Merge pull request #1557 from qdrant/ecosystem-section

[docs] Ecosystem Section

qdrant-landing/content/documentation/agentic-rag-camelai-discord.md

@@ -1,6 +1,6 @@
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 title: Agentic RAG Discord Bot with CAMEL-AI
-weight: 14
+weight: 4
 partition: build
 social_preview_image: /documentation/examples/agentic-rag-camelai-discord/social-preview.png
 ---

qdrant-landing/content/documentation/agentic-rag-crewai-zoom.md

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 title: Simple Agentic RAG System
-weight: 12
+weight: 2
 partition: build
 social_preview_image: /documentation/examples/agentic-rag-crewai-zoom/social_preview.png
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qdrant-landing/content/documentation/agentic-rag-langgraph.md

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 title: Agentic RAG With LangGraph 
-weight: 13
+weight: 3
 partition: build
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 # Agentic RAG With LangGraph and Qdrant

qdrant-landing/content/documentation/data-ingestion-beginners.md

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 title: Data Ingestion for Beginners
-weight: 11
+weight: 2
 partition: build
 social_preview_image: /documentation/examples/data-ingestion-beginners/social_preview.png
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qdrant-landing/content/documentation/dl-ecosystem.md

@@ -0,0 +1,11 @@
+---
+#Delimiter files are used to separate the list of documentation pages into sections.
+title: "Ecosystem"
+type: delimiter
+weight: 10 # Position before Integrations (weight 17)
+sitemapExclude: True
+_build:
+  publishResources: false
+  render: never
+partition: build
+--- 

qdrant-landing/content/documentation/dl-essentials.md

@@ -2,7 +2,7 @@
 #Delimiter files are used to separate the list of documentation pages into sections.
 title: "Essentials"
 type: delimiter
-weight: 10 # Change this weight to change order of sections
+weight: 1 # Change this weight to change order of sections
 sitemapExclude: True
 _build:
   publishResources: false

qdrant-landing/content/documentation/fastembed/_index.md

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-title: "FastEmbed" 
-weight: 7
-partition: qdrant
+title: "FastEmbed"
+weight: 11
+partition: build
 ---
 
 # What is FastEmbed?
@@ -21,7 +21,7 @@ FastEmbed easily integrates with Qdrant for a variety of multimodal search purpo
 
 - Light: Unlike other inference frameworks, such as PyTorch, FastEmbed requires very little external dependencies. Because it uses the ONNX runtime, it is perfect for serverless environments like AWS Lambda.
 - Fast: By using ONNX, FastEmbed ensures high-performance inference across various hardware platforms.
-- Accurate: FastEmbed aims for better accuracy and recall than models like OpenAI’s `Ada-002`. It always uses model which demonstrate strong results on the MTEB leaderboard.
+- Accurate: FastEmbed aims for better accuracy and recall than models like OpenAI's `Ada-002`. It always uses model which demonstrate strong results on the MTEB leaderboard.
 - Support: FastEmbed supports a wide range of models, including multilingual ones, to meet diverse use case needs.
 
 

qdrant-landing/content/documentation/multimodal-search.md

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 title: Multilingual & Multimodal RAG with LlamaIndex
-weight: 14
+weight: 5
 partition: build
 social_preview_image: /documentation/examples/multimodal-search/social_preview.png
 aliases:

qdrant-landing/content/documentation/qdrant-mcp-server.md

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+---
+title: "Qdrant MCP Server"
+weight: 12
+type: external-link
+external_url: https://github.com/qdrant/mcp-server-qdrant
+partition: build
+---

qdrant-landing/content/documentation/rag-deepseek.md

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 title: 5 Minute RAG with Qdrant and DeepSeek
-weight: 15
+weight: 6
 partition: build
 social_preview_image: /documentation/examples/rag-deepseek/social_preview.png
 ---