How do I build a RAG chatbot over SharePoint and Google Drive?

To build a RAG chatbot over SharePoint + Google Drive, think of it as 5 parts:

1. Connect to the sources
2. Extract and clean documents
3. Chunk + embed + index
4. Retrieve relevant chunks at question time
5. Generate an answer with citations

Here’s a practical blueprint.

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1) Architecture

Ingestion path

• SharePoint connector → fetch files/pages
• Google Drive connector → fetch files/docs
• Text extraction/OCR → normalize content
• Chunking → split into searchable passages
• Embeddings → convert chunks to vectors
• Indexing → store in vector DB + metadata store

Query path

• User asks question
• Query is embedded
• Retrieve top-k relevant chunks from vector DB
• Optionally filter by permissions, source, date, file type
• LLM answers using retrieved context
• Return citations/links back to SharePoint/Drive

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2) Choose your stack

Common options

Ingestion / orchestration

• Python
• LangChain or LlamaIndex
• Airflow / Prefect / Celery for scheduled syncs

Connectors

• SharePoint / Microsoft Graph API
• Google Drive API

Parsing

• Unstructured
• Apache Tika
• Pandoc / pdfplumber / docx / python-pptx for specific formats

Vector DB

• Pinecone, Weaviate, Qdrant, Milvus, pgvector, Elasticsearch/OpenSearch vector search

LLM / embeddings

• OpenAI, Azure OpenAI, Anthropic, or open-source models
• Sentence-transformers or hosted embedding models

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3) Connect to SharePoint

Use Microsoft Graph API to access SharePoint content.

Typical steps:

• Register an Azure app
• Grant permissions like Sites.Read.All or least-privilege site-specific access
• Use OAuth2 client credentials or delegated auth
• Traverse:
  • Sites
  • Document libraries
  • Lists / pages if needed
• Download files and capture metadata:
  • site_id
  • library/folder path
  • file_id
  • last_modified
  • owner
  • permissions

Important:

• Respect tenant policies
• Handle incremental sync using delta queries where possible
• Cache auth tokens securely

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4) Connect to Google Drive

Use the Google Drive API.

Typical steps:

• Create a Google Cloud project
• Enable Drive API
• Use OAuth or service account with domain-wide delegation if needed
• Traverse folders/files
• Download native docs as exported text where appropriate:
  • Google Docs → text/HTML
  • Sheets → CSV/TSV or selected ranges
  • Slides → text export
  • PDFs/Office docs → raw file download
• Capture metadata:
  • file_id
  • mime_type
  • path/folder
  • modified_time
  • permissions

Important:

• Incremental sync via changes.list
• Use shared drive support if applicable

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5) Document extraction and normalization

You want a single internal text format.

For each file:

• Detect type
• Extract text
• Preserve headings, tables, bullets if possible
• Remove boilerplate
• Store metadata and source URL

Examples:

• PDF: pdfplumber, pymupdf, or OCR if scanned
• DOCX: python-docx
• PPTX: python-pptx
• HTML/Pages: parse as structured text
• Google Docs: export as plain text or HTML
• Images/scans: OCR via Tesseract, Azure Document Intelligence, AWS Textract, or Google Document AI

Good practice:

• Keep original file link
• Keep page number / slide number / section name if available
• Keep access control metadata

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6) Chunking strategy

Split text into retrievable units.

Recommended:

• Chunk by headings/paragraphs first
• Aim for ~300–800 tokens per chunk
• Use 10–20% overlap
• Attach metadata:
  • source = SharePoint / Google Drive
  • document title
  • file URL
  • page number / section
  • modified date
  • ACL / groups

For long docs:

• hierarchical chunking works well:
  • document summary
  • section chunks
  • paragraph chunks

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7) Embeddings + vector index

For each chunk:

• Generate embedding
• Store in vector DB with metadata

Index design:

• chunk_id
• doc_id
• text
• embedding
• metadata fields for filtering:
  • source system
  • tenant/site/folder
  • access groups
  • date
  • doc type

Optional:

• Keep a keyword index too for hybrid search
• Use BM25 + vector retrieval for better recall

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8) Permission-aware retrieval

This is critical for enterprise RAG.

At query time:

• Authenticate the user
• Determine user groups / identity
• Filter retrieval to chunks the user is allowed to see
• Never send unauthorized chunks to the LLM

Implementation options:

• Store ACL metadata per document/chunk
• Filter in vector DB if supported
• Or retrieve then post-filter before generation
• Best is pre-filtering + security trimming

If you skip this, you risk data leakage.

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9) Query flow

A solid query pipeline:

1. User asks: “What’s our expense policy for international travel?”
2. Embed query
3. Retrieve:
   • top 10–30 chunks
   • optionally hybrid search
   • filter by permissions
4. Re-rank results with a cross-encoder or LLM-based reranker
5. Build prompt with:
   • question
   • retrieved passages
   • instructions to cite sources and not hallucinate
6. LLM generates answer
7. Return answer + citations + links

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10) Prompting pattern

Use a constrained prompt like:

• Answer only from provided context
• If context is insufficient, say so
• Cite sources per claim
• Prefer recent policy docs when conflicts exist

Example instruction:

You are an enterprise assistant. Use only the provided context. If the answer is not in the context, say you don’t know. Include citations with document title and link.

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11) Sync and updates

You need ongoing ingestion:

• Schedule refresh every N minutes/hours
• Use incremental change APIs:
  • SharePoint delta queries
  • Google Drive changes feed
• Re-embed changed docs
• Delete removed docs/chunks
• Version documents if you need auditability

A common pattern:

• Store document hash
• Only reprocess if content changed
• Maintain tombstones for deleted files

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12) Evaluation

Test with real user questions:

• Recall: did retrieval find the right chunks?
• Precision: did it include irrelevant chunks?
• Faithfulness: is the answer grounded in retrieved text?
• Coverage: are the right sources indexed?
• Permission correctness: can users only see allowed content?

Useful metrics:

• top-k hit rate
• answer correctness
• citation accuracy
• latency
• ingestion freshness

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13) Minimal implementation outline

Ingestion pseudocode

for source in [sharepoint, google_drive]:
    docs = source.list_changed_documents(since=last_sync)
    for doc in docs:
        raw = source.download(doc)
        text = extract_text(raw)
        chunks = chunk_text(text)
        embeddings = embed(chunks)
        vector_db.upsert([
            {
                "id": chunk.id,
                "vector": embeddings[i],
                "text": chunk.text,
                "metadata": doc.metadata
            }
            for i, chunk in enumerate(chunks)
        ])

Query pseudocode

user = authenticate()
query_vec = embed([question])[0]
results = vector_db.search(
    query_vec,
    top_k=10,
    filter={"acl": {"$in": user.groups}}
)
answer = llm.generate(question, results)
return answer

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14) Recommended production architecture

If you want something robust:

• API: FastAPI
• Workers: Celery/RQ + Redis
• Connectors: Microsoft Graph + Google Drive APIs
• Parsing: Unstructured + OCR fallback
• Storage:
  • object store for originals
  • Postgres for metadata
  • Qdrant/Weaviate/Pinecone for vectors
• Auth: Azure AD / Google SSO / OIDC
• Observability: logs, tracing, prompt/answer audit
• Guardrails: permission checks, citation requirement, PII redaction if needed

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15) Practical shortcuts

If you want to move fast, consider:

• LlamaIndex has good connectors and RAG abstractions
• LangChain has many integrations
• Microsoft Graph connectors can simplify SharePoint access
• Google Drive loader utilities are available in both ecosystems

But still verify:

• permission handling
• incremental sync
• file-type extraction quality

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16) Common pitfalls

• Ignoring ACLs and leaking documents
• Chunking too large or too small
• Not preserving source metadata
• Treating scanned PDFs as text-only
• Using only vector search and missing keyword exact matches
• Failing to handle duplicates and file versions
• Not refreshing indexes after updates
• No evaluation set before launch

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17) Suggested MVP plan

Week 1

• Connect SharePoint and Drive
• Ingest a small set of docs
• Build extraction + chunking
• Store in vector DB

Week 2

• Build retrieval + chat endpoint
• Add citations
• Add basic ACL filtering

Week 3

• Incremental sync
• Hybrid search
• Evaluation with real questions

Week 4

• Improve reranking
• Add monitoring, admin tools, and audit logs

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If you want, I can also give you:

1. a reference architecture diagram,
2. a Python starter project, or
3. a specific implementation using LangChain/LlamaIndex + Pinecone/Qdrant + Microsoft Graph + Google Drive API.