minima-llm 0.2.2

Minimal async LLM backend with caching and batch execution

minima-llm

Minimal async LLM backend with caching and batch execution.

Features

• Zero Dependencies: Core package uses only Python stdlib (asyncio, urllib, sqlite3)
• SQLite Cache: Automatic prompt caching with WAL mode for multi-process safety
• Batch Execution: Worker pool pattern with heartbeat, failure tracking, and early abort
• Rate Limiting: RPM pacing with server-learned limits from rate limit headers
• Retry Logic: Exponential backoff with jitter, cooldown after overload
• OpenAI Compatible: Works with any OpenAI-compatible endpoint
• DSPy Integration: Optional adapter for DSPy framework (requires [dspy] extra)

Installation

# Core only (no dependencies)
pip install minima-llm

# With DSPy support
pip install minima-llm[dspy]

# With YAML config support
pip install minima-llm[yaml]

# Development
pip install minima-llm[dev]

Quick Start

Basic Usage

import asyncio
from minima_llm import MinimaLlmConfig, OpenAIMinimaLlm, MinimaLlmRequest

async def main():
    # Configure from environment or explicit values
    config = MinimaLlmConfig(
        base_url="https://api.openai.com/v1",
        model="gpt-4",
        api_key="sk-...",
        cache_dir="./cache",
    )

    backend = OpenAIMinimaLlm(config)

    # Single request
    request = MinimaLlmRequest(
        request_id="q1",
        messages=[{"role": "user", "content": "What is 2+2?"}],
        temperature=0.0,
    )

    result = await backend.generate(request)
    print(result.text)

    await backend.aclose()

asyncio.run(main())

Batch Execution

import asyncio
from minima_llm import MinimaLlmConfig, OpenAIMinimaLlm, MinimaLlmRequest

async def main():
    config = MinimaLlmConfig.from_env()
    backend = OpenAIMinimaLlm(config)

    requests = [
        MinimaLlmRequest(
            request_id=f"q{i}",
            messages=[{"role": "user", "content": f"Question {i}"}],
        )
        for i in range(100)
    ]

    # Run batch with progress heartbeat
    results = await backend.run_batched(requests)

    for r in results:
        if hasattr(r, 'text'):
            print(f"{r.request_id}: {r.text[:50]}...")

    await backend.aclose()

asyncio.run(main())

With DSPy

import asyncio
import dspy
from minima_llm import MinimaLlmConfig, OpenAIMinimaLlm
from minima_llm.dspy_adapter import MinimaLlmDSPyLM

class QA(dspy.Signature):
    question = dspy.InputField()
    answer = dspy.OutputField()

async def main():
    config = MinimaLlmConfig.from_env()
    backend = OpenAIMinimaLlm(config)
    lm = MinimaLlmDSPyLM(backend)

    dspy.configure(lm=lm)

    predictor = dspy.ChainOfThought(QA)
    result = await predictor.acall(question="What is the capital of France?")
    print(result.answer)

    await backend.aclose()

asyncio.run(main())

Batch Management

For long-running batch jobs using the OpenAI batch API, minima-llm provides batch state management with local state files for resumption after interruption.

Configuration

Enable Parasail batch mode in your config:

parasail:
  llm_batch_prefix: "my-project"  # Prefix for batch state files
  state_dir: "./batch-state"      # Directory for state files (defaults to cache_dir)
  poll_interval_s: 30             # How often to poll for completion
  max_poll_hours: 24              # Maximum time to wait

Batch Management Functions

These functions are available for programmatic batch management:

from minima_llm import (
    batch_status_overview,
    cancel_batch,
    cancel_all_batches,
    cancel_all_local_batches,
    MinimaLlmConfig,
)

config = MinimaLlmConfig.from_yaml("config.yml")

# Show status of all local batch state files
batch_status_overview(config)

# Cancel a specific batch by remote batch ID
cancel_batch("batch_abc123", config)

# Cancel all batches matching a prefix
cancel_all_batches(config, prefix="my-project")

# Cancel ALL local batches
cancel_all_local_batches(config)

Command Line Interface

minima-llm provides a standalone CLI for batch management:

# Show status of all batches (uses CACHE_DIR from environment)
minima-llm batch-status

# With explicit config file
minima-llm batch-status --config config.yml

# Cancel batches matching a prefix
minima-llm batch-status --cancel my-prefix

# Cancel a specific remote batch by ID
minima-llm batch-status --cancel-remote batch_abc123

# Cancel ALL local batches
minima-llm batch-status --cancel-all

When calling from a different directory, use absolute paths or set environment variables:

# Absolute path to config
minima-llm batch-status --config /path/to/project/config.yml

# Or set CACHE_DIR to find batch state files
CACHE_DIR=/path/to/project/cache minima-llm batch-status

Configuration

Environment Variables

Variable	Description	Default
OPENAI_BASE_URL	API endpoint URL	(required)
OPENAI_MODEL	Model identifier	(required)
OPENAI_API_KEY	API key	None
CACHE_DIR	SQLite cache directory	None (disabled)
BATCH_NUM_WORKERS	Concurrent workers	64
MAX_OUTSTANDING	Max in-flight HTTP requests	32
RPM	Requests per minute (0=unlimited)	600
TIMEOUT_S	Per-request timeout	60.0
MAX_ATTEMPTS	Max retry attempts (0=infinite)	6
CACHE_FORCE_REFRESH	Skip cache reads, still write	0 (disabled)
MINIMA_TRACE_FILE	Cache key debug log (JSONL)	None (disabled)

YAML Configuration

base_url: "https://api.openai.com/v1"
model: "gpt-4"
api_key: "sk-..."
cache_dir: "./cache"

# Optional batch settings
batch:
  num_workers: 64
  max_failures: 25
  heartbeat_s: 10.0

Load with:

config = MinimaLlmConfig.from_yaml("config.yml")

Prompt Caching

minima-llm includes an SQLite-backed prompt cache that stores LLM responses keyed by a SHA-256 hash of the request parameters (model, messages, temperature, max_tokens, extras). The database uses WAL mode for multi-process safety.

Enable / Disable

• Enable: Set cache_dir to a directory path via environment variable, YAML, or code. The cache database is created at {cache_dir}/minima_llm.db.
• Disable: Leave cache_dir unset (default). No cache files are created.

cache_dir: "./my-cache"

Force Refresh

Force refresh bypasses cache reads but still writes new responses to the cache, useful for regenerating stale entries.

• Config-wide: Set CACHE_FORCE_REFRESH=1 env var, or force_refresh: true in YAML.
• Per-request: Pass force_refresh=True to generate():

result = await backend.generate(request, force_refresh=True)

Debug Tracing

To diagnose cache misses, set MINIMA_TRACE_FILE to a file path. Every cache key computation is logged as a JSONL line containing the canonical JSON used for hashing and the resulting SHA-256 key:

MINIMA_TRACE_FILE=trace.jsonl python my_script.py

Each line has the form {"key": "<sha256>", "canonical": "<json>"}. Compare canonical JSON between runs to spot differences causing cache misses.

Architecture

minima_llm/
├── protocol.py      # AsyncMinimaLlmBackend protocol, Request/Response types
├── config.py        # MinimaLlmConfig, BatchConfig, ParasailBatchConfig
├── backend.py       # OpenAIMinimaLlm - full async backend with cache
├── batch.py         # run_batched_callable, Parasail batch support, batch management
├── cli.py           # Command-line interface (minima-llm command)
└── dspy_adapter.py  # MinimaLlmDSPyLM, TolerantChatAdapter (optional)

Multi-Loop Support

The backend is designed to be reused across multiple asyncio.run() calls:

backend = OpenAIMinimaLlm(config)

# First asyncio.run()
asyncio.run(batch1(backend))

# Second asyncio.run() - works correctly
asyncio.run(batch2(backend))

This is achieved through lazy per-loop initialization of async primitives.

License

MIT