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
sandbox/data/ stores the data, agent_card, input for evaluation run local_evaluation.py to evaluate the agent performance
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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
Main Branch targets at solving the problem with prompts only with minimum coding. Working branch targets at practical solution as of right now technologies (giving basic rag code example, code functions e.g. writing/reading files, ...)