From c3cda6dabb09d3f4e3ff09e42d89cd4e3bce9ad2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Istv=C3=A1n=20Zolt=C3=A1n=20Szab=C3=B3?= Date: Wed, 10 Jun 2026 10:42:31 +0200 Subject: [PATCH] Use .md link pattern. --- .../content/documentation/_index.md | 54 +++++++++---------- 1 file changed, 27 insertions(+), 27 deletions(-) diff --git a/qdrant-landing/content/documentation/_index.md b/qdrant-landing/content/documentation/_index.md index f1e284ce3..f010f4434 100644 --- a/qdrant-landing/content/documentation/_index.md +++ b/qdrant-landing/content/documentation/_index.md @@ -93,42 +93,42 @@ Qdrant is an AI-native vector search engine for storing, indexing, and searching ## Getting Started -- [Local Quickstart](/documentation/quickstart/) — Run Qdrant locally with Docker, connect a client SDK, and create your first collection. -- [Cloud Quickstart](/documentation/cloud-quickstart/) — Create a free Qdrant Cloud cluster on AWS, GCP, or Azure and query it in minutes. -- [Overview](/documentation/overview/) — How vector search works, the client-server architecture, and core data structures (points, vectors, payloads, collections). -- [API & SDKs](/documentation/interfaces/) — Connect via REST or gRPC with official client libraries for Python, JavaScript/TypeScript, Rust, Go, Java, and .NET. +- [Local Quickstart](/documentation/quickstart/index.md) — Run Qdrant locally with Docker, connect a client SDK, and create your first collection. +- [Cloud Quickstart](/documentation/cloud-quickstart/index.md) — Create a free Qdrant Cloud cluster on AWS, GCP, or Azure and query it in minutes. +- [Overview](/documentation/overview/index.md) — How vector search works, the client-server architecture, and core data structures (points, vectors, payloads, collections). +- [API & SDKs](/documentation/interfaces/index.md) — Connect via REST or gRPC with official client libraries for Python, JavaScript/TypeScript, Rust, Go, Java, and .NET. ## Develop -- [Manage Data](/documentation/manage-data/) — Create collections, insert and update points and payloads, configure vector indexes, quantization, and multitenancy. -- [Search](/documentation/search/) — Similarity search, filtering, hybrid and multimodal queries, multi-stage pipelines, and relevance tuning. -- [Inference](/documentation/inference/) — Configure dense, sparse, and multi-vector embeddings; use cloud-hosted embedding models directly within Qdrant. -- [Qdrant Edge](/documentation/edge/) — Lightweight embedded vector search for in-process, offline-capable retrieval on robots, kiosks, and mobile devices. +- [Manage Data](/documentation/manage-data/index.md) — Create collections, insert and update points and payloads, configure vector indexes, quantization, and multitenancy. +- [Search](/documentation/search/index.md) — Similarity search, filtering, hybrid and multimodal queries, multi-stage pipelines, and relevance tuning. +- [Inference](/documentation/inference/index.md) — Configure dense, sparse, and multi-vector embeddings; use cloud-hosted embedding models directly within Qdrant. +- [Qdrant Edge](/documentation/edge/index.md) — Lightweight embedded vector search for in-process, offline-capable retrieval on robots, kiosks, and mobile devices. ## Deploy -- [Deploy Overview](/documentation/deploy-intro/) — Compare all Qdrant deployment options: Managed Cloud, Hybrid Cloud, Private Cloud, and self-hosted. -- [Installation](/documentation/installation/) — Install Qdrant via Docker, Kubernetes, or binary on Linux, macOS, or Windows. -- [Managed Cloud](/documentation/cloud/) — Qdrant as a managed service on AWS, GCP, or Azure with automatic scaling, backups, and zero-downtime upgrades. -- [Hybrid Cloud](/documentation/hybrid-cloud/) — Deploy into your own Kubernetes cluster while managing through Qdrant Cloud. -- [Private Cloud](/documentation/private-cloud/) — Fully air-gapped deployment in your own Kubernetes cluster with no Qdrant Cloud connectivity required. -- [Distributed Deployment](/documentation/distributed_deployment/) — Multi-node clusters with horizontal sharding and replication for scale and fault tolerance. -- [Security](/documentation/security/) — API keys, JWT-based collection-scoped access control, TLS encryption, and network binding. -- [Configuration](/documentation/ops-configuration/) — Customize Qdrant via config files and environment variables; runtime administration tools; GPU-accelerated vector indexing. -- [Monitoring & Telemetry](/documentation/ops-monitoring/) — Monitor Qdrant with Prometheus and Grafana via built-in OpenMetrics endpoints. -- [Optimization](/documentation/ops-optimization/) — Tune for high-speed search, high precision, or low memory usage; understand how the background optimizer works. -- [Production Checklist](/documentation/production-checklist/) — Pre-launch review of sharding, replication, quantization, load balancing, and observability. -- [Capacity Planning](/documentation/capacity-planning/) — Estimate RAM and disk for vectors, payloads, indexes, and replication factors. -- [Snapshots](/documentation/snapshots/) — Back up and restore collections with snapshots for disaster recovery and cross-cluster replication. -- [Troubleshooting](/documentation/common-errors/) — Diagnose common runtime errors: open-file limits, filesystem incompatibilities, corrupted collection metadata. +- [Deploy Overview](/documentation/deploy-intro/index.md) — Compare all Qdrant deployment options: Managed Cloud, Hybrid Cloud, Private Cloud, and self-hosted. +- [Installation](/documentation/installation/index.md) — Install Qdrant via Docker, Kubernetes, or binary on Linux, macOS, or Windows. +- [Managed Cloud](/documentation/cloud/index.md) — Qdrant as a managed service on AWS, GCP, or Azure with automatic scaling, backups, and zero-downtime upgrades. +- [Hybrid Cloud](/documentation/hybrid-cloud/index.md) — Deploy into your own Kubernetes cluster while managing through Qdrant Cloud. +- [Private Cloud](/documentation/private-cloud/index.md) — Fully air-gapped deployment in your own Kubernetes cluster with no Qdrant Cloud connectivity required. +- [Distributed Deployment](/documentation/distributed_deployment/index.md) — Multi-node clusters with horizontal sharding and replication for scale and fault tolerance. +- [Security](/documentation/security/index.md) — API keys, JWT-based collection-scoped access control, TLS encryption, and network binding. +- [Configuration](/documentation/ops-configuration/index.md) — Customize Qdrant via config files and environment variables; runtime administration tools; GPU-accelerated vector indexing. +- [Monitoring & Telemetry](/documentation/ops-monitoring/index.md) — Monitor Qdrant with Prometheus and Grafana via built-in OpenMetrics endpoints. +- [Optimization](/documentation/ops-optimization/index.md) — Tune for high-speed search, high precision, or low memory usage; understand how the background optimizer works. +- [Production Checklist](/documentation/production-checklist/index.md) — Pre-launch review of sharding, replication, quantization, load balancing, and observability. +- [Capacity Planning](/documentation/capacity-planning/index.md) — Estimate RAM and disk for vectors, payloads, indexes, and replication factors. +- [Snapshots](/documentation/snapshots/index.md) — Back up and restore collections with snapshots for disaster recovery and cross-cluster replication. +- [Troubleshooting](/documentation/common-errors/index.md) — Diagnose common runtime errors: open-file limits, filesystem incompatibilities, corrupted collection metadata. ## Ecosystem -- [Frameworks](/documentation/frameworks/) — Integrations with 40+ AI agent and RAG frameworks: LangChain, LlamaIndex, Haystack, CrewAI, AutoGen, Spring AI, and more. -- [Embedding Providers](/documentation/embeddings/) — Connect to 30+ providers: OpenAI, Cohere, Jina, Mistral, AWS Bedrock, Voyage AI, Ollama, and more. -- [Platforms](/documentation/platforms/) — No-code and low-code integrations with n8n, Make, MuleSoft, Pipedream, and more. +- [Frameworks](/documentation/frameworks/index.md) — Integrations with 40+ AI agent and RAG frameworks: LangChain, LlamaIndex, Haystack, CrewAI, AutoGen, Spring AI, and more. +- [Embedding Providers](/documentation/embeddings/index.md) — Connect to 30+ providers: OpenAI, Cohere, Jina, Mistral, AWS Bedrock, Voyage AI, Ollama, and more. +- [Platforms](/documentation/platforms/index.md) — No-code and low-code integrations with n8n, Make, MuleSoft, Pipedream, and more. ## Tutorials & Examples -- [Tutorials](/documentation/tutorials-lp-overview/) — Hub for all tutorials covering basics, search engineering, retrieval quality, operations, migrations, and ecosystem integrations. -- [Examples](/documentation/examples/) — End-to-end code samples for RAG pipelines, hybrid search, multitenancy, recommendations, and multimodal search. +- [Tutorials](/documentation/tutorials-lp-overview/index.md) — Hub for all tutorials covering basics, search engineering, retrieval quality, operations, migrations, and ecosystem integrations. +- [Examples](/documentation/examples/index.md) — End-to-end code samples for RAG pipelines, hybrid search, multitenancy, recommendations, and multimodal search.