--- title: "vec2text" type: "tool" slug: "vec2text-vec2text" canonical_url: "https://www.graphcanon.com/tools/vec2text-vec2text" github_url: "https://github.com/vec2text/vec2text" homepage_url: null stars: 1127 forks: 117 primary_language: "Python" license: "Other" archived: false categories: ["llm-frameworks", "vector-databases", "model-training"] tags: ["python"] updated_at: "2026-07-11T23:06:34.554735+00:00" --- # vec2text > utilities for decoding deep representations (like sentence embeddings) back to text utilities for decoding deep representations (like sentence embeddings) back to text ## Facts - Repository: https://github.com/vec2text/vec2text - Stars: 1,127 · Forks: 117 · Open issues: 27 · Watchers: 13 - Primary language: Python - License: Other - Last pushed: 2025-12-27T17:22:44+00:00 ## Trust & health _Signals computed from public GitHub metadata. Not a security guarantee._ - Maintenance: Slowing (computed 2026-07-11T23:06:24.241Z) - Security scan: No findings reported (0 critical, 0 high, 0 medium, 0 low) · last scan 2026-07-11T23:06:24.805Z - Full report: [trust report](/tools/vec2text-vec2text/trust.md) · [JSON](https://www.graphcanon.com/api/graphcanon/tools/vec2text-vec2text/trust) ## Categories - [LLM Frameworks](/categories/llm-frameworks.md) - [Vector Databases](/categories/vector-databases.md) - [Model Training](/categories/model-training.md) ## Tags python ## Category neighbours (exploratory) _Same-category tools for discovery only - not curated alternatives. 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To get started, install this on PyPI: ```bash pip install vec2text ``` [Link to Colab Demo](https://colab.research.google.com/drive/14RQFRF2It2Kb8gG3_YDhP_6qE0780L8h?usp=sharing) ### Development If you're training a model you'll need to set up nltk: ```python import nltk nltk.download('punkt') ``` Before pushing any code, please run precommit: ```bash pre-commit run --all ``` ## Usage The library can be used to embed text and then invert it, or invert directly from embeddings. First you'll need to construct a `Corrector` object which wraps the necessary models, embedders, and tokenizers: ### Load a model via `load_pretrained_corrector` ```python corrector = vec2text.load_pretrained_corrector("text-embedding-ada-002") ``` ### Load a model via `load_corrector` If you have trained you own custom models using vec2text, you can load them in using the `load_corrector` function. ```python inversion_model = vec2text.models.InversionModel.from_pretrained("jxm/gtr__nq__32") corrector_model = vec2text.models.CorrectorEncoderModel.from_pretrained("jxm/gtr__nq__32__correct") corrector = vec2text.load_corrector(inversion_model, corrector_model) ``` Both `vec2text.models.InversionModel` and `vec2text.models.CorrectorEncoderModel` classes inherit `transformers.PreTrainedModel` therefore you can pass in a Hugging Face model name or path to a local directory. ### Invert text with `invert_strings` ```python vec2text.invert_strings( [ "Jack Morris is a PhD student at Cornell Tech in New York City", "It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity" ], corrector=corrector, ) ['Morris is a PhD student at Cornell University in New York City', 'It was the age of incredulity, the age of wisdom, the age of apocalypse, the age of apocalypse, it was the age of faith, the age of best faith, it was the age of foolishness'] ``` By default, this will make a single guess (using the hypothesizer). For better results, you can make multiple steps: ```python vec2text.invert_strings( [ "Jack Morris is a PhD student at Cornell Tech in New York City", "It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity" ], corrector=corrector, num_steps=20, ) ['Jack Morris is a PhD student in tech at Cornell University in New York City', 'It was the best time of the epoch, it was the worst time of the epoch, it was the best time of the age of wisdom, it was the age of incredulity, it was the age of betrayal'] ``` And for even better results, you can increase the size of the search space by setting `sequence_beam_width` to a positive integer: ```python vec2text.invert_strings( [ "Jack Morris is a PhD student at Cornell Tech in New York City", "It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity" ], corrector=corrector, num_steps=20, sequence_beam_width=4, ) ['Jack Morris is a PhD student at Cornell Tech in New York City', 'It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity'] ``` Note that this technique has to store `sequence_beam_width * sequence_beam_width` hypotheses at each step, so ```` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/vec2text-vec2text`](/api/graphcanon/tools/vec2text-vec2text) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_