llm-batch-py 0.1.5

Cron-friendly batch LLM processing for Polars.

llm-batch-py

llm-batch-py is a cron-friendly batch LLM runner for Polars.

Install with pip install llm-batch-py.

It lets you:

• define structured-output or embedding jobs against a Polars DataFrame or LazyFrame
• build prompts with Rust-backed row templates, with Python UDFs available as fallback
• auto-poll prior batches, auto-submit only missing rows, and materialize a fresh Polars result table
• process large inputs incrementally with Runner.run_stream(...) from a DataFrame, LazyFrame, or host-supplied Iterable[DataFrame]
• persist manifests and raw artifacts in local or S3-backed parquet storage

Reruns are grouped implicitly by job.name inside one shared result_cache.root_uri. If you run the same job again against the same cache store, llm-batch-py reuses completed results, skips duplicate submission for still-active prior work, and only submits rows whose effective request identity changed or whose retries are still allowed.

llm-batch-py is cron-friendly, but it does not schedule itself. Your scheduler invokes Runner.run(), and each rerun acquires a short-lived job lock, polls prior batches, reuses completed results, keeps matching in-flight rows pending, and submits only new or changed rows. Changing BatchConfig.batch_size affects only future submissions; it does not invalidate result-cache hits for completed rows.

Provider batch submission is sequential. When one run needs multiple small provider batches, llm-batch-py submits them one by one rather than firing all submits concurrently.

If a small batch submit fails transiently before the provider accepts it, llm-batch-py does a short inline retry loop in the same run(). If the submit still fails, the batch stays recoverable in the result cache and later reruns retry that same small batch before submitting any later chunks. BatchConfig.max_retries applies per retryable small-batch submit and per retryable row-level provider failure.

LazyFrame support does not make Runner.run() fully streaming by itself. Runner.run() still materializes the full job input before validation and request building. Use Runner.run_stream(...) when you want chunked execution over a DataFrame, LazyFrame, or host-supplied Iterable[DataFrame].

Quickstart

import polars as pl
from pydantic import BaseModel

from llm_batch_py import (
    BatchConfig,
    LockConfig,
    OpenAIConfig,
    PromptCacheConfig,
    ResultCacheStoreConfig,
    Runner,
    StructuredOutputJob,
    structured_template,
)


class CompanyLabel(BaseModel):
    label: str


build_prompt = structured_template(
    system="Return JSON only.",
    messages="Label this company: {{ row.company_name }}",
    name="build_prompt",
    version="v1",
)


job = StructuredOutputJob(
    name="company_labels",
    key_cols=["id"],
    input_df=pl.DataFrame({"id": [1], "company_name": ["OpenAI"]}),
    prompt_builder=build_prompt,
    output_model=CompanyLabel,
    provider=OpenAIConfig(model="gpt-4o-mini"),
    result_cache=ResultCacheStoreConfig(root_uri="./.llm_batch_py"),
    prompt_cache=PromptCacheConfig(mode="auto"),
    lock=LockConfig(ttl_seconds=3600),
    batch=BatchConfig(batch_size=500),
)

runner = Runner()
result_df = runner.run(job)
print(result_df)
print(runner.last_summary)

slim_df = runner.run(job, metadata_columns=["llm_batch_py_status"])
print(slim_df)

Streaming Large Inputs

import polars as pl

lazy_input = (
    pl.scan_parquet("./companies.parquet")
    .select(["id", "company_name"])
)

runner = Runner()
for chunk_df in runner.run_stream(
    job=job.__class__(**{**job.__dict__, "input_df": lazy_input}),
    input_batch_rows=10_000,
):
    print(chunk_df.select(["id", "llm_batch_py_status"]))

print(runner.last_stream_summary)

run_stream() yields one result DataFrame per input chunk. Each chunk uses the same result-cache, polling, retry, and lock semantics as a normal short-lived run().

If your host already pages from Postgres or another source, you can also push batches directly:

def pg_batches() -> list[pl.DataFrame]:
    return [
        pl.DataFrame({"id": [1], "company_name": ["OpenAI"]}),
        pl.DataFrame({"id": [2], "company_name": ["Anthropic"]}),
    ]


for chunk_df in runner.run_stream(job=job, input_batches=pg_batches()):
    print(chunk_df.select(["id", "llm_batch_py_status"]))

For private S3-backed result caches, pass filesystem options through ResultCacheStoreConfig:

result_cache = ResultCacheStoreConfig(
    root_uri="s3://my-bucket/llm_batch_py-prod",
    storage_options={
        "profile": "my-profile",
        "client_kwargs": {"region_name": "us-west-2"},
    },
)

For a private AWS S3 bucket, the cache store can authenticate with any normal s3fs credential path:

• AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY
• optional AWS_SESSION_TOKEN
• AWS_PROFILE
• an attached IAM role

If you want explicit credentials in code:

result_cache = ResultCacheStoreConfig(
    root_uri="s3://my-bucket/llm_batch_py-prod",
    storage_options={
        "key": "AWS_ACCESS_KEY_ID",
        "secret": "AWS_SECRET_ACCESS_KEY",
        "token": "AWS_SESSION_TOKEN",
        "client_kwargs": {"region_name": "us-west-2"},
    },
)

The cache principal needs list, read, write, and delete access under the configured prefix because llm-batch-py lists manifests, reads and writes artifacts, and creates and removes lock files there.

If you point storage_options at a custom S3-compatible endpoint_url, llm-batch-py now rejects job locking by default because the lock protocol depends on AWS S3 exclusive-create semantics. Only bypass that with LockConfig(allow_unsafe_s3_compatible_locks=True) if you already serialize runners externally and accept possible cache corruption risk.

structured_template(...) and embedding_template(...) are the primary prompt-building APIs. They render {{ row.field }} placeholders through the Rust template engine for lower per-row overhead.

prompt_udf(...) remains supported as a compatibility fallback when templating is not expressive enough. A prompt_udf should return structured Python data such as dicts, lists, Pydantic models, and datetime-like values. llm-batch-py canonically serializes the rendered payload before computing result-cache keys, so dict insertion order does not affect cache hits.

Returned result frames include llm_batch_py_* metadata columns for provider status, token counts, and raw request/response inspection fields such as llm_batch_py_input_raw_json, llm_batch_py_request_raw_json, llm_batch_py_output_raw_json, and llm_batch_py_output_raw_text.

If you do not need the full metadata surface on the returned frame, pass metadata_columns=[...] to Runner.run() or Runner.run_stream() to join back only the requested llm_batch_py_* columns. Pass metadata_columns=[] to suppress metadata columns entirely.

Docs

• Prompt building guide
• Provider config reference
• Result cache guide
• S3 cache storage guide
• Prompt caching guide
• Locking guide
• Batching guide
• Streaming guide

Customizing provider prompt cache

from llm_batch_py import PromptCacheConfig

PromptCacheConfig(
    mode="auto",
    verbose=False,
)

• mode: Literal["off", "auto"] = "auto"
• verbose: bool = False

OpenAIConfig

from llm_batch_py import OpenAIConfig

OpenAIConfig(
    model="gpt-4o-mini",
    api_key=None,
    max_output_tokens=None,
    temperature=None,
    timeout=60.0,
    organization=None,
    base_url=None,
    dimensions=None,
    pricing=None,
)

• model: str
• api_key: str | None = None
• max_output_tokens: int | None = None
• temperature: float | None = None
• timeout: float | None = 60.0
• organization: str | None = None
• base_url: str | None = None
• dimensions: int | None = None
• pricing: ModelPricing | None = None
• provider_name: Literal["openai"] = "openai"

OpenAIConfig is used for:

• structured-output jobs
• embedding jobs

AnthropicConfig

from llm_batch_py import AnthropicConfig

AnthropicConfig(
    model="claude-3-5-haiku-latest",
    api_key=None,
    max_output_tokens=1024,
    temperature=None,
    timeout=60.0,
    pricing=None,
)

• model: str
• api_key: str | None = None
• max_output_tokens: int = 1024
• temperature: float | None = None
• timeout: float | None = 60.0
• pricing: ModelPricing | None = None
• provider_name: Literal["anthropic"] = "anthropic"

AnthropicConfig is used for structured-output jobs only.

Customizing result cache store config

result_cache=ResultCacheStoreConfig(...) controls the persistent result cache catalog used to reuse prior results. It is separate from the result_df returned by runner.run(job).

Completed results are stored in the result cache catalog and reused on later runs until you delete or replace that catalog. There is no result TTL or automatic result-cache eviction in llm-batch-py today.

from llm_batch_py import ResultCacheStoreConfig

result_cache = ResultCacheStoreConfig(
    root_uri="s3://my-bucket/llm_batch_py-prod",
)

• root_uri: base location for manifests, raw artifacts, and reusable cached results. This can be a local path like "./.llm_batch_py" or any fsspec URI such as s3://....
• storage_options: optional fsspec / s3fs filesystem options such as profile, key, secret, token, client_kwargs.region_name, or client_kwargs.endpoint_url.

For private S3-backed cache stores, see the S3 cache storage guide.

Default result cache key

Each request gets a content-addressed cache key for result reuse. llm-batch-py hashes the request payload together with the job/provider context, using SHA-256 over a canonical JSON representation.

By default the cache key includes:

• the rendered request payload from your prompt_builder or text_builder
• job.name
• prompt_builder.version or text_builder.version
• provider name and model
• endpoint kind (structured or embeddings)
• structured-output schema, when applicable
• provider options that affect outputs: temperature, max_output_tokens, dimensions, base_url, and organization
• shared prompt-cache config, when set on a structured-output job

Notably, row IDs and key_cols are not part of the result cache key. If two rows in the same job render the same payload under the same config, they will hit the same cached result.

Cache key customization

There is no explicit cache_key_fn or cache-key override API today.

The supported ways to change cache identity are:

• change job.name to create a separate cache namespace
• bump the prompt builder version when prompt semantics change
• change the rendered payload or provider/model settings
• point root_uri at a different result cache store if you want fully isolated cached results

Customizing lock config

lock=LockConfig(...) controls how llm-batch-py recovers from abandoned job locks.

from llm_batch_py import LockConfig

lock = LockConfig(ttl_seconds=6 * 60 * 60)

• ttl_seconds: if a previous run left a lock behind and it is older than this TTL, a new run can reclaim it. Default: 3600.
• allow_unsafe_s3_compatible_locks: bypasses the safeguard that rejects custom S3-compatible endpoint_url lock backends. Default: False.

allow_unsafe_s3_compatible_locks=True does not make S3-compatible locking safe. It only suppresses the fail-fast guard for users who already serialize runners externally.

If you mean provider prompt caching rather than the llm-batch-py result cache store, configure that on the job:

from llm_batch_py import PromptCacheConfig

job = StructuredOutputJob(
    ...,
    prompt_cache=PromptCacheConfig(mode="auto"),
)

PromptCacheConfig(...) controls shared provider-side prompt caching behavior.

• mode="off" disables provider prompt caching.
• mode="auto" uses provider-managed prompt caching.
• verbose=True logs one INFO estimated-analysis diagnostic per distinct prompt shape showing the likely cacheable prefix candidate and likely trailing dynamic content candidate.
• llm-batch-py does not expose provider-specific prompt-cache options; it only enables shared automatic caching behavior.

Customizing batch config

batch=BatchConfig(...) controls how pending rows are grouped into provider batch submissions.

from llm_batch_py import BatchConfig

batch = BatchConfig(
    batch_size=500,
    max_retries=3,
)

• batch_size: hard cap on requests per submitted batch. If unset, llm-batch-py uses the provider's built-in batch request limit.
• max_retries: maximum retries for retryable failed rows before they are held as failed. Default: 2.

Provider payload byte caps and batch completion windows are derived internally from the selected provider adapter rather than configured in BatchConfig.