Weaviate: как использовать vector database для RAG и семантического поиска

import os
from openai import OpenAI
import weaviate
import weaviate.classes.config as wc

openai = OpenAI(
    base_url="https://speshu.ai/api/v1",
    api_key=os.environ["SPESHU_AI_API_KEY"],
)

texts = [
    "SpeShu.AI предоставляет доступ к LLM через совместимые API.",
    "Weaviate хранит векторы и metadata для семантического поиска.",
]

embeddings = openai.embeddings.create(
    model="text-embedding-3-large",
    input=texts,
).data

client = weaviate.connect_to_local()

try:
    if client.collections.exists("Article"):
        client.collections.delete("Article")

    articles = client.collections.create(
        name="Article",
        properties=[wc.Property(name="text", data_type=wc.DataType.TEXT)],
        vector_config=wc.Configure.Vectors.self_provided(),
    )

    for text, item in zip(texts, embeddings):
        articles.data.insert(properties={"text": text}, vector=item.embedding)

    query_vector = openai.embeddings.create(
        model="text-embedding-3-large",
        input="Как хранить документы для RAG?",
    ).data[0].embedding

    result = articles.query.near_vector(near_vector=query_vector, limit=2)
    for obj in result.objects:
        print(obj.properties["text"])
finally:
    client.close()