https://github.com/pahul0303/llassist
A tool for processing and analyzing research articles using NLP and Large Language Models (LLMs).
https://github.com/pahul0303/llassist
csharp dotnet literature-review llm nlp
Last synced: 3 months ago
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A tool for processing and analyzing research articles using NLP and Large Language Models (LLMs).
- Host: GitHub
- URL: https://github.com/pahul0303/llassist
- Owner: pahul0303
- License: other
- Created: 2025-07-03T20:55:26.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-04T14:25:35.000Z (3 months ago)
- Last Synced: 2025-07-04T15:58:33.814Z (3 months ago)
- Topics: csharp, dotnet, literature-review, llm, nlp
- Language: C#
- Homepage:
- Size: 18.6 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LLAssist
LLAssist is a tool for processing and analyzing research articles using Natural Language Processing (NLP) techniques and Large Language Models (LLMs).
Note:
- The paper with title [LLAssist: Simple Tools for Automating Literature Review Using Large Language Models](https://doi.org/10.48550/arXiv.2407.13993) uses [commit versions prior to `07caad7`](https://github.com/cyharyanto/llassist/tree/07caad7d954f9e64933ffa5aa34d0b745006feea), specifically [commit `3bf51a6`](https://github.com/cyharyanto/llassist/tree/3bf51a695b945e07c77eaa0a323c9aa3e57372bd).
## Features
- Read articles from CSV files
- Extract key semantics (topics, entities, keywords) from article titles and abstracts
- Estimate relevance of articles to research questions
- Generate embeddings for keywords
- Output results in both JSON and CSV formats
## Components
### Program.cs
The main entry point of the application. It orchestrates the process of:
1. Reading articles from a CSV file
2. Processing each article to extract semantics and estimate relevance
3. Writing results incrementally to a CSV file
4. Generating a final JSON output
### Services
#### NLPService
Handles Natural Language Processing tasks:
- Extracting key semantics from text
- Estimating relevance of content to research questions
- Generating embeddings for keywords
#### LLMService
Manages connections to various Large Language Models:
- Ollama Gemma 2 (local)
- GPT-3.5 Turbo (OpenAI)
- GPT-4 (OpenAI)
- Text Embedding model (OpenAI)
#### ArticleService
Handles file I/O operations:
- Reading articles from CSV files
- Writing articles to JSON files
- Writing results to CSV files
## Usage
### Console Mode
```
dotnet run --project llassist.AppConsole
```
Where:
- `` is the path to the CSV file containing the articles
- `` is the path to a text file containing the research questions (one per line)
### Web Application
Run docker compose in the root directory
```
docker-compose up -d
```
Run DB migrations in ApiService dir
```
dotnet ef database update
```
## Output
The program generates two output files:
1. A JSON file (`-result.json`) containing all processed articles with their semantics and relevance scores
2. A CSV file (`-result.csv`) with the same information in a tabular format
## Dependencies
- Microsoft.SemanticKernel
- CsvHelper
- Microsoft.Extensions.Logging
## Notes
- The program uses a local Ollama instance for the Gemma 2 model. Ensure it's running on `http://localhost:11434` before executing the program.
- OpenAI API key is required for GPT models and embeddings. Set it in the `LLMService` constructor.
## Disclaimer
This tool is for research purposes.