We implemented a multi-step process to find the required context:
1. Quick Context
Shows the most relevant files based on a pagerank algorithm (static analysis) and semantic embeddings (JLama inference engine). The input are the instructions and the AI workspace fragments (i.e. files).
2. Deep Scan
A richer LLM receives the summaries of the AI workspace files (+instructions) and returns a recommendation of files and tests. It also recommends the type of inclusion (editable, read-only, summary/skeleton).
3. Agentic Search
The AI has access to a set of tools for finding the required files. But the tools are not limited to grep/rg. Instead you can:
- find symbols (classes, methods, ...) in the project
- ask for summaries/skeletons of files
- provide class or method implementations
- find usages of symbols (where is x used?)
- call sites (in/out)
...
The amazing thing here is that the Brokk AI can access your code like an IDE, can ask for usages or gather the summary of a file before deciding to get the implementation of a method!
It mimics like a Dev is navigating the codebase.
And this is more reliable and token-efficient than the usual grep/rg approach