List Qdrant among the other vector databases

examples/Question_answering_using_embeddings.ipynb

@@ -316,7 +316,7 @@
    "source": [
     "We preprocess the document sections by creating an embedding vector for each section. An embedding is a vector of numbers that helps us understand how semantically similar or different the texts are. The closer two embeddings are to each other, the more similar are their contents. See the [documentation on OpenAI embeddings](https://beta.openai.com/docs/guides/embeddings) for more information.\n",
     "\n",
-    "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](https://www.pinecone.io/) or [Weaviate](https://github.com/semi-technologies/weaviate) to power the search."
+    "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](https://www.pinecone.io/), [Weaviate](https://github.com/semi-technologies/weaviate) or [Qdrant](https://qdrant.tech) to power the search."
    ]
   },
   {
@@ -419,7 +419,7 @@
     "\n",
     "# 2) Find the most similar document embeddings to the question embedding\n",
     "\n",
-    "At the time of question-answering, to answer the user's query we compute the query embedding of the question and use it to find the most similar document sections. Since this is a small example, we store and search the embeddings locally. If you have a larger dataset, consider using a vector search engine like [Pinecone](https://www.pinecone.io/) or [Weaviate](https://github.com/semi-technologies/weaviate) to power the search."
+    "At the time of question-answering, to answer the user's query we compute the query embedding of the question and use it to find the most similar document sections. Since this is a small example, we store and search the embeddings locally. If you have a larger dataset, consider using a vector search engine like [Pinecone](https://www.pinecone.io/), [Weaviate](https://github.com/semi-technologies/weaviate) or [Qdrant](https://qdrant.tech) to power the search."
    ]
   },
   {

text_comparison_examples.md

@@ -20,7 +20,7 @@ The simplest way to use embeddings for search is as follows:
 * Before the search (precompute):
   * Split your text corpus into chunks smaller than the token limit (8,191 tokens for `text-embedding-ada-002`)
   * Embed each chunk of text
-  * Store those embeddings in your own database or in a vector search provider like [Pinecone](https://www.pinecone.io) or [Weaviate](https://weaviate.io)
+  * Store those embeddings in your own database or in a vector search provider like [Pinecone](https://www.pinecone.io), [Weaviate](https://weaviate.io) or [Qdrant](https://qdrant.tech)
 * At the time of the search (live compute):
   * Embed the search query
   * Find the closest embeddings in your database