The Pinecone Text Client is a Python package that provides text utilities designed for seamless integration with Pinecone's sparse-dense (hybrid) semantic search.
⚠️ WarningThis is a public preview ("Beta") version.
For any issues or requests, please reach out to our support team.
To install the Pinecone Text Client, use the following command:
pip install pinecone-textIf you wish to use SpladeEncoder, you will need to install the splade extra:
pip install pinecone-text[splade]If you wish to use SentenceTransformerEncoder dense encoder, you will need to install the dense extra:
pip install pinecone-text[dense]If you wish to use OpenAIEncoder dense encoder, you will need to install the openai extra:
pip install pinecone-text[openai]To convert your own text corpus to sparse vectors, you can either use BM25 or SPLADE.
To encode your documents and queries using BM25 as vector for dot product search, you can use the BM25Encoder class.
📝 NOTE:
Our current implementation of BM25 supports only static document frequency (meaning that the document frequency values are precomputed and fixed, and do not change dynamically based on new documents added to the collection).
When conducting a search, you may come across queries that contain terms not found in the training corpus but are present in the database. To address this scenario, BM25Encoder uses a default document frequency value of 1 when encoding such terms.
For an end-to-end example, you can refer to our Quora dataset generation with BM25 notebook.
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)
# Encode a new document (for upsert to Pinecone index)
doc_sparse_vector = bm25.encode_documents("The brown fox is quick")
# {"indices": [102, 18, 12, ...], "values": [0.22, 0.38, 0.15, ...]}
# Encode a query (for search in Pinecone index)
query_sparse_vector = bm25.encode_queries("Which fox is brown?")
# {"indices": [102, 16, 18, ...], "values": [0.22, 0.11, 0.15, ...]}
# store BM25 params as json
bm25.dump("bm25_params.json")
# load BM25 params from json
bm25.load("bm25_params.json")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.
from pinecone_text.sparse import BM25Encoder
bm25 = BM25Encoder.default()The BM25Encoder class offers configurable parameters to customize the encoding:
b: Controls document length normalization (default: 0.75).k1: Controls term frequency saturation (default: 1.2).- Tokenization Options: Allows customization of the tokenization process, including options for handling case, punctuation, stopwords, stemming, and language selection.
These parameters can be specified when initializing the BM25Encoder class. Please read the BM25Encoder documentation for more details.
Currently the SpladeEncoder class supprts only the naver/splade-cocondenser-ensembledistil model, and follows SPLADE V2 implementation.
📝 NOTE:
Currently pinecone text not supoorts SPLADE with python 3.12 due to compatibility issues with pytorch
For an end-to-end example, you can refer to our Quora dataset generation with SPLADE notebook.
Note: If cuda is available, the model will automatically run on GPU. You can explicitly override the device using the device parameter in the constructor.
from pinecone_text.sparse import SpladeEncoder
# Initialize Splade
splade = SpladeEncoder()
# encode a batch of documents
documents = ["The quick brown fox jumps over the lazy dog",
"The lazy dog is brown",
"The fox is brown"]
document_vectors = splade.encode_documents(documents)
# [{"indices": [102, 18, 12, ...], "values": [0.21, 0.38, 0.15, ...]}, ...]
# encode a query
query = "Which fox is brown?"
query_vectors = splade.encode_queries(query)
# {"indices": [102, 18, 12, ...], "values": [0.21, 0.38, 0.15, ...]}For dense embedding we also provide a thin wrapper for the following models:
- All Sentence Transformers models hosted on huggingface See full list of models
- All OpenAI API supported embedding models See full list of models
When using SentenceTransformerEncoder, the models are downloaded from huggingface and run locally. Also, if cuda is available, the model will automatically run on GPU. You can explicitly override the device using the device parameter in the constructor.
📝 NOTE:
Currently pinecone text not supoorts sentence transformers with python 3.12 due to compatibility issues with pytorch
from pinecone_text.dense import SentenceTransformerEncoder
encoder = SentenceTransformerEncoder("sentence-transformers/all-MiniLM-L6-v2")
encoder.encode_documents(["The quick brown fox jumps over the lazy dog"])
# [[0.21, 0.38, 0.15, ...]]
encoder.encode_queries(["Who jumped over the lazy dog?"])
# [[0.11, 0.43, 0.67, ...]]When using the OpenAIEncoder, you need to provide an API key for the OpenAI API, and store it in the OPENAI_API_KEY environment variable before you import the encoder.
By default the encoder will use text-embedding-3-small as recommended by OpenAI. You can also specify a different model name using the model_name parameter.
from pinecone_text.dense import OpenAIEncoder
encoder = OpenAIEncoder() # defaults to the recommended model - "text-embedding-3-small"
encoder.encode_documents(["The quick brown fox jumps over the lazy dog"])
# [[0.21, 0.38, 0.15, ...]]
encoder.encode_queries(["Who jumped over the lazy dog?"])
# [[0.11, 0.43, 0.67, ...]]Pinecone text also supports Azure OpenAI API. To use it, you need to import the AzureOpenAIEncoder class instead of OpenAIEncoder. You also need to pass Azure specific environment variables to the constructor, along with your specific embeddings deployment as the model name. For more information please follow the AzureOpenAIEncoder documentation.
When using the JinaEncoder, you need to provide an API key for the Jina Embeddings API, and store it in the JINA_API_KEY environment variable before you import the encoder.
By default the encoder will use jina-embeddings-v2-base-en. You can also specify a different model name using the model_name parameter.
from pinecone_text.dense import JinaEncoder
encoder = JinaEncoder()
encoder.encode_documents(["The quick brown fox jumps over the lazy dog"])
# [[-0.62586284, -0.54578537, 0.5570845, ...]]
encoder.encode_queries(["Who jumped over the lazy dog?"])
# [[-0.43374294, -0.42069837, 0.773763, ...]]To combine sparse and dense encodings for hybrid search, you can use the hybrid_convex_scale method on your query.
This method receives both a dense vector and a sparse vector, along with a convex scaling parameter alpha. It returns a tuple consisting of the scaled dense and sparse vectors according to the following formula: (alpha * dense_vector, (1 - alpha) * sparse_vector).
from pinecone_text.hybrid import hybrid_convex_scale
from pinecone_text.sparse import SpladeEncoder
from pinecone_text.dense import SentenceTransformerEncoder
# Initialize Splade
splade = SpladeEncoder()
# Initialize Sentence Transformer
sentence_transformer = SentenceTransformerEncoder("sentence-transformers/all-MiniLM-L6-v2")
# encode a query
sparse_vector = splade.encode_queries("Which fox is brown?")
dense_vector = sentence_transformer.encode_queries("Which fox is brown?")
# combine sparse and dense vectors
hybrid_dense, hybrid_sparse = hybrid_convex_scale(dense_vector, sparse_vector, alpha=0.8)
# ([-0.21, 0.38, 0.15, ...], {"indices": [102, 16, 18, ...], "values": [0.21, 0.11, 0.18, ...]})