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 byrag.embedding_cachevia 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.
1 maintainer
Metadata
1 maintainer
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