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import google.generativeai as genai
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import os
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genai.configure(api_key=os.environ["GEMINI_API_KEY"])
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prompt = """
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<role>Database Architect & AI Specialist</role>
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<task>Design a JSON schema for a RAG Knowledge Base.</task>
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<constraints>
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Use standard TypeScript interfaces. Include Vector dimensions (1536) and Metadata (Author, Date).
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</constraints>
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"""
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model = genai.GenerativeModel('gemini-1.5-pro')
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response = model.generate_content(prompt)
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print(response.text)

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import { GoogleGenerativeAI } from "@google/generative-ai";
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const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);
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async function designSchema() {
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    const prompt = `
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    <role>Database Architect & AI Specialist</role>
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    <task>Design a JSON schema for a RAG Knowledge Base using TypeScript interfaces.</task>
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    <constraints>Support Vector Embeddings (1536d) and 'Technical Tale' metadata.</constraints>
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    `;
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    const model = genAI.getGenerativeModel({ model: "gemini-1.5-pro" });
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    const result = await model.generateContent(prompt);
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    console.log(result.response.text());
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}
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designSchema();

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import com.google.cloud.vertexai.VertexAI;
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import com.google.cloud.vertexai.generativeai.GenerativeModel;
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import com.google.cloud.vertexai.generativeai.ResponseHandler;
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public class SchemaDesign {
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    public static void main(String[] args) throws Exception {
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        try (VertexAI vertexAi = new VertexAI("your-project-id", "us-central1")) {
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            String prompt = """
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                <role>Database Architect & AI Specialist</role>
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                <task>Design a JSON schema for a RAG Knowledge Base.</task>
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                <constraints>Output TypeScript interfaces for Vector/Metadata layers.</constraints>
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                """;
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            GenerativeModel model = new GenerativeModel("gemini-1.5-pro", vertexAi);
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            var response = model.generateContent(prompt);
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            System.out.println(ResponseHandler.getText(response));
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        }
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    }
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}