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LiveAgent stands out with its ultra-fast performance, robust ticketing system, and user-friendly interface. It is a scalable solution equipped with over 180+ features and 200+ integrations, capable of growing as your customer service needs expand.
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LiveAgent offers 24/7 availability, an exceptional 20-second average response time, and extraordinary usability. Suitable for any type of business, its unbeatable value for money makes it a top choice for reliable and efficient customer service.
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Our primary audience consists of businesses of all sizes seeking to enhance their customer service experience. This includes startups, SMEs, and large corporations across various industries.
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Born out of the need for better customer interactions, LiveAgent was founded in 2004. Driven by the philosophy 'to treat customers as people, not tickets,' we've grown into a leading customer service solution.
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As a cloud-based solution, LiveAgent employs cutting-edge technologies to ensure a fast, secure, and reliable customer service platform. Our intricate infrastructure guarantees optimal functionality and high performance at all times.
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Renowned brands like Huawei, Yamaha, BMW, and Oxford University are proud users of LiveAgent, trusting us for world-class customer service.
Based on our record, Weaviate seems to be more popular. It has been mentiond 28 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Weaviate: An open-source, cloud-native vector database built for scalable and fast vector searches. It's particularly effective for semantic search applications, combining full-text search with vector search for AI-powered insights. - Source: dev.to / 4 months ago
Weaviate is an open-source vector search engine with out-of-the-box support for vectorization, classification, and semantic search. It is designed to make vector search accessible and scalable, supporting use cases such as semantic text search, automatic classification, and more. - Source: dev.to / 4 months ago
Congrats to them! What have your experiences with vector databases been? I've been using https://weaviate.io/ which works great, but just for little tech demos, so I'm not really sure how to compare one versus another or even what to look for really. - Source: Hacker News / 5 months ago
A RAG implementation's quality and performance highly depend on the similarity-based search of embeddings. The challenge arises from the fact that embeddings are usually high-dimensional vectors, and the knowledge base may have many documents. It's not surprising that the popularity of LLM catalyzed the development of specialized vector databases like Pinecone and Weaviate. However, SQL databases are also evolving... - Source: dev.to / 6 months ago
To find semantically similar texts we need to calculate the distance between vectors. While we have just a few short texts we can brute-force it: calculate the distance between our query and each text embedding one by one and see which one is the closest. When we deal with thousands or even millions of entries in our database, however, we need a more efficient way of comparing vectors. Just like for any other way... - Source: dev.to / 7 months ago
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Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
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pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs
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