{"id":25688863,"url":"https://github.com/eshaffer321/elasticstar","last_synced_at":"2025-02-24T21:17:07.647Z","repository":{"id":265777324,"uuid":"896621853","full_name":"eshaffer321/ElasticSTAR","owner":"eshaffer321","description":" A personal knowledge database powered by Elasticsearch, designed to index experience and answer STAR questions with Retrieval-Augmented Generation.","archived":false,"fork":false,"pushed_at":"2024-11-30T21:27:03.000Z","size":5,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-11-30T22:35:27.392Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/eshaffer321.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-30T21:17:13.000Z","updated_at":"2024-11-30T21:27:06.000Z","dependencies_parsed_at":"2024-11-30T22:35:34.991Z","dependency_job_id":"89039343-3ebd-4105-b53d-1f6a889c6ca6","html_url":"https://github.com/eshaffer321/ElasticSTAR","commit_stats":null,"previous_names":["eshaffer321/elasticstar"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eshaffer321%2FElasticSTAR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eshaffer321%2FElasticSTAR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eshaffer321%2FElasticSTAR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eshaffer321%2FElasticSTAR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/eshaffer321","download_url":"https://codeload.github.com/eshaffer321/ElasticSTAR/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240557490,"owners_count":19820360,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-02-24T21:17:07.008Z","updated_at":"2025-02-24T21:17:07.634Z","avatar_url":"https://github.com/eshaffer321.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# ElasticSTAR\n\n**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.\n\n---\n\n## Features\n\n- **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.  \n- **Elasticsearch Integration**: Transform parsed data into a format suitable for indexing in Elasticsearch, enabling fast and accurate search capabilities.  \n- **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.  \n- **Retrieval-Augmented Generation (RAG)**: Combine Elasticsearch's search capabilities with ChatGPT's language understanding to create an efficient and intelligent Q\u0026A pipeline.\n\n---\n\n## How It Works\n\n1. **Data Ingestion**: Input professional experience data from various formats (e.g., plain text, JSON).  \n2. **Data Processing**:  \n   - Parse and structure the data.  \n   - Summarize and tag the data with relevant technologies, skills, and work themes using ChatGPT.  \n3. **Indexing**: Store the structured and tagged data into Elasticsearch for fast retrieval.  \n4. **Query Pipeline**:  \n   - Use the CLI to ask a question.  \n   - Query Elasticsearch to fetch the most relevant documents.  \n   - Pass the documents and your question to ChatGPT for a detailed, context-aware response.\n\n---\n\n## Use Cases\n\n- **Personal Knowledge Management**: Easily organize, retrieve, and query your professional achievements and experiences.  \n- **Interview Preparation**: Quickly generate STAR-style responses based on indexed data for interview questions.  \n- **Professional Insights**: Retrieve insights or examples of work you've done based on specific technologies or challenges.\n\n---\n\n## Technology Stack\n\n- **Python**: Core language for development.  \n- **Elasticsearch**: Backend for indexing and querying data.  \n- **ChatGPT**: For summarization, tagging, and contextual Q\u0026A.  \n- **CLI Interface**: Simple command-line interface for queries and interaction.\n\n---\n\n## Installation\n\n### Prerequisites\n- Python 3.8+  \n- Elasticsearch (local or cloud instance)  \n- OpenAI API key for ChatGPT  \n\n### Steps\n1. Clone the repository:  \n   ```bash  \n   git clone https://github.com/yourusername/elasticstar.git  \n   cd elasticstar  \n   ```\n2. Install dependencies:  \n   ```bash  \n   pip install -r requirements.txt  \n   ```\n3. Configure Elasticsearch and OpenAI API:  \n   - Update `config.yaml` with your Elasticsearch connection details and OpenAI API key.\n\n---\n\n## Usage\n\n### CLI Commands\n\n1. **Index Data**: Parse and index professional data into Elasticsearch:  \n   ```bash  \n   python elasticstar.py index --input data_file.json  \n   ```\n\n2. **Ask Questions**: Query your database for context-aware answers:  \n   ```bash  \n   python elasticstar.py query --question \"Tell me about a time I optimized a system's performance.\"  \n   ```\n\n### Example Output\n```plaintext  \nQuestion: \"Tell me about a time I optimized a system's performance.\"  \nAnswer: 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.  \n```\n\n---\n\n## Roadmap\n\n- Add a web-based interface for queries and data visualization.  \n- Expand data formats supported for ingestion.  \n- Integrate additional LLMs for summarization and analysis.  \n- Enhance tagging with advanced NLP techniques for more precise categorization.\n\n---\n\n## Contributing\n\nContributions are welcome! Feel free to open issues or submit pull requests to improve ElasticSTAR.\n\n---\n\n## License\n\nThis project is licensed under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feshaffer321%2Felasticstar","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feshaffer321%2Felasticstar","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feshaffer321%2Felasticstar/lists"}