An AI Copilot is an artificial intelligence system that assists developers, programmers, or other professionals in various tasks related to software development, coding, or content creation. AI Copilots can help programmers by providing code suggestions, identifying errors, and offering code snippets that align with the developer’s coding style. AI Copilots can work within integrated development environments (IDEs), assist in collaborative coding projects, and help in the content generation in LLMs.
AI Copilots can learn from the developer’s coding patterns and adapt to their preferences over time, which enhances the user’s assistance experience. Well-known AI Copilots include GitHub Copilot and OpenAI GPT-3. AI Copilots leverage various artificial intelligence, natural language processing (NLP), machine learning, and code analysis. AI Copilots are often updated regularly to incorporate new programming languages, frameworks, and best practices, ensuring they remain valuable to developers as technology evolves.
Now, a team of researchers design OpenCopilot. It is a user’s own AI copilot, trained specifically for their product and their requirement. Unlike generic AI models, OpenCopilot deeply integrates with a product’s underlying APIs by the primary function and effortlessly executes API calls whenever required. It uses LLMs to determine if the user’s request requires calling an API endpoint. It stands as a tool that can significantly improve efficiency and reduce the manual work involved in interfacing with APIs.
OpenCopilot can call your underlying APIs and transform the responses into meaningful texts. It can also automatically produce certain request payload fields based on the context. Users need to provide their API/backend definition as well as their public endpoints to call them. Users can also embed OpenCopilot’s chat bubble into their SaaS applications. OpenCopilot ensures the provided schema is valid to produce optimized results.
However, the limitations of this product as of now are that it cannot call multiple endpoints simultaneously and is not designed for large or complex APIs. It doesn’t retain chat history and treats each message as a standalone interaction.
Users need to create unlimited copilots and embed the copilot into their SaaS product using standard JS calls. They need to provide Swagger definitions for their APIs and embed the validator and recommender to it. Users can add chat memory and Vector DB support for large Swagger files.
Their future work will include making the platform more versatile by introducing a plugin system catering to various authentication methods. They also plan on incorporating offline LLMs as they can process sensitive or confidential information without the need to transmit data over the internet. This will reduce the risk of data breaches and unauthorized access. They are also working on expanding OpenCopilot’s data ingestion capabilities with plans to support a range of formats, from texts and PDFs to websites and other data sources.
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