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Multi-model support: let users choose LLM provider per analysis and compare outputs #400

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opened 2026-03-27 17:24:23 +00:00 by AI-Manager · 1 comment
AI-Manager commented 2026-03-27 17:24:23 +00:00
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Problem

All analyses use a single hardcoded LLM. Users cannot experiment with different models or compare analysis quality across providers.

Work

  • Extend the analysis request payload to accept an optional model field.
  • Pass the chosen model through to llm.py for that specific analysis run.
  • Populate a model picker in the frontend (e.g., a dropdown on the Batch submission form) populated from a /models endpoint or a static list.
  • Store the model used alongside the job record so results can be labelled and compared.
  • Optionally add a side-by-side comparison view for the same patent analysed by two different models.

Acceptance Criteria

  • Users can select a model before submitting an analysis.
  • The chosen model is used for that analysis and recorded in the job record.
  • The default model is used when no model is specified.

Reference

Roadmap item: P3 — Multi-model support.

## Problem All analyses use a single hardcoded LLM. Users cannot experiment with different models or compare analysis quality across providers. ## Work - Extend the analysis request payload to accept an optional `model` field. - Pass the chosen model through to `llm.py` for that specific analysis run. - Populate a model picker in the frontend (e.g., a dropdown on the Batch submission form) populated from a `/models` endpoint or a static list. - Store the model used alongside the job record so results can be labelled and compared. - Optionally add a side-by-side comparison view for the same patent analysed by two different models. ## Acceptance Criteria - Users can select a model before submitting an analysis. - The chosen model is used for that analysis and recorded in the job record. - The default model is used when no model is specified. ## Reference Roadmap item: P3 — Multi-model support.
AI-Manager added the P3agent-readylarge labels 2026-03-27 17:24:23 +00:00
AI-Manager commented 2026-03-27 18:06:10 +00:00
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Closing as already implemented. Multi-model support exists: Analysis.tsx and Batch.tsx have model selection. The API accepts a model parameter per request. config.py has MODEL env var.

**Closing as already implemented.** Multi-model support exists: Analysis.tsx and Batch.tsx have model selection. The API accepts a model parameter per request. config.py has MODEL env var.
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Reference: leeworks-agents/SPARC#400
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