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Merge pull request #1288 from qdrant/tutorials-section
[devportal] Create a Separate Tutorials Section
This commit is contained in:
David Myriel
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---
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title: Advanced Retrieval
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weight: 17
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# If the index.md file is empty, the link to the section will be hidden from the sidebar
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is_empty: false
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aliases:
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- how-to
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- tutorials
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partition: qdrant
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---
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# Advanced Tutorials
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| |
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|----------------------------------------------------------|
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| [Use Collaborative Filtering to Build a Movie Recommendation System with Qdrant](/documentation/advanced-tutorials/collaborative-filtering/) |
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| [Build a Text/Image Multimodal Search System with Qdrant and FastEmbed](/documentation/advanced-tutorials/multimodal-search-fastembed/) |
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| [Navigate Your Codebase with Semantic Search and Qdrant](/documentation/advanced-tutorials/code-search/) |
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---
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title: Semantic code search
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weight: 22
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title: Search Through Your Codebase
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aliases:
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- /documentation/tutorials/code-search/
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weight: 2
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---
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# Use semantic search to navigate your codebase
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# Navigate Your Codebase with Semantic Search and Qdrant
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| Time: 45 min | Level: Intermediate | [](https://colab.research.google.com/github/qdrant/examples/blob/master/code-search/code-search.ipynb) | |
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|--------------|---------------------|--|----|
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---
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title: Collaborative filtering
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title: Build a Recommendation System with Collaborative Filtering
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aliases:
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- /documentation/tutorials/collaborative-filtering/
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short_description: "Build an effective movie recommendation system using collaborative filtering and Qdrant's similarity search."
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description: "Build an effective movie recommendation system using collaborative filtering and Qdrant's similarity search."
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preview_image: /blog/collaborative-filtering/social_preview.png
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social_preview_image: /blog/collaborative-filtering/social_preview.png
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weight: 23
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weight: 3
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---
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# Create a collaborative filtering system
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# Use Collaborative Filtering to Build a Movie Recommendation System with Qdrant
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| Time: 45 min | Level: Intermediate | [](https://githubtocolab.com/qdrant/examples/blob/master/collaborative-filtering/collaborative-filtering.ipynb) | |
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|--------------|---------------------|--|----|
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---
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title: Multimodal Search
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weight: 4
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title: Setup Text/Image Multimodal Search
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aliases:
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- /documentation/tutorials/multimodal-search-fastembed/
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weight: 1
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---
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# Multimodal Search with Qdrant and FastEmbed
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# Build a Multimodal Search System with Qdrant and FastEmbed
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| Time: 15 min | Level: Beginner |Output: [GitHub](https://github.com/qdrant/examples/blob/master/multimodal-search/Multimodal_Search_with_FastEmbed.ipynb)|[](https://githubtocolab.com/qdrant/examples/blob/master/multimodal-search/Multimodal_Search_with_FastEmbed.ipynb) |
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| --- | ----------- | ----------- | ----------- |
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---
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title: Vector Search Basics
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aliases:
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- /documentation/tutorials/
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weight: 16
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# If the index.md file is empty, the link to the section will be hidden from the sidebar
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is_empty: false
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aliases:
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- how-to
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- tutorials
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partition: qdrant
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---
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# Beginner Tutorials
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| |
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|----------------------------------------------------|
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| [Build Your First Semantic Search Engine in 5 Minutes](/documentation/beginner-tutorials/search-beginners/) |
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| [Build a Neural Search Service with Sentence Transformers and Qdrant](/documentation/beginner-tutorials/neural-search/) |
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| [Build a Hybrid Search Service with FastEmbed and Qdrant](/documentation/beginner-tutorials/hybrid-search-fastembed/) |
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| [Measure and Improve Retrieval Quality in Semantic Search](/documentation/beginner-tutorials/retrieval-quality/) |
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---
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title: Hybrid Search with Fastembed
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weight: 2
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title: Setup Hybrid Search with FastEmbed
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aliases:
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- /documentation/tutorials/neural-search-fastembed/
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- /documentation/tutorials/hybrid-search-fastembed/
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weight: 3
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---
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# Create a Hybrid Search Service with Fastembed
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# Build a Hybrid Search Service with FastEmbed and Qdrant
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| Time: 20 min | Level: Beginner | Output: [GitHub](https://github.com/qdrant/qdrant_demo/) |
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| --- | ----------- | ----------- |----------- |
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---
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title: Neural Search Service
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weight: 1
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title: Build a Neural Search Service
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aliases:
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- /documentation/tutorials/neural-search/
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weight: 2
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---
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# Create a Simple Neural Search Service
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# Build a Neural Search Service with Sentence Transformers and Qdrant
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| Time: 30 min | Level: Beginner | Output: [GitHub](https://github.com/qdrant/qdrant_demo/tree/sentense-transformers) | [](https://colab.research.google.com/drive/1kPktoudAP8Tu8n8l-iVMOQhVmHkWV_L9?usp=sharing) |
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| --- | ----------- | ----------- |----------- |
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This tutorial shows you how to build and deploy your own neural search service to look through descriptions of companies from [startups-list.com](https://www.startups-list.com/) and pick the most similar ones to your query. The website contains the company names, descriptions, locations, and a picture for each entry.
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A neural search service uses artificial neural networks to improve the accuracy and relevance of search results. Besides offering simple keyword results, this system can retrieve results by meaning. It can understand and interpret complex search queries and provide more contextually relevant output, effectively enhancing the user's search experience.
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<aside role="status">
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There is a version of this tutorial that uses <a href="https://github.com/qdrant/fastembed">Fastembed</a> model inference engine instead of Sentence Transformers.
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Check it out <a href="/documentation/tutorials/hybrid-search-fastembed/">here</a>.
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Check it out <a href="/documentation/beginner-tutorials/hybrid-search-fastembed/">here</a>.
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</aside>
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---
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title: Measure retrieval quality
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weight: 21
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title: Measure Search Quality
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aliases:
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- /documentation/tutorials/retrieval-quality/
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weight: 4
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---
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# Measure retrieval quality
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# Measure and Improve Retrieval Quality in Semantic Search
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| Time: 30 min | Level: Intermediate | | |
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|--------------|---------------------|--|----|
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---
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title: Semantic Search 101
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weight: -100
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weight: 1
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aliases:
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- /documentation/tutorials/mighty.md/
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- /documentation/tutorials/search-beginners/
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---
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# Semantic Search for Beginners
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# Build Your First Semantic Search Engine in 5 Minutes
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| Time: 5 - 15 min | Level: Beginner | | |
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| --- | ----------- | ----------- |----------- |
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@@ -24,7 +24,7 @@ content:
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title: Search
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description: Build a simple neural search service with Qdrant and FastEmbed. Learn how to upload data, create indexes, and run search queries.
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link:
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url: /documentation/tutorials/hybrid-search-fastembed/
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url: /documentation/beginner-tutorials/hybrid-search-fastembed/
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text: Read More
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- id: 2
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image:
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---
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title: Using the Database
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weight: 18
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# If the index.md file is empty, the link to the section will be hidden from the sidebar
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is_empty: false
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aliases:
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- how-to
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- tutorials
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partition: qdrant
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---
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# Database Tutorials
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|--------------------------------------------|
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| [Bulk Upload Vectors to a Qdrant Collection](/documentation/database-tutorials/bulk-upload/) |
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| [Backup and Restore Qdrant Collections Using Snapshots](/documentation/database-tutorials/create-snapshot/) |
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| [Load and Search Hugging Face Datasets with Qdrant](/documentation/database-tutorials/huggingface-datasets/) |
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| [Using Qdrant’s Async API for Efficient Python Applications](/documentation/database-tutorials/async-api/) |
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---
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title: Asynchronous API
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weight: 14
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title: Build With Async API
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aliases:
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- /documentation/tutorials/async-api/
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weight: 4
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---
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# Using Qdrant asynchronously
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# Using Qdrant’s Async API for Efficient Python Applications
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Asynchronous programming is being broadly adopted in the Python ecosystem. Tools such as FastAPI [have embraced this new
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paradigm](https://fastapi.tiangolo.com/async/), but it is also becoming a standard for ML models served as SaaS. For example, the Cohere SDK
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---
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title: Bulk Upload Vectors
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weight: 13
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aliases:
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- /documentation/tutorials/bulk-upload/
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weight: 1
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---
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# Bulk upload a large number of vectors
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# Bulk Upload Vectors to a Qdrant Collection
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Uploading a large-scale dataset fast might be a challenge, but Qdrant has a few tricks to help you with that.
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---
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title: Create and restore from snapshot
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weight: 14
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title: Create & Restore Snapshots
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aliases:
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- /documentation/tutorials/create-snapshot/
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weight: 2
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---
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# Create and restore collections from snapshot
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# Backup and Restore Qdrant Collections Using Snapshots
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| Time: 20 min | Level: Beginner | | |
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|--------------|-----------------|--|----|
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---
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title: Load Hugging Face dataset
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weight: 19
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title: Load a HuggingFace Dataset
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aliases:
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- /documentation/tutorials/huggingface-datasets/
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weight: 3
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---
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# Loading a dataset from Hugging Face hub
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# Load and Search Hugging Face Datasets with Qdrant
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[Hugging Face](https://huggingface.co/) provides a platform for sharing and using ML models and
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datasets. [Qdrant](https://huggingface.co/Qdrant) also publishes datasets along with the
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---
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#Delimiter files are used to separate the list of documentation pages into sections.
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title: "Tutorials"
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type: delimiter
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weight: 15 # Change this weight to change order of sections
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sitemapExclude: True
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_build:
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publishResources: false
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render: never
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partition: qdrant
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---
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---
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title: Tutorials
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weight: 10
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# If the index.md file is empty, the link to the section will be hidden from the sidebar
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is_empty: false
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aliases:
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- how-to
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- tutorials
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partition: qdrant
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---
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# Tutorials
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These tutorials demonstrate different ways you can build vector search into your applications.
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| Essential How-Tos | Description | Stack |
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|---------------------------------------------------------------------------------|-------------------------------------------------------------------|---------------------------------------------|
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| [Semantic Search for Beginners](/documentation/tutorials/search-beginners/) | Create a simple search engine locally in minutes. | Qdrant |
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| [Simple Neural Search](/documentation/tutorials/neural-search/) | Build and deploy a neural search that browses startup data. | Qdrant, BERT, FastAPI |
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| [Neural Search with FastEmbed](/documentation/tutorials/neural-search-fastembed/) | Build and deploy a neural search with our FastEmbed library. | Qdrant |
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| [Multimodal Search](/documentation/tutorials/multimodal-search-fastembed/) | Create a simple multimodal search. | Qdrant |
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| [Bulk Upload Vectors](/documentation/tutorials/bulk-upload/) | Upload a large scale dataset. | Qdrant |
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| [Asynchronous API](/documentation/tutorials/async-api/) | Communicate with Qdrant server asynchronously with Python SDK. | Qdrant, Python |
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| [Create Dataset Snapshots](/documentation/tutorials/create-snapshot/) | Turn a dataset into a snapshot by exporting it from a collection. | Qdrant |
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| [Load HuggingFace Dataset](/documentation/tutorials/huggingface-datasets/) | Load a Hugging Face dataset to Qdrant | Qdrant, Python, datasets |
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| [Measure Retrieval Quality](/documentation/tutorials/retrieval-quality/) | Measure and fine-tune the retrieval quality | Qdrant, Python, datasets |
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| [Search Through Code](/documentation/tutorials/code-search/) | Implement semantic search application for code search tasks | Qdrant, Python, sentence-transformers, Jina |
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| [Setup Collaborative Filtering](/documentation/tutorials/collaborative-filtering/) | Implement a collaborative filtering system for recommendation engines | Qdrant|
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