mirror of
https://github.com/qdrant/landing_page.git
synced 2026-10-04 10:28:29 +02:00
updated aliases and relative links
This commit is contained in:
kanungle
@@ -13,10 +13,10 @@ partition: qdrant
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| Tutorial | Objective | Stack | Time | Level |
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| Tutorial | Objective | Stack | Time | Level |
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| :--- | :--- | :--- | :--- | :--- |
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| :--- | :--- | :--- | :--- | :--- |
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| [Embedding Migration](https://qdrant.tech/documentation/tutorials-ecosystem/migration/) | Move dense and sparse embeddings to Qdrant. | CLI | 30m | Intermediate |
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| [Embedding Migration](/documentation/tutorials-ecosystem/migration/) | Move dense and sparse embeddings to Qdrant. | CLI | 30m | Intermediate |
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| [S3 Ingestion with LangChain](https://qdrant.tech/documentation/data-ingestion-beginners/) | Stream data from AWS S3 to vector store. | LangChain | 30m | Beginner |
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| [S3 Ingestion with LangChain](/documentation/data-ingestion-beginners/) | Stream data from AWS S3 to vector store. | LangChain | 30m | Beginner |
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| [Hugging Face Datasets](https://qdrant.tech/documentation/tutorials-ecosystem/huggingface-datasets/) | Load and search public ML datasets. | Python | 15m | Beginner |
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| [Hugging Face Datasets](/documentation/tutorials-ecosystem/huggingface-datasets/) | Load and search public ML datasets. | Python | 15m | Beginner |
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| [Databricks Integration](https://qdrant.tech/documentation/send-data/databricks/) | Vectorize datasets using FastEmbed on Databricks. | Databricks | 30m | Intermediate |
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| [Databricks Integration](/documentation/send-data/databricks/) | Vectorize datasets using FastEmbed on Databricks. | Databricks | 30m | Intermediate |
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| [Airflow & Astronomer](https://qdrant.tech/documentation/send-data/qdrant-airflow-astronomer/) | Orchestrate data engineering workflows. | Airflow | 45m | Intermediate |
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| [Airflow & Astronomer](/documentation/send-data/qdrant-airflow-astronomer/) | Orchestrate data engineering workflows. | Airflow | 45m | Intermediate |
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| [Kafka Data Streaming](https://qdrant.tech/documentation/send-data/data-streaming-kafka-qdrant/) | Setup Qdrant Sink Connector for real-time data. | Kafka | 60m | Advanced |
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| [Kafka Data Streaming](/documentation/send-data/data-streaming-kafka-qdrant/) | Setup Qdrant Sink Connector for real-time data. | Kafka | 60m | Advanced |
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| [No-Code Automation (n8n)](https://qdrant.tech/documentation/qdrant-n8n/) | Combine Qdrant with low-code n8n workflows. | n8n | 45m | Intermediate |
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| [No-Code Automation (n8n)](/documentation/qdrant-n8n/) | Combine Qdrant with low-code n8n workflows. | n8n | 45m | Intermediate |
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@@ -2,6 +2,7 @@
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title: Load a HuggingFace Dataset
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title: Load a HuggingFace Dataset
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aliases:
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aliases:
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- /documentation/tutorials/huggingface-datasets/
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- /documentation/tutorials/huggingface-datasets/
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- /documentation/database-tutorials/huggingface-datasets/
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weight: 3
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weight: 3
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---
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---
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@@ -1,5 +1,7 @@
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---
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---
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title: Migration to Qdrant
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title: Migration to Qdrant
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aliases:
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- /documentation/database-tutorials/migration/
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weight: 180
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weight: 180
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---
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---
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@@ -13,10 +13,10 @@ partition: qdrant
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| Tutorial | Objective | Stack | Time | Level |
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| Tutorial | Objective | Stack | Time | Level |
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| :--- | :--- | :--- | :--- | :--- |
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| :--- | :--- | :--- | :--- | :--- |
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| [Bulk Data Uploads](https://qdrant.tech/documentation/tutorials-operations/bulk-upload/) | High-scale ingestion tricks for power users. | Python | 20m | Intermediate |
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| [Bulk Data Uploads](/documentation/tutorials-operations/bulk-upload/) | High-scale ingestion tricks for power users. | Python | 20m | Intermediate |
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| [Snapshot & Backup](https://qdrant.tech/documentation/tutorials-operations/create-snapshot/) | Create and restore collection snapshots. | Python | 20m | Beginner |
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| [Snapshot & Backup](/documentation/tutorials-operations/create-snapshot/) | Create and restore collection snapshots. | Python | 20m | Beginner |
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| [Billion-Scale Search](https://qdrant.tech/documentation/tutorials-operations/large-scale-search/) | Cost-efficient search for LAION-400M datasets. | None | 2 days | Advanced |
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| [Billion-Scale Search](/documentation/tutorials-operations/large-scale-search/) | Cost-efficient search for LAION-400M datasets. | None | 2 days | Advanced |
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| [Python Async API](https://qdrant.tech/documentation/tutorials-operations/async-api/) | Use Asynchronous programming for efficiency. | Python | 25m | Intermediate |
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| [Python Async API](/documentation/tutorials-operations/async-api/) | Use Asynchronous programming for efficiency. | Python | 25m | Intermediate |
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| [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 |
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| [Cloud Inference Search](/documentation/tutorials-and-examples/cloud-inference-hybrid-search/) | Hybrid search using Qdrant's built-in inference. | Any | 20m | Beginner |
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| [Monitor Managed Cloud](https://qdrant.tech/documentation/tutorials-and-examples/managed-cloud-prometheus/) | Observability with Prometheus and Grafana. | Prometheus | 30m | Intermediate |
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| [Monitor Managed Cloud](/documentation/tutorials-and-examples/managed-cloud-prometheus/) | Observability with Prometheus and Grafana. | Prometheus | 30m | Intermediate |
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| [Monitor Private Cloud](https://qdrant.tech/documentation/tutorials-and-examples/hybrid-cloud-prometheus/) | Observability for hybrid/private cloud setups. | Prometheus | 30m | Intermediate |
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| [Monitor Private Cloud](/documentation/tutorials-and-examples/hybrid-cloud-prometheus/) | Observability for hybrid/private cloud setups. | Prometheus | 30m | Intermediate |
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@@ -2,6 +2,7 @@
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title: Build With Async API
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title: Build With Async API
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aliases:
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aliases:
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- /documentation/tutorials/async-api/
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- /documentation/tutorials/async-api/
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- /documentation/database-tutorials/async-api/
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weight: 4
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weight: 4
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---
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---
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title: Bulk Upload Vectors
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title: Bulk Upload Vectors
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aliases:
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aliases:
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- /documentation/tutorials/bulk-upload/
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- /documentation/tutorials/bulk-upload/
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- /documentation/database-tutorials/bulk-upload/
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weight: 1
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weight: 1
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---
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---
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title: Create & Restore Snapshots
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title: Create & Restore Snapshots
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aliases:
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aliases:
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- /documentation/tutorials/create-snapshot/
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- /documentation/tutorials/create-snapshot/
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- /documentation/database-tutorials/create-snapshot/
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weight: 2
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weight: 2
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---
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---
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---
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---
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title: Large Scale Search
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title: Large Scale Search
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aliases:
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- /documentation/database-tutorials/large-scale-search/
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weight: 2
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weight: 2
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---
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---
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@@ -15,9 +15,9 @@ partition: qdrant
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| Tutorial | Objective | Stack | Time | Level |
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| Tutorial | Objective | Stack | Time | Level |
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| :--- | :--- | :--- | :--- | :--- |
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| :--- | :--- | :--- | :--- | :--- |
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| [Local Qdrant Setup](https://qdrant.tech/documentation/quickstart/) | Basic CRUD operations and local deployment. | Python | 10m | Beginner |
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| [Local Qdrant Setup](/documentation/quickstart/) | Basic CRUD operations and local deployment. | Python | 10m | Beginner |
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| [5-Minute Semantic Search](https://qdrant.tech/documentation/tutorials-quickstart/search-beginners/) | Build a search engine for science fiction books. | Python | 5m | Beginner |
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| [5-Minute Semantic Search](/documentation/tutorials-quickstart/search-beginners/) | Build a search engine for science fiction books. | Python | 5m | Beginner |
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| [5-Minute RAG with DeepSeek](https://qdrant.tech/documentation/tutorials-quickstart/rag-deepseek/) | Build a RAG pipeline with DeepSeek enrichment. | Python | 5m | Beginner |
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| [5-Minute RAG with DeepSeek](/documentation/tutorials-quickstart/rag-deepseek/) | Build a RAG pipeline with DeepSeek enrichment. | Python | 5m | Beginner |
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---
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---
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@@ -26,15 +26,15 @@ partition: qdrant
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| Tutorial | Objective | Stack | Time | Level |
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| Tutorial | Objective | Stack | Time | Level |
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| :--- | :--- | :--- | :--- | :--- |
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| :--- | :--- | :--- | :--- | :--- |
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| [Neural Search Service](https://qdrant.tech/documentation/tutorials-search-engineering/neural-search/) | Deploy a search service for company descriptions. | FastAPI | 30m | Beginner |
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| [Neural Search Service](/documentation/tutorials-search-engineering/neural-search/) | Deploy a search service for company descriptions. | FastAPI | 30m | Beginner |
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| [Hybrid Search with FastEmbed](https://qdrant.tech/documentation/tutorials-search-engineering/hybrid-search-fastembed/) | Combine dense and sparse search for startups. | FastAPI | 20m | Beginner |
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| [Hybrid Search with FastEmbed](/documentation/tutorials-search-engineering/hybrid-search-fastembed/) | Combine dense and sparse search for startups. | FastAPI | 20m | Beginner |
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| [Movie Recommendations](https://qdrant.tech/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | Python | 45m | Intermediate |
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| [Movie Recommendations](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | Python | 45m | Intermediate |
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| [Advanced PDF Retrieval](https://qdrant.tech/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | Python | 30m | Intermediate |
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| [Advanced PDF Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | Python | 30m | Intermediate |
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| [Retrieval Quality Benchmarking](https://qdrant.tech/documentation/tutorials-search-engineering/retrieval-quality/) | Measure quality and tune HNSW parameters. | Python | 30m | Intermediate |
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| [Retrieval Quality Benchmarking](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure quality and tune HNSW parameters. | Python | 30m | Intermediate |
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| [Multivector Reranking](https://qdrant.tech/documentation/search-precision/reranking-semantic-search/) | Use multivector representations for better ranking. | Python | 30m | Intermediate |
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| [Multivector Reranking](/documentation/search-precision/reranking-semantic-search/) | Use multivector representations for better ranking. | Python | 30m | Intermediate |
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| [Hybrid Search Reranking](https://qdrant.tech/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | Python | 40m | Intermediate |
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| [Hybrid Search Reranking](/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | Python | 40m | Intermediate |
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| [Semantic Code Search](https://qdrant.tech/documentation/tutorials-search-engineering/code-search/) | Navigate codebases using vector similarity. | Python | 45m | Intermediate |
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| [Semantic Code Search](/documentation/tutorials-search-engineering/code-search/) | Navigate codebases using vector similarity. | Python | 45m | Intermediate |
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| [Static Embeddings Analysis](https://qdrant.tech/documentation/tutorials-search-engineering/static-embeddings/) | Evaluate the renaissance of static embeddings. | Python | 20m | Intermediate |
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| [Static Embeddings Analysis](/documentation/tutorials-search-engineering/static-embeddings/) | Evaluate the renaissance of static embeddings. | Python | 20m | Intermediate |
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---
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---
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@@ -43,11 +43,11 @@ partition: qdrant
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| Tutorial | Objective | Stack | Time | Level |
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| Tutorial | Objective | Stack | Time | Level |
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| :--- | :--- | :--- | :--- | :--- |
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| :--- | :--- | :--- | :--- | :--- |
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| [Agentic RAG with CrewAI](https://qdrant.tech/documentation/agentic-rag-crewai-zoom/) | Step-by-step multi-agent RAG system. | CrewAI | 45m | Beginner |
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| [Agentic RAG with CrewAI](/documentation/agentic-rag-crewai-zoom/) | Step-by-step multi-agent RAG system. | CrewAI | 45m | Beginner |
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| [Agentic RAG with LangGraph](https://qdrant.tech/documentation/agentic-rag-langgraph/) | Build AI agents to answer library documentation. | LangGraph | 45m | Intermediate |
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| [Agentic RAG with LangGraph](/documentation/agentic-rag-langgraph/) | Build AI agents to answer library documentation. | LangGraph | 45m | Intermediate |
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| [Agentic Discord ChatBot](https://qdrant.tech/documentation/agentic-rag-camelai-discord/) | Develop a functional bot with CAMEL-AI. | OpenAI | 45m | Intermediate |
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| [Agentic Discord ChatBot](/documentation/agentic-rag-camelai-discord/) | Develop a functional bot with CAMEL-AI. | OpenAI | 45m | Intermediate |
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| [Multimodal Search (LlamaIndex)](https://qdrant.tech/documentation/multimodal-search/) | Search across image and text modalities. | LlamaIndex | 15m | Beginner |
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| [Multimodal Search (LlamaIndex)](/documentation/multimodal-search/) | Search across image and text modalities. | LlamaIndex | 15m | Beginner |
|
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| [Automate Metadata Filtering](https://qdrant.tech/documentation/search-precision/automate-filtering-with-llms/) | Use LLM structured output for dynamic filters. | Python | 30m | Intermediate |
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| [Automate Metadata Filtering](/documentation/search-precision/automate-filtering-with-llms/) | Use LLM structured output for dynamic filters. | Python | 30m | Intermediate |
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---
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---
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@@ -56,13 +56,13 @@ partition: qdrant
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| Tutorial | Objective | Stack | Time | Level |
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| Tutorial | Objective | Stack | Time | Level |
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| :--- | :--- | :--- | :--- | :--- |
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| :--- | :--- | :--- | :--- | :--- |
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| [Embedding Migration](https://qdrant.tech/documentation/tutorials-ecosystem/migration/) | Move dense and sparse embeddings to Qdrant. | CLI | 30m | Intermediate |
|
| [Embedding Migration](/documentation/tutorials-ecosystem/migration/) | Move dense and sparse embeddings to Qdrant. | CLI | 30m | Intermediate |
|
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| [S3 Ingestion with LangChain](https://qdrant.tech/documentation/data-ingestion-beginners/) | Stream data from AWS S3 to vector store. | LangChain | 30m | Beginner |
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| [S3 Ingestion with LangChain](/documentation/data-ingestion-beginners/) | Stream data from AWS S3 to vector store. | LangChain | 30m | Beginner |
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| [Hugging Face Datasets](https://qdrant.tech/documentation/tutorials-ecosystem/huggingface-datasets/) | Load and search public ML datasets. | Python | 15m | Beginner |
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| [Hugging Face Datasets](/documentation/tutorials-ecosystem/huggingface-datasets/) | Load and search public ML datasets. | Python | 15m | Beginner |
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| [Databricks Integration](https://qdrant.tech/documentation/send-data/databricks/) | Vectorize datasets using FastEmbed on Databricks. | Databricks | 30m | Intermediate |
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| [Databricks Integration](/documentation/send-data/databricks/) | Vectorize datasets using FastEmbed on Databricks. | Databricks | 30m | Intermediate |
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| [Airflow & Astronomer](https://qdrant.tech/documentation/send-data/qdrant-airflow-astronomer/) | Orchestrate data engineering workflows. | Airflow | 45m | Intermediate |
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| [Airflow & Astronomer](/documentation/send-data/qdrant-airflow-astronomer/) | Orchestrate data engineering workflows. | Airflow | 45m | Intermediate |
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| [Kafka Data Streaming](https://qdrant.tech/documentation/send-data/data-streaming-kafka-qdrant/) | Setup Qdrant Sink Connector for real-time data. | Kafka | 60m | Advanced |
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| [Kafka Data Streaming](/documentation/send-data/data-streaming-kafka-qdrant/) | Setup Qdrant Sink Connector for real-time data. | Kafka | 60m | Advanced |
|
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| [No-Code Automation (n8n)](https://qdrant.tech/documentation/qdrant-n8n/) | Combine Qdrant with low-code n8n workflows. | n8n | 45m | Intermediate |
|
| [No-Code Automation (n8n)](/documentation/qdrant-n8n/) | Combine Qdrant with low-code n8n workflows. | n8n | 45m | Intermediate |
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---
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---
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@@ -71,10 +71,10 @@ partition: qdrant
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| Tutorial | Objective | Stack | Time | Level |
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| Tutorial | Objective | Stack | Time | Level |
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| :--- | :--- | :--- | :--- | :--- |
|
| :--- | :--- | :--- | :--- | :--- |
|
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| [Bulk Data Uploads](https://qdrant.tech/documentation/tutorials-operations/bulk-upload/) | High-scale ingestion tricks for power users. | Python | 20m | Intermediate |
|
| [Bulk Data Uploads](/documentation/tutorials-operations/bulk-upload/) | High-scale ingestion tricks for power users. | Python | 20m | Intermediate |
|
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| [Snapshot & Backup](https://qdrant.tech/documentation/tutorials-operations/create-snapshot/) | Create and restore collection snapshots. | Python | 20m | Beginner |
|
| [Snapshot & Backup](/documentation/tutorials-operations/create-snapshot/) | Create and restore collection snapshots. | Python | 20m | Beginner |
|
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| [Billion-Scale Search](https://qdrant.tech/documentation/tutorials-operations/large-scale-search/) | Cost-efficient search for LAION-400M datasets. | None | 2 days | Advanced |
|
| [Billion-Scale Search](/documentation/tutorials-operations/large-scale-search/) | Cost-efficient search for LAION-400M datasets. | None | 2 days | Advanced |
|
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| [Python Async API](https://qdrant.tech/documentation/tutorials-operations/async-api/) | Use Asynchronous programming for efficiency. | Python | 25m | Intermediate |
|
| [Python Async API](/documentation/tutorials-operations/async-api/) | Use Asynchronous programming for efficiency. | Python | 25m | Intermediate |
|
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| [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 |
|
| [Cloud Inference Search](/documentation/tutorials-and-examples/cloud-inference-hybrid-search/) | Hybrid search using Qdrant's built-in inference. | Any | 20m | Beginner |
|
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| [Monitor Managed Cloud](https://qdrant.tech/documentation/tutorials-and-examples/managed-cloud-prometheus/) | Observability with Prometheus and Grafana. | Prometheus | 30m | Intermediate |
|
| [Monitor Managed Cloud](/documentation/tutorials-and-examples/managed-cloud-prometheus/) | Observability with Prometheus and Grafana. | Prometheus | 30m | Intermediate |
|
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| [Monitor Private Cloud](https://qdrant.tech/documentation/tutorials-and-examples/hybrid-cloud-prometheus/) | Observability for hybrid/private cloud setups. | Prometheus | 30m | Intermediate |
|
| [Monitor Private Cloud](/documentation/tutorials-and-examples/hybrid-cloud-prometheus/) | Observability for hybrid/private cloud setups. | Prometheus | 30m | Intermediate |
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@@ -13,6 +13,6 @@ partition: qdrant
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| Tutorial | Objective | Stack | Time | Level |
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| Tutorial | Objective | Stack | Time | Level |
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| :--- | :--- | :--- | :--- | :--- |
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| :--- | :--- | :--- | :--- | :--- |
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| [Local Qdrant Setup](https://qdrant.tech/documentation/quickstart/) | Basic CRUD operations and local deployment. | Python | 10m | Beginner |
|
| [Local Qdrant Setup](/documentation/quickstart/) | Basic CRUD operations and local deployment. | Python | 10m | Beginner |
|
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| [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 Semantic Search](/documentation/tutorials-quickstart/search-beginners/) | Build a search engine for science fiction books. | Python | 5m | Beginner |
|
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| [5-Minute RAG with DeepSeek](https://qdrant.tech/documentation/tutorials-quickstart/rag-deepseek/) | Build a RAG pipeline with DeepSeek enrichment. | Python | 5m | Beginner |
|
| [5-Minute RAG with DeepSeek](/documentation/tutorials-quickstart/rag-deepseek/) | Build a RAG pipeline with DeepSeek enrichment. | Python | 5m | Beginner |
|
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aliases:
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aliases:
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- /documentation/tutorials/mighty.md/
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- /documentation/tutorials/mighty.md/
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- /documentation/tutorials/search-beginners/
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- /documentation/tutorials/search-beginners/
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- /documentation/beginner-tutorials/search-beginners/
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---
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---
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# Build Your First Semantic Search Engine in 5 Minutes
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# Build Your First Semantic Search Engine in 5 Minutes
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@@ -13,8 +13,8 @@ partition: qdrant
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| Tutorial | Objective | Stack | Time | Level |
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| Tutorial | Objective | Stack | Time | Level |
|
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| :--- | :--- | :--- | :--- | :--- |
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| :--- | :--- | :--- | :--- | :--- |
|
||||||
| [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 CrewAI](/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 RAG with LangGraph](/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 |
|
| [Agentic Discord ChatBot](/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 |
|
| [Multimodal Search (LlamaIndex)](/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 |
|
| [Automate Metadata Filtering](/documentation/search-precision/automate-filtering-with-llms/) | Use LLM structured output for dynamic filters. | Python | 30m | Intermediate |
|
||||||
@@ -13,12 +13,12 @@ partition: qdrant
|
|||||||
|
|
||||||
| Tutorial | Objective | Stack | Time | Level |
|
| 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 |
|
| [Neural Search Service](/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 |
|
| [Hybrid Search with FastEmbed](/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 |
|
| [Movie Recommendations](/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 |
|
| [Advanced PDF Retrieval](/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 |
|
| [Retrieval Quality Benchmarking](/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 |
|
| [Multivector Reranking](/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 |
|
| [Hybrid Search Reranking](/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 |
|
| [Semantic Code Search](/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 |
|
| [Static Embeddings Analysis](/documentation/tutorials-search-engineering/static-embeddings/) | Evaluate the renaissance of static embeddings. | Python | 20m | Intermediate |
|
||||||
@@ -2,6 +2,7 @@
|
|||||||
title: Search Through Your Codebase
|
title: Search Through Your Codebase
|
||||||
aliases:
|
aliases:
|
||||||
- /documentation/tutorials/code-search/
|
- /documentation/tutorials/code-search/
|
||||||
|
- /documentation/advanced-tutorials/code-search/
|
||||||
weight: 2
|
weight: 2
|
||||||
---
|
---
|
||||||
|
|
||||||
|
|||||||
+1
@@ -2,6 +2,7 @@
|
|||||||
title: Build a Recommendation System with Collaborative Filtering
|
title: Build a Recommendation System with Collaborative Filtering
|
||||||
aliases:
|
aliases:
|
||||||
- /documentation/tutorials/collaborative-filtering/
|
- /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."
|
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."
|
description: "Build an effective movie recommendation system using collaborative filtering and Qdrant's similarity search."
|
||||||
preview_image: /blog/collaborative-filtering/social_preview.png
|
preview_image: /blog/collaborative-filtering/social_preview.png
|
||||||
|
|||||||
+1
@@ -2,6 +2,7 @@
|
|||||||
title: Setup Hybrid Search with FastEmbed
|
title: Setup Hybrid Search with FastEmbed
|
||||||
aliases:
|
aliases:
|
||||||
- /documentation/tutorials/hybrid-search-fastembed/
|
- /documentation/tutorials/hybrid-search-fastembed/
|
||||||
|
- /documentation/beginner-tutorials/hybrid-search-fastembed/
|
||||||
weight: 3
|
weight: 3
|
||||||
---
|
---
|
||||||
|
|
||||||
|
|||||||
@@ -2,6 +2,7 @@
|
|||||||
title: Build a Neural Search Service
|
title: Build a Neural Search Service
|
||||||
aliases:
|
aliases:
|
||||||
- /documentation/tutorials/neural-search/
|
- /documentation/tutorials/neural-search/
|
||||||
|
- /documentation/beginner-tutorials/neural-search/
|
||||||
weight: 2
|
weight: 2
|
||||||
---
|
---
|
||||||
|
|
||||||
|
|||||||
+1
@@ -2,6 +2,7 @@
|
|||||||
title: Scaling PDF Retrieval with Qdrant
|
title: Scaling PDF Retrieval with Qdrant
|
||||||
aliases:
|
aliases:
|
||||||
- /documentation/tutorials/pdf-retrieval-at-scale/
|
- /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."
|
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."
|
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
|
weight: 4
|
||||||
|
|||||||
+1
@@ -3,6 +3,7 @@ title: Reranking in Hybrid Search
|
|||||||
weight: 2
|
weight: 2
|
||||||
aliases:
|
aliases:
|
||||||
- /documentation/search-precision/reranking-hybrid-search/
|
- /documentation/search-precision/reranking-hybrid-search/
|
||||||
|
- /documentation/advanced-tutorials/reranking-hybrid-search/
|
||||||
---
|
---
|
||||||
|
|
||||||
# Reranking Hybrid Search Results with Qdrant Vector Database
|
# Reranking Hybrid Search Results with Qdrant Vector Database
|
||||||
|
|||||||
@@ -2,6 +2,7 @@
|
|||||||
title: Measure Search Quality
|
title: Measure Search Quality
|
||||||
aliases:
|
aliases:
|
||||||
- /documentation/tutorials/retrieval-quality/
|
- /documentation/tutorials/retrieval-quality/
|
||||||
|
- /documentation/beginner-tutorials/retrieval-quality/
|
||||||
weight: 4
|
weight: 4
|
||||||
---
|
---
|
||||||
|
|
||||||
|
|||||||
@@ -3,6 +3,7 @@ title: Static Embeddings. Should you pay attention?
|
|||||||
weight: 181
|
weight: 181
|
||||||
aliases:
|
aliases:
|
||||||
- /blog/static-embeddings/
|
- /blog/static-embeddings/
|
||||||
|
- /documentation/database-tutorials/static-embeddings/
|
||||||
---
|
---
|
||||||
# Static Embeddings: should you pay attention?
|
# Static Embeddings: should you pay attention?
|
||||||
In the world of resource-constrained computing, a quiet revolution is taking place. While transformers dominate
|
In the world of resource-constrained computing, a quiet revolution is taking place. While transformers dominate
|
||||||
|
|||||||
+1
@@ -3,6 +3,7 @@ title: How to Use Multivector Representations with Qdrant Effectively
|
|||||||
weight: 2
|
weight: 2
|
||||||
aliases:
|
aliases:
|
||||||
- /documentation/search-precision/multivector-representations-with-Qdrant/
|
- /documentation/search-precision/multivector-representations-with-Qdrant/
|
||||||
|
- /documentation/advanced-tutorials/using-multivector-representations/
|
||||||
---
|
---
|
||||||
# How to Effectively Use Multivector Representations in Qdrant for Reranking
|
# 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.
|
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.
|
||||||
|
|||||||
Reference in New Issue
Block a user