ai-labs

A professional collection of Proof of Concepts (PoCs), prototypes, and architectural patterns leveraging Artificial Intelligence, Large Language Models (LLMs), Generative AI, and Foundation Models.

This repository acts as a polyglot testing ground, implementing AI capabilities across multiple programming languages to evaluate performance, SDK ecosystems, and integration patterns.

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🛠️ Repository Structure

To maintain clean dependencies and avoid build conflicts, this repository is strictly organized by programming language at the root level.

ai-labs/
├── go/                  # Go (Golang) implementations
│   ├── .golangci.yml
│   └── go.mod
├── java/                # Java implementations
│   └── pom.xml / build.gradle
├── javascript/          # Node.js / JavaScript implementations
│   └── package.json
├── perl/                # Perl 5 legacy/modern integrations
│   └── cpanfile
├── python/              # Python implementations
│   └── requirements.txt
└── README.md

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🚀 Core Focus Areas

The PoCs within this repository explore the following domains:

• Foundation Model Orchestration: Interfacing with frontier models via native SDKs and APIs.
• RAG (Retrieval-Augmented Generation): Vector database integrations and semantic search pipelines.
• Agentic Workflows: Autonomous tool-use and multi-agent reasoning chains.
• Prompt Engineering: Programmatic prompt optimization and structured data extraction.

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💻 Language & Stack Overview

🐍 Python (/python)

• Primary Focus: Heavy data processing, rapid prototyping, and framework evaluation.
• Key Tech: LangChain, LlamaIndex, OpenAI/Anthropic SDKs, Hugging Face.

🦫 Go (/go)

• Primary Focus: High-performance concurrent services and lightweight deployments.
• Key Tech: LangChainGo, official cloud provider SDKs.

⬢ JavaScript / Node.js (/javascript)

• Primary Focus: Full-stack integrations, edge functions, and real-time streaming interfaces.
• Key Tech: LangChain.js, Vercel AI SDK.

☕ Java (/java)

• Primary Focus: Enterprise integration patterns, strict typing architectures, and robust pipelines.
• Key Tech: LangChain4j.

🐪 Perl 5 (/perl)

• Primary Focus: Text processing, legacy integrations, and lightweight API clients.
• Key Tech: REST::Client, JSON::MaybeXS, custom model wrappers.

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⚙️ Prerequisites & Setup

Each language directory contains its own localized setup instructions. However, all projects generally require:

1. API Credentials: Set up your environment variables in a root or localized .env file.

   OPENAI_API_KEY="your_key_here"
   ANTHROPIC_API_KEY="your_key_here"

2. Language Runtimes: Ensure you have the appropriate runtimes installed (e.g., Python 3.10+, Node 18+, Go 1.20+, JDK 17+, Perl 5.30+).

Please navigate to the specific language subdirectory for detailed installation and execution commands.

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📄 License

This repository is available under the MIT License. Feel free to use, modify, and distribute the code patterns demonstrated here.