Closed nkwangleiGIT closed 6 months ago
LGTM.
Allow to define a CR and configure the input audio/pdf files (pdf, wav, mp3, etc ...), then the controller can transcribe pdf/audio to text file. Then we can use the text for QA or summary.
Handle audio file:
apiVersion: converter.kubeagi.k8s.com.cn/v1alpha1 kind: Audio2Text metadata: name: auto2text-converter1 namespace: kubeagi-test spec: files: - <audio file from MinIO> status: condition: Processing result: <text from audio>
Handle pdf file:
apiVersion: converter.kubeagi.k8s.com.cn/v1alpha1 kind: Pdf2Text metadata: name: pdf2text-converter1 namespace: kubeagi-test spec: files: - <pdf file from MinIO> status: condition: Processing result: <text from pdf>
The converter can invoke the APIs or start a long-running job to handle the data. For other data processing, we can also follow the similar approach, and use these data process CRs to orchestrate LLM agent.
LGTM! Similar to kubeagi evaluations,we can use k8s jobs as the converter. Utilize core-library to devlop the converter cli.
We can use this CLI(deveoped with python) as the job runner
cli convert xxx
Add a CRD for now to handle document loader: https://github.com/kubeagi/arcadia/pull/771 We can change the logic(use different document loader implementation) to use Job(cli) as needed later
Mark it done as we can support it using documentloader in chat mode.
Allow to define a CR and configure the input audio/pdf files (pdf, wav, mp3, etc ...), then the controller can transcribe pdf/audio to text file. Then we can use the text for QA or summary.
Handle audio file:
Handle pdf file:
The converter can invoke the APIs or start a long-running job to handle the data. For other data processing, we can also follow the similar approach, and use these data process CRs to orchestrate LLM agent.