nlproxy 0.1.0

High-performance prompt compression and LLM proxy SDK by IntelliDeep

nlproxy (Python SDK)

A high-performance, native Python library for semantic prompt compression, prompt firewalling (jailbreak protection), and secure LLM orchestration. Developed and owned by IntelliDeep.

Compiled using PyO3 and Maturin directly from Rust, nlproxy offers sub-millisecond execution times and ~1500x higher throughput than pure Python-based middleware solutions.

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🚀 Key Features

• PII Shielding & Masking: Redact sensitive data (IPs, emails, API keys, credentials) before they reach cloud LLMs.
• Semantic Prompt Compression: Locally segment and compress long prompts using KMeans semantic clustering on quantized weights.
• Candle Inference Integration: Utilizes Hugging Face Candle to run Sentence-Transformers locally and offline with zero network calls.
• Unified Pipeline Orchestration: Combine cache checks, local prompt firewalls, prompt compression, LLM generation (Gemini, OpenAI, Claude), and post-LLM validation.

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📦 Installation

Ensure you have Python 3.8+ installed, then run:

pip install nlproxy

Maturin will link the precompiled binary (.so on Linux/macOS or .pyd on Windows) directly into your virtual environment.

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🐍 Python API Quickstart

1. Initialize the Offline Model Engine

Before processing prompts, load your local quantized Sentence-Transformers model weights (e.g. all-MiniLM-L6-v2) offline:

import nlproxy

success = nlproxy.init_engine(
    "models/model.onnx",
    "models/config.json",
    "models/tokenizer.json"
)

if success:
    print("IntelliDeep semantic engine successfully loaded offline!")

2. Shield and Compress Prompts

import nlproxy

# Create the request payload
request = nlproxy.CompressRequest(
    text="The database server is located at 192.168.50.22. Please run checkup.",
    mode="general",
    aggressiveness=0.5
)

# Run the native compression
response = nlproxy.compress_prompt(request)

print("Processed text:", response.processed_text)
# Output: "The database server is located at __PROT_82736284__. Please run checkup."

print("Extracted Placeholders:", response.placeholders)
# { "__PROT_82736284__": "192.168.50.22" }

3. Unified Async Orchestration Pipeline

import nlproxy

request = nlproxy.CompressUnifiedRequest(
    prompt="Generate status report for 192.168.1.1",
    domain="general",
    aggressiveness=0.0,
    provider="gemini",
    model="gemini-1.5-pro",
    bypass_cache=False,
    check_firewall=True,
    semantic_drift_threshold=0.75
)

try:
    response = nlproxy.run_unified_pipeline(request)
    if response.allowed:
        print("Raw response from LLM:", response.raw_response)
        print("Final response with PII restored:", response.final_response)
        print("Execution overhead:", response.latency_ms, "ms")
    else:
        print("Blocked by Prompt Firewall:", response.violations)
except Exception as e:
    print("Execution failed:", str(e))

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🛠️ Architectural Open Core Model

This Python SDK is built on top of nlproxy-core using a hybrid commercial model:

• Open Core (Default): Single-threaded limit, artificial 150 ms delay per unified execution. Recommended for local developer evaluation.
• Enterprise Basic: Up to 4 high-speed parallel threads, 0 ms artificial delays.
• Enterprise Unlimited: Uses all CPU cores, 0 ms artificial overhead.

To upgrade, load a valid license key into your environment variables:

export NLPROXY_LICENSE_KEY="your_base64_or_jwt_license_key"

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🏢 Authors & Attribution

Developed and maintained exclusively by IntelliDeep Solutions.

• B-GUST (Co-founder / Lead Developer)
• luiserb (Co-founder / Architect)

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© 2026 IntelliDeep Solutions. All rights reserved.