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Embeddings

Embeddings help Elephant Agent recover meaning when words change. They are part of contextual recall and search, not a replacement for the Personal Model.

Why embeddings matter

Personal context is multilingual, indirect, and time-sensitive. The same idea may appear as a Chinese note, an English command, a project nickname, or a conversation fragment from weeks ago.

Embeddings give Elephant Agent a semantic path through that material while the Personal Model keeps durable truth correctable.

Local default

Elephant Agent includes a local semantic recall path by default.

PieceRole
elephant-local-embedLocal embedding provider selection.
elephant-embeddings-v1-text-smallCompact local model used for semantic retrieval.
64 / 256 / 768 dimensionsDifferent latency and depth postures from the same model family.
normalized vectorsStable similarity behavior across retrieval paths.
Local-first recall

The local default means claim and conversation retrieval can run without sending personal context to an external embedding provider.

Retrieval posture

SignalWhy it exists
Lexical and exact matchProtects precise names, IDs, and explicit phrases.
CJK n-gramsHelps mixed Chinese/English recall without a global alias table.
Semantic searchRecovers meaning when wording changes.
Time intentRespects explicit windows and recency/historical intent.
Match statusPrevents weak similarity from becoming false memory.

Provider override

The default is local. Advanced operators can configure one OpenAI-compatible embedding override when they intentionally want an external embedding endpoint.

elephant provider embeddings status
elephant provider embeddings local
elephant provider embeddings openai-compatible \
--base-url https://api.example.com/v1 \
--model text-embedding-3-large \
--dimensions 1536 \
--api-key "$OPENAI_API_KEY"
ModeUse it when...Tradeoff
LocalYou want private, built-in recall.Smaller model, local resource use.
OpenAI-compatible overrideYou need a specific embedding endpoint or larger dimension.External service and credential management.

Boundary with memory

Embeddings answer: what is semantically nearby?

The Personal Model answers: what should Elephant Agent treat as current understanding?

That boundary is why retrieval can be powerful without turning every retrieved chunk into truth.