From 5ec3c5a1e200131f09e6d7fcf28c34a937e77bd2 Mon Sep 17 00:00:00 2001 From: administrator Date: Sun, 2 Apr 2023 09:52:10 +0800 Subject: [PATCH] =?UTF-8?q?qdrant=E6=95=B0=E6=8D=AE=E5=BA=93?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- gpt_0_create_qdrant.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/gpt_0_create_qdrant.py b/gpt_0_create_qdrant.py index c02d4b2..abbcd0c 100644 --- a/gpt_0_create_qdrant.py +++ b/gpt_0_create_qdrant.py @@ -1,6 +1,15 @@ from qdrant_client import QdrantClient from qdrant_client.http import models +# 在下文中提醒如果数据量大, 需要使用专用的向量搜索引擎, 如Pinecone, Weaviate, Qdrant +# https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb +""" +This indexing stage can be executed offline and only runs once to precompute the indexes for the dataset +so that each piece of content can be retrieved later. Since this is a small example, we will store and search the +embeddings locally. If you have a larger dataset, consider using a vector search engine like Pinecone, +Weaviate or Qdrant to power the search. +""" + collection_name = "qyt_gpt_collection" qdrant_url = "http://localhost:6333"