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Running on Zero
Running on Zero
| Welcome to the ProfillyBot Documents Directory | |
| =================================================== | |
| This is a sample document to demonstrate the document processing capabilities. | |
| About This Chatbot | |
| ------------------ | |
| ProfillyBot uses Retrieval Augmented Generation (RAG) to answer questions about | |
| a professional profile. It works by: | |
| 1. Processing documents (PDF, Word, HTML, text files) | |
| 2. Splitting them into semantic chunks | |
| 3. Creating vector embeddings | |
| 4. Storing in ChromaDB vector database | |
| 5. Retrieving relevant context for user queries | |
| 6. Generating answers using a local LLM (Ollama) | |
| How to Use | |
| ---------- | |
| 1. DELETE this sample file | |
| 2. Add your actual profile documents: | |
| - Resume in PDF or Word format | |
| - Project reports and case studies | |
| - LinkedIn profile (export as PDF or HTML) | |
| - Publications, certifications | |
| - Portfolio descriptions | |
| - Any other professional documents | |
| 3. Build the vector store: | |
| python -m src.build_vectorstore | |
| 4. Run the Streamlit app: | |
| streamlit run app.py | |
| Supported File Types | |
| -------------------- | |
| - PDF (.pdf) | |
| - Microsoft Word (.docx, .doc) | |
| - HTML (.html, .htm) | |
| - Plain text (.txt) | |
| - Markdown (.md) | |
| Tips for Better Results | |
| ----------------------- | |
| - Use well-structured documents with clear headings | |
| - Include detailed information about your experience | |
| - Add context about projects (challenges, solutions, results) | |
| - Keep documents professional and relevant | |
| - Update regularly as you gain new experience | |
| Sample Profile Information | |
| -------------------------- | |
| Name: John Doe | |
| Title: Software Engineer & AI Enthusiast | |
| Location: San Francisco, CA | |
| Skills: | |
| - Python, JavaScript, TypeScript | |
| - Machine Learning & AI | |
| - Web Development (React, Node.js) | |
| - Cloud Platforms (AWS, GCP) | |
| - Docker, Kubernetes | |
| Experience: | |
| - Senior Software Engineer at Tech Corp (2020-Present) | |
| * Led development of AI-powered features | |
| * Improved system performance by 40% | |
| * Mentored junior developers | |
| - Software Engineer at StartupXYZ (2018-2020) | |
| * Built scalable microservices | |
| * Implemented CI/CD pipelines | |
| * Contributed to open-source projects | |
| Education: | |
| - M.S. Computer Science, Stanford University (2018) | |
| - B.S. Computer Science, UC Berkeley (2016) | |
| Projects: | |
| - Open-source RAG Framework (2024) | |
| * Built modular RAG system for document Q&A | |
| * 500+ GitHub stars | |
| * Used by Fortune 500 companies | |
| - AI Resume Analyzer (2023) | |
| * ML-powered resume screening tool | |
| * 90% accuracy in skill extraction | |
| * Reduced hiring time by 60% | |
| Certifications: | |
| - AWS Certified Solutions Architect | |
| - Google Cloud Professional ML Engineer | |
| - Certified Kubernetes Administrator (CKA) | |
| Interests: | |
| - Contributing to open-source AI projects | |
| - Writing technical blog posts | |
| - Speaking at tech conferences | |
| - Mentoring aspiring engineers | |
| --- | |
| REMEMBER: Replace this sample content with your actual profile documents! | |