Merge branch 'mongodb-developer:main' into RealtimeVoiceTS

Author: Pash10g

README.md

@@ -38,3 +38,4 @@ As you work through these examples, if you encounter any problems, please [open
 
 * [AI Learning Hub](https://www.mongodb.com/resources/use-cases/artificial-intelligence)
 * [GenAI Community Forum](https://www.mongodb.com/community/forums/c/generative-ai/162)
+* [Tutorials and code examples from our official docs](https://github.com/mongodb/docs-notebooks)

apps/mongo-feed/.gitignore

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+node_modules
+.env
+.env.local
+package-lock.json
+.next

apps/mongo-feed/README.md

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+# MongoFeed
+
+MongoFeed is a comprehensive platform for product feedback analysis and sentiment tracking. It leverages MongoDB for data storage and Amazon Bedrock for AI-powered sentiment analysis, providing valuable insights into customer feedback and product reviews.
+
+> ♥️ Inpired by a customer success story : [Syncly](https://www.mongodb.com/customers/syncly)
+
+## Hosted Version
+
+https://mongo-feed.vercel.app
+
+## Features
+
+- File upload for product feedback analysis (JSON, HTML, images)
+- Chat paste functionality for direct input of customer interactions
+- Sentiment analysis using Amazon Bedrock AI
+- Real-time processing queue for feedback analysis
+- Interactive charts and visualizations:
+  - Feedback trends over time
+  - Sentiment distribution
+  - Top issues identification
+- Agent performance tracking and sentiment analysis
+
+## Prerequisites
+
+Before you begin, ensure you have the following installed:
+- Node.js (v14 or later)
+- npm (v6 or later)
+- MongoDB (6.0+)
+- An AWS account with access to Amazon Bedrock and Claude 3.5 V2 model
+
+## Installation
+
+1. **Clone the repository:**
+   ```bash
+   git clone <repository-url>
+   cd mongo-feed
+   ```
+
+2. **Install dependencies:**
+   ```bash
+   npm install
+   ```
+
+3. **Configure environment variables:**
+   - Create a `.env.local` file in the root directory.
+   - Add your MongoDB connection string and AWS Bedrock credentials.
+     ```env
+     MONGODB_URI=your_mongodb_connection_string
+     AWS_REGION=your_aws_region
+     AWS_ACCESS_KEY_ID=your_aws_access_key_id
+     AWS_SECRET_ACCESS_KEY=your_aws_secret_access_key
+     ```
+     **Note:** Ensure you have the necessary permissions for Amazon Bedrock and MongoDB.
+
+## Development
+
+1. **Run the development server:**
+   ```bash
+   npm run dev
+   ```
+   Open [http://localhost:3000](http://localhost:3000) in your browser to view the application.
+
+## Building for Production
+
+1. **Build the application:**
+   ```bash
+   npm run build
+   ```
+
+2. **Start the production server:**
+   ```bash
+   npm run start
+   ```
+
+## Usage
+
+To use MongoFeed:
+
+1. **Access the application** in your browser at [http://localhost:3000](http://localhost:3000) after running the development or production server.
+2. **Upload Feedback Files or Paste Chat Interactions:**
+   - Navigate to the feedback input section.
+   - Choose to upload files (JSON, HTML, images) or paste text from chat interactions.
+   - Follow the on-screen instructions to input your feedback data.
+3. **View Sentiment Analysis Results and Visualizations:**
+   - Once the feedback is processed, navigate to the dashboard.
+   - Explore interactive charts and visualizations to understand:
+     - Feedback trends over time
+     - Sentiment distribution across feedback
+     - Top issues identified from the feedback
+4. **Navigate the Dashboard:**
+   - Use the dashboard to access different features, such as:
+     - Real-time processing queue monitoring.
+     - Agent performance tracking and sentiment analysis (if applicable).
+     - Detailed views of individual feedback entries and their sentiment analysis.
+
+## Configuration
+
+- **Environment Variables:**
+  - `MONGODB_URI`: MongoDB connection string for your MongoDB database.
+  - `AWS_REGION`: AWS region where your Bedrock service is configured.
+  - `AWS_ACCESS_KEY_ID`: AWS access key ID for authentication.
+  - `AWS_SECRET_ACCESS_KEY`: AWS secret access key for authentication.
+
+- **Other configurations:**
+  - The application may have additional configurations that can be set in the `.env.local` file or through the application's settings panel. Refer to the application documentation for advanced configuration options.
+
+## Contributing
+
+If you'd like to contribute to MongoFeed, please follow these guidelines:
+1. Fork the repository.
+2. Create a branch for your feature or bug fix.
+3. Ensure your code adheres to the project's coding standards.
+4. Submit a pull request with a clear description of your changes.
+
+## License
+
+[Specify the project license, e.g., MIT License]

apps/mongo-feed/app/api/agent-analysis/route.ts

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+import { NextResponse } from "next/server"
+import clientPromise from "@/lib/mongodb"
+
+export const dynamic = "force-dynamic"
+export const revalidate = 0
+
+export async function GET() {
+  try {
+    const client = await clientPromise
+    const db = client.db("mongofeed")
+
+    const agentAnalysis = await db
+      .collection("chat_analyses")
+      .aggregate([
+        { $unwind: "$messages" },
+        { $match: { "messages.role": "Agent" } },
+        {
+          $group: {
+            _id: "$messages.agentName",
+            positiveSentiment: { $sum: { $cond: [{ $eq: ["$messages.sentiment", "positive"] }, 1, 0] } },
+            neutralSentiment: { $sum: { $cond: [{ $eq: ["$messages.sentiment", "neutral"] }, 1, 0] } },
+            negativeSentiment: { $sum: { $cond: [{ $eq: ["$messages.sentiment", "negative"] }, 1, 0] } },
+            totalInteractions: { $sum: 1 },
+          },
+        },
+        {
+          $project: {
+            agentName: "$_id",
+            positiveSentiment: 1,
+            neutralSentiment: 1,
+            negativeSentiment: 1,
+            totalInteractions: 1,
+            _id: 0,
+          },
+        },
+        { $sort: { totalInteractions: -1 } },
+      ])
+      .toArray()
+
+    return NextResponse.json(agentAnalysis)
+  } catch (error) {
+    console.error("Error fetching agent analysis:", error)
+    return NextResponse.json({ error: "An error occurred while fetching agent analysis." }, { status: 500 })
+  }
+}

apps/mongo-feed/app/api/agent-sentiment/route.ts

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+import { NextResponse } from "next/server"
+import clientPromise from "@/lib/mongodb"
+
+export const dynamic = "force-dynamic"
+export const revalidate = 0
+
+export async function GET() {
+  try {
+    const client = await clientPromise
+    const db = client.db("mongofeed")
+
+    // First get all agents with their sentiment data
+    const agentData = await db
+      .collection("agent_sentiment")
+      .aggregate([
+        {
+          $lookup: {
+            from: "chat_analyses",
+            let: { agentName: "$agentName" },
+            pipeline: [
+              {
+                $match: {
+                  $expr: { $eq: ["$analysis.agentName", "$$agentName"] },
+                },
+              },
+              { $sort: { createdAt: -1 } },
+              { $limit: 5 },
+              {
+                $project: {
+                  id: { $toString: "$_id" },
+                  date: "$createdAt",
+                  summary: "$analysis.summary",
+                  sentiment: "$analysis.overallSentiment",
+                  issues: "$analysis.mainTopics",
+                },
+              },
+            ],
+            as: "recentChats",
+          },
+        },
+        { $sort: { totalInteractions: -1 } },
+      ])
+      .toArray()
+
+    return NextResponse.json(agentData)
+  } catch (error) {
+    console.error("Error fetching agent sentiment:", error)
+    return NextResponse.json({ error: "An error occurred while fetching agent sentiment." }, { status: 500 })
+  }
+}

apps/mongo-feed/app/api/analyze-feedback/route.ts

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+import { type NextRequest, NextResponse } from "next/server"
+import { analyzeAgentFeedback, analyzeProductReview } from "@/lib/analyze-content"
+import clientPromise from "@/lib/mongodb"
+
+export const dynamic = "force-dynamic"
+export const runtime = "nodejs"
+
+export async function POST(req: NextRequest) {
+  try {
+    const formData = await req.formData()
+    const file = formData.get("file") as File
+    const type = formData.get("type") as string
+
+    if (!file) {
+      return NextResponse.json({ error: "No file uploaded" }, { status: 400 })
+    }
+
+    const contentType = file.type
+    const fileName = file.name
+
+    let content: string | ArrayBuffer
+    if (contentType.startsWith("image/")) {
+      content = await file.arrayBuffer()
+    } else {
+      const buffer = await file.arrayBuffer()
+      content = new TextDecoder().decode(buffer)
+    }
+
+    let analysisResult
+
+    if (type === "agent") {
+      analysisResult = await analyzeAgentFeedback(content as string)
+    } else if (type === "product") {
+      analysisResult = await analyzeProductReview(content, contentType, fileName)
+    } else {
+      return NextResponse.json({ error: "Invalid analysis type" }, { status: 400 })
+    }
+
+    // Store the analysis result in MongoDB
+    const client = await clientPromise
+    const db = client.db("mongofeed")
+    await db.collection("chat_analyses").insertOne({
+      type,
+      contentType,
+      fileName,
+      analysis: analysisResult,
+      createdAt: new Date(),
+    })
+
+    return NextResponse.json({ sentiments: analysisResult })
+  } catch (error) {
+    console.error("Error analyzing feedback:", error)
+    return NextResponse.json({ error: "An error occurred while analyzing feedback." }, { status: 500 })
+  }
+}

apps/mongo-feed/app/api/analyze-sentiment/route.ts

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+import { NextRequest, NextResponse } from 'next/server';
+import { bedrock } from '@/lib/bedrock';
+import { generateText } from 'ai';
+
+export async function POST(req: NextRequest) {
+  try {
+    const data = await req.json();
+    const documents = data.documents;
+
+    if (!Array.isArray(documents)) {
+      return NextResponse.json({ error: 'Invalid input. Expected an array of documents.' }, { status: 400 });
+    }
+
+    const sentiments = await Promise.all(
+      documents.map(async (doc) => {
+        try {
+          const { text } = await generateText({
+            model: bedrock('aanthropic.claude-3-5-sonnet-20241022-v2:0'),
+            prompt: `Analyze the sentiment of the following text and respond with only one word: "positive", "negative", or "neutral". Text: "${doc}"`,
+          });
+          return text.trim().toLowerCase();
+        } catch (error) {
+          console.error('Error analyzing individual document:', error);
+          return 'error';
+        }
+      })
+    );
+
+    return NextResponse.json({ sentiments });
+  } catch (error) {
+    console.error('Error analyzing sentiment:', error);
+    return NextResponse.json({ error: 'An error occurred while analyzing sentiment.' }, { status: 500 });
+  }
+}