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| author | Dmitry Vyukov <dvyukov@google.com> | 2026-01-15 20:53:57 +0100 |
|---|---|---|
| committer | Dmitry Vyukov <dvyukov@google.com> | 2026-01-20 21:12:57 +0000 |
| commit | 7f5908e77ae0e7fef4b7901341b8c2c4bbb74b28 (patch) | |
| tree | 2ccbc85132a170d046837de6bdd8be3317f94060 /pkg/aflow/flow/patching/patching.go | |
| parent | 2494e18d5ced59fc7f0522749041e499d3082a9e (diff) | |
pkg/aflow: make LLM model per-agent rather than per-flow
Having LLM model per-agent is even more flexible than per-flow.
We can have some more complex tasks during patch generation with the most elaborate model,
but also some simpler ones with less elaborate models.
Diffstat (limited to 'pkg/aflow/flow/patching/patching.go')
| -rw-r--r-- | pkg/aflow/flow/patching/patching.go | 4 |
1 files changed, 3 insertions, 1 deletions
diff --git a/pkg/aflow/flow/patching/patching.go b/pkg/aflow/flow/patching/patching.go index 766cf089f..856962e6c 100644 --- a/pkg/aflow/flow/patching/patching.go +++ b/pkg/aflow/flow/patching/patching.go @@ -43,7 +43,6 @@ func init() { ai.WorkflowPatching, "generate a kernel patch fixing a provided bug reproducer", &aflow.Flow{ - Model: aflow.BestExpensiveModel, Root: &aflow.Pipeline{ Actions: []aflow.Action{ baseCommitPicker, @@ -54,6 +53,7 @@ func init() { codesearcher.PrepareIndex, &aflow.LLMAgent{ Name: "debugger", + Model: aflow.BestExpensiveModel, Reply: "BugExplanation", Temperature: 1, Instruction: debuggingInstruction, @@ -62,6 +62,7 @@ func init() { }, &aflow.LLMAgent{ Name: "diff-generator", + Model: aflow.BestExpensiveModel, Reply: "PatchDiff", Temperature: 1, Instruction: diffInstruction, @@ -70,6 +71,7 @@ func init() { }, &aflow.LLMAgent{ Name: "description-generator", + Model: aflow.BestExpensiveModel, Reply: "PatchDescription", Temperature: 1, Instruction: descriptionInstruction, |