1. What is a Vector Database?

A Vector Database stores and retrieves data based on meaning, not exact match.
Instead of storing plain text, it stores vectors (embeddings) and finds which ones are closest to your query vector.

Think of it as: Google for meaning

• It doesn’t care about the exact words, just the semantic similarity

2. Why Not Use a Regular Database?

A traditional SQL database is great for:

• Exact lookups
• Structured queries

But it can’t natively search for “things that are similar” in high-dimensional space.

Example:

• SQL can find “car” = “car”
• Vector DB can find “car” ≈ “automobile” ≈ “sedan”

3. How Does It Work?

Workflow:

1. You create embeddings from your data (using an embedding model)
2. Store them as vectors in the vector database
3. When a user queries:
   • Create an embedding for the query
   • Database finds nearest vectors using similarity search
   • Return related content

Similarity Search Methods:

• Cosine Similarity (angle between vectors)
• Euclidean Distance (straight-line distance)
• Dot Product (magnitude-based match)

4. Popular Vector Databases

• Pinecone → Fully managed, scalable
• Weaviate → Open-source + cloud options
• Milvus → Large-scale similarity search
• FAISS (Facebook AI Similarity Search) → Local, super fast
• Qdrant → Rust-based, blazing performance

5. Where Do Vector Databases Fit in AI?

They are the memory layer for your AI system.
Example in a Retrieval-Augmented Generation (RAG) pipeline:

1. User Query → Create embedding
2. Vector DB → Retrieve top-k similar documents
3. LLM → Uses those docs to answer

This makes:

• Chatbots that remember
• AI search engines
• Context-aware assistants
• Recommendation systems

6. Key Questions

• Q: How do you measure similarity between embeddings?
  A: Cosine similarity, Euclidean distance, dot product.
• Q: Difference between FAISS and Pinecone?
  A: FAISS is local/open-source, Pinecone is managed and scalable.
• Q: Why use a Vector DB over relational DB?
  A: Handles high-dimensional similarity search efficiently.

1. What Are Embeddings?

Imagine you want AI to understand that “car” and “automobile” are similar in meaning. Computers don’t inherently understand words — they understand numbers.
Embeddings are how we convert words, sentences, or documents into numerical form, so AI can compare them mathematically.

An embedding is:

• A vector (a list of numbers)
• Each number represents a learned feature
• Similar meanings → similar vectors

Example:

cssCopyEditcar        → [0.12, -0.44, 0.88, ...]
automobile → [0.10, -0.47, 0.91, ...]

Their numbers are close → AI knows they’re related.

2. Why Do We Need Embeddings?

Without embeddings:

• AI would compare raw text → poor at finding meaning
  With embeddings:
• We can search by meaning, not exact words
• Example: Search “How to bake bread” → also finds “Steps for making loaf”

Uses:

• Semantic search
• Chatbots with memory
• Recommendation systems
• Clustering similar content
• Detecting spam or sentiment

3. How Are Embeddings Created?

Embeddings come from embedding models trained on huge datasets.
Popular ones:

• OpenAI text-embedding-ada-002
• BERT / Sentence-BERT
• Cohere embeddings
• Hugging Face models

The model:

1. Takes your text
2. Tokenizes it (breaks into words/pieces)
3. Maps tokens into a high-dimensional vector space (often 512–1536 dimensions)
4. Ensures semantically similar things are closer

4. How to Use Embeddings in Practice

Basic workflow:

1. Create embeddings for all your data
   (e.g., product descriptions, FAQs, documents)
2. Store them in a Vector Database (Pinecone, Weaviate, Milvus, FAISS)
3. When user asks a question:
   • Create embedding for the question
   • Find the nearest embeddings in your database
   • Use those as context for your LLM response

5. Key Concepts to Remember

• Dimensionality: How many numbers in the vector (higher = more detail)
• Cosine Similarity: Common way to measure “closeness” between vectors
• Context Window: Embeddings help you extend LLM memory by storing/retrieving past information