Fix deprecated default Gemini model to latest version#368
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📝 WalkthroughWalkthroughThe PR updates the default Gemini language model and documents the configuration option. The code changes the provider detection default from ChangesGemini Model Configuration
Estimated code review effort🎯 1 (Trivial) | ⏱️ ~3 minutes Possibly related PRs
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* chore(providers): bump Gemini defaults to current GA models Bundles two upstream PRs into one chore — both are blocking real users today and both are simple default-string bumps with no API contract change. LLM default (was PR #368, @yut304) - `gemini-2.0-flash` is deprecated in Google's Gemini API and returns 429 rate-limit errors under load. Replace the default with `gemini-flash-latest`. Users on a pinned `GEMINI_MODEL` in `~/.agentmemory/.env` are unaffected. Embedding default (was PR #246, @AmmarSaleh50) - `text-embedding-004` is deprecated (shutdown Jan 14 2026). Replace with `gemini-embedding-001` (GA): 100+ languages, MRL dims (768 / 1536 / 3072), 2048-token input. - URL path changes from `:batchEmbedContent` to `:batchEmbedContents` (plural — the new model's batch endpoint). - Each request now sends `outputDimensionality: 768` so the returned vectors match the existing index dim guard from #248 — no reindex needed. - L2-normalize each returned vector before pushing to the result array. `gemini-embedding-001` does not normalize by default, unlike `text-embedding-004`. Without this the cosine-similarity math elsewhere in the search pipeline (which assumes unit-length vectors) collapses. Verified - `npm test` clean: 903 / 903. - `npm run build` clean. Closes #368, closes #246. * fix(gemini): pin LLM default to gemini-2.5-flash + warn-once on zero-norm Addresses CodeRabbit findings on PR #370. 1. Pin Gemini LLM default to gemini-2.5-flash. `gemini-flash-latest` is a moving alias that points to whatever Google promotes next. Production behaviour should be deterministic from a release perspective — users who upgrade agentmemory should not also get a Gemini model rotation in the same step. Switch the default to the current stable GA model `gemini-2.5-flash`. Users who want the moving alias keep getting it via `GEMINI_MODEL=gemini-flash-latest` in `~/.agentmemory/.env`. 2. Warn-once on zero-norm embedding in l2Normalize. `gemini-embedding-001` can return a zero-norm vector for degenerate input. The previous code silently returned the zero vector — downstream cosine-similarity math then divides by zero and the call site sees `NaN` scores with no signal as to why. Emit a one-time stderr warning naming the model + vector length so operators can correlate index quality dips with upstream embedding regressions. Behaviour otherwise unchanged: return the zero vector and let BM25 carry the search signal. Throwing was the other option — rejected because a single bad embedding in a 100-item batch would abort the whole batch and surface as an indexing pipeline halt. Soft-fail + warn matches the rest of the embedding provider error handling. Skipped finding: - `outputDimensionality` → `output_dimensionality` snake_case rename. CodeRabbit asserts the REST API expects snake_case. The Gemini REST API actually uses camelCase on the wire — confirmed against ai.google.dev/api/embeddings (field labelled `outputDimensionality` in the REST schema; the Python SDK alone uses snake_case and translates internally). Current code is correct as-shipped; the snake_case rename would silently break the dim override. Verified: 903 / 903 tests pass; build clean.
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Summary
gemini-2.0-flash deprecated, always get return 429 rate limit.
Changes
Replace gemini-2.0-flash to gemini-flash-latest
Summary by CodeRabbit
Documentation
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