matrx-batch

Cost-shield foundation for AI workloads in the Matrx ecosystem.

Three primitives:

OpenAI Batch API + Anthropic Message Batches — submit JSONL, poll status, fetch results. Async wrappers over the provider SDKs.
Urgency router — BatchRouter.submit(job) routes a job to live inference, to the provider's Batch API (50% discount, 24h SLA), or makes a cost-based decision automatically.
Embedding cache — content-addressable (SHA256 of model || text) reuse of embedding vectors across the org. Backed by rag.embedding_cache via a host-supplied asyncpg pool.

Dependency posture

matrx-batch sits one notch above matrx-utils in the dependency graph and is consumed by matrx-rag (embedding cache) and matrx-ai (urgency router, future). It MUST NOT import from aidream/ or from any other matrx-* sibling — every cross-boundary dependency comes in via matrx_batch.configure(...).

Usage (host-side wiring)

import matrx_batch
from openai import AsyncOpenAI
from anthropic import AsyncAnthropic

matrx_batch.configure(
    openai_client=AsyncOpenAI(api_key=settings.OPENAI_API_KEY),
    anthropic_client=AsyncAnthropic(api_key=settings.ANTHROPIC_API_KEY),
    pool_factory=lambda: get_async_pg_pool(),
    cost_check=_aidream_budget_precheck,
)

# Wire the cache into matrx-rag's embed() funnel
import matrx_rag
matrx_rag.configure(embedding_cache=matrx_batch.get_embedding_cache())

Usage (consumer)

from matrx_batch import BatchRouter, BatchableJob

router = BatchRouter()
receipt = await router.submit(
    BatchableJob(
        kind="chat",
        provider="anthropic",
        urgency="auto",
        payload={"model": "claude-haiku-4-5", "messages": [...]},
        estimated_tokens_in=8200,
    )
)

Metadata

Release files for matrx-batch 0.2.150

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Source distribution for matrx-batch 0.2.150
File	Size	Uploaded
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Table of built distributions (wheels) for matrx-batch 0.2.150
File	Interpreter	ABI	Platform	Reset
matrx_batch-0.2.150-py3-none-any.whl 81.2 kB Oct 11, 2026	Python 3	none	any	Details

Total release size: 175.1 kB

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Size	81.2 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`1c222932ed171e917b99e07d1ea78898bae98bbcabc397ff2552c79bbb09b246`
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Upload date	Oct 11, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Publishing platform: GitHub Actions
Publishing repository: github.com/AI-Matrix-Engine/aidream
Publishing commit: github.com/AI-Matrix-Engine/aidream/tree/126a78003ff374d7b80eda78e98a121d6858cbe3

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