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Embeddings

Embeddings are numerical representations of data that capture semantic meaning for AI and machine learning systems. They convert data such as text, images, or audio into numerical vectors, allowing AI systems to measure similarity, understand context, and power applications such as semantic search, recommendation systems, clustering, and Retrieval-Augmented Generation (RAG).

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RE-call

Memory that abstains instead of guessing: agent memory on your own Postgres with a verdict, confidence and provenance on every hit, and a calibrated refusal when nothing clears the threshold.

  • Updated Oct 6, 2026
  • Python

Self-hosted, OpenAI-compatible inference for the agentic era: reasoning LLMs, universal tool calling, and the Responses API alongside embeddings, speech, and image models — many models sharing your GPUs, one gateway. Powered by Ray Serve.

  • Updated Oct 6, 2026
  • Python

MCP server that hands coding agents the one Markdown section that answers the question, not the whole file. Local hybrid search: BM25 keywords + ONNX embeddings in SQLite. No API keys, no network.

  • Updated Oct 6, 2026
  • Python