{ "cells": [ { "cell_type": "markdown", "id": "85b93c49", "metadata": {}, "source": [ "# Expert Resume Creator" ] }, { "cell_type": "markdown", "id": "8f90fe9a", "metadata": {}, "source": [ " In this exercise, we'll build a RAG-powered resume refinement tool that helps tailor resumes to specific job descriptions.\n", " \n", " What We'll Build\n", " An AI assistant that takes a job description and current resume, then produces an optimized version using resume writing best practices.\n", " \n", " The Approach (RAG)\n", " 1. **Generate Knowledge Base** - Use an LLM to create expert resume writing guides\n", " 2. **Create Vector Database** - Store the knowledge in Chroma for semantic search\n", " 3. **Build Interface** - Create a Gradio app where users can refine their resumes\n", " \n", " Steps\n", " - **STEP 1**: Generate synthetic resume writing knowledge using LLM\n", " - **STEP 2**: Load documents and create RAG with Chroma vector database\n", " - **STEP 3**: Build Gradio interface for users to input job description and resume\n", " \n", " ---\n", " \n", " Let's get started! šŸš€" ] }, { "cell_type": "code", "execution_count": null, "id": "1f889c1d", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import glob\n", "from dotenv import load_dotenv\n", "import gradio as gr" ] }, { "cell_type": "code", "execution_count": null, "id": "3711bc34", "metadata": {}, "outputs": [], "source": [ "from langchain.document_loaders import DirectoryLoader, TextLoader\n", "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain.schema import Document\n", "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n", "from langchain_chroma import Chroma\n", "from langchain.memory import ConversationBufferMemory\n", "from langchain.chains import ConversationalRetrievalChain\n", "from langchain.embeddings import HuggingFaceEmbeddings" ] }, { "cell_type": "code", "execution_count": null, "id": "840999d8", "metadata": {}, "outputs": [], "source": [ "# Configuration\n", "MODEL = \"gpt-4o-mini\"\n", "db_name = \"resume_vector_db\"\n", "KNOWLEDGE_BASE_DIR = \"resume-knowledge-base\"" ] }, { "cell_type": "code", "execution_count": null, "id": "4695238c", "metadata": {}, "outputs": [], "source": [ "#load environment variables\n", "load_dotenv(override=True)\n", "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')" ] }, { "cell_type": "markdown", "id": "37ce61e4", "metadata": {}, "source": [ "### STEP 1 - Programmatically Generate Synthetic Resume Knowledge Base" ] }, { "cell_type": "code", "execution_count": null, "id": "f6257788", "metadata": {}, "outputs": [], "source": [ "def generate_content_with_llm(topic, category):\n", " \"\"\"Use LLM to generate content for a specific topic\"\"\"\n", " \n", " llm = ChatOpenAI(temperature=0.8, model_name=MODEL)\n", " \n", " prompts = {\n", " \"best-practices\": f\"\"\"You are an expert resume writer and career coach. Write a comprehensive guide about: {topic}\n", "\n", " Create a detailed markdown document with:\n", " - Clear section headers\n", " - Specific, actionable advice\n", " - Multiple concrete examples\n", " - Do's and don'ts\n", " - Real-world tips that hiring managers look for\n", "\n", " Write 500-800 words in markdown format. Be specific and practical.\"\"\",\n", " \n", " \"industry-specific\": f\"\"\"You are an expert resume writer specializing in {topic} industry resumes.\n", "\n", " Write a comprehensive industry guide covering:\n", " - Key skills and technologies to highlight for {topic} roles\n", " - How to structure experience for this industry\n", " - Important keywords and terminology\n", " - 5-8 example bullet points showing strong achievements with specific metrics\n", " - Common mistakes to avoid\n", " - What hiring managers in {topic} look for\n", "\n", " Write 600-900 words in markdown format with specific examples.\"\"\",\n", " \n", " \"examples\": f\"\"\"You are an expert resume writer. Create detailed examples for: {topic}\n", "\n", " Provide:\n", " - 3-4 complete, realistic examples showing proper formatting\n", " - Each example should include company name, dates, and 4-6 bullet points\n", " - Bullet points must include quantified achievements (numbers, percentages, dollar amounts)\n", " - Show variety in roles (junior, mid-level, senior)\n", " - Use strong action verbs\n", " - Demonstrate clear impact and results\n", "\n", " Write in markdown format. Make examples realistic and impressive.\"\"\",\n", " \n", " \"specialized\": f\"\"\"You are an expert in resume writing for {topic}.\n", "\n", " Create a comprehensive guide covering:\n", " - Unique considerations for {topic}\n", " - Best practices and formatting tips\n", " - 6-10 strong example bullet points with metrics\n", " - Common questions and how to address them\n", " - What makes a standout resume in this area\n", "\n", " Write 500-700 words in markdown format.\"\"\"\n", " }\n", " \n", " prompt = prompts.get(category, prompts[\"best-practices\"])\n", " response = llm.invoke(prompt)\n", " return response.content" ] }, { "cell_type": "code", "execution_count": null, "id": "6a3e0c62", "metadata": {}, "outputs": [], "source": [ "def create_resume_knowledge_base():\n", " \"\"\"Programmatically generate comprehensive resume knowledge base using LLM\"\"\"\n", " \n", " print(\"šŸ¤– Starting LLM-powered knowledge base generation...\")\n", " print(\"ā³ This may take 2-3 minutes to generate all content...\\n\")\n", " \n", " # Create directory structure\n", " os.makedirs(f\"{KNOWLEDGE_BASE_DIR}/best-practices\", exist_ok=True)\n", " os.makedirs(f\"{KNOWLEDGE_BASE_DIR}/examples\", exist_ok=True)\n", " os.makedirs(f\"{KNOWLEDGE_BASE_DIR}/industry-specific\", exist_ok=True)\n", " os.makedirs(f\"{KNOWLEDGE_BASE_DIR}/specialized\", exist_ok=True)\n", " \n", " # Define topics for each category\n", " topics = {\n", " \"best-practices\": [\n", " \"Resume Formatting and Structure\",\n", " \"Powerful Action Verbs and Keywords\",\n", " \"Quantifying Achievements and Impact\",\n", " \"Tailoring Resume to Job Descriptions\",\n", " \"ATS (Applicant Tracking System) Optimization\",\n", " \"Common Resume Mistakes to Avoid\"\n", " ],\n", " \"industry-specific\": [\n", " \"Software Engineering and Technology\",\n", " \"Data Science and Machine Learning\",\n", " \"Business and Marketing\",\n", " \"Finance and Accounting\",\n", " \"Healthcare and Medical\",\n", " \"Product Management\"\n", " ],\n", " \"examples\": [\n", " \"Strong Experience Section Examples\",\n", " \"Skills Section Formatting\",\n", " \"Project Descriptions for Technical Roles\",\n", " \"Leadership and Management Achievements\",\n", " \"Entry-Level Resume Examples\"\n", " ],\n", " \"specialized\": [\n", " \"Career Changers and Transitions\",\n", " \"Recent Graduates and Internships\",\n", " \"Executive and C-Level Resumes\",\n", " \"Freelance and Contract Work\",\n", " \"Career Gaps and Explanations\"\n", " ]\n", " }\n", " \n", " total_files = sum(len(topic_list) for topic_list in topics.values())\n", " current_file = 0\n", " \n", " # Generate content for each category and topic\n", " for category, topic_list in topics.items():\n", " for topic in topic_list:\n", " current_file += 1\n", " print(f\"[{current_file}/{total_files}] Generating: {category}/{topic}...\")\n", " \n", " try:\n", " # Generate content using LLM\n", " content = generate_content_with_llm(topic, category)\n", " \n", " # Create filename from topic\n", " filename = topic.lower().replace(\" \", \"-\").replace(\"(\", \"\").replace(\")\", \"\") + \".md\"\n", " filepath = f\"{KNOWLEDGE_BASE_DIR}/{category}/{filename}\"\n", " \n", " # Add title to content\n", " full_content = f\"# {topic}\\n\\n{content}\"\n", " \n", " # Write to file\n", " with open(filepath, \"w\", encoding=\"utf-8\") as f:\n", " f.write(full_content)\n", " \n", " print(f\" āœ… Saved to {category}/{filename}\")\n", " \n", " except Exception as e:\n", " print(f\" āŒ Error generating {topic}: {str(e)}\")\n", " continue\n", " \n", " print(f\"\\nāœ… Knowledge base generation complete!\")\n", " print(f\"šŸ“ Created {total_files} files across 4 categories:\")\n", " print(f\" - {len(topics['best-practices'])} best practice guides\")\n", " print(f\" - {len(topics['industry-specific'])} industry-specific guides\")\n", " print(f\" - {len(topics['examples'])} example collections\")\n", " print(f\" - {len(topics['specialized'])} specialized guides\")\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "a7257b2b", "metadata": {}, "outputs": [], "source": [ "# Run this to create the knowledge base\n", "create_resume_knowledge_base()" ] }, { "cell_type": "markdown", "id": "292a8d84", "metadata": {}, "source": [ "### Load and Process Documents" ] }, { "cell_type": "code", "execution_count": null, "id": "d8c18a52", "metadata": {}, "outputs": [], "source": [ " # Read in documents using LangChain's loaders\n", "folders = glob.glob(f\"{KNOWLEDGE_BASE_DIR}/*\")\n", "\n", "def add_metadata(doc, doc_type):\n", " doc.metadata[\"doc_type\"] = doc_type\n", " return doc\n", "\n", "text_loader_kwargs = {'encoding': 'utf-8'}\n", "\n", "documents = []\n", "for folder in folders:\n", " doc_type = os.path.basename(folder)\n", " loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n", " folder_docs = loader.load()\n", " documents.extend([add_metadata(doc, doc_type) for doc in folder_docs])\n", "\n", "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", "chunks = text_splitter.split_documents(documents)" ] }, { "cell_type": "code", "execution_count": null, "id": "567829d5", "metadata": {}, "outputs": [], "source": [ "print(f\"Total number of chunks: {len(chunks)}\")\n", "print(f\"Document types found: {set(doc.metadata['doc_type'] for doc in documents)}\")" ] }, { "cell_type": "markdown", "id": "12e5dfb1", "metadata": {}, "source": [ "Create Vector Store" ] }, { "cell_type": "code", "execution_count": null, "id": "94239c9d", "metadata": {}, "outputs": [], "source": [ "# Using OpenAI embeddings (you can switch to HuggingFace for free alternative)\n", "embeddings = OpenAIEmbeddings()\n", "\n", "# Alternative free option:\n", "# embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n", "\n", "# Delete if already exists\n", "if os.path.exists(db_name):\n", " Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n", "\n", "# Create vectorstore\n", "vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n", "print(f\"āœ… Vectorstore created with {vectorstore._collection.count()} documents\")\n" ] }, { "cell_type": "markdown", "id": "62554189", "metadata": {}, "source": [ "Set up RAG Chain" ] }, { "cell_type": "code", "execution_count": null, "id": "e2b349f5", "metadata": {}, "outputs": [], "source": [ "\n", "llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n", "\n", "# Alternative - use Ollama locally:\n", "# llm = ChatOpenAI(temperature=0.7, model_name='llama3.2', base_url='http://localhost:11434/v1', api_key='ollama')\n", "\n", "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", "retriever = vectorstore.as_retriever(search_kwargs={\"k\": 10})\n", "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)\n", "\n", "print(\"āœ… RAG chain configured and ready\")\n" ] }, { "cell_type": "markdown", "id": "280ad157", "metadata": {}, "source": [ "Create Resume Refinement Function" ] }, { "cell_type": "code", "execution_count": null, "id": "f54e5573", "metadata": {}, "outputs": [], "source": [ "def refine_resume(job_description, current_resume, history=None):\n", " \"\"\"\n", " Refines a resume based on job description using RAG knowledge base\n", " \"\"\"\n", " # Reset memory for each new refinement\n", " global conversation_chain, memory, llm, retriever\n", " memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", " conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)\n", " \n", " prompt = f\"\"\"You are an expert resume writer with access to best practices and successful examples.\n", "\n", " JOB DESCRIPTION:\n", " {job_description}\n", "\n", " CURRENT RESUME:\n", " {current_resume}\n", "\n", " Please analyze the current resume and provide a refined version that:\n", " 1. Aligns keywords and skills with the job description\n", " 2. Uses strong action verbs and quantified achievements\n", " 3. Follows formatting best practices\n", " 4. Highlights most relevant experience for this role\n", " 5. Removes or de-emphasizes less relevant information\n", "\n", " Provide the refined resume in a clear, professional format. Also include a brief \"KEY IMPROVEMENTS\" section at the end explaining the main changes you made and why.\n", " \"\"\"\n", " \n", " result = conversation_chain.invoke({\"question\": prompt})\n", " return result[\"answer\"]\n" ] }, { "cell_type": "markdown", "id": "4efdfe8b", "metadata": {}, "source": [ "Create Gradio Interface" ] }, { "cell_type": "code", "execution_count": null, "id": "dacb51de", "metadata": {}, "outputs": [], "source": [ "def create_gradio_interface():\n", " with gr.Blocks(title=\"Expert Resume Creator\") as interface:\n", " gr.Markdown(\"# šŸ“„ Expert Resume Creator\")\n", " gr.Markdown(\"Refine your resume using AI-powered best practices and tailored optimization\")\n", " \n", " with gr.Row():\n", " with gr.Column():\n", " job_desc_input = gr.Textbox(\n", " label=\"Job Description\",\n", " placeholder=\"Paste the job description here...\",\n", " lines=10\n", " )\n", " resume_input = gr.Textbox(\n", " label=\"Your Current Resume\",\n", " placeholder=\"Paste your current resume here...\",\n", " lines=15\n", " )\n", " submit_btn = gr.Button(\"āœØ Refine My Resume\", variant=\"primary\", size=\"lg\")\n", " \n", " with gr.Column():\n", " output = gr.Textbox(\n", " label=\"Refined Resume\",\n", " lines=30,\n", " show_copy_button=True\n", " )\n", " \n", " gr.Markdown(\"### šŸ’” Tips\")\n", " gr.Markdown(\"\"\"\n", " - Include complete job description with requirements and responsibilities\n", " - Paste your full resume including experience, education, and skills\n", " - The AI will optimize your resume to match the job requirements\n", " - Review the KEY IMPROVEMENTS section to understand the changes\n", " \"\"\")\n", " \n", " submit_btn.click(\n", " fn=refine_resume,\n", " inputs=[job_desc_input, resume_input],\n", " outputs=output\n", " )\n", " \n", " return interface" ] }, { "cell_type": "code", "execution_count": null, "id": "e01ddd13", "metadata": {}, "outputs": [], "source": [ "# Launch the interface\n", "interface = create_gradio_interface()\n", "interface.launch(inbrowser=True, share=False)" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 }