fieldkit 0.1.0

Verified-on-Spark patterns lifted from the ai-field-notes blog into one importable Python package.

fieldkit

Verified-on-Spark patterns lifted from the ai-field-notes blog into one importable Python package.

Every essay in ai-field-notes ends with evidence/ — a folder of working code that produced the article's numbers. After 25+ articles the same patterns kept reappearing: the same NIM client wrapper, the same chunk-embed-store dance, the same bench harness. fieldkit is what those evidence/ folders look like once the boilerplate is lifted into a real package.

The blog stays the long-form rationale. fieldkit is the pip install-able surface so you can reproduce — and extend — the work without re-pasting 80 lines of NIM-client setup per article.

Install

pip install fieldkit

For the bleeding edge between releases, install from the git tag instead:

pip install "git+https://github.com/manavsehgal/ai-field-notes.git@fieldkit/v0.1.0#subdirectory=fieldkit"

Quickstart

from fieldkit.nim import NIMClient

client = NIMClient(base_url="http://localhost:8000/v1", model="meta/llama-3.1-8b-instruct")
print(client.chat([{"role": "user", "content": "Hello, Spark."}]))

What's in v0.1.0

Module	Purpose	Source articles
fieldkit.capabilities	Typed Python facade over spark-capabilities.json — KV cache math, weight bytes, inference envelope.	kv-cache-arithmetic-at-inference, gpu-sizing-math-for-fine-tuning
fieldkit.nim	OpenAI-compatible NIM client wrapper with retry, chunking, and the 8192-token context guard.	nim-first-inference-dgx-spark and friends
fieldkit.rag	Pipeline(embed_url, rerank_url, pgvector_dsn, generator) — ingest → retrieve → rerank → fuse.	naive-rag-on-spark and friends
fieldkit.eval	Bench, Judge, Trajectory — the recurring eval harness shapes.	every article with a bench.py or benchmark.py
fieldkit.cli	fieldkit bench rag, fieldkit feasibility <id>, fieldkit envelope <size>.	discoverability

Modules deferred to v0.2: retriever, ft, guardrails, agents. To v0.3: train, observe.

Hardware

v0.1 is Spark-only. Every code path is verified on a DGX Spark (GB10, 128 GB unified memory, NIM 8B + embed NIM + pgvector co-resident). Portability to other CUDA 12.x boxes lands in v0.2+ when there's demand.

License

Apache-2.0. See LICENSE.

Links

• Blog: https://ainative.business/field-notes/
• Docs: https://ainative.business/fieldkit/
• Source: https://github.com/manavsehgal/ai-field-notes/tree/main/fieldkit
• Changelog: CHANGELOG.md