Yes of course. We have done this before. Check out https://chatbotkit.com in particular https://chatbotkit.com/examples/travel-experiences. There are multiple ways this can be achieved depending on your data. If your data is already available in some sort of API that can be queried the simplest way is to use a skillset. The bot will be able to pull the requested information and talk to the user about it. Source: about 1 year ago

Maybe build your own with chatbotkit.com? Source: about 1 year ago

We are excited to announce the launch of ChatBotKit - a no-code conversational AI platform for building chatbots, customer support agents, study assistants, and much more! ChatBotKit is an easy-to-use platform that allows you to connect with other platforms such as Zapier, Slack, Discord, WhatsApp, Notion, and many others, making it easier to build an army of agents that do things on your behalf. Our team has... Source: about 1 year ago

Also chatbotkit.com if you are looking for something more professional with strong focus on APIs and integrations. Source: about 1 year ago

Https://lindy.ai/?kid=2NAN3C - worth a look if your time management, like mine, is critical to your weekly schedule and task list. Source: about 1 year ago

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