Tag: ai

Based on open source made available as open web service in Eyevinn Open Source Cloud we can have AI to assist with code reviewing of submitted pull requests to a GitHub repository.

The AI Code Reviewer is an open source project that provides an API and user interface to review code in a public GitHub repository. It analyzes code for quality, best practices and potential improvements, providing actionable feedback to improve a code base. This is achieved by carefully prompting a GPT4 model to perform the task and return a structured response with scores and suggested improvements.

This project has been made available as an open web service in Eyevinn Open Source Cloud which means that you can instantly start to integrate this into your solution.

AI Code Review GitHub Action

For example, we might want to incorporate this AI Code Reviewer in our Pull Request workflow, and use this to provide an automated first review of all opened pull requests. It could add a comment on overall score and suggested improvements.

In order to add this to our GitHub workflow we need a GitHub action to perform this task based on this open web service in Eyevinn Open Source Cloud. We will create a GitHub action that uses the client libraries for Eyevinn OSC to create an AI Code Reviewer instance.

core.info('Setting up Code Reviewer');
const ctx = new Context();
let reviewer = await getEyevinnAiCodeReviewerInstance(ctx, 'ghaction');
if (!reviewer) {
  reviewer = await createEyevinnAiCodeReviewerInstance(ctx, {
    name: 'ghaction',
    OpenAiApiKey: '{{secrets.openaikey}}'
  });
  await delay(1000);
}
core.info(`Reviewer available, requesting review of ${gitHubUrl.toString()}`);

These lines of code will create an instance called “ghaction” if it does not already exists. When that is available we can use the API that this service provides to perform the actual code review. The following lines of codes takes care of this.

const reviewRequestUrl = new URL('/api/v1/review', reviewer.url);
const sat = await ctx.getServiceAccessToken('eyevinn-ai-code-reviewer');
const response = await fetch(reviewRequestUrl, {
  method: 'POST',
  headers: {
    Authorization: `Bearer ${sat}`,
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    githubUrl: gitHubUrl.toString()
  })
});
if (response.ok) {
  const review = await response.json();
  return review;
} else {
  throw new Error('Failed to get review');
}

We package this together into a GitHub action and makes it available on the GitHub action marketplace.

Add review to pull request workflow

Now it is time to add this to our pull request workflow in GitHub. We add a step to review the branch for the pull request using the GitHub action we created. The input variable “repo_url” contains the URL to this branch and in addition we need to provide the access token to Eyevinn OSC as an environment variable.

  - name: Review branch
    id: review
    uses: EyevinnOSC/code-review-action@v1
    with:
      repo_url: ${{ github.server_url }}/${{ github.repository }}/tree/${{ github.head_ref}}
    env:
      OSC_ACCESS_TOKEN: ${{ secrets.OSC_ACCESS_TOKEN }}

Next step is to take the outputs “score” and “suggestions” of this step and add this as a comment to the pull request.

  - name: comment
    uses: actions/github-script@v7
    with:
      github-token: ${{secrets.GITHUB_TOKEN}}
      script: |
        github.rest.issues.createComment({
          issue_number: context.issue.number,
          owner: context.repo.owner,
          repo: context.repo.repo,
          body: 'Code review score: ${{ steps.review.outputs.score }}\n${{ join(fromJSON(steps.review.outputs.suggestions), ', ') }}'
        })

When a pull request is open the workflow is run and result of the code review is posted as a comment to the pull request.

Conclusion

This is an example on how you can integrate open web services to enhance your software development processes. We are continuing our journey to advance and democratize web services through open source and a sustainable business model for creators.

Install

If you want to try this out yourself you can follow these steps. A prerequisite is that you have an account on Eyevinn Open Source Cloud and at least 6 services available on your plan.

1. Download and install Claude Desktop.
2. In the Eyevinn OSC web console go to API settings (in Settings > API settings)
3. Copy the Personal Access Token
4. add the following to your claude_desktop_config.json:

{ "mcpServers": { "eyevinn-osc": { "command": "npx", "args": ["-y", "@osaas/mcp-server"], "env": { "OSC_ACCESS_TOKEN": "YOUR_PERSONAL_ACCESS_TOKEN" } } } }

5. Restart Claude

If everything is correctly installed you should see an icon of a hammer in the bottom of the chat input.

Now you can ask Claude to create a VOD from a file that you have available online as shown in the video above.

Client SDK

This MCP server uses the Typescript client SDK for Eyevinn Open Source Cloud. With this SDK you can create and remove instances and automate what you can do in the web console. Here is an example of how to create a VOD package using the client SDK which is basically what one of the tools currently can do.

import { Context, Log } from '@osaas/client-core'; import { createVod, createVodPipeline } from '@osaas/client-transcode'; async function main() { const ctx = new Context(); try { const ctx = new Context({ environment }); Log().info('Creating VOD pipeline'); const pipeline = await createVodPipeline(name, ctx); Log().info('VOD pipeline created, starting job to create VOD'); const job = await createVod(pipeline, source, ctx); if (job) { Log().info('Created VOD will be available at: ' + job.vodUrl); } } catch (err) { Log().error(err); } } main();

This gives you an example of what you can do and the possibilities are “endless”. It feels as it is only creativity that stands in the way of what you can do.

Share your ideas either in the comments below or with a contribution to the Eyevinn OSC MCP server that is open source. Be creative!