # type: ignore[index]
+            except Exception:
+                continue
+            mapping.setdefault(doc_id, []).append(idx)
+        self._docid_to_indices = mapping
+
     def search(
         self, data: np.ndarray, topk: int, first_stage_k: int = 50
     ) -> list[tuple[float, int]]:
@@ -180,3 +202,139 @@ class LeannMultiVector:
 
         scores = sorted(((v, k) for k, v in doc_scores.items()), key=lambda x: x[0], reverse=True)
         return scores[:topk] if len(scores) >= topk else scores
+
+    def search_exact(
+        self,
+        data: np.ndarray,
+        topk: int,
+        *,
+        first_stage_k: int = 200,
+        max_workers: int = 32,
+    ) -> list[tuple[float, int]]:
+        """
+        High-precision MaxSim reranking over candidate documents.
+
+        Steps:
+        1) Run a first-stage ANN to collect candidate doc_ids (using seq-level neighbors).
+        2) For each candidate doc, load all its token embeddings and compute
+           MaxSim(query_tokens, doc_tokens) exactly: sum(max(dot(q_i, d_j))).
+
+        Returns top-k list of (score, doc_id).
+        """
+        # Normalize inputs
+        if data.ndim == 1:
+            data = data.reshape(1, -1)
+        if data.dtype != np.float32:
+            data = data.astype(np.float32)
+
+        self._load_labels_meta_if_needed()
+        self._build_docid_to_indices_if_needed()
+
+        emb_path = self._embeddings_path()
+        if not emb_path.exists():
+            # Fallback to approximate if we don't have persisted embeddings
+            return self.search(data, topk, first_stage_k=first_stage_k)
+
+        # Memory-map embeddings to avoid loading all into RAM
+        all_embeddings = np.load(emb_path, mmap_mode="r")
+        if all_embeddings.dtype != np.float32:
+            all_embeddings = all_embeddings.astype(np.float32)
+
+        # First-stage ANN to collect candidate doc_ids
+        searcher = HNSWSearcher(self.index_path, meta=self._meta_dict())
+        raw = searcher.search(
+            data,
+            first_stage_k,
+            recompute_embeddings=False,
+            complexity=128,
+            beam_width=1,
+            prune_ratio=0.0,
+            batch_size=0,
+        )
+        labels = raw.get("labels")
+        if labels is None:
+            return []
+        candidate_doc_ids: set[int] = set()
+        for batch in labels:
+            for sid in batch:
+                try:
+                    idx = int(sid)
+                except Exception:
+                    continue
+                if 0 <= idx < len(self._labels_meta):
+                    candidate_doc_ids.add(int(self._labels_meta[idx]["doc_id"]))  # type: ignore[index]
+
+        # Exact scoring per doc (parallelized)
+        assert self._docid_to_indices is not None
+
+        def _score_one(doc_id: int) -> tuple[float, int]:
+            token_indices = self._docid_to_indices.get(doc_id, [])
+            if not token_indices:
+                return (0.0, doc_id)
+            doc_vecs = np.asarray(all_embeddings[token_indices], dtype=np.float32)
+            # (Q, D) x (P, D)^T -> (Q, P) then MaxSim over P, sum over Q
+            sim = np.dot(data, doc_vecs.T)
+            # nan-safe
+            sim = np.nan_to_num(sim, nan=-1e30, posinf=1e30, neginf=-1e30)
+            score = sim.max(axis=2).sum(axis=1) if sim.ndim == 3 else sim.max(axis=1).sum()
+            return (float(score), doc_id)
+
+        scores: list[tuple[float, int]] = []
+        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as ex:
+            futures = [ex.submit(_score_one, doc_id) for doc_id in candidate_doc_ids]
+            for fut in concurrent.futures.as_completed(futures):
+                scores.append(fut.result())
+
+        scores.sort(key=lambda x: x[0], reverse=True)
+        return scores[:topk] if len(scores) >= topk else scores
+
+    def search_exact_all(
+        self,
+        data: np.ndarray,
+        topk: int,
+        *,
+        max_workers: int = 32,
+    ) -> list[tuple[float, int]]:
+        """
+        Exact MaxSim over ALL documents (no ANN pre-filtering).
+
+        This computes, for each document, sum_i max_j dot(q_i, d_j).
+        It memory-maps the persisted token-embedding matrix for scalability.
+        """
+        if data.ndim == 1:
+            data = data.reshape(1, -1)
+        if data.dtype != np.float32:
+            data = data.astype(np.float32)
+
+        self._load_labels_meta_if_needed()
+        self._build_docid_to_indices_if_needed()
+
+        emb_path = self._embeddings_path()
+        if not emb_path.exists():
+            return self.search(data, topk)
+
+        all_embeddings = np.load(emb_path, mmap_mode="r")
+        if all_embeddings.dtype != np.float32:
+            all_embeddings = all_embeddings.astype(np.float32)
+
+        assert self._docid_to_indices is not None
+        candidate_doc_ids = list(self._docid_to_indices.keys())
+
+        def _score_one(doc_id: int) -> tuple[float, int]:
+            token_indices = self._docid_to_indices.get(doc_id, [])
+            if not token_indices:
+                return (0.0, doc_id)
+            doc_vecs = np.asarray(all_embeddings[token_indices], dtype=np.float32)
+            sim = np.dot(data, doc_vecs.T)
+            sim = np.nan_to_num(sim, nan=-1e30, posinf=1e30, neginf=-1e30)
+            score = sim.max(axis=2).sum(axis=1) if sim.ndim == 3 else sim.max(axis=1).sum()
+            return (float(score), doc_id)
+
+        scores: list[tuple[float, int]] = []
+        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as ex:
+            futures = [ex.submit(_score_one, d) for d in candidate_doc_ids]
+            for fut in concurrent.futures.as_completed(futures):
+                scores.append(fut.result())
+
+        scores.sort(key=lambda x: x[0], reverse=True)
+        return scores[:topk] if len(scores) >= topk else scores

apps/multimodal/vision-based-pdf-multi-vector/multi-vector-leann-similarity-map.py

@@ -2,6 +2,7 @@
 # %%
 # uv pip install matplotlib qwen_vl_utils
 import os
+import json
 import re
 import sys
 from pathlib import Path
@@ -230,12 +231,18 @@ def _build_index(index_path: str, doc_vecs: list[Any], filepaths: list[str]) ->
     return retriever
 
 
-def _load_retriever_if_index_exists(index_path: str, dim: int) -> Optional[LeannMultiVector]:
+def _load_retriever_if_index_exists(index_path: str) -> Optional[LeannMultiVector]:
     index_base = Path(index_path)
     # Rough heuristic: index dir exists AND meta+labels files exist
     meta = index_base.parent / f"{index_base.name}.meta.json"
     labels = index_base.parent / f"{index_base.name}.labels.json"
     if index_base.exists() and meta.exists() and labels.exists():
+        try:
+            with open(meta, "r", encoding="utf-8") as f:
+                meta_json = json.load(f)
+            dim = int(meta_json.get("dimensions", 128))
+        except Exception:
+            dim = 128
         return LeannMultiVector(index_path=index_path, dim=dim)
     return None
 
@@ -390,11 +397,7 @@ print(f"Using model={model_name}, device={device_str}, dtype={dtype}")
 # Step 3: Build or load index
 retriever: Optional[LeannMultiVector] = None
 if not REBUILD_INDEX:
-    try:
-        one_vec = _embed_images(model, processor, [images[0]])[0]
-        retriever = _load_retriever_if_index_exists(INDEX_PATH, dim=int(one_vec.shape[-1]))
-    except Exception:
-        retriever = None
+    retriever = _load_retriever_if_index_exists(INDEX_PATH)
 
 if retriever is None:
     doc_vecs = _embed_images(model, processor, images)