iliad_embeddings
InactiveTool of AXIS Toolbox — Agentic Commerce Codebase Intelligence
Convert text into dense vectors. Accepts a single string or a batch (max 2048). Returns one vector per input. AXIS-owned in-process inference by default (node-llama-cpp + an embedding-capable GGUF at AXIS_EMBEDDING_MODEL_PATH — no upstream provider call); an optional OpenAI /v1/embeddings backend is available behind AXIS_EMBEDDING_BACKEND=openai (model: text-embedding-3-small by default, overridable via OPENAI_EMBEDDING_MODEL; reports token usage). Requires Authorization: Bearer <api_key> to call. When the selected backend is not provisioned (local: GGUF file absent; openai: OPENAI_API_KEY unset), returns a structured `_not_configured: true` envelope naming the backend and remediation. Pairs natively with iliad_vector_database — feed `vectors` from this tool's output into `vector` of the vector_database upsert/query calls. Engineer mode (X-Agent-Mode: engineer — Domain Embeddings, $0.08): pass `dimensions` (Matryoshka truncation → smaller vectors) and/or `corpus_adapter: true` (mean-center the batch to sharpen retrieval on your data); returns an `engineer` block with the fitted adapter_mean for query alignment.
Parameters schema
{
"type": "object",
"required": [
"input"
],
"properties": {
"input": {
"type": [
"string",
"array"
],
"description": "A single string or an array of strings to embed. Empty strings and entries > 32k chars are rejected (chunk before calling)."
},
"dimensions": {
"type": "number",
"description": "Engineer mode: truncate each vector to this many leading dims (Matryoshka) + renormalize. Smaller, cheaper vectors."
},
"corpus_adapter": {
"type": "boolean",
"description": "Engineer mode: mean-center the batch (all-but-the-mean) to sharpen retrieval; returns the fitted adapter_mean."
}
}
}No endpoints wrapped at confidence ≥ 0.70.
Parent server
AXIS Toolbox — Agentic Commerce Codebase Intelligence
https://github.com/lastmanupinc-hub/Toolbox
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