📘 Can a Document Database be used as a Vector Database?

👉 Short answer:
No, a Document DB is not a Vector DB by design.
But yes, you can extend or integrate a Document DB to act like one. Let me explain:

⚡ What’s the Difference?

🔹 Document Database (e.g., MongoDB, CouchDB, Firestore)

Stores semi-structured data (JSON-like documents).
Great for flexible schemas, nested objects, user profiles, and app data.
Optimized for queries on fields (e.g., name, age, category).

🔹 Vector Database (e.g., Pinecone, Weaviate, Milvus, FAISS)

Stores embeddings (numerical vector representations).
Optimized for similarity search using algorithms like cosine similarity, dot product, Euclidean distance.
Used for AI/ML workloads: semantic search, RAG (Retrieval-Augmented Generation), recommendations, etc.

🛠️ Using Document DB as Vector DB

Document DBs don’t natively support vector indexing & similarity search, but you can still store vectors inside documents.

Example in MongoDB:

{
  "id": "doc_1",
  "title": "AI in Healthcare",
  "content": "AI improves diagnosis accuracy",
  "embedding": [0.123, 0.456, 0.789, ...]   // vector representation
}

✅ You can store vectors inside the document.
❌ But searching for "nearest vectors" (like semantic search) is slow without special support.

🚀 Good News!

Some modern Document DBs are adding vector search capabilities:

MongoDB Atlas (latest versions) → supports Vector Search Indexes.
Amazon DocumentDB → experimenting with vector search integration.
Elasticsearch (though not purely document DB) → has strong vector search support.

🎯 When to Use What?

If your app is document-heavy with occasional vector search needs → Use MongoDB with vector search extension.
If your app is AI-driven (chatbots, semantic search, recommendations) → Use a dedicated Vector DB like Pinecone or Weaviate.
You can also hybrid → store documents in MongoDB & embeddings in Pinecone (common in RAG setups).

🌟 In summary:

A Document DB is not a Vector DB by nature.
You can store embeddings in it, but performance won’t match a true vector DB.
Some Document DBs (like MongoDB Atlas) now support vector search, bridging the gap.