Observed Signal · May 21, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Private Local LLM Queries Git and Project Data
A developer built a private, offline AI assistant that answers natural-language questions about git history and project-management data by translating user questions into SQL. The system ingests commits and project board data into a single SQLite database (via Python collectors), uses an auto-discovery step to surface exact values, and runs a local LLM (Ollama with qwen2.5-coder:7b) to generate Text-to-SQL queries and summarize results. The project emphasizes privacy (no cloud or API keys), avoids vector RAG/embedding stores for structured data, and is implemented as a small CLI codebase (~8 files, ~400 lines). Planned enhancements include hourly refresh cron jobs, adding chat history as a data source, and a simple web UI.
Demonstrates a practical, privacy-preserving local LLM Text-to-SQL pattern for connecting engineering and project-management systems, which is useful to teams but not an industry-shifting platform announcement.
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Key Takeaways & Evidence Grounding
- Author implemented a local natural-language interface that converts questions to SQL and queries a SQLite database.
- Two Python collectors populate the database: a git history collector and a project-management collector (example: Monday.com GraphQL API).
- System uses Text-to-SQL (not vector embeddings/RAG) plus auto-discovery and self-correcting query retries for structured data.
- Local inference runs on Ollama with the qwen2.5-coder:7b model; the author reports good SQL generation performance on Apple Silicon.
- Project codebase is ~8 files and ~400 lines of Python, with two external dependencies (rich, requests); no cloud services or LangChain are used.
Connected Companies & Entities
3 Entities mappedOntology Mapping & Concepts
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