FeaturedRetrieval Augmented Generation
Think building your own Retrieval-Augmented Generation (RAG) system will give you a competitive edge? Think again. Companies are sinking months and hundreds of thousands of dollars into in-house AI projects, only to end up with sluggish, unsecure, and overpriced systems that can't hold a candle to existing solutions.
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Conversational AI has absolutely transformed the way we interact with our data. If you are yet to experience this as a consumer or business user, then you’re in for a real awakening. Having a conversation with website data as a customer can simplify your decision making process while asking complex questions to your business data can help you find instant answers. These are two practical use cases for conversational AI that can be adopted off the shelf. Let us explain how this is possible.
Ingesting data from your database into Vectara: a step by step guide.
Today, we’re incredibly excited to announce that we’re releasing a new “reranking” capability built into Vectara: one that’s focused on increasing the amount of diversity of the results near the top of the result list.
Today, we’re happy to announce that Vectara’s hybrid search capabilities have just gotten even stronger with the ability to apply updates to which metadata fields can have filters and what their data types are. This blog will walk through some common use cases and how you can use this new feature.
AI has raised the stakes for customer communications with your Brand. Are you hitting the mark? Or do you want to make sure your strategy isn’t a failure to launch. Learn what makes a smart chatbot
Vectara launches open-source Hallucination Evaluation Model (HEM) that provides a FICO-like score for grading how often a generative LLM hallucinates in Retrieval Augmented Generation (RAG) systems.
Vectara launches the open source Hughes Hallucination Evaluation Model (HHEM) and uses it to compare hallucination rates across top LLMs including OpenAI, Cohere, PaLM, Anthropic’s Claude 2 and more.
The top large language models along with recommendations for when to use each based upon needs like API, tunable, or fully hosted.
In RAG, retrieving the right facts from your data is crucial, and choosing the right embedding model to power your retrieval matters!
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