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Hybrid search combines vector similarity search with keyword-based full-text search to provide more accurate and comprehensive results. This guide shows you how to implement hybrid search in Chroma. Hybrid search leverages two complementary approaches:
  1. Vector Search (KNN) - Finds semantically similar content using embedding vectors
  2. Full-Text Search (BM25) - Finds keyword matches using traditional information retrieval
Results are combined using Reciprocal Rank Fusion (RRF) to produce a unified ranking.

​
Setting Up

First, create a collection with hybrid search support: 
import chromadb
from chromadb.utils.embedding_functions import DefaultEmbeddingFunction
from chromadb.utils.embedding_functions import ChromaBM25EmbeddingFunction

client = chromadb.Client()

# Create embedding functions
vector_ef = DefaultEmbeddingFunction()
bm25_ef = ChromaBM25EmbeddingFunction()

collection = client.create_collection(
    name="hybrid_collection",
    embedding_function=vector_ef,
    metadata={"hnsw:space": "cosine"}
)

​
Adding Documents

collection.add(
    documents=[
        "The quick brown fox jumps over the lazy dog",
        "Machine learning is a subset of artificial intelligence",
        "Python is a popular programming language for data science",
        "Neural networks are inspired by biological neural networks",
        "Deep learning uses multiple layers of neural networks"
    ],
    ids=["doc1", "doc2", "doc3", "doc4", "doc5"]
)

from chromadb.search import Knn, Rrf

# Hybrid search: combines vector similarity and keyword matching
results = collection.search(
    search=[
        Knn(
            query_texts=["machine learning algorithms"],
            n_results=10
        ),
        Rrf(
            query_texts=["machine learning algorithms"]
        )
    ],
    n_results=5
)

for i, (id, doc, distance) in enumerate(zip(
    results['ids'][0],
    results['documents'][0],
    results['distances'][0]
)):
    print(f"{i+1}. [{id}] {doc} (score: {distance:.3f})")

​
Understanding the Components

​
KNN (K-Nearest Neighbors)

Vector similarity search finds semantically similar content: 
from chromadb.search import Knn

# Pure vector search
results = collection.search(
    search=[Knn(
        query_texts=["artificial intelligence"],
        n_results=5
    )]
)
KNN is excellent for:
  • Finding semantically related content
  • Handling synonyms and paraphrases
  • Cross-lingual search (with multilingual models)

​
BM25 (Full-Text Search)

Keyword-based search using the BM25 algorithm: 
# Full-text search with BM25
results = collection.search(
    search=[Rrf(
        query_texts=["Python programming"],
        k=60  # RRF parameter
    )]
)
BM25 excels at:
  • Exact keyword matching
  • Technical terms and jargon
  • Proper nouns and specific phrases

​
RRF (Reciprocal Rank Fusion)

Combines multiple search results: 
# Hybrid search with custom RRF parameter
results = collection.search(
    search=[
        Knn(query_texts=["query"], n_results=20),
        Rrf(query_texts=["query"], k=60)
    ],
    n_results=10
)
The k parameter controls the fusion:
  • Lower k (e.g., 20): Emphasizes top-ranked results
  • Higher k (e.g., 100): Gives more weight to lower-ranked results
  • Default: k=60 (balanced)

​
With Metadata Filtering

# Add documents with metadata
collection.add(
    documents=[
        "Introduction to machine learning",
        "Advanced deep learning techniques",
        "Python for beginners"
    ],
    metadatas=[
        {"category": "ml", "level": "beginner"},
        {"category": "ml", "level": "advanced"},
        {"category": "programming", "level": "beginner"}
    ],
    ids=["doc1", "doc2", "doc3"]
)

# Hybrid search with filters
results = collection.search(
    search=[
        Knn(
            query_texts=["learning Python"],
            n_results=10,
            where={"category": "programming"}
        ),
        Rrf(
            query_texts=["learning Python"],
            where={"category": "programming"}
        )
    ],
    n_results=5
)
Search with multiple queries: 
# Search multiple queries simultaneously
queries = [
    "machine learning algorithms",
    "deep learning models"
]

results = collection.search(
    search=[
        Knn(query_texts=queries, n_results=10),
        Rrf(query_texts=queries)
    ],
    n_results=5
)

# Results are returned per query
for query_idx, query in enumerate(queries):
    print(f"\nResults for: {query}")
    for doc in results['documents'][query_idx]:
        print(f"  - {doc}")

​
Custom Scoring

Adjust the balance between vector and keyword search: 
# Emphasize vector search (more results from KNN)
results = collection.search(
    search=[
        Knn(query_texts=["query"], n_results=30),  # More KNN results
        Rrf(query_texts=["query"], k=100)  # Less weight to BM25
    ],
    n_results=10
)

# Emphasize keyword search (more results from BM25)
results = collection.search(
    search=[
        Knn(query_texts=["query"], n_results=10),  # Fewer KNN results
        Rrf(query_texts=["query"], k=20)  # More weight to BM25
    ],
    n_results=10
)

​
Using BM25 Embedding Function

Chroma provides a built-in BM25 embedding function for full-text search: 
from chromadb.utils.embedding_functions import ChromaBM25EmbeddingFunction

# Create BM25 embedding function
bm25_ef = ChromaBM25EmbeddingFunction()

# Create collection with BM25
bm25_collection = client.create_collection(
    name="bm25_collection",
    embedding_function=bm25_ef
)

# Add documents
bm25_collection.add(
    documents=[
        "Document about Python programming",
        "Tutorial on machine learning",
        "Guide to data science"
    ],
    ids=["1", "2", "3"]
)

# Search with BM25
results = bm25_collection.query(
    query_texts=["Python data science"],
    n_results=3
)

​
Real-World Use Cases

# Combine semantic understanding with exact matches
results = collection.search(
    search=[
        Knn(
            query_texts=["comfortable running shoes"],
            n_results=20,
            where={"in_stock": True}
        ),
        Rrf(
            query_texts=["comfortable running shoes"],
            where={"in_stock": True}
        )
    ],
    n_results=10
)
Benefits:
  • Finds products by features (semantic)
  • Matches exact brand/model names (keyword)
  • Ranks by relevance (hybrid)

​
Document Retrieval

# Find relevant documents with specific terms
results = collection.search(
    search=[
        Knn(
            query_texts=["quarterly financial performance"],
            n_results=20,
            where={"year": 2024}
        ),
        Rrf(
            query_texts=["quarterly financial performance"],
            where={"year": 2024},
            k=40  # Emphasize keyword matching for exact terms
        )
    ],
    n_results=5
)

​
Question Answering

# Find answers to user questions
def search_for_answer(question: str):
    results = collection.search(
        search=[
            Knn(
                query_texts=[question],
                n_results=15,
                where={"type": "answer"}
            ),
            Rrf(
                query_texts=[question],
                where={"type": "answer"}
            )
        ],
        n_results=3
    )
    return results['documents'][0][0]

answer = search_for_answer("How do I install Python?")
print(answer)

​
Performance Optimization

​
Tuning Parameters

# For better precision (more relevant, fewer results)
results = collection.search(
    search=[
        Knn(query_texts=["query"], n_results=10),
        Rrf(query_texts=["query"], k=30)
    ],
    n_results=5
)

# For better recall (more comprehensive results)
results = collection.search(
    search=[
        Knn(query_texts=["query"], n_results=50),
        Rrf(query_texts=["query"], k=100)
    ],
    n_results=20
)

​
Caching

from functools import lru_cache

@lru_cache(maxsize=100)
def cached_hybrid_search(query: str):
    return collection.search(
        search=[
            Knn(query_texts=[query], n_results=10),
            Rrf(query_texts=[query])
        ],
        n_results=5
    )

​
Batch Processing

# Search multiple queries efficiently
queries = ["query1", "query2", "query3"]

results = collection.search(
    search=[
        Knn(query_texts=queries, n_results=10),
        Rrf(query_texts=queries)
    ],
    n_results=5
)

​
Comparison: Hybrid vs. Single Method

✅ Use hybrid search when:
  • You need both semantic and exact matching
  • Queries contain technical terms or proper nouns
  • You want robust results across query types
  • Handling diverse user queries

​
When to Use Vector Search Only

✅ Use vector search when:
  • Semantic similarity is more important than keywords
  • Working with multilingual content
  • Queries are questions or natural language
  • Handling paraphrases and synonyms

​
When to Use Keyword Search Only

✅ Use keyword search when:
  • Exact term matching is critical
  • Searching for codes, IDs, or specific phrases
  • Working with structured data
  • Speed is the priority

​
Troubleshooting

​
Poor Hybrid Results

# Adjust the balance
# Too much weight on keywords? Increase KNN results
results = collection.search(
    search=[
        Knn(query_texts=["query"], n_results=30),  # More vector results
        Rrf(query_texts=["query"], k=80)  # Reduce keyword weight
    ],
    n_results=10
)

​
No BM25 Results

Ensure collection has enough documents and diverse vocabulary: 
# Check collection size
count = collection.count()
if count < 10:
    print("Collection too small for effective BM25")

​
Slow Hybrid Queries

# Reduce search space
results = collection.search(
    search=[
        Knn(query_texts=["query"], n_results=5),  # Fewer results
        Rrf(query_texts=["query"])
    ],
    n_results=3,
    where={"recent": True}  # Pre-filter with metadata
)

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