Semantic search technology has been a game-changer for tech giants like Netflix, Google, and Amazon for years. Now, with increased accessibility, organizations of all sizes are rapidly adopting this powerful technology.
At BetterFeedback, we're excited to be at the forefront of this movement. We've integrated the Weaviate vector database into our platform to harness the full potential of semantic search for creating insightful and actionable analyses.
Our primary goal is to elevate the quality of feedback analyses. By employing a more sophisticated approach to data interpretation, we empower businesses to generate more meaningful, accurate, and impactful insights.
What is Semantic Search?
Ever been frustrated by irrelevant search results? Traditional search engines often struggle to grasp the meaning behind your queries. Semantic search addresses this by understanding the context and intent of your words. It's especially useful when dealing with ambiguous terms or nuanced concepts. Instead of just matching keywords, it aims to understand the meaning behind your query and provide results that are truly relevant.
Why a Vector Search Engine?
Weaviate is a cloud-native, modular, real-time vector search engine that employs machine learning to vectorize and store data, enabling it to answer natural language queries with remarkable accuracy. But what does this actually mean?
Think of it this way:
If you work with data, you probably work with search engine technology. The best search engines are amazing pieces of software, but because of their core architecture, they come with limitations when it comes to finding the data you are looking for.
Take for example the data: "The Eiffel Tower is a wrought iron lattice tower on the Champ de Mars in Paris."
Storing this in a traditional search engine might leverage inverted indices to index the data. This means that to retrieve the data; you need to search for “Eiffel Tower” or “wrought iron lattice”, etc. to find it. But what if you have vast amounts of data and you want the document about the Eiffel Tower but you search for: “landmarks in France”? Traditional search engines can’t help you there and this is where vector search engines show their superiority.
Weaviate uses vector indexing mechanisms at its core to represent the data. The vectorization modules (e.g., the NLP module) vectorizes the above-mentioned data object in a vector-space where the data object sits near the text ”landmarks in France”. This means that Weaviate can’t make a 100% match, but a very high one to show you the results.
Source: weaviate.io
We chose to leverage the power of semantic search to revolutionize our analysis generation process. With our understanding of success and semantic search technology, we're developing cutting-edge features. We can now swiftly rank documents by semantic relevance, presenting information in an easily digestible format.
Semantic search allows users to input any information, regardless of spelling or potential inaccuracies, without compromising the search's effectiveness. This enhanced flexibility unlocks more powerful and intuitive data exploration.
The Weaviate vector database emerged as the ideal choice for several compelling reasons:
- It's open-source.
- It boasts GraphQL integration.
- It benefits from a thriving community.
- It's incredibly fast.
- It integrates seamlessly with OpenAI.
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It's supported by a dedicated and helpful team.
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By integrating Weaviate into our tech stack, we're building more efficient feedback analyses. This will allow our users to gain a deeper understanding of their customers and plan effectively for the future. Ultimately, this empowers them to deliver higher quality products and maintain a competitive advantage. Ready to experience the future of feedback analysis? Try it yourself!