Weaviate's vision of using graph QL knowledge graphs enables targeted data object search without relying on specific ontologies or keywords.
Embeddings in search engines allow for more intuitive understanding of search outcomes based on similarities and associations, while maintaining user data privacy.
The modular design of Weaviate allows for seamless integration of different components and models, paving the way for multimodal learning and future innovation in vector search engines.
Deep dives
The Vision of Weeviate: Graph QL Knowledge Graphs
The CEO and co-founder of Semi Technologies discusses the concept behind Weeviate's vision of using graph QL knowledge graphs. This idea arose from the complexities of ontologies, where people often do not agree on definitions. This lack of consensus causes problems when searching for specific data sets. By using factorization in natural language processing (NLP), Weeviate developed a method to target data objects in a graph without relying on specific ontologies or keywords. The use of graph QL provides a developer-friendly interface for traversing the graph, balancing expressiveness and ease of use.
The Power of Embeddings and Human Intuition
The podcast explores the potential of embeddings in search engines. The guest speaker shares their experience of using embeddings for various use cases, including image search, cybersecurity, genomics, and customer behavior analysis. Embeddings enable search engines to show relevant results based on similarities and associations, providing a more intuitive understanding of search outcomes. The conversation highlights the flexibility and privacy-safe nature of embeddings, allowing for personalized search experiences without compromising user data.
The Versatility of Weeviate in Research and Applications
The episode delves into the modular design of Weeviate and its potential for research and practical applications. The discussion emphasizes the value of modularity in integrating different components and models seamlessly. This modularity enables researchers to experiment and swap models easily, improving the overall user experience. The conversation also touches on the possibilities of combining graph embeddings, text embeddings, and other modalities, such as images and scientific literature. Multimodal learning is seen as a promising avenue for future research and innovation in vector search engines.
Advancements in Information Retrieval Techniques
The podcast discusses advancements in information retrieval techniques, emphasizing the importance of contextual understanding and the use of search engines like Bing API. It highlights the transition from vector search indexes to reinforcement learning-based models like OpenAI's web GPT for interpreting search results. Weeviate is introduced as a neuro symbolic search tool that combines symbolic filtering, graph traversal, and information retrieval components. The power of information retrieval in question answering and its potential for various industries, including banking, insurance, and startups, is highlighted.
The Role of Fine-tuning and General Purpose Models
The podcast delves into the debate of fine-tuning models versus using general purpose models for different use cases. It acknowledges that while fine-tuning produces better results, the already impressive performance of general purpose models makes them accessible and beneficial for many users. The discussion includes references to research papers like 'Don't Stop Pre-training' and 'VNL Adapter,' which explore the effectiveness and trade-offs of fine-tuning. The optimistic perspective is presented that as general purpose models improve over time, their usage for diverse applications becomes easier, enabling users with varying expertise levels to leverage these models effectively.
Weaviate Podcast #3. Join Connor Shorten and Bob van Luijt (SeMI Technologies) for the third Weaviate vector search engine Podcast. During the show, they will be discussing use cases, the GraphQL API, knowledge graphs, Weaviate as a product, vector search engine use cases, and a vision for the future of vector search.
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