Hybrid Search in Pinecone and pgvector
Configure hybrid search in Pinecone using sparse-dense index mode and in pgvector using parallel queries with RRF merging, benchmarking retrieval quality on your dataset.
Native Hybrid Search in Vector Databases
While you can implement hybrid search yourself using two separate indexes and RRF fusion, production vector databases increasingly offer built-in hybrid search that handles sparse and dense retrieval in a single query. Pinecone and pgvector both support hybrid modes, but with different architecture choices and trade-offs. Understanding both options lets you pick the right tool for your infrastructure.
Pinecone Sparse-Dense Index Mode
Pinecone supports a sparse-dense index where each vector record stores both a dense embedding (as a float array) and a sparse vector (as a dictionary of token ID to weight). Queries can specify both a dense query vector and a sparse query vector, and Pinecone merges results using a weighted linear combination. This is different from RRF — Pinecone uses raw score fusion with configurable weights called alpha.
from pinecone import Pinecone, ServerlessSpec
pc = Pinecone(api_key='YOUR_API_KEY')
# Create a sparse-dense index
pc.create_index(
name='hybrid-index',
dimension=1536, # dense vector dimension
metric='dotproduct', # must use dotproduct for hybrid
spec=ServerlessSpec(cloud='aws', region='us-east-1'),
)
index = pc.Index('hybrid-index')All lessons in this course
- Dense vs Sparse Retrieval: Trade-offs
- Implementing BM25 Keyword Search
- Reciprocal Rank Fusion for Score Merging
- Hybrid Search in Pinecone and pgvector