{"categories":[{"label":"Text Processing","url":"https://skillfed.io/packages/category/text-processing/2"}],"enrichment":{"capability":"Provides sparse and dense text encoders for converting documents and queries into vectors compatible with Pinecone's hybrid search, supporting BM25, SPLADE, Sentence Transformers, and OpenAI embeddings.","skillfed_tags":["vector-embeddings","semantic-search","pinecone-integration"],"use_cases":["Prepare a corpus of documents for BM25-based sparse vector indexing in Pinecone without implementing tokenization and IDF calculation yourself.","Encode queries and documents using SPLADE for learned sparse retrieval when BM25 alone is insufficient.","Generate dense embeddings via OpenAI's API and store them in Pinecone for semantic search without managing API calls directly.","Combine BM25 sparse vectors with Sentence Transformer dense vectors for hybrid search in a single pipeline.","Load and reuse precomputed BM25 parameters (fitted on MS MARCO) to encode new documents without retraining."],"what_it_does":"Pinecone Text is a utility library that bridges text data and Pinecone's vector search engine by providing encoders that convert documents and queries into sparse or dense vectors. It wraps multiple encoding strategies\u2014BM25 for traditional sparse vectors, SPLADE for learned sparse representations, and integrations with Sentence Transformers and OpenAI's embedding models for dense vectors\u2014allowing developers to prepare text for hybrid search without writing encoding logic themselves.\n\nThe package is designed for use with Pinecone's hybrid search, which combines sparse and dense vectors for improved retrieval. It handles tokenization, model loading, and vector formatting, but requires explicit installation of optional dependencies for SPLADE, Sentence Transformers, or OpenAI support. BM25 is available by default and can be initialized with precomputed parameters or fitted to a custom corpus; SPLADE uses a fixed HuggingFace model; dense encoders delegate to external services or local models.","worth_installing":"Yes, with conditions. The package is useful for developers building hybrid search on Pinecone and want to avoid writing encoding logic, but maintenance is aging (368 days since last release) and license terms are unclear. Install if you are committed to Pinecone's platform and can work within Python 3.9\u20133.11 (avoid 3.12 for SPLADE and Sentence Transformers). Verify the license before use in proprietary contexts. No known security vulnerabilities."},"id":"pinecone-text","links":{"html":"https://skillfed.io/packages/pinecone-text","md":"https://skillfed.io/packages/pinecone-text.md","pypi":"https://pypi.org/project/pinecone-text/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-08-11","license_spdx":null,"license_treatment":"unclear","name":"pinecone-text","python_support":"supports_current","summary":"Text utilities library by Pinecone.io"},"popularity":{"monthly_downloads":514004,"position":6242,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.11.0"}