First commit of hr vercel agent

Author: Pash10g

apps/vercel_sdk_hr_agent/.gitignore

@@ -0,0 +1,5 @@
+node_modules
+.env
+.env.local
+package-lock.json
+.next

apps/vercel_sdk_hr_agent/README.md

@@ -0,0 +1,115 @@
+# HR Team Matcher
+
+A smart HR assistant that helps managers build optimal teams for projects using MongoDB Vector Search for semantic skills matching and the Vercel AI SDK for agentic capabilities.
+
+## Features
+
+- **Advanced Skill Matching**: Uses MongoDB Vector Search to find employees with the right skills, even when they're not an exact keyword match
+- **Multi-step Reasoning**: Uses Vercel AI SDK's agentic functionality to analyze projects, search for candidates, and evaluate team fit
+- **Team Analysis**: Evaluates skill coverage, team diversity, and past collaboration success
+- **Recommendations with Rationale**: Provides detailed justification for each team member suggestion
+- **Risk Assessment**: Identifies potential issues with proposed teams and suggests mitigation strategies
+
+## Architecture
+
+The HR Team Matcher is built with the following technologies:
+
+- **Next.js**: Frontend and API routes
+- **MongoDB**: Database with Vector Search for semantic skill matching
+- **Vercel AI SDK**: Agentic AI capabilities with multi-step reasoning
+- **Voyage AI**: Generation of text embeddings for semantic search
+- **OpenAI**: Language model for team analysis and recommendations
+- **Tailwind CSS**: Styling
+
+## How It Works
+
+1. **Project Analysis**: The agent analyzes the project description to extract required skills, team size, and timeline constraints
+2. **Skill Matching**: Using MongoDB Vector Search, the system finds employees with matching skills
+3. **Team Formation**: The agent explores different combinations to form an optimal team
+4. **Team Evaluation**: Each potential team is evaluated for skill coverage, diversity, and collaboration history
+5. **Recommendation**: The best team is recommended with detailed justifications and risk assessments
+
+## Getting Started
+
+### Prerequisites
+
+- Node.js 18.x or higher
+- MongoDB Atlas account (with Vector Search capability)
+- OpenAI API key
+- Voyage AI API key
+
+### Environment Setup
+
+1. Clone this repository
+2. Install dependencies:
+   ```
+   npm install
+   ```
+3. Create a `.env.local` file with the following variables:
+   ```
+   MONGODB_URI=your_mongodb_connection_string
+   OPENAI_API_KEY=your_openai_api_key
+   VOYAGE_API_KEY=your_voyage_api_key
+   ```
+
+### Database Setup
+
+1. Create a MongoDB Atlas cluster
+2. Create a database named `hr_database`
+3. Create the following collections:
+   - `employees`
+   - `teams`
+4. Set up the Vector Search index on the `employees` collection named `skill_vector_index`:
+   ```json
+   {
+        "fields": [
+        {
+        "type": "vector",
+        "path": "embedding",
+        "numDimensions": 1024,
+        "similarity": "cosine"
+        }
+    ]
+    }
+   ```
+
+### Running the Application
+
+1. Start the development server:
+   ```
+   npm run dev
+   ```
+2. Open your browser and navigate to `http://localhost:3000`
+
+## Usage
+
+1. Navigate to the "Build New Team" tab
+2. Enter a detailed project description, including required skills, timeline, and any special requirements
+3. Click "Build Team" and wait for the AI to generate a team recommendation
+4. Review the recommended team, including skill coverage, member details, and risk assessment
+5. Saved teams can be viewed and approved in the "Team History" tab
+
+## Implementation Details
+
+### MongoDB Vector Search
+
+Skills are matched using semantic search, allowing the system to understand that "React experience" is related to "Frontend development" even without exact keyword matches.
+
+### Voyage AI Embeddings
+
+The Voyage AI model converts skill descriptions into vector embeddings that capture semantic meaning, enabling more intelligent matching.
+
+### Vercel AI SDK Agent
+
+The agent uses multiple tools in sequence to:
+1. Analyze projects with `analyzeProjectRequirements`
+2. Search for employees with `searchEmployeesBySkill`
+3. Analyze team compositions with `analyzeTeamComposition`
+4. Save recommended teams with `saveTeamToDatabase`
+5. Generate final recommendations with `generateTeamRecommendation`
+
+The `maxSteps: 15` parameter enables the agent to perform multiple tool calls in sequence, making it a true agentic application rather than a simple API wrapper.
+
+## License
+
+MIT

apps/vercel_sdk_hr_agent/app/.gitignore

@@ -0,0 +1,81 @@
+import { NextRequest, NextResponse } from 'next/server';
+import { openai } from '@ai-sdk/openai';
+import { generateText, tool } from 'ai';
+import { z } from 'zod';
+import {
+  analyzeProjectRequirements,
+  searchEmployeesBySkill,
+  analyzeTeamComposition,
+  saveTeamToDatabase,
+  generateTeamRecommendation
+} from '../../../utils/tools';
+
+export async function POST(req: NextRequest) {
+  try {
+    const { projectDescription } = await req.json();
+
+    if (!projectDescription) {
+      return NextResponse.json({ error: 'Project description is required' }, { status: 400 });
+    }
+
+    const result = await buildTeam(projectDescription);
+
+    return NextResponse.json(result);
+  } catch (error) {
+    console.error('Error building team:', error);
+    return NextResponse.json({ error: 'Failed to build team' }, { status: 500 });
+  }
+}
+
+async function buildTeam(projectDescription: string) {
+  const { steps ,  toolCalls } = await generateText({
+    model: openai('o3-mini', { structuredOutputs: true }),
+    tools: {
+      analyzeProjectRequirements,
+      searchEmployeesBySkill,
+      analyzeTeamComposition,
+      saveTeamToDatabase,
+      generateTeamRecommendation,
+    },
+    toolChoice: 'auto', // Force structured output using the generateTeamRecommendation tool
+    maxSteps: 15, // This is the key to enabling agentic behavior!
+    system: `You are an expert HR assistant that builds optimal teams for projects.
+      Given a project description follow this set of steps:
+      1. Analyze the requirements to identify needed skills (analyzeProjectRequirements)
+      2. Search for employees with matching skills (searchEmployeesBySkill)
+      3. Evaluate possible team compositions (analyzeTeamComposition)
+      4. MUST Save the recommended team to the database (saveTeamToDatabase)
+      5. Generate a final recommendation with justification (generateTeamRecommendation)
+
+      Consider skill coverage, team diversity, availability, and past collaboration success.
+      Be thorough in your analysis but aim to build the smallest effective team possible. If unable to complete a team, please provide a reason why but do place people with potential.
+
+      After your final recommendation is complete, save the team to the database for record-keeping.`,
+    prompt: projectDescription,
+  });
+
+  // Find the last generateTeamRecommendation call for the final recommendation
+  const recommendationCall = toolCalls.find(
+    call => call.toolName === 'generateTeamRecommendation'
+  );
+
+  // Find the database save call to get the team ID
+  const databaseCall = toolCalls.find(
+    call => call.toolName === 'saveTeamToDatabase'
+  );
+
+  // Combine the recommendation with the database result
+  if (recommendationCall && databaseCall) {
+    return {
+      recommendation: recommendationCall.args,
+      databaseResult: databaseCall.args
+    };
+  }
+
+  // Fallback to just the recommendation if database save failed
+  if (recommendationCall) {
+    return { recommendation: recommendationCall.args };
+  }
+
+  return { error: "Failed to generate team recommendation" };
+}

apps/vercel_sdk_hr_agent/app/api/build-team/route.ts

@@ -0,0 +1,38 @@
+import { NextResponse } from 'next/server';
+import { getCollections } from '../../../../../utils/db';
+import { ObjectId } from 'mongodb';
+
+interface Params {
+  id: string;
+}
+
+export async function POST(
+  request: Request,
+  { params }: { params: Params }
+) {
+  try {
+    const id = params.id;
+
+    if (!ObjectId.isValid(id)) {
+      return NextResponse.json(
+        { error: "Invalid team ID" },
+        { status: 400 }
+      );
+    }
+
+    // In a real implementation, we would update the team in MongoDB
+    // For this demo, we'll just return a success response
+
+    return NextResponse.json({
+      success: true,
+      message: `Team ${id} has been approved`,
+      status: 'approved'
+    });
+  } catch (error) {
+    console.error('Error approving team:', error);
+    return NextResponse.json(
+      { error: "Failed to approve team" },
+      { status: 500 }
+    );
+  }
+}

apps/vercel_sdk_hr_agent/app/api/teams/[id]/approve/route.ts

@@ -0,0 +1,16 @@
+import { NextRequest, NextResponse } from 'next/server';
+import { getCollections } from '../../../utils/db';
+
+export async function GET(req: NextRequest) {
+  try {
+    const { teams } = await getCollections();
+
+    // Get all teams, sorted by creation date (newest first)
+    const teamsList = await teams.find({}).sort({ createdAt: -1 }).toArray();
+
+    return NextResponse.json({ teams: teamsList });
+  } catch (error) {
+    console.error('Error retrieving teams:', error);
+    return NextResponse.json({ error: 'Failed to retrieve teams' }, { status: 500 });
+  }
+}

apps/vercel_sdk_hr_agent/app/api/teams/route.ts

@@ -0,0 +1,20 @@
+import React from 'react';
+
+interface LoadingProps {
+  message?: string;
+}
+
+const Loading: React.FC<LoadingProps> = ({ message = "Building your team..." }) => {
+  return (
+    <div className="flex flex-col items-center justify-center py-8">
+      <div className="relative w-16 h-16 mb-4">
+        <div className="absolute top-0 left-0 w-full h-full border-4 border-blue-100 rounded-full"></div>
+        <div className="absolute top-0 left-0 w-full h-full border-4 border-blue-500 rounded-full border-t-transparent animate-spin"></div>
+      </div>
+      <p className="text-gray-600">{message}</p>
+      <p className="text-sm text-gray-500 mt-2">This might take a moment as we analyze the skills and build the perfect team</p>
+    </div>
+  );
+};
+
+export default Loading;

apps/vercel_sdk_hr_agent/app/components/Loading.tsx

@@ -0,0 +1,50 @@
+import React from 'react';
+
+interface TeamMemberProps {
+  name: string;
+  role: string;
+  keySkills: string[];
+  justification: string;
+}
+
+export const TeamMember: React.FC<TeamMemberProps> = ({
+  name,
+  role,
+  keySkills,
+  justification
+}) => {
+  return (
+    <div className="border p-4 rounded-lg shadow-sm bg-white hover:shadow-md transition-shadow">
+      <div className="flex items-center mb-3">
+        <div className="bg-blue-100 rounded-full h-10 w-10 flex items-center justify-center text-blue-500 font-bold mr-3">
+          {name.split(' ').map(n => n[0]).join('')}
+        </div>
+        <div>
+          <div className="font-bold">{name}</div>
+          <div className="text-sm text-gray-600">{role}</div>
+        </div>
+      </div>
+
+      <div className="mt-3">
+        <div className="text-sm font-semibold mb-1">Key Skills:</div>
+        <div className="flex flex-wrap gap-1">
+          {keySkills.map((skill, i) => (
+            <span
+              key={i}
+              className="bg-blue-50 text-blue-700 text-xs px-2 py-1 rounded"
+            >
+              {skill}
+            </span>
+          ))}
+        </div>
+      </div>
+
+      <div className="mt-3">
+        <div className="text-sm font-semibold mb-1">Why this person?</div>
+        <p className="text-sm text-gray-700">{justification}</p>
+      </div>
+    </div>
+  );
+};
+
+export default TeamMember;

apps/vercel_sdk_hr_agent/app/components/TeamMember.tsx

@@ -0,0 +1,14 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+:root {
+  --foreground-rgb: 0, 0, 0;
+  --background-start-rgb: 214, 219, 220;
+  --background-end-rgb: 255, 255, 255;
+}
+
+body {
+  color: rgb(var(--foreground-rgb));
+  background: white;
+}

apps/vercel_sdk_hr_agent/app/globals.css

@@ -0,0 +1,22 @@
+import type { Metadata } from 'next';
+import { Inter } from 'next/font/google';
+import './globals.css';
+
+const inter = Inter({ subsets: ['latin'] });
+
+export const metadata: Metadata = {
+  title: 'HR Team Matcher',
+  description: 'Build optimal teams for projects with MongoDB Vector Search and AI',
+};
+
+export default function RootLayout({
+  children,
+}: {
+  children: React.ReactNode;
+}) {
+  return (
+    <html lang="en">
+      <body className={inter.className}>{children}</body>
+    </html>
+  );
+}