pref0 + LlamaIndex

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.chat_engine import CondensePlusContextChatEngine
import requests

PREF0_API = "https://api.pref0.com"
PREF0_KEY = "pref0_sk_..."

def get_preferences(user_id: str) -> str:
    res = requests.get(
        f"{PREF0_API}/v1/profiles/{user_id}",
        headers={"Authorization": f"Bearer {PREF0_KEY}"},
    )
    prefs = res.json().get("preferences", [])
    return "\n".join(
        f"- {p['key']}: {p['value']}"
        for p in prefs if p["confidence"] >= 0.5
    )

# LlamaIndex handles knowledge retrieval
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)

# pref0 handles user preferences
learned = get_preferences("user_abc123")

chat_engine = index.as_chat_engine(
    system_prompt=f"You are a helpful assistant.\n\nLearned user preferences:\n{learned}",
)

response = chat_engine.chat("How do I deploy this project?")