VertexAI

[ngoiyaeric]

User description

vertex

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PR Type

enhancement

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Description

• Enhanced the sanitizeUrl function to integrate Google Vertex AI with OpenAI.
• Added logic to retrieve access tokens and configure models dynamically.
• Cleaned up commented-out code and adjusted the function flow for better readability and functionality.

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Changes walkthrough 📝

	Relevant files
Enhancement	index.ts Integrate Google Vertex AI with OpenAI in utility functions lib/utils/index.ts Uncommented and modified code to integrate Google Vertex AI with OpenAI. Added logic to handle access token retrieval and model configuration. Removed commented-out code and adjusted function flow. +14/-14

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PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Key issues to review Async Handling The new code introduces asynchronous operations (`await auth.getClient()` and `await client.getAccessToken()`) but the function `sanitizeUrl` is not declared as async. This will lead to runtime errors as the `await` keyword requires the function to be asynchronous. Error Handling The new code checks if `token.token` is not present but does not throw an error or handle the case where the token is missing. This could lead to issues downstream where an API key is expected but not provided.

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PR Code Suggestions ✨

Category	Suggestion	Score
Reliability	Add error handling for asynchronous operations ___ **Add error handling for asynchronous operations to prevent runtime errors and improve reliability.** [lib/utils/index.ts [105-106]](https://github.com/QueueLab/mapgpt/pull/57/files#diff-76051e819bd9ea2a35405d89d3fbf1831f45019a2b12d5e0a915668d1bd46462R105-R106) ```diff -const client = await auth.getClient() -const token = await client.getAccessToken() +let client; +try { + client = await auth.getClient(); +} catch (error) { + console.error('Error getting client:', error); + throw new Error('Failed to get client'); +} +let token; +try { + token = await client.getAccessToken(); +} catch (error) { + console.error('Error getting access token:', error); + throw new Error('Failed to get access token'); +} ``` - [ ] **Apply this suggestion** Suggestion importance[1-10]: 9 Why: Adding error handling for asynchronous operations significantly improves the reliability and robustness of the code by preventing potential runtime errors.	9
	Add error handling for token validation ___ **Ensure that the if condition checking for token.token is followed by appropriate action or error handling.** [lib/utils/index.ts [107-109]](https://github.com/QueueLab/mapgpt/pull/57/files#diff-76051e819bd9ea2a35405d89d3fbf1831f45019a2b12d5e0a915668d1bd46462R107-R109) ```diff if (!token.token) { - // throw new Error('Failed to obtain access token') -// } + throw new Error('Failed to obtain access token'); +} ``` - [ ] **Apply this suggestion** Suggestion importance[1-10]: 8 Why: Adding error handling for token validation ensures that the code properly handles cases where the token is not obtained, improving reliability and preventing potential runtime errors.	8
Maintainability	Improve code readability by fixing indentation ___ **Ensure proper indentation for the newly added code to maintain code readability and consistency.** [lib/utils/index.ts [104-112]](https://github.com/QueueLab/mapgpt/pull/57/files#diff-76051e819bd9ea2a35405d89d3fbf1831f45019a2b12d5e0a915668d1bd46462R104-R112) ```diff -const model = new VertexAI({ project: projectId, location: region }) -const client = await auth.getClient() -const token = await client.getAccessToken() +const model = new VertexAI({ project: projectId, location: region }); +const client = await auth.getClient(); +const token = await client.getAccessToken(); if (!token.token) { - // throw new Error('Failed to obtain access token') -// } + // throw new Error('Failed to obtain access token') +} return new OpenAI({ apiKey: token.token, baseURL: `https://${region}-aiplatform.googleapis.com/v1/projects/${projectId}/locations/${region}/publishers/google/models/publishers/meta/models/llama3-405b-instruct-maas` -} +}); ``` - [ ] **Apply this suggestion** Suggestion importance[1-10]: 7 Why: The suggestion improves code readability and consistency by ensuring proper indentation and adding missing semicolons, which are good practices for maintainability.	7
	Remove commented-out code ___ **Remove commented-out code to enhance code cleanliness and maintainability.** [lib/utils/index.ts [108-109]](https://github.com/QueueLab/mapgpt/pull/57/files#diff-76051e819bd9ea2a35405d89d3fbf1831f45019a2b12d5e0a915668d1bd46462R108-R109) ```diff -// throw new Error('Failed to obtain access token') -// } + ``` - [ ] **Apply this suggestion** Suggestion importance[1-10]: 6 Why: Removing commented-out code enhances code cleanliness and maintainability, although it is a minor improvement.	6

[ngoiyaeric]

/review --pr_reviewer.extra_instructions=" In the possible issues section, emphasize the following:

• Is the code logic efficient?
• google-auth-library intergration throws child_process missing error, ts, fs missing error model is deployed on vertex is using openai sdk somehow curl -X POST \ -H "Authorization: Bearer $(gcloud auth print-access-token)" \ -H "Content-Type: application/json" \ https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/publishers/google/models/${MODEL_ID}:streamGenerateContent \ -d '{ "contents": [{ ... }], "generationConfig": { ... }, "safetySettings": { ... } ... }' " --pr_reviewer.num_code_suggestions="0" --pr_reviewer.inline_code_comments=true -i --pr_reviewer.require_score_review=true --pr_reviewer.require_can_be_split_review=true --pr_reviewer.require_soc2_ticket=false

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PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪

🏅 Score: 75

🧪 No relevant tests

🔒 No security concerns identified

🔀 No multiple PR themes

⚡ Key issues to review Efficiency Concern The integration of Google Vertex AI with OpenAI involves multiple asynchronous operations (auth.getClient() and client.getAccessToken()). Consider optimizing these operations to avoid potential performance bottlenecks, especially in scenarios where this function might be called frequently. Error Handling The commented-out error handling code for missing tokens was uncommented and modified, but it lacks proper implementation. It's crucial to handle cases where the token is not retrieved successfully to avoid runtime errors. Integration Issue The integration code for Google Vertex AI and OpenAI might throw errors related to missing modules like 'child_process', 'ts', and 'fs' when deployed on Vertex AI using the OpenAI SDK. Ensure that all dependencies are correctly managed and compatible with the deployment environment.

[ngoiyaeric]

/improve --pr_code_suggestions.num_code_suggestions_per_chunk="4" --pr_code_suggestions.commitable_code_suggestions=true

[ngoiyaeric]

/ask how to integrate this with @ai-sdk nextjs for the model to natively be embedded into the codebase.

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Ask❓

how to integrate this with @ai-sdk nextjs for the model to natively be embedded into the codebase.

Answer:

To integrate the enhanced sanitizeUrl function with @ai-sdk/nextjs for the model to be natively embedded into the codebase, follow these steps:

1. Install Required Packages: Ensure you have the necessary packages installed. You might need @ai-sdk/nextjs, google-auth-library, and any other dependencies required for Google Vertex AI and OpenAI.

   npm install @ai-sdk/nextjs google-auth-library

2. Configure Environment Variables: Set up environment variables for projectId, region, and any other necessary configurations. These can be added to a .env.local file in your Next.js project.

   NEXT_PUBLIC_PROJECT_ID=your-google-cloud-project-id
   NEXT_PUBLIC_REGION=your-region

3. Create a Utility Function: Create a utility function that integrates with @ai-sdk/nextjs and uses the enhanced sanitizeUrl function. This function will handle the model configuration and token retrieval.

   // lib/utils/aiIntegration.ts
   import { VertexAI } from '@google-cloud/ai-platform';
   import { auth } from 'google-auth-library';
   import { OpenAI } from '@ai-sdk/nextjs';
   
   export async function getModelInstance() {
    const projectId = process.env.NEXT_PUBLIC_PROJECT_ID;
    const region = process.env.NEXT_PUBLIC_REGION;
   
    if (!projectId || !region) {
      throw new Error('Project ID and region must be set in environment variables');
    }
   
    const model = new VertexAI({ project: projectId, location: region });
    const client = await auth.getClient();
    const token = await client.getAccessToken();
   
    if (!token.token) {
      throw new Error('Failed to obtain access token');
    }
   
    return new OpenAI({
      apiKey: token.token,
      baseURL: `https://${region}-aiplatform.googleapis.com/v1/projects/${projectId}/locations/${region}/publishers/google/models/publishers/meta/models/llama3-405b-instruct-maas`
    });
   }

4. Use the Utility Function in Your Next.js Application: Integrate the utility function into your Next.js application. You can use it in API routes or directly in your components.

   // pages/api/getModel.ts
   import type { NextApiRequest, NextApiResponse } from 'next';
   import { getModelInstance } from '../../lib/utils/aiIntegration';
   
   export default async function handler(req: NextApiRequest, res: NextApiResponse) {
    try {
      const modelInstance = await getModelInstance();
      res.status(200).json({ message: 'Model instance created successfully', modelInstance });
    } catch (error) {
      res.status(500).json({ error: error.message });
    }
   }

   // pages/index.tsx
   import { useEffect, useState } from 'react';
   
   export default function Home() {
    const [modelInstance, setModelInstance] = useState(null);
   
    useEffect(() => {
      async function fetchModelInstance() {
        const response = await fetch('/api/getModel');
        const data = await response.json();
        setModelInstance(data.modelInstance);
      }
   
      fetchModelInstance();
    }, []);
   
    return (
      <div>
        <h1>AI Model Integration</h1>
        {modelInstance ? (
          <pre>{JSON.stringify(modelInstance, null, 2)}</pre>
        ) : (
          <p>Loading model instance...</p>
        )}
      </div>
    );
   }

By following these steps, you can integrate the enhanced sanitizeUrl function with @ai-sdk/nextjs and embed the model natively into your Next.js codebase. This setup ensures that the model configuration and token retrieval are handled dynamically and securely.

[ngoiyaeric]

/analyze if system would work if the only api key provided was googleapikey

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