resumesh-llm

A professional-grade, lightweight, and production-ready Python library powering the intelligent LLM features of the ResuMesh portfolio and CV builder. Architected strictly on SOLID and Domain-Driven Design (DDD) principles, it provides robust, schema-validated, and provider-agnostic abstractions to analyze repositories, optimize resume metrics, and build active career journals.

🎯 The "Aha!" Moment: See it in Action

High-impact resumes need metrics, actions, and structure. Here is how resumesh-llm transforms weak descriptions into Google-style XYZ Formula (Accomplished [X], as measured by [Y], by doing [Z]) statements:

🌟 Flagship Features

• 🧠 Self-Reflecting Critique Agents: Implements a graph-based state machine (StateGraph) that orchestrates a multi-step agentic reflection loop to refine work history.
• 🚀 Asynchronous RAG Pipeline: Ingests compliance regulations and ATS formatting guidelines, retrieving relevant chunks on the fly without blocking execution.
• 📊 Dynamic Gap Analysis: Gamifies CV-to-Job alignment, detecting missing hard/soft skills and offering suggestions.
• 💾 State Graph Checkpointing: Built-in Memory and Disk checkpointers (MemoryCheckpointer / FileCheckpointer) to freeze state and resume execution if rate limits or API outages occur.
• 🔌 Pluggable Provider Registry: Out-of-the-box support for OpenAI, Groq, Ollama, and Mock clients. Extend or inject custom proprietary LLM models dynamically.
• 🔒 Strict Pydantic V2 Validation: Uses a dedicated OutputParser layer to ensure LLM outputs conform exactly to models, triggering auto-retries on validation errors.

⚡ Frictionless Quickstart

Get up and running in under 60 seconds.

Step 1: Install the Package

pip install -e .

Step 2: Configure API Key (Optional)

export OPENAI_API_KEY="your-api-key-here"

Step 3: Run the Code

import asyncio
from resumesh_llm import LLMClientFactory, CVOptimizer

async def main():
    # Initialize the client (uses MockClient offline by default)
    client = LLMClientFactory.get_client(provider="mock")
    optimizer = CVOptimizer(client=client)

    # Optimize experience using the Google XYZ formula
    result = await optimizer.optimize_bullet_point(
        raw_bullet="I worked on fixing bugs and writing tests",
        context="Backend Developer"
    )

    print(f"Original: {result.original}")
    print(f"Optimized: {result.optimized}")
    print(f"Rationale: {result.explanation}")

asyncio.run(main())

🛠️ Architecture & Workflow Pipeline

We partition domains cleanly using the Facade Pattern to export simplified entry points, avoiding circular imports.

Agentic Workflow & Checkpoint Pipeline

graph TD
    User([Client Application]) -->|Triggers run| StateGraph[StateGraph Orchestrator]
    StateGraph -->|Loads Checkpoint| Checkpointer[(BaseCheckpointer)]
    StateGraph -->|Invokes| RouterAgent[RouterAgent]
    RouterAgent -->|Queries| RAG[AsyncRAGPipeline]
    RAG -.->|Retrieves Chunks| Regulations[(Regulations & ATS Guidelines)]
    RouterAgent -->|Decides route| Decision{Routing Decision}
    Decision -->|ats_optimization| ATSNode[ATS Optimization Node]
    Decision -->|regulatory_alignment| RegNode[Regulatory Alignment Node]
    Decision -->|standard_critique| CritiqueNode[Standard Critique Node]
    ATSNode -->|Updates State| Save[Save Checkpoint]
    RegNode -->|Updates State| Save
    CritiqueNode -->|Updates State| Save
    Save -->|Persists state & pointer| Checkpointer

📚 Documentation Map

Delve deeper into the architecture and subdomains of resumesh-llm:

• 📖 Getting Started Guide: Configuration reference, environment setup, and registering custom LLM providers.
• 📐 Architecture Overview: Detailed SOLID design patterns, class structures, and exception hierarchies.
• ✏️ CV Optimizer Reference: XYZ formulations, skill classifiers, and job description alignment analyzer.
• 🐙 GitHub Summarizer Reference: Scraped commit processing, career journals, and repository summarization.
• 🧪 Testing Documentation: Local testing, mock provider verification, and formatting commands.

🤝 Community & Contributing

We welcome issues, feedback, and pull requests! Please read our Contributing Guide and Code of Conduct to get started.

If you find resumesh-llm useful or are using it to build your developer portfolio, support us by leaving a star! ⭐️

resumesh-llm is maintained by the ResuMesh Team.