README.md

---
language: en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- drilling-engineering
datasets:
- oil-gas-engineering-docs
- drilling-engineering-manuals
- well-drilling-reports
- casing-design-guidelines
- mud-logging-data
- managed-pressure-drilling-reports
- directional-drilling-studies
pipeline_tag: sentence-similarity
base_model:
- sentence-transformers/all-MiniLM-L6-v2
---

# OGAI-Embedder
This is a [sentence-transformers](https://www.SBERT.net) model fine-tuned specifically for **drilling engineering** applications in the oil and gas industry. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for tasks like **technical document retrieval, automated report analysis, and intelligent search** within **drilling-related datasets**.

[![Hugging Face](https://img.shields.io/badge/HuggingFace-OGAI--Embedder-blue)](https://huggingface.co/GainEnergy/OGAI-Embedder)
[![License](https://img.shields.io/github/license/huggingface/transformers.svg)](LICENSE)

## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```bash
pip install -U sentence-transformers
```

Then you can use the model like this:
```python
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)
```

## 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.

### Key Datasets Used:
| Dataset | Description |
|---------|------------|
| Well Drilling Reports | Real-world drilling reports from operators |
| Casing Design Guidelines | Technical best practices for casing design | 
| Mud Logging Data | Drilling fluid parameters and performance records | 

## 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.

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

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

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