briefcase-python 2.1.27

Python bindings for Briefcase AI
Documentation
briefcase-python-2.1.27 has been yanked.

Briefcase AI Python SDK

Python SDK for AI observability, replay, and decision tracking. briefcase-ai provides Rust-powered performance with a Python-native API.

Crates.io PyPI

Overview

Briefcase AI helps you:

  • Capture AI decision inputs/outputs as structured snapshots
  • Track estimated model costs and monitor drift signals
  • Sanitize sensitive values before storage or transport
  • Store, query, and replay decisions with SQLite or lakeFS backends

The package is built from the briefcase-core Rust runtime and exposed through PyO3 bindings.

Installation

pip install briefcase-ai

Requirements:

  • Python >=3.10
  • Rust toolchain only if building from source

Artifact note:

  • Releases can include native wheels and a source distribution (sdist).
  • If a wheel is unavailable for your environment, pip can install from sdist.

Compatibility

briefcase-ai supports both namespaces in this release:

  • Modern namespace: briefcase_ai
  • Legacy namespace: briefcase

Examples:

# Modern
from briefcase_ai import DecisionSnapshot, versioned

# Legacy (still supported)
from briefcase.integrations.lakefs import versioned_context
from briefcase.correlation import briefcase_workflow

Compatibility aliases exposed in briefcase_ai include:

  • versioned
  • lakefs_versioned
  • versioned_context
  • lakefs_context
  • briefcase_workflow

Alias usage may emit DeprecationWarning to encourage migration to canonical module paths.

Quick Start

import briefcase_ai

briefcase_ai.init()

decision = briefcase_ai.DecisionSnapshot("chat_completion")
decision.add_input(briefcase_ai.Input("prompt", "Summarize this text", "string"))
decision.add_input(briefcase_ai.Input("model", "gpt-4o-mini", "string"))
decision.add_output(
    briefcase_ai.Output("response", "Summary output", "string").with_confidence(0.94)
)
decision.with_execution_time(42.5)
decision.add_tag("environment", "prod")

storage = briefcase_ai.SqliteBackend.in_memory()
decision_id = storage.save_decision(decision)

loaded = storage.load_decision(decision_id)
print(loaded.function_name)

Core Capabilities

1) Decision Snapshot Modeling

  • DecisionSnapshot, Input, Output, ModelParameters, ExecutionContext
  • Build reproducible records for AI decisions and annotate with tags/metadata

2) Storage and Query

  • SqliteBackend for local/in-memory usage
  • LakeFSBackend for versioned storage workflows
  • Save/load/query snapshots and decision records

3) Drift Monitoring

calculator = briefcase_ai.DriftCalculator()
metrics = calculator.calculate_drift(["answer A", "answer A", "answer B"])
print(metrics.consistency_score, metrics.drift_score)

4) Cost Estimation

cost = briefcase_ai.CostCalculator()
estimate = cost.estimate_cost("gpt-4", 1200, 300)
print(estimate.total_cost, estimate.currency)

5) PII Sanitization

sanitizer = briefcase_ai.Sanitizer()
result = sanitizer.sanitize("Email me at user@example.com")
print(result.sanitized)

6) Replay

  • ReplayEngine can replay persisted snapshots/decisions from a configured backend
  • Useful for debugging determinism and policy validation workflows

7) Legacy Feature Modules

The distribution bundles legacy feature modules under briefcase.*, including:

  • briefcase.integrations (lakeFS, framework handlers, VCS adapters)
  • briefcase.correlation
  • briefcase.compliance
  • briefcase.rag
  • briefcase.external_data
  • briefcase.validation

Optional provider libraries (for example Pinecone/Weaviate/Chroma or framework-specific SDKs) may still be required at runtime depending on the integration you use.

Documentation Links

License

This project is licensed under GNU General Public License v3.0 (GPL-3.0-or-later). See the LICENSE file.