BM25 vs Vector Search 

BM25 and Vector Search are two different approaches used in information retrieval systems, especially in search engines and RAG (Retrieval-Augmented Generation) systems.

1. BM25 (Keyword-Based Search)

BM25 is a traditional lexical search algorithm used by search engines like Elasticsearch and Apache Solr.

It works mainly by:

• Matching exact keywords

• Calculating relevance using:

  • Term frequency (TF)

  • Inverse document frequency (IDF)

  • Document length normalization

Example

Query:

"best laptop for coding"

BM25 searches for documents containing:

• "best"

• "laptop"

• "coding"

Documents with more matching keywords rank higher.

2. Vector Search (Semantic Search)

Vector search is a semantic retrieval method used in modern AI systems and RAG pipelines.

Text is converted into embeddings (vectors) using embedding models from companies like OpenAI or frameworks like Hugging Face.

Instead of exact keyword matching, it retrieves documents based on meaning.

Example

Query:

"best laptop for coding"

Vector search may retrieve:

• "Top notebooks for software developers"

• "Best computers for programming"

even if the exact words "laptop" or "coding" are absent.

3. Core Difference

Feature	BM25	Vector Search
Search Type	Keyword-based	Semantic/meaning-based
Matching	Exact words	Similar meaning
Works on	Tokens/terms	Embeddings/vectors
Handles synonyms	Poorly	Very well
Speed	Faster	Usually slower
Explainability	Easy	Harder
Storage	Inverted index	Vector index
Best for	Exact search	Contextual understanding

4. How BM25 Works

BM25 scoring is based on:

• How often a word appears

• How rare the word is across documents

• Document length

Common words like “the” get low importance.

Rare words get higher importance.

The relevance formula is:

\text{BM25}(D,Q)=\sum_{i=1}^{n} IDF(q_i)\cdot\frac{f(q_i,D)(k_1+1)}{f(q_i,D)+k_1\left(1-b+b\cdot\frac{|D|}{avgdl}\right)}

You do not need to memorize the entire formula in interviews, but you should know:

• TF

• IDF

• Length normalization

5. How Vector Search Works

Steps:

1. Convert text into embeddings

2. Store vectors in a vector database

3. Convert query into vector

4. Find nearest vectors using similarity metrics

Common similarity metrics:

• Cosine similarity

• Euclidean distance

• Dot product

Example cosine similarity:

\cos(\theta)=\frac{A\cdot B}{|A||B|}

Popular vector databases:

• Pinecone

• Weaviate

• Milvus

• MongoDB Atlas Vector Search

6. Example Comparison

Query

"car repair shop"

BM25 may miss:

• "automobile mechanic service"

because keywords differ.

Vector search can retrieve it

because meanings are semantically similar.

7. Advantages of BM25

Pros

• Fast

• Cheap

• Easy to implement

• Highly explainable

• Great for exact keyword matching

Cons

• Cannot understand meaning

• Weak with synonyms/paraphrasing

• Poor contextual understanding

8. Advantages of Vector Search

Pros

• Understands semantics

• Better for natural language queries

• Excellent for RAG systems

• Handles paraphrases and synonyms

Cons

• More computationally expensive

• Requires embedding models

• Harder to explain rankings

• May retrieve semantically similar but irrelevant results

9. Hybrid Search (Very Important for Interviews)

Modern RAG systems often combine:

• BM25

• Vector search

This is called Hybrid Search.

Why?

• BM25 gives precise keyword matching

• Vector search gives semantic understanding

Together they improve retrieval quality.

Used heavily in:

• Enterprise search

• AI chatbots

• Document retrieval systems

10. Interview Answer (Short Version)

If interviewer asks:

“What is the difference between BM25 and vector search?”

You can answer:

“BM25 is a traditional keyword-based retrieval algorithm that ranks documents using term frequency and inverse document frequency. It works well for exact keyword matching but lacks semantic understanding. Vector search converts text into embeddings and retrieves documents based on semantic similarity, making it better for understanding meaning and paraphrases. Modern RAG systems often combine both using hybrid search.”

11. Common Interview Follow-Up Questions

Q1. Which is faster?

BM25 is generally faster and cheaper.

Q2. Which is better for RAG?

Vector search or hybrid search.

Q3. Why combine both?

To get:

• Exact keyword precision

• Semantic understanding

Q4. What databases support vector search?

Examples:

• Pinecone

• Weaviate

• Milvus

• Elastic

• MongoDB

12. Easy Way to Remember

• BM25 → words

• Vector Search → meaning

or

• BM25 = lexical search

• Vector search = semantic search