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fix index
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davidmyriel
@@ -49,12 +49,12 @@ features:
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- id: 5
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image:
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src: /img/advanced-search-use-cases/hybrid-search-service-fastembed.svg
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alt: Create a Hybrid Search Service with Fastembed
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title: Create a Hybrid Search Service with Fastembed
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description: This tutorial guides you through building and deploying your own hybrid search service using Fastembed.
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alt: Create a Hybrid Search Service with FastEmbed
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title: Create a Hybrid Search Service with FastEmbed
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description: This tutorial guides you through building and deploying your own hybrid search service using FastEmbed.
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link:
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text: View Tutorial
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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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sitemapExclude: true
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---
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@@ -11,8 +11,8 @@ partition: qdrant
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# Advanced Tutorials
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| Tutorial | Description |
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|----------------------------------------------------------|--------------------------------------------------------------------|
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| [Collaborative Filtering](/documentation/advanced-tutorials/collaborative-filtering/) | Set up a recommendation engine purely with vector search. |
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| [Multimodal Search](/documentation/advanced-tutorials/multimodal-search-fastembed/) | Create a simple multimodal search engine. |
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| [Search Through Code](/documentation/advanced-tutorials/code-search/) | Implement semantic search application for code search tasks. |
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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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@@ -1,11 +1,11 @@
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---
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title: Semantic Search Over Code
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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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@@ -1,5 +1,5 @@
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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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@@ -9,7 +9,7 @@ social_preview_image: /blog/collaborative-filtering/social_preview.png
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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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+2
-2
@@ -1,11 +1,11 @@
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---
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title: Multimodal Search
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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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@@ -13,9 +13,9 @@ partition: qdrant
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# Beginner Tutorials
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| Tutorial | Description |
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|----------------------------------------------------|---------------------------------------------------------|
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| [Semantic Search for Beginners](/documentation/beginner-tutorials/search-beginners/) | Create a simple search engine locally in minutes. |
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| [Simple Neural Search](/documentation/beginner-tutorials/neural-search/) | Build and deploy a neural search that browses startup data. |
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| [Hybrid Search with FastEmbed](/documentation/beginner-tutorials/hybrid-search-fastembed/) | Build and deploy a hybrid search with our FastEmbed library. |
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| [Measure Retrieval Quality](/documentation/beginner-tutorials/retrieval-quality/) | Measure and fine-tune the retrieval quality |
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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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@@ -1,14 +1,11 @@
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---
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title: Hybrid Search with FastEmbed
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title: Setup Hybrid Search with FastEmbed
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aliases:
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- /documentation/tutorials/hybrid-search-fastembed/
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weight: 3
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aliases:
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- /documentation/tutorials/neural-search-fastembed/
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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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@@ -1,17 +1,15 @@
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---
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title: Neural Search Service
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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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@@ -5,7 +5,7 @@ aliases:
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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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@@ -6,7 +6,7 @@ aliases:
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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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@@ -11,9 +11,9 @@ partition: qdrant
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# Database Tutorials
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| Tutorial | Description |
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|--------------------------------------------|----------------------------------------------------------------|
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| [Bulk Upload Vectors](/documentation/database-tutorials/bulk-upload/) | Upload a large scale dataset. |
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| [Asynchronous API](/documentation/database-tutorials/async-api/) | Communicate with Qdrant server asynchronously with Python SDK. |
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| [Create Dataset Snapshots](/documentation/database-tutorials/create-snapshot/) | Turn a dataset into a snapshot by exporting it from a collection. |
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| [Load HuggingFace Dataset](/documentation/database-tutorials/huggingface-datasets/) | Load a Hugging Face dataset to Qdrant |
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| |
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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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@@ -1,11 +1,11 @@
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---
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title: Using the Async API
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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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@@ -5,7 +5,7 @@ aliases:
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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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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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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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