Simply link your data sources to create a chatbot similar to ChatGPT for your data. You can then incorporate it into your website as a widget or interact with it via our integrations or API.
LLMRails employs a hybrid search approach, combining large language model (LLM) based retrieval, also known as Vector Search, with precise boolean exact match techniques. This enables the system to swiftly identify the most pertinent products, support cases, and documents that directly address your users' inquiries. As a result, your chatbots, Q&A systems, conversational apps, and websites powered by LLMRails can deliver responses focused on the information that matters to you and your users while disregarding irrelevant content.
It is a game-changer! Unlike our English language embed model, we have trained this one using dot product calculations, resulting in a non-normalized similarity score that reflects the magnitude of the two compared vectors. This approach has proven more effective than standard embeddings, and our multilingual embeddings boast an impressive 768 dimensions. With our technology, you can expect top-notch performance and unmatched accuracy.
In the industry, there have been instances where chatbots have fabricated information, awkwardly expressed affection toward users, or even promoted rival products. These occurrences are partly because most generative systems comprehend language but lack a deep understanding of your business or the ability to acknowledge when unsure. LLMRails, on the other hand, uses Grounded Generation, a methodology that ensures its generative system solely relies on the factual information and data provided by you.
In the realm of generative systems, it is concerning that some platforms utilize user data for training purposes, potentially compromising sensitive information such as personally identifiable information, trade secrets, and authentication details. However, with LLMRails, we take a different approach. We do not require your data for training our models. Our zero-shot large language models are expertly designed to comprehend various types of business data and the corresponding inquiries. Through our Grounded Generation methodology, retraining models using your data to generate accurate summaries and chatbot-style responses is unnecessary. This is because the retrieval process has already optimized our generative models' available data.
All you need to do is sign up with a company email address. You will then get access to the LLMRails to get started with ingesting documents and testing the search engine.
LLMRails supports PDF, Microsoft Word, Microsoft PowerPoint, Open Office, HTML, JSON, XML, email in RFC822, text, RTF, ePUB, and Common Mark.
You can index data from any text document in a supported file format via LLM Rails APIs.
There's no need for extra or specialized engineering, hardware, or infrastructure resources to implement LLMRails effectively. LLMRails was designed to simplify the process for web and app developers to incorporate semantic search into their sites and applications, eliminating the need for extra training or resources.
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