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

AgentCloud uses Qdrant as the vector store to efficiently store and manage large sets of vector embeddings. For a given user query the RAG application fetches relevant documents from vector store by analyzing how similar their vector representation is compared to the query vector. - Source: dev.to / 15 days ago

Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker. - Source: dev.to / 26 days ago

I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours. - Source: dev.to / about 1 month ago

I'm currently looking to implement locally, using QDrant [1] for instance. I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2]. [1]. https://qdrant.tech/. - Source: Hacker News / about 2 months ago

There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb. - Source: Hacker News / 3 months ago