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#!/usr/bin/env python3
"""
Script to build user vectors for PersonaMem 32k dataset.
1. Load shared contexts.
2. Convert to ChatTurns.
3. Extract preferences -> MemoryCards.
4. Compute embeddings & item vectors.
5. Aggregate to user vectors.
"""
import sys
import os
import json
import uuid
import numpy as np
from tqdm import tqdm
from typing import List, Dict
# Add src to sys.path
sys.path.append(os.path.join(os.path.dirname(__file__), "../src"))
from personalization.config.settings import load_local_models_config
from personalization.config.registry import get_preference_extractor
from personalization.models.embedding.qwen3_8b import Qwen3Embedding8B
from personalization.retrieval.preference_store.schemas import MemoryCard, ChatTurn, PreferenceList
from personalization.data.personamem_loader import load_personamem_contexts_32k, PersonaMemContext
from personalization.user_model.features import ItemProjection
def context_to_chatturns(context: PersonaMemContext) -> List[ChatTurn]:
turns = []
for i, msg in enumerate(context.messages):
if isinstance(msg, dict):
role = msg.get("role", "user")
text = msg.get("content", "")
elif isinstance(msg, str):
# Fallback for string messages, assume user? or skip?
# print(f"Warning: msg is string: {msg[:50]}...")
role = "user"
text = msg
else:
continue
turns.append(ChatTurn(
user_id=context.shared_context_id, # Use context id as user id equivalent for building vector
session_id=context.shared_context_id,
turn_id=i,
role="user" if role == "user" else "assistant",
text=text,
timestamp=None,
meta={}
))
return turns
def sliding_windows(seq: List, window_size: int, step: int):
for i in range(0, len(seq), step):
yield seq[i : i + window_size]
def find_last_user_text(window: List[ChatTurn]) -> str:
for t in reversed(window):
if t.role == "user":
return t.text
return ""
def main():
# Paths (adjust as needed, assume downloaded to data/raw_datasets/personamem)
ctx_path = "data/raw_datasets/personamem/shared_contexts_32k.jsonl"
item_proj_path = "data/corpora/item_projection.npz"
output_vec_path = "data/personamem/user_vectors.npz"
output_cards_path = "data/personamem/memory_cards.jsonl"
# Ensure dirs
os.makedirs(os.path.dirname(output_vec_path), exist_ok=True)
if not os.path.exists(ctx_path):
print(f"Error: Context file not found at {ctx_path}")
return
if not os.path.exists(item_proj_path):
print(f"Error: Item projection not found at {item_proj_path}. Run build_item_space.py first.")
return
# Load Models
print("Loading models...")
cfg = load_local_models_config()
# Explicitly use Qwen3Embedding8B
embed_model = Qwen3Embedding8B.from_config(cfg)
# Use registry for extractor (SFT model)
extractor_name = "qwen3_0_6b_sft"
try:
extractor = get_preference_extractor(extractor_name)
except:
print(f"Fallback to rule extractor for {extractor_name} not found.")
extractor = get_preference_extractor("rule")
# Load Projection
proj_data = np.load(item_proj_path)
projection = ItemProjection(P=proj_data["P"], mean=proj_data["mean"])
# Load Contexts
print("Loading contexts...")
contexts = load_personamem_contexts_32k(ctx_path)
print(f"Loaded {len(contexts)} contexts.")
all_cards = []
# Process each context
print("Extracting preferences...")
# For demo speed, maybe limit? Or full run. Full run might take time.
# Assuming batch processing or just loop for now.
for ctx_id, ctx in tqdm(contexts.items()):
turns = context_to_chatturns(ctx)
# print(f"Context {ctx_id}: {len(turns)} turns")
# Sliding window extraction
for window in sliding_windows(turns, window_size=6, step=3):
# Only extract if window has user turns
if not any(t.role == "user" for t in window):
continue
try:
prefs = extractor.extract_turn(window)
# if prefs.preferences:
# print(f" Found {len(prefs.preferences)} preferences")
except Exception as e:
print(f"Extraction failed: {e}")
continue
if not prefs.preferences:
continue
source_query = find_last_user_text(window)
if not source_query:
continue
# Embed
e_m = embed_model.encode([source_query], return_tensor=False)[0]
e_m_np = np.array(e_m)
v_m = projection.transform_vector(e_m_np)
# Serialize note
notes = [f"When {p.condition}, {p.action}." for p in prefs.preferences]
note_text = " ".join(notes)
card = MemoryCard(
card_id=str(uuid.uuid4()),
user_id=ctx_id, # persona_id/context_id
source_session_id=ctx_id,
source_turn_ids=[t.turn_id for t in window if t.role == "user"],
raw_queries=[source_query],
preference_list=prefs,
note_text=note_text,
embedding_e=e_m,
kind="pref"
)
all_cards.append(card)
print(f"Extracted {len(all_cards)} memory cards.")
# Build User Vectors
print("Building user vectors...")
z_by_user = {}
# Group cards by user
cards_by_user = {}
for c in all_cards:
if c.user_id not in cards_by_user:
cards_by_user[c.user_id] = []
cards_by_user[c.user_id].append(c)
for uid, u_cards in cards_by_user.items():
# Stack v_m: [M_u, k]
V = np.stack([projection.transform_vector(np.array(c.embedding_e, dtype=np.float32)) for c in u_cards], axis=0)
z = np.mean(V, axis=0)
z_by_user[uid] = z
# Save
print(f"Saving {len(all_cards)} cards to {output_cards_path}...")
with open(output_cards_path, "w", encoding="utf-8") as f:
for c in all_cards:
f.write(c.model_dump_json() + "\n")
print(f"Saving user vectors to {output_vec_path}...")
user_ids = list(z_by_user.keys())
Z = np.array([z_by_user[uid] for uid in user_ids], dtype=np.float32)
np.savez(output_vec_path, user_ids=user_ids, Z=Z)
print("Done.")
if __name__ == "__main__":
main()
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