Smart Search
Last updated
Last updated
The Smart Search is an advanced, AI-driven search feature designed to simplify and enhance the way users interact with the Solution Explorer. Powered by a hybrid semantic search engine, it combines traditional keyword matching with contextual understanding to deliver highly relevant results.
How It Works:
Semantic Search: Uses AI to understand the meaning behind user queries, rather than just matching keywords.
Technology Stack:
Vectorization: Powered by OpenAI’s text-embedding-3-large
model, which converts solutions and queries into vector representations for contextual matching.
Vector Store: Built on Typesense, a high-performance vector database optimized for semantic and hybrid search.
In simple terms: The system doesn’t just look for exact matches but understands what you’re asking for, even if the words don’t match perfectly.
Previously, the search relied purely on keywords, often returning irrelevant or no results for nuanced queries. Now, with the Smart Search feature, integrated in the default search bar, contextual understanding ensures a significantly better user experience.
Example Query:
Before (Keyword Search):
Query: "Alternative materials for insulation in France"
Result: 1 result, as the phrase contained many keywords that did not match solution titles or descriptions.
After (Smart Search):
Query: "Alternative materials for insulation in France"
Result: Multiple relevant solutions (102)
The new filtering system on the Solution Explorer provides replicates the previous robust and intuitive way to refine searches, ensuring relevant results are tailored user needs. Below are the filtering categories, complete with examples for practical application:
Filter by;
This filter structure ensures users can easily narrow their searches while the AI does the heavy lifting to provide contextually relevant results.
Here are examples of how the new search capabilities excel in addressing complex and thematic queries:
Results:
Biological Pest Control: Natural predators and biopesticides to manage pests without harmful chemicals.
Integrated Pest Management (IPM) Systems: Strategies combining crop monitoring, natural controls, and minimal chemical usage.
Precision Spraying Technologies: AI-driven tools that target pests directly, reducing overall pesticide use.
Rationale:
The search engine identifies the query's focus on minimizing chemical use in pest control within agriculture. Results showcase innovative solutions that leverage biological methods, integrated approaches, and precision technologies to reduce environmental impact while maintaining agricultural productivity. This highlights the AI's ability to connect sustainability goals with practical applications in agriculture.
Results:
Offshore Wind Turbines: Robust turbines designed to withstand harsh weather conditions and provide renewable energy in coastal regions.
Hurricane-Resistant Solar Panels: Solar panel systems engineered for durability in storm-prone areas, ensuring sustained energy generation.
Hybrid Microgrids with Battery Backup: Resilient energy systems combining solar, wind, and battery storage to maintain power supply during and after storms.
Rationale:
The search engine interprets the query's emphasis on renewable energy and coastal storm challenges, retrieving solutions specifically designed for these environments. The results reflect technologies optimized for resilience, ensuring energy reliability despite extreme weather conditions, demonstrating the AI's capability to address geographic and situational complexities.
Results:
Solar-Powered Desalination Units: Portable systems that convert saline water into fresh drinking water using renewable energy.
Rainwater Harvesting Technologies: Low-cost systems designed to collect and store rainwater in arid regions.
Drip Irrigation Systems: Water-efficient technologies tailored for agricultural use in regions with scarce water resources.
Rationale:
The search engine interprets the query's focus on water security and regional challenges (Sahel) and retrieves solutions addressing these needs. Results include technologies for water generation, efficient usage, and sustainable practices suited to arid environments. This demonstrates the AI's contextual understanding of the query's goals and regional specifics.
The Smart Search feature represents a significant advancement in usability and relevance, enabling users to find the solutions they need with greater ease and confidence. Environmental challenges are often highly context-specific, requiring an understanding of nuanced problems and needs. Smart Search bridges this gap by leveraging semantic AI and vector search technologies to interpret natural queries in a way that aligns with users’ specific requirements.
By understanding the key components of environmental problems, Smart Search empowers users to navigate complex challenges and discover clean technologies that are truly relevant to their context. This makes the process of finding solutions more intuitive, impactful, and tailored to individual needs.