Embedding Alignment

Fri, 01 Aug 2025 00:00:00 +0000

Embedding Alignment

August 2025

Why?

Given the increasing use of vendored vector store solutions and dependencies on AI for mission critical systems, it is important to determine ways to ensure systems stay reliable when third party APIs degrade. Embedding alignment can be an approach to re-map embeddings from one vendored system to another in the hope that systems stay reliable at the cost of minimal degradation at inference time. This approach requires only maintaining a linear mapping between embeddings rather than duplicating vector stores for multiple vendors which can be expensive in terms of ownership, processes and infrastructure.

LLMs - in Review (2024)

Sun, 01 Dec 2024 00:00:00 +0000

LLMs - in Review (2024)

December 2024

2024 was the first year where I took LLMs seriously. I successfully hosted a Llama 70b parameter model in production which was used as with continue.dev for a self-hosted co-pilot replacement, along with a code autocomplete like Qwen Coder or Deepseek, these were fine replacements and surprisingly robust. Huggingface’s TGI along with Triton Server were the main heroes for this project, (Triton was used to serve onnx models for embeddings) though I’ve yet to find a “good” embedding model. At this stage in time, most of the vector database solutions “feel” the same and can all seemingly be trivially hosted via Kubernetes.

Embeddings on NoRaincheck

Embedding Alignment