vector search Google Drive Slack Confluence

If you want to build vector search over Google Drive, Slack, and Confluence, the usual pattern is:

High-level architecture

1. Connect sources

   • Google Drive API
   • Slack API
   • Confluence REST API

2. Extract content

   • Pull text from files/messages/pages
   • Keep metadata: source, author, timestamp, URL, permissions, workspace, channel, folder, etc.

3. Chunk the content

   • Split documents/messages into searchable chunks
   • Common sizes: 300–1,000 tokens with overlap

4. Create embeddings

   • Use an embedding model to convert each chunk into a vector

5. Store vectors in a vector database

   • Pinecone, Weaviate, Milvus, Qdrant, pgvector, OpenSearch, etc.

6. Search workflow

   • User query → embed query → vector similarity search
   • Optionally rerank results with keyword or semantic reranker
   • Enforce source permissions before returning results

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Important implementation details

Google Drive

• Support docs, PDFs, spreadsheets, slides
• Use OCR / text extraction for scanned files if needed
• Watch for folder-level permissions and shared drives

Slack

• Index channels, threads, and attachments
• Keep thread structure so results can show conversation context
• Respect private channel and DM access controls

Confluence

• Index pages, comments, and attachments
• Use page hierarchy and space metadata
• Include page versioning if you need freshness

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Best practices

• Hybrid search: combine vector + keyword/BM25 for better recall
• Reranking: improves result quality a lot
• ACL filtering: absolutely necessary for enterprise search
• Incremental sync: use webhooks/polling to update embeddings
• Deduplication: avoid indexing near-identical chunks
• Citations: return the exact source link and snippet

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Common stack choices

• Ingestion: Python + scheduled jobs / serverless workers
• Parsing: unstructured, Apache Tika, Google export APIs
• Embeddings: OpenAI, Cohere, Voyage, Sentence Transformers
• Vector DB: Qdrant or Pinecone for simplicity; pgvector if you already use Postgres
• Search API: FastAPI / Node.js

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Example search pipeline

User query
  → embed query
  → vector DB search top 20
  → keyword filter / reranker
  → ACL filter
  → return top 5 with snippets + source links

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If you want, I can also give you:

1. a reference architecture diagram,
2. a Python sample ingestion pipeline, or
3. a product comparison of vector DBs for this use case.