{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Provides Python access to Voyage AI's embedding and reranking APIs for converting documents and queries into semantic vectors and scoring document relevance.","skillfed_tags":["embeddings","rag","semantic-search"],"use_cases":["Build semantic search systems where user queries are embedded and matched against document vectors in a vector store.","Implement RAG chatbots that retrieve relevant documents by embedding queries and reranking results for accuracy.","Rerank initial retrieval results from lexical search to refine document relevance before passing to a language model.","Convert multimodal data (documents, images, audio) into unified vector representations for cross-modal search.","Prototype embedding-based applications locally using voyage-4-nano without API keys."],"what_it_does":"Voyageai is a Python client for Voyage AI's embedding and reranking services. It converts unstructured data\u2014documents, queries, images, audio, video, or tabular data\u2014into dense numerical vectors that capture semantic meaning, or scores document relevance for reranking. These vectors are core building blocks for semantic search and retrieval-augmented generation (RAG) systems, where they integrate with vector stores and language models to improve retrieval accuracy.\n\nThe library supports both hosted API endpoints and a local voyage-4-nano model that runs without an API key on Python 3.10+. It handles input validation, token accounting, and batch limits transparently. The local path mirrors the hosted API's behavior, though some output data types are not yet supported locally. Runtime dependencies include requests, aiohttp, tenacity for retry logic, pydantic for validation, and langchain-text-splitters for text chunking.","worth_installing":"Yes. Voyageai is actively maintained, has low install friction, carries a permissive MIT license, and integrates cleanly into RAG and semantic search workflows. The optional local model is a genuine advantage for prototyping. No known security vulnerabilities. Install it if you need embeddings or reranking from Voyage AI; the API-only path has minimal overhead."},"id":"voyageai","links":{"html":"https://skillfed.io/packages/voyageai","md":"https://skillfed.io/packages/voyageai.md","pypi":"https://pypi.org/project/voyageai/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-10","license_spdx":null,"license_treatment":"permissive","name":"voyageai","python_support":"supports_current","summary":null},"popularity":{"monthly_downloads":3882848,"position":2462,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.5.0"}