refactor: chat and base searcher
This commit is contained in:
Andy Lee
@@ -3,16 +3,9 @@ import os
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import json
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from pathlib import Path
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from typing import Dict, Any, List
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import contextlib
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import threading
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import time
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import atexit
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import socket
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import subprocess
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import sys
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import pickle
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from leann.embedding_server_manager import EmbeddingServerManager
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from leann.searcher_base import BaseSearcher
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from .convert_to_csr import convert_hnsw_graph_to_csr
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from leann.registry import register_backend
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@@ -38,306 +31,120 @@ class HNSWBackend(LeannBackendFactoryInterface):
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@staticmethod
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def searcher(index_path: str, **kwargs) -> LeannBackendSearcherInterface:
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path = Path(index_path)
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meta_path = path.parent / f"{path.name}.meta.json"
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if not meta_path.exists():
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raise FileNotFoundError(f"Leann metadata file not found at {meta_path}.")
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with open(meta_path, 'r') as f:
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meta = json.load(f)
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kwargs['meta'] = meta
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return HNSWSearcher(index_path, **kwargs)
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class HNSWBuilder(LeannBackendBuilderInterface):
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def __init__(self, **kwargs):
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self.build_params = kwargs.copy()
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# --- Configuration defaults with standardized names ---
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self.is_compact = self.build_params.setdefault("is_compact", True)
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self.is_recompute = self.build_params.setdefault("is_recompute", True)
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# --- Additional Options ---
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self.is_skip_neighbors = self.build_params.setdefault("is_skip_neighbors", False)
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self.disk_cache_ratio = self.build_params.setdefault("disk_cache_ratio", 0.0)
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self.external_storage_path = self.build_params.get("external_storage_path", None)
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# --- Standard HNSW parameters ---
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self.M = self.build_params.setdefault("M", 32)
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self.efConstruction = self.build_params.setdefault("efConstruction", 200)
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self.distance_metric = self.build_params.setdefault("distance_metric", "mips")
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self.dimensions = self.build_params.get("dimensions")
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if self.is_skip_neighbors and not self.is_compact:
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raise ValueError("is_skip_neighbors can only be used with is_compact=True")
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if self.is_recompute and not self.is_compact:
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raise ValueError("is_recompute requires is_compact=True for efficiency")
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def build(self, data: np.ndarray, ids: List[str], index_path: str, **kwargs):
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"""Build HNSW index using FAISS"""
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from . import faiss
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path = Path(index_path)
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index_dir = path.parent
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index_prefix = path.stem
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index_dir.mkdir(parents=True, exist_ok=True)
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if data.dtype != np.float32:
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data = data.astype(np.float32)
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if not data.flags['C_CONTIGUOUS']:
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data = np.ascontiguousarray(data)
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# Create label map: integer -> string_id
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label_map = {i: str_id for i, str_id in enumerate(ids)}
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label_map_file = index_dir / "leann.labels.map"
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with open(label_map_file, 'wb') as f:
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pickle.dump(label_map, f)
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metric_str = self.distance_metric.lower()
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metric_enum = get_metric_map().get(metric_str)
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metric_enum = get_metric_map().get(self.distance_metric.lower())
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if metric_enum is None:
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raise ValueError(f"Unsupported distance_metric '{metric_str}'.")
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raise ValueError(f"Unsupported distance_metric '{self.distance_metric}'.")
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M = self.M
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efConstruction = self.efConstruction
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dim = self.dimensions
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if not dim:
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dim = data.shape[1]
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dim = self.dimensions or data.shape[1]
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index = faiss.IndexHNSWFlat(dim, self.M, metric_enum)
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index.hnsw.efConstruction = self.efConstruction
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print(f"INFO: Building HNSW index for {data.shape[0]} vectors with metric {metric_enum}...")
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try:
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index = faiss.IndexHNSWFlat(dim, M, metric_enum)
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index.hnsw.efConstruction = efConstruction
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if metric_str == "cosine":
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faiss.normalize_L2(data)
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index.add(data.shape[0], faiss.swig_ptr(data))
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index_file = index_dir / f"{index_prefix}.index"
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faiss.write_index(index, str(index_file))
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print(f"✅ HNSW index built successfully at '{index_file}'")
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if self.distance_metric.lower() == "cosine":
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faiss.normalize_L2(data)
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if self.is_compact:
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self._convert_to_csr(index_file)
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except Exception as e:
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print(f"💥 ERROR: HNSW index build failed. Exception: {e}")
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raise
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index.add(data.shape[0], faiss.swig_ptr(data))
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index_file = index_dir / f"{index_prefix}.index"
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faiss.write_index(index, str(index_file))
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if self.is_compact:
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self._convert_to_csr(index_file)
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def _convert_to_csr(self, index_file: Path):
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"""Convert built index to CSR format"""
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try:
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mode_str = "CSR-pruned" if self.is_recompute else "CSR-standard"
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print(f"INFO: Converting HNSW index to {mode_str} format...")
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csr_temp_file = index_file.with_suffix(".csr.tmp")
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success = convert_hnsw_graph_to_csr(
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str(index_file),
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str(csr_temp_file),
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prune_embeddings=self.is_recompute
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)
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if success:
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print("✅ CSR conversion successful.")
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import shutil
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shutil.move(str(csr_temp_file), str(index_file))
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print(f"INFO: Replaced original index with {mode_str} version at '{index_file}'")
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else:
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# Clean up and fail fast
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if csr_temp_file.exists():
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os.remove(csr_temp_file)
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raise RuntimeError("CSR conversion failed - cannot proceed with compact format")
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except Exception as e:
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print(f"💥 ERROR: CSR conversion failed. Exception: {e}")
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raise
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class HNSWSearcher(LeannBackendSearcherInterface):
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def _get_index_storage_status_from_meta(self) -> tuple[bool, bool]:
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"""
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Get storage status from metadata with sensible defaults.
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Returns:
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A tuple (is_compact, is_pruned).
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"""
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# Check if metadata has these flags
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is_compact = self.meta.get('is_compact', True) # Default to compact (CSR format)
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is_pruned = self.meta.get('is_pruned', True) # Default to pruned (embeddings removed)
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print(f"INFO: Storage status from metadata: is_compact={is_compact}, is_pruned={is_pruned}")
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return is_compact, is_pruned
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csr_temp_file = index_file.with_suffix(".csr.tmp")
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success = convert_hnsw_graph_to_csr(
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str(index_file), str(csr_temp_file), prune_embeddings=self.is_recompute
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)
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if success:
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import shutil
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shutil.move(str(csr_temp_file), str(index_file))
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else:
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if csr_temp_file.exists():
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os.remove(csr_temp_file)
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raise RuntimeError("CSR conversion failed")
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class HNSWSearcher(BaseSearcher):
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def __init__(self, index_path: str, **kwargs):
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super().__init__(index_path, backend_module_name="leann_backend_hnsw.hnsw_embedding_server", **kwargs)
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from . import faiss
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self.meta = kwargs.get("meta", {})
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if not self.meta:
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raise ValueError("HNSWSearcher requires metadata from .meta.json.")
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self.dimensions = self.meta.get("dimensions")
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if not self.dimensions:
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raise ValueError("Dimensions not found in Leann metadata.")
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self.distance_metric = self.meta.get("distance_metric", "mips").lower()
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metric_enum = get_metric_map().get(self.distance_metric)
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if metric_enum is None:
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raise ValueError(f"Unsupported distance_metric '{self.distance_metric}'.")
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self.embedding_model = self.meta.get("embedding_model")
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if not self.embedding_model:
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print("WARNING: embedding_model not found in meta.json. Recompute will fail if attempted.")
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self.is_compact, self.is_pruned = self._get_index_storage_status_from_meta()
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# Check for embedding model override (not allowed)
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if 'embedding_model' in kwargs and kwargs['embedding_model'] != self.embedding_model:
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raise ValueError(f"Embedding model override not allowed. Index uses '{self.embedding_model}', but got '{kwargs['embedding_model']}'")
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path = Path(index_path)
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self.index_dir = path.parent
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self.index_prefix = path.stem
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# Load the label map
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label_map_file = self.index_dir / "leann.labels.map"
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if not label_map_file.exists():
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raise FileNotFoundError(f"Label map file not found: {label_map_file}")
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with open(label_map_file, 'rb') as f:
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self.label_map = pickle.load(f)
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index_file = self.index_dir / f"{self.index_prefix}.index"
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index_file = self.index_dir / f"{self.index_path.stem}.index"
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if not index_file.exists():
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raise FileNotFoundError(f"HNSW index file not found at {index_file}")
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# Get storage status from metadata with user overrides
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self.is_compact, self.is_pruned = self._get_index_storage_status_from_meta()
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# Allow override of storage parameters via kwargs
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if 'is_compact' in kwargs:
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self.is_compact = kwargs['is_compact']
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if 'is_pruned' in kwargs:
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self.is_pruned = kwargs['is_pruned']
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# Validate configuration constraints
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if not self.is_compact and kwargs.get("is_skip_neighbors", False):
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raise ValueError("is_skip_neighbors can only be used with is_compact=True")
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if kwargs.get("is_recompute", False) and kwargs.get("external_storage_path"):
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raise ValueError("Cannot use both is_recompute and external_storage_path simultaneously")
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hnsw_config = faiss.HNSWIndexConfig()
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hnsw_config.is_compact = self.is_compact
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# Apply additional configuration options with strict validation
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hnsw_config.is_skip_neighbors = kwargs.get("is_skip_neighbors", False)
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hnsw_config.is_recompute = self.is_pruned or kwargs.get("is_recompute", False)
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hnsw_config.disk_cache_ratio = kwargs.get("disk_cache_ratio", 0.0)
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hnsw_config.external_storage_path = kwargs.get("external_storage_path")
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self.zmq_port = kwargs.get("zmq_port", 5557)
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if self.is_pruned and not hnsw_config.is_recompute:
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raise RuntimeError("Index is pruned (embeddings removed) but recompute is disabled. This is impossible - recompute must be enabled for pruned indices.")
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print(f"INFO: Loading index with is_compact={self.is_compact}, is_pruned={self.is_pruned}")
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print(f"INFO: Config - skip_neighbors={hnsw_config.is_skip_neighbors}, recompute={hnsw_config.is_recompute}")
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self._index = faiss.read_index(str(index_file), faiss.IO_FLAG_MMAP, hnsw_config)
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if self.is_compact:
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print("✅ Compact CSR format HNSW index loaded successfully.")
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else:
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print("✅ Standard HNSW index loaded successfully.")
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self.embedding_server_manager = EmbeddingServerManager(
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backend_module_name="leann_backend_hnsw.hnsw_embedding_server"
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)
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if self.is_pruned and not hnsw_config.is_recompute:
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raise RuntimeError("Index is pruned but recompute is disabled.")
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self._index = faiss.read_index(str(index_file), faiss.IO_FLAG_MMAP, hnsw_config)
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def _get_index_storage_status_from_meta(self) -> tuple[bool, bool]:
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is_compact = self.meta.get('is_compact', True)
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is_pruned = self.meta.get('is_pruned', True)
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return is_compact, is_pruned
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def search(self, query: np.ndarray, top_k: int, **kwargs) -> Dict[str, Any]:
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"""Search using HNSW index with optional recompute functionality"""
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from . import faiss
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ef = kwargs.get("ef", 128)
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if self.is_pruned:
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print(f"INFO: Index is pruned - ensuring embedding server is running for recompute.")
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if not self.embedding_model:
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raise ValueError("Cannot use recompute mode without 'embedding_model' in meta.json.")
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passages_file = kwargs.get("passages_file")
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if not passages_file:
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# Pass the metadata file instead of a single passage file
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meta_file_path = self.index_dir / f"{self.index_prefix}.index.meta.json"
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if meta_file_path.exists():
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passages_file = str(meta_file_path)
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print(f"INFO: Using metadata file for lazy loading: {passages_file}")
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else:
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raise RuntimeError(f"FATAL: Index is pruned but metadata file not found: {meta_file_path}")
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meta_file_path = self.index_dir / f"{self.index_path.name}.meta.json"
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if not meta_file_path.exists():
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raise RuntimeError(f"FATAL: Index is pruned but metadata file not found: {meta_file_path}")
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zmq_port = kwargs.get("zmq_port", 5557)
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server_started = self.embedding_server_manager.start_server(
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port=zmq_port,
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model_name=self.embedding_model,
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passages_file=passages_file,
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distance_metric=self.distance_metric
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)
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if not server_started:
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raise RuntimeError(f"Failed to start HNSW embedding server on port {zmq_port}")
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self._ensure_server_running(str(meta_file_path), port=zmq_port, **kwargs)
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if query.dtype != np.float32:
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query = query.astype(np.float32)
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if query.ndim == 1:
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query = np.expand_dims(query, axis=0)
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if self.distance_metric == "cosine":
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faiss.normalize_L2(query)
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try:
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self._index.hnsw.efSearch = ef
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params = faiss.SearchParametersHNSW()
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params.zmq_port = kwargs.get("zmq_port", self.zmq_port)
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params.efSearch = ef
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params.beam_size = 2 # Match research system beam_size
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batch_size = query.shape[0]
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distances = np.empty((batch_size, top_k), dtype=np.float32)
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labels = np.empty((batch_size, top_k), dtype=np.int64)
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self._index.search(query.shape[0], faiss.swig_ptr(query), top_k, faiss.swig_ptr(distances), faiss.swig_ptr(labels), params)
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# 🐛 DEBUG: Print raw faiss results before conversion
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print(f"🔍 DEBUG HNSW Search Results:")
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print(f" Query shape: {query.shape}")
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print(f" Top_k: {top_k}")
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print(f" Raw faiss indices: {labels[0] if len(labels) > 0 else 'No results'}")
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print(f" Raw faiss distances: {distances[0] if len(distances) > 0 else 'No results'}")
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# Convert integer labels to string IDs
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string_labels = []
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for batch_idx, batch_labels in enumerate(labels):
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batch_string_labels = []
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print(f" Batch {batch_idx} conversion:")
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for i, int_label in enumerate(batch_labels):
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if int_label in self.label_map:
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string_id = self.label_map[int_label]
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batch_string_labels.append(string_id)
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print(f" faiss[{int_label}] -> passage_id '{string_id}' (distance: {distances[batch_idx][i]:.4f})")
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else:
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unknown_id = f"unknown_{int_label}"
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batch_string_labels.append(unknown_id)
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print(f" faiss[{int_label}] -> {unknown_id} (NOT FOUND in label_map!)")
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string_labels.append(batch_string_labels)
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return {"labels": string_labels, "distances": distances}
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except Exception as e:
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print(f"💥 ERROR: HNSW search failed. Exception: {e}")
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raise
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def __del__(self):
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if hasattr(self, 'embedding_server_manager'):
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self.embedding_server_manager.stop_server()
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params = faiss.SearchParametersHNSW()
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params.zmq_port = kwargs.get("zmq_port", 5557)
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params.efSearch = kwargs.get("ef", 128)
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params.beam_size = 2
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batch_size = query.shape[0]
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distances = np.empty((batch_size, top_k), dtype=np.float32)
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labels = np.empty((batch_size, top_k), dtype=np.int64)
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self._index.search(query.shape[0], faiss.swig_ptr(query), top_k, faiss.swig_ptr(distances), faiss.swig_ptr(labels), params)
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string_labels = [[self.label_map.get(int_label, f"unknown_{int_label}") for int_label in batch_labels] for batch_labels in labels]
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return {"labels": string_labels, "distances": distances}
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