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Standardize and refactor tutorials titles and metadata for improved clarity, organization, and SEO
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kanungle
@@ -3,7 +3,7 @@ title: Essential Examples
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weight: 21
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partition: build
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---
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# Essential Examples
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# Integration Examples
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*Step-by-step guides for connecting Qdrant to the broader AI ecosystem and data stacks.*
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@@ -1,5 +1,5 @@
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---
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title: Agentic RAG Discord Bot with CAMEL-AI
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title: Discord RAG Bot
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weight: 4
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#partition: build
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social_preview_image: /documentation/examples/agentic-rag-camelai-discord/social-preview.png
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@@ -7,9 +7,9 @@ aliases:
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- /documentation/agentic-rag-camelai-discord/
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---
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<!--  -->
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# Agentic RAG Discord ChatBot with Qdrant, CAMEL-AI, & OpenAI
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# Qdrant Agentic RAG Discord Bot with CAMEL-AI and OpenAI
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| Time: 45 min | Level: Intermediate | [](https://colab.research.google.com/drive/1Ymqzm6ySoyVOekY7fteQBCFCXYiYyHxw#scrollTo=QQZXwzqmNfaS) |
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| --- | ----------- | ----------- |----------- |
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---
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title: Simple Agentic RAG System
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title: Agentic RAG with CrewAI
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weight: 2
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partition: build
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social_preview_image: /documentation/examples/agentic-rag-crewai-zoom/social_preview.png
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aliases:
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- /documentation/agentic-rag-crewai-zoom/
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---
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<!--  -->
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# Agentic RAG With CrewAI & Qdrant Vector Database
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# Qdrant Agentic RAG System with CrewAI
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| Time: 45 min | Level: Beginner | Output: [GitHub](https://github.com/qdrant/examples/tree/master/agentic_rag_zoom_crewai) |
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| --- | ----------- | ----------- |----------- |
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@@ -86,7 +86,7 @@ The system is built on three main components:
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## Getting Started
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<!--  -->
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1. **Get API Credentials for Qdrant**:
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- Sign up for an account at [Qdrant Cloud](https://cloud.qdrant.io/signup).
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@@ -352,7 +352,7 @@ This combination of features creates an interface that's both powerful and appro
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---
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## Conclusion
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<!--  -->
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This tutorial has demonstrated how to build a sophisticated meeting analysis system that combines vector search with AI agents. Let's recap the key components we've covered:
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@@ -384,5 +384,3 @@ This foundation can be extended in many ways, such as:
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- Integrating with other data sources
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The code is available in the [repository](https://github.com/qdrant/examples/tree/master/agentic_rag_zoom_crewai), and we encourage you to experiment with your own modifications and improvements.
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---
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@@ -1,12 +1,15 @@
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---
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title: Agentic RAG With LangGraph
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title: Agentic RAG with LangGraph
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weight: 3
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partition: build
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hideInSidebar: true
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aliases:
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- /documentation/agentic-rag-langgraph/
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---
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# Agentic RAG With LangGraph and Qdrant
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# Agentic RAG with LangGraph and Qdrant
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| Time: 45 min | Level: Intermediate |
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| --- | ----------- |
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Traditional Retrieval-Augmented Generation (RAG) systems follow a straightforward path: query → retrieve → generate. Sure, this works well for many scenarios. But let’s face it—this linear approach often struggles when you're dealing with complex queries that demand multiple steps or pulling together diverse types of information.
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---
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title: Data Ingestion for Beginners
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title: S3 Ingestion with LangChain
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weight: 2
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partition: build
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hideInSidebar: true
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@@ -7,9 +7,9 @@ social_preview_image: /documentation/examples/data-ingestion-beginners/social_pr
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aliases:
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- /documentation/data-ingestion-beginners/
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---
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<!--  -->
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# Send S3 Data to Qdrant Vector Store with LangChain
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# S3 Ingestion with LangChain and Qdrant
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| Time: 30 min | Level: Beginner | | |
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| --- | ----------- | ----------- |----------- |
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---
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title: Multilingual & Multimodal RAG with LlamaIndex
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title: Multimodal and Multilingual RAG
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weight: 5
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hideInSidebar: true
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partition: build
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@@ -10,9 +10,9 @@ aliases:
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- /documentation/multimodal-search/
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---
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# Multilingual & Multimodal Search with LlamaIndex
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# Multimodal and Multilingual RAG with LlamaIndex and Qdrant
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<!--  -->
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| Time: 15 min | Level: Beginner |Output: [GitHub](https://github.com/qdrant/examples/blob/master/multimodal-search/Multimodal_Search_with_LlamaIndex.ipynb)|[](https://githubtocolab.com/qdrant/examples/blob/master/multimodal-search/Multimodal_Search_with_LlamaIndex.ipynb) |
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| --- | ----------- | ----------- | ----------- |
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@@ -1,5 +1,5 @@
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---
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title: Automating Processes with Qdrant and n8n
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title: n8n Workflow Automation
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weight: 7
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#partition: build
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social_preview_image: /documentation/examples/qdrant-n8n-2/preview/social_preview.png
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@@ -9,9 +9,9 @@ aliases:
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---
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<!--  -->
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# Automating Processes with Qdrant and n8n beyond simple RAG
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# Automate Qdrant Workflows with n8n
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| Time: 45 min | Level: Intermediate |
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| --- | ----------- |
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@@ -1,5 +1,5 @@
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---
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title: 5 Minute RAG with Qdrant and DeepSeek
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title: 5-Minute RAG with DeepSeek
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weight: 6
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partition: build
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social_preview_image: /documentation/examples/rag-deepseek/social_preview.png
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@@ -7,9 +7,9 @@ aliases:
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- /documentation/rag-deepseek/
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---
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<!--  -->
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# 5 Minute RAG with Qdrant and DeepSeek
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# RAG in 5 Minutes with DeepSeek and Qdrant
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| Time: 5 min | Level: Beginner | Output: [GitHub](https://github.com/qdrant/examples/blob/master/rag-with-qdrant-deepseek/deepseek-qdrant.ipynb) |
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| --- | ----------- | ----------- |----------- |
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