haystack-hybrid-embedding 0.1.0

Sparse-Dense Embeddings for Pinecone in Haystack

haystack-hybrid-embedding

Recently, Pinecone announced support for Sparse-dense embeddings, allowing for hybrid vector search (both semantic and keyword search).

haystack is a fantastic NLP framework that does not yet support hybrid vectors for Retrievers.

This little library helps temporarily bridge the gap!

Installation

$ pip install haystack-hybrid-embedding

Usage

from haystack_hybrid_embedding import SpladeEmbeddingEncoder
from haystack_hybrid_embedding.pinecone import PineconeHybridDocumentStore, SparseDenseRetriever

document_store = PineconeHybridDocumentStore(...)

retriever = SparseDenseRetriever(
  sparse_encoder=SpladeEmbeddingEncoder(),
  alpha=0.8,
  ...
)

Replacing EmbeddingRetriever

Simply replace your imports of PineconeDocumentStore and EmbeddingRetriever/MultihopEmbeddingRetriever.

1,2c1,3
< from haystack.document_stores.pinecone import PineconeDocumentStore
< from haystack.nodes import EmbeddingRetriever, MultihopEmbeddingRetriever
---
> from haystack_hybrid_embedding import SpladeEmbeddingEncoder
> from haystack_hybrid_embedding.pinecone import PineconeHybridDocumentStore, SparseDenseRetriever, SparseDenseMultihopRetriever
>
4c5
< document_store = PineconeDocumentStore(...)
---
> document_store = PineconeHybridDocumentStore(...)
6c7
< retriever = EmbeddingRetriever(
---
> retriever = SparseDenseRetriever(
7a9,10
>     sparse_encoder=SpladeEmbeddingEncoder(),
>     alpha=0.8,

Config

There are only two additional parameters exposed on SparseDenseRetriever over EmbeddingRetriever:

• sparse_encoder embeds both queries and documents into sparse vectors
• alpha controls the weighting between the sparse and dense vectors (0 is all sparse, and 1 is all dense)