Adaptive RAG (Adaptive Retrieval-Augmented Generation)
This workflow utilizes adaptive retrieval-augmented generation technology to intelligently classify user queries and dynamically adjust retrieval and generation strategies, thereby providing more accurate and diverse responses. By integrating large language models and vector databases, it formulates customized strategies for different types of queries (factual, analytical, opinion-based, contextual), enhancing user experience and information retrieval efficiency. It is suitable for scenarios such as intelligent Q&A, enterprise knowledge bases, and customer service robots, effectively addressing the issues of accuracy and personalization found in traditional methods.
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Workflow Name
Adaptive RAG (Adaptive Retrieval-Augmented Generation)
Key Features and Highlights
This workflow implements Adaptive Retrieval-Augmented Generation (Adaptive RAG) technology, which intelligently classifies user queries and dynamically adjusts retrieval and generation strategies based on the query type. This approach delivers more precise, comprehensive, diverse, and contextually relevant responses.
- Utilizes Google Gemini large language model for query classification and strategy generation
- Applies customized query enhancement strategies for four query types: factual, analytical, opinion-based, and contextual
- Integrates Qdrant vector database for efficient semantic retrieval
- Supports dialogue memory management to maintain contextual continuity
- Flexible input normalization with support for chat interface or other workflow triggers
Core Problems Addressed
Traditional retrieval-augmented generation often applies a uniform strategy to all queries, which struggles to adapt to the characteristics of different questions, resulting in responses that lack precision or comprehensiveness. This workflow intelligently classifies query types and optimizes retrieval prompts and generation strategies accordingly, addressing:
- Efficiency decline caused by diverse query types in retrieval and generation
- Inability to provide personalized answers incorporating user context
- Lack of diverse presentation of subjective opinions and multi-dimensional analysis
Application Scenarios
- Intelligent Q&A systems: Automatically adjust response logic based on user query type to enhance user experience
- Enterprise knowledge base search: Precisely deliver factual data, analytical reports, opinion summaries, or personalized recommendations for various business questions
- Customer service bots: Flexibly handle user inquiries, balancing objective information and subjective needs
- Research assistance tools: Help researchers quickly access multi-perspective information and contextually relevant knowledge
Main Workflow Steps
- Input Trigger: Receive user query, chat session ID, and vector database ID via built-in chat interface or other workflow triggers
- Query Classification: Use Google Gemini model to classify user query into factual, analytical, opinion-based, or contextual types
- Branch Routing: Route the query to the corresponding strategy node based on classification results
- Strategy Execution:
- Factual: Reconstruct query focusing on precise entities and relationships
- Analytical: Decompose into sub-questions to ensure comprehensive coverage
- Opinion-based: Extract diverse perspectives and present multiple viewpoints
- Contextual: Infer implicit context by incorporating user background
- Set Retrieval Prompts: Construct retrieval prompts and system instructions according to the strategy output
- Document Retrieval: Query Qdrant vector database using the strategy-adjusted query to retrieve relevant documents from the knowledge base
- Context Concatenation: Merge retrieved document contents to form the contextual input
- Answer Generation: Leverage Google Gemini model to generate the final answer by combining retrieval context, chat memory, and system prompts
- Response Return: Send the generated answer back to the caller via a Webhook node
Involved Systems and Services
- Google Gemini (PaLM) Large Language Model: Used for query classification, strategy generation, and final answer generation
- Qdrant Vector Database: Stores and retrieves vector representations of knowledge documents
- n8n Platform: Automates the workflow, managing triggers, logic branches, and data processing nodes
- Webhook: Handles external calls and enables system integration
- Chat Interface (Chat Trigger): Supports conversational interaction and maintains session memory
Target Users and Value
- Developers and Data Engineers: Quickly build intelligent Q&A and knowledge retrieval systems to enhance system intelligence
- Enterprise Information Managers: Optimize knowledge base utilization and achieve precise responses for multiple query types
- Customer Service and Support Teams: Automate handling of complex customer inquiries to improve response quality and efficiency
- Researchers and Content Creators: Assist in acquiring multi-dimensional information and support in-depth analysis and opinion exploration
This Adaptive RAG workflow combines advanced large model capabilities with flexible retrieval strategies, adaptively adjusting retrieval and generation logic according to the nature of user queries, significantly enhancing the intelligence, personalization, and practicality of Q&A systems.
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