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79 lines
4.2 KiB
Markdown
79 lines
4.2 KiB
Markdown
---
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title: Explore the Qdrant Ecosystem
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short_description: "Explore the Qdrant ecosystem of integrations, partners, frameworks, and community tools built around vector search."
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description: "Discover the Qdrant ecosystem — integrations with frameworks, embedding providers, cloud platforms, and community-built tools for vector search."
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slug: ecosystem
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aliases:
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- /documentation/build/
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breadcrumb: false
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content:
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- partial: documentation/banners/banner-c
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title: Explore the Qdrant Ecosystem
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description: Dev-portal Ecosystem
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image:
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src: /img/dev-portal-build/spanish-ai-app-hero.png
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alt: Spanish AI app.png
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startedButton:
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text: Get Started
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url: https://qdrant.to/cloud
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- partial: documentation/sections/cards-section
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title: Start Building
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description: Deploy and manage high-performance vector search clusters across cloud environments. Easily scale with fully managed cloud solutions, integrate seamlessly across hybrid setups, or maintain complete control with private cloud deployments in Kubernetes.
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cardsPartial: documentation/cards/docs-cards
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cards:
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- id: 1
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image:
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src: /img/dev-portal-build/search.png
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alt: Search
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title: Search
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description: Build a simple neural search service with Qdrant and FastEmbed. Learn how to upload data, create indexes, and run search queries.
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link:
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url: /documentation/tutorials-develop/hybrid-search-fastembed/
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text: Read More
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- id: 2
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image:
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src: /img/dev-portal-build/rag.png
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alt: RAG
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title: RAG
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description: Build end-to-end prototype chatbots. Learn how Qdrant integrates with popular RAG frameworks like LangChain and LlamaIndex.
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link:
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url: /documentation/frameworks/langchain/
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text: Read More
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- id: 3
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image:
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src: /img/dev-portal-build/pipelines.png
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alt: Pipelines
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title: Pipelines
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description: Integrate Qdrant into your data infrastructure by connecting with popular data engineering tools.
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link:
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url: /documentation/send-data/
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text: Read More
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partition: ecosystem
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hideInSidebar: true
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build:
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render: always
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---
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# Explore the Qdrant Ecosystem
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## Migration
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- [Migration Tool](/documentation/migrate-to-qdrant/index.md) — Move vectors, payloads, and sparse embeddings from Chroma, Pinecone, Weaviate, Milvus, pgvector, Elasticsearch, and OpenSearch.
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- [Migration Guidance](/documentation/migration-guidance/index.md) — Structured framework for verifying migrations and catching silent data and search regressions.
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- [Data Synchronization](/documentation/data-synchronization/index.md) — Keep Qdrant in sync with source databases using CDC, batch reindexing, or dual-write patterns for fresh search results.
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## Integrations
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- [Data Management](/documentation/data-management/index.md) — Connect Qdrant to ETL and streaming tools to ingest, transform, and sync vectors at scale.
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- [Embedding Providers](/documentation/embeddings/index.md) — Connect to 30+ providers: OpenAI, Cohere, Jina, Mistral, AWS Bedrock, Voyage AI, Ollama, and more.
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- [Frameworks](/documentation/frameworks/index.md) — Integrations with 40+ AI agent and RAG frameworks: LangChain, LlamaIndex, Haystack, CrewAI, AutoGen, Spring AI, and more.
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- [Observability](/documentation/observability/index.md) — Connect Qdrant to Datadog, OpenLIT, and OpenLLMetry to monitor performance and traces.
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- [Platforms](/documentation/platforms/index.md) — No-code and low-code integrations with n8n, Make, MuleSoft, Pipedream, Power Apps, and more.
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## Ecosystem Guides
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- [Essential Examples](/documentation/tutorials-build-essentials/index.md) — Hands-on tutorials for agentic RAG, multimodal search, data ingestion, and automation integrations.
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- [Build Prototypes](/documentation/examples/index.md) — End-to-end code samples for RAG pipelines, hybrid search, multitenancy, recommendations, and multimodal search.
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- [Improve Search](/documentation/improve-search/index.md) — Techniques for improving retrieval relevance and pipeline output quality.
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- [Practice Datasets](/documentation/datasets/index.md) — Ready-made Qdrant snapshots of public datasets you can import and explore without the embedding step.
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