> For the complete documentation index, see [llms.txt](https://docs.elna.ai/elna-whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.elna.ai/elna-whitepaper/roadmap.md). # Roadmap ### Q1 2024 1. Deployment of the vector database, the vector database indexes and stores vector embeddings for fast retrieval and similarity search, with capabilities like CRUD operations, metadata filtering, and horizontal scaling. It has been implemented in Canisters (the virtual machines of IC that both data and code are stored and read from) enhancing similarity search capabilities. 2. Agent creation platform beta is launched and open to the public. People can now create agents and showcase their agents on the ELNA platform. ### Q2 2024 1. Developing a minimalist AI agent creation toolkit, empowering users to craft bespoke AI solutions - with the ability to integrate multiple file formats (text, images, video, other specialized files), multiple data sources, and interact with different external applications helping users to automate tasks, fetch data from various software tools etc 2. A significant upcoming feature is the ELNA Marketplace which allows creators an avenue to derive value from their innovations, monetizing their custom agents. Additionally, enhancing our platform to support multi-model functionality with the capability to integrate multiple machine-learning model pipelines. This will be a minimalist implementation and will be iterating the same with additional features. ### Q3 2024 1. The initial version of the developer ecosystem will be open for beta users to list tools and data. The monetise model will also be integrated. 2. Elaborating the features and functionalities within the marketplace, which includes the monetization model deployment. ### Q4 2024 1. For comprehensive decentralization, ELNA is working towards on-chain LLM model deployment tools, including large language inference engines, allowing on-chain fine-tuning, and pre-training capabilities. These models can be customized and improved directly on the blockchain by a wide range of contributors, and thus be driven by the community. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.elna.ai/elna-whitepaper/roadmap.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.