https://github.com/eshaffer321/elasticstar
A personal knowledge database powered by Elasticsearch, designed to index experience and answer STAR questions with Retrieval-Augmented Generation.
https://github.com/eshaffer321/elasticstar
Last synced: over 1 year ago
JSON representation
A personal knowledge database powered by Elasticsearch, designed to index experience and answer STAR questions with Retrieval-Augmented Generation.
- Host: GitHub
- URL: https://github.com/eshaffer321/elasticstar
- Owner: eshaffer321
- License: mit
- Created: 2024-11-30T21:17:13.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-30T21:27:03.000Z (over 1 year ago)
- Last Synced: 2024-11-30T22:35:27.392Z (over 1 year ago)
- Size: 4.88 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# ElasticSTAR
**ElasticSTAR** is a personal knowledge database built to index professional experiences and achievements, providing concise and context-rich answers to your questions. It leverages Elasticsearch for efficient data retrieval and ChatGPT for retrieval-augmented generation (RAG), creating a powerful pipeline for querying and summarizing your personal knowledge.
---
## Features
- **Data Parsing and Summarization**: Parse professional experience data from various formats and send it through prompt-engineered requests to ChatGPT for consistent summaries and tagging.
- **Elasticsearch Integration**: Transform parsed data into a format suitable for indexing in Elasticsearch, enabling fast and accurate search capabilities.
- **Query and Contextual Answers**: Use a Python CLI to ask questions, retrieve relevant documents from Elasticsearch, and get detailed answers enriched with context via ChatGPT.
- **Retrieval-Augmented Generation (RAG)**: Combine Elasticsearch's search capabilities with ChatGPT's language understanding to create an efficient and intelligent Q&A pipeline.
---
## How It Works
1. **Data Ingestion**: Input professional experience data from various formats (e.g., plain text, JSON).
2. **Data Processing**:
- Parse and structure the data.
- Summarize and tag the data with relevant technologies, skills, and work themes using ChatGPT.
3. **Indexing**: Store the structured and tagged data into Elasticsearch for fast retrieval.
4. **Query Pipeline**:
- Use the CLI to ask a question.
- Query Elasticsearch to fetch the most relevant documents.
- Pass the documents and your question to ChatGPT for a detailed, context-aware response.
---
## Use Cases
- **Personal Knowledge Management**: Easily organize, retrieve, and query your professional achievements and experiences.
- **Interview Preparation**: Quickly generate STAR-style responses based on indexed data for interview questions.
- **Professional Insights**: Retrieve insights or examples of work you've done based on specific technologies or challenges.
---
## Technology Stack
- **Python**: Core language for development.
- **Elasticsearch**: Backend for indexing and querying data.
- **ChatGPT**: For summarization, tagging, and contextual Q&A.
- **CLI Interface**: Simple command-line interface for queries and interaction.
---
## Installation
### Prerequisites
- Python 3.8+
- Elasticsearch (local or cloud instance)
- OpenAI API key for ChatGPT
### Steps
1. Clone the repository:
```bash
git clone https://github.com/yourusername/elasticstar.git
cd elasticstar
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Configure Elasticsearch and OpenAI API:
- Update `config.yaml` with your Elasticsearch connection details and OpenAI API key.
---
## Usage
### CLI Commands
1. **Index Data**: Parse and index professional data into Elasticsearch:
```bash
python elasticstar.py index --input data_file.json
```
2. **Ask Questions**: Query your database for context-aware answers:
```bash
python elasticstar.py query --question "Tell me about a time I optimized a system's performance."
```
### Example Output
```plaintext
Question: "Tell me about a time I optimized a system's performance."
Answer: Based on your past experiences, one example includes optimizing test infrastructure by implementing Redis streams, which improved performance by reducing feedback time from 20 minutes to 30 seconds.
```
---
## Roadmap
- Add a web-based interface for queries and data visualization.
- Expand data formats supported for ingestion.
- Integrate additional LLMs for summarization and analysis.
- Enhance tagging with advanced NLP techniques for more precise categorization.
---
## Contributing
Contributions are welcome! Feel free to open issues or submit pull requests to improve ElasticSTAR.
---
## License
This project is licensed under the MIT License.