AI Orchestrator – Semantic Intelligence Engine

Overview The AI Orchestrator is a modular intelligence system that transforms natural language inputs into structured, simulation-ready scientific outputs (spec.json). It integrates hybrid parsing, vector-based retrieval, research enrichment, and strict validation into a unified pipeline. This system acts as the core reasoning layer of the Prana-G AI pipeline, enabling downstream simulation systems to operate on reliable, structured data.

Core Capabilities

Hybrid parsing (rules + LLM) for high accuracy Semantic retrieval using vector embeddings Trait-to-mechanism scientific mapping Strict schema validation using Pydantic Retry and fallback mechanisms for robustness Modular architecture for scalability

Architecture User Input ↓Hybrid Parser (Rules + LLM) ↓Vector Search (ChromaDB + Embeddings) ↓Research Layer (Trait → Mechanism) ↓Spec Generator ↓Pydantic Validator ↓Final spec.json Workflow Execution Parse → Search → Research → Generate → Validate

Parsing extracts structured intent

Search retrieves relevant traits Research adds scientific reasoning

Generator builds final spec Validator ensures correctness

Semantic-AI/ │ ├── main.py # Entry point (CLI pipeline) ├── workflow.py # LangGraph workflow orchestration ├── llm_parser.py # Hybrid parsing system ├── vector_search.py # ChromaDB-based semantic retrieval ├── research.py # Scientific insight mapping ├── spec_generator.py # Final JSON generation logic ├── validator.py # Pydantic validation layer ├── models.py # Schema definitions ├── app.py # Streamlit UI (optional) └── requirements.txt

Example Input rice in hot dry climate

Example Output { "crop": "rice", "location": "hot dry climate", "temperature": 25, "stress": ["heat", "drought"], "traits": [ "deep root system", "heat shock protein expression" ], "scientific_basis": [ "Improves water uptake from deeper soil layers", "Stabilizes proteins under heat stress" ], "confidence": 0.85}

Installation git clone https://github.com/your-username/semantic-ai.gitcd semantic-aipython -m venv venvvenv\Scripts\activatepip install -r requirements.txt

Usage CLI python main.py

Streamlit UI streamlit run app.py

Tech Stack

Python LangGraph Sentence Transformers ChromaDB Pydantic Streamlit Ollama (Llama3)

Design Principles

Reliability — validation + retry logic Scalability — vector database integration Modularity — independent components

Explainability — scientific reasoning layer Performance — optimized execution pipeline

Performance Highlights

Reduced hallucinations using hybrid parsing Context-aware retrieval using metadata filtering Structured output with strict schema validation Fault-tolerant pipeline with retry handling

Future Roadmap

Integration with Semantic Scholar / ArXiv APIs

FastAPI deployment for production

Async execution for low latency

Large-scale trait ingestion (Parquet + vector DB)

Advanced ranking and scoring models

Contribution Developed as part of the Prana-G AI system, focusing on building the core orchestration and intelligence layer.

License MIT License

👨‍💻 Author

Harshit