The article discusses a newly developed component within Salesforce, designed to operate locally on a computer and utilize Salesforce records for personalized responses. Inspired by Agentforce, which utilizes decision-making agents, the author opted to create an assistant instead due to complexities in building a local agent with Pico LLM. The component accommodates multiple open-source models, accesses single and related records for context, and offers a configurable user interface. This innovation aims to deliver contextual and relevant insights while protecting user data privacy.
The evolution of AI-driven components, such as local LLMs in Salesforce, signifies a shift towards more personalized and privacy-centric user experiences in tech.
By integrating Salesforce records, my assistant can generate context-aware responses, thereby enhancing the utility of basic open-source models to meet specific business needs.
The key feature of this component is its adaptability; it can utilize multiple models, tailoring the solution based on the computational resources available.
Although designing a local agent with Pico LLM is feasible, the complexity involved led me to focus on creating a more manageable assistant.
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