This page shows you how to use the Pinecone Text Client to encode sparse vectors for use in hybrid search.

To convert your own text corpus to sparse vectors, you can either use BM25 or SPLADE. This guide uses BM25, which is more common.

This feature is in public preview.

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Install the Pinecone Text Client

The Pinecone Text Client is a public Python package that provides text utilities designed for seamless integration with Pinecone’s sparse-dense (hybrid) search.

To install the Pinecone Text Client, use the following command:

Shell
pip install pinecone-text

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Initialize the encoder and fit the corpus

Before encoding sparse vectors for use in hybrid search, you must initialize the BM25 encoder and fit it to your corpus of documents.

Example

The following example initializes a BM25Encoder object and calls the fit() function on the corpus, formatted as an array of strings.

Python
from pinecone_text.sparse import BM25Encoder

corpus = ["The quick brown fox jumps over the lazy dog",
          "The lazy dog is brown",
          "The fox is brown"]

# Initialize BM25 and fit the corpus.
bm25 = BM25Encoder()
bm25.fit(corpus)

If you want to use the default parameters for BM25Encoder, you can call the default method. The default parameters were fitted on the MS MARCO passage ranking dataset.

Example

Python
bm25 = BM25Encoder.default()

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Encode documents for Pinecone

After the encoder is initialized and fit, you can encode documents as sparse vectors.

Example

The following example encodes a new document as a sparse vector for upsert to a Pinecone index.

Python
doc_sparse_vector = bm25.encode_documents("The brown fox is quick")

Now, the contents of doc_sparse_vector are like the following:

JSON
{"indices": [102, 18, 12, ...], "values": [0.21, 0.38, 0.15, ...]}

You can encode a string as a sparse vector for use in a hybrid search query in the same way.

Example

The following example encodes a string as a sparse vector for use in a hybrid search query.

Python
query_sparse_vector = bm25.encode_queries("Which fox is brown?")

Now, the contents of query_sparse_vector are like the following:

JSON
{"indices": [102, 16, 18, ...], "values": [0.21, 0.11, 0.15, ...]}

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See also

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Understanding hybrid searchUpsert sparse-dense vectors