From ed2b006ea1aa22f3316d10e2be483341311cd0a8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Kacper=20=C5=81ukawski?=
 <kacperlukawski@users.noreply.github.com>
Date: Fri, 8 Jul 2022 20:22:27 +0200
Subject: [PATCH] Update neural search tutorial to use 0.8.4 API (#66)

* Update neural search tutorial to use 0.8.4 API

* Update neural-search-tutorial.md

Co-authored-by: Andrey Vasnetsov <andrey@vasnetsov.com>
---
 .../content/articles/neural-search-tutorial.md    | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/qdrant-landing/content/articles/neural-search-tutorial.md b/qdrant-landing/content/articles/neural-search-tutorial.md
index ef90c5823..3ef4f1bb8 100644
--- a/qdrant-landing/content/articles/neural-search-tutorial.md
+++ b/qdrant-landing/content/articles/neural-search-tutorial.md
@@ -171,7 +171,11 @@ Many independent vector collections can exist on one service at the same time.
 Let's create a new collection for our startup vectors.
 
 ```python
-qdrant_client.recreate_collection(collection_name='startups', vector_size=768)
+qdrant_client.recreate_collection(
+    collection_name='startups', 
+    vector_size=768, 
+    distance="Cosine"
+)
 ```
 
 The `recreate_collection` function first tries to remove an existing collection with the same name.
@@ -182,6 +186,8 @@ It tells the service the size of the vectors in that collection.
 All vectors in a collection must have the same size, otherwise, it is impossible to calculate the distance between them.
 `768` is the output dimensionality of the encoder we are using.
 
+The `distance` parameter allows specifying the function used to measure the distance between two points.
+
 The Qdrant client library defines a special function that allows you to load datasets into the service.
 However, since there may be too much data to fit a single computer memory, the function takes an iterator over the data as input.
 
@@ -252,7 +258,7 @@ The search function looks as simple as possible:
 ```python
     def search(self, text: str):
         # Convert text query into vector
-        vector = self.model.encode(text)
+        vector = self.model.encode(text).tolist()
 
         # Use `vector` for search for closest vectors in the collection
         search_result = self.qdrant_client.search(
@@ -261,10 +267,9 @@ The search function looks as simple as possible:
             query_filter=None,  # We don't want any filters for now
             top=5  # 5 the most closest results is enough
         )
-
         # `search_result` contains found vector ids with similarity scores along with the stored payload
         # In this function we are interested in payload only
-        payloads = [payload for point, payload in search_result]
+        payloads = [hit.payload for hit in search_result]
         return payloads
 ```
 
@@ -272,7 +277,7 @@ With Qdrant it is also feasible to add some conditions to the search.
 For example, if we wanted to search for startups in a certain city, the search query could look like this:
 
 ```python
-from qdrant_openapi_client.models.models import Filter
+from qdrant_client.http.models.models import Filter
 
     ...