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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.