Gensim
Gensim is a Python library for topic modeling and vector space modeling, providing tools to generate high-dimensional vector embeddings from text data. These embeddings can be stored and efficiently searched in vector databases, making Gensim directly relevant to vector search use cases.
About this tool
Gensim
Gensim is an open-source Python library for topic modeling and vector space modeling, widely used for generating high-dimensional vector embeddings from text data. These embeddings can be used for efficient vector search and semantic analysis.
Features
- Large-scale semantic NLP model training: Efficiently trains models for semantic analysis and topic modeling.
- Text representation as semantic vectors: Converts text into high-dimensional vector embeddings suitable for vector search and similarity tasks.
- Semantic similarity search: Finds semantically related documents based on vector representations.
- Fast and optimized: Core algorithms use highly optimized and parallelized C routines for speed.
- Data streaming: Capable of processing arbitrarily large corpora with data-streamed algorithms (no requirement for data to fit in RAM).
- Cross-platform: Runs on Linux, Windows, Mac OS X, and other platforms supporting Python and NumPy.
- Pretrained models: Access to ready-to-use pretrained models for specific domains (e.g., legal, health) via the Gensim-data project.
- Open-source: Source code available under the GNU LGPL license and maintained by the open source community.
- Easy installation: Available via pip and conda.
- Continuous integration: Automatically tested across multiple platforms and environments.
Category
- SDKs & Libraries
Tags
- python
- vector-embeddings
- open-source
- topic-modeling
Pricing
Gensim is free and open-source software, released under the GNU LGPL license. No pricing plans are required for usage.
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Information
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