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As I was building LLM projects, I found I was re-implementing a new vector database for each one. So I built RagTag (https://ragtag.weaveapi.com https://ragtag
by takinola 3y ago
As I was building LLM projects, I found I was re-implementing a new vector database for each one. So I built RagTag (https://ragtag.weaveapi.com https://ragtag.weaveapi.com), a vectordb/RAG as a service to make the process faster. This provides a CRUD interface to push and retrieve documents, which are automatically chunked and converted to embeddings.
AgentX (https://theagentx.com https://theagentx.com), an LLM chat support app is one of the projects I built on this framework. It is a self-updating customer support agent that is trained on your support docs. Not only does this answer your customer questions, it provides summaries of the queries so you get a sense of where your product and/or documentation is deficient.