OGAI Embedder

🚀 OGAI-Embedder

This is a sentence-transformers model fine-tuned for drilling engineering applications in the oil and gas industry. It maps sentences and paragraphs to a 384-dimensional dense vector space, useful for technical document retrieval, automated report analysis, and intelligent search in drilling-related datasets.

🚀 Quick Start

This model is specifically fine - tuned for drilling engineering applications in the oil and gas industry. It maps sentences and paragraphs to a 384 - dimensional dense vector space and can be used for tasks such as technical document retrieval, automated report analysis, and intelligent search within drilling - related datasets.

✨ Features

• Semantic Search: OGAI - Embedder can be used in document search engines for drilling operations, enabling semantic search across various drilling - related documents.
• Real - time AI Integration: Designed for real - time AI integration into oil and gas platforms, it supports automated report analysis, intelligent document retrieval, context - aware AI assistants, and enhanced decision - making.

📦 Installation

Using this model becomes easy when you have sentence - transformers installed:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer
sentences = ["What is the optimal mud weight for a high-angle well?", "How does managed pressure drilling improve well control?"]

model = SentenceTransformer('OGAI-Embedder')
embeddings = model.encode(sentences)
print(embeddings)

Advanced Usage

This model can be used with llama.cpp for efficient inference in drilling engineering applications.

To run a single query:

brew install llama.cpp
llama-cli --hf-repo OGAI-Embedder --hf-file ogai-embedder-q5_0.gguf -p "What are the key challenges in managed pressure drilling?"

To run a server:

llama-server --hf-repo OGAI-Embedder --hf-file ogai-embedder-q5_0.gguf -c 2048

📚 Documentation

Drilling - Specific Search and Retrieval

OGAI - Embedder can be used in document search engines for drilling operations, enabling semantic search across:

• Well drilling reports
• Casing design manuals
• Mud logging data
• Directional drilling surveys
• Equipment specifications
• Well control procedures

Training Data for Drilling Engineering

The model has been fine - tuned using a curated dataset of drilling engineering documents, manuals, and field reports.

Deployment for AI - Powered Drilling Engineering Assistance

OGAI - Embedder is designed for real - time AI integration into oil and gas platforms. It enables:

• Automated report analysis for drilling engineers.
• Intelligent document retrieval from large drilling knowledge bases.
• Context - aware AI assistants for well planning and execution.
• Enhanced decision - making based on historical well performance data.

📄 License

This model is available on Hugging Face for research and commercial use under the Apache 2.0 license.