qdrant数据库

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"