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-0.2.73.tar.gz (73.9 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-0.2.73-py3-none-any.whl (66.2 kB view details)

Uploaded Python 3

File details

Details for the file matrx_batch-0.2.73.tar.gz.

File metadata

  • Download URL: matrx_batch-0.2.73.tar.gz
  • Upload date:
  • Size: 73.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for matrx_batch-0.2.73.tar.gz
Algorithm Hash digest
SHA256 8bb359444de4aa11b7b04240852cff1c00a9faefdffc5129826fcddbdc453acc
MD5 59c35005e34d59887421f29467bb97c3
BLAKE2b-256 01631c3273876311c4f0552f4752d734771fdb4c6e05a00cf6fee05f5638859c

See more details on using hashes here.

Provenance

The following attestation bundles were made for matrx_batch-0.2.73.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-0.2.73-py3-none-any.whl.

File metadata

  • Download URL: matrx_batch-0.2.73-py3-none-any.whl
  • Upload date:
  • Size: 66.2 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-0.2.73-py3-none-any.whl
Algorithm Hash digest
SHA256 694697a2d2adef61b1715878bd55794c04973f27803a3f29ee390e0417542ad0
MD5 6c0a28ea81eb17c0126799e9fd2f0bd9
BLAKE2b-256 1eb3da9788ea6ef71929a8786450e45410912395c94ea803c09d2dfafbb0081e

See more details on using hashes here.

Provenance

The following attestation bundles were made for matrx_batch-0.2.73-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

This release

0.2.73 This release

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

0.2.64

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