Assignment: Hybrid Search#

Assignment Metadata#

Field	Description
Assignment Name	Hybrid Search with BM25 and Reciprocal Rank Fusion
Course	RAG and Optimization
Project Name	`hybrid-search-rag`
Estimated Time	90 minutes
Framework	Python 3.10+, LangChain, rank-bm25, Sentence-Transformers, ChromaDB

Learning Objectives#

By completing this assignment, you will be able to:

Implement BM25 keyword search alongside vector-based semantic search
Apply Reciprocal Rank Fusion (RRF) to merge results from multiple retrievers
Compare the effectiveness of Vector Search, BM25, and Hybrid Search
Configure the fusion parameters to optimize retrieval quality
Analyze scenarios where Hybrid Search outperforms single-method approaches

Problem Description#

Your RAG system currently relies solely on Vector Search for retrieval. While this works well for semantic queries, users report poor results when searching for:

Specific error codes (e.g., “Error 503 Service Unavailable”)
Product SKUs and model numbers
Technical terms and acronyms
Proper names and exact phrases

Your task is to implement a Hybrid Search system that combines BM25 keyword matching with Vector Search, using RRF to merge the results.

Technical Requirements#

Environment Setup#

Python 3.10 or higher
Required packages:
- langchain >= 0.1.0
- rank-bm25 >= 0.2.2
- sentence-transformers >= 2.2.0
- chromadb >= 0.4.0
- nltk >= 3.8.0 (for tokenization)

Dataset#

Prepare a dataset that includes documents with:

Technical specifications with codes/numbers
Natural language descriptions
Mixed content (code snippets, prose, tables)
At least 100 documents for meaningful comparison

Tasks#

Task 1: Implement BM25 Retriever (25 points)#

Build a BM25 retriever that:
- Tokenizes documents properly (handle punctuation, case normalization)
- Indexes all documents in your corpus
- Returns top-K documents with BM25 scores
Test with keyword-heavy queries:
- Create at least 5 queries containing specific codes, numbers, or technical terms
- Verify that BM25 correctly retrieves documents with exact keyword matches

Task 2: Implement Hybrid Search with RRF (35 points)#

Create a Hybrid Retriever that:
- Executes both BM25 and Vector Search in parallel
- Implements RRF score calculation: RRF(d) = Σ 1/(k + rank(d))
- Uses configurable k constant (default: 60)
- Returns merged and re-ranked results
Handle edge cases:
- Documents appearing in only one result list
- Ties in RRF scores
- Empty results from one retriever

Task 3: Comparative Evaluation (40 points)#

Create a test set with 20 queries categorized as:
- Keyword queries (5): Exact matches, codes, identifiers
- Semantic queries (5): Conceptual questions, synonyms
- Hybrid queries (10): Mix of keywords and semantic intent
Evaluate each retrieval method (Vector, BM25, Hybrid):
- Precision@5: Proportion of relevant documents in top 5
- Recall@10: Proportion of all relevant documents retrieved in top 10
- Mean Reciprocal Rank (MRR): Average of 1/rank of first relevant result
Create a comparison table showing:

Query Type	Method	Precision@5	Recall@10	MRR
Keyword	Vector
Keyword	BM25
Keyword	Hybrid
Semantic	Vector
Semantic	BM25
Semantic	Hybrid
Hybrid	Vector
Hybrid	BM25
Hybrid	Hybrid

Evaluation Criteria#

Criteria	Points
BM25 implementation correctness	15
Tokenization and preprocessing	10
RRF implementation accuracy	25
Hybrid retriever edge case handling	10
Evaluation methodology	15
Comparative analysis quality	15
Code quality and documentation	10
Total	100

Hints#

The rank-bm25 library provides easy BM25 implementation
Use nltk.word_tokenize() for consistent tokenization
Test RRF with small examples first to verify your formula
Consider using the companion notebook 02-hybrid-search-rag.ipynb as reference
For the evaluation, manually label at least the top 10 results per query as relevant/not relevant