Skip to main content

matrx-batch

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

Three primitives:

  1. OpenAI Batch API + Anthropic Message Batches — submit JSONL, poll status, fetch results. Async wrappers over the provider SDKs.
  2. Urgency routerBatchRouter.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.
  3. 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,
    )
)

See also

  • CLAUDE.md — package rules and injection-point contract.
  • matrx_batch/embedding_cache.py — chunk-level embedding reuse.
  • matrx_batch/router.py — urgency routing decisions.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

matrx_batch-0.2.64.tar.gz (72.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

matrx_batch-0.2.64-py3-none-any.whl (64.9 kB view details)

Uploaded Python 3

File details

Details for the file matrx_batch-0.2.64.tar.gz.

File metadata

  • Download URL: matrx_batch-0.2.64.tar.gz
  • Upload date:
  • Size: 72.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for matrx_batch-0.2.64.tar.gz
Algorithm Hash digest
SHA256 95472223bcb5d4793191f6e51cbd641b00b17a13cf7b52cc121b40f078087f1e
MD5 afa8f119c1c6624eb87962652d823c56
BLAKE2b-256 d0ba091cc8b60f059e038c17ecb29fbc78b8cce5d8ba08b7d0b0cc62dc7a0f7e

See more details on using hashes here.

Provenance

The following attestation bundles were made for matrx_batch-0.2.64.tar.gz:

Publisher: publish-package.yml on AI-Matrix-Engine/aidream

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file matrx_batch-0.2.64-py3-none-any.whl.

File metadata

  • Download URL: matrx_batch-0.2.64-py3-none-any.whl
  • Upload date:
  • Size: 64.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for matrx_batch-0.2.64-py3-none-any.whl
Algorithm Hash digest
SHA256 618846453ceb213b35e0c022ab36d1d610a2351f59d78a708fc191742095458e
MD5 9024d8f82fb61cbea0aae4bc24166229
BLAKE2b-256 573c6d0bf51e188cad9f399b161ba7521d53df4cdd7976f9fd185e70abb0b917

See more details on using hashes here.

Provenance

The following attestation bundles were made for matrx_batch-0.2.64-py3-none-any.whl:

Publisher: publish-package.yml on AI-Matrix-Engine/aidream

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.2.86

2 files

0.2.84

2 files

0.2.83

2 files

0.2.82

2 files

0.2.81

2 files

0.2.80

2 files

0.2.79

2 files

0.2.78

2 files

0.2.77

2 files

0.2.76

2 files

0.2.75

2 files

0.2.74

2 files

0.2.73

2 files

0.2.72

2 files

0.2.71

2 files

0.2.70

2 files

0.2.69

2 files

0.2.68

2 files

0.2.67

2 files

0.2.66

2 files

0.2.65

2 files

This release

0.2.64 This release

2 files

0.2.63

2 files

0.2.62

2 files

0.2.61

2 files

0.2.60

2 files

0.2.59

2 files

0.2.58

2 files

0.2.57

2 files

0.2.56

2 files

0.2.55

2 files

0.2.54

2 files

0.2.53

2 files

0.2.52

2 files

0.2.51

2 files

0.2.50

2 files

0.2.49

2 files

0.2.48

2 files

0.2.47

2 files

0.2.46

2 files

0.2.45

2 files

0.2.44

2 files

0.2.43

2 files

0.2.42

2 files

0.2.41

2 files

0.2.40

2 files

0.2.39

2 files

0.2.38

2 files

0.2.37

2 files

0.2.36

2 files

0.2.35

2 files

0.2.34

2 files

0.2.33

2 files

0.2.32

2 files

0.2.31

2 files

0.2.30

2 files

0.2.29

2 files

0.2.28

2 files

0.2.27

2 files

0.2.26

2 files

0.2.25

2 files

0.2.24

2 files

0.2.23

2 files

0.2.22

2 files

0.2.21

2 files

0.2.20

2 files

0.2.19

2 files

0.2.18

2 files

0.2.17

2 files

0.2.16

2 files

0.2.15

2 files

0.2.14

2 files

0.2.13

2 files

0.2.12

2 files

0.2.11

2 files

0.2.10

2 files

0.2.9

2 files

0.2.8

2 files

0.2.7

2 files

0.2.6

2 files

0.2.5

2 files

0.2.4

2 files

0.2.3

2 files

0.2.2

2 files

0.2.1

2 files

0.2.0

2 files

0.1.1

2 files

0.1.0

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page