deepset-ai--haystack/docs-website/versioned_docs/version-2.31/pipeline-components/retrievers/mssharepointretriever.mdx

---
title: "MSSharePointRetriever"
id: mssharepointretriever
slug: "/mssharepointretriever"
description: "Retrieves content from Microsoft SharePoint and OneDrive via the Microsoft Search (Graph) API."
---

# MSSharePointRetriever

Retrieves content from Microsoft SharePoint and OneDrive via the Microsoft Search (Graph) API.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | At the start of a query pipeline, after an [`OAuthTokenResolver`](../connectors/oauthtokenresolver.mdx) that provides the `access_token` |
| **Mandatory init variables** | None |
| **Mandatory run variables** | `query`: The search query string  <br /> <br />`access_token`: A delegated Microsoft Graph bearer token, typically wired from an upstream `OAuthTokenResolver` |
| **Output variables** | `documents`: A list of [Documents](../../concepts/data-classes.mdx) holding the search snippets and resource metadata |
| **API reference** | [Microsoft SharePoint](/reference/integrations-microsoft-sharepoint) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/microsoft_sharepoint |
| **Package name** | `microsoft-sharepoint-haystack` |

</div>

## Overview

`MSSharePointRetriever` searches a user's Microsoft SharePoint and OneDrive content through the [Microsoft Search (Graph) API](https://learn.microsoft.com/en-us/graph/api/resources/search-api-overview). Given a query, it calls `POST /search/query` and maps each hit to a Haystack `Document` whose `content` is the search snippet and whose `meta` carries the resource metadata: `file_name`, `web_url`, `entity_type`, `created_date_time`, `last_modified_date_time`, `created_by`, `last_modified_by`, `mime_type`, and `file_extension`. It also stores the SharePoint identifiers a downstream fetcher needs to read list items and pages by ID (`site_id`, `list_id`, `list_item_id`, `list_item_unique_id`).

The retriever does **not** download or convert the underlying files – it only returns Search snippets and metadata. To download the full content of the hits, compose it with [`MSSharePointFetcher`](../fetchers/mssharepointfetcher.mdx) followed by a converter.

### Authentication

The retriever takes a per-user `access_token` as a run input. The token must carry **delegated** Microsoft Graph permissions (for example `Files.Read.All`, plus `Sites.Read.All` for site and list scoping); the Search API supports delegated permissions only. Typically you wire the token from an upstream [`OAuthTokenResolver`](../connectors/oauthtokenresolver.mdx), which emits a plain string. A `Secret` is also accepted and resolved internally.

### Scoping and filtering the search

You can narrow what is searched in several ways:

- `entity_types`: which Microsoft Search entity types to query. Defaults to `["driveItem", "listItem"]`, which covers files, folders, SharePoint pages and news, and list items. Other valid values are `"list"` and `"site"`.
- KQL operators embedded directly in the query, for example `filetype:docx`, `author:"Jane Doe"`, or `path:"https://contoso.sharepoint.com/sites/Team"`. See the [Keyword Query Language (KQL) syntax reference](https://learn.microsoft.com/en-us/sharepoint/dev/general-development/keyword-query-language-kql-syntax-reference).
- `query_template`: a reusable template such as `'{searchTerms} path:"https://contoso.sharepoint.com/sites/Team"'`, where the literal `{searchTerms}` placeholder is replaced by the run-time query.

### Installation

Install the Microsoft SharePoint integration with:

```shell
pip install microsoft-sharepoint-haystack
```

## Usage

### On its own

`access_token` below is a per-user delegated Microsoft Graph bearer token. In production you would obtain it from an [`OAuthTokenResolver`](../connectors/oauthtokenresolver.mdx) rather than pasting it in.

```python
from haystack_integrations.components.retrievers.microsoft_sharepoint import (
    MSSharePointRetriever,
)

retriever = MSSharePointRetriever(top_k=5)

result = retriever.run(
    query="quarterly roadmap",
    access_token="my-delegated-graph-token",
)

for doc in result["documents"]:
    print(doc.meta["file_name"], "-", doc.meta["web_url"])
```

### In a pipeline

The following pipeline obtains a token from an `OAuthTokenResolver` and feeds it into the retriever, so that running the pipeline requires only the query:

```python
from haystack import Pipeline
from haystack.utils import Secret
from haystack_integrations.components.connectors.oauth import OAuthTokenResolver
from haystack_integrations.utils.oauth import OAuthRefreshTokenSource
from haystack_integrations.components.retrievers.microsoft_sharepoint import (
    MSSharePointRetriever,
)

pipeline = Pipeline()
pipeline.add_component(
    "resolver",
    OAuthTokenResolver(
        token_source=OAuthRefreshTokenSource(
            token_url="https://login.microsoftonline.com/common/oauth2/v2.0/token",
            client_id="aaa-bbb-ccc",
            refresh_token=Secret.from_env_var("MS_REFRESH_TOKEN"),
            scopes=[
                "https://graph.microsoft.com/Files.Read.All",
                "https://graph.microsoft.com/Sites.Read.All",
                "offline_access",
            ],
        ),
    ),
)
pipeline.add_component("retriever", MSSharePointRetriever(top_k=5))
pipeline.connect("resolver.access_token", "retriever.access_token")

result = pipeline.run({"retriever": {"query": "quarterly roadmap"}})
documents = result["retriever"]["documents"]
```

To download and convert the full content of the retrieved hits, connect the retriever's `documents` output to a [`MSSharePointFetcher`](../fetchers/mssharepointfetcher.mdx). See that page for an end-to-end retrieve-fetch-convert example.