Use .md link pattern.

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.