privysha 0.4.1

PrivySHA (privacy focused secure hashing library) - drop-in security + optimization layer for LLM apps (developer preview)

PrivySHA

Drop-in security and token optimization for LLM apps — mask PII, block injection patterns, and compress prompts before they reach any model.

v0.4.1 developer preview — architecture-stable, API may evolve before 1.0.0. Pin your version in production. See docs/developer-preview.md.

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What it does

Your app  →  process() / wrap_llm()  →  safe, smaller prompt  →  LLM

PrivySHA sits between your application and the model. One function call can:

• Mask emails, phones, API keys, and other PII
• Run prompt-injection checks
• Compress verbose prompts to save tokens
• Return typed results with metrics and optional traces

No global config. No pipeline boilerplate. Works without API keys for preprocessing.

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Install

pip install privysha

Python 3.10+ required. From source:

pip install -e .

Optional extras:

pip install privysha[openai]        # OpenAI client wrapping
pip install privysha[ml]              # Hybrid ML PII detection
pip install privysha[integrations]  # FastAPI, LangChain, etc.

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60-second example

from privysha import process

result = process("Contact [e-mail] — analyze this sales data")
print(result)                    # str(result) → optimized output
print(result.output)             # same text, typed access
print(result.security.pii_detected)
print(result.metrics.token_reduction_pct)

Wrap an existing client (recommended for production):

from privysha.integrations import wrap_llm
import openai

client = wrap_llm(openai.OpenAI(), mode="balanced")
client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Email me at [e-mail]"}],
)

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Public API

Root package exports five symbols only:

from privysha import process, sanitize, optimize, Agent

Everything else uses explicit subpackage imports:

from privysha.integrations import wrap_llm, auto_patch
from privysha.runtime import PromptProcessor
from privysha.types import ProcessResult, SanitizeResult
from privysha.core.policy_config import PolicyConfig

Function	What it does
process()	Security → compile → optimize (full path)
sanitize()	Security / PII only
optimize()	Token compression only
Agent	Preprocess + call an LLM adapter
wrap_llm()	Transparent SDK wrapper (integrations)

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Modes

process(prompt, mode="balanced")  # default — fail-open with fallback
process(prompt, mode="strict")    # fail-closed — raises on total failure
process(prompt, mode="lite")      # minimal policy features
process(prompt, mode="off")       # passthrough, no changes

Advanced options go in PolicyConfig, not loose kwargs:

from privysha.core.policy_config import PolicyConfig

process(
    prompt,
    policy=PolicyConfig(
        pii_mode="hybrid",      # needs privysha[ml]
        reversible=True,
        preserve_intent=True,
    ),
)

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Agent (preprocess + LLM)

from privysha import Agent

agent = Agent(model="mock")  # no API key needed for mock
print(agent.run("Summarize data from [e-mail]"))

With a real provider, set OPENAI_API_KEY and use model="gpt-4o-mini".

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Architecture (v0.4.1)

privysha/
├── core/           # engines: security, compiler, policy
├── runtime/        # PromptProcessor, Agent, adapters
├── integrations/   # wrap_llm, auto_patch, framework middleware
├── types/          # ProcessResult, SanitizeResult
├── utils/          # drop-in functions
├── compat/         # opt-in legacy dict helpers
└── cli/            # privysha command

process() → PromptProcessor → three engines: security, compile, optimize.

Details: docs/architecture.md

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Documentation

Guide	Description
Quickstart	5-minute walkthrough
Getting Started	Install, modes, CLI
API Reference	Full signatures
Security	PII, masking, fail-closed
Migration v0.4	Upgrading from 0.3.x
Deprecations	Removed symbols

Build docs locally:

pip install -e ".[docs]"
mkdocs serve

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Tests

pip install -e ".[dev]"
pytest tests -q

CI runs on Ubuntu, Windows, and macOS (Python 3.10–3.12).

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Status

Ready for	Not yet
Pinned production pilots (privysha==0.4.1)	Stable 1.0 API guarantee
process() / wrap_llm() drop-in use	Certified compliance product
Architecture-frozen 0.4.x line	Unpinned dep without migration budget

Stable public API is planned for 1.0.0 after a freeze period on 0.5.x. See docs/versioning.md.

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License

Apache 2.0 — see LICENSE.