Add API

.gitignore

@@ -396,3 +396,9 @@ FodyWeavers.xsd
 
 # JetBrains Rider
 *.sln.iml
+
+# Google creds
+service-principal-gemini-credential.json
+
+# Remove local env 
+.env

API/README.md

@@ -0,0 +1,18 @@
+# AI API
+API endpoint providing access to Google Gemini on /api/ai/gemini-pro
+
+## Prerequisites
+The API requires nodeJS v18 minimum.
+
+## Running the API
+Start by installing the modules
+'''
+npm i
+'''
+
+Run the server
+'''
+node server.js
+'''
+
+The API will be listening on port 3001 by default. 

API/helpers/gemini.js

@@ -0,0 +1,62 @@
+const { VertexAI, HarmCategory, HarmBlockThreshold } = require('@google-cloud/vertexai');
+require('dotenv').config();
+
+// Constants for project and location should be defined at the top level.
+const PROJECT_ID = process.env.PROJECT_ID;
+const LOCATION = process.env.LOCATION;
+
+// Initialize Vertex AI with the necessary project and location information once.
+const vertexAiOptions = { project: PROJECT_ID, location: LOCATION };
+const vertex_ai = new VertexAI(vertexAiOptions);
+
+// Define model names as constants to avoid magic strings and improve readability.
+const GEMINI_PRO_MODEL_NAME = process.env.GEMINI_PRO_MODEL_NAME
+
+// Safety settings can be moved outside of the model instantiation, 
+// if they are static and reused across multiple instances.
+const safetySettings = [{
+    category: HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
+    threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+}];
+
+// Instantiate models once outside of functions to avoid repeated initializations.
+const generativeModelOptions = {
+    model: GEMINI_PRO_MODEL_NAME,
+    safety_settings: safetySettings,
+    generation_config: { max_output_tokens: 512 },
+};
+const generativeModel = vertex_ai.preview.getGenerativeModel(generativeModelOptions);
+
+// The streamGenerateContent function does not need to be an async declaration since it returns a Promise implicitly.
+async function streamGenerateContent(promptInitText, question) {
+    const request = {
+        contents: [
+            {
+                role: 'user',
+                parts: [{ text: promptInitText + question }]
+            }
+        ],
+    };
+
+    console.log(request)
+
+    // Using implicit return for the async arrow function.
+    let geminiResponse;
+    try {
+        const streamingResp = await generativeModel.generateContentStream(request);
+        for await (const item of streamingResp.stream) {
+            console.log('stream chunk: ', item.candidates[0].content.parts[0]);
+        }
+        const aggregatedResponse = await streamingResp.response;
+        console.log("Original question: " + question)
+        console.log('Gemini response: ', aggregatedResponse.candidates[0].content.parts[0].text);
+        geminiResponse = aggregatedResponse.candidates[0].content.parts[0].text;
+    } catch (error) {
+        console.error('An error occurred during content generation:', error);
+        geminiResponse = "An error occurred during content generation:" + error;
+    }
+
+    return geminiResponse;
+}
+
+module.exports = { streamGenerateContent };