From b996e7967b1822fcf9e883b5f038ec7e3e4605fe Mon Sep 17 00:00:00 2001
From: David Sertic <62056091+davidmyriel@users.noreply.github.com>
Date: Thu, 8 Jun 2023 11:06:36 +0200
Subject: [PATCH] fix steps in build api

---
 .../content/documentation/tutorials/neural-search.md        | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qdrant-landing/content/documentation/tutorials/neural-search.md b/qdrant-landing/content/documentation/tutorials/neural-search.md
index 9b04954d0..5cfb3851c 100644
--- a/qdrant-landing/content/documentation/tutorials/neural-search.md
+++ b/qdrant-landing/content/documentation/tutorials/neural-search.md
@@ -207,7 +207,7 @@ pip install sentence-transformers numpy
 
 In order to process incoming requests neural search will need 2 things: 1) a model to convert the query into a vector and 2) the Qdrant client to perform search queries.
 
-Create a file named `neural_searcher.py` and specify the following.
+1. Create a file named `neural_searcher.py` and specify the following.
 
 ```python
 
@@ -225,7 +225,7 @@ class NeuralSearcher:
         self.qdrant_client = QdrantClient(host='localhost', port=6333)
 ```
 
-1. Write the search function.
+2. Write the search function.
 
 ```python
     def search(self, text: str):
@@ -245,7 +245,7 @@ class NeuralSearcher:
         return payloads
 ```
 
-2. Add search filters.
+3. Add search filters.
 
 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: