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#!/usr/bin/env python3
"""
Script to migrate raw queries into MemoryCards by extracting preferences.
It reads from data/raw_datasets/pilot_study_1000.jsonl and outputs:
- data/corpora/memory_cards.jsonl
- data/corpora/memory_embeddings.npy
"""
import json
import os
import sys
# Add src to sys.path so we can import personalization
sys.path.append(os.path.join(os.path.dirname(__file__), "../src"))
import uuid
import numpy as np
import torch
from pathlib import Path
from tqdm import tqdm
from typing import List
from personalization.config.settings import load_local_models_config
# from personalization.models.preference_extractor.rule_extractor import QwenRuleExtractor
from personalization.models.preference_extractor.gpt4o_extractor import GPT4OExtractor
from personalization.models.embedding.qwen3_8b import Qwen3Embedding8B
from personalization.retrieval.preference_store.schemas import ChatTurn, MemoryCard, PreferenceList
def ensure_dir(path: str):
Path(path).parent.mkdir(parents=True, exist_ok=True)
def main():
# 1. Setup paths
input_path = "data/corpora/oasst1_labeled.jsonl"
# input_path = "data/raw_datasets/oasst1_queries.jsonl"
output_cards_path = "data/corpora/memory_cards.jsonl"
output_emb_path = "data/corpora/memory_embeddings.npy"
ensure_dir(output_cards_path)
print("Loading models configuration...")
cfg = load_local_models_config()
# 2. Initialize models
# print("Initializing Preference Extractor (GPT-4o)...")
# extractor = GPT4OExtractor.from_config(cfg)
print("Initializing Embedding Model...")
embedder = Qwen3Embedding8B.from_config(cfg)
# 3. Process data
print(f"Reading from {input_path}...")
memory_cards: List[MemoryCard] = []
# We will process in small batches to manage memory if needed,
# but for 1000 items, we can iterate one by one for extraction
# and maybe batch for embedding if we want optimization.
# Given the complexity, let's just do sequential for simplicity and safety.
with open(input_path, "r", encoding="utf-8") as f:
lines = f.readlines()
# Synthetic user distribution (round robin for 10 users)
users = [f"user_{i}" for i in range(10)]
print("Extracting preferences...")
# Use tqdm for progress
for idx, line in enumerate(tqdm(lines)):
# if idx >= 100: # LIMIT to 100 items
# break
row = json.loads(line)
query = row.get("original_query", "").strip()
if not query:
continue
# Use real metadata from dataset
user_id = row.get("user_id", f"user_{idx}")
session_id = row.get("session_id", f"sess_{idx}")
turn_id = row.get("turn_id", 0)
# Load pre-extracted preferences
has_pref = row.get("has_preference", False)
extracted_data = row.get("extracted_json", {})
# Skip if no preference (according to label)
if not has_pref:
continue
try:
pref_list = PreferenceList.model_validate(extracted_data)
except Exception:
# Fallback or skip if validation fails
continue
# If we have preferences, create a memory card
if pref_list.preferences:
# Construct a note text: "condition: action"
notes = [f"{p.condition}: {p.action}" for p in pref_list.preferences]
note_summary = "; ".join(notes)
# Create MemoryCard (embedding will be filled later)
card = MemoryCard(
card_id=str(uuid.uuid4()),
user_id=user_id,
source_session_id=session_id,
source_turn_ids=[turn_id],
raw_queries=[query],
preference_list=pref_list,
note_text=note_summary,
embedding_e=[], # To be filled
kind="pref"
)
memory_cards.append(card)
print(f"Found {len(memory_cards)} memory cards. Generating embeddings...")
if not memory_cards:
print("No preferences found. Exiting.")
return
# 4. Generate Embeddings
# We'll embed the `raw_queries` (joined) or `note_text`?
# The design doc says: "Qwen3Embedding8B.encode([turn.text])"
# So we embed the original query that generated the memory.
texts_to_embed = [card.raw_queries[0] for card in memory_cards]
print(f"Embedding {len(texts_to_embed)} memories...")
embeddings_list = []
chunk_size = 2000 # Process in chunks to avoid OOM
for i in range(0, len(texts_to_embed), chunk_size):
print(f" Embedding chunk {i} to {min(i+chunk_size, len(texts_to_embed))}...")
chunk = texts_to_embed[i : i + chunk_size]
# Batch encode with larger batch_size for A40
chunk_emb = embedder.encode(
chunk,
batch_size=128,
normalize=True,
return_tensor=False
)
embeddings_list.extend(chunk_emb)
# Assign back to cards and prepare matrix
emb_matrix = []
for card, emb in zip(memory_cards, embeddings_list):
card.embedding_e = emb
emb_matrix.append(emb)
# 5. Save
print(f"Saving {len(memory_cards)} cards to {output_cards_path}...")
with open(output_cards_path, "w", encoding="utf-8") as f:
for card in memory_cards:
f.write(card.model_dump_json() + "\n")
print(f"Saving embeddings matrix to {output_emb_path}...")
np_emb = np.array(emb_matrix, dtype=np.float32)
np.save(output_emb_path, np_emb)
print("Done!")
if __name__ == "__main__":
main()
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