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# retriever/index_dense.py
import os
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
import hashlib
import threading
import numpy as np
import pickle
import torch
from pathlib import Path
from sentence_transformers import SentenceTransformer
from classifier.utils import DEVICE
try:
import faiss # type: ignore
_HAS_FAISS = True
except Exception:
_HAS_FAISS = False
def _chunks(lst, n):
for i in range(0, len(lst), n):
yield lst[i:i+n]
def _compute_cache_key(docs, model_name):
"""Compute a hash key for caching based on documents and model."""
# Create a hash from document IDs/texts and model name
doc_ids = "".join([d.id for d in docs])
content = f"{model_name}:{doc_ids}"
return hashlib.md5(content.encode()).hexdigest()
class DenseIndex:
def __init__(self, docs, model_name="sentence-transformers/embeddinggemma-300m-medical",
batch_size=64, embedding_model=None, cache_dir=".cache/embeddings"):
self.docs = docs
self.batch_size = batch_size
self.cache_dir = cache_dir
# Thread safety
self.lock = threading.Lock()
self.ready_count = 0
self.emb_batches = [] # List of numpy arrays for fallback
torch.set_num_threads(1)
if embedding_model:
self.model = embedding_model
self.device = self.model.device
actual_model_name = getattr(self.model, 'model_card_data', {}).get('base_model', model_name)
if hasattr(self.model, '_model_card_vars') and 'model_id' in self.model._model_card_vars:
actual_model_name = self.model._model_card_vars['model_id']
else:
self.model = SentenceTransformer(model_name, device=DEVICE)
self.device = DEVICE
actual_model_name = model_name
self.cache_key = _compute_cache_key(docs, actual_model_name)
self.cache_path = Path(cache_dir) / f"{self.cache_key}.pkl"
# Initialize index structure
if _HAS_FAISS:
# We need to know dimension to init FAISS.
# We'll init it when the first batch arrives or if we load full cache.
self.index = None
else:
self.index = None
# Start background ingestion
self.ingest_thread = threading.Thread(target=self._ingest_embeddings, daemon=True)
self.ingest_thread.start()
def _generate_embeddings(self):
"""Yields batches of embeddings from cache or computation."""
texts = [d.text for d in self.docs]
# 1. Try full cache first
if self.cache_path.exists():
print(f"Loading embeddings from cache: {self.cache_path}")
try:
with open(self.cache_path, 'rb') as f:
full_emb = pickle.load(f)
print(f"✓ Loaded {len(full_emb)} cached embeddings")
# Yield as a single large batch
yield full_emb
return
except Exception as e:
print(f"Cache load failed: {e}, recomputing...")
# 2. Partial cache logic
partial_cache_path = self.cache_path.parent / f"{self.cache_path.stem}.partial.pkl"
start_index = 0
existing_embs = []
if partial_cache_path.exists():
try:
with open(partial_cache_path, 'rb') as f:
existing_embs = pickle.load(f)
# Yield existing chunks
# We assume existing_embs is a list of batches from previous run
# But wait, previous implementation saved list of batches.
# Let's verify if it saved list of batches or vstacked array.
# Previous impl: pickle.dump(embs, f) where embs is list of arrays.
for batch in existing_embs:
yield batch
start_index = sum(len(e) for e in existing_embs)
except Exception as e:
existing_embs = []
start_index = 0
# 3. Compute remaining
texts_to_process = texts[start_index:]
if not texts_to_process:
return
# We need to keep track of all embs (existing + new) to save partial/full cache
# But `existing_embs` might be large.
# We will append new batches to `existing_embs` locally to save partials.
with torch.inference_mode():
total_processed = start_index
total_batches = (len(texts) + self.batch_size - 1) // self.batch_size
start_batch = len(existing_embs)
for i, part in enumerate(_chunks(texts_to_process, self.batch_size), 1):
part_emb = self.model.encode(
part,
batch_size=self.batch_size,
normalize_embeddings=True,
convert_to_numpy=True,
show_progress_bar=False,
device=self.device,
)
batch_emb = part_emb.astype(np.float32)
yield batch_emb
existing_embs.append(batch_emb)
total_processed += len(part)
# Save partial
with open(partial_cache_path, 'wb') as f:
pickle.dump(existing_embs, f)
def _ingest_embeddings(self):
"""Background thread to ingest embeddings from generator."""
all_embs = []
for batch_emb in self._generate_embeddings():
with self.lock:
if _HAS_FAISS:
if self.index is None:
d = batch_emb.shape[1]
self.index = faiss.IndexFlatIP(d)
self.index.add(batch_emb)
# We also keep track for fallback or saving
self.emb_batches.append(batch_emb)
self.ready_count += len(batch_emb)
all_embs.append(batch_emb)
# Finalize
full_emb = np.vstack(all_embs).astype(np.float32)
# Save full cache
self.cache_path.parent.mkdir(parents=True, exist_ok=True)
with open(self.cache_path, 'wb') as f:
pickle.dump(full_emb, f)
print(f"✓ Saved embeddings to cache: {self.cache_path}")
# Cleanup partial
partial_cache_path = self.cache_path.parent / f"{self.cache_path.stem}.partial.pkl"
if partial_cache_path.exists():
partial_cache_path.unlink()
def search(self, query: str, k: int = 50):
qv = self.model.encode(
[query],
normalize_embeddings=True,
convert_to_numpy=True,
show_progress_bar=False,
device=self.device,
).astype(np.float32)[0]
with self.lock:
current_count = self.ready_count
if current_count == 0:
print("Warning: Index not yet initialized, returning empty results.")
return []
# If we have partial data, we search it.
if _HAS_FAISS and self.index is not None:
# FAISS index is updated incrementally
D, I = self.index.search(qv.reshape(1, -1), min(k, current_count))
return [(self.docs[int(i)], float(D[0][j])) for j, i in enumerate(I[0]) if i != -1]
# NumPy fallback
# We might have multiple batches, need to stack them for search
# Optimization: cache the stacked version if it hasn't changed?
# For now, just stack what we have.
curr_emb = np.vstack(self.emb_batches)
sims = curr_emb @ qv
effective_k = min(k, len(sims))
if effective_k >= len(sims):
order = np.argsort(-sims)
else:
idx = np.argpartition(-sims, kth=effective_k-1)[:effective_k]
order = idx[np.argsort(-sims[idx])]
return [(self.docs[int(i)], float(sims[int(i)])) for i in order]
def get_progress(self):
"""Returns (current_count, total_count) of indexed documents."""
with self.lock:
return self.ready_count, len(self.docs)
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