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.

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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.

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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.

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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.

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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.

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Installation

Prerequisites

• Python 3.8+
• Elasticsearch (local or cloud instance)
• OpenAI API key for ChatGPT

Steps

1. Clone the repository:

   git clone https://github.com/yourusername/elasticstar.git  
   cd elasticstar  

2. Install dependencies:

   pip install -r requirements.txt  

3. Configure Elasticsearch and OpenAI API:
   • Update config.yaml with your Elasticsearch connection details and OpenAI API key.

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Usage

CLI Commands

1. Index Data: Parse and index professional data into Elasticsearch:

   python elasticstar.py index --input data_file.json  

2. Ask Questions: Query your database for context-aware answers:

   python elasticstar.py query --question "Tell me about a time I optimized a system's performance."  

Example Output

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.  

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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.

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Contributing

Contributions are welcome! Feel free to open issues or submit pull requests to improve ElasticSTAR.

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License

This project is licensed under the MIT License.