From 9c3399b7b48e06c0ca3d6f015ffa58cb21790531 Mon Sep 17 00:00:00 2001 From: davidmyriel Date: Thu, 12 Sep 2024 22:26:59 -0700 Subject: [PATCH] add guidebooks section --- qdrant-landing/content/documentation/_index.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/qdrant-landing/content/documentation/_index.md b/qdrant-landing/content/documentation/_index.md index 0e699af55..ababe9636 100644 --- a/qdrant-landing/content/documentation/_index.md +++ b/qdrant-landing/content/documentation/_index.md @@ -23,4 +23,10 @@ Qdrant is an AI-native vector dabatase and a semantic search engine. You can use |:-|:-|:-| |[Filtrable HNSW](/documentation/filtering/)
Single-stage payload filtering | [Recommendations & Context Search](/documentation/concepts/explore/#explore-the-data)
Exploratory advanced search| [Pure-Vector Hybrid Search](/documentation/hybrid-queries/)
Full text and semantic search in one| |[Multitenancy](/documentation/guides/multiple-partitions/)
Payload-based partitioning|[Custom Sharding](/documentation/guides/distributed_deployment/#sharding)
For data isolation and distribution|[Role Based Access Control](/documentation/guides/security/?q=jwt#granular-access-control-with-jwt)
Secure JWT-based access | -|[Quantization](/documentation/guides/quantization/)
Compress data for drastic speedups|[Multivector Support](/documentation/concepts/vectors/?q=multivect#multivectors)
For ColBERT late interaction |[Built-in IDF](/documentation/concepts/indexing/?q=inverse+docu#idf-modifier)
Cutting-edge similarity calculation| \ No newline at end of file +|[Quantization](/documentation/guides/quantization/)
Compress data for drastic speedups|[Multivector Support](/documentation/concepts/vectors/?q=multivect#multivectors)
For ColBERT late interaction |[Built-in IDF](/documentation/concepts/indexing/?q=inverse+docu#idf-modifier)
Cutting-edge similarity calculation| + +## Developer guidebooks: + +| [A Complete Guide to Filtering in Vector Search](/articles/vector-search-filtering/)
Beginner & advanced examples showing how to improve precision in vector search.| [Building Hybrid Search with Query API](/articles/hybrid-search/)
Build a pure vector-based hybrid search system with our new fusion feature.| +|----------------------------------------------|-------------------------------| +| [Multitenancy and Sharding: Best Practices](/articles/multitenancy/)
Combine two powerful features for complete data isolation and scaling.| [Benefits of Binary Quantization in Vector Search](/articles/binary-quantization/)
Compress data points while retaining essential meaning for extreme search performance.| \ No newline at end of file