krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-09-26 06:28:30 +02:00
Code Issues Packages Projects Releases Wiki Activity

fix search beginners and tables

Browse Source
This commit is contained in:
David Sertic
2023-07-04 14:19:55 +02:00
parent 852b4e2e48
commit 66edeed09f
3 changed files with 6 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/documentation/_index.md
+2 -1
View File
@@ -34,9 +34,10 @@ Qdrant is ideal for deploying applications based on the matching of embeddings p
| Use Case | Description | Stack |
|-----------------------|----------------------------------------------|--------|
| [Intro to Semantic Search and Recommendations Systems](https://githubtocolab.com/qdrant/examples/blob/master/qdrant_101_getting_started/getting_started.ipynb) | Learn how to get started building semantic search and recommendation systems. | Qdrant |
| [Semantic Search for Beginners](tutorials/search-beginners/) | Build a search engine locally with our most basic instruction set. | Qdrant |
| [Build a Simple Neural Search](tutorials/neural-search/) | Build and deploy a neural search. [Check out the live demo app.](https://demo.qdrant.tech/#/) | Qdrant, BERT, FastAPI |
| [Build a Search with Aleph Alpha](tutorials/aleph-alpha-search/) | Build a simple semantic search that combines text and image data. | Qdrant, Aleph Alpha |
| [Developing Recommendations Systems](https://githubtocolab.com/qdrant/examples/blob/master/qdrant_101_getting_started/getting_started.ipynb) | Learn how to get started building semantic search and recommendation systems. | Qdrant |
| [Search and Recommend Newspaper Articles](https://githubtocolab.com/qdrant/examples/blob/master/qdrant_101_text_data/qdrant_and_text_data.ipynb) | Work with text data to develop a semantic search and a recommendation engine for news articles. | Qdrant |
| [Recommendation System for Songs](https://githubtocolab.com/qdrant/examples/blob/master/qdrant_101_audio_data/03_qdrant_101_audio.ipynb) | Use Qdrant to develop a music recommendation engine based on audio embeddings. | Qdrant |
| [Image Comparison System for Skin Conditions](https://colab.research.google.com/github/qdrant/examples/blob/master/qdrant_101_image_data/04_qdrant_101_cv.ipynb) | Use Qdrant to compare challenging images with labels representing different skin diseases. | Qdrant |
qdrant-landing/content/documentation/tutorials/_index.md
+1
View File
@@ -17,6 +17,7 @@ These tutorials demonstrate different ways you can build vector search into your
| [Configure Optimal Use](../tutorials/optimize/) | Configure Qdrant collections for best resource use. | Qdrant |
| [Separate Partitions](../tutorials/multiple-partitions/) | Serve vectors for many independent users. | Qdrant |
| [Bulk Upload Vectors](../tutorials/bulk-upload/) | Upload a large scale dataset. | Qdrant |
| [Semantic Search for Beginners](../tutorials/search-beginners/) | Create a simple search engine locally in minutes. | Qdrant |
| [Simple Neural Search](../tutorials/neural-search/) | Build and deploy a neural search that browses startup data. | Qdrant, BERT, FastAPI |
| [Aleph Alpha Search](../tutorials/aleph-alpha-search/) | Build a multimodal search that combines text and image data. | Qdrant, Aleph Alpha |
| [Troubleshooting](../tutorials/common-errors/) | Solutions to common errors and fixes | Qdrant |
qdrant-landing/content/documentation/tutorials/semantic-search-beginners.md → qdrant-landing/content/documentation/tutorials/search-beginners.md
+3 -5
View File
@@ -5,11 +5,9 @@ weight: 14
# Semantic Search for Beginners
| Time: 5 - 15 min | Level: Beginner | Output: [GitHub]() | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)]() |
| Time: 5 - 15 min | Level: Beginner | | |
| --- | ----------- | ----------- |----------- |
<center><iframe src="https://www.youtube.com/embed/hFc7xv1tsos" width="900" height="500" title="Semantic Search for Beginners" frameborder="0" allowfullscreen></iframe></center>
If you are new to vector databases, this tutorial is for you. In 5 minutes you will build a semantic search engine for science fiction books. After you set it up, you will ask the engine about an impending alien threat. Your creation will recommend books as preparation for a potential space attack.
Before you begin, you need to have a [recent version of Python](https://www.python.org/downloads/) installed. If you don't know how to run this code in a virtual environment, follow [this tutorial](https://towardsdatascience.com/creating-and-using-virtual-environment-on-jupyter-notebook-with-python-db3f5afdd56a) first.
@@ -34,14 +32,14 @@ from qdrant_client import models, QdrantClient
from sentence_transformers import SentenceTransformer
```
The [Sentence Transformers] framework contains many Large Language Models. However, [all-MiniLM-L6-v2] is the fastest encoder for this tutorial.
The [Sentence Transformers](https://www.sbert.net/index.html) framework contains many Large Language Models. However, [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) is the fastest encoder for this tutorial.
```python
encoder = SentenceTransformer('all-MiniLM-L6-v2')
```
## 2. Add the dataset
[all-MiniLM-L6-v2] will encode the data you provide. Here you will list all the science fiction books in your library. Each book has metadata, a name, author, publication year and a short description.
[all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) will encode the data you provide. Here you will list all the science fiction books in your library. Each book has metadata, a name, author, publication year and a short description.
```python
documents = [