Classify a query into one of four categories: Factual, Analytical, Opinion, or Contextual.

Returns: str: Query category

Retrieval strategy for factual queries focusing on precision.

Retrieval strategy for analytical queries focusing on comprehensive coverage.

Retrieval strategy for opinion queries focusing on diverse perspectives.

Retrieval strategy for contextual queries integrating user context.

Factual Strategy

Retrieve precise facts and figures.

Analytical Strategy

Provide comprehensive coverage of a topics and exploring different aspects.

Opinion Strategy

Gather diverse viewpoints on a subjective issue.

Contextual Strategy

Incorporate user-specific context to fine-tune the retrieval.

Perform adaptive retrieval

Find document considering both query and context.

User query classification

Classify the query into one of four categories: Factual, Analytical, Opinion, or Contextual.

Adaptive RAG Workflow

This 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.

How it Works

1. Input Trigger:

   • The workflow can be initiated via the built-in Chat interface or triggered by another n8n workflow.
   • It expects inputs: user_query, chat_memory_key (for conversation history), and vector_store_id (specifying the Qdrant collection).
   • A Set node (Combined Fields) standardizes these inputs.

2. Query Classification:

   • A Google Gemini agent (Query Classification) analyzes the user_query.
   • It classifies the query into one of four categories:
     • Factual: Seeking specific, verifiable information.
     • Analytical: Requiring comprehensive analysis or explanation.
     • Opinion: Asking about subjective matters or seeking diverse viewpoints.
     • Contextual: Depending on user-specific or implied context.

3. Adaptive Strategy Routing:

   • A Switch node routes the workflow based on the classification result from the previous step.

4. Strategy Implementation (Query Adaptation):

   • Depending on the route, a specific Google Gemini agent adapts the query or approach:
     • Factual Strategy: Rewrites the query for better precision, focusing on key entities (Factual Strategy - Focus on Precision).
     • Analytical Strategy: Breaks down the main query into multiple sub-questions to ensure comprehensive coverage (Analytical Strategy - Comprehensive Coverage).
     • Opinion Strategy: Identifies different potential perspectives or angles related to the query (Opinion Strategy - Diverse Perspectives).
     • Contextual Strategy: Infers implied context needed to answer the query effectively (Contextual Strategy - User Context Integration).
   • Each strategy path uses its own chat memory buffer for the adaptation step.

5. Retrieval Prompt & Output Setup:

   • 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:
     • The output from the strategy step (e.g., rewritten query, sub-questions, perspectives).
     • 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).

6. Document Retrieval (RAG):

   • 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).
   • It retrieves the top relevant document chunks using Google Gemini embeddings.

7. Context Preparation:

   • The content from the retrieved document chunks is concatenated (Concatenate Context) to form a single context block for the final answer generation.

8. Answer Generation:

   • The final Answer agent (powered by Google Gemini) generates the response.
   • It uses:
     • The tailored system prompt set in step 5.
     • The concatenated context from retrieved documents (step 7).
     • The original user_query.
     • The shared chat history (Chat Buffer Memory using chat_memory_key).

9. Response:

   • The generated answer is sent back to the user via the Respond to Webhook node.

⚠️ If using in Chat mode

Update the vector_store_id variable to the corresponding Qdrant ID needed to perform the documents retrieval.

Adaptive RAG

Purpose and structure

This folder contains the Adaptive RAG workflow. Its executable nodes use agent, chatTrigger, embeddingsGoogleGemini, executeWorkflowTrigger, lmChatGoogleGemini, memoryBufferWindow, respondToWebhook, set, summarize, switch, vectorStoreQdrant. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.

Setup

Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.

Entry points

Chat, When Executed by Another Workflow

Verification

Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.

Attribution

https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Summarize/1829_Summarize_Respondtowebhook_Automation_Webhook.json MIT; preserve source notices.