EDA is an on-premises data analytics solution by leveraging LLM's code generation capability.
Rather than requiring users to upload raw data to cloud platforms, EDA keeps everything local, providing a privacy-preserving and efficient way to interact with and analyze data.
EDA converts a user’s own data into a local on-demand agent service by leveraging the latest auto code generation capabilities of LLMs
- Agent code is generated on the fly the first time the user interacts with the database
- No coding knowledge (maybe even no installation needed if possible) is required for users to build and use
- In contrast to centralized AI platform, the user do not need uploading raw data
- The goal is to allow users to interact with, digest, or serve their own data on-demand without prior application development
(WIP)
sandbox/data/ stores the data, agent_card, input for evaluation run local_evaluation.py to evaluate the agent performance
=======
Currently, we are leveraging smolagents and llamaindex framework: read / write files from the local directory. Agent for writing up the retrieval code
- The accuracy and robustness of LLM code generation improves to human level (SWEBench https://www.swebench.com/)
- The cost of autonomous coding goes to negligible
- The accuracy and user experience can be further improved by local/personal knowledge of the data base and the information of user query history