refactor: passage structure

.gitignore

@@ -71,3 +71,6 @@ test_indices*/
 test_*.py
 !tests/**
 packages/leann-backend-diskann/third_party/DiskANN/_deps/
+
+*.meta.json
+*.passages.json

examples/document_search.py

@@ -74,7 +74,7 @@ def main():
     print(f"⏱️  Basic search time: {basic_time:.3f} seconds")
     print(">>> Basic search results <<<")
     for i, res in enumerate(results, 1):
-        print(f"  {i}. ID: {res['id']}, Score: {res['score']:.4f}, Text: '{res['text']}', Metadata: {res['metadata']}")
+        print(f"  {i}. ID: {res.id}, Score: {res.score:.4f}, Text: '{res.text}', Metadata: {res.metadata}")
 
     # --- 3. Recompute search demo ---
     print(f"\n[PHASE 3] Recompute search using embedding server...")
@@ -107,7 +107,7 @@ def main():
         print(f"⏱️  Recompute search time: {recompute_time:.3f} seconds")
         print(">>> Recompute search results <<<")
         for i, res in enumerate(recompute_results, 1):
-            print(f"  {i}. ID: {res['id']}, Score: {res['score']:.4f}, Text: '{res['text']}', Metadata: {res['metadata']}")
+            print(f"  {i}. ID: {res.id}, Score: {res.score:.4f}, Text: '{res.text}', Metadata: {res.metadata}")
         
         # Compare results
         print(f"\n--- Result comparison ---")
@@ -116,8 +116,8 @@ def main():
         
         print("\nBasic search vs Recompute results:")
         for i in range(min(len(results), len(recompute_results))):
-            basic_score = results[i]['score']
+            basic_score = results[i].score
-            recompute_score = recompute_results[i]['score']
+            recompute_score = recompute_results[i].score
             score_diff = abs(basic_score - recompute_score)
             print(f"  Position {i+1}: PQ={basic_score:.4f}, Recompute={recompute_score:.4f}, Difference={score_diff:.4f}")
         

examples/main_cli_example.py

@@ -10,7 +10,6 @@ import asyncio
 import os
 import dotenv
 from leann.api import LeannBuilder, LeannSearcher, LeannChat
-import leann_backend_hnsw # Import to ensure backend registration
 import shutil
 from pathlib import Path
 
@@ -39,7 +38,7 @@ all_texts = []
 for doc in documents:
     nodes = node_parser.get_nodes_from_documents([doc])
     for node in nodes:
-        all_texts.append(node.text)
+        all_texts.append(node.get_content())
 
 INDEX_DIR = Path("./test_pdf_index")
 INDEX_PATH = str(INDEX_DIR / "pdf_documents.leann")
@@ -51,7 +50,7 @@ if not INDEX_DIR.exists():
 
     # CSR compact mode with recompute
     builder = LeannBuilder(
-        backend_name="hnsw",
+        backend_name="diskann",
         embedding_model="facebook/contriever",
         graph_degree=32, 
         complexity=64,
@@ -74,7 +73,7 @@ async def main():
     
     query = "Based on the paper, what are the main techniques LEANN explores to reduce the storage overhead and DLPM explore to achieve Fairness and Efiiciency trade-off?"
     print(f"You: {query}")
-    chat_response = chat.ask(query, top_k=20, recompute_beighbor_embeddings=True,embedding_model="facebook/contriever")
+    chat_response = chat.ask(query, top_k=20, recompute_beighbor_embeddings=True)
     print(f"Leann: {chat_response}")
 
 if __name__ == "__main__":

packages/leann-backend-diskann/leann_backend_diskann/diskann_backend.py

@@ -3,7 +3,7 @@ import os
 import json
 import struct
 from pathlib import Path
-from typing import Dict
+from typing import Dict, Any, List
 import contextlib
 import threading
 import time
@@ -11,6 +11,7 @@ import atexit
 import socket
 import subprocess
 import sys
+import pickle
 
 from leann.embedding_server_manager import EmbeddingServerManager
 from leann.registry import register_backend
@@ -19,13 +20,13 @@ from leann.interface import (
     LeannBackendBuilderInterface,
     LeannBackendSearcherInterface
 )
-from . import _diskannpy as diskannpy
+def _get_diskann_metrics():
-
+    from . import _diskannpy as diskannpy
-METRIC_MAP = {
+    return {
-    "mips": diskannpy.Metric.INNER_PRODUCT,
+        "mips": diskannpy.Metric.INNER_PRODUCT,
-    "l2": diskannpy.Metric.L2,
+        "l2": diskannpy.Metric.L2,
-    "cosine": diskannpy.Metric.COSINE,
+        "cosine": diskannpy.Metric.COSINE,
-}
+    }
 
 @contextlib.contextmanager
 def chdir(path):
@@ -36,7 +37,7 @@ def chdir(path):
     finally:
         os.chdir(original_dir)
 
-def _write_vectors_to_bin(data: np.ndarray, file_path: str):
+def _write_vectors_to_bin(data: np.ndarray, file_path: Path):
     num_vectors, dim = data.shape
     with open(file_path, 'wb') as f:
         f.write(struct.pack('I', num_vectors))
@@ -67,7 +68,8 @@ class DiskannBuilder(LeannBackendBuilderInterface):
     def __init__(self, **kwargs):
         self.build_params = kwargs
 
-    def build(self, data: np.ndarray, index_path: str, **kwargs):
+
+    def build(self, data: np.ndarray, ids: List[str], index_path: str, **kwargs):
         path = Path(index_path)
         index_dir = path.parent
         index_prefix = path.stem
@@ -82,8 +84,15 @@ class DiskannBuilder(LeannBackendBuilderInterface):
         data_filename = f"{index_prefix}_data.bin"
         _write_vectors_to_bin(data, index_dir / data_filename)
 
+        # Create label map: integer -> string_id
+        label_map = {i: str_id for i, str_id in enumerate(ids)}
+        label_map_file = index_dir / "leann.labels.map"
+        with open(label_map_file, 'wb') as f:
+            pickle.dump(label_map, f)
+
         build_kwargs = {**self.build_params, **kwargs}
         metric_str = build_kwargs.get("distance_metric", "mips").lower()
+        METRIC_MAP = _get_diskann_metrics()
         metric_enum = METRIC_MAP.get(metric_str)
         if metric_enum is None:
             raise ValueError(f"Unsupported distance_metric '{metric_str}'.")
@@ -99,6 +108,7 @@ class DiskannBuilder(LeannBackendBuilderInterface):
         print(f"INFO: Building DiskANN index for {data.shape[0]} vectors with metric {metric_enum}...")
         
         try:
+            from . import _diskannpy as diskannpy
             with chdir(index_dir):
                 diskannpy.build_disk_float_index(
                     metric_enum,
@@ -127,30 +137,38 @@ class DiskannSearcher(LeannBackendSearcherInterface):
         if not self.meta:
             raise ValueError("DiskannSearcher requires metadata from .meta.json.")
 
-        dimensions = self.meta.get("dimensions")
-        if not dimensions:
-            raise ValueError("Dimensions not found in Leann metadata.")
-        
-        self.distance_metric = self.meta.get("distance_metric", "mips").lower()
-        metric_enum = METRIC_MAP.get(self.distance_metric)
-        if metric_enum is None:
-            raise ValueError(f"Unsupported distance_metric '{self.distance_metric}'.")
-
         self.embedding_model = self.meta.get("embedding_model")
         if not self.embedding_model:
             print("WARNING: embedding_model not found in meta.json. Recompute will fail if attempted.")
 
         path = Path(index_path)
-        index_dir = path.parent
+        self.index_dir = path.parent
-        index_prefix = path.stem
+        self.index_prefix = path.stem
+        
+        # Load the label map
+        label_map_file = self.index_dir / "leann.labels.map"
+        if not label_map_file.exists():
+            raise FileNotFoundError(f"Label map file not found: {label_map_file}")
+        
+        with open(label_map_file, 'rb') as f:
+            self.label_map = pickle.load(f)
+        
+        # Extract parameters for DiskANN
+        distance_metric = kwargs.get("distance_metric", "mips").lower()
+        METRIC_MAP = _get_diskann_metrics()
+        metric_enum = METRIC_MAP.get(distance_metric)
+        if metric_enum is None:
+            raise ValueError(f"Unsupported distance_metric '{distance_metric}'.")
         
         num_threads = kwargs.get("num_threads", 8)
         num_nodes_to_cache = kwargs.get("num_nodes_to_cache", 0)
+        self.zmq_port = kwargs.get("zmq_port", 6666)
         
         try:
-            full_index_prefix = str(index_dir / index_prefix)
+            from . import _diskannpy as diskannpy
+            full_index_prefix = str(self.index_dir / self.index_prefix)
             self._index = diskannpy.StaticDiskFloatIndex(
-                metric_enum, full_index_prefix, num_threads, num_nodes_to_cache, 1, "", ""
+                metric_enum, full_index_prefix, num_threads, num_nodes_to_cache, 1, self.zmq_port, "", ""
             )
             self.num_threads = num_threads
             self.embedding_server_manager = EmbeddingServerManager(
@@ -161,7 +179,7 @@ class DiskannSearcher(LeannBackendSearcherInterface):
             print(f"💥 ERROR: Failed to load DiskANN index. Exception: {e}")
             raise
 
-    def search(self, query: np.ndarray, top_k: int, **kwargs) -> Dict[str, any]:
+    def search(self, query: np.ndarray, top_k: int, **kwargs) -> Dict[str, Any]:
         complexity = kwargs.get("complexity", 256)
         beam_width = kwargs.get("beam_width", 4)
         
@@ -172,23 +190,32 @@ class DiskannSearcher(LeannBackendSearcherInterface):
         prune_ratio = kwargs.get("prune_ratio", 0.0)
         batch_recompute = kwargs.get("batch_recompute", False)
         global_pruning = kwargs.get("global_pruning", False)
+        port = kwargs.get("zmq_port", self.zmq_port)
         
         if recompute_beighbor_embeddings:
             print(f"INFO: DiskANN ZMQ mode enabled - ensuring embedding server is running")
             if not self.embedding_model:
                 raise ValueError("Cannot use recompute_beighbor_embeddings without 'embedding_model' in meta.json.")
-            
+
-            zmq_port = kwargs.get("zmq_port", 6666)
+            passages_file = kwargs.get("passages_file")
-            
+            if not passages_file:
+                # Get the passages file path from meta.json
+                if 'passage_sources' in self.meta and self.meta['passage_sources']:
+                    passage_source = self.meta['passage_sources'][0]
+                    passages_file = passage_source['path']
+                    print(f"INFO: Found passages file from metadata: {passages_file}")
+                else:
+                    raise RuntimeError(f"FATAL: Recompute mode enabled but no passage_sources found in metadata.")
+
             server_started = self.embedding_server_manager.start_server(
-                port=zmq_port,
+                port=self.zmq_port,
                 model_name=self.embedding_model,
-                distance_metric=self.distance_metric
+                distance_metric=kwargs.get("distance_metric", "mips"),
+                passages_file=passages_file
             )
             
             if not server_started:
-                print(f"WARNING: Failed to start embedding server, falling back to PQ computation")
+                raise RuntimeError(f"Failed to start DiskANN embedding server on port {self.zmq_port}")
-                recompute_beighbor_embeddings = False
         
         if query.dtype != np.float32:
             query = query.astype(np.float32)
@@ -211,11 +238,23 @@ class DiskannSearcher(LeannBackendSearcherInterface):
                 batch_recompute,
                 global_pruning
             )
-            return {"labels": labels, "distances": distances}
+            
+            # Convert integer labels to string IDs
+            string_labels = []
+            for batch_labels in labels:
+                batch_string_labels = []
+                for int_label in batch_labels:
+                    if int_label in self.label_map:
+                        batch_string_labels.append(self.label_map[int_label])
+                    else:
+                        batch_string_labels.append(f"unknown_{int_label}")
+                string_labels.append(batch_string_labels)
+            
+            return {"labels": string_labels, "distances": distances}
         except Exception as e:
             print(f"💥 ERROR: DiskANN search failed. Exception: {e}")
             batch_size = query.shape[0]
-            return {"labels": np.full((batch_size, top_k), -1, dtype=np.int64), 
+            return {"labels": [[f"error_{i}" for i in range(top_k)] for _ in range(batch_size)], 
                    "distances": np.full((batch_size, top_k), float('inf'), dtype=np.float32)}
     
     def __del__(self):

packages/leann-backend-diskann/leann_backend_diskann/embedding_server.py

@@ -7,6 +7,8 @@ import pickle
 import argparse
 import threading
 import time
+import json
+from typing import Dict, Any, Optional, Union
 
 from transformers import AutoTokenizer, AutoModel
 import os
@@ -17,49 +19,76 @@ import numpy as np
 RED = "\033[91m"
 RESET = "\033[0m"
 
-# 简化的文档存储 - 替代 LazyPassages
+# --- New Passage Loader from HNSW backend ---
-class SimpleDocumentStore:
+class SimplePassageLoader:
-    """简化的文档存储，支持任意ID"""
+    """
-    def __init__(self, documents: dict = None):
+    Simple passage loader that replaces config.py dependencies
-        self.documents = documents or {}
+    """
-        # 默认演示文档
+    def __init__(self, passages_data: Optional[Dict[str, Any]] = None):
-        self.default_docs = {
+        self.passages_data = passages_data or {}
-            0: "Python is a high-level, interpreted language known for simplicity.",
-            1: "Machine learning builds systems that learn from data.", 
-            2: "Data structures like arrays, lists, and graphs organize data.",
-        }
     
-    def __getitem__(self, doc_id):
+    def __getitem__(self, passage_id: Union[str, int]) -> Dict[str, str]:
-        doc_id = int(doc_id)
+        """Get passage by ID"""
-        
+        str_id = str(passage_id)
-        # 优先使用指定的文档
+        if str_id in self.passages_data:
-        if doc_id in self.documents:
+            return {"text": self.passages_data[str_id]}
-            return {"text": self.documents[doc_id]}
+        else:
-        
+            # Return empty text for missing passages
-        # 其次使用默认演示文档
+            return {"text": ""}
-        if doc_id in self.default_docs:
-            return {"text": self.default_docs[doc_id]}
-        
-        # 对于任意其他ID，返回通用文档
-        fallback_docs = [
-            "This is a general document about technology and programming concepts.",
-            "This document discusses machine learning and artificial intelligence topics.", 
-            "This content covers data structures, algorithms, and computer science fundamentals.",
-            "This is a document about software engineering and development practices.",
-            "This content focuses on databases, data management, and information systems."
-        ]
-        
-        # 根据ID选择一个fallback文档
-        fallback_text = fallback_docs[doc_id % len(fallback_docs)]
-        return {"text": f"[ID:{doc_id}] {fallback_text}"}
     
-    def __len__(self):
+    def __len__(self) -> int:
-        return len(self.documents) + len(self.default_docs)
+        return len(self.passages_data)
+
+def load_passages_from_file(passages_file: str) -> SimplePassageLoader:
+    """
+    Load passages from a JSONL file with label map support
+    Expected format: {"id": "passage_id", "text": "passage_text", "metadata": {...}} (one per line)
+    """
+    from pathlib import Path
+    import pickle
+    
+    if not os.path.exists(passages_file):
+        raise FileNotFoundError(f"Passages file {passages_file} not found.")
+    
+    if not passages_file.endswith('.jsonl'):
+        raise ValueError(f"Expected .jsonl file format, got: {passages_file}")
+    
+    # Load label map (int -> string_id)
+    passages_dir = Path(passages_file).parent
+    label_map_file = passages_dir / "leann.labels.map"
+    
+    label_map = {}
+    if label_map_file.exists():
+        with open(label_map_file, 'rb') as f:
+            label_map = pickle.load(f)
+        print(f"Loaded label map with {len(label_map)} entries")
+    else:
+        raise FileNotFoundError(f"Label map file not found: {label_map_file}")
+    
+    # Load passages by string ID
+    string_id_passages = {}
+    with open(passages_file, 'r', encoding='utf-8') as f:
+        for line in f:
+            if line.strip():
+                passage = json.loads(line)
+                string_id_passages[passage['id']] = passage['text']
+    
+    # Create int ID -> text mapping using label map
+    passages_data = {}
+    for int_id, string_id in label_map.items():
+        if string_id in string_id_passages:
+            passages_data[str(int_id)] = string_id_passages[string_id]
+        else:
+            print(f"WARNING: String ID {string_id} from label map not found in passages")
+    
+    print(f"Loaded {len(passages_data)} passages from JSONL file {passages_file} using label map")
+    return SimplePassageLoader(passages_data)
 
 def create_embedding_server_thread(
     zmq_port=5555,
     model_name="sentence-transformers/all-mpnet-base-v2",
     max_batch_size=128,
+    passages_file: Optional[str] = None,
 ):
     """
     在当前线程中创建并运行 embedding server
@@ -109,15 +138,14 @@ def create_embedding_server_thread(
             except Exception as e:
                 print(f"WARNING: Model optimization failed: {e}")
 
-        # 默认演示文档
+        # Load passages from file if provided
-        demo_documents = {
+        if passages_file and os.path.exists(passages_file):
-            0: "Python is a high-level, interpreted language known for simplicity.",
+            passages = load_passages_from_file(passages_file)
-            1: "Machine learning builds systems that learn from data.", 
+        else:
-            2: "Data structures like arrays, lists, and graphs organize data.",
+            print("WARNING: No passages file provided or file not found. Using an empty passage loader.")
-        }
+            passages = SimplePassageLoader()
 
-        passages = SimpleDocumentStore(demo_documents)
+        print(f"INFO: Loaded {len(passages)} passages.")
-        print(f"INFO: Loaded {len(passages)} demo documents")
 
         class DeviceTimer:
             """设备计时器"""
@@ -264,7 +292,13 @@ def create_embedding_server_thread(
                     for nid in node_ids:
                         txtinfo = passages[nid]
                         txt = txtinfo["text"]
-                        texts.append(txt)
+                        if txt:
+                            texts.append(txt)
+                        else:
+                            # 如果文本为空，我们仍然需要一个占位符来进行批处理，
+                            # 但将其ID记录为缺失
+                            texts.append("") 
+                            missing_ids.append(nid)
                 lookup_timer.print_elapsed()
 
                 if missing_ids:
@@ -360,18 +394,20 @@ def create_embedding_server(
     max_batch_size=128,
     lazy_load_passages=False,
     model_name="sentence-transformers/all-mpnet-base-v2",
+    passages_file: Optional[str] = None,
 ):
     """
     原有的 create_embedding_server 函数保持不变
     这个是阻塞版本，用于直接运行
     """
-    create_embedding_server_thread(zmq_port, model_name, max_batch_size)
+    create_embedding_server_thread(zmq_port, model_name, max_batch_size, passages_file)
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Embedding service")
     parser.add_argument("--zmq-port", type=int, default=5555, help="ZMQ port to run on")
     parser.add_argument("--domain", type=str, default="demo", help="Domain name")
+    parser.add_argument("--passages-file", type=str, help="JSON file containing passage ID to text mapping")
     parser.add_argument("--load-passages", action="store_true", default=True)
     parser.add_argument("--load-embeddings", action="store_true", default=False)
     parser.add_argument("--use-fp16", action="store_true", default=False)
@@ -394,4 +430,5 @@ if __name__ == "__main__":
         max_batch_size=args.max_batch_size,
         lazy_load_passages=args.lazy_load_passages,
         model_name=args.model_name,
+        passages_file=args.passages_file,
     )

packages/leann-backend-hnsw/leann_backend_hnsw/convert_to_csr.py

@@ -468,16 +468,27 @@ def convert_hnsw_graph_to_csr(input_filename, output_filename, prune_embeddings=
             # --- Write CSR HNSW graph data using unified function ---
             print(f"[{time.time() - start_time:.2f}s] Writing CSR HNSW graph data in FAISS-compatible order...")
             
-            # Determine storage fourcc based on prune_embeddings
+            # Determine storage fourcc and data based on prune_embeddings
-            output_storage_fourcc = NULL_INDEX_FOURCC if prune_embeddings else (storage_fourcc if 'storage_fourcc' in locals() else NULL_INDEX_FOURCC)
             if prune_embeddings:
                 print(f"   Pruning embeddings: Writing NULL storage marker.")
-            storage_data = b''
+                output_storage_fourcc = NULL_INDEX_FOURCC
+                storage_data = b''
+            else:
+                # Keep embeddings - read and preserve original storage data
+                if storage_fourcc and storage_fourcc != NULL_INDEX_FOURCC:
+                    print(f"   Preserving embeddings: Reading original storage data...")
+                    storage_data = f_in.read()  # Read remaining storage data
+                    output_storage_fourcc = storage_fourcc
+                    print(f"   Read {len(storage_data)} bytes of storage data")
+                else:
+                    print(f"   No embeddings found in original file (NULL storage)")
+                    output_storage_fourcc = NULL_INDEX_FOURCC
+                    storage_data = b''
             
             # Use the unified write function
             write_compact_format(f_out, original_hnsw_data, assign_probas_np, cum_nneighbor_per_level_np, 
                                levels_np, compact_level_ptr, compact_node_offsets_np, 
-                               compact_neighbors_data, output_storage_fourcc, storage_data if not prune_embeddings else b'')
+                               compact_neighbors_data, output_storage_fourcc, storage_data)
             
             # Clean up memory
             del assign_probas_np, cum_nneighbor_per_level_np, levels_np

packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py

@@ -3,7 +3,7 @@ import os
 import json
 import struct
 from pathlib import Path
-from typing import Dict, Any
+from typing import Dict, Any, List
 import contextlib
 import threading
 import time
@@ -11,6 +11,7 @@ import atexit
 import socket
 import subprocess
 import sys
+import pickle
 
 from leann.embedding_server_manager import EmbeddingServerManager
 from .convert_to_csr import convert_hnsw_graph_to_csr
@@ -74,7 +75,7 @@ class HNSWBuilder(LeannBackendBuilderInterface):
         if self.is_recompute and not self.is_compact:
             raise ValueError("is_recompute requires is_compact=True for efficiency")
 
-    def build(self, data: np.ndarray, index_path: str, **kwargs):
+    def build(self, data: np.ndarray, ids: List[str], index_path: str, **kwargs):
         """Build HNSW index using FAISS"""
         from . import faiss
         
@@ -89,6 +90,12 @@ class HNSWBuilder(LeannBackendBuilderInterface):
         if not data.flags['C_CONTIGUOUS']:
             data = np.ascontiguousarray(data)
             
+        # Create label map: integer -> string_id
+        label_map = {i: str_id for i, str_id in enumerate(ids)}
+        label_map_file = index_dir / "leann.labels.map"
+        with open(label_map_file, 'wb') as f:
+            pickle.dump(label_map, f)
+            
         metric_str = self.distance_metric.lower()
         metric_enum = get_metric_map().get(metric_str)
         if metric_enum is None:
@@ -119,9 +126,6 @@ class HNSWBuilder(LeannBackendBuilderInterface):
             if self.is_compact:
                 self._convert_to_csr(index_file)
             
-            if self.is_recompute:
-                self._generate_passages_file(index_dir, index_prefix, **kwargs)
-            
         except Exception as e:
             print(f"💥 ERROR: HNSW index build failed. Exception: {e}")
             raise
@@ -155,30 +159,6 @@ class HNSWBuilder(LeannBackendBuilderInterface):
             print(f"💥 ERROR: CSR conversion failed. Exception: {e}")
             raise
 
-    def _generate_passages_file(self, index_dir: Path, index_prefix: str, **kwargs):
-        """Generate passages file for recompute mode"""
-        try:
-            chunks = kwargs.get('chunks', [])
-            if not chunks:
-                print("INFO: No chunks data provided, skipping passages file generation")
-                return
-            
-            # Generate node_id to text mapping
-            passages_data = {}
-            for node_id, chunk in enumerate(chunks):
-                passages_data[str(node_id)] = chunk["text"]
-            
-            # Save passages file
-            passages_file = index_dir / f"{index_prefix}.passages.json"
-            with open(passages_file, 'w', encoding='utf-8') as f:
-                json.dump(passages_data, f, ensure_ascii=False, indent=2)
-            
-            print(f"✅ Generated passages file for recompute mode at '{passages_file}' ({len(passages_data)} passages)")
-            
-        except Exception as e:
-            print(f"💥 ERROR: Failed to generate passages file. Exception: {e}")
-            # Don't raise - this is not critical for index building
-            pass
 
 class HNSWSearcher(LeannBackendSearcherInterface):
     def _get_index_storage_status(self, index_file: Path) -> tuple[bool, bool]:
@@ -282,6 +262,14 @@ class HNSWSearcher(LeannBackendSearcherInterface):
         self.index_dir = path.parent
         self.index_prefix = path.stem
         
+        # Load the label map
+        label_map_file = self.index_dir / "leann.labels.map"
+        if not label_map_file.exists():
+            raise FileNotFoundError(f"Label map file not found: {label_map_file}")
+        
+        with open(label_map_file, 'rb') as f:
+            self.label_map = pickle.load(f)
+        
         index_file = self.index_dir / f"{self.index_prefix}.index"
         if not index_file.exists():
             raise FileNotFoundError(f"HNSW index file not found at {index_file}")
@@ -303,7 +291,8 @@ class HNSWSearcher(LeannBackendSearcherInterface):
         hnsw_config.is_recompute = self.is_pruned or kwargs.get("is_recompute", False)
         hnsw_config.disk_cache_ratio = kwargs.get("disk_cache_ratio", 0.0)
         hnsw_config.external_storage_path = kwargs.get("external_storage_path")
-        hnsw_config.zmq_port = kwargs.get("zmq_port", 5557)
+        
+        self.zmq_port = kwargs.get("zmq_port", 5557)
         
         if self.is_pruned and not hnsw_config.is_recompute:
             raise RuntimeError("Index is pruned (embeddings removed) but recompute is disabled. This is impossible - recompute must be enabled for pruned indices.")
@@ -335,12 +324,13 @@ class HNSWSearcher(LeannBackendSearcherInterface):
 
             passages_file = kwargs.get("passages_file")
             if not passages_file:
-                potential_passages_file = self.index_dir / f"{self.index_prefix}.passages.json"
+                # Get the passages file path from meta.json
-                if potential_passages_file.exists():
+                if 'passage_sources' in self.meta and self.meta['passage_sources']:
-                    passages_file = str(potential_passages_file)
+                    passage_source = self.meta['passage_sources'][0]
-                    print(f"INFO: Automatically found passages file: {passages_file}")
+                    passages_file = passage_source['path']
+                    print(f"INFO: Found passages file from metadata: {passages_file}")
                 else:
-                    raise RuntimeError(f"FATAL: Index is pruned but no passages file found.")
+                    raise RuntimeError(f"FATAL: Index is pruned but no passage_sources found in metadata.")
 
             zmq_port = kwargs.get("zmq_port", 5557)
             server_started = self.embedding_server_manager.start_server(
@@ -361,15 +351,28 @@ class HNSWSearcher(LeannBackendSearcherInterface):
             faiss.normalize_L2(query)
         
         try:
-            self._index.hnsw.efSearch = ef
+            params = faiss.SearchParametersHNSW()
+            params.efSearch = ef
+            params.zmq_port = kwargs.get("zmq_port", self.zmq_port)
             
             batch_size = query.shape[0]
             distances = np.empty((batch_size, top_k), dtype=np.float32)
             labels = np.empty((batch_size, top_k), dtype=np.int64)
             
-            self._index.search(query.shape[0], faiss.swig_ptr(query), top_k, faiss.swig_ptr(distances), faiss.swig_ptr(labels))
+            self._index.search(query.shape[0], faiss.swig_ptr(query), top_k, faiss.swig_ptr(distances), faiss.swig_ptr(labels), params)
             
-            return {"labels": labels, "distances": distances}
+            # Convert integer labels to string IDs
+            string_labels = []
+            for batch_labels in labels:
+                batch_string_labels = []
+                for int_label in batch_labels:
+                    if int_label in self.label_map:
+                        batch_string_labels.append(self.label_map[int_label])
+                    else:
+                        batch_string_labels.append(f"unknown_{int_label}")
+                string_labels.append(batch_string_labels)
+            
+            return {"labels": string_labels, "distances": distances}
             
         except Exception as e:
             print(f"💥 ERROR: HNSW search failed. Exception: {e}")

packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_embedding_server.py

@@ -58,21 +58,46 @@ class SimplePassageLoader:
 
 def load_passages_from_file(passages_file: str) -> SimplePassageLoader:
     """
-    Load passages from a JSON file
+    Load passages from a JSONL file with label map support
-    Expected format: {"passage_id": "passage_text", ...}
+    Expected format: {"id": "passage_id", "text": "passage_text", "metadata": {...}} (one per line)
     """
     if not os.path.exists(passages_file):
-        print(f"Warning: Passages file {passages_file} not found. Using empty loader.")
+        raise FileNotFoundError(f"Passages file {passages_file} not found.")
-        return SimplePassageLoader()
     
-    try:
+    if not passages_file.endswith('.jsonl'):
-        with open(passages_file, 'r', encoding='utf-8') as f:
+        raise ValueError(f"Expected .jsonl file format, got: {passages_file}")
-            passages_data = json.load(f)
+    
-        print(f"Loaded {len(passages_data)} passages from {passages_file}")
+    # Load label map (int -> string_id)
-        return SimplePassageLoader(passages_data)
+    passages_dir = Path(passages_file).parent
-    except Exception as e:
+    label_map_file = passages_dir / "leann.labels.map"
-        print(f"Error loading passages from {passages_file}: {e}")
+    
-        return SimplePassageLoader()
+    label_map = {}
+    if label_map_file.exists():
+        import pickle
+        with open(label_map_file, 'rb') as f:
+            label_map = pickle.load(f)
+        print(f"Loaded label map with {len(label_map)} entries")
+    else:
+        raise FileNotFoundError(f"Label map file not found: {label_map_file}")
+    
+    # Load passages by string ID
+    string_id_passages = {}
+    with open(passages_file, 'r', encoding='utf-8') as f:
+        for line in f:
+            if line.strip():
+                passage = json.loads(line)
+                string_id_passages[passage['id']] = passage['text']
+    
+    # Create int ID -> text mapping using label map
+    passages_data = {}
+    for int_id, string_id in label_map.items():
+        if string_id in string_id_passages:
+            passages_data[str(int_id)] = string_id_passages[string_id]
+        else:
+            print(f"WARNING: String ID {string_id} from label map not found in passages")
+    
+    print(f"Loaded {len(passages_data)} passages from JSONL file {passages_file} using label map")
+    return SimplePassageLoader(passages_data)
 
 def create_hnsw_embedding_server(
     passages_file: Optional[str] = None,

packages/leann-core/src/leann/__init__.py

@@ -0,0 +1,7 @@
+# packages/leann-core/src/leann/__init__.py
+from .api import LeannBuilder, LeannChat, LeannSearcher
+from .registry import BACKEND_REGISTRY, autodiscover_backends
+
+autodiscover_backends()
+
+__all__ = ["LeannBuilder", "LeannSearcher", "LeannChat", "BACKEND_REGISTRY"]

packages/leann-core/src/leann/api.py

@@ -7,6 +7,8 @@ import json
 from pathlib import Path
 import openai
 from dataclasses import dataclass, field
+import uuid
+import pickle
 
 # --- Helper Functions for Embeddings ---
 
@@ -56,11 +58,45 @@ def _get_embedding_dimensions(model_name: str) -> int:
 @dataclass
 class SearchResult:
     """Represents a single search result."""
-    id: int
+    id: str
     score: float
     text: str
     metadata: Dict[str, Any] = field(default_factory=dict)
 
+
+class PassageManager:
+    """Manages passage data and lazy loading from JSONL files."""
+    
+    def __init__(self, passage_sources: List[Dict[str, Any]]):
+        self.offset_maps = {}
+        self.passage_files = {}
+        
+        for source in passage_sources:
+            if source["type"] == "jsonl":
+                passage_file = source["path"]
+                index_file = source["index_path"]
+                
+                if not os.path.exists(index_file):
+                    raise FileNotFoundError(f"Passage index file not found: {index_file}")
+                
+                with open(index_file, 'rb') as f:
+                    offset_map = pickle.load(f)
+                
+                self.offset_maps[passage_file] = offset_map
+                self.passage_files[passage_file] = passage_file
+    
+    def get_passage(self, passage_id: str) -> Dict[str, Any]:
+        """Lazy load a passage by ID."""
+        for passage_file, offset_map in self.offset_maps.items():
+            if passage_id in offset_map:
+                offset = offset_map[passage_id]
+                with open(passage_file, 'r', encoding='utf-8') as f:
+                    f.seek(offset)
+                    line = f.readline()
+                    return json.loads(line)
+        
+        raise KeyError(f"Passage ID not found: {passage_id}")
+
 # --- Core Classes ---
 
 class LeannBuilder:
@@ -82,7 +118,26 @@ class LeannBuilder:
         print(f"INFO: LeannBuilder initialized with '{backend_name}' backend.")
 
     def add_text(self, text: str, metadata: Optional[Dict[str, Any]] = None):
-        self.chunks.append({"text": text, "metadata": metadata or {}})
+        if metadata is None:
+            metadata = {}
+        
+        # Check if ID is provided in metadata
+        passage_id = metadata.get('id')
+        if passage_id is None:
+            passage_id = str(uuid.uuid4())
+        else:
+            # Validate uniqueness
+            existing_ids = {chunk['id'] for chunk in self.chunks}
+            if passage_id in existing_ids:
+                raise ValueError(f"Duplicate passage ID: {passage_id}")
+        
+        # Store the definitive ID with the chunk
+        chunk_data = {
+            "id": passage_id,
+            "text": text,
+            "metadata": metadata
+        }
+        self.chunks.append(chunk_data)
 
     def build_index(self, index_path: str):
         if not self.chunks:
@@ -92,28 +147,65 @@ class LeannBuilder:
             self.dimensions = _get_embedding_dimensions(self.embedding_model)
             print(f"INFO: Auto-detected dimensions for '{self.embedding_model}': {self.dimensions}")
 
+        path = Path(index_path)
+        index_dir = path.parent
+        index_name = path.name
+        
+        # Ensure the directory exists
+        index_dir.mkdir(parents=True, exist_ok=True)
+        
+        # Create the passages.jsonl file and offset index
+        passages_file = index_dir / f"{index_name}.passages.jsonl"
+        offset_file = index_dir / f"{index_name}.passages.idx"
+        
+        offset_map = {}
+        
+        with open(passages_file, 'w', encoding='utf-8') as f:
+            for chunk in self.chunks:
+                offset = f.tell()
+                passage_data = {
+                    "id": chunk["id"],
+                    "text": chunk["text"],
+                    "metadata": chunk["metadata"]
+                }
+                json.dump(passage_data, f, ensure_ascii=False)
+                f.write('\n')
+                offset_map[chunk["id"]] = offset
+        
+        # Save the offset map
+        with open(offset_file, 'wb') as f:
+            pickle.dump(offset_map, f)
+        
+        # Compute embeddings
         texts_to_embed = [c["text"] for c in self.chunks]
         embeddings = _compute_embeddings(texts_to_embed, self.embedding_model)
-
+        
+        # Extract string IDs for the backend
+        string_ids = [chunk["id"] for chunk in self.chunks]
+        
+        # Build the vector index
         current_backend_kwargs = self.backend_kwargs.copy()
         current_backend_kwargs['dimensions'] = self.dimensions
         builder_instance = self.backend_factory.builder(**current_backend_kwargs)
         
-        build_kwargs = current_backend_kwargs.copy()
+        builder_instance.build(embeddings, string_ids, index_path, **current_backend_kwargs)
-        build_kwargs['chunks'] = self.chunks
-        builder_instance.build(embeddings, index_path, **build_kwargs)
 
-        index_dir = Path(index_path).parent
+        # Create the lightweight meta.json file
-        leann_meta_path = index_dir / f"{Path(index_path).name}.meta.json"
+        leann_meta_path = index_dir / f"{index_name}.meta.json"
         
         meta_data = {
-            "version": "0.1.0",
+            "version": "1.0",
             "backend_name": self.backend_name,
             "embedding_model": self.embedding_model,
             "dimensions": self.dimensions,
             "backend_kwargs": self.backend_kwargs,
-            "num_chunks": len(self.chunks),
+            "passage_sources": [
-            "chunks": self.chunks,
+                {
+                    "type": "jsonl",
+                    "path": str(passages_file),
+                    "index_path": str(offset_file)
+                }
+            ]
         }
         with open(leann_meta_path, 'w', encoding='utf-8') as f:
             json.dump(meta_data, f, indent=2)
@@ -136,14 +228,16 @@ class LeannSearcher:
         backend_name = self.meta_data['backend_name']
         self.embedding_model = self.meta_data['embedding_model']
         
+        # Initialize the passage manager
+        passage_sources = self.meta_data.get('passage_sources', [])
+        self.passage_manager = PassageManager(passage_sources)
+        
         backend_factory = BACKEND_REGISTRY.get(backend_name)
         if backend_factory is None:
             raise ValueError(f"Backend '{backend_name}' (from index file) not found or not registered.")
 
-        final_kwargs = self.meta_data.get("backend_kwargs", {})
+        final_kwargs = backend_kwargs.copy()
-        final_kwargs.update(backend_kwargs)
+        final_kwargs['meta'] = self.meta_data
-        if 'dimensions' not in final_kwargs:
-            final_kwargs['dimensions'] = self.meta_data.get('dimensions')
 
         self.backend_impl = backend_factory.searcher(index_path, **final_kwargs)
         print(f"INFO: LeannSearcher initialized with '{backend_name}' backend using index '{index_path}'.")
@@ -155,15 +249,17 @@ class LeannSearcher:
         results = self.backend_impl.search(query_embedding, top_k, **search_kwargs)
         
         enriched_results = []
-        for label, dist in zip(results['labels'][0], results['distances'][0]):
+        for string_id, dist in zip(results['labels'][0], results['distances'][0]):
-            if label < len(self.meta_data['chunks']):
+            try:
-                chunk_info = self.meta_data['chunks'][label]
+                passage_data = self.passage_manager.get_passage(string_id)
                 enriched_results.append(SearchResult(
-                    id=label,
+                    id=string_id,
                     score=dist,
-                    text=chunk_info['text'],
+                    text=passage_data['text'],
-                    metadata=chunk_info.get('metadata', {})
+                    metadata=passage_data.get('metadata', {})
                 ))
+            except KeyError:
+                print(f"WARNING: Passage ID '{string_id}' not found in passage files")
         return enriched_results
 
 

packages/leann-core/src/leann/registry.py

@@ -1,6 +1,9 @@
 # packages/leann-core/src/leann/registry.py
 
 from typing import Dict, TYPE_CHECKING
+import importlib
+import importlib.metadata
+
 if TYPE_CHECKING:
     from leann.interface import LeannBackendFactoryInterface
 
@@ -12,4 +15,22 @@ def register_backend(name: str):
         print(f"INFO: Registering backend '{name}'")
         BACKEND_REGISTRY[name] = cls
         return cls
-    return decorator
+    return decorator
+
+def autodiscover_backends():
+    """Automatically discovers and imports all 'leann-backend-*' packages."""
+    print("INFO: Starting backend auto-discovery...")
+    discovered_backends = []
+    for dist in importlib.metadata.distributions():
+        dist_name = dist.metadata['name']
+        if dist_name.startswith('leann-backend-'):
+            backend_module_name = dist_name.replace('-', '_')
+            discovered_backends.append(backend_module_name)
+            
+    for backend_module_name in sorted(discovered_backends): # sort for deterministic loading
+        try:
+            importlib.import_module(backend_module_name)
+            # Registration message is printed by the decorator
+        except ImportError as e:
+            print(f"WARN: Could not import backend module '{backend_module_name}': {e}")
+    print("INFO: Backend auto-discovery finished.")

tests/sanity_checks/README_hnsw_pruning.md

@@ -0,0 +1,68 @@
+# HNSW Index Storage Optimization
+
+This document explains the storage optimization features available in the HNSW backend.
+
+## Storage Modes
+
+The HNSW backend supports two orthogonal optimization techniques:
+
+### 1. CSR Compression (`is_compact=True`)
+- Converts the graph structure from standard format to Compressed Sparse Row (CSR) format
+- Reduces memory overhead from graph adjacency storage
+- Maintains all embedding data for direct access
+
+### 2. Embedding Pruning (`is_recompute=True`) 
+- Removes embedding vectors from the index file
+- Replaces them with a NULL storage marker
+- Requires recomputation via embedding server during search
+- Must be used with `is_compact=True` for efficiency
+
+## Performance Impact
+
+**Storage Reduction (100 vectors, 384 dimensions):**
+```
+Standard format:     168 KB (embeddings + graph)
+CSR only:           160 KB (embeddings + compressed graph)  
+CSR + Pruned:         6 KB (compressed graph only)
+```
+
+**Key Benefits:**
+- **CSR compression**: ~5% size reduction from graph optimization
+- **Embedding pruning**: ~95% size reduction by removing embeddings
+- **Combined**: Up to 96% total storage reduction
+
+## Usage
+
+```python
+# Standard format (largest)
+builder = LeannBuilder(
+    backend_name="hnsw",
+    is_compact=False,
+    is_recompute=False
+)
+
+# CSR compressed (medium)
+builder = LeannBuilder(
+    backend_name="hnsw", 
+    is_compact=True,
+    is_recompute=False
+)
+
+# CSR + Pruned (smallest, requires embedding server)
+builder = LeannBuilder(
+    backend_name="hnsw",
+    is_compact=True,      # Required for pruning
+    is_recompute=True     # Default: enabled
+)
+```
+
+## Trade-offs
+
+| Mode | Storage | Search Speed | Memory Usage | Setup Complexity |
+|------|---------|--------------|--------------|------------------|
+| Standard | Largest | Fastest | Highest | Simple |
+| CSR | Medium | Fast | Medium | Simple |
+| CSR + Pruned | Smallest | Slower* | Lowest | Complex** |
+
+*Requires network round-trip to embedding server for recomputation  
+**Needs embedding server and passages file for search

tests/sanity_checks/test_hnsw_pruning.py

@@ -0,0 +1,156 @@
+#!/usr/bin/env python3
+"""
+Sanity check script to verify HNSW index pruning effectiveness.
+Tests the difference in file sizes between pruned and non-pruned indices.
+"""
+
+import os
+import sys
+import tempfile
+from pathlib import Path
+import numpy as np
+import json
+
+# Add the project root to the Python path
+project_root = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+# Import backend packages to trigger plugin registration
+import leann_backend_hnsw
+
+from leann.api import LeannBuilder
+
+def create_sample_documents(num_docs=1000):
+    """Create sample documents for testing"""
+    documents = []
+    for i in range(num_docs):
+        documents.append(f"Sample document {i} with some random text content for testing purposes.")
+    return documents
+
+def build_index(documents, output_dir, is_recompute=True):
+    """Build HNSW index with specified recompute setting"""
+    index_path = os.path.join(output_dir, "test_index.hnsw")
+    
+    builder = LeannBuilder(
+        backend_name="hnsw",
+        embedding_model="sentence-transformers/all-MiniLM-L6-v2",
+        M=16,
+        efConstruction=100,
+        distance_metric="mips",
+        is_compact=True,
+        is_recompute=is_recompute
+    )
+    
+    for doc in documents:
+        builder.add_text(doc)
+    
+    builder.build_index(index_path)
+    
+    return index_path
+
+def get_file_size(filepath):
+    """Get file size in bytes"""
+    return os.path.getsize(filepath)
+
+def main():
+    print("🔍 HNSW Pruning Sanity Check")
+    print("=" * 50)
+    
+    # Create sample data
+    print("📊 Creating sample documents...")
+    documents = create_sample_documents(num_docs=1000)
+    print(f"   Number of documents: {len(documents)}")
+    
+    with tempfile.TemporaryDirectory() as temp_dir:
+        print(f"📁 Working in temporary directory: {temp_dir}")
+        
+        # Build index with pruning (is_recompute=True)
+        print("\n🔨 Building index with pruning enabled (is_recompute=True)...")
+        pruned_dir = os.path.join(temp_dir, "pruned")
+        os.makedirs(pruned_dir, exist_ok=True)
+        
+        pruned_index_path = build_index(documents, pruned_dir, is_recompute=True)
+        # Check what files were actually created
+        print(f"   Looking for index files at: {pruned_index_path}")
+        import glob
+        files = glob.glob(f"{pruned_index_path}*")
+        print(f"   Found files: {files}")
+        
+        # Try to find the actual index file
+        if os.path.exists(f"{pruned_index_path}.index"):
+            pruned_index_file = f"{pruned_index_path}.index"
+        else:
+            # Look for any .index file in the directory
+            index_files = glob.glob(f"{pruned_dir}/*.index")
+            if index_files:
+                pruned_index_file = index_files[0]
+            else:
+                raise FileNotFoundError(f"No .index file found in {pruned_dir}")
+        
+        pruned_size = get_file_size(pruned_index_file)
+        print(f"   ✅ Pruned index built successfully")
+        print(f"   📏 Pruned index size: {pruned_size:,} bytes ({pruned_size/1024:.1f} KB)")
+        
+        # Build index without pruning (is_recompute=False)
+        print("\n🔨 Building index without pruning (is_recompute=False)...")
+        non_pruned_dir = os.path.join(temp_dir, "non_pruned")
+        os.makedirs(non_pruned_dir, exist_ok=True)
+        
+        non_pruned_index_path = build_index(documents, non_pruned_dir, is_recompute=False)
+        # Check what files were actually created
+        print(f"   Looking for index files at: {non_pruned_index_path}")
+        files = glob.glob(f"{non_pruned_index_path}*")
+        print(f"   Found files: {files}")
+        
+        # Try to find the actual index file
+        if os.path.exists(f"{non_pruned_index_path}.index"):
+            non_pruned_index_file = f"{non_pruned_index_path}.index"
+        else:
+            # Look for any .index file in the directory
+            index_files = glob.glob(f"{non_pruned_dir}/*.index")
+            if index_files:
+                non_pruned_index_file = index_files[0]
+            else:
+                raise FileNotFoundError(f"No .index file found in {non_pruned_dir}")
+        
+        non_pruned_size = get_file_size(non_pruned_index_file)
+        print(f"   ✅ Non-pruned index built successfully")
+        print(f"   📏 Non-pruned index size: {non_pruned_size:,} bytes ({non_pruned_size/1024:.1f} KB)")
+        
+        # Compare sizes
+        print("\n📊 Comparison Results:")
+        print("=" * 30)
+        size_diff = non_pruned_size - pruned_size
+        size_ratio = pruned_size / non_pruned_size if non_pruned_size > 0 else 0
+        reduction_percent = (1 - size_ratio) * 100
+        
+        print(f"Non-pruned index: {non_pruned_size:,} bytes ({non_pruned_size/1024:.1f} KB)")
+        print(f"Pruned index:     {pruned_size:,} bytes ({pruned_size/1024:.1f} KB)")
+        print(f"Size difference:  {size_diff:,} bytes ({size_diff/1024:.1f} KB)")
+        print(f"Size ratio:       {size_ratio:.3f}")
+        print(f"Size reduction:   {reduction_percent:.1f}%")
+        
+        # Verify pruning effectiveness
+        print("\n🔍 Verification:")
+        if size_diff > 0:
+            print("   ✅ Pruning is effective - pruned index is smaller")
+            if reduction_percent > 10:
+                print(f"   ✅ Significant size reduction: {reduction_percent:.1f}%")
+            else:
+                print(f"   ⚠️  Small size reduction: {reduction_percent:.1f}%")
+        else:
+            print("   ❌ Pruning appears ineffective - no size reduction")
+        
+        # Check if passages files were created
+        pruned_passages = f"{pruned_index_path}.passages.json"
+        non_pruned_passages = f"{non_pruned_index_path}.passages.json"
+        
+        print(f"\n📄 Passages files:")
+        print(f"   Pruned passages file exists: {os.path.exists(pruned_passages)}")
+        print(f"   Non-pruned passages file exists: {os.path.exists(non_pruned_passages)}")
+        
+        return True
+
+if __name__ == "__main__":
+    success = main()
+    sys.exit(0 if success else 1)