sharepoint

Sharepoint Connector

This connector extracts search documents from pages on Sharepoint sites. It retrieves all pages on all sites that it has access to.

Setup

This connector makes use of a Microsoft Azure App Registration. Ensure that you apply either the Microsoft Graph / Sites.Read.All permission or configure Microsoft Graph / Sites.Selected appropriately to allow access to the desired site(s).

Configure the client ID, client secret, and tenant ID using the information from the registered App.

Additionally, this connector requires Azure OpenAI services or an OpenAI API key to generate embedding vectors for documents.

Config File

Create a YAML config file based on the following template.

Required Configurations

sharepoint_client_id: sharepoint_client_id
sharepoint_client_secret: sharepoint_client_secret
sharepoint_tenant_id: sharepoint_tenant_id

embedding_model:
  azure_openai:
    key: <key>
    endpoint: <endpoint>

Note that an embedding model needs to be appropriately configured. This example shows how to configure an Azure OpenAI services model, but you can use other supported models.

Optional Configurations

These defaults are provided; you don't have to manually configure them.

include_text specifies whether to include the original document text alongside the embedded content.

embedding_model:  # in the same block as above
  azure_openai:
    version: <version> # "2024-03-01-preview"
    deployment_name: <deployment_name> # "Embedding_3_small"
    model: <model> # "text-embedding-3-small"
  chunk_size: 512
  chunk_overlap: 50

include_text: <include_text> # False

Output Destination

See Output Config for more information on the optional output config.

Testing

Follow the installation instructions to install metaphor-connectors in your environment (or virtualenv). Make sure to include the sharepoint or all extra.

Run the following command to test the connector locally:

metaphor sharepoint <config_file>

Manually verify the output after the run finishes.