Metadata-Version: 2.4
Name: aipea
Version: 1.3.0
Summary: AI Prompt Engineer Agent — prompt preprocessing, security screening, and context enrichment for LLM systems
Project-URL: Homepage, https://github.com/undercurrentai/AIPEA
Project-URL: Repository, https://github.com/undercurrentai/AIPEA
Project-URL: Issues, https://github.com/undercurrentai/AIPEA/issues
Author-email: Undercurrent Holdings <dev@undercurrentholdings.com>
License-Expression: MIT
License-File: LICENSE
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Typing :: Typed
Requires-Python: >=3.11
Requires-Dist: httpx>=0.27.0
Provides-Extra: cli
Requires-Dist: typer[all]>=0.12; extra == 'cli'
Provides-Extra: dev
Requires-Dist: mypy>=1.13; extra == 'dev'
Requires-Dist: pytest-asyncio>=0.23; extra == 'dev'
Requires-Dist: pytest-cov>=5.0; extra == 'dev'
Requires-Dist: pytest>=8.0; extra == 'dev'
Requires-Dist: ruff>=0.8.0; extra == 'dev'
Requires-Dist: typer[all]>=0.12; extra == 'dev'
Description-Content-Type: text/markdown

# AIPEA — AI Prompt Engineer Agent

[![Python 3.11+](https://img.shields.io/badge/python-3.11%2B-blue)](https://python.org)
[![License: MIT](https://img.shields.io/badge/license-MIT-green)](LICENSE)
[![Tests](https://img.shields.io/badge/tests-698%20passing-brightgreen)]()
[![Coverage](https://img.shields.io/badge/coverage-91.42%25-brightgreen)]()
[![PyPI](https://img.shields.io/pypi/v/aipea)](https://pypi.org/project/aipea/)

A standalone Python library for prompt preprocessing, security screening, query analysis, and context enrichment for LLM systems. Extracted from [Agora IV](https://github.com/undercurrentai/agora-iv) production (v4.1.49).

## Architecture

AIPEA processes prompts through a multi-stage pipeline:

```
User Query → SecurityScanner → QueryAnalyzer → SearchOrchestrator → PromptEngine → Enhanced Prompt
                  │                   │                 │                  │
              PII/PHI scan      Tier routing      Context fetch     Model-specific
              Classification    Domain detect     Knowledge base    prompt formatting
              Injection guard   Complexity score   MCP providers    Tier processing
```

### Core Modules

| Module | Purpose |
|--------|---------|
| `security` | PII/PHI detection, classification markers, injection prevention, compliance modes |
| `analyzer` | Query complexity scoring, domain detection, temporal awareness, tier routing |
| `search` | Multi-provider search orchestration (Exa, Firecrawl, Context7) |
| `knowledge` | Offline knowledge base with SQLite storage and domain-aware retrieval |
| `engine` | Model-specific prompt formatting, tier-based processing (Offline/Tactical/Strategic) |
| `enhancer` | High-level facade coordinating the full pipeline |

### Processing Tiers

| Tier | Latency | Use Case |
|------|---------|----------|
| **Offline** | <2s | Air-gapped, classified, simple queries |
| **Tactical** | 2-5s | Standard queries with search context |
| **Strategic** | 5-15s | Complex research, multi-source synthesis |

## Installation

```bash
# Library only (no CLI)
pip install aipea

# With CLI tools (adds Typer + Rich)
pip install aipea[cli]

# From source (development)
pip install -e ".[dev]"
```

## Getting Started

AIPEA works out of the box with zero configuration. API keys and Ollama are entirely optional — they unlock richer enhancement when available.

**Path 1: Minimal (no setup needed)**

```bash
pip install aipea
```

```python
from aipea import enhance_prompt
result = await enhance_prompt("What is quantum computing?", model_id="gpt-4")
# Works immediately with template-based enhancement — no API keys required
```

**Path 2: With Search Providers (real-time web context)**

[Exa](https://exa.ai) provides AI-powered web search; [Firecrawl](https://firecrawl.dev) provides structured web content retrieval. Both offer free tiers.

```bash
pip install aipea[cli]
aipea configure          # interactive wizard — press Enter to skip any key
```

**Path 3: With Ollama (local LLM enhancement)**

Ollama runs open-source LLMs locally for richer offline enhancement. AIPEA auto-detects it when available.

```bash
# macOS
brew install ollama

# Linux
curl -fsSL https://ollama.ai/install.sh | sh

# Pull a lightweight model
ollama pull gemma3:1b

# Populate the offline knowledge base
aipea seed-kb
```

Run `aipea doctor` at any time to see what capabilities are active and what you can add.

## Configuration

All API keys are optional. AIPEA can be configured via environment variables, a `.env` file, or `~/.aipea/config.toml`. Priority: env vars > `.env` > global TOML > defaults.

```bash
# Interactive setup wizard (requires [cli] extra)
aipea configure

# Save to global config instead of project .env
aipea configure --global

# Check current configuration
aipea check

# Full diagnostic report
aipea doctor
```

Or configure manually with environment variables:

```bash
export EXA_API_KEY="your-exa-key"
export FIRECRAWL_API_KEY="your-firecrawl-key"
export AIPEA_HTTP_TIMEOUT=30  # seconds (optional)
```

Or create a `.env` file in your project root:

```
EXA_API_KEY="your-exa-key"
FIRECRAWL_API_KEY="your-firecrawl-key"
```

## Usage

### Quick Start — `enhance_prompt`

The simplest way to use AIPEA is through the `enhance_prompt` facade:

```python
import asyncio
from aipea import enhance_prompt

async def main():
    result = await enhance_prompt(
        "What are the latest advances in transformer architectures?",
        model_id="gpt-5.2",
    )
    print(result.enhanced_prompt)
    print(f"Processing tier: {result.processing_tier}")
    print(f"Security context: {result.security_context.security_level}")

asyncio.run(main())
```

### Security Scanning

```python
from aipea import SecurityScanner, SecurityContext, SecurityLevel, ComplianceMode

scanner = SecurityScanner()
context = SecurityContext(
    security_level=SecurityLevel.UNCLASSIFIED,
    compliance_mode=ComplianceMode.HIPAA,
)

# Scan for PII, PHI, classification markers, and injection attempts
result = scanner.scan("Patient John Doe, SSN 123-45-6789, diagnosed with...", context)
print(result.has_pii())       # True
print(result.has_phi())       # True
print(result.is_blocked)      # False (PII is flagged, not blocked)
print(result.flags)           # ["pii_detected:ssn", "phi_detected:diagnosis", ...]
```

### Query Analysis

```python
from aipea import QueryAnalyzer

analyzer = QueryAnalyzer()
analysis = analyzer.analyze("Compare CRISPR-Cas9 efficiency across cell types in 2026 studies")

print(analysis.query_type)          # QueryType.RESEARCH
print(analysis.suggested_tier)      # ProcessingTier.STRATEGIC
print(analysis.complexity)          # 0.85
print(analysis.needs_current_info)  # True (detected temporal reference)
print(analysis.domain_indicators)   # ["biology", "genetics"]
```

### Offline Knowledge Base

```python
import asyncio
from aipea import OfflineKnowledgeBase, StorageTier, KnowledgeDomain

async def main():
    kb = OfflineKnowledgeBase("/path/to/knowledge.db", StorageTier.STANDARD)

    # Add domain knowledge
    await kb.add_knowledge(
        "Transformer attention mechanism computes Q*K^T/sqrt(d_k) for scaled dot-product attention.",
        domain=KnowledgeDomain.TECHNICAL,
    )

    # Search knowledge base
    results = await kb.search("attention mechanism", limit=5)
    for node in results.nodes:
        print(f"[{node.relevance_score:.2f}] {node.content[:100]}")

    kb.close()

asyncio.run(main())
```

### Search Orchestration

```python
import asyncio
from aipea import SearchOrchestrator

async def main():
    orchestrator = SearchOrchestrator()

    # Multi-provider search with strategy selection
    results = await orchestrator.search(
        "quantum error correction 2026",
        strategy="multi_source",
        num_results=10,
    )

    for result in results.results:
        print(f"[{results.source}] {result.title}: {result.url}")

asyncio.run(main())
```

## Integration

AIPEA is designed as a standalone preprocessing layer for LLM systems. It integrates with:

- **[AEGIS Governance](https://github.com/undercurrentai/aegis-governance)** — engineering standards & compliance SDK (`pip install aegis-governance[aipea]`)
- **Agora IV** — multi-model orchestration platform (uses AIPEA for prompt preprocessing)

## Enterprise & Governance

AIPEA is free and open-source. For organizations that need full AI governance — risk registers, model cards, compliance auditing, and policy enforcement — see [AEGIS](https://github.com/undercurrentai/aegis-governance), Undercurrent AI's governance platform.

AIPEA's compliance modes (HIPAA, TACTICAL) provide runtime security controls, with FEDRAMP support planned. AEGIS adds the organizational layer: audit trails, human oversight workflows, and regulatory reporting.

Learn more at [undercurrentholdings.com](https://undercurrentholdings.com).

## Development

```bash
# Install with dev dependencies
pip install -e ".[dev]"

# Run all checks
make all        # format + lint + type check + tests

# Individual commands
make fmt         # Ruff format + auto-fix
make lint        # Ruff check + format check
make type        # mypy strict mode
make test        # pytest with coverage (75% minimum)
make sec         # Security-focused lint rules
make ci          # CI parity (lint + type + test, no autofix)
```

## Testing

```bash
# Full test suite with coverage
pytest tests/ -v --cov=src/aipea --cov-report=term-missing

# Run specific module tests
pytest tests/test_security.py -v
pytest tests/test_analyzer.py -v
pytest tests/test_engine.py -v
```

## License

MIT
