briefcase-ai 2.4.1

Python SDK for AI observability, replay, and decision tracking - Rust-powered performance

Briefcase AI Python SDK

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

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

For live lakeFS integrations:

pip install "briefcase-ai[lakefs]"

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.

Canonical Import Surface

Use briefcase_ai for all new code. Compatibility aliases exposed in briefcase_ai include:

• versioned
• lakefs_versioned
• versioned_context
• lakefs_context
• briefcase_workflow

The legacy briefcase namespace remains available in 2.1.30 as a compatibility alias and emits DeprecationWarning. Alias removal is planned for 2.1.31.

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 [e-mail]")
print(result.sanitized)

6) Replay

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

7) Feature Modules via briefcase_ai

The distribution exposes feature modules through briefcase_ai.*, including:

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

• Product docs: https://docs.jalebiventures.com
• Getting started: https://docs.jalebiventures.com/docs/getting-started/installation
• Quick start: https://docs.jalebiventures.com/docs/getting-started/quickstart

License

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