sayit-archive-tw / spark-scripts /build_index.py
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Add DGX Spark fine-tuning plan, scripts, and Apertus 70B as deep tier
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#!/usr/bin/env python3
"""Build FAISS index from RAG chunks for retrieval."""
import json
import faiss
import numpy as np
import pickle
from sentence_transformers import SentenceTransformer
from pathlib import Path
CHUNKS_PATH = Path("dataset/data/chunks.jsonl")
INDEX_DIR = Path("index")
INDEX_DIR.mkdir(exist_ok=True)
# Load chunks
print("Loading chunks...")
chunks = []
with open(CHUNKS_PATH) as f:
for line in f:
chunks.append(json.loads(line))
print(f"Loaded {len(chunks)} chunks")
# Build embedding text: combine question + text for richer retrieval
texts = []
for c in chunks:
parts = []
if c.get("question"):
parts.append(f"Q: {c['question']}")
parts.append(c["text"])
texts.append("\n".join(parts))
# Encode with bge-m3
print("Loading bge-m3...")
model = SentenceTransformer("./models/bge-m3")
print("Encoding chunks (this takes a few minutes)...")
embeddings = model.encode(
texts,
batch_size=256,
show_progress_bar=True,
normalize_embeddings=True,
)
embeddings = np.array(embeddings, dtype=np.float32)
print(f"Embeddings shape: {embeddings.shape}")
# Build FAISS index (inner product since embeddings are normalized = cosine sim)
dim = embeddings.shape[1]
index = faiss.IndexFlatIP(dim)
# Optional: use IVF for faster search on large indices
# nlist = 256
# quantizer = faiss.IndexFlatIP(dim)
# index = faiss.IndexIVFFlat(quantizer, dim, nlist, faiss.METRIC_INNER_PRODUCT)
# index.train(embeddings)
index.add(embeddings)
print(f"FAISS index built: {index.ntotal} vectors")
# Save
faiss.write_index(index, str(INDEX_DIR / "chunks.faiss"))
with open(INDEX_DIR / "chunks_meta.pkl", "wb") as f:
pickle.dump(chunks, f)
# Also save the lexicon for terminology lookup
lexicon = []
with open("dataset/data/lexicon.jsonl") as f:
for line in f:
lexicon.append(json.loads(line))
with open(INDEX_DIR / "lexicon.pkl", "wb") as f:
pickle.dump(lexicon, f)
print(f"Saved index to {INDEX_DIR}/")
print(f"Index size: {INDEX_DIR / 'chunks.faiss'}")