How do I set up retrieval for a chatbot over SharePoint and Google Drive?

To set up retrieval for a chatbot over SharePoint and Google Drive, you typically build a RAG pipeline (Retrieval-Augmented Generation):

1. Connect to each data source
2. Extract documents and metadata
3. Chunk the content
4. Embed the chunks
5. Store them in a vector database
6. At query time, retrieve relevant chunks
7. Feed them to the LLM to answer the user

High-level architecture

SharePoint / Google Drive → ingestion pipeline → chunking → embeddings → vector store → retriever → chatbot LLM

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1) Connect to SharePoint and Google Drive

SharePoint

Common options:

• Microsoft Graph API for SharePoint documents
• A connector tool/framework if available in your stack
• Scheduled sync using service account / app registration

What you’ll need:

• Azure app registration
• OAuth credentials
• Permissions to read sites/files
• Access to document libraries

Google Drive

Common options:

• Google Drive API
• Service account or OAuth
• Read access to the relevant folders/shared drives

What you’ll need:

• Google Cloud project
• Enable Drive API
• OAuth client or service account
• Folder/file permissions

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2) Extract documents

Pull file contents and metadata such as:

• filename
• source system
• URL
• last modified time
• owner
• permissions/groups
• folder/site path

Supported formats usually include:

• PDF
• DOCX
• PPTX
• TXT
• HTML
• sometimes spreadsheets

For Office docs and PDFs, use parsers/loaders that preserve text reasonably well.

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3) Chunk the content

Split documents into smaller pieces before embedding.

Good practices:

• Chunk by headings/sections if possible
• Use overlap to preserve context
• Keep chunks roughly 300–1,000 tokens depending on doc type and model
• Attach metadata to every chunk

Example chunk metadata:

{
  "source": "sharepoint",
  "title": "HR Policy",
  "url": "https://...",
  "path": "/Policies/HR/",
  "last_modified": "2026-01-10T12:00:00Z"
}

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4) Create embeddings

Convert each chunk into a vector using an embedding model.

Popular choices:

• OpenAI embeddings
• Azure OpenAI embeddings
• Cohere
• sentence-transformers
• other hosted embedding services

You store:

• chunk text
• embedding vector
• metadata

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5) Store in a vector database

Use a vector store that supports:

• similarity search
• metadata filtering
• incremental updates
• deletion by source document

Examples:

• Pinecone
• Weaviate
• Qdrant
• Milvus
• Elasticsearch / OpenSearch vector search
• FAISS for local/simple setups

For enterprise use, metadata filters are very helpful, especially for:

• source = SharePoint or Drive
• permissions
• department
• date ranges
• document type

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6) Build retrieval at query time

When the user asks a question:

1. Embed the user query
2. Search vector DB for top-k similar chunks
3. Optionally re-rank results
4. Apply metadata/permission filters
5. Send retrieved context to the LLM
6. Generate answer with citations

You’ll usually want:

• Top-k retrieval: 3–10 chunks
• Re-ranking: to improve precision
• Hybrid search: combine semantic + keyword search for better results
• Citations: return source links for trust

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7) Handle permissions properly

This is critical for SharePoint/Drive.

You should enforce:

• document-level ACLs
• user-group mapping
• filtering at retrieval time

Two common patterns:

A. Index ACL metadata

Store allowed users/groups per chunk and filter search results by the current user.

B. Separate indexes by security domain

For example, one index per team or department.

If you skip this, your chatbot may expose restricted content.

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8) Keep the index fresh

Set up incremental updates:

• poll for file changes
• use webhooks where possible
• re-embed changed docs
• delete removed docs

Typical sync strategy:

• nightly full sync
• frequent incremental sync every 5–15 minutes

Track:

• document ID
• modified timestamp
• hash/version

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9) Recommended implementation stack

A common stack could be:

• Ingestion: Python
• SharePoint: Microsoft Graph API
• Google Drive: Google Drive API
• Parsing: unstructured, PyPDF, python-docx, python-pptx
• Chunking/Orchestration: LangChain or LlamaIndex
• Embeddings: OpenAI / Azure OpenAI / sentence-transformers
• Vector DB: Qdrant / Pinecone / Weaviate
• Chatbot LLM: GPT-style model with retrieved context

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10) Example retrieval flow

User asks:

“What is our PTO policy for new hires?”

System:

1. Search index for chunks from SharePoint and Drive
2. Retrieve relevant policy sections
3. Filter by user access
4. Send chunks to LLM
5. LLM answers and cites the policy document URL

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11) Tools that can speed this up

Depending on your needs:

• LlamaIndex: very good for document ingestion and retrieval pipelines
• LangChain: flexible orchestration for retrieval + chat
• Microsoft Graph connectors: for SharePoint
• Google Drive API: for Drive ingestion

If you want less custom code, LlamaIndex often makes multi-source retrieval easier.

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12) Practical tips

• Normalize text extraction quality before embedding
• Deduplicate repeated content
• Use semantic chunking for long policies/manuals
• Add citations in every answer
• Log retrieved chunks for debugging
• Evaluate retrieval separately from generation
• Consider hybrid search if users ask exact phrases, acronyms, or document names

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Simple architecture example

SharePoint ----\
                --> Ingestion --> Chunking --> Embeddings --> Vector DB --> Retriever --> LLM
Google Drive --/

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If you want a more concrete setup

I can give you one of these next:

1. Python example using LlamaIndex
2. Python example using LangChain
3. Architecture for Azure-only stack
4. Permission-aware retrieval design
5. End-to-end example with SharePoint + Google Drive connectors

If you want, I can show you a working reference implementation next.