n8n templatesn8n templates

Intelligent Web Query and Semantic Re-Ranking Flow

This workflow aims to enhance the intelligence and accuracy of online searches. After the user inputs a research question, the system automatically generates the optimal search query and retrieves results through the Brave Web Search API. By leveraging advanced large language models, it conducts multi-dimensional semantic analysis and result re-ranking, ultimately outputting the top ten high-quality links and key information that closely match the user's needs. This process is suitable for scenarios such as academic research, market analysis, and media editing, effectively addressing the issues of imprecise traditional search queries and difficulties in information extraction.

Workflow Diagram
Intelligent Web Query and Semantic Re-Ranking Flow Workflow diagram

Workflow Name

Intelligent Web Query and Semantic Re-Ranking Flow

Key Features and Highlights

This workflow implements intelligent web search query generation and semantic result re-ranking. Upon receiving a user’s research question, the system automatically generates an optimal search query through multi-step reasoning chains, invokes the free Brave Web Search API to retrieve results, and then leverages advanced large language models (such as Google Gemini, OpenAI GPT, and Anthropic Claude) to perform multidimensional semantic analysis and relevance re-ranking of the search results. The final output delivers the top ten high-quality links and extracted key information that closely match the user’s intent.

Highlights include:

  • Automated generation of precise search queries, avoiding ineffective long-tail queries.
  • Integration of temporal information to ensure timeliness and relevance of search results.
  • Deep understanding of user intent through multi-step reasoning chains, enhancing search quality.
  • Semantic re-ranking to improve result accuracy by filtering irrelevant or low-quality content.
  • Support for customizable replacement among multiple advanced large language models, providing flexibility to meet diverse needs.

Core Problems Addressed

Traditional search often struggles with imprecise query expression, overwhelming and disorganized results, and difficulty in information extraction. This workflow addresses:

  • The challenge of accurately expressing complex user needs in search queries.
  • Difficulty in quickly locating high-value information amid large volumes of search results.
  • Lack of deep semantic analysis and information extraction from search results.
  • Delays in search result updates or lack of sensitivity to time relevance.

Application Scenarios

  • Academic researchers rapidly obtaining highly relevant literature and materials.
  • Market analysts customizing the collection of the latest industry trends and reports.
  • Media editors filtering and distilling core information from news coverage.
  • Product managers and developers conducting technical research and trend tracking.
  • Any scenario requiring automated, intelligent web information retrieval and refinement.

Main Process Steps

  1. Webhook Node Receives User Input — Users submit research questions via the Webhook interface.
  2. Obtain Current Time Information — Provides temporal context for subsequent analysis.
  3. Semantic Query Generation (Semantic Search - Query Maker) — Decomposes the question through multi-step reasoning, extracts keywords, and formulates the optimal search query.
  4. Invoke Brave Web Search API to Execute Query — Sends HTTP requests to retrieve search results.
  5. Result Aggregation (Query-1 Combined) — Consolidates titles, URLs, and descriptions returned by the web search.
  6. Semantic Re-Ranking (Semantic Search - Result Re-Ranker) — Utilizes large language models to interpret user intent, analyze, and rank search results by relevance while extracting key information.
  7. Result Parsing and Auto-Correction (Auto-fixing Output Parser) — Ensures output format compliance and content completeness.
  8. Return Top Ten Ranked Results and Extracted Information via Respond to Webhook Node — Outputs structured JSON format.

Involved Systems or Services

  • Brave Web Search API: Free web search data source.
  • Webhook: Receives external query requests.
  • n8n Nodes: Including HTTP request, code processing, time nodes, etc.
  • Large Language Model Integration:
    • Google Gemini (PaLM)
    • OpenAI GPT
    • Anthropic Claude
  • Auto-fixing Output Parser: Ensures data format and content accuracy.

Target Users and Value

  • Researchers and analysts requiring efficient and precise web information retrieval.
  • Enterprise users relying on real-time, authoritative data to support decision-making.
  • Technical developers automating content aggregation and information extraction.
  • Content editors aiming to reduce manual screening workload and improve search quality.

By leveraging intelligent multi-model fusion, this workflow significantly enhances the precision of web queries and the usability of information, making it suitable for professional users with high demands on information depth and quality.