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Adaptive RAG with Google Gemini & Qdrant: Context-Aware Query Answering

by Nisaadapted from n8n official workflow galleryUpdated Aug 2026
RequiresAAI AgentEmbeddings Google GeminiEmbeddings Google GeminiGoogle Gemini Chat ModelQdrant Vector StoreQdrant Vector StoreSSimple Memory
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AgQuery ClassificationQuery Classific…SwSwitchAgFactual Strategy - Focus on PrecisionFactual Strateg…AgAnalytical Strategy - Comprehensive CoverageAnalytical Stra…AgOpinion Strategy - Diverse PerspectivesOpinion Strateg…AgContextual Strategy - User Context IntegrationContextual Stra…ChChatSeFactual Prompt and OutputFactual Prompt …SeContextual Prompt and OutputContextual Prom…SeOpinion Prompt and OutputOpinion Prompt …SeAnalytical Prompt and OutputAnalytical Prom…Gemini ClassificationGemini Classifi…Gemini FactualGemini AnalyticalGemini Analytic…MBChat Buffer Memory AnalyticalChat Buffer Mem…MBChat Buffer Memory FactualChat Buffer Mem…Gemini OpinionMBChat Buffer Memory OpinionChat Buffer Mem…Gemini ContextualGemini Contextu…MBChat Buffer Memory ContextualChat Buffer Mem…EGEmbeddingsSuConcatenate ContextConcatenate Con…VSRetrieve Documents from Vector StoreRetrieve Docume…SeSet Prompt and OutputSet Prompt and …Gemini AnswerAgAnswerMBChat Buffer MemoryChat Buffer Mem…RTRespond to WebhookRespond to Webh…EWWhen Executed by Another WorkflowWhen Executed b…SeCombined Fields1234567891011121314151617181920212223242526272829303132
1/5
STEPS · 32
Starts from a chat message

Description This workflow automatically classifies user queries and retrieves the most relevant information based on the query type. 🌟 It uses adaptive strategies like; Factual, Analytical, Opinion, and Contextual to deliver more precise and meaningful responses by leveraging n8n's flexibility. Integrated with Qdrant vector store and Google Gemini, it processes each query faster and more effectively. 🚀 How It Works? Query Reception: A user query is triggered (e.g., through a chatbot interface). 💬 Classification: The query is classified into one of four categories: Factual: Queries seeking verifiable information. Analytical: Queries that require in-depth analysis or explanation. Opinion: Queries looking for different perspectives or subjective viewpoints. Contextual: Queries specific to the user or certain contextual conditions. Adaptive Strategy Application: Based on classification, the query is restructured using the relevant strategy for better results. Response Generation**: The most relevant documents and context are used to generate a tailored response.

Tags

n8nreference-onlyagentembeddings-google-geminilm-chat-google-geminimemory-buffer-windowvector-store-qdrant
Connects
AAI AgentembeddingsgooglegeminiEmbeddings Google GeminiGoogle Gemini Chat ModelqdrantvectorstoreQdrant Vector StoreSSimple Memory
CategoryAI Automation
Triggermanual
Complexityadvanced
Nodes30
AddedMay 14, 2025

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