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Hi Community,

This is a detailed, candid walkthrough of the IRIS AI Studio platform. I speak out loud on my thoughts while trying different examples, some of which fail to deliver expected results - which I believe is a need for such a platform to explore different models, configurations and limitations. This will be helpful if you're interested in how to build 'Chat with PDF' or data recommendation systems using IRIS DB and LLM models.

https://www.youtube.com/embed/bcu1gt0BDhY
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In the previous article, we saw in detail about Connectors, that let user upload their file and get it converted into embeddings and store it to IRIS DB. In this article, we'll explore different retrieval options that IRIS AI Studio offers - Semantic Search, Chat, Recommender and Similarity.

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Hi Community,

Here is a brief walkthrough on the capabilities of IRIS AI Studio platform. It covers one complete flow from loading data into IRIS DB as vector embeddings and retrieving information through 4 different channels (search, chat, recommender and similarity). In the latest release, added docker support for local installation and live version to explore.

https://www.youtube.com/embed/X1gzz3Qs2dw
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In the previous article, we saw different modules in IRIS AI Studio and how it could help explore GenAI capabilities out of IRIS DB seamlessly, even for a non-technical stakeholder. In this article, we will deep dive into "Connectors" module, the one that enables users to seamlessly load data from local or cloud sources (AWS S3, Airtable, Azure Blob) into IRIS DB as vector embeddings, by also configuring embedding settings like model and dimensions.

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Problem

Do you resonate with this - A capability and impact of a technology being truly discovered when it's packaged in a right way to it's audience. Finest example would be, how the Generative AI took off when ChatGPT was put in the public for easy access and not when Transformers/RAG's capabilities were identified. At least a much higher usage came in, when the audience were empowered to explore the possibilities.

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FHIR has revolutionized the healthcare industry by providing a standardized data model for building healthcare applications and promoting data exchange between different healthcare systems. As the FHIR standard is based on modern API-driven approaches, making it more accessible to mobile and web developers. However, interacting with FHIR APIs can still be challenging especially when it comes to querying data using natural language.

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Problem

In a fast-paced clinical environment, where quick decision-making is crucial, the lack of streamlined document storage and access systems poses several obstacles. While storage solutions for documents exist (e.g, FHIR), accessing and effectively searching for specific patient data within those documents meaningfully can be a significant challenge.

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