Create adaptive RAG chat agent with Google Gemini and Qdrant

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

Unlock adaptive, context aware AI chat in your automations—no coding required! This template is a plug and play n8n workflow that transforms how your chatbots, support agents, and knowledge systems...

Best for

Support Chatbot automation workflows
AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.agent, n8n-nodes-base.switch, @n8n/n8n-nodes-langchain.chattrigger, n8n-nodes-base.set, @n8n/n8n-nodes-langchain.lmchatgooglegemini, @n8n/n8n-nodes-langchain.memorybufferwindow, @n8n/n8n-nodes-langchain.embeddingsgooglegemini, n8n-nodes-base.stickynote

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Brandon Crenshaw.

Original n8n.io source

1.1 Workflow description

Title: Create adaptive RAG chat agent with Google Gemini and Qdrant
Workflow name: Create adaptive RAG chat agent with Google Gemini and Qdrant

Unlock adaptive, context-aware AI chat in your automations—no coding required!

This template is a plug-and-play n8n workflow that transforms how your chatbots, support agents, and knowledge systems respond to users. Powered by Google Gemini and a Qdrant vector database, it automatically classifies every incoming query and applies a tailor-made strategy for Factual, Analytical, Opinion, or Contextual requests—delivering the right answer, every time.

🛠️ Key Features Automatic Query Classification: Seamlessly detects whether the user wants facts, a deep analysis, opinions, or context—then routes each input to the best answering strategy.

Four Dynamic Retrieval Modes:

Factual: Delivers precise, accurate information
Analytical: Breaks down complex topics for deep dives
Opinion: Surfaces diverse viewpoints and perspectives
Contextual: Connects the dots using implied or user-specific context

End-to-End RAG Pipeline: Uses Gemini to classify and answer, while Qdrant powers fast, smart knowledge retrieval.

No-Code Visual Editing: Import into n8n, connect your LLM and vector database credentials, and you’re live—customize, extend, and scale with zero backend code.

Reusable in Any Project: Perfect for customer support, research, onboarding bots, internal knowledgebases, or any adaptive AI chat interface.

🚀 How it Works

User submits a query (via chat or API)
Query is auto-classified as Factual, Analytical, Opinion, or Contextual
Adaptive retrieval strategy is triggered (each with its own prompt logic and memory buffer)
Smart knowledge search is performed using Gemini and Qdrant
Response is generated and sent back to the user—tailored to the query type!

🧩 What’s Included

Full n8n workflow (.json)
Step-by-step setup instructions
Sample prompts and system messages for each strategy
Lifetime updates (as the workflow evolves)

💡 Use Cases

Chatbots that adapt to every user’s intent
Internal/external FAQ and helpdesk automations
AI research and summarization agents
Product support and onboarding flows

Any scenario where smarter, more relevant answers = better user experience

Ready to build smarter automations? Import this template, connect your Gemini & Qdrant accounts, and let your AI agent adapt to every conversation.

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Query Classification

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.8

Block 2 - Switch

Type / Role: n8n-nodes-base.switch - switch
Config choices: Version 3.2

Block 3 - Factual Strategy - Focus on Precision

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.7

Block 4 - Analytical Strategy - Comprehensive Coverage

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.7

Block 5 - Opinion Strategy - Diverse Perspectives

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.7

Block 6 - Contextual Strategy - User Context Integration

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.7

Block 7 - Chat

Type / Role: @n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices: Version 1.1

Block 8 - Factual Prompt and Output

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 9 - Contextual Prompt and Output

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 10 - Opinion Prompt and Output

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 11 - Analytical Prompt and Output

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 12 - Gemini Classification

Type / Role: @n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices: Version 1

Block 13 - Gemini Factual

Type / Role: @n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices: Version 1

Block 14 - Gemini Analytical

Type / Role: @n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices: Version 1

Block 15 - Chat Buffer Memory Analytical

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.3

Block 16 - Chat Buffer Memory Factual

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.3

Block 17 - Gemini Opinion

Type / Role: @n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices: Version 1

Block 18 - Chat Buffer Memory Opinion

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.3

Block 19 - Gemini Contextual

Type / Role: @n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices: Version 1

Block 20 - Chat Buffer Memory Contextual

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.3

Block 21 - Embeddings

Type / Role: @n8n/n8n-nodes-langchain.embeddingsGoogleGemini - embeddingsGoogleGemini
Config choices: Version 1

Block 22 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 23 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 24 - Sticky Note2

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Showing the first 24 of 40 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Create adaptive RAG chat agent with Google Gemini and Qdrant
Complexity	advanced
Nodes	40
Categories	Support Chatbot, AI RAG
Author	Brandon Crenshaw
Published	22 Jun 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/5111/5111.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Support Chatbot workflows AI RAG workflows

Frequently asked questions

What does Create adaptive RAG chat agent with Google Gemini and Qdrant do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Support Chatbot, AI RAG use case.

Brandon Crenshaw

1 workflow

Nodes

@n8n/n8n-nodes-langchain.agent n8n-nodes-base.switch @n8n/n8n-nodes-langchain.chattrigger n8n-nodes-base.set @n8n/n8n-nodes-langchain.lmchatgooglegemini @n8n/n8n-nodes-langchain.memorybufferwindow @n8n/n8n-nodes-langchain.embeddingsgooglegemini n8n-nodes-base.stickynote

Complexity

advanced

Published 22 Jun 2025

Likes 0

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Install path: /data/workflows/5111/5111.json

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Create adaptive RAG chat agent with Google Gemini and Qdrant

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Query Classification

Block 2 - Switch

Block 3 - Factual Strategy - Focus on Precision

Block 4 - Analytical Strategy - Comprehensive Coverage

Block 5 - Opinion Strategy - Diverse Perspectives

Block 6 - Contextual Strategy - User Context Integration

Block 7 - Chat

Block 8 - Factual Prompt and Output

Block 9 - Contextual Prompt and Output

Block 10 - Opinion Prompt and Output

Block 11 - Analytical Prompt and Output

Block 12 - Gemini Classification

Block 13 - Gemini Factual

Block 14 - Gemini Analytical

Block 15 - Chat Buffer Memory Analytical

Block 16 - Chat Buffer Memory Factual

Block 17 - Gemini Opinion

Block 18 - Chat Buffer Memory Opinion

Block 19 - Gemini Contextual

Block 20 - Chat Buffer Memory Contextual

Block 21 - Embeddings

Block 22 - Sticky Note

Block 23 - Sticky Note1

Block 24 - Sticky Note2

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions