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kanungle
2025-12-23 13:18:14 -08:00
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qdrant-landing/content/documentation/tutorials-ecosystem/_index.md
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| Tutorial | Objective | Stack | Time | Level |
| :--- | :--- | :--- | :--- | :--- |
| [Embedding Migration](https://qdrant.tech/documentation/tutorials-ecosystem/migration/) | Move dense and sparse embeddings to Qdrant. | CLI | 30m | Intermediate |
| [S3 Ingestion with LangChain](https://qdrant.tech/documentation/data-ingestion-beginners/) | Stream data from AWS S3 to vector store. | LangChain | 30m | Beginner |
| [Hugging Face Datasets](https://qdrant.tech/documentation/tutorials-ecosystem/huggingface-datasets/) | Load and search public ML datasets. | Python | 15m | Beginner |
| [Databricks Integration](https://qdrant.tech/documentation/send-data/databricks/) | Vectorize datasets using FastEmbed on Databricks. | Databricks | 30m | Intermediate |
| [Airflow & Astronomer](https://qdrant.tech/documentation/send-data/qdrant-airflow-astronomer/) | Orchestrate data engineering workflows. | Airflow | 45m | Intermediate |
| [Kafka Data Streaming](https://qdrant.tech/documentation/send-data/data-streaming-kafka-qdrant/) | Setup Qdrant Sink Connector for real-time data. | Kafka | 60m | Advanced |
| [No-Code Automation (n8n)](https://qdrant.tech/documentation/qdrant-n8n/) | Combine Qdrant with low-code n8n workflows. | n8n | 45m | Intermediate |
| [Embedding Migration](/documentation/tutorials-ecosystem/migration/) | Move dense and sparse embeddings to Qdrant. | CLI | 30m | Intermediate |
| [S3 Ingestion with LangChain](/documentation/data-ingestion-beginners/) | Stream data from AWS S3 to vector store. | LangChain | 30m | Beginner |
| [Hugging Face Datasets](/documentation/tutorials-ecosystem/huggingface-datasets/) | Load and search public ML datasets. | Python | 15m | Beginner |
| [Databricks Integration](/documentation/send-data/databricks/) | Vectorize datasets using FastEmbed on Databricks. | Databricks | 30m | Intermediate |
| [Airflow & Astronomer](/documentation/send-data/qdrant-airflow-astronomer/) | Orchestrate data engineering workflows. | Airflow | 45m | Intermediate |
| [Kafka Data Streaming](/documentation/send-data/data-streaming-kafka-qdrant/) | Setup Qdrant Sink Connector for real-time data. | Kafka | 60m | Advanced |
| [No-Code Automation (n8n)](/documentation/qdrant-n8n/) | Combine Qdrant with low-code n8n workflows. | n8n | 45m | Intermediate |
qdrant-landing/content/documentation/tutorials-ecosystem/huggingface-datasets.md
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title: Load a HuggingFace Dataset
aliases:
- /documentation/tutorials/huggingface-datasets/
- /documentation/database-tutorials/huggingface-datasets/
weight: 3
---
qdrant-landing/content/documentation/tutorials-ecosystem/migration.md
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---
title: Migration to Qdrant
aliases:
- /documentation/database-tutorials/migration/
weight: 180
---
qdrant-landing/content/documentation/tutorials-operations/_index.md
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@@ -13,10 +13,10 @@ partition: qdrant
| Tutorial | Objective | Stack | Time | Level |
| :--- | :--- | :--- | :--- | :--- |
| [Bulk Data Uploads](https://qdrant.tech/documentation/tutorials-operations/bulk-upload/) | High-scale ingestion tricks for power users. | Python | 20m | Intermediate |
| [Snapshot & Backup](https://qdrant.tech/documentation/tutorials-operations/create-snapshot/) | Create and restore collection snapshots. | Python | 20m | Beginner |
| [Billion-Scale Search](https://qdrant.tech/documentation/tutorials-operations/large-scale-search/) | Cost-efficient search for LAION-400M datasets. | None | 2 days | Advanced |
| [Python Async API](https://qdrant.tech/documentation/tutorials-operations/async-api/) | Use Asynchronous programming for efficiency. | Python | 25m | Intermediate |
| [Cloud Inference Search](https://qdrant.tech/documentation/tutorials-and-examples/cloud-inference-hybrid-search/) | Hybrid search using Qdrant's built-in inference. | Any | 20m | Beginner |
| [Monitor Managed Cloud](https://qdrant.tech/documentation/tutorials-and-examples/managed-cloud-prometheus/) | Observability with Prometheus and Grafana. | Prometheus | 30m | Intermediate |
| [Monitor Private Cloud](https://qdrant.tech/documentation/tutorials-and-examples/hybrid-cloud-prometheus/) | Observability for hybrid/private cloud setups. | Prometheus | 30m | Intermediate |
| [Bulk Data Uploads](/documentation/tutorials-operations/bulk-upload/) | High-scale ingestion tricks for power users. | Python | 20m | Intermediate |
| [Snapshot & Backup](/documentation/tutorials-operations/create-snapshot/) | Create and restore collection snapshots. | Python | 20m | Beginner |
| [Billion-Scale Search](/documentation/tutorials-operations/large-scale-search/) | Cost-efficient search for LAION-400M datasets. | None | 2 days | Advanced |
| [Python Async API](/documentation/tutorials-operations/async-api/) | Use Asynchronous programming for efficiency. | Python | 25m | Intermediate |
| [Cloud Inference Search](/documentation/tutorials-and-examples/cloud-inference-hybrid-search/) | Hybrid search using Qdrant's built-in inference. | Any | 20m | Beginner |
| [Monitor Managed Cloud](/documentation/tutorials-and-examples/managed-cloud-prometheus/) | Observability with Prometheus and Grafana. | Prometheus | 30m | Intermediate |
| [Monitor Private Cloud](/documentation/tutorials-and-examples/hybrid-cloud-prometheus/) | Observability for hybrid/private cloud setups. | Prometheus | 30m | Intermediate |
qdrant-landing/content/documentation/tutorials-operations/async-api.md
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title: Build With Async API
aliases:
- /documentation/tutorials/async-api/
- /documentation/database-tutorials/async-api/
weight: 4
---
qdrant-landing/content/documentation/tutorials-operations/bulk-upload.md
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title: Bulk Upload Vectors
aliases:
- /documentation/tutorials/bulk-upload/
- /documentation/database-tutorials/bulk-upload/
weight: 1
---
qdrant-landing/content/documentation/tutorials-operations/create-snapshot.md
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title: Create & Restore Snapshots
aliases:
- /documentation/tutorials/create-snapshot/
- /documentation/database-tutorials/create-snapshot/
weight: 2
---
qdrant-landing/content/documentation/tutorials-operations/large-scale-search.md
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---
title: Large Scale Search
aliases:
- /documentation/database-tutorials/large-scale-search/
weight: 2
---
qdrant-landing/content/documentation/tutorials-overview/_index.md
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@@ -15,9 +15,9 @@ partition: qdrant
| Tutorial | Objective | Stack | Time | Level |
| :--- | :--- | :--- | :--- | :--- |
| [Local Qdrant Setup](https://qdrant.tech/documentation/quickstart/) | Basic CRUD operations and local deployment. | Python | 10m | Beginner |
| [5-Minute Semantic Search](https://qdrant.tech/documentation/tutorials-quickstart/search-beginners/) | Build a search engine for science fiction books. | Python | 5m | Beginner |
| [5-Minute RAG with DeepSeek](https://qdrant.tech/documentation/tutorials-quickstart/rag-deepseek/) | Build a RAG pipeline with DeepSeek enrichment. | Python | 5m | Beginner |
| [Local Qdrant Setup](/documentation/quickstart/) | Basic CRUD operations and local deployment. | Python | 10m | Beginner |
| [5-Minute Semantic Search](/documentation/tutorials-quickstart/search-beginners/) | Build a search engine for science fiction books. | Python | 5m | Beginner |
| [5-Minute RAG with DeepSeek](/documentation/tutorials-quickstart/rag-deepseek/) | Build a RAG pipeline with DeepSeek enrichment. | Python | 5m | Beginner |
---
@@ -26,15 +26,15 @@ partition: qdrant
| Tutorial | Objective | Stack | Time | Level |
| :--- | :--- | :--- | :--- | :--- |
| [Neural Search Service](https://qdrant.tech/documentation/tutorials-search-engineering/neural-search/) | Deploy a search service for company descriptions. | FastAPI | 30m | Beginner |
| [Hybrid Search with FastEmbed](https://qdrant.tech/documentation/tutorials-search-engineering/hybrid-search-fastembed/) | Combine dense and sparse search for startups. | FastAPI | 20m | Beginner |
| [Movie Recommendations](https://qdrant.tech/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | Python | 45m | Intermediate |
| [Advanced PDF Retrieval](https://qdrant.tech/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | Python | 30m | Intermediate |
| [Retrieval Quality Benchmarking](https://qdrant.tech/documentation/tutorials-search-engineering/retrieval-quality/) | Measure quality and tune HNSW parameters. | Python | 30m | Intermediate |
| [Multivector Reranking](https://qdrant.tech/documentation/search-precision/reranking-semantic-search/) | Use multivector representations for better ranking. | Python | 30m | Intermediate |
| [Hybrid Search Reranking](https://qdrant.tech/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | Python | 40m | Intermediate |
| [Semantic Code Search](https://qdrant.tech/documentation/tutorials-search-engineering/code-search/) | Navigate codebases using vector similarity. | Python | 45m | Intermediate |
| [Static Embeddings Analysis](https://qdrant.tech/documentation/tutorials-search-engineering/static-embeddings/) | Evaluate the renaissance of static embeddings. | Python | 20m | Intermediate |
| [Neural Search Service](/documentation/tutorials-search-engineering/neural-search/) | Deploy a search service for company descriptions. | FastAPI | 30m | Beginner |
| [Hybrid Search with FastEmbed](/documentation/tutorials-search-engineering/hybrid-search-fastembed/) | Combine dense and sparse search for startups. | FastAPI | 20m | Beginner |
| [Movie Recommendations](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | Python | 45m | Intermediate |
| [Advanced PDF Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | Python | 30m | Intermediate |
| [Retrieval Quality Benchmarking](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure quality and tune HNSW parameters. | Python | 30m | Intermediate |
| [Multivector Reranking](/documentation/search-precision/reranking-semantic-search/) | Use multivector representations for better ranking. | Python | 30m | Intermediate |
| [Hybrid Search Reranking](/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | Python | 40m | Intermediate |
| [Semantic Code Search](/documentation/tutorials-search-engineering/code-search/) | Navigate codebases using vector similarity. | Python | 45m | Intermediate |
| [Static Embeddings Analysis](/documentation/tutorials-search-engineering/static-embeddings/) | Evaluate the renaissance of static embeddings. | Python | 20m | Intermediate |
---
@@ -43,11 +43,11 @@ partition: qdrant
| Tutorial | Objective | Stack | Time | Level |
| :--- | :--- | :--- | :--- | :--- |
| [Agentic RAG with CrewAI](https://qdrant.tech/documentation/agentic-rag-crewai-zoom/) | Step-by-step multi-agent RAG system. | CrewAI | 45m | Beginner |
| [Agentic RAG with LangGraph](https://qdrant.tech/documentation/agentic-rag-langgraph/) | Build AI agents to answer library documentation. | LangGraph | 45m | Intermediate |
| [Agentic Discord ChatBot](https://qdrant.tech/documentation/agentic-rag-camelai-discord/) | Develop a functional bot with CAMEL-AI. | OpenAI | 45m | Intermediate |
| [Multimodal Search (LlamaIndex)](https://qdrant.tech/documentation/multimodal-search/) | Search across image and text modalities. | LlamaIndex | 15m | Beginner |
| [Automate Metadata Filtering](https://qdrant.tech/documentation/search-precision/automate-filtering-with-llms/) | Use LLM structured output for dynamic filters. | Python | 30m | Intermediate |
| [Agentic RAG with CrewAI](/documentation/agentic-rag-crewai-zoom/) | Step-by-step multi-agent RAG system. | CrewAI | 45m | Beginner |
| [Agentic RAG with LangGraph](/documentation/agentic-rag-langgraph/) | Build AI agents to answer library documentation. | LangGraph | 45m | Intermediate |
| [Agentic Discord ChatBot](/documentation/agentic-rag-camelai-discord/) | Develop a functional bot with CAMEL-AI. | OpenAI | 45m | Intermediate |
| [Multimodal Search (LlamaIndex)](/documentation/multimodal-search/) | Search across image and text modalities. | LlamaIndex | 15m | Beginner |
| [Automate Metadata Filtering](/documentation/search-precision/automate-filtering-with-llms/) | Use LLM structured output for dynamic filters. | Python | 30m | Intermediate |
---
@@ -56,13 +56,13 @@ partition: qdrant
| Tutorial | Objective | Stack | Time | Level |
| :--- | :--- | :--- | :--- | :--- |
| [Embedding Migration](https://qdrant.tech/documentation/tutorials-ecosystem/migration/) | Move dense and sparse embeddings to Qdrant. | CLI | 30m | Intermediate |
| [S3 Ingestion with LangChain](https://qdrant.tech/documentation/data-ingestion-beginners/) | Stream data from AWS S3 to vector store. | LangChain | 30m | Beginner |
| [Hugging Face Datasets](https://qdrant.tech/documentation/tutorials-ecosystem/huggingface-datasets/) | Load and search public ML datasets. | Python | 15m | Beginner |
| [Databricks Integration](https://qdrant.tech/documentation/send-data/databricks/) | Vectorize datasets using FastEmbed on Databricks. | Databricks | 30m | Intermediate |
| [Airflow & Astronomer](https://qdrant.tech/documentation/send-data/qdrant-airflow-astronomer/) | Orchestrate data engineering workflows. | Airflow | 45m | Intermediate |
| [Kafka Data Streaming](https://qdrant.tech/documentation/send-data/data-streaming-kafka-qdrant/) | Setup Qdrant Sink Connector for real-time data. | Kafka | 60m | Advanced |
| [No-Code Automation (n8n)](https://qdrant.tech/documentation/qdrant-n8n/) | Combine Qdrant with low-code n8n workflows. | n8n | 45m | Intermediate |
| [Embedding Migration](/documentation/tutorials-ecosystem/migration/) | Move dense and sparse embeddings to Qdrant. | CLI | 30m | Intermediate |
| [S3 Ingestion with LangChain](/documentation/data-ingestion-beginners/) | Stream data from AWS S3 to vector store. | LangChain | 30m | Beginner |
| [Hugging Face Datasets](/documentation/tutorials-ecosystem/huggingface-datasets/) | Load and search public ML datasets. | Python | 15m | Beginner |
| [Databricks Integration](/documentation/send-data/databricks/) | Vectorize datasets using FastEmbed on Databricks. | Databricks | 30m | Intermediate |
| [Airflow & Astronomer](/documentation/send-data/qdrant-airflow-astronomer/) | Orchestrate data engineering workflows. | Airflow | 45m | Intermediate |
| [Kafka Data Streaming](/documentation/send-data/data-streaming-kafka-qdrant/) | Setup Qdrant Sink Connector for real-time data. | Kafka | 60m | Advanced |
| [No-Code Automation (n8n)](/documentation/qdrant-n8n/) | Combine Qdrant with low-code n8n workflows. | n8n | 45m | Intermediate |
---
@@ -71,10 +71,10 @@ partition: qdrant
| Tutorial | Objective | Stack | Time | Level |
| :--- | :--- | :--- | :--- | :--- |
| [Bulk Data Uploads](https://qdrant.tech/documentation/tutorials-operations/bulk-upload/) | High-scale ingestion tricks for power users. | Python | 20m | Intermediate |
| [Snapshot & Backup](https://qdrant.tech/documentation/tutorials-operations/create-snapshot/) | Create and restore collection snapshots. | Python | 20m | Beginner |
| [Billion-Scale Search](https://qdrant.tech/documentation/tutorials-operations/large-scale-search/) | Cost-efficient search for LAION-400M datasets. | None | 2 days | Advanced |
| [Python Async API](https://qdrant.tech/documentation/tutorials-operations/async-api/) | Use Asynchronous programming for efficiency. | Python | 25m | Intermediate |
| [Cloud Inference Search](https://qdrant.tech/documentation/tutorials-and-examples/cloud-inference-hybrid-search/) | Hybrid search using Qdrant's built-in inference. | Any | 20m | Beginner |
| [Monitor Managed Cloud](https://qdrant.tech/documentation/tutorials-and-examples/managed-cloud-prometheus/) | Observability with Prometheus and Grafana. | Prometheus | 30m | Intermediate |
| [Monitor Private Cloud](https://qdrant.tech/documentation/tutorials-and-examples/hybrid-cloud-prometheus/) | Observability for hybrid/private cloud setups. | Prometheus | 30m | Intermediate |
| [Bulk Data Uploads](/documentation/tutorials-operations/bulk-upload/) | High-scale ingestion tricks for power users. | Python | 20m | Intermediate |
| [Snapshot & Backup](/documentation/tutorials-operations/create-snapshot/) | Create and restore collection snapshots. | Python | 20m | Beginner |
| [Billion-Scale Search](/documentation/tutorials-operations/large-scale-search/) | Cost-efficient search for LAION-400M datasets. | None | 2 days | Advanced |
| [Python Async API](/documentation/tutorials-operations/async-api/) | Use Asynchronous programming for efficiency. | Python | 25m | Intermediate |
| [Cloud Inference Search](/documentation/tutorials-and-examples/cloud-inference-hybrid-search/) | Hybrid search using Qdrant's built-in inference. | Any | 20m | Beginner |
| [Monitor Managed Cloud](/documentation/tutorials-and-examples/managed-cloud-prometheus/) | Observability with Prometheus and Grafana. | Prometheus | 30m | Intermediate |
| [Monitor Private Cloud](/documentation/tutorials-and-examples/hybrid-cloud-prometheus/) | Observability for hybrid/private cloud setups. | Prometheus | 30m | Intermediate |
qdrant-landing/content/documentation/tutorials-quickstart/_index.md
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| Tutorial | Objective | Stack | Time | Level |
| :--- | :--- | :--- | :--- | :--- |
| [Local Qdrant Setup](https://qdrant.tech/documentation/quickstart/) | Basic CRUD operations and local deployment. | Python | 10m | Beginner |
| [5-Minute Semantic Search](https://qdrant.tech/documentation/tutorials-quickstart/search-beginners/) | Build a search engine for science fiction books. | Python | 5m | Beginner |
| [5-Minute RAG with DeepSeek](https://qdrant.tech/documentation/tutorials-quickstart/rag-deepseek/) | Build a RAG pipeline with DeepSeek enrichment. | Python | 5m | Beginner |
| [Local Qdrant Setup](/documentation/quickstart/) | Basic CRUD operations and local deployment. | Python | 10m | Beginner |
| [5-Minute Semantic Search](/documentation/tutorials-quickstart/search-beginners/) | Build a search engine for science fiction books. | Python | 5m | Beginner |
| [5-Minute RAG with DeepSeek](/documentation/tutorials-quickstart/rag-deepseek/) | Build a RAG pipeline with DeepSeek enrichment. | Python | 5m | Beginner |
qdrant-landing/content/documentation/tutorials-quickstart/search-beginners.md
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aliases:
- /documentation/tutorials/mighty.md/
- /documentation/tutorials/search-beginners/
- /documentation/beginner-tutorials/search-beginners/
---
# Build Your First Semantic Search Engine in 5 Minutes
qdrant-landing/content/documentation/tutorials-rag-and-agents/_index.md
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| Tutorial | Objective | Stack | Time | Level |
| :--- | :--- | :--- | :--- | :--- |
| [Agentic RAG with CrewAI](https://qdrant.tech/documentation/agentic-rag-crewai-zoom/) | Step-by-step multi-agent RAG system. | CrewAI | 45m | Beginner |
| [Agentic RAG with LangGraph](https://qdrant.tech/documentation/agentic-rag-langgraph/) | Build AI agents to answer library documentation. | LangGraph | 45m | Intermediate |
| [Agentic Discord ChatBot](https://qdrant.tech/documentation/agentic-rag-camelai-discord/) | Develop a functional bot with CAMEL-AI. | OpenAI | 45m | Intermediate |
| [Multimodal Search (LlamaIndex)](https://qdrant.tech/documentation/multimodal-search/) | Search across image and text modalities. | LlamaIndex | 15m | Beginner |
| [Automate Metadata Filtering](https://qdrant.tech/documentation/search-precision/automate-filtering-with-llms/) | Use LLM structured output for dynamic filters. | Python | 30m | Intermediate |
| [Agentic RAG with CrewAI](/documentation/agentic-rag-crewai-zoom/) | Step-by-step multi-agent RAG system. | CrewAI | 45m | Beginner |
| [Agentic RAG with LangGraph](/documentation/agentic-rag-langgraph/) | Build AI agents to answer library documentation. | LangGraph | 45m | Intermediate |
| [Agentic Discord ChatBot](/documentation/agentic-rag-camelai-discord/) | Develop a functional bot with CAMEL-AI. | OpenAI | 45m | Intermediate |
| [Multimodal Search (LlamaIndex)](/documentation/multimodal-search/) | Search across image and text modalities. | LlamaIndex | 15m | Beginner |
| [Automate Metadata Filtering](/documentation/search-precision/automate-filtering-with-llms/) | Use LLM structured output for dynamic filters. | Python | 30m | Intermediate |
qdrant-landing/content/documentation/tutorials-search-engineering/_index.md
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@@ -13,12 +13,12 @@ partition: qdrant
| Tutorial | Objective | Stack | Time | Level |
| :--- | :--- | :--- | :--- | :--- |
| [Neural Search Service](https://qdrant.tech/documentation/tutorials-search-engineering/neural-search/) | Deploy a search service for company descriptions. | FastAPI | 30m | Beginner |
| [Hybrid Search with FastEmbed](https://qdrant.tech/documentation/tutorials-search-engineering/hybrid-search-fastembed/) | Combine dense and sparse search for startups. | FastAPI | 20m | Beginner |
| [Movie Recommendations](https://qdrant.tech/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | Python | 45m | Intermediate |
| [Advanced PDF Retrieval](https://qdrant.tech/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | Python | 30m | Intermediate |
| [Retrieval Quality Benchmarking](https://qdrant.tech/documentation/tutorials-search-engineering/retrieval-quality/) | Measure quality and tune HNSW parameters. | Python | 30m | Intermediate |
| [Multivector Reranking](https://qdrant.tech/documentation/search-precision/reranking-semantic-search/) | Use multivector representations for better ranking. | Python | 30m | Intermediate |
| [Hybrid Search Reranking](https://qdrant.tech/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | Python | 40m | Intermediate |
| [Semantic Code Search](https://qdrant.tech/documentation/tutorials-search-engineering/code-search/) | Navigate codebases using vector similarity. | Python | 45m | Intermediate |
| [Static Embeddings Analysis](https://qdrant.tech/documentation/tutorials-search-engineering/static-embeddings/) | Evaluate the renaissance of static embeddings. | Python | 20m | Intermediate |
| [Neural Search Service](/documentation/tutorials-search-engineering/neural-search/) | Deploy a search service for company descriptions. | FastAPI | 30m | Beginner |
| [Hybrid Search with FastEmbed](/documentation/tutorials-search-engineering/hybrid-search-fastembed/) | Combine dense and sparse search for startups. | FastAPI | 20m | Beginner |
| [Movie Recommendations](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | Python | 45m | Intermediate |
| [Advanced PDF Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | Python | 30m | Intermediate |
| [Retrieval Quality Benchmarking](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure quality and tune HNSW parameters. | Python | 30m | Intermediate |
| [Multivector Reranking](/documentation/search-precision/reranking-semantic-search/) | Use multivector representations for better ranking. | Python | 30m | Intermediate |
| [Hybrid Search Reranking](/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | Python | 40m | Intermediate |
| [Semantic Code Search](/documentation/tutorials-search-engineering/code-search/) | Navigate codebases using vector similarity. | Python | 45m | Intermediate |
| [Static Embeddings Analysis](/documentation/tutorials-search-engineering/static-embeddings/) | Evaluate the renaissance of static embeddings. | Python | 20m | Intermediate |
qdrant-landing/content/documentation/tutorials-search-engineering/code-search.md
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title: Search Through Your Codebase
aliases:
- /documentation/tutorials/code-search/
- /documentation/advanced-tutorials/code-search/
weight: 2
---
qdrant-landing/content/documentation/tutorials-search-engineering/collaborative-filtering.md
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title: Build a Recommendation System with Collaborative Filtering
aliases:
- /documentation/tutorials/collaborative-filtering/
- /documentation/advanced-tutorials/collaborative-filtering/
short_description: "Build an effective movie recommendation system using collaborative filtering and Qdrant's similarity search."
description: "Build an effective movie recommendation system using collaborative filtering and Qdrant's similarity search."
preview_image: /blog/collaborative-filtering/social_preview.png
qdrant-landing/content/documentation/tutorials-search-engineering/hybrid-search-fastembed.md
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title: Setup Hybrid Search with FastEmbed
aliases:
- /documentation/tutorials/hybrid-search-fastembed/
- /documentation/beginner-tutorials/hybrid-search-fastembed/
weight: 3
---
qdrant-landing/content/documentation/tutorials-search-engineering/neural-search.md
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title: Build a Neural Search Service
aliases:
- /documentation/tutorials/neural-search/
- /documentation/beginner-tutorials/neural-search/
weight: 2
---
qdrant-landing/content/documentation/tutorials-search-engineering/pdf-retrieval-at-scale.md
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title: Scaling PDF Retrieval with Qdrant
aliases:
- /documentation/tutorials/pdf-retrieval-at-scale/
- /documentation/advanced-tutorials/pdf-retrieval-at-scale/
short_description: "Optimizing PDF retrieval at scale with Qdrant and Vision Large Language Models (VLLMs) such as ColPali and ColQwen."
description: "Optimizing PDF retrieval at scale with Qdrant and Vision Large Language Models (VLLMs) such as ColPali and ColQwen. Two-stage retrieval with multivector representations mean pooling."
weight: 4
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weight: 2
aliases:
- /documentation/search-precision/reranking-hybrid-search/
- /documentation/advanced-tutorials/reranking-hybrid-search/
---
# Reranking Hybrid Search Results with Qdrant Vector Database
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title: Measure Search Quality
aliases:
- /documentation/tutorials/retrieval-quality/
- /documentation/beginner-tutorials/retrieval-quality/
weight: 4
---
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aliases:
- /blog/static-embeddings/
- /documentation/database-tutorials/static-embeddings/
---
# Static Embeddings: should you pay attention?
In the world of resource-constrained computing, a quiet revolution is taking place. While transformers dominate
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- /documentation/search-precision/multivector-representations-with-Qdrant/
- /documentation/advanced-tutorials/using-multivector-representations/
---
# How to Effectively Use Multivector Representations in Qdrant for Reranking
Multivector Representations are one of the most powerful features of Qdrant. However, most people don't use them effectively, resulting in massive RAM overhead, slow inserts, and wasted compute.