diff --git "a/Notebooks/CodeForge.ipynb" "b/Notebooks/CodeForge.ipynb"
--- "a/Notebooks/CodeForge.ipynb"
+++ "b/Notebooks/CodeForge.ipynb"
@@ -265,6 +265,14 @@
"pdf_file = get_pdf_content(test_resume_url)\n"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "26688a4a",
+ "metadata": {},
+ "source": [
+ "**Start**"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -645,6 +653,17 @@
" onboarding_summary: str"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "679a529f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class SearchCourse(BaseModel):\n",
+ " query:str=Field(..., description=\"The skill to find with semantic terms (e.g., 'FastAPI', 'PostgreSQL', 'Docker','Enterprise VMS Strategy','Utilization Management')\")"
+ ]
+ },
{
"cell_type": "markdown",
"id": "604e9728",
@@ -663,7 +682,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 78,
"id": "14dab004",
"metadata": {},
"outputs": [],
@@ -693,7 +712,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 79,
"id": "472dae2f",
"metadata": {},
"outputs": [],
@@ -723,13 +742,13 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 80,
"id": "0d5e3b17",
"metadata": {},
"outputs": [],
"source": [
"gap_analysis_agent=ChatGroq(\n",
- " model=\"openai/gpt-oss-120b\",\n",
+ " model=\"moonshotai/kimi-k2-instruct-0905\",\n",
" temperature=0.2,\n",
")\n",
"\n",
@@ -755,14 +774,14 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 81,
"id": "0ccc026b",
"metadata": {},
"outputs": [],
"source": [
- "roadmap_planner_agent=ChatGroq(\n",
- " model=\"moonshotai/kimi-k2-instruct-0905\",\n",
- " temperature=0.2,\n",
+ "rd_planner_agent=ChatGroq(\n",
+ " model=\"openai/gpt-oss-120b\",\n",
+ " temperature=0.1,\n",
")"
]
},
@@ -776,7 +795,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"id": "c8827093",
"metadata": {},
"outputs": [
@@ -787,12 +806,12 @@
"Index ready: {'_response_info': {'raw_headers': {'connection': 'keep-alive',\n",
" 'content-length': '187',\n",
" 'content-type': 'application/json',\n",
- " 'date': 'Mon, 23 Mar 2026 20:11:40 GMT',\n",
+ " 'date': 'Tue, 24 Mar 2026 14:52:30 GMT',\n",
" 'grpc-status': '0',\n",
" 'server': 'envoy',\n",
- " 'x-envoy-upstream-service-time': '62',\n",
- " 'x-pinecone-request-latency-ms': '61',\n",
- " 'x-pinecone-response-duration-ms': '64'}},\n",
+ " 'x-envoy-upstream-service-time': '64',\n",
+ " 'x-pinecone-request-latency-ms': '67',\n",
+ " 'x-pinecone-response-duration-ms': '69'}},\n",
" 'dimension': 384,\n",
" 'index_fullness': 0.0,\n",
" 'memoryFullness': 0.0,\n",
@@ -887,14 +906,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "C:\\Users\\ATHARVA\\AppData\\Local\\Temp\\ipykernel_30068\\2526755923.py:1: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 1.0. An updated version of the class exists in the `langchain-huggingface package and should be used instead. To use it run `pip install -U `langchain-huggingface` and import as `from `langchain_huggingface import HuggingFaceEmbeddings``.\n",
+ "C:\\Users\\ATHARVA\\AppData\\Local\\Temp\\ipykernel_7492\\2526755923.py:1: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 1.0. An updated version of the class exists in the `langchain-huggingface package and should be used instead. To use it run `pip install -U `langchain-huggingface` and import as `from `langchain_huggingface import HuggingFaceEmbeddings``.\n",
" embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\", model_kwargs={\"device\": device})\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3af10b64d4584d53952822157482186f",
+ "model_id": "86dbcd8effee4deaac275a8cd43f19c3",
"version_major": 2,
"version_minor": 0
},
@@ -925,18 +944,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "6bc7292f",
"metadata": {},
"outputs": [
{
- "ename": "",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31mnotebook controller is DISPOSED. \n",
- "\u001b[1;31mView Jupyter log for further details."
- ]
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2c3b9d66d82549a8bd12615b72b8732c",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/50 [00:00log for further details."
- ]
- }
- ],
+ "outputs": [],
"source": [
"from langchain_core.tools import tool\n",
"from typing import Optional\n",
"\n",
- "@tool\n",
+ "@tool(args_schema=SearchCourse)\n",
"def search_courses(query: str):\n",
" \"\"\"\n",
" Search the course catalog for relevant modules based on a skill query \n",
- " \n",
- " \n",
- " Args:\n",
- " \n",
- " query:the skill to find with semantic terms (e.g., 'FastAPI', 'PostgreSQL', 'Docker','Enterprise VMS Strategy','Utilization Management').\n",
- " \n",
+ " Args:query:the skill to find with semantic terms (e.g., 'FastAPI', 'PostgreSQL', 'Docker','Enterprise VMS Strategy','Utilization Management').\n",
" \"\"\"\n",
" \n",
" results = retriever.invoke(\n",
@@ -1009,7 +1018,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 20,
"id": "9db28710",
"metadata": {},
"outputs": [],
@@ -1060,7 +1069,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 21,
"id": "09d238ab",
"metadata": {},
"outputs": [],
@@ -1093,7 +1102,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"id": "6ad04bc6",
"metadata": {},
"outputs": [],
@@ -1115,7 +1124,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"id": "285f74bb",
"metadata": {},
"outputs": [],
@@ -1130,7 +1139,7 @@
"metadata": {},
"outputs": [],
"source": [
- "roadmap_planner_agent=roadmap_planner_agent.bind_tools(roadmap_planner_agent_tools)"
+ "# roadmap_planner_agent=roadmap_planner_agent.bind_tools(roadmap_planner_agent_tools)"
]
},
{
@@ -1141,7 +1150,7 @@
"outputs": [],
"source": [
"# Replace 'roadmap_planner_agent' with your bound model variable\n",
- "print(roadmap_planner_agent.kwargs.get(\"tools\"))\n"
+ "# print(roadmap_planner_agent.kwargs.get(\"tools\"))\n"
]
},
{
@@ -1172,7 +1181,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 24,
"id": "5deda2bb",
"metadata": {},
"outputs": [],
@@ -1205,7 +1214,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 25,
"id": "c8df9934",
"metadata": {},
"outputs": [],
@@ -1242,7 +1251,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 26,
"id": "608efafd",
"metadata": {},
"outputs": [],
@@ -1282,7 +1291,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 27,
"id": "8a6c1483",
"metadata": {},
"outputs": [],
@@ -1319,7 +1328,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 28,
"id": "059e5f86",
"metadata": {},
"outputs": [],
@@ -1383,7 +1392,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 29,
"id": "9c4dea1e",
"metadata": {},
"outputs": [],
@@ -1422,7 +1431,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 30,
"id": "eb13ffc0",
"metadata": {},
"outputs": [],
@@ -1460,7 +1469,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 31,
"id": "330acef6",
"metadata": {},
"outputs": [],
@@ -1515,7 +1524,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 54,
"id": "7352181c",
"metadata": {},
"outputs": [],
@@ -1562,8 +1571,21 @@
"\n",
" \n",
" result = gap_analysis_agent.invoke(messages)\n",
+ " analysis_data = result[\"parsed\"] \n",
+ "\n",
+ "\n",
+ " task_message = HumanMessage(\n",
+ " content=f\"\"\"\n",
+ " Skill Gap Analysis Completed for {state.get('candidate_name')}.\n",
+ " \n",
+ " Gaps Identified:\n",
+ " {analysis_data.model_dump_json(indent=2)}\n",
+ " \n",
+ " Please proceed to search for courses and build the learning roadmap now.\n",
+ " \"\"\"\n",
+ " )\n",
"\n",
- " return {\"skill_gap_analysis_data\": result[\"parsed\"]}\n",
+ " return {\"skill_gap_analysis_data\": result[\"parsed\"],\"messages\": [task_message]}\n",
"\n",
"\n",
" \n",
@@ -1578,26 +1600,28 @@
"metadata": {},
"outputs": [],
"source": [
- "def roadmap_planning_node(state: OnboardingState):\n",
- " \"\"\"\n",
- " The agent's 'thinking' node. It looks at the Skill Gaps and \n",
- " decides which tool to call next.\n",
- " \"\"\"\n",
- " skill_gap_data = state[\"skill_gap_analysis_data\"]\n",
+ "# def roadmap_planning_node(state: OnboardingState):\n",
+ "# \"\"\"\n",
+ "# The agent's 'thinking' node. It looks at the Skill Gaps and \n",
+ "# decides which tool to call next.\n",
+ "# \"\"\"\n",
+ "# skill_gap_data = state[\"skill_gap_analysis_data\"]\n",
+ "\n",
+ "# skill_gap_data= skill_gap_data.model_dump()\n",
"\n",
- " skill_gap_data= skill_gap_data.model_dump()\n",
+ "# system_prompt = SystemMessage(content=roadmap_planner_agent_prompt)\n",
+ "# input_msg = HumanMessage(content=f\" {skill_gap_data} \")\n",
+ " \n",
+ "# response = roadmap_planner_agent.invoke([system_prompt, input_msg] + state[\"messages\"])\n",
"\n",
- " system_prompt = SystemMessage(content=roadmap_planner_agent_prompt)\n",
- " input_msg = HumanMessage(content=f\" {skill_gap_data} \")\n",
" \n",
- " response = roadmap_planner_agent.invoke([system_prompt, input_msg] + state[\"messages\"])\n",
" \n",
- " return {\"messages\": [response]}"
+ "# return {\"messages\": [response]}"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 55,
"id": "cea90664",
"metadata": {},
"outputs": [],
@@ -1638,7 +1662,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 34,
"id": "ba9f22e1",
"metadata": {},
"outputs": [],
@@ -1648,7 +1672,35 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 87,
+ "id": "668b6787",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from langchain.agents import create_agent\n",
+ "from langchain.agents.middleware import ToolCallLimitMiddleware\n",
+ "\n",
+ "\n",
+ "plan_agent=create_agent(\n",
+ " model=rd_planner_agent, \n",
+ " tools=roadmap_planner_agent_tools,\n",
+ " system_prompt=roadmap_planner_agent_prompt,\n",
+ " middleware=[\n",
+ " ToolCallLimitMiddleware[Any,None](\n",
+ " tool_name=\"search_courses\",\n",
+ " run_limit=4,\n",
+ " thread_limit=10,\n",
+ "\n",
+ "\n",
+ " )\n",
+ "\n",
+ " ] ,\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
"id": "b5cfe4c3",
"metadata": {},
"outputs": [],
@@ -1660,54 +1712,68 @@
"builder.add_node(\"resume_data_extraction\", extractResumeDataNode)\n",
"builder.add_node(\"jd_data_extraction\", extractJDDataNode)\n",
"builder.add_node(\"skill_gap_analysis\", skill_gap_node)\n",
- "builder.add_node(\"roadmap_planning_agent\", roadmap_planning_node)\n",
- "builder.add_node(\"tools\", tool_node) # Named 'tools' for tools_condition compatibility\n",
+ "\n",
+ "# The ReAct Agent Node\n",
+ "builder.add_node(\"roadmap_planning_agent\", plan_agent)\n",
+ "\n",
+ "# The Tool Execution Node (Required for the loop)\n",
+ "builder.add_node(\"tools\", ToolNode(roadmap_planner_agent_tools))\n",
+ "\n",
"builder.add_node(\"finalize_state\", finalize_state_node)\n",
"\n",
- "# Define Entry Point and initial Extraction Parallelism\n",
- "builder.set_entry_point(\"input_node\")\n",
+ "# 5. Define Edges and Workflow\n",
+ "builder.add_edge(START, \"input_node\")\n",
"builder.add_edge(\"input_node\", \"resume_data_extraction\")\n",
"builder.add_edge(\"input_node\", \"jd_data_extraction\")\n",
"\n",
- "# Join Extractions into Gap Analysis\n",
+ "# Join Parallel Extractions\n",
"builder.add_edge(\"resume_data_extraction\", \"skill_gap_analysis\")\n",
"builder.add_edge(\"jd_data_extraction\", \"skill_gap_analysis\")\n",
"\n",
- "# Transition from Analysis to Planning Agent\n",
+ "# Start the Planning Phase\n",
"builder.add_edge(\"skill_gap_analysis\", \"roadmap_planning_agent\")\n",
"\n",
- "# Agentic ReAct Loop (Planning Agent <-> Tools)\n",
+ "# Agentic ReAct Loop\n",
"builder.add_conditional_edges(\n",
" \"roadmap_planning_agent\",\n",
- " tools_condition,\n",
+ " tools_condition, # Built-in: routes to \"tools\" if the model calls a tool\n",
" {\n",
- " \"tools\": \"tools\", # If tool_calls exist, go to tools\n",
- " \"__end__\": \"finalize_state\" # If finished, go to finalize_state\n",
+ " \"tools\": \"tools\",\n",
+ " END: \"finalize_state\" # Routes to finalize if the model gives a final answer\n",
" }\n",
")\n",
"\n",
- "# 2. Loop back to agent after tools\n",
+ "# Loop back to agent after tool execution\n",
"builder.add_edge(\"tools\", \"roadmap_planning_agent\")\n",
"\n",
- "\n",
- "\n",
- "# Compile the Graph\n",
+ "# 6. Compile\n",
"graph = builder.compile()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 84,
"id": "53588a77",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"display(graph)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 69,
"id": "0edf8d59",
"metadata": {},
"outputs": [],
@@ -1736,7 +1802,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 60,
"id": "da3df5a4",
"metadata": {},
"outputs": [],
@@ -1777,7 +1843,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 70,
"id": "a95b4db7",
"metadata": {},
"outputs": [],
@@ -1806,13 +1872,13 @@
"\n",
"config = {\"configurable\": {\"thread_id\": THREAD_ID,\"langgraph_user_id\": \"Chirayu Jain\"}}\n",
"\n",
- "final_state = graph.invoke(initial_input, config=config)\n",
+ "# final_state = graph.invoke(initial_input, config=config)\n",
"\n"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 90,
"id": "093bdd6e",
"metadata": {},
"outputs": [],
@@ -1872,20 +1938,127 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "7a36ecb1",
+ "id": "db595a54",
"metadata": {},
"outputs": [],
"source": [
- "config = {\"configurable\": {\"thread_id\": \"debug_123\"}}\n",
- "final_result = run_graph_with_stream(graph, initial_input, config)"
+ "///break"
]
},
{
"cell_type": "code",
"execution_count": null,
+ "id": "7a36ecb1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "🚀 Starting Graph Stream...\n",
+ "\n",
+ "📂 File path received: C:\\Users\\ATHARVA\\Downloads\\my codes\\web\\AdaptiveEngineService\\AI_Engine_Evaluation\\Testcases\\Testresume\\ChirayuResume.pdf\n",
+ "📂 File exists: True\n",
+ "📄 Pages loaded: 1\n",
+ "📄 Text length: 3156\n",
+ "--- 📍 Node: input_node ---\n",
+ "\n",
+ "==================================================\n",
+ "\n",
+ "DEBUGGER: Sending 525 characters to JD Agent...\n",
+ "📄 Resume text length: 3156 chars\n",
+ "DEBUGGER SUCCESS: Extracted Backend Developer\n",
+ "✅ Resume extracted: Software Developer\n",
+ "--- 📍 Node: jd_data_extraction ---\n",
+ "✅ Extracted JD: Backend Developer\n",
+ "\n",
+ "==================================================\n",
+ "\n",
+ "--- 📍 Node: resume_data_extraction ---\n",
+ "✅ Extracted Resume for: Chirayu Jain\n",
+ "\n",
+ "==================================================\n",
+ "\n",
+ "--- 📍 Node: skill_gap_analysis ---\n",
+ "🧠 AI THOUGHT: Skill Gap Analysis Completed for Chirayu Jain. Gaps Identified: { \"job_title\": \"Backend Developer\", \"candidate_name\": \"C...\n",
+ "🎯 Skill Gap Analysis Completed.\n",
+ "\n",
+ "==================================================\n",
+ "\n",
+ "--- 📍 Node: roadmap_planning_agent ---\n",
+ "🧠 AI THOUGHT: The learning roadmap for **Chirayu Jain** has been finalized and visualized. **Roadmap Summary** - **Target Role:** Backend Developer - **Onboardin...\n",
+ "\n",
+ "==================================================\n",
+ "\n",
+ "--- 📍 Node: finalize_state ---\n",
+ "🏁 Final Roadmap Constructed.\n",
+ "\n",
+ "==================================================\n",
+ "\n",
+ "✨ Stream Finished. Final state captured.\n"
+ ]
+ }
+ ],
+ "source": [
+ "config = {\"configurable\": {\"thread_id\": \"gyujj\"}}\n",
+ "final_result = run_graph_with_stream(graph, initial_input, config)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
"id": "4e502949",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'candidate_name': 'Chirayu Jain',\n",
+ " 'resume_text': 'Chirayu Jain\\nSOFTWARE DEVELOPER\\n+91-9462128878 | chirayujain93@gmail.com | LinkedIn | GitHub | CodeChef | Leetcode\\nEDUCATION\\nIndian Institute of Information Technology, Kota (CGPA-8.1)\\nKota, Rajasthan\\nB.Tech in Electronics and Communication Engineering\\n2022 - Present\\nWORK EXPERIENCE\\nNAV India\\nJan 2026 - Present\\nTechnology Intern\\nOnsite – Jaipur, Rajasthan\\n• As part of the AI/ML team, built Flask API for the RTA investor verification team, implementing 2\\nconfidence-scoring pipelines using OCR extraction (PyMuPDF, Docling) to parse PDF/forms and\\nextract 3 key KYC investor attributes, reducing verification time from 20–30 minutes to 2–3 minutes.\\n• Developed a scalable document intelligence pipeline with parallel CPU processing using Docling OCR,\\nsemantic chunking, and LLM-based extraction to process 60+ page financial documents and identify\\ninvestor/investee details, reducing extraction time by 35%.\\n• Implementing Cron-based background workers to generate automated weekly email analytics reports\\ntracking API accuracy and confidence match-mismatch scores, improving scoring precision and reducing\\noverall document processing latency by 40%.\\nVestbox\\nApril 2025 - May 2025\\nSoftware Development intern\\nRemote\\n• Developed Groceazy app in Flutter (MVVM), integrating 10+ modules for grocery service , order management\\nand Applied clean code practices (SOLID, modular design) for scalability.\\n• Implemented secure user authentication and role-based access, ensuring smooth login and data protection.\\n• Built image-to-item price matcher using APIs across 5+ platforms, enabling best-price suggestions.\\nPROJECTS\\nHostel Bites – IIITK Canteen App | Flutter, Dart, Firebase, Cloud Firestore, Mapbox API\\n• Developed a Flutter app for hostel residents to order food, increasing canteen efficiency by 35%.\\n• Implemented role-based access for students, staff, and admin, increasing operational efficiency by 40%.\\n• Integrated Firebase Auth, Firestore, Storage, and Mapbox API for secure logins, and live order tracking.\\n• GitHub: Live link\\nJobShield - Fake Job Detector | Flutter, Dart, Python, Flask, SQLite\\n• Developed a Flask+Python fake job detection API with a custom NLP model, with 95% classification accuracy.\\n• Built a responsive web interface using Flutter (Web), integrated with the backend, enabling seamless real-time\\ndetection for 500+ job postings.\\n• Github: Live Link\\nTECHNICAL SKILLS\\nLanguages: C/C++ , Java , Python , JavaScript, Dart\\nFrontend: Flutter, HTML/CSS, ReactJS\\nBackend: Firebase, REST APIs, Flask, Django, NodeJS, ExpressJS\\nDatabase Management: MySQL, MongoDB, SQLite\\nTools and Frameworks: GitHub, Android Studio, Visual Studio Code, MATLAB, Cursor, Github Copilot\\nLibraries: Provider, Bloc, Scikit-learn, tensorflow\\nACHIEVEMENTS & CERTIFICATIONS\\n• Ranked 81 in Inter IIIT Coding Contest Optigo, competing against top-tier programmers - View credential.\\n• Obtained Certification in Graph Algorithms from AlgoUniversity - View credential.\\n• Achieved a 4-star CodeChef rating and earned the Knight(Max. 1888) badge on LeetCode.\\n• Developed an e-waste facility locator application for SIH’23, successfully advancing to the regionals.',\n",
+ " 'file_path': 'C:\\\\Users\\\\ATHARVA\\\\Downloads\\\\my codes\\\\web\\\\AdaptiveEngineService\\\\AI_Engine_Evaluation\\\\Testcases\\\\Testresume\\\\ChirayuResume.pdf',\n",
+ " 'job_description': 'Job Title: Backend Developer\\n\\nCompany name: CodeForge\\nWe are hiring a Backend Developer to build scalable APIs and backend systems.\\n\\nResponsibilities:\\n- Develop REST APIs using FastAPI\\n- Design and manage PostgreSQL databases\\n- Implement authentication and authorization systems\\n- Optimize performance and scalability\\n\\nRequirements:\\n- Strong knowledge of Python\\n- Experience with FastAPI or Django\\n- Good understanding of SQL and database design\\n- Familiarity with Docker\\n\\nConstraints:\\n- Location: Pune only\\n- Full-time role ',\n",
+ " 'messages': [HumanMessage(content='\\n Skill Gap Analysis Completed for Chirayu Jain.\\n\\n Gaps Identified:\\n {\\n \"job_title\": \"Backend Developer\",\\n \"candidate_name\": \"Chirayu Jain\",\\n \"analyzed_gaps\": [\\n {\\n \"skill_name\": \"FastAPI\",\\n \"gap_type\": \"missing_foundation\",\\n \"priority\": \"high\",\\n \"reasoning\": \"JD explicitly lists FastAPI; resume shows Flask/Django but no exposure to FastAPI, creating a core framework gap.\",\\n \"target_competency\": \"Build async REST endpoints with FastAPI, Pydantic models and dependency injection\"\\n },\\n {\\n \"skill_name\": \"PostgreSQL\",\\n \"gap_type\": \"missing_foundation\",\\n \"priority\": \"high\",\\n \"reasoning\": \"JD requires PostgreSQL; candidate only lists MySQL, SQLite and MongoDB, so no evidence of PostgreSQL-specific features or deployment experience.\",\\n \"target_competency\": \"Design relational schemas, write optimized queries and manage transactions in PostgreSQL\"\\n },\\n {\\n \"skill_name\": \"Docker\",\\n \"gap_type\": \"missing_foundation\",\\n \"priority\": \"high\",\\n \"reasoning\": \"Docker appears in JD but is absent from skills, experience and projects, indicating no containerization background.\",\\n \"target_competency\": \"Containerize FastAPI services, write Dockerfiles and orchestrate multi-container dev environments\"\\n }\\n ],\\n \"is_fresher_adaptation_needed\": false,\\n \"executive_summary\": \"Chirayu is a near-ready backend candidate with solid Python, Django and SQL experience, but must quickly add FastAPI, PostgreSQL and Docker to meet CodeForge’s stack requirements.\"\\n}\\n\\n Please proceed to search for courses and build the learning roadmap now.\\n ', additional_kwargs={}, response_metadata={}, id='61350c0a-f333-4b0b-bce8-b002d5c13832'),\n",
+ " AIMessage(content='', additional_kwargs={'reasoning_content': \"We need to follow workflow.\\n\\nStep 1: For each gap skill, call search_courses with query.\\n\\nWe have three gaps: FastAPI, PostgreSQL, Docker.\\n\\nWe need to get course IDs.\\n\\nLet's call search_courses for FastAPI.\", 'tool_calls': [{'id': 'fc_6c1f8c83-94ad-438c-8ff4-772fc7e3a5a2', 'function': {'arguments': '{\"query\":\"FastAPI\"}', 'name': 'search_courses'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 78, 'prompt_tokens': 1285, 'total_tokens': 1363, 'completion_time': 0.167971032, 'completion_tokens_details': {'reasoning_tokens': 50}, 'prompt_time': 0.061565999, 'prompt_tokens_details': None, 'queue_time': 0.04593067, 'total_time': 0.229537031}, 'model_name': 'openai/gpt-oss-120b', 'system_fingerprint': 'fp_a09bde29de', 'service_tier': 'on_demand', 'finish_reason': 'tool_calls', 'logprobs': None, 'model_provider': 'groq'}, id='lc_run--019d206b-c546-75f2-9bee-3a75dc5dc28a-0', tool_calls=[{'name': 'search_courses', 'args': {'query': 'FastAPI'}, 'id': 'fc_6c1f8c83-94ad-438c-8ff4-772fc7e3a5a2', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 1285, 'output_tokens': 78, 'total_tokens': 1363, 'output_token_details': {'reasoning': 50}}),\n",
+ " ToolMessage(content=\"ID: CS-FAST-101\\nCourse Title: REST API Development with FastAPI\\nCategory: Backend\\nDescription: A project-driven course for building production-quality REST APIs with Python's FastAPI framework. Students learn to define typed routes with path and query parameters, leverage Pydantic models for automatic request validation and serialization, implement dependency injection, and secure endpoints using JWT-based Bearer token authentication.\\nLearning Outcomes: Create REST endpoints with path and query params, Validate request/response with Pydantic models, Implement JWT-based authentication\\nPrerequisites: CS-PY-101\\n---\\nID: CS-FAST-201\\nCourse Title: Advanced API Design with FastAPI\\nCategory: Backend\\nDescription: An advanced course for engineers building high-performance, secure backend services with FastAPI. Students implement full OAuth2 authorization code and client credentials flows, build async database CRUD endpoints using SQLAlchemy 2.0 with asyncpg, design layered dependency injection architectures, create custom middleware for logging and rate limiting, and run Celery background task queues.\\nLearning Outcomes: Implement OAuth2 authentication flows, Build async database endpoints with SQLAlchemy, Use dependency injection and middleware patterns\\nPrerequisites: CS-PY-101, CS-DB-101, CS-FAST-101\\n---\\nID: CS-MLOPS-301\\nCourse Title: MLOps — Model Deployment & Monitoring\\nCategory: AI Engineering\\nDescription: An end-to-end MLOps course covering the full lifecycle of production machine learning systems. Students track experiments and version datasets and models using MLflow, build model serving APIs with FastAPI, containerize inference services with Docker, set up automated retraining pipelines triggered by data drift detection using Evidently AI, and monitor prediction quality and infrastructure health with Grafana dashboards.\\nLearning Outcomes: Track experiments and version models with MLflow, Serve ML models via FastAPI and Docker, Monitor model drift and set up retraining triggers\\nPrerequisites: CS-ML-101, CS-DOCKER-101, CS-FAST-101\\n---\\nID: OPS-K8S-101\\nCourse Title: Kubernetes for AI Workloads\\nCategory: MLOps\\nDescription: An introduction to K8s specifically for ML engineers. Covers pod orchestration, GPU resource allocation, and scaling FastAPI backends on clusters.\\nLearning Outcomes: Deploy ML models as K8s services, Configure GPU-enabled worker nodes, Manage cluster auto-scaling for inference bursts\\nPrerequisites: None\\n---\", name='search_courses', id='be522420-9402-497c-b37a-55f859017f39', tool_call_id='fc_6c1f8c83-94ad-438c-8ff4-772fc7e3a5a2'),\n",
+ " AIMessage(content='', additional_kwargs={'reasoning_content': 'We have FastAPI courses. Need also PostgreSQL and Docker courses. Search for PostgreSQL.', 'tool_calls': [{'id': 'fc_cfcddbeb-b919-4f79-98e2-2000cdf9c354', 'function': {'arguments': '{\"query\":\"PostgreSQL\"}', 'name': 'search_courses'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 45, 'prompt_tokens': 1809, 'total_tokens': 1854, 'completion_time': 0.09460185, 'completion_tokens_details': {'reasoning_tokens': 20}, 'prompt_time': 0.614039222, 'prompt_tokens_details': {'cached_tokens': 768}, 'queue_time': 0.045160052, 'total_time': 0.708641072}, 'model_name': 'openai/gpt-oss-120b', 'system_fingerprint': 'fp_626f3fc5e0', 'service_tier': 'on_demand', 'finish_reason': 'tool_calls', 'logprobs': None, 'model_provider': 'groq'}, id='lc_run--019d206b-dc84-7312-8d72-172aa51c0a1b-0', tool_calls=[{'name': 'search_courses', 'args': {'query': 'PostgreSQL'}, 'id': 'fc_cfcddbeb-b919-4f79-98e2-2000cdf9c354', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 1809, 'output_tokens': 45, 'total_tokens': 1854, 'input_token_details': {'cache_read': 768}, 'output_token_details': {'reasoning': 20}}),\n",
+ " ToolMessage(content='ID: CS-DB-101\\nCourse Title: SQL Fundamentals for Backend Developers\\nCategory: Database\\nDescription: A hands-on introduction to relational database theory and practice using SQLite and PostgreSQL. Students learn to model data with schemas, enforce referential integrity through primary and foreign keys, and interact with data using full CRUD operations and multi-table JOINs.\\nLearning Outcomes: Write basic SELECT queries with filters, Understand Primary and Foreign Keys, Perform data insertion, updates, and deletions\\nPrerequisites: None\\n---\\nID: CS-DB-201\\nCourse Title: Advanced SQL — Indexing, Transactions & Query Optimization\\nCategory: Database\\nDescription: A deep dive into production-grade SQL performance and reliability. Students master analytical window functions, Common Table Expressions (CTEs), query execution plan analysis using EXPLAIN/ANALYZE, B-tree and partial index strategies, and ACID-compliant transaction management including deadlock detection and prevention.\\nLearning Outcomes: Use window functions and CTEs, Analyze and optimize slow queries with EXPLAIN, Manage ACID transactions and deadlock prevention\\nPrerequisites: CS-DB-101\\n---\\nID: BUS-SQL-201\\nCourse Title: SQL for Workforce Analytics\\nCategory: Data Science\\nDescription: Learn to query internal talent databases. Covers JOINs, aggregations, and subqueries to find hiring trends and predict churn.\\nLearning Outcomes: Write SQL queries to extract talent metrics, Analyze historical hiring data for trends, Build basic labor supply reports\\nPrerequisites: BUS-DATA-101\\n---\\nID: CS-DB-301\\nCourse Title: NoSQL Databases — MongoDB & Redis\\nCategory: Database\\nDescription: Practical training in document and key-value store paradigms for modern applications. Covers MongoDB schema design, flexible document modeling, the aggregation pipeline for complex data transformations, and Redis as both a high-speed caching layer and a pub/sub message broker with TTL-based eviction strategies.\\nLearning Outcomes: Design document schemas in MongoDB, Build aggregation pipelines, Implement caching with Redis TTL strategies\\nPrerequisites: CS-DB-101\\n---', name='search_courses', id='5f602c21-b2c8-4611-9f68-1e4f127958cb', tool_call_id='fc_cfcddbeb-b919-4f79-98e2-2000cdf9c354'),\n",
+ " AIMessage(content='', additional_kwargs={'reasoning_content': 'We have three gaps: FastAPI, PostgreSQL, Docker.\\n\\nWe need to search for Docker courses.', 'tool_calls': [{'id': 'fc_68440688-509a-4a4d-ae5f-b46d16b4a8fc', 'function': {'arguments': '{\"query\":\"Docker\"}', 'name': 'search_courses'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 45, 'prompt_tokens': 2260, 'total_tokens': 2305, 'completion_time': 0.095418158, 'completion_tokens_details': {'reasoning_tokens': 22}, 'prompt_time': 0.28257033, 'prompt_tokens_details': {'cached_tokens': 1280}, 'queue_time': 0.045485127, 'total_time': 0.377988488}, 'model_name': 'openai/gpt-oss-120b', 'system_fingerprint': 'fp_a09bde29de', 'service_tier': 'on_demand', 'finish_reason': 'tool_calls', 'logprobs': None, 'model_provider': 'groq'}, id='lc_run--019d206b-e355-7100-8e37-9e09f7cf0a97-0', tool_calls=[{'name': 'search_courses', 'args': {'query': 'Docker'}, 'id': 'fc_68440688-509a-4a4d-ae5f-b46d16b4a8fc', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 2260, 'output_tokens': 45, 'total_tokens': 2305, 'input_token_details': {'cache_read': 1280}, 'output_token_details': {'reasoning': 22}}),\n",
+ " ToolMessage(content='ID: CS-DOCKER-101\\nCourse Title: Docker & Containerization Fundamentals\\nCategory: DevOps\\nDescription: A practical guide to application containerization using Docker. Students learn to write optimized multi-stage Dockerfiles, manage images and container lifecycles, configure bridge and overlay networking, use named volumes for data persistence, and orchestrate multi-service applications with Docker Compose for local and staging environments.\\nLearning Outcomes: Write efficient multi-stage Dockerfiles, Manage container lifecycle and networking, Orchestrate multi-service apps with Docker Compose\\nPrerequisites: None\\n---\\nID: CS-CICD-201\\nCourse Title: CI/CD Pipelines with GitHub Actions\\nCategory: DevOps\\nDescription: A hands-on course in automating the full software delivery lifecycle using GitHub Actions. Students define multi-job workflows with matrix builds, integrate linting and pytest test suites as required CI checks, automate Docker image builds and pushes to registries, and manage environment secrets and deployment approvals for staging and production environments.\\nLearning Outcomes: Create GitHub Actions workflows for CI/CD, Automate Docker image builds and pushes, Manage secrets and environment variables securely\\nPrerequisites: CS-DOCKER-101\\n---\\nID: CS-MLOPS-301\\nCourse Title: MLOps — Model Deployment & Monitoring\\nCategory: AI Engineering\\nDescription: An end-to-end MLOps course covering the full lifecycle of production machine learning systems. Students track experiments and version datasets and models using MLflow, build model serving APIs with FastAPI, containerize inference services with Docker, set up automated retraining pipelines triggered by data drift detection using Evidently AI, and monitor prediction quality and infrastructure health with Grafana dashboards.\\nLearning Outcomes: Track experiments and version models with MLflow, Serve ML models via FastAPI and Docker, Monitor model drift and set up retraining triggers\\nPrerequisites: CS-ML-101, CS-DOCKER-101, CS-FAST-101\\n---\\nID: CS-K8S-301\\nCourse Title: Kubernetes — Container Orchestration at Scale\\nCategory: DevOps\\nDescription: A comprehensive course on running containerized workloads in production with Kubernetes. Students deploy stateless and stateful applications using Deployments and StatefulSets, expose services through ClusterIP, NodePort, and Ingress controllers, manage configuration and secrets with ConfigMaps and Secrets, autoscale workloads with HPA, and package applications for repeatable releases using Helm charts.\\nLearning Outcomes: Deploy applications using Deployments and StatefulSets, Configure Services, Ingress, and ConfigMaps, Manage releases with Helm charts\\nPrerequisites: CS-DOCKER-101, CS-CICD-201\\n---', name='search_courses', id='00e1a0f1-f97e-46b6-9f4d-ee55fc23c17d', tool_call_id='fc_68440688-509a-4a4d-ae5f-b46d16b4a8fc'),\n",
+ " AIMessage(content='', additional_kwargs={'reasoning_content': 'We have identified gaps: FastAPI, PostgreSQL, Docker. Need to search for courses for each. Already have courses:\\n\\nFastAPI: CS-FAST-101 (prereq CS-PY-101). Also CS-FAST-201 (prereq CS-PY-101, CS-DB-101, CS-FAST-101). Likely choose CS-FAST-101 as foundation.\\n\\nPostgreSQL: CS-DB-101 covers SQLite and PostgreSQL fundamentals. No explicit prerequisite. Also CS-DB-201 advanced. Choose CS-DB-101.\\n\\nDocker: CS-DOCKER-101 fundamentals, no prereq.\\n\\nNow need to resolve prerequisites: For CS-FAST-101, prerequisite CS-PY-101 (Python Fundamentals). Candidate has solid Python, Django experience. Likely resume shows Python mastery, so we can consider that as known. The instruction: \"Skip courses the candidate already demonstrates via projects or experience.\" So we can treat CS-PY-101 as known, not needed to include.\\n\\nCheck other courses for prerequisites: none else.\\n\\nThus roadmap includes three courses: CS-FAST-101, CS-DB-101, CS-DOCKER-101. Sequence: prerequisites before target modules. Since no dependencies among these three (except maybe Docker before FastAPI? Not required). Could order logically: start with Docker fundamentals (foundation for containerizing FastAPI later), then PostgreSQL fundamentals, then FastAPI. Or any order. We\\'ll assign sequence_order 1,2,3.\\n\\nis_fresher_adaptation_needed false, so no professional fundamentals module.\\n\\nNow need to build roadmap array with fields: course_id, is_foundation (boolean). Probably true for foundation courses? Let\\'s set is_foundation true for all three as they are foundational.\\n\\nReasoning: brief.\\n\\nNow need to create mermaid visualization following rules: gap courses ::gap, known prerequisites ::known, start node ::start, end node ::done. Since we are not adding known courses (CS-PY-101) as nodes, but we could include as known? The rules: known prerequisites should be marked ::known. Since we are skipping CS-PY-101, maybe we don\\'t include it. But we could include it as known to show prerequisite satisfied. Let\\'s include a node for CS-PY-101 as known.\\n\\nAlso start node: candidate\\'s current skills.\\n\\nEnd node: Role-ready — Backend Developer.\\n\\nGroup by week using subgraph. Let\\'s assign weeks: Week 1 Docker, Week 2 PostgreSQL, Week 3 FastAPI.\\n\\nMermaid code accordingly.\\n\\nNow need to call submit_final_roadmap with candidate_name, onboarding_summary (maybe executive summary), roadmap array, target_role \"Backend Developer\".\\n\\nThen submit_mermaid_visualization with code.\\n\\nProceed.', 'tool_calls': [{'id': 'fc_e1fad88b-3a4b-4877-a3df-369362707126', 'function': {'arguments': '{\"candidate_name\":\"Chirayu Jain\",\"onboarding_summary\":\"Chirayu is a near-ready backend candidate with solid Python, Django and SQL experience, but must quickly add FastAPI, PostgreSQL and Docker to meet CodeForge’s stack requirements.\",\"roadmap\":[{\"course_id\":\"CS-DOCKER-101\",\"is_foundation\":true,\"reasoning\":\"Provides essential containerization skills required to package FastAPI services for deployment.\",\"sequence_order\":1,\"title\":\"Docker \\\\u0026 Containerization Fundamentals\"},{\"course_id\":\"CS-DB-101\",\"is_foundation\":true,\"reasoning\":\"Covers PostgreSQL fundamentals, enabling design of relational schemas and optimized queries for the target role.\",\"sequence_order\":2,\"title\":\"SQL Fundamentals for Backend Developers\"},{\"course_id\":\"CS-FAST-101\",\"is_foundation\":true,\"reasoning\":\"Introduces FastAPI, the primary framework listed in the JD, building async REST endpoints and Pydantic models.\",\"sequence_order\":3,\"title\":\"REST API Development with FastAPI\"}],\"target_role\":\"Backend Developer\"}', 'name': 'submit_final_roadmap'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 864, 'prompt_tokens': 2825, 'total_tokens': 3689, 'completion_time': 1.891397937, 'completion_tokens_details': {'reasoning_tokens': 564}, 'prompt_time': 0.145777084, 'prompt_tokens_details': {'cached_tokens': 1792}, 'queue_time': 0.045268224, 'total_time': 2.037175021}, 'model_name': 'openai/gpt-oss-120b', 'system_fingerprint': 'fp_626f3fc5e0', 'service_tier': 'on_demand', 'finish_reason': 'tool_calls', 'logprobs': None, 'model_provider': 'groq'}, id='lc_run--019d206b-ee19-7900-a734-70259374603f-0', tool_calls=[{'name': 'submit_final_roadmap', 'args': {'candidate_name': 'Chirayu Jain', 'onboarding_summary': 'Chirayu is a near-ready backend candidate with solid Python, Django and SQL experience, but must quickly add FastAPI, PostgreSQL and Docker to meet CodeForge’s stack requirements.', 'roadmap': [{'course_id': 'CS-DOCKER-101', 'is_foundation': True, 'reasoning': 'Provides essential containerization skills required to package FastAPI services for deployment.', 'sequence_order': 1, 'title': 'Docker & Containerization Fundamentals'}, {'course_id': 'CS-DB-101', 'is_foundation': True, 'reasoning': 'Covers PostgreSQL fundamentals, enabling design of relational schemas and optimized queries for the target role.', 'sequence_order': 2, 'title': 'SQL Fundamentals for Backend Developers'}, {'course_id': 'CS-FAST-101', 'is_foundation': True, 'reasoning': 'Introduces FastAPI, the primary framework listed in the JD, building async REST endpoints and Pydantic models.', 'sequence_order': 3, 'title': 'REST API Development with FastAPI'}], 'target_role': 'Backend Developer'}, 'id': 'fc_e1fad88b-3a4b-4877-a3df-369362707126', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 2825, 'output_tokens': 864, 'total_tokens': 3689, 'input_token_details': {'cache_read': 1792}, 'output_token_details': {'reasoning': 564}}),\n",
+ " ToolMessage(content='{\"candidate_name\": \"Chirayu Jain\", \"target_role\": \"Backend Developer\", \"onboarding_summary\": \"Chirayu is a near-ready backend candidate with solid Python, Django and SQL experience, but must quickly add FastAPI, PostgreSQL and Docker to meet CodeForge’s stack requirements.\", \"roadmap\": [{\"course_id\": \"CS-DOCKER-101\", \"title\": \"Docker & Containerization Fundamentals\", \"reasoning\": \"Provides essential containerization skills required to package FastAPI services for deployment.\", \"is_foundation\": true, \"sequence_order\": 1}, {\"course_id\": \"CS-DB-101\", \"title\": \"SQL Fundamentals for Backend Developers\", \"reasoning\": \"Covers PostgreSQL fundamentals, enabling design of relational schemas and optimized queries for the target role.\", \"is_foundation\": true, \"sequence_order\": 2}, {\"course_id\": \"CS-FAST-101\", \"title\": \"REST API Development with FastAPI\", \"reasoning\": \"Introduces FastAPI, the primary framework listed in the JD, building async REST endpoints and Pydantic models.\", \"is_foundation\": true, \"sequence_order\": 3}]}', name='submit_final_roadmap', id='5fb8c861-d353-4034-a0c8-d1c17aeabcd6', tool_call_id='fc_e1fad88b-3a4b-4877-a3df-369362707126'),\n",
+ " AIMessage(content='', additional_kwargs={'reasoning_content': \"We have submitted final roadmap. Need to also submit mermaid visualization. Must follow rules: gap courses ::gap, known prerequisites ::known, start ::start, end ::done. In this case all three are gaps (they are missing foundations). However they have prerequisites: CS-FAST-101 prerequisite CS-PY-101, CS-DB-101 has none, CS-DOCKER-101 none. Candidate already has Python foundation, so we can treat CS-PY-101 as known prerequisite. Need to include it as known node. Sequence: start -> CS-DOCKER-101 (gap) -> CS-DB-101 (gap) -> CS-FAST-101 (gap) -> end. Also include CS-PY-101 as known prerequisite for FastAPI, placed before FastAPI maybe in same week or earlier. Since FastAPI depends on Python fundamentals, we should include known node before FastAPI. Could be in same week as FastAPI but as known. We'll create weeks: Week1 Docker, Week2 PostgreSQL, Week3 Python fundamentals (known) and FastAPI (gap). Or maybe include known before gap in same week.\\n\\nMermaid format: flowchart TD. Use subgraph W1, W2, W3.\\n\\nAdd class definitions.\\n\\nNow call submit_mermaid_visualization.\", 'tool_calls': [{'id': 'fc_ea8b8113-e478-45dd-a8b4-bf2eae927d9a', 'function': {'arguments': '{\"mermaid_code\":\"flowchart TD\\\\n A([Start — Current Skills]):::start\\\\n subgraph W1[\\\\\"Week 1 — Containerization\\\\\"]\\\\n B[CS-DOCKER-101\\\\\\\\nDocker \\\\u0026 Containerization Fundamentals]:::gap\\\\n end\\\\n subgraph W2[\\\\\"Week 2 — Relational DB\\\\\"]\\\\n C[CS-DB-101\\\\\\\\nSQL Fundamentals for Backend Developers]:::gap\\\\n end\\\\n subgraph W3[\\\\\"Week 3 — API Development\\\\\"]\\\\n D[CS-PY-101\\\\\\\\nPython Fundamentals]:::known\\\\n E[CS-FAST-101\\\\\\\\nREST API Development with FastAPI]:::gap\\\\n end\\\\n Z([Backend Developer — Role Ready]):::done\\\\n A --\\\\u003e B --\\\\u003e C --\\\\u003e D --\\\\u003e E --\\\\u003e Z\\\\n classDef gap fill:#EEEDFE,stroke:#534AB7,color:#26215C\\\\n classDef known fill:#E1F5EE,stroke:#0F6E56,color:#085041\\\\n classDef start fill:#1D9E75,stroke:#0F6E56,color:#E1F5EE\\\\n classDef done fill:#534AB7,stroke:#3C3489,color:#EEEDFE\"}', 'name': 'submit_mermaid_visualization'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 557, 'prompt_tokens': 3339, 'total_tokens': 3896, 'completion_time': 1.196030062, 'completion_tokens_details': {'reasoning_tokens': 266}, 'prompt_time': 0.160108793, 'prompt_tokens_details': None, 'queue_time': 0.046081046, 'total_time': 1.356138855}, 'model_name': 'openai/gpt-oss-120b', 'system_fingerprint': 'fp_8a618bed98', 'service_tier': 'on_demand', 'finish_reason': 'tool_calls', 'logprobs': None, 'model_provider': 'groq'}, id='lc_run--019d206b-f96f-7ad0-9a9e-fc4e668a8516-0', tool_calls=[{'name': 'submit_mermaid_visualization', 'args': {'mermaid_code': 'flowchart TD\\n A([Start — Current Skills]):::start\\n subgraph W1[\"Week 1 — Containerization\"]\\n B[CS-DOCKER-101\\\\nDocker & Containerization Fundamentals]:::gap\\n end\\n subgraph W2[\"Week 2 — Relational DB\"]\\n C[CS-DB-101\\\\nSQL Fundamentals for Backend Developers]:::gap\\n end\\n subgraph W3[\"Week 3 — API Development\"]\\n D[CS-PY-101\\\\nPython Fundamentals]:::known\\n E[CS-FAST-101\\\\nREST API Development with FastAPI]:::gap\\n end\\n Z([Backend Developer — Role Ready]):::done\\n A --> B --> C --> D --> E --> Z\\n classDef gap fill:#EEEDFE,stroke:#534AB7,color:#26215C\\n classDef known fill:#E1F5EE,stroke:#0F6E56,color:#085041\\n classDef start fill:#1D9E75,stroke:#0F6E56,color:#E1F5EE\\n classDef done fill:#534AB7,stroke:#3C3489,color:#EEEDFE'}, 'id': 'fc_ea8b8113-e478-45dd-a8b4-bf2eae927d9a', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 3339, 'output_tokens': 557, 'total_tokens': 3896, 'output_token_details': {'reasoning': 266}}),\n",
+ " ToolMessage(content='Mermaid visualization saved successfully.', name='submit_mermaid_visualization', id='e07fe033-a45e-41ee-b421-caa15afa997d', tool_call_id='fc_ea8b8113-e478-45dd-a8b4-bf2eae927d9a'),\n",
+ " AIMessage(content='The learning roadmap for **Chirayu Jain** has been finalized and visualized.\\n\\n**Roadmap Summary**\\n- **Target Role:** Backend Developer \\n- **Onboarding Summary:** Near‑ready backend candidate with solid Python, Django and SQL experience; needs FastAPI, PostgreSQL, and Docker to meet CodeForge’s stack. \\n\\n**Course Sequence**\\n1. **CS-DOCKER-101 – Docker & Containerization Fundamentals** (Foundation) \\n2. **CS-DB-101 – SQL Fundamentals for Backend Developers** (Foundation) \\n3. **CS-FAST-101 – REST API Development with FastAPI** (Foundation) \\n\\n**Mermaid Visualization**\\n```mermaid\\nflowchart TD\\n A([Start — Current Skills]):::start\\n subgraph W1[\"Week 1 — Containerization\"]\\n B[CS-DOCKER-101\\\\nDocker & Containerization Fundamentals]:::gap\\n end\\n subgraph W2[\"Week 2 — Relational DB\"]\\n C[CS-DB-101\\\\nSQL Fundamentals for Backend Developers]:::gap\\n end\\n subgraph W3[\"Week 3 — API Development\"]\\n D[CS-PY-101\\\\nPython Fundamentals]:::known\\n E[CS-FAST-101\\\\nREST API Development with FastAPI]:::gap\\n end\\n Z([Backend Developer — Role Ready]):::done\\n A --> B --> C --> D --> E --> Z\\n classDef gap fill:#EEEDFE,stroke:#534AB7,color:#26215C\\n classDef known fill:#E1F5EE,stroke:#0F6E56,color:#085041\\n classDef start fill:#1D9E75,stroke:#0F6E56,color:#E1F5EE\\n classDef done fill:#534AB7,stroke:#3C3489,color:#EEEDFE\\n```\\n\\nAll steps are complete.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 406, 'prompt_tokens': 3646, 'total_tokens': 4052, 'completion_time': 1.438615073, 'completion_tokens_details': None, 'prompt_time': 0.141632128, 'prompt_tokens_details': {'cached_tokens': 2816}, 'queue_time': 0.04638064, 'total_time': 1.5802472010000002}, 'model_name': 'openai/gpt-oss-120b', 'system_fingerprint': 'fp_626f3fc5e0', 'service_tier': 'on_demand', 'finish_reason': 'stop', 'logprobs': None, 'model_provider': 'groq'}, id='lc_run--019d206c-010d-7133-92a9-18ed936df441-0', tool_calls=[], invalid_tool_calls=[], usage_metadata={'input_tokens': 3646, 'output_tokens': 406, 'total_tokens': 4052, 'input_token_details': {'cache_read': 2816}})],\n",
+ " 'skill_gap_analysis_data': SkillGapAnalysis(job_title='Backend Developer', candidate_name='Chirayu Jain', analyzed_gaps=[SkillGap(skill_name='FastAPI', gap_type='missing_foundation', priority='high', reasoning='JD explicitly lists FastAPI; resume shows Flask/Django but no exposure to FastAPI, creating a core framework gap.', target_competency='Build async REST endpoints with FastAPI, Pydantic models and dependency injection'), SkillGap(skill_name='PostgreSQL', gap_type='missing_foundation', priority='high', reasoning='JD requires PostgreSQL; candidate only lists MySQL, SQLite and MongoDB, so no evidence of PostgreSQL-specific features or deployment experience.', target_competency='Design relational schemas, write optimized queries and manage transactions in PostgreSQL'), SkillGap(skill_name='Docker', gap_type='missing_foundation', priority='high', reasoning='Docker appears in JD but is absent from skills, experience and projects, indicating no containerization background.', target_competency='Containerize FastAPI services, write Dockerfiles and orchestrate multi-container dev environments')], is_fresher_adaptation_needed=False, executive_summary='Chirayu is a near-ready backend candidate with solid Python, Django and SQL experience, but must quickly add FastAPI, PostgreSQL and Docker to meet CodeForge’s stack requirements.'),\n",
+ " 'resume_data': ResumeExtract(candidate_name='Chirayu Jain', job_title='Software Developer', skills=[Skill(name='C/C++', category='Other'), Skill(name='Java', category='Other'), Skill(name='Python', category='Backend'), Skill(name='JavaScript', category='Frontend'), Skill(name='Dart', category='Frontend'), Skill(name='Flutter', category='Frontend'), Skill(name='HTML/CSS', category='Frontend'), Skill(name='ReactJS', category='Frontend'), Skill(name='Firebase', category='Backend'), Skill(name='REST APIs', category='Backend'), Skill(name='Flask', category='Backend'), Skill(name='Django', category='Backend'), Skill(name='NodeJS', category='Backend'), Skill(name='ExpressJS', category='Backend'), Skill(name='MySQL', category='Other'), Skill(name='MongoDB', category='Other'), Skill(name='SQLite', category='Other'), Skill(name='GitHub', category='Other'), Skill(name='Android Studio', category='Other'), Skill(name='Visual Studio Code', category='Other'), Skill(name='MATLAB', category='Other'), Skill(name='Cursor', category='Other'), Skill(name='Github Copilot', category='Other'), Skill(name='Provider', category='Other'), Skill(name='Bloc', category='Other'), Skill(name='Scikit-learn', category='ML'), Skill(name='tensorflow', category='ML')], experience=[ExperienceItem(job_title='Technology Intern', experience_type='internship', technologies=['Flask', 'PyMuPDF', 'Docling', 'OCR', 'Python'], responsibilities=['Built Flask API for RTA investor verification with confidence-scoring pipelines', 'Developed document intelligence pipeline with parallel CPU processing', 'Implementing Cron-based background workers for weekly email analytics reports']), ExperienceItem(job_title='Software Development Intern', experience_type='internship', technologies=['Flutter', 'MVVM', 'Dart'], responsibilities=['Developed Grocezy app in Flutter with 10+ modules', 'Implemented secure user authentication and role-based access', 'Built image-to-item price matcher using APIs'])], projects=[ProjectItem(name='Hostel Bites – IIITK Canteen App', technologies=['Flutter', 'Dart', 'Firebase', 'Cloud Firestore', 'Mapbox API']), ProjectItem(name='JobShield - Fake Job Detector', technologies=['Flutter', 'Dart', 'Python', 'Flask', 'SQLite'])], certifications=[CertificationItem(name='Graph Algorithms Certification from AlgoUniversity', topics_covered=['Graph Algorithms'])], is_fresher=False),\n",
+ " 'extraction_error': None,\n",
+ " 'JobDescriptionExtract_data': JobDescriptionExtract(job_title='Backend Developer', company_name='CodeForge', location='Pune', employment_type='Full-time role', duration_months=None, is_fresher_allowed=None, skills_required=[SkillRequirement(name='Python', level=None), SkillRequirement(name='FastAPI', level=None), SkillRequirement(name='Django', level=None), SkillRequirement(name='SQL', level=None), SkillRequirement(name='PostgreSQL', level=None), SkillRequirement(name='Docker', level=None)], tools_technologies=None, responsibilities=[ResponsibilityItem(description='Develop REST APIs using FastAPI'), ResponsibilityItem(description='Design and manage PostgreSQL databases'), ResponsibilityItem(description='Implement authentication and authorization systems'), ResponsibilityItem(description='Optimize performance and scalability')], requirements=[RequirementItem(description='Strong knowledge of Python'), RequirementItem(description='Experience with FastAPI or Django'), RequirementItem(description='Good understanding of SQL and database design'), RequirementItem(description='Familiarity with Docker')], constraints=[ConstraintItem(type='location', value='Pune only'), ConstraintItem(type='employment type', value='Full-time role')]),\n",
+ " 'mermaid_code': 'flowchart TD\\n A([Start — Current Skills]):::start\\n subgraph W1[\"Week 1 — Containerization\"]\\n B[CS-DOCKER-101\\\\nDocker & Containerization Fundamentals]:::gap\\n end\\n subgraph W2[\"Week 2 — Relational DB\"]\\n C[CS-DB-101\\\\nSQL Fundamentals for Backend Developers]:::gap\\n end\\n subgraph W3[\"Week 3 — API Development\"]\\n D[CS-PY-101\\\\nPython Fundamentals]:::known\\n E[CS-FAST-101\\\\nREST API Development with FastAPI]:::gap\\n end\\n Z([Backend Developer — Role Ready]):::done\\n A --> B --> C --> D --> E --> Z\\n classDef gap fill:#EEEDFE,stroke:#534AB7,color:#26215C\\n classDef known fill:#E1F5EE,stroke:#0F6E56,color:#085041\\n classDef start fill:#1D9E75,stroke:#0F6E56,color:#E1F5EE\\n classDef done fill:#534AB7,stroke:#3C3489,color:#EEEDFE',\n",
+ " 'final_roadmap': {'candidate_name': 'Chirayu Jain',\n",
+ " 'onboarding_summary': 'Chirayu is a near-ready backend candidate with solid Python, Django and SQL experience, but must quickly add FastAPI, PostgreSQL and Docker to meet CodeForge’s stack requirements.',\n",
+ " 'roadmap': [{'course_id': 'CS-DOCKER-101',\n",
+ " 'is_foundation': True,\n",
+ " 'reasoning': 'Provides essential containerization skills required to package FastAPI services for deployment.',\n",
+ " 'sequence_order': 1,\n",
+ " 'title': 'Docker & Containerization Fundamentals'},\n",
+ " {'course_id': 'CS-DB-101',\n",
+ " 'is_foundation': True,\n",
+ " 'reasoning': 'Covers PostgreSQL fundamentals, enabling design of relational schemas and optimized queries for the target role.',\n",
+ " 'sequence_order': 2,\n",
+ " 'title': 'SQL Fundamentals for Backend Developers'},\n",
+ " {'course_id': 'CS-FAST-101',\n",
+ " 'is_foundation': True,\n",
+ " 'reasoning': 'Introduces FastAPI, the primary framework listed in the JD, building async REST endpoints and Pydantic models.',\n",
+ " 'sequence_order': 3,\n",
+ " 'title': 'REST API Development with FastAPI'}],\n",
+ " 'target_role': 'Backend Developer'}}"
+ ]
+ },
+ "execution_count": 73,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"final_result"
]
@@ -1902,17 +2075,39 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 74,
"id": "5afbce5b",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[HumanMessage(content='\\n Skill Gap Analysis Completed for Chirayu Jain.\\n\\n Gaps Identified:\\n {\\n \"job_title\": \"Backend Developer\",\\n \"candidate_name\": \"Chirayu Jain\",\\n \"analyzed_gaps\": [\\n {\\n \"skill_name\": \"FastAPI\",\\n \"gap_type\": \"missing_foundation\",\\n \"priority\": \"high\",\\n \"reasoning\": \"JD explicitly lists FastAPI; resume shows Flask/Django but no exposure to FastAPI, creating a core framework gap.\",\\n \"target_competency\": \"Build async REST endpoints with FastAPI, Pydantic models and dependency injection\"\\n },\\n {\\n \"skill_name\": \"PostgreSQL\",\\n \"gap_type\": \"missing_foundation\",\\n \"priority\": \"high\",\\n \"reasoning\": \"JD requires PostgreSQL; candidate only lists MySQL, SQLite and MongoDB, so no evidence of PostgreSQL-specific features or deployment experience.\",\\n \"target_competency\": \"Design relational schemas, write optimized queries and manage transactions in PostgreSQL\"\\n },\\n {\\n \"skill_name\": \"Docker\",\\n \"gap_type\": \"missing_foundation\",\\n \"priority\": \"high\",\\n \"reasoning\": \"Docker appears in JD but is absent from skills, experience and projects, indicating no containerization background.\",\\n \"target_competency\": \"Containerize FastAPI services, write Dockerfiles and orchestrate multi-container dev environments\"\\n }\\n ],\\n \"is_fresher_adaptation_needed\": false,\\n \"executive_summary\": \"Chirayu is a near-ready backend candidate with solid Python, Django and SQL experience, but must quickly add FastAPI, PostgreSQL and Docker to meet CodeForge’s stack requirements.\"\\n}\\n\\n Please proceed to search for courses and build the learning roadmap now.\\n ', additional_kwargs={}, response_metadata={}, id='61350c0a-f333-4b0b-bce8-b002d5c13832'),\n",
+ " AIMessage(content='', additional_kwargs={'reasoning_content': \"We need to follow workflow.\\n\\nStep 1: For each gap skill, call search_courses with query.\\n\\nWe have three gaps: FastAPI, PostgreSQL, Docker.\\n\\nWe need to get course IDs.\\n\\nLet's call search_courses for FastAPI.\", 'tool_calls': [{'id': 'fc_6c1f8c83-94ad-438c-8ff4-772fc7e3a5a2', 'function': {'arguments': '{\"query\":\"FastAPI\"}', 'name': 'search_courses'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 78, 'prompt_tokens': 1285, 'total_tokens': 1363, 'completion_time': 0.167971032, 'completion_tokens_details': {'reasoning_tokens': 50}, 'prompt_time': 0.061565999, 'prompt_tokens_details': None, 'queue_time': 0.04593067, 'total_time': 0.229537031}, 'model_name': 'openai/gpt-oss-120b', 'system_fingerprint': 'fp_a09bde29de', 'service_tier': 'on_demand', 'finish_reason': 'tool_calls', 'logprobs': None, 'model_provider': 'groq'}, id='lc_run--019d206b-c546-75f2-9bee-3a75dc5dc28a-0', tool_calls=[{'name': 'search_courses', 'args': {'query': 'FastAPI'}, 'id': 'fc_6c1f8c83-94ad-438c-8ff4-772fc7e3a5a2', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 1285, 'output_tokens': 78, 'total_tokens': 1363, 'output_token_details': {'reasoning': 50}}),\n",
+ " ToolMessage(content=\"ID: CS-FAST-101\\nCourse Title: REST API Development with FastAPI\\nCategory: Backend\\nDescription: A project-driven course for building production-quality REST APIs with Python's FastAPI framework. Students learn to define typed routes with path and query parameters, leverage Pydantic models for automatic request validation and serialization, implement dependency injection, and secure endpoints using JWT-based Bearer token authentication.\\nLearning Outcomes: Create REST endpoints with path and query params, Validate request/response with Pydantic models, Implement JWT-based authentication\\nPrerequisites: CS-PY-101\\n---\\nID: CS-FAST-201\\nCourse Title: Advanced API Design with FastAPI\\nCategory: Backend\\nDescription: An advanced course for engineers building high-performance, secure backend services with FastAPI. Students implement full OAuth2 authorization code and client credentials flows, build async database CRUD endpoints using SQLAlchemy 2.0 with asyncpg, design layered dependency injection architectures, create custom middleware for logging and rate limiting, and run Celery background task queues.\\nLearning Outcomes: Implement OAuth2 authentication flows, Build async database endpoints with SQLAlchemy, Use dependency injection and middleware patterns\\nPrerequisites: CS-PY-101, CS-DB-101, CS-FAST-101\\n---\\nID: CS-MLOPS-301\\nCourse Title: MLOps — Model Deployment & Monitoring\\nCategory: AI Engineering\\nDescription: An end-to-end MLOps course covering the full lifecycle of production machine learning systems. Students track experiments and version datasets and models using MLflow, build model serving APIs with FastAPI, containerize inference services with Docker, set up automated retraining pipelines triggered by data drift detection using Evidently AI, and monitor prediction quality and infrastructure health with Grafana dashboards.\\nLearning Outcomes: Track experiments and version models with MLflow, Serve ML models via FastAPI and Docker, Monitor model drift and set up retraining triggers\\nPrerequisites: CS-ML-101, CS-DOCKER-101, CS-FAST-101\\n---\\nID: OPS-K8S-101\\nCourse Title: Kubernetes for AI Workloads\\nCategory: MLOps\\nDescription: An introduction to K8s specifically for ML engineers. Covers pod orchestration, GPU resource allocation, and scaling FastAPI backends on clusters.\\nLearning Outcomes: Deploy ML models as K8s services, Configure GPU-enabled worker nodes, Manage cluster auto-scaling for inference bursts\\nPrerequisites: None\\n---\", name='search_courses', id='be522420-9402-497c-b37a-55f859017f39', tool_call_id='fc_6c1f8c83-94ad-438c-8ff4-772fc7e3a5a2'),\n",
+ " AIMessage(content='', additional_kwargs={'reasoning_content': 'We have FastAPI courses. Need also PostgreSQL and Docker courses. Search for PostgreSQL.', 'tool_calls': [{'id': 'fc_cfcddbeb-b919-4f79-98e2-2000cdf9c354', 'function': {'arguments': '{\"query\":\"PostgreSQL\"}', 'name': 'search_courses'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 45, 'prompt_tokens': 1809, 'total_tokens': 1854, 'completion_time': 0.09460185, 'completion_tokens_details': {'reasoning_tokens': 20}, 'prompt_time': 0.614039222, 'prompt_tokens_details': {'cached_tokens': 768}, 'queue_time': 0.045160052, 'total_time': 0.708641072}, 'model_name': 'openai/gpt-oss-120b', 'system_fingerprint': 'fp_626f3fc5e0', 'service_tier': 'on_demand', 'finish_reason': 'tool_calls', 'logprobs': None, 'model_provider': 'groq'}, id='lc_run--019d206b-dc84-7312-8d72-172aa51c0a1b-0', tool_calls=[{'name': 'search_courses', 'args': {'query': 'PostgreSQL'}, 'id': 'fc_cfcddbeb-b919-4f79-98e2-2000cdf9c354', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 1809, 'output_tokens': 45, 'total_tokens': 1854, 'input_token_details': {'cache_read': 768}, 'output_token_details': {'reasoning': 20}}),\n",
+ " ToolMessage(content='ID: CS-DB-101\\nCourse Title: SQL Fundamentals for Backend Developers\\nCategory: Database\\nDescription: A hands-on introduction to relational database theory and practice using SQLite and PostgreSQL. Students learn to model data with schemas, enforce referential integrity through primary and foreign keys, and interact with data using full CRUD operations and multi-table JOINs.\\nLearning Outcomes: Write basic SELECT queries with filters, Understand Primary and Foreign Keys, Perform data insertion, updates, and deletions\\nPrerequisites: None\\n---\\nID: CS-DB-201\\nCourse Title: Advanced SQL — Indexing, Transactions & Query Optimization\\nCategory: Database\\nDescription: A deep dive into production-grade SQL performance and reliability. Students master analytical window functions, Common Table Expressions (CTEs), query execution plan analysis using EXPLAIN/ANALYZE, B-tree and partial index strategies, and ACID-compliant transaction management including deadlock detection and prevention.\\nLearning Outcomes: Use window functions and CTEs, Analyze and optimize slow queries with EXPLAIN, Manage ACID transactions and deadlock prevention\\nPrerequisites: CS-DB-101\\n---\\nID: BUS-SQL-201\\nCourse Title: SQL for Workforce Analytics\\nCategory: Data Science\\nDescription: Learn to query internal talent databases. Covers JOINs, aggregations, and subqueries to find hiring trends and predict churn.\\nLearning Outcomes: Write SQL queries to extract talent metrics, Analyze historical hiring data for trends, Build basic labor supply reports\\nPrerequisites: BUS-DATA-101\\n---\\nID: CS-DB-301\\nCourse Title: NoSQL Databases — MongoDB & Redis\\nCategory: Database\\nDescription: Practical training in document and key-value store paradigms for modern applications. Covers MongoDB schema design, flexible document modeling, the aggregation pipeline for complex data transformations, and Redis as both a high-speed caching layer and a pub/sub message broker with TTL-based eviction strategies.\\nLearning Outcomes: Design document schemas in MongoDB, Build aggregation pipelines, Implement caching with Redis TTL strategies\\nPrerequisites: CS-DB-101\\n---', name='search_courses', id='5f602c21-b2c8-4611-9f68-1e4f127958cb', tool_call_id='fc_cfcddbeb-b919-4f79-98e2-2000cdf9c354'),\n",
+ " AIMessage(content='', additional_kwargs={'reasoning_content': 'We have three gaps: FastAPI, PostgreSQL, Docker.\\n\\nWe need to search for Docker courses.', 'tool_calls': [{'id': 'fc_68440688-509a-4a4d-ae5f-b46d16b4a8fc', 'function': {'arguments': '{\"query\":\"Docker\"}', 'name': 'search_courses'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 45, 'prompt_tokens': 2260, 'total_tokens': 2305, 'completion_time': 0.095418158, 'completion_tokens_details': {'reasoning_tokens': 22}, 'prompt_time': 0.28257033, 'prompt_tokens_details': {'cached_tokens': 1280}, 'queue_time': 0.045485127, 'total_time': 0.377988488}, 'model_name': 'openai/gpt-oss-120b', 'system_fingerprint': 'fp_a09bde29de', 'service_tier': 'on_demand', 'finish_reason': 'tool_calls', 'logprobs': None, 'model_provider': 'groq'}, id='lc_run--019d206b-e355-7100-8e37-9e09f7cf0a97-0', tool_calls=[{'name': 'search_courses', 'args': {'query': 'Docker'}, 'id': 'fc_68440688-509a-4a4d-ae5f-b46d16b4a8fc', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 2260, 'output_tokens': 45, 'total_tokens': 2305, 'input_token_details': {'cache_read': 1280}, 'output_token_details': {'reasoning': 22}}),\n",
+ " ToolMessage(content='ID: CS-DOCKER-101\\nCourse Title: Docker & Containerization Fundamentals\\nCategory: DevOps\\nDescription: A practical guide to application containerization using Docker. Students learn to write optimized multi-stage Dockerfiles, manage images and container lifecycles, configure bridge and overlay networking, use named volumes for data persistence, and orchestrate multi-service applications with Docker Compose for local and staging environments.\\nLearning Outcomes: Write efficient multi-stage Dockerfiles, Manage container lifecycle and networking, Orchestrate multi-service apps with Docker Compose\\nPrerequisites: None\\n---\\nID: CS-CICD-201\\nCourse Title: CI/CD Pipelines with GitHub Actions\\nCategory: DevOps\\nDescription: A hands-on course in automating the full software delivery lifecycle using GitHub Actions. Students define multi-job workflows with matrix builds, integrate linting and pytest test suites as required CI checks, automate Docker image builds and pushes to registries, and manage environment secrets and deployment approvals for staging and production environments.\\nLearning Outcomes: Create GitHub Actions workflows for CI/CD, Automate Docker image builds and pushes, Manage secrets and environment variables securely\\nPrerequisites: CS-DOCKER-101\\n---\\nID: CS-MLOPS-301\\nCourse Title: MLOps — Model Deployment & Monitoring\\nCategory: AI Engineering\\nDescription: An end-to-end MLOps course covering the full lifecycle of production machine learning systems. Students track experiments and version datasets and models using MLflow, build model serving APIs with FastAPI, containerize inference services with Docker, set up automated retraining pipelines triggered by data drift detection using Evidently AI, and monitor prediction quality and infrastructure health with Grafana dashboards.\\nLearning Outcomes: Track experiments and version models with MLflow, Serve ML models via FastAPI and Docker, Monitor model drift and set up retraining triggers\\nPrerequisites: CS-ML-101, CS-DOCKER-101, CS-FAST-101\\n---\\nID: CS-K8S-301\\nCourse Title: Kubernetes — Container Orchestration at Scale\\nCategory: DevOps\\nDescription: A comprehensive course on running containerized workloads in production with Kubernetes. Students deploy stateless and stateful applications using Deployments and StatefulSets, expose services through ClusterIP, NodePort, and Ingress controllers, manage configuration and secrets with ConfigMaps and Secrets, autoscale workloads with HPA, and package applications for repeatable releases using Helm charts.\\nLearning Outcomes: Deploy applications using Deployments and StatefulSets, Configure Services, Ingress, and ConfigMaps, Manage releases with Helm charts\\nPrerequisites: CS-DOCKER-101, CS-CICD-201\\n---', name='search_courses', id='00e1a0f1-f97e-46b6-9f4d-ee55fc23c17d', tool_call_id='fc_68440688-509a-4a4d-ae5f-b46d16b4a8fc'),\n",
+ " AIMessage(content='', additional_kwargs={'reasoning_content': 'We have identified gaps: FastAPI, PostgreSQL, Docker. Need to search for courses for each. Already have courses:\\n\\nFastAPI: CS-FAST-101 (prereq CS-PY-101). Also CS-FAST-201 (prereq CS-PY-101, CS-DB-101, CS-FAST-101). Likely choose CS-FAST-101 as foundation.\\n\\nPostgreSQL: CS-DB-101 covers SQLite and PostgreSQL fundamentals. No explicit prerequisite. Also CS-DB-201 advanced. Choose CS-DB-101.\\n\\nDocker: CS-DOCKER-101 fundamentals, no prereq.\\n\\nNow need to resolve prerequisites: For CS-FAST-101, prerequisite CS-PY-101 (Python Fundamentals). Candidate has solid Python, Django experience. Likely resume shows Python mastery, so we can consider that as known. The instruction: \"Skip courses the candidate already demonstrates via projects or experience.\" So we can treat CS-PY-101 as known, not needed to include.\\n\\nCheck other courses for prerequisites: none else.\\n\\nThus roadmap includes three courses: CS-FAST-101, CS-DB-101, CS-DOCKER-101. Sequence: prerequisites before target modules. Since no dependencies among these three (except maybe Docker before FastAPI? Not required). Could order logically: start with Docker fundamentals (foundation for containerizing FastAPI later), then PostgreSQL fundamentals, then FastAPI. Or any order. We\\'ll assign sequence_order 1,2,3.\\n\\nis_fresher_adaptation_needed false, so no professional fundamentals module.\\n\\nNow need to build roadmap array with fields: course_id, is_foundation (boolean). Probably true for foundation courses? Let\\'s set is_foundation true for all three as they are foundational.\\n\\nReasoning: brief.\\n\\nNow need to create mermaid visualization following rules: gap courses ::gap, known prerequisites ::known, start node ::start, end node ::done. Since we are not adding known courses (CS-PY-101) as nodes, but we could include as known? The rules: known prerequisites should be marked ::known. Since we are skipping CS-PY-101, maybe we don\\'t include it. But we could include it as known to show prerequisite satisfied. Let\\'s include a node for CS-PY-101 as known.\\n\\nAlso start node: candidate\\'s current skills.\\n\\nEnd node: Role-ready — Backend Developer.\\n\\nGroup by week using subgraph. Let\\'s assign weeks: Week 1 Docker, Week 2 PostgreSQL, Week 3 FastAPI.\\n\\nMermaid code accordingly.\\n\\nNow need to call submit_final_roadmap with candidate_name, onboarding_summary (maybe executive summary), roadmap array, target_role \"Backend Developer\".\\n\\nThen submit_mermaid_visualization with code.\\n\\nProceed.', 'tool_calls': [{'id': 'fc_e1fad88b-3a4b-4877-a3df-369362707126', 'function': {'arguments': '{\"candidate_name\":\"Chirayu Jain\",\"onboarding_summary\":\"Chirayu is a near-ready backend candidate with solid Python, Django and SQL experience, but must quickly add FastAPI, PostgreSQL and Docker to meet CodeForge’s stack requirements.\",\"roadmap\":[{\"course_id\":\"CS-DOCKER-101\",\"is_foundation\":true,\"reasoning\":\"Provides essential containerization skills required to package FastAPI services for deployment.\",\"sequence_order\":1,\"title\":\"Docker \\\\u0026 Containerization Fundamentals\"},{\"course_id\":\"CS-DB-101\",\"is_foundation\":true,\"reasoning\":\"Covers PostgreSQL fundamentals, enabling design of relational schemas and optimized queries for the target role.\",\"sequence_order\":2,\"title\":\"SQL Fundamentals for Backend Developers\"},{\"course_id\":\"CS-FAST-101\",\"is_foundation\":true,\"reasoning\":\"Introduces FastAPI, the primary framework listed in the JD, building async REST endpoints and Pydantic models.\",\"sequence_order\":3,\"title\":\"REST API Development with FastAPI\"}],\"target_role\":\"Backend Developer\"}', 'name': 'submit_final_roadmap'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 864, 'prompt_tokens': 2825, 'total_tokens': 3689, 'completion_time': 1.891397937, 'completion_tokens_details': {'reasoning_tokens': 564}, 'prompt_time': 0.145777084, 'prompt_tokens_details': {'cached_tokens': 1792}, 'queue_time': 0.045268224, 'total_time': 2.037175021}, 'model_name': 'openai/gpt-oss-120b', 'system_fingerprint': 'fp_626f3fc5e0', 'service_tier': 'on_demand', 'finish_reason': 'tool_calls', 'logprobs': None, 'model_provider': 'groq'}, id='lc_run--019d206b-ee19-7900-a734-70259374603f-0', tool_calls=[{'name': 'submit_final_roadmap', 'args': {'candidate_name': 'Chirayu Jain', 'onboarding_summary': 'Chirayu is a near-ready backend candidate with solid Python, Django and SQL experience, but must quickly add FastAPI, PostgreSQL and Docker to meet CodeForge’s stack requirements.', 'roadmap': [{'course_id': 'CS-DOCKER-101', 'is_foundation': True, 'reasoning': 'Provides essential containerization skills required to package FastAPI services for deployment.', 'sequence_order': 1, 'title': 'Docker & Containerization Fundamentals'}, {'course_id': 'CS-DB-101', 'is_foundation': True, 'reasoning': 'Covers PostgreSQL fundamentals, enabling design of relational schemas and optimized queries for the target role.', 'sequence_order': 2, 'title': 'SQL Fundamentals for Backend Developers'}, {'course_id': 'CS-FAST-101', 'is_foundation': True, 'reasoning': 'Introduces FastAPI, the primary framework listed in the JD, building async REST endpoints and Pydantic models.', 'sequence_order': 3, 'title': 'REST API Development with FastAPI'}], 'target_role': 'Backend Developer'}, 'id': 'fc_e1fad88b-3a4b-4877-a3df-369362707126', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 2825, 'output_tokens': 864, 'total_tokens': 3689, 'input_token_details': {'cache_read': 1792}, 'output_token_details': {'reasoning': 564}}),\n",
+ " ToolMessage(content='{\"candidate_name\": \"Chirayu Jain\", \"target_role\": \"Backend Developer\", \"onboarding_summary\": \"Chirayu is a near-ready backend candidate with solid Python, Django and SQL experience, but must quickly add FastAPI, PostgreSQL and Docker to meet CodeForge’s stack requirements.\", \"roadmap\": [{\"course_id\": \"CS-DOCKER-101\", \"title\": \"Docker & Containerization Fundamentals\", \"reasoning\": \"Provides essential containerization skills required to package FastAPI services for deployment.\", \"is_foundation\": true, \"sequence_order\": 1}, {\"course_id\": \"CS-DB-101\", \"title\": \"SQL Fundamentals for Backend Developers\", \"reasoning\": \"Covers PostgreSQL fundamentals, enabling design of relational schemas and optimized queries for the target role.\", \"is_foundation\": true, \"sequence_order\": 2}, {\"course_id\": \"CS-FAST-101\", \"title\": \"REST API Development with FastAPI\", \"reasoning\": \"Introduces FastAPI, the primary framework listed in the JD, building async REST endpoints and Pydantic models.\", \"is_foundation\": true, \"sequence_order\": 3}]}', name='submit_final_roadmap', id='5fb8c861-d353-4034-a0c8-d1c17aeabcd6', tool_call_id='fc_e1fad88b-3a4b-4877-a3df-369362707126'),\n",
+ " AIMessage(content='', additional_kwargs={'reasoning_content': \"We have submitted final roadmap. Need to also submit mermaid visualization. Must follow rules: gap courses ::gap, known prerequisites ::known, start ::start, end ::done. In this case all three are gaps (they are missing foundations). However they have prerequisites: CS-FAST-101 prerequisite CS-PY-101, CS-DB-101 has none, CS-DOCKER-101 none. Candidate already has Python foundation, so we can treat CS-PY-101 as known prerequisite. Need to include it as known node. Sequence: start -> CS-DOCKER-101 (gap) -> CS-DB-101 (gap) -> CS-FAST-101 (gap) -> end. Also include CS-PY-101 as known prerequisite for FastAPI, placed before FastAPI maybe in same week or earlier. Since FastAPI depends on Python fundamentals, we should include known node before FastAPI. Could be in same week as FastAPI but as known. We'll create weeks: Week1 Docker, Week2 PostgreSQL, Week3 Python fundamentals (known) and FastAPI (gap). Or maybe include known before gap in same week.\\n\\nMermaid format: flowchart TD. Use subgraph W1, W2, W3.\\n\\nAdd class definitions.\\n\\nNow call submit_mermaid_visualization.\", 'tool_calls': [{'id': 'fc_ea8b8113-e478-45dd-a8b4-bf2eae927d9a', 'function': {'arguments': '{\"mermaid_code\":\"flowchart TD\\\\n A([Start — Current Skills]):::start\\\\n subgraph W1[\\\\\"Week 1 — Containerization\\\\\"]\\\\n B[CS-DOCKER-101\\\\\\\\nDocker \\\\u0026 Containerization Fundamentals]:::gap\\\\n end\\\\n subgraph W2[\\\\\"Week 2 — Relational DB\\\\\"]\\\\n C[CS-DB-101\\\\\\\\nSQL Fundamentals for Backend Developers]:::gap\\\\n end\\\\n subgraph W3[\\\\\"Week 3 — API Development\\\\\"]\\\\n D[CS-PY-101\\\\\\\\nPython Fundamentals]:::known\\\\n E[CS-FAST-101\\\\\\\\nREST API Development with FastAPI]:::gap\\\\n end\\\\n Z([Backend Developer — Role Ready]):::done\\\\n A --\\\\u003e B --\\\\u003e C --\\\\u003e D --\\\\u003e E --\\\\u003e Z\\\\n classDef gap fill:#EEEDFE,stroke:#534AB7,color:#26215C\\\\n classDef known fill:#E1F5EE,stroke:#0F6E56,color:#085041\\\\n classDef start fill:#1D9E75,stroke:#0F6E56,color:#E1F5EE\\\\n classDef done fill:#534AB7,stroke:#3C3489,color:#EEEDFE\"}', 'name': 'submit_mermaid_visualization'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 557, 'prompt_tokens': 3339, 'total_tokens': 3896, 'completion_time': 1.196030062, 'completion_tokens_details': {'reasoning_tokens': 266}, 'prompt_time': 0.160108793, 'prompt_tokens_details': None, 'queue_time': 0.046081046, 'total_time': 1.356138855}, 'model_name': 'openai/gpt-oss-120b', 'system_fingerprint': 'fp_8a618bed98', 'service_tier': 'on_demand', 'finish_reason': 'tool_calls', 'logprobs': None, 'model_provider': 'groq'}, id='lc_run--019d206b-f96f-7ad0-9a9e-fc4e668a8516-0', tool_calls=[{'name': 'submit_mermaid_visualization', 'args': {'mermaid_code': 'flowchart TD\\n A([Start — Current Skills]):::start\\n subgraph W1[\"Week 1 — Containerization\"]\\n B[CS-DOCKER-101\\\\nDocker & Containerization Fundamentals]:::gap\\n end\\n subgraph W2[\"Week 2 — Relational DB\"]\\n C[CS-DB-101\\\\nSQL Fundamentals for Backend Developers]:::gap\\n end\\n subgraph W3[\"Week 3 — API Development\"]\\n D[CS-PY-101\\\\nPython Fundamentals]:::known\\n E[CS-FAST-101\\\\nREST API Development with FastAPI]:::gap\\n end\\n Z([Backend Developer — Role Ready]):::done\\n A --> B --> C --> D --> E --> Z\\n classDef gap fill:#EEEDFE,stroke:#534AB7,color:#26215C\\n classDef known fill:#E1F5EE,stroke:#0F6E56,color:#085041\\n classDef start fill:#1D9E75,stroke:#0F6E56,color:#E1F5EE\\n classDef done fill:#534AB7,stroke:#3C3489,color:#EEEDFE'}, 'id': 'fc_ea8b8113-e478-45dd-a8b4-bf2eae927d9a', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 3339, 'output_tokens': 557, 'total_tokens': 3896, 'output_token_details': {'reasoning': 266}}),\n",
+ " ToolMessage(content='Mermaid visualization saved successfully.', name='submit_mermaid_visualization', id='e07fe033-a45e-41ee-b421-caa15afa997d', tool_call_id='fc_ea8b8113-e478-45dd-a8b4-bf2eae927d9a'),\n",
+ " AIMessage(content='The learning roadmap for **Chirayu Jain** has been finalized and visualized.\\n\\n**Roadmap Summary**\\n- **Target Role:** Backend Developer \\n- **Onboarding Summary:** Near‑ready backend candidate with solid Python, Django and SQL experience; needs FastAPI, PostgreSQL, and Docker to meet CodeForge’s stack. \\n\\n**Course Sequence**\\n1. **CS-DOCKER-101 – Docker & Containerization Fundamentals** (Foundation) \\n2. **CS-DB-101 – SQL Fundamentals for Backend Developers** (Foundation) \\n3. **CS-FAST-101 – REST API Development with FastAPI** (Foundation) \\n\\n**Mermaid Visualization**\\n```mermaid\\nflowchart TD\\n A([Start — Current Skills]):::start\\n subgraph W1[\"Week 1 — Containerization\"]\\n B[CS-DOCKER-101\\\\nDocker & Containerization Fundamentals]:::gap\\n end\\n subgraph W2[\"Week 2 — Relational DB\"]\\n C[CS-DB-101\\\\nSQL Fundamentals for Backend Developers]:::gap\\n end\\n subgraph W3[\"Week 3 — API Development\"]\\n D[CS-PY-101\\\\nPython Fundamentals]:::known\\n E[CS-FAST-101\\\\nREST API Development with FastAPI]:::gap\\n end\\n Z([Backend Developer — Role Ready]):::done\\n A --> B --> C --> D --> E --> Z\\n classDef gap fill:#EEEDFE,stroke:#534AB7,color:#26215C\\n classDef known fill:#E1F5EE,stroke:#0F6E56,color:#085041\\n classDef start fill:#1D9E75,stroke:#0F6E56,color:#E1F5EE\\n classDef done fill:#534AB7,stroke:#3C3489,color:#EEEDFE\\n```\\n\\nAll steps are complete.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 406, 'prompt_tokens': 3646, 'total_tokens': 4052, 'completion_time': 1.438615073, 'completion_tokens_details': None, 'prompt_time': 0.141632128, 'prompt_tokens_details': {'cached_tokens': 2816}, 'queue_time': 0.04638064, 'total_time': 1.5802472010000002}, 'model_name': 'openai/gpt-oss-120b', 'system_fingerprint': 'fp_626f3fc5e0', 'service_tier': 'on_demand', 'finish_reason': 'stop', 'logprobs': None, 'model_provider': 'groq'}, id='lc_run--019d206c-010d-7133-92a9-18ed936df441-0', tool_calls=[], invalid_tool_calls=[], usage_metadata={'input_tokens': 3646, 'output_tokens': 406, 'total_tokens': 4052, 'input_token_details': {'cache_read': 2816}})]"
+ ]
+ },
+ "execution_count": 74,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "final_state"
+ "final_result['messages']"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 75,
"id": "25a6b5b4",
"metadata": {},
"outputs": [],
@@ -1951,16 +2146,6 @@
" print(f\"✅ UI Payload successfully exported to {filename}\")"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "26c10157",
- "metadata": {},
- "outputs": [],
- "source": [
- "export_ui_payload(final_state)"
- ]
- },
{
"cell_type": "code",
"execution_count": null,
@@ -1988,7 +2173,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 91,
"id": "5b29b7ea",
"metadata": {},
"outputs": [],
@@ -2001,159 +2186,220 @@
"# --- Configuration & Paths ---\n",
"\n",
"test_map = [\n",
- " {\"resume\": \"ATGPDF.pdf\", \"data\": \"atgdata.json\", \"name\": \"Atharva_Gaykar\"},\n",
- " \n",
+ " {\"resume\": \"ATGPDF.pdf\", \"data\": \"atgdata.json\", \"name\": \"Atharva_Gaykar\"},\n",
"]\n",
"\n",
"RESUME_DIR = r\"C:\\Users\\ATHARVA\\Downloads\\my codes\\web\\AdaptiveEngineService\\AI_Engine_Evaluation\\Testcases\\Testresume\"\n",
- "DATA_DIR = r\"C:\\Users\\ATHARVA\\Downloads\\my codes\\web\\AdaptiveEngineService\\AI_Engine_Evaluation\\Testcases\\data\"\n",
+ "DATA_DIR = r\"C:\\Users\\ATHARVA\\Downloads\\my codes\\web\\AdaptiveEngineService\\AI_Engine_Evaluation\\Testcases\\data\"\n",
"\n",
"# Windows-safe absolute output path\n",
"OUTPUT_DIR = os.path.join(os.getcwd(), \"predictions\")\n",
- "\n",
"if not os.path.exists(OUTPUT_DIR):\n",
" os.makedirs(OUTPUT_DIR)\n",
"\n",
+ "# Global list to store final states if needed for debugging\n",
+ "store_state = []\n",
"\n",
"# --- Helper Functions ---\n",
"\n",
"def get_job_description_string(data_filename: str) -> str | None:\n",
- " \"\"\"\n",
- " Extracts the Job Description from the test data JSON and\n",
- " formats it as a clean string for the extraction node.\n",
- " \"\"\"\n",
" path = os.path.join(DATA_DIR, data_filename)\n",
- "\n",
" if not os.path.exists(path):\n",
" print(f\"⚠️ Data file not found: {path}\")\n",
" return None\n",
- "\n",
" try:\n",
" with open(path, \"r\", encoding=\"utf-8\") as f:\n",
" suite = json.load(f)\n",
- "\n",
" jd_obj = suite.get(\"job_description\") or suite.get(\"job_description_requirements\")\n",
- "\n",
" if not jd_obj:\n",
- " print(f\"⚠️ No JD key found in {data_filename}\")\n",
" return None\n",
- "\n",
" title = jd_obj.get(\"title\") or jd_obj.get(\"job_title\", \"N/A\")\n",
- " desc = jd_obj.get(\"description\", \"\")\n",
- " reqs = jd_obj.get(\"requirements\", [])\n",
- "\n",
+ " desc = jd_obj.get(\"description\", \"\")\n",
+ " reqs = jd_obj.get(\"requirements\", [])\n",
" jd_string = f\"JOB TITLE: {title}\\n\\n\"\n",
- " if desc:\n",
- " jd_string += f\"OVERVIEW: {desc}\\n\\n\"\n",
+ " if desc: jd_string += f\"OVERVIEW: {desc}\\n\\n\"\n",
" jd_string += \"REQUIREMENTS:\\n\" + \"\\n\".join([f\"- {r}\" for r in reqs])\n",
- "\n",
" return jd_string\n",
- "\n",
" except Exception as e:\n",
- " print(f\"❌ Error loading JD from {data_filename}: {e}\")\n",
+ " print(f\"❌ Error loading JD: {e}\")\n",
" return None\n",
"\n",
- "\n",
"def export_ui_payload(state: dict) -> dict:\n",
- " \"\"\"\n",
- " Extracts required keys from graph state.\n",
- " Converts Pydantic objects to dicts via .model_dump().\n",
- " \"\"\"\n",
- " REQUIRED_KEYS = [\n",
- " \"candidate_name\",\n",
- " \"skill_gap_analysis_data\",\n",
- " \"mermaid_code\",\n",
- " \"final_roadmap\",\n",
- " ]\n",
+ " REQUIRED_KEYS = [\"candidate_name\", \"skill_gap_analysis_data\", \"mermaid_code\", \"final_roadmap\"]\n",
" ui_data = {}\n",
- "\n",
" for key in REQUIRED_KEYS:\n",
" val = state.get(key)\n",
- " if val is None:\n",
- " continue\n",
- " if hasattr(val, \"model_dump\"):\n",
- " ui_data[key] = val.model_dump()\n",
- " else:\n",
- " ui_data[key] = val\n",
- "\n",
+ " if val is None: continue\n",
+ " ui_data[key] = val.model_dump() if hasattr(val, \"model_dump\") else val\n",
" return ui_data\n",
"\n",
- "\n",
"# --- Execution Loop ---\n",
"\n",
"def run_evaluation_suite(graph_instance):\n",
- " \"\"\"\n",
- " Runs the graph for every resume in test_map.\n",
- " Saves UI-ready payloads as predicted_{name}.json in OUTPUT_DIR.\n",
- " \"\"\"\n",
" print(f\"\\n📁 Output directory: {OUTPUT_DIR}\\n\")\n",
"\n",
" for case in test_map:\n",
" print(f\"🚀 Processing: {case['resume']}...\")\n",
- "\n",
- " # 1. Validate resume file exists\n",
" resume_path = os.path.join(RESUME_DIR, case[\"resume\"])\n",
" if not os.path.exists(resume_path):\n",
- " print(f\"⚠️ Resume not found, skipping: {resume_path}\")\n",
+ " print(f\"⚠️ Resume not found: {resume_path}\")\n",
" continue\n",
"\n",
- " # 2. Load JD string\n",
" jd_content = get_job_description_string(case[\"data\"])\n",
" if not jd_content:\n",
- " print(f\"⚠️ Skipping {case['resume']}: JD not found in {case['data']}\")\n",
+ " print(f\"⚠️ JD not found in {case['data']}\")\n",
" continue\n",
"\n",
- " # 3. Build initial state\n",
" initial_input = {\n",
- " \"candidate_name\": case[\"name\"].replace(\"_\", \" \"),\n",
- " \"file_path\": resume_path,\n",
- " \"job_description\": jd_content,\n",
- " \"resume_text\": None,\n",
- " \"resume_data\": None,\n",
- " \"extraction_error\": None,\n",
+ " \"candidate_name\": case[\"name\"].replace(\"_\", \" \"),\n",
+ " \"file_path\": resume_path,\n",
+ " \"job_description\": jd_content,\n",
+ " \"resume_text\": None,\n",
+ " \"resume_data\": None,\n",
+ " \"extraction_error\": None,\n",
" \"JobDescriptionExtract_data\": None,\n",
" \"skill_gap_analysis_data\": None,\n",
- " \"messages\": [],\n",
- " \"mermaid_code\": None,\n",
- " \"final_roadmap\": None,\n",
+ " \"messages\": [],\n",
+ " \"mermaid_code\": None,\n",
+ " \"final_roadmap\": None,\n",
" }\n",
"\n",
- " # 4. Invoke graph\n",
" config = {\"configurable\": {\"thread_id\": str(uuid.uuid4())}}\n",
"\n",
" try:\n",
- " final_state = graph_instance.invoke(initial_input, config=config)\n",
+ " print(f\"--- 🛰️ Starting Stream for {case['name']} ---\")\n",
+ " final_state = initial_input.copy()\n",
+ "\n",
+ " # The stream loop MUST be indented inside the try block\n",
+ " for chunk in graph_instance.stream(initial_input, config=config, stream_mode=\"updates\"):\n",
+ " for node_name, node_update in chunk.items():\n",
+ " print(f\"\\n📍 [NODE]: {node_name}\")\n",
+ " \n",
+ " # Update local final_state with changes from the node\n",
+ " final_state.update(node_update)\n",
+ "\n",
+ " # Monitor agent messages for tool calls\n",
+ " if \"messages\" in node_update and node_update[\"messages\"]:\n",
+ " last_msg = node_update[\"messages\"][-1]\n",
+ " if hasattr(last_msg, \"content\") and last_msg.content:\n",
+ " print(f\"💬 Thought: {last_msg.content[:100]}...\")\n",
+ " if hasattr(last_msg, \"tool_calls\") and last_msg.tool_calls:\n",
+ " print(f\"🛠️ TOOL CALL DETECTED: {last_msg.tool_calls}\")\n",
"\n",
" store_state.append(final_state)\n",
+ " print(f\"✅ Graph finished for {case['name']}\\n\")\n",
"\n",
- " print(f\"✅ Graph execution successful for {case['resume']}\\n\")\n",
- "\n",
- " # 5. Export payload\n",
- " prediction = export_ui_payload(final_state)\n",
+ " # Save the result\n",
+ " prediction = export_ui_payload(final_state)\n",
" output_file = f\"predicted_{case['name'].lower()}.json\"\n",
" output_path = os.path.join(OUTPUT_DIR, output_file)\n",
- "\n",
" with open(output_path, \"w\", encoding=\"utf-8\") as f:\n",
" json.dump(prediction, f, indent=2, ensure_ascii=False)\n",
- "\n",
- " print(f\"✅ Saved: {output_path}\\n\")\n",
+ " print(f\"💾 Saved: {output_path}\\n\")\n",
"\n",
" except Exception as e:\n",
- " print(f\"❌ Error during graph execution for {case['resume']}: {e}\\n\")"
+ " print(f\"❌ Error during graph execution for {case['resume']}: {e}\")\n",
+ "\n"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 92,
"id": "c1638a3f",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "📁 Output directory: c:\\Users\\ATHARVA\\Downloads\\my codes\\web\\AdaptiveEngineService\\Notebooks\\predictions\n",
+ "\n",
+ "🚀 Processing: ATGPDF.pdf...\n",
+ "--- 🛰️ Starting Stream for Atharva_Gaykar ---\n",
+ "📂 File path received: C:\\Users\\ATHARVA\\Downloads\\my codes\\web\\AdaptiveEngineService\\AI_Engine_Evaluation\\Testcases\\Testresume\\ATGPDF.pdf\n",
+ "📂 File exists: True\n",
+ "📄 Pages loaded: 1\n",
+ "📄 Text length: 2671\n",
+ "\n",
+ "📍 [NODE]: input_node\n",
+ "DEBUGGER: Sending 995 characters to JD Agent...\n",
+ "📄 Resume text length: 2671 chars\n",
+ "DEBUGGER SUCCESS: Extracted Senior ML & MLOps Architect\n",
+ "\n",
+ "📍 [NODE]: jd_data_extraction\n",
+ "✅ Resume extracted: AI Engineer\n",
+ "\n",
+ "📍 [NODE]: resume_data_extraction\n",
+ "\n",
+ "📍 [NODE]: skill_gap_analysis\n",
+ "💬 Thought: \n",
+ " Skill Gap Analysis Completed for Atharva Gaykar.\n",
+ "\n",
+ " Gaps Identified:\n",
+ " {\n",
+ " \"job...\n",
+ "\n",
+ "📍 [NODE]: roadmap_planning_agent\n",
+ "💬 Thought: The learning roadmap for **Atharva Gaykar** has been finalized and saved, along with its Mermaid vis...\n",
+ "\n",
+ "📍 [NODE]: finalize_state\n",
+ "✅ Graph finished for Atharva_Gaykar\n",
+ "\n",
+ "💾 Saved: c:\\Users\\ATHARVA\\Downloads\\my codes\\web\\AdaptiveEngineService\\Notebooks\\predictions\\predicted_atharva_gaykar.json\n",
+ "\n"
+ ]
+ }
+ ],
"source": [
"if __name__ == \"__main__\":\n",
" # Assuming your graph is already compiled and named 'graph'\n",
" run_evaluation_suite(graph)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "id": "d6b44b85",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "flowchart TD\n",
+ " A([Start — Current Skills]):::start\n",
+ " subgraph W1[\"Week 1 — Kubernetes Foundations\"]\n",
+ " B[OPS-K8S-101\\nKubernetes for AI Workloads]:::gap\n",
+ " end\n",
+ " subgraph W2[\"Week 2 — GPU Inference Serving\"]\n",
+ " C[OPS-TRITON-201\\nHigh-Performance Serving with Triton]:::gap\n",
+ " end\n",
+ " subgraph W3[\"Week 3 — Self‑Hosted Vector DB (Milvus)\"]\n",
+ " D[DTA-MIL-401\\nBillion-Scale Vector Ops]:::gap\n",
+ " end\n",
+ " subgraph W4[\"Week 4 — Open‑Source Vector DB (Weaviate)\"]\n",
+ " E[CS-VEC-101\\nVector Databases & Semantic Search]:::gap\n",
+ " end\n",
+ " subgraph W5[\"Week 5 — LLM Security & Red‑Teaming\"]\n",
+ " F[SEC-LLM-301\\nLLM Red Teaming & Guardrails]:::gap\n",
+ " end\n",
+ " subgraph W6[\"Week 6 — End‑to‑End MLOps & Observability\"]\n",
+ " G[CS-MLOPS-301\\nMLOps — Model Deployment & Monitoring]:::gap\n",
+ " end\n",
+ " Z([Role‑Ready — Senior ML & MLOps Architect]):::done\n",
+ " A --> B --> C --> D --> E --> F --> G --> Z\n",
+ " classDef gap fill:#EEEDFE,stroke:#534AB7,color:#26215C\n",
+ " classDef known fill:#E1F5EE,stroke:#0F6E56,color:#085041\n",
+ " classDef start fill:#1D9E75,stroke:#0F6E56,color:#E1F5EE\n",
+ " classDef done fill:#534AB7,stroke:#3C3489,color:#EEEDFE\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(store_state[0]['mermaid_code'])"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -2703,7 +2949,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "venv",
+ "display_name": "pro_env",
"language": "python",
"name": "python3"
},