Update README.md

README.md

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 title: Mongodb Gemini Rag
-emoji: 📚
+emoji: ♊️
 colorFrom: indigo
 colorTo: purple
 sdk: gradio
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 license: apache-2.0
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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Atlas Vector Search Chat with MongoDB and Google Gemini
+
+Welcome to the Atlas Vector Search Chat! This application demonstrates how to use MongoDB [Atlas Vector Search](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/) with [Google Gemini](https://ai.google.dev/) for semantic search and retrieval tasks.
+
+## Features
+- **Interactive Chat**: Ask questions related to the embedded documents.
+- **Vector Search**: Utilizes MongoDB Atlas Vector Search to find relevant documents based on similarity.
+- **Google Gemini Integration**: Embeds text and generates responses.
+
+## Requirements
+- Python 3.7 or later
+- MongoDB Atlas account
+-   Atlas cluster enabled with `0.0.0.0/0` connection and connetion string
+- Google Cloud account with access to Gemini
+
+## Installation
+1. **Clone the space**:
+    - Click [...] and clone the space to your repo, make sure to input the variables: 
+
+3. **Set up environment variables**:
+    - `GOOGLE_API_KEY`: Your Google API key for Gemini.
+    - `MONGODB_ATLAS_URI`: Your MongoDB Atlas connection string.
+
+## Running the Application
+1. **Start the application**:
+    ```bash
+    python app.py
+    ```
+2. **Access the interface**:
+    Open your browser on the `App` tab.
+
+## Vector Search Index Configuration
+To create a [vector search index](https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/) on the `google-ai.embedded_docs` collection, use the following configuration:
+```
+{
+  "fields": [
+    {
+      "numDimensions": 768,
+      "path": "embedding",
+      "similarity": "cosine",
+      "type": "vector"
+    }
+  ]
+}
+```
+## MongoDB Trigger to Embed Results
+
+This Atlas enviroment use an Atlas Database trigger on collection `google-ai.embedded_docs` to capture any `insert` operation and embed the content as specified in this [article](https://www.mongodb.com/developer/products/atlas/semantic-search-mongodb-atlas-vector-search/).
+
+```
+// Get the API key from Realm's Values & Secrets
+const apiKey = context.values.get('google-api-key');
+
+// Set up the URL for the Google Generative Language API - embedding endpoint
+const url = `https://generativelanguage.googleapis.com/v1beta/models/embedding-001:embedContent?key=${apiKey}`;
+// batch example
+// const url = `https://generativelanguage.googleapis.com/v1beta/models/embedding-001:batchEmbedContents?key=${apiKey}`;
+
+// Get the full document from the change event
+const doc = changeEvent.fullDocument;
+
+try {
+    console.log(`Processing document with id: ${doc._id}`);
+
+    // Prepare the request body
+    const requestBody = `{
+    "model": "models/embedding-001",
+    "content": {
+    "parts":[{
+      "text": '${doc.content}'}]}}`;
+    
+
+    // Make the HTTP POST request
+    const response = await context.http.post({
+        url: url,
+        headers: { 'Content-Type': ['application/json'] },
+        body: requestBody
+    });
+
+    // Parse the JSON response
+    const responseData = EJSON.parse(response.body.text());
+    console.log(JSON.stringify(responseData))
+
+    if(response.statusCode === 200) {
+        console.log("Successfully received embedding response from the API.");
+
+        // Extract the embedding from the response
+        const embedding = responseData.embedding.values; // Adjust based on actual response structure
+
+        // Use the name of your MongoDB Atlas Cluster
+        const collection = context.services.get("mongodb-atlas").db("google-ai").collection("embedded_docs");
+
+        // Update the document in MongoDB with the embedding
+        const updateResult = await collection.updateOne(
+            { _id: doc._id },
+            { $set: { embedding: embedding }}
+        );
+
+        if(updateResult.modifiedCount === 1) {
+            console.log("Successfully updated the document.");
+        } else {
+            console.log("Failed to update the document.");
+        }
+    } else {
+        console.log(`Failed to receive embedding. Status code: ${response.statusCode} -  ${JSON.stringify(response)}`);
+    }
+} catch(err) {
+    console.error(`Error making request to API: ${err}`);
+}
+```