LangChain Sharepoint Loader

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AI Summary

Summary of SharePoint Document Loader for AI Project Development

• The video discusses the challenges of using Lang chain’s SharePoint document loader for AI project development and provides a solution.
• The author encountered issues with local storage requirements, metadata inconsistency, integration complexity, and compliance concerns.
• Lang chain’s SharePoint loader has limitations such as two-step authentication, format restrictions, and manual configuration.
• The author created a custom SharePoint client to overcome these challenges.

Detailed Instructions and Tips

• Tenant Name Retrieval:
  • Found in the SharePoint site URL or via Azure AD B2C in the Azure portal.
• Collection ID and Subsite ID Retrieval:
  • Requires manual URL creation including the tenant name and SharePoint site.
  • Look for values inside the GD tag.
• SharePoint S ID:
  • Use the Graph Explorer playground to run a query with the SharePoint S ID.
  • Look for the ID in the query payload.
• VSS Code Project:
  • Create an .env file with variables for tenant name, collection ID, and subsite ID.
  • Set the token to false initially for authentication, then true to reuse the token.
• Custom SharePoint Client:
  • Create an app registration on the Azure portal for SharePoint access.
  • Use the SharePoint API to define functions for retrieving site ID, drive ID, etc.
  • The client allows downloading files and creating custom loader classes without saving files locally.
• Text Splitter:
  • Use Lang chain character text splitter with carriage return as a separator.
  • Set chunk size and overlap according to requirements.

URLs and Code Availability

• The author’s Medium article link is provided in the video description.
• The custom code for the SharePoint client is available on the author’s GitHub, with the link in the video description.

Demonstration

• The author demonstrates how to use the custom SharePoint client to:
  • Retrieve site ID, drive ID, and root folder content.
  • Download files locally (optional).
  • Create text chunks for embeddings with proper metadata, including source path and page number when applicable.

Conclusion

• The author concludes by showcasing the successful creation of custom Lang chain loaders that work with various file types, including Word, PDF, PowerPoint, Excel, and text files.
• The custom solution addresses the limitations of the original SharePoint loader provided by Lang chain.