⚡n8n Workflow
Expert

Adaptive RAG

Implement an advanced Adaptive RAG workflow in n8n. Dynamically classify user queries to tailor retrieval and generation strategies using Google Gemini and Qdrant for more accurate AI responses.

Overview

Builds an intelligent AI chatbot that classifies user queries to dynamically adapt its information retrieval strategy from a Qdrant vector store, powered by Google Gemini.

🧠 Description & Use Case

This workflow implements an Adaptive Retrieval-Augmented Generation (RAG) system. Unlike a standard RAG pipeline that uses the same strategy for all queries, this workflow first classifies the user's intent and then dynamically adapts its retrieval and generation process. By understanding whether a query is Factual, Analytical, Opinion-based, or Contextual, it can retrieve more relevant information from a Qdrant vector store and generate a more nuanced and accurate answer using Google Gemini.

🔄 How It Works:

  1. Trigger and Input: The workflow starts when a user sends a message via the Chat interface or when called by another workflow. It receives the user_query, a chat_memory_key for session history, and a vector_store_id for the Qdrant collection.

  2. Query Classification: The Query Classification agent, powered by Google Gemini, analyzes the user's query and categorizes it into one of four types:

    • Factual: Seeking specific, verifiable facts.
    • Analytical: Requiring in-depth explanation or analysis.
    • Opinion: Asking for subjective viewpoints.
    • Contextual: Depending on user-specific or implied context.

  3. Strategy Routing: A Switch node directs the workflow to a specific path based on the query classification.

  4. Query Adaptation: Each path uses a specialized Gemini agent to enhance the query for optimal retrieval:

    • Factual Strategy: Rewrites the query for precision, focusing on key entities.
    • Analytical Strategy: Breaks the query into multiple sub-questions for comprehensive coverage.
    • Opinion Strategy: Identifies different perspectives to search for diverse results.
    • Contextual Strategy: Infers implied context to enrich the search.

  5. Tailored Document Retrieval: The adapted query is used to search the specified Qdrant vector store. The system retrieves the most relevant document chunks based on semantic similarity using Google Gemini embeddings.

  6. Context-Aware Generation: The retrieved documents are concatenated into a single context. A final Answer agent generates the response by combining:

    • The original user query.
    • The rich context from the Qdrant store.
    • The conversation history.
    • A custom system prompt tailored to the initial query type (e.g., "be precise" for Factual, "present diverse views" for Opinion).

  7. Response: The final, contextually-aware answer is sent back to the user.

✅ Real-World Use Cases:

  • Advanced Q&A Systems: For corporate knowledge bases where employees ask varied questions, from specific policy details (Factual) to strategic analysis (Analytical).
  • Intelligent Customer Support: A chatbot that can differentiate between a customer asking "What is the return policy?" (Factual) and "Which product is better for my needs?" (Opinion/Contextual).
  • Research Assistants: Help researchers and students explore topics by breaking down complex questions or sourcing multiple viewpoints on a subject.
  • Personalized Financial Advisors: A bot that provides factual stock data, analyzes market trends, and discusses different investment philosophies based on the user's query type.

Instructions

Basic steps

  1. Download workflow JSON
  2. Import into n8n
  3. Configure node parameters as needed
  4. Test and enable the workflow

💡 Tips

After importing, verify all connectors and credentials match your environment.

Preview

Workflow Visualization

39 nodes
100%
Query Classification@n8n/langchain.agentSwitchswitchFactual Strategy - Focus on Precision@n8n/langchain.agentAnalytical Strategy - Comprehensive Coverage@n8n/langchain.agentOpinion Strategy - Diverse Perspectives@n8n/langchain.agentContextual Strategy - User Context Integration@n8n/langchain.agentChat@n8n/langchain.chatTriggerFactual Prompt and OutputsetContextual Prompt and OutputsetOpinion Prompt and OutputsetAnalytical Prompt and OutputsetGemini Classification@n8n/langchain.lmChatGoogleGeminiGemini Factual@n8n/langchain.lmChatGoogleGeminiGemini Analytical@n8n/langchain.lmChatGoogleGeminiChat Buffer Memory Analytical@n8n/langchain.memoryBufferWindowChat Buffer Memory Factual@n8n/langchain.memoryBufferWindowGemini Opinion@n8n/langchain.lmChatGoogleGeminiChat Buffer Memory Opinion@n8n/langchain.memoryBufferWindowGemini Contextual@n8n/langchain.lmChatGoogleGeminiChat Buffer Memory Contextual@n8n/langchain.memoryBufferWindowEmbeddings@n8n/langchain.embeddingsGoogleGeminiSticky NotestickyNoteSticky Note1stickyNoteSticky Note2stickyNoteSticky Note3stickyNoteConcatenate ContextsummarizeRetrieve Documents from Vector Store@n8n/langchain.vectorStoreQdrantSet Prompt and OutputsetGemini Answer@n8n/langchain.lmChatGoogleGeminiAnswer@n8n/langchain.agentChat Buffer Memory@n8n/langchain.memoryBufferWindowSticky Note4stickyNoteSticky Note5stickyNoteRespond to WebhookrespondToWebhookSticky Note6stickyNoteWhen Executed by Another WorkflowexecuteWorkflowTriggerCombined FieldssetSticky Note7stickyNoteSticky Note8stickyNote

Nodes

1

Query Classification

@n8n/langchain.agent

v1.8
2

Switch

switch

v3.2
3

Factual Strategy - Focus on Precision

@n8n/langchain.agent

v1.7
4

Analytical Strategy - Comprehensive Coverage

@n8n/langchain.agent

v1.7
5

Opinion Strategy - Diverse Perspectives

@n8n/langchain.agent

v1.7
6

Contextual Strategy - User Context Integration

@n8n/langchain.agent

v1.7
7

Chat

@n8n/langchain.chatTrigger

v1.1
8

Factual Prompt and Output

set

v3.4
9

Contextual Prompt and Output

set

v3.4
10

Opinion Prompt and Output

set

v3.4
11

Analytical Prompt and Output

set

v3.4
12

Gemini Classification

@n8n/langchain.lmChatGoogleGemini

v1
13

Gemini Factual

@n8n/langchain.lmChatGoogleGemini

v1
14

Gemini Analytical

@n8n/langchain.lmChatGoogleGemini

v1
15

Chat Buffer Memory Analytical

@n8n/langchain.memoryBufferWindow

v1.3
16

Chat Buffer Memory Factual

@n8n/langchain.memoryBufferWindow

v1.3
17

Gemini Opinion

@n8n/langchain.lmChatGoogleGemini

v1
18

Chat Buffer Memory Opinion

@n8n/langchain.memoryBufferWindow

v1.3
19

Gemini Contextual

@n8n/langchain.lmChatGoogleGemini

v1
20

Chat Buffer Memory Contextual

@n8n/langchain.memoryBufferWindow

v1.3
21

Embeddings

@n8n/langchain.embeddingsGoogleGemini

v1
22

Sticky Note

stickyNote

v1
23

Sticky Note1

stickyNote

v1
24

Sticky Note2

stickyNote

v1
25

Sticky Note3

stickyNote

v1
26

Concatenate Context

summarize

v1.1
27

Retrieve Documents from Vector Store

@n8n/langchain.vectorStoreQdrant

v1.1
28

Set Prompt and Output

set

v3.4
29

Gemini Answer

@n8n/langchain.lmChatGoogleGemini

v1
30

Answer

@n8n/langchain.agent

v1.8
31

Chat Buffer Memory

@n8n/langchain.memoryBufferWindow

v1.3
32

Sticky Note4

stickyNote

v1
33

Sticky Note5

stickyNote

v1
34

Respond to Webhook

respondToWebhook

v1.1
35

Sticky Note6

stickyNote

v1
36

When Executed by Another Workflow

executeWorkflowTrigger

v1.1
37

Combined Fields

set

v3.4
38

Sticky Note7

stickyNote

v1
39

Sticky Note8

stickyNote

v1

Statistics

Total nodes:39
Disabled nodes:0
Node types:12
Connections:29
View full JSON structure
{
  "nodes": [
    {
      "id": "856bd809-8f41-41af-8f72-a3828229c2a5",
      "name": "Query Classification",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "notes": "Classify a query into one of four categories: Factual, Analytical, Opinion, or Contextual.\n        \nReturns:\nstr: Query category",
      "position": [
        380,
        -20
      ],
      "parameters": {
        "text": "=Classify this query: {{ $('Combined Fields').item.json.user_query }}",
        "options": {
          "systemMessage": "You are an expert at classifying questions. \n\nClassify the given query into exactly one of these categories:\n- Factual: Queries seeking specific, verifiable information.\n- Analytical: Queries requiring comprehensive analysis or explanation.\n- Opinion: Queries about subjective matters or seeking diverse viewpoints.\n- Contextual: Queries that depend on user-specific context.\n\nReturn ONLY the category name, without any explanation or additional text."
        },
        "promptType": "define"
      },
      "typeVersion": 1.8
    },
    {
      "id": "cc2106fc-f1a8-45ef-b37b-ab981ac13466",
      "name": "Switch",
      "type": "n8n-nodes-base.switch",
      "position": [
        740,
        -40
      ],
      "parameters": {
        "rules": {
          "values": [
            {
              "outputKey": "Factual",
              "conditions": {
                "options": {
                  "version": 2,
                  "leftValue": "",
                  "caseSensitive": true,
                  "typeValidation": "strict"
                },
                "combinator": "and",
                "conditions": [
                  {
                    "id": "87f3b50c-9f32-4260-ac76-19c05b28d0b4",
                    "operator": {
                      "type": "string",
                      "operation": "equals"
                    },
                    "leftValue": "={{ $json.output.trim() }}",
                    "rightValue": "Factual"
                  }
                ]
              },
              "renameOutput": true
            },
            {
              "outputKey": "Analytical",
              "conditions": {
                "options": {
                  "version": 2,
                  "leftValue": "",
                  "caseSensitive": true,
                  "typeValidation": "strict"
                },
                "combinator": "and",
                "conditions": [
                  {
                    "id": "f8651b36-79fa-4be4-91fb-0e6d7deea18f",
                    "operator": {
                      "name": "filter.operator.equals",
                      "type": "string",
                      "operation": "equals"
                    },
                    "leftValue": "={{ $json.output.trim() }}",
                    "rightValue": "Analytical"
                  }
                ]
              },
              "renameOutput": true
            },
            {
              "outputKey": "Opinion",
              "conditions": {
                "options": {
                  "version": 2,
                  "leftValue": "",
                  "caseSensitive": true,
                  "typeValidation": "strict"
                },
                "combinator": "and",
                "conditions": [
                  {
                    "id": "5dde06bc-5fe1-4dca-b6e2-6857c5e96d49",
                    "operator": {
                      "name": "filter.operator.equals",
                      "type": "string",
                      "operation": "equals"
                    },
                    "leftValue": "={{ $json.output.trim() }}",
                    "rightValue": "Opinion"
                  }
                ]
              },
              "renameOutput": true
            },
            {
              "outputKey": "Contextual",
              "conditions": {
                "options": {
                  "version": 2,
                  "leftValue": "",
                  "caseSensitive": true,
                  "typeValidation": "strict"
                },
                "combinator": "and",
                "conditions": [
                  {
                    "id": "bf97926d-7a0b-4e2f-aac0-a820f73344d8",
                    "operator": {
                      "name": "filter.operator.equals",
                      "type": "string",
                      "operation": "equals"
                    },
                    "leftValue": "={{ $json.output.trim() }}",
                    "rightValue": "Contextual"
                  }
                ]
              },
              "renameOutput": true
            }
          ]
        },
        "options": {
          "fallbackOutput": 0
        }
      },
      "typeVersion": 3.2
    },
    {
      "id": "63889cad-1283-4dbf-ba16-2b6cf575f24a",
      "name": "Factual Strategy - Focus on Precision",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "notes": "Retrieval strategy for factual queries focusing on precision.",
      "position": [
        1140,
        -780
      ],
      "parameters": {
        "text": "=Enhance this factual query: {{ $('Combined Fields').item.json.user_query }}",
        "options": {
          "systemMessage": "=You are an expert at enhancing search queries.\n\nYour task is to reformulate the given factual query to make it more precise and specific for information retrieval. Focus on key entities and their relationships.\n\nProvide ONLY the enhanced query without any explanation."
        },
        "promptType": "define"
      },
      "typeVersion": 1.7
    },
    {
      "id": "020d2201-9590-400d-b496-48c65801271c",
      "name": "Analytical Strategy - Comprehensive Coverage",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "notes": "Retrieval strategy for analytical queries focusing on comprehensive coverage.",
      "position": [
        1140,
        -240
      ],
      "parameters": {
        "text": "=Generate sub-questions for this analytical query: {{ $('Combined Fields').item.json.user_query }}",
        "options": {
          "systemMessage": "=You are an expert at breaking down complex questions.\n\nGenerate sub-questions that explore different aspects of the main analytical query.\nThese sub-questions should cover the breadth of the topic and help retrieve comprehensive information.\n\nReturn a list of exactly 3 sub-questions, one per line."
        },
        "promptType": "define"
      },
      "typeVersion": 1.7
    },
    {
      "id": "c35d1b95-68c8-4237-932d-4744f620760d",
      "name": "Opinion Strategy - Diverse Perspectives",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "notes": "Retrieval strategy for opinion queries focusing on diverse perspectives.",
      "position": [
        1140,
        300
      ],
      "parameters": {
        "text": "=Identify different perspectives on: {{ $('Combined Fields').item.json.user_query }}",
        "options": {
          "systemMessage": "=You are an expert at identifying different perspectives on a topic.\n\nFor the given query about opinions or viewpoints, identify different perspectives that people might have on this topic.\n\nReturn a list of exactly 3 different viewpoint angles, one per line."
        },
        "promptType": "define"
      },
      "typeVersion": 1.7
    },
    {
      "id": "363a3fc3-112f-40df-891e-0a5aa3669245",
      "name": "Contextual Strategy - User Context Integration",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "notes": "Retrieval strategy for contextual queries integrating user context.",
      "position": [
        1140,
        840
      ],
      "parameters": {
        "text": "=Infer the implied context in this query: {{ $('Combined Fields').item.json.user_query }}",
        "options": {
          "systemMessage": "=You are an expert at understanding implied context in questions.\n\nFor the given query, infer what contextual information might be relevant or implied but not explicitly stated. Focus on what background would help answering this query.\n\nReturn a brief description of the implied context."
        },
        "promptType": "define"
      },
      "typeVersion": 1.7
    },
    {
      "id": "45887701-5ea5-48b4-9b2b-40a80238ab0c",
      "name": "Chat",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        -280,
        120
      ],
      "webhookId": "56f626b5-339e-48af-857f-1d4198fc8a4d",
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "7f7df364-4829-4e29-be3d-d13a63f65b8f",
      "name": "Factual Prompt and Output",
      "type": "n8n-nodes-base.set",
      "position": [
        1540,
        -780
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "a4a28ac2-4a56-46f6-8b86-f5d1a34b2ced",
              "name": "output",
              "type": "string",
              "value": "={{ $json.output }}"
            },
            {
              "id": "7aa6ce13-afbf-4871-b81c-6e9c722a53dc",
              "name": "prompt",
              "type": "string",
              "value": "You are a helpful assistant providing factual information. Answer the question based on the provided context. Focus on accuracy and precision. If the context doesn't contain the information needed, acknowledge the limitations."
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "590d8667-69eb-4db2-b5be-714c602b319a",
      "name": "Contextual Prompt and Output",
      "type": "n8n-nodes-base.set",
      "position": [
        1540,
        840
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "a4a28ac2-4a56-46f6-8b86-f5d1a34b2ced",
              "name": "output",
              "type": "string",
              "value": "={{ $json.output }}"
            },
            {
              "id": "7aa6ce13-afbf-4871-b81c-6e9c722a53dc",
              "name": "prompt",
              "type": "string",
              "value": "You are a helpful assistant providing contextually relevant information. Answer the question considering both the query and its context. Make connections between the query context and the information in the provided documents. If the context doesn't fully address the specific situation, acknowledge the limitations."
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "fa3228ee-62d8-4c02-9dca-8a1ebc6afc74",
      "name": "Opinion Prompt and Output",
      "type": "n8n-nodes-base.set",
      "position": [
        1540,
        300
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "a4a28ac2-4a56-46f6-8b86-f5d1a34b2ced",
              "name": "output",
              "type": "string",
              "value": "={{ $json.output }}"
            },
            {
              "id": "7aa6ce13-afbf-4871-b81c-6e9c722a53dc",
              "name": "prompt",
              "type": "string",
              "value": "You are a helpful assistant discussing topics with multiple viewpoints. Based on the provided context, present different perspectives on the topic. Ensure fair representation of diverse opinions without showing bias. Acknowledge where the context presents limited viewpoints."
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "c769a76a-fb26-46a1-a00d-825b689d5f7a",
      "name": "Analytical Prompt and Output",
      "type": "n8n-nodes-base.set",
      "position": [
        1540,
        -240
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "a4a28ac2-4a56-46f6-8b86-f5d1a34b2ced",
              "name": "output",
              "type": "string",
              "value": "={{ $json.output }}"
            },
            {
              "id": "7aa6ce13-afbf-4871-b81c-6e9c722a53dc",
              "name": "prompt",
              "type": "string",
              "value": "You are a helpful assistant providing analytical insights. Based on the provided context, offer a comprehensive analysis of the topic. Cover different aspects and perspectives in your explanation. If the context has gaps, acknowledge them while providing the best analysis possible."
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "fcd29f6b-17e8-442c-93f9-b93fbad7cd10",
      "name": "Gemini Classification",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        360,
        180
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-2.0-flash-lite"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "2zwuT5znDglBrUCO",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "c0828ee3-f184-41f5-9a25-0f1059b03711",
      "name": "Gemini Factual",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        1120,
        -560
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-2.0-flash"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "2zwuT5znDglBrUCO",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "98f9981d-ea8e-45cb-b91d-3c8d1fe33e25",
      "name": "Gemini Analytical",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        1120,
        -20
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-2.0-flash"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "2zwuT5znDglBrUCO",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "c85f270d-3224-4e60-9acf-91f173dfe377",
      "name": "Chat Buffer Memory Analytical",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        1280,
        -20
      ],
      "parameters": {
        "sessionKey": "={{ $('Combined Fields').item.json.chat_memory_key }}",
        "sessionIdType": "customKey",
        "contextWindowLength": 10
      },
      "typeVersion": 1.3
    },
    {
      "id": "c39ba907-7388-4152-965a-e28e626bc9b2",
      "name": "Chat Buffer Memory Factual",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        1280,
        -560
      ],
      "parameters": {
        "sessionKey": "={{ $('Combined Fields').item.json.chat_memory_key }}",
        "sessionIdType": "customKey",
        "contextWindowLength": 10
      },
      "typeVersion": 1.3
    },
    {
      "id": "52dcd9f0-e6b3-4d33-bc6f-621ef880178e",
      "name": "Gemini Opinion",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        1120,
        520
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-2.0-flash"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "2zwuT5znDglBrUCO",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "147a709a-4b46-4835-82cf-7d6b633acd4c",
      "name": "Chat Buffer Memory Opinion",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        1280,
        520
      ],
      "parameters": {
        "sessionKey": "={{ $('Combined Fields').item.json.chat_memory_key }}",
        "sessionIdType": "customKey",
        "contextWindowLength": 10
      },
      "typeVersion": 1.3
    },
    {
      "id": "3cb6bf32-5937-49b9-acf7-d7d01dc2ddd1",
      "name": "Gemini Contextual",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        1120,
        1060
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-2.0-flash"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "2zwuT5znDglBrUCO",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "5916c4f1-4369-4d66-8553-2fff006b7e69",
      "name": "Chat Buffer Memory Contextual",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        1280,
        1060
      ],
      "parameters": {
        "sessionKey": "={{ $('Combined Fields').item.json.chat_memory_key }}",
        "sessionIdType": "customKey",
        "contextWindowLength": 10
      },
      "typeVersion": 1.3
    },
    {
      "id": "d33377c2-6b98-4e4d-968f-f3085354ae50",
      "name": "Embeddings",
      "type": "@n8n/n8n-nodes-langchain.embeddingsGoogleGemini",
      "position": [
        2060,
        200
      ],
      "parameters": {
        "modelName": "models/text-embedding-004"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "2zwuT5znDglBrUCO",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "32d9a0c0-0889-4cb2-a088-8ee9cfecacd3",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1040,
        -900
      ],
      "parameters": {
        "color": 7,
        "width": 700,
        "height": 520,
        "content": "## Factual Strategy\n**Retrieve precise facts and figures.**"
      },
      "typeVersion": 1
    },
    {
      "id": "064a4729-717c-40c8-824a-508406610a13",
      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1040,
        -360
      ],
      "parameters": {
        "color": 7,
        "width": 700,
        "height": 520,
        "content": "## Analytical Strategy\n**Provide comprehensive coverage of a topics and exploring different aspects.**"
      },
      "typeVersion": 1
    },
    {
      "id": "9fd52a28-44bc-4dfd-bdb7-90987cc2f4fb",
      "name": "Sticky Note2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1040,
        180
      ],
      "parameters": {
        "color": 7,
        "width": 700,
        "height": 520,
        "content": "## Opinion Strategy\n**Gather diverse viewpoints on a subjective issue.**"
      },
      "typeVersion": 1
    },
    {
      "id": "3797b21f-cc2a-4210-aa63-6d181d413c5e",
      "name": "Sticky Note3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1040,
        720
      ],
      "parameters": {
        "color": 7,
        "width": 700,
        "height": 520,
        "content": "## Contextual Strategy\n**Incorporate user-specific context to fine-tune the retrieval.**"
      },
      "typeVersion": 1
    },
    {
      "id": "16fa1531-9fb9-4b12-961c-be12e20b2134",
      "name": "Concatenate Context",
      "type": "n8n-nodes-base.summarize",
      "position": [
        2440,
        -20
      ],
      "parameters": {
        "options": {},
        "fieldsToSummarize": {
          "values": [
            {
              "field": "document.pageContent",
              "separateBy": "other",
              "aggregation": "concatenate",
              "customSeparator": "={{ \"\\n\\n---\\n\\n\" }}"
            }
          ]
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "4d6147d1-7a3d-42ab-b23f-cdafe8ea30b0",
      "name": "Retrieve Documents from Vector Store",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
      "position": [
        2080,
        -20
      ],
      "parameters": {
        "mode": "load",
        "topK": 10,
        "prompt": "={{ $json.prompt }}\n\nUser query: \n{{ $json.output }}",
        "options": {},
        "qdrantCollection": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $('Combined Fields').item.json.vector_store_id }}"
        }
      },
      "credentials": {
        "qdrantApi": {
          "id": "mb8rw8tmUeP6aPJm",
          "name": "QdrantApi account"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "7e68f9cb-0a0d-4215-8083-3b9ef92cd237",
      "name": "Set Prompt and Output",
      "type": "n8n-nodes-base.set",
      "position": [
        1880,
        -20
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "1d782243-0571-4845-b8fe-4c6c4b55379e",
              "name": "output",
              "type": "string",
              "value": "={{ $json.output }}"
            },
            {
              "id": "547091fb-367c-44d4-ac39-24d073da70e0",
              "name": "prompt",
              "type": "string",
              "value": "={{ $json.prompt }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "0c623ca1-da85-48a3-9d8b-90d97283a015",
      "name": "Gemini Answer",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        2720,
        200
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-2.0-flash"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "2zwuT5znDglBrUCO",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "fab91e48-1c62-46a8-b9fc-39704f225274",
      "name": "Answer",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        2760,
        -20
      ],
      "parameters": {
        "text": "=User query: {{ $('Combined Fields').item.json.user_query }}",
        "options": {
          "systemMessage": "={{ $('Set Prompt and Output').item.json.prompt }}\n\nUse the following context (delimited by <ctx></ctx>) and the chat history to answer the user query.\n<ctx>\n{{ $json.concatenated_document_pageContent }}\n</ctx>"
        },
        "promptType": "define"
      },
      "typeVersion": 1.8
    },
    {
      "id": "d69f8d62-3064-40a8-b490-22772fbc38cd",
      "name": "Chat Buffer Memory",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        2900,
        200
      ],
      "parameters": {
        "sessionKey": "={{ $('Combined Fields').item.json.chat_memory_key }}",
        "sessionIdType": "customKey",
        "contextWindowLength": 10
      },
      "typeVersion": 1.3
    },
    {
      "id": "a399f8e6-fafd-4f73-a2de-894f1e3c4bec",
      "name": "Sticky Note4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1800,
        -220
      ],
      "parameters": {
        "color": 7,
        "width": 820,
        "height": 580,
        "content": "## Perform adaptive retrieval\n**Find document considering both query and context.**"
      },
      "typeVersion": 1
    },
    {
      "id": "7f10fe70-1af8-47ad-a9b5-2850412c43f8",
      "name": "Sticky Note5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2640,
        -220
      ],
      "parameters": {
        "color": 7,
        "width": 740,
        "height": 580,
        "content": "## Reply to the user integrating retrieval context"
      },
      "typeVersion": 1
    },
    {
      "id": "5cd0dd02-65f4-4351-aeae-c70ecf5f1d66",
      "name": "Respond to Webhook",
      "type": "n8n-nodes-base.respondToWebhook",
      "position": [
        3120,
        -20
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "4c56ef8f-8fce-4525-bb87-15df37e91cc4",
      "name": "Sticky Note6",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        280,
        -220
      ],
      "parameters": {
        "color": 7,
        "width": 700,
        "height": 580,
        "content": "## User query classification\n**Classify the query into one of four categories: Factual, Analytical, Opinion, or Contextual.**"
      },
      "typeVersion": 1
    },
    {
      "id": "3ef73405-89de-4bed-9673-90e2c1f2e74b",
      "name": "When Executed by Another Workflow",
      "type": "n8n-nodes-base.executeWorkflowTrigger",
      "position": [
        -280,
        -140
      ],
      "parameters": {
        "workflowInputs": {
          "values": [
            {
              "name": "user_query"
            },
            {
              "name": "chat_memory_key"
            },
            {
              "name": "vector_store_id"
            }
          ]
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "0785714f-c45c-4eda-9937-c97e44c9a449",
      "name": "Combined Fields",
      "type": "n8n-nodes-base.set",
      "position": [
        40,
        -20
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "90ab73a2-fe01-451a-b9df-bffe950b1599",
              "name": "user_query",
              "type": "string",
              "value": "={{ $json.user_query || $json.chatInput }}"
            },
            {
              "id": "36686ff5-09fc-40a4-8335-a5dd1576e941",
              "name": "chat_memory_key",
              "type": "string",
              "value": "={{ $json.chat_memory_key || $('Chat').item.json.sessionId }}"
            },
            {
              "id": "4230c8f3-644c-4985-b710-a4099ccee77c",
              "name": "vector_store_id",
              "type": "string",
              "value": "={{ $json.vector_store_id || \"<ID HERE>\" }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "57a93b72-4233-4ba2-b8c7-99d88f0ed572",
      "name": "Sticky Note7",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -300,
        400
      ],
      "parameters": {
        "width": 1280,
        "height": 1300,
        "content": "# Adaptive RAG Workflow\n\nThis n8n workflow implements a version of the Adaptive Retrieval-Augmented Generation (RAG) approach. It classifies user queries and applies different retrieval and generation strategies based on the query type (Factual, Analytical, Opinion, or Contextual) to provide more relevant and tailored answers from a knowledge base stored in a Qdrant vector store.\n\n## How it Works\n\n1.  **Input Trigger:**\n    * The workflow can be initiated via the built-in Chat interface or triggered by another n8n workflow.\n    * It expects inputs: `user_query`, `chat_memory_key` (for conversation history), and `vector_store_id` (specifying the Qdrant collection).\n    * A `Set` node (`Combined Fields`) standardizes these inputs.\n\n2.  **Query Classification:**\n    * A Google Gemini agent (`Query Classification`) analyzes the `user_query`.\n    * It classifies the query into one of four categories:\n        * **Factual:** Seeking specific, verifiable information.\n        * **Analytical:** Requiring comprehensive analysis or explanation.\n        * **Opinion:** Asking about subjective matters or seeking diverse viewpoints.\n        * **Contextual:** Depending on user-specific or implied context.\n\n3.  **Adaptive Strategy Routing:**\n    * A `Switch` node routes the workflow based on the classification result from the previous step.\n\n4.  **Strategy Implementation (Query Adaptation):**\n    * Depending on the route, a specific Google Gemini agent adapts the query or approach:\n        * **Factual Strategy:** Rewrites the query for better precision, focusing on key entities (`Factual Strategy - Focus on Precision`).\n        * **Analytical Strategy:** Breaks down the main query into multiple sub-questions to ensure comprehensive coverage (`Analytical Strategy - Comprehensive Coverage`).\n        * **Opinion Strategy:** Identifies different potential perspectives or angles related to the query (`Opinion Strategy - Diverse Perspectives`).\n        * **Contextual Strategy:** Infers implied context needed to answer the query effectively (`Contextual Strategy - User Context Integration`).\n    * Each strategy path uses its own chat memory buffer for the adaptation step.\n\n5.  **Retrieval Prompt & Output Setup:**\n    * Based on the *original* query classification, a `Set` node (`Factual/Analytical/Opinion/Contextual Prompt and Output`, combined via connections to `Set Prompt and Output`) prepares:\n        * The output from the strategy step (e.g., rewritten query, sub-questions, perspectives).\n        * A tailored system prompt for the final answer generation agent, instructing it how to behave based on the query type (e.g., focus on precision for Factual, present diverse views for Opinion).\n\n6.  **Document Retrieval (RAG):**\n    * The `Retrieve Documents from Vector Store` node uses the adapted query/output from the strategy step to search the specified Qdrant collection (`vector_store_id`).\n    * It retrieves the top relevant document chunks using Google Gemini embeddings.\n\n7.  **Context Preparation:**\n    * The content from the retrieved document chunks is concatenated (`Concatenate Context`) to form a single context block for the final answer generation.\n\n8.  **Answer Generation:**\n    * The final `Answer` agent (powered by Google Gemini) generates the response.\n    * It uses:\n        * The tailored system prompt set in step 5.\n        * The concatenated context from retrieved documents (step 7).\n        * The original `user_query`.\n        * The shared chat history (`Chat Buffer Memory` using `chat_memory_key`).\n\n9.  **Response:**\n    * The generated answer is sent back to the user via the `Respond to Webhook` node."
      },
      "typeVersion": 1
    },
    {
      "id": "bec8070f-2ce9-4930-b71e-685a2b21d3f2",
      "name": "Sticky Note8",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -60,
        -220
      ],
      "parameters": {
        "color": 7,
        "width": 320,
        "height": 580,
        "content": "## ⚠️  If using in Chat mode\n\nUpdate the `vector_store_id` variable to the corresponding Qdrant ID needed to perform the documents retrieval."
      },
      "typeVersion": 1
    }
  ],
  "connections": {
    "Chat": {
      "main": [
        [
          {
            "node": "Combined Fields",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Answer": {
      "main": [
        [
          {
            "node": "Respond to Webhook",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Switch": {
      "main": [
        [
          {
            "node": "Factual Strategy - Focus on Precision",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Analytical Strategy - Comprehensive Coverage",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Opinion Strategy - Diverse Perspectives",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Contextual Strategy - User Context Integration",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings": {
      "ai_embedding": [
        [
          {
            "node": "Retrieve Documents from Vector Store",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "Gemini Answer": {
      "ai_languageModel": [
        [
          {
            "node": "Answer",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Gemini Factual": {
      "ai_languageModel": [
        [
          {
            "node": "Factual Strategy - Focus on Precision",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Gemini Opinion": {
      "ai_languageModel": [
        [
          {
            "node": "Opinion Strategy - Diverse Perspectives",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Combined Fields": {
      "main": [
        [
          {
            "node": "Query Classification",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Gemini Analytical": {
      "ai_languageModel": [
        [
          {
            "node": "Analytical Strategy - Comprehensive Coverage",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Gemini Contextual": {
      "ai_languageModel": [
        [
          {
            "node": "Contextual Strategy - User Context Integration",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Chat Buffer Memory": {
      "ai_memory": [
        [
          {
            "node": "Answer",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "Concatenate Context": {
      "main": [
        [
          {
            "node": "Answer",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Query Classification": {
      "main": [
        [
          {
            "node": "Switch",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Gemini Classification": {
      "ai_languageModel": [
        [
          {
            "node": "Query Classification",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Set Prompt and Output": {
      "main": [
        [
          {
            "node": "Retrieve Documents from Vector Store",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Factual Prompt and Output": {
      "main": [
        [
          {
            "node": "Set Prompt and Output",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Opinion Prompt and Output": {
      "main": [
        [
          {
            "node": "Set Prompt and Output",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Chat Buffer Memory Factual": {
      "ai_memory": [
        [
          {
            "node": "Factual Strategy - Focus on Precision",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "Chat Buffer Memory Opinion": {
      "ai_memory": [
        [
          {
            "node": "Opinion Strategy - Diverse Perspectives",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "Analytical Prompt and Output": {
      "main": [
        [
          {
            "node": "Set Prompt and Output",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Contextual Prompt and Output": {
      "main": [
        [
          {
            "node": "Set Prompt and Output",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Chat Buffer Memory Analytical": {
      "ai_memory": [
        [
          {
            "node": "Analytical Strategy - Comprehensive Coverage",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "Chat Buffer Memory Contextual": {
      "ai_memory": [
        [
          {
            "node": "Contextual Strategy - User Context Integration",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "When Executed by Another Workflow": {
      "main": [
        [
          {
            "node": "Combined Fields",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Retrieve Documents from Vector Store": {
      "main": [
        [
          {
            "node": "Concatenate Context",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Factual Strategy - Focus on Precision": {
      "main": [
        [
          {
            "node": "Factual Prompt and Output",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Opinion Strategy - Diverse Perspectives": {
      "main": [
        [
          {
            "node": "Opinion Prompt and Output",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Analytical Strategy - Comprehensive Coverage": {
      "main": [
        [
          {
            "node": "Analytical Prompt and Output",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Contextual Strategy - User Context Integration": {
      "main": [
        [
          {
            "node": "Contextual Prompt and Output",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}

Actions

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Categories:
Data Workflow
Tags:
setswitchstickynoteexecuteworkflowtriggerrespondtowebhooksummarize

Technical Specs

Complexity:
Expert
Node Count:
39 nodes
Published:11 months ago
Updated:10 days ago

Compatibility

Platform:
N8N Cloud & Self-hosted
Min Version:
N8N v1.0.0+
Supports cloud and self-hosted deployment

License

This workflow template follows MIT license, you can freely use, modify and distribute.

Please comply with relevant third-party service terms when using.

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