Model Releases
Built a self-hostable AI data analyst — upload a file or connect a DB, 4 agents analyze it, works with Ollama, OpenAI, Anthropic, or DeepSeek
Every time I needed to explore a dataset quickly, the same wall appeared — cleaning boilerplate, encoding errors, deciding which chart actually makes sense. The mechanical parts aren't interesting, th
Every time I needed to explore a dataset quickly, the same wall appeared — cleaning boilerplate, encoding errors, deciding which chart actually makes sense. The mechanical parts aren't interesting, they're just in the way. So I built something to handle them. You connect a file (CSV, Excel, Parquet, JSON) or a database (Postgres, MySQL, SQLite, DuckDB), and four agents run sequentially — one cleans the data, one generates hypotheses grounded in actual stats, one scores and debates them, one builds Plotly charts. Then you ask follow-up questions in plain English. Supports Ollama (fully local, no API key), OpenAI, Anthropic, and DeepSeek — switchable at runtime, no restart needed. The part I spent the most time on: the NLQ agent writes Python and runs it, so I had to sandbox it properly. It goes through RestrictedPython — AST check before anything executes, then a stripped context with no os, no open, no network. The model can use pandas and Plotly, nothing else. One command to get started: curl -fsSL https://raw.githubusercontent.com/laban254/insight-orchestra/main/install.sh | bash Repo: https://github.com/laban254/insight-orchestra submitted by /u/kibe254 [link] [comments]
Source: r/ollama | 2026-10-01