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ROADMAP.md

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LangLang Development Roadmap

This document outlines the strategic development roadmap for LangLang, detailing our planned features and improvements for the coming quarters.

Q2 2024: Enhanced Multi-Modal Capabilities

Vision-Language Integration

We are excited to announce the upcoming integration of advanced vision-language capabilities into LangLang. This enhancement will enable seamless processing of both textual and visual inputs through our unified interface.

from langlang import VisionProcessor, MultiModalChain

# Initialize the vision processor
vision = VisionProcessor()

# Create a multi-modal chain
chain = MultiModalChain(
    vision_processor=vision,
    llm=OpenAI(),
    prompt="Describe this image in the style of {style}"
)

# Process both image and text
result = chain.process(
    image="path/to/image.jpg",
    style="Shakespearean"
)

Key features:

  • Advanced Image Processing: Leveraging state-of-the-art vision models
  • Seamless Integration: Unified API for both text and image inputs
  • Context-Aware Processing: Intelligent understanding of visual context
  • Enterprise-Grade Features: Production-ready image processing pipeline

Q3 2024: Cross-Platform Expansion

F# Integration

LangLang is expanding beyond Python to provide a comprehensive, cross-platform solution. Our F# integration will bring the power of LangLang to the .NET ecosystem.

open LangLang

let llm = OpenAI(apiKey = "your-api-key")

let generateStory (prompt: string) =
    let chain = LangChain.Chain(
        llm = llm,
        prompt = "Write a story about {topic}"
    )
    chain.Run(topic = prompt)

Key features:

  • Native F# Support: Full integration with F# type system
  • Seamless Interop: Easy interaction between F# and Python components
  • Performance Optimization: Leveraging F# performance characteristics
  • Enterprise-Grade Features: Production-ready F# integration

Q4 2024: Distributed Computing

LangLang MCP (Master Control Program) Server

Introducing the LangLang MCP Server, a distributed computing framework that enables seamless coordination of multiple LangLang instances across your infrastructure.

from langlang.mcp import MCPServer, WorkerNode

# Initialize the MCP server
mcp = MCPServer(
    host="localhost",
    port=8080,
    max_workers=1000
)

# Register worker nodes
worker = WorkerNode(
    capabilities=["text", "vision", "audio"],
    load_balancing=True
)
mcp.register_worker(worker)

# Deploy distributed chains
chain = DistributedChain(
    mcp=mcp,
    strategy="dynamic-load-balancing"
)
result = chain.process(
    input="Hello, distributed world!",
    workers=10
)

Key features:

  • Distributed Processing: Seamless coordination across multiple nodes
  • Dynamic Load Balancing: Intelligent resource allocation
  • Fault Tolerance: Automatic recovery from node failures
  • Enterprise-Grade Features: Production-ready distributed computing

Future Considerations

Quantum Computing Integration

We are exploring the potential of quantum computing to enhance LangLang's processing capabilities. This is currently in the research phase.

Blockchain-Based Model Registry

A decentralized system for managing and versioning LLM models, ensuring transparency and reproducibility.

Neural Interface Support

Direct integration with neural interfaces for enhanced human-AI interaction (subject to regulatory approval).

Contributing to the Roadmap

We welcome community contributions to help shape LangLang's future. Please visit our GitHub repository to submit proposals or discuss potential features.

Version Timeline

  • v0.1.0 (Current): Core functionality and G-UNIT integration
  • v0.2.0 (Q2 2024): Multi-modal support
  • v0.3.0 (Q3 2024): F# integration
  • v0.4.0 (Q4 2024): MCP Server
  • v1.0.0 (2025): Production-ready release