https://github.com/parthapray/neuro-symbolic_abductive_reasoning_ollama_fault_diagnosis
This repo presents codes that allows user to run localized Ollama based Reasoning LLM to integrate with neuro-symbolic abductive reasoning for fault diagnosis
https://github.com/parthapray/neuro-symbolic_abductive_reasoning_ollama_fault_diagnosis
abductive-reasoning fault-diagnosis industrial large-language-models neurosymbolic ollama-api restful-api
Last synced: 24 days ago
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This repo presents codes that allows user to run localized Ollama based Reasoning LLM to integrate with neuro-symbolic abductive reasoning for fault diagnosis
- Host: GitHub
- URL: https://github.com/parthapray/neuro-symbolic_abductive_reasoning_ollama_fault_diagnosis
- Owner: ParthaPRay
- License: mit
- Created: 2025-06-27T06:43:14.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-27T06:46:51.000Z (4 months ago)
- Last Synced: 2025-06-27T07:41:54.050Z (4 months ago)
- Topics: abductive-reasoning, fault-diagnosis, industrial, large-language-models, neurosymbolic, ollama-api, restful-api
- Language: Python
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Neuro-Symbolic Abductive Reasoning with Ollama Local LLMs
**Author:** Partha Pratim Ray
**Contact:** [parthapratimray1986@gmail.com](mailto:parthapratimray1986@gmail.com)
**Date:** 27 June, 2025
---
## Introduction
This project is an implementation of **neuro-symbolic AI** for machine fault diagnosis, combining symbolic domain knowledge (engineering rules, hypotheses) with the powerful pattern recognition and language capabilities of local large language models (LLMs) through [Ollama](https://ollama.com/).
It applies **abductive reasoning**—a form of logical inference that seeks the best explanation for observed evidence—enabling transparent, step-wise diagnostic reasoning for sensor-rich environments such as industrial machinery and IoT deployments.
**Why Neuro-Symbolic and Abductive Reasoning?**
Traditional LLMs excel at pattern matching but struggle with explicit causal inference, logical explanation, and traceable diagnosis. By blending LLM “neural” capabilities with **symbolic hypothesis generation and evaluation**, this tool can:
* Generate hypotheses grounded in engineering knowledge,
* Use LLMs to explain and prioritize these hypotheses,
* Deliver transparent, auditable reasoning trails for every diagnosis.
---
## Key Features
* **Neuro-symbolic abduction:** Marries symbolic hypothesis spaces with LLM-powered neural reasoning, for robust “explainable AI” in fault analysis.
* **Local and private:** All reasoning runs on your own machine, leveraging Ollama’s local LLM serving—no cloud or data sharing required.
* **Supports Ollama ‘thinking’ LLMs:** Out-of-the-box compatibility with Qwen3, DeepSeek R1, and future models supporting the `think` API.
* **Step-wise inference:** For every sensor event, the system generates plausible faults, technical explanations, and Bayesian-style prior probabilities—all with detailed LLM “thinking.”
* **Full audit trail:** Logs every LLM response, explanation, and key metrics (duration, token rates, etc.) to CSV for further study and benchmarking.
* **Ready for real-world prompts:** Comes with a suite of 17 realistic, diverse event prompts for immediate demonstration and testing.
---
## Example Use Cases
Feed the tool descriptions such as:
* **Simple bearing fault:** Sudden bearing temperature increase with persistent vibration at 0.10 Hz.
* **Sensor noise event:** Vibration amplitude sharply peaks at 0.11 Hz, temperature stable.
* **Multi-symptom event:** High mean/variance in temperature and a strong vibration peak at 0.15 Hz.
* **Perfectly healthy/null case:** All sensors normal; no anomalies detected.
The tool performs **abductive reasoning** to infer, explain, and rank possible root causes—logging the complete thought process.
---
## How It Works
1. **Describe a sensor event:** The user enters an observation (natural language).
2. **Hypothesis generation:** The LLM, guided by engineering context, proposes plausible fault hypotheses (symbolic step).
3. **Neural reasoning:** For each hypothesis, the LLM explains the mechanism and estimates its likelihood (prior).
4. **All outputs are logged:** Explanations, reasoning traces, and evaluation metrics are stored in a CSV row for each case.
5. **Transparent reporting:** The diagnosis and full rationale are printed for user inspection.
---
## Getting Started
### Prerequisites
* **Python 3.11+**
* **Ollama** running locally ([installation guide](https://ollama.com/download))
* At least one supported model:
* e.g., `ollama pull qwen3:8b` or `ollama pull deepseek-r1:8b`
* Python packages:
```bash
pip install requests numpy
```
### Usage
1. Set the `OLLAMA_MODEL` variable in the code to your preferred model (e.g., `"deepseek-r1:8b"`).
2. Start the Ollama server and ensure your model is available.
3. Run the script:
```bash
python app.py
```
4. Enter your sensor event description when prompted.
5. Review the diagnosis on-screen and in `llm_abduction_log.csv`.
---
## Output
* **Comprehensive CSV log:**
Each event’s hypotheses, explanations, priors, model thinking, and performance metrics are logged for scientific analysis and reproducibility.
---
## Model Compatibility
Tested on and compatible with:
* **Qwen3 (Alibaba)**
* **DeepSeek R1**
* Any other Ollama model supporting the “thinking” (`think=true`) API.
---
## Why Use This?
* **Explainable neuro-symbolic AI** for safety-critical and industrial environments.
* **Abductive reasoning**: Go beyond pattern matching—find the best *explanation* for anomalies.
* **Transparent & auditable**: Every step, score, and rationale is logged and reproducible.
* **Private & offline**: No data leaves your computer.
---
## Reference
* [Thinking in Ollama (Blog)](https://ollama.com/blog/thinking)
* See code and output for more.
---
## Citation
If you use this tool for research or deployment, please cite:
> Partha Pratim Ray, “Neuro-symbolic Abductive Reasoning with Ollama Local Reasoning LLMs,” 2025.
> [github.com/ParthaPRay/llm-abduction-ollama](https://github.com/ParthaPRay/llm-abduction-ollama)
---
## License
MIT License
---
**For questions or collaboration:**
[parthapratimray1986@gmail.com](mailto:parthapratimray1986@gmail.com)
---
***Bridging neural and symbolic reasoning for real-world diagnostics.***
---