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InstructLab and Deepsearch
This is the proposal to start integrating th document conversion system Deepsearch from IBM Research and InstructLab Co-authored-by: Ming Zhao <[email protected]> Co-authored-by: BJ Hargrave <[email protected]> Signed-off-by: JJ Asghar <[email protected]>
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# DeepSearch + InstructLab Integration Proposal
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<https://github.com/DS4SD>
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## Why is a Conversion Tool Necessary?
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Managing submissions for the open-source InstructLab project has revealed a significant bottleneck in processing
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knowledge documents. For the InstructLab backend to effectively utilize these documents, they must be in markdown
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format. Currently, we only accept Wikipedia articles, but the built-in conversion tool is inadequate. Internally at
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IBM, and other companies, many knowledge submissions are in multiple document formats, including PDF format,
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necessitating conversion to markdown before being used in InstructLab.
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Existing open-source methods, such as PanDoc, are inconsistent. While they preserve text, they struggle with parsing
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tables and special symbols, as evidenced by issues in PR #1154 of the taxonomy repo in the InstructLab project. Other
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open-source solutions have similar shortcomings.
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## Why DeepSearch?
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IBM's DeepSearch software excels in document conversion, outperforming traditional open-source methods. Utilizing a
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computer vision model layer, it accurately parses content in the files, including titles, headers, and tables.
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Additionally, it automatically implements RAG layers for models, which could benefit the InstructLab process in
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the future.
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## Integration Proposal
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To maintain the open-source nature of the project while leveraging the strengths of DeepSearch, we propose a
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two-pronged approach:
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### Open-Source Conversion
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- Implement a basic document conversion tool in the UI using an open-source method such as PanDoc. This tool will be
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lightweight and easily hosted, ensuring it can be used and improved by the community.
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### DeepSearch Integration
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- Enable the UI to switch the conversion endpoint to DeepSearch, allowing high-fidelity markdown conversions for
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backend use. This approach maintains an open-source version while benefiting from DeepSearch's superior
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conversion capabilities.
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IBM Research and the DeepSearch team will host the DeepSearch endpoint for the open-source community. This
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arrangement benefits the community by streamlining contributions and provides data and exposure for the DeepSearch
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project. IBM's contribution underscores its commitment to supporting and improving open-source projects.
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This integration will highlight the value of DeepSearch, highlighting their potential for those integrating
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InstructLab into their workflows. If the volume of community requests becomes unsustainable for the DeepSearch team,
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we hope for ample notification to allow the community to find alternative solutions. By then, we anticipate that the
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open-source versions will have improved sufficiently, or the value of the integration will justify continued support.
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By adopting this two-pronged approach, we ensure the integrity of the open-source project while leveraging IBM's
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advanced DeepSearch capabilities. This strategy balances community collaboration with innovative technology,
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fostering innovation and improvement in document processing for the InstructLab project.

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