Weaviate vs Kronvex
Base vectorielle vs API mémoire

import weaviate
import openai
from datetime import datetime

# Étape 1 : Connexion et définition du schéma
client = weaviate.connect_to_weaviate_cloud(
    cluster_url="...",
    auth_credentials=weaviate.Auth.api_key("...")
)
# Vous devez d'abord définir le schéma de collection
client.collections.create(
    "Memory",
    properties=[
        weaviate.Property(name="content", ...),
        weaviate.Property(name="agent_id", ...),
        weaviate.Property(name="created_at", ...),
        weaviate.Property(name="access_count", ...)
    ]
)

# Étape 2 : Embed la requête manuellement
oai = openai.OpenAI()
def embed(text):
    r = oai.embeddings.create(
        model="text-embedding-3-small",
        input=text
    )
    return r.data[0].embedding

# Étape 3 : Requête avec filtres
query_vec = embed("What does the user like?")
collection = client.collections.get("Memory")
results = collection.query.near_vector(
    near_vector=query_vec,
    filters=weaviate.Filter.by_property("agent_id").equal("user-123"),
    limit=10
)

# Étape 4 : Scoring récence + fréquence manuel
def confidence(obj, sim):
    age_days = (datetime.now() - obj.properties['created_at']).days
    recency = 1 / (1 + 0.023 * age_days)
    freq = min(obj.properties['access_count'], 10) / 10
    return sim * 0.6 + recency * 0.2 + freq * 0.2

scored = sorted([
    (obj, confidence(obj, obj.metadata.certainty))
    for obj in results.objects
], key=lambda x: x[1], reverse=True)[:5]

# Étape 5 : Formatter manuellement comme bloc de prompt
context = "Memory context:\n" + "\n".join(
    f"- {o.properties['content']} (confidence: {s:.2f})"
    for o, s in scored
)
# ~45 lignes juste pour injecter le contexte