Top Free Embedding tools, APIs, and Open Source models

Eden AI
6 min readNov 28, 2023

What is Text Embedding API?

Text embeddings are a common practice in NLP (Natural Language Processing) that involves expressing words or phrases numerically within a high-dimensional vector space. Embedding captures underlying meanings and semantic connections within a text corpus. Its key purpose is to map related words to close positions and unrelated words to distant points.

As traditional machine learning algorithms and models demand numerical input, working with raw text can pose a real challenge. However, by preserving semantic similarity when translating phrases or words into high-dimensional vectors, text embeddings provide a solution to this problem. Embeddings are widely used in NLP applications such as text categorization, sentiment analysis, machine translation and question-answering systems.

Top Open Source (Free) Embedding models on the market

For users seeking a cost-effective engine, opting for an open-source model is recommended. Here is the list of the best Embedding Open Source Models:

1‍. Word2Vec

Word2Vec is a pioneering model for word embeddings. It represents words using vectors in a continuous vector space, capturing semantic relationships among them.

2. GloVe

GloVe is a well-known method for acquiring word embeddings. It concentrates on collecting global statistics of word co-occurrence from a vast text corpus.

3. bert

BERT is a transformer model that takes into account the context from both left and right directions, resulting in bidirectional embeddings. It has attained cutting-edge outcomes in a range of natural language processing assignments.

4. LexVec

This is an implementation of the LexVec word embedding model (similar to word2vec and GloVe) that achieves state-of-the-art results in multiple NLP tasks

5. txtai

txtai is an all-in-one embedding database for semantic search, LLM orchestration and language model workflows.

6. chroma

This is a text-embedding open source model.

Cons of Using Open Source AI models

‍While open-source models offer many advantages, they also have potential drawbacks and challenges. Here are some cons of using open-source models:

  • Not Entirely Cost Free: Open-source models, while providing valuable resources to users, may not always be entirely free of cost. Users often need to bear hosting and server usage expenses, especially when dealing with large or resource-intensive data sets.
  • Lack of Support: Open-source models may not have official support channels or dedicated customer support teams. If you encounter issues or need assistance, you might have to rely on community forums or the goodwill of volunteers, which can be less reliable than commercial support.
  • Limited Documentation: Some open source models may need more complete or better-maintained documentation. This can make it difficult for developers to understand how to use the model effectively, leading to frustration and wasted time.
  • Security Concerns: Security vulnerabilities can exist in open-source models, and it may take longer for these issues to be addressed compared to commercially supported models. Users of open-source models may need to monitor for security updates and patches actively.
  • Scalability and Performance: Open source models may not be as optimized for performance and scalability as commercial models. If your application requires high performance or needs to handle a large number of requests, you may need to invest more time in optimization.

Why choose Eden AI?

Given the potential costs and challenges related to open-source models, one cost-effective solution is to use APIs. Eden AI smoothens the incorporation and implementation of AI technologies with its API, connecting to multiple AI engines.

Eden AI presents a broad range of AI APIs on its platform, customized to suit your needs and financial limitations. These technologies include data parsing, language identification, sentiment analysis, logo recognition, question answering, data anonymization, speech recognition, and numerous other capabilities.

To get started, we offer free $10 credits for you to explore our APIs.

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Access Embedding providers with one API

Our standardized API enables you to integrate Invoice Parser APIs into your system with ease by utilizing various providers on Eden AI. Here is the list (in alphabetical order):

  • Cohere
  • Google
  • OpenAI

1. Cohere- Available on Eden AI

Cohere’s Embedding API is highly proficient in processing concise texts with less than 512 tokens. It is modeled on the method devised by Reimers and Gurevych, and the API produces contextualized embeddings for every token, which are then averaged to yield comprehensive representations even for brief texts.

For texts exceeding the 512-token limit, the API truncates the input to accommodate the maximum context length while making the best use of its dominant embedding capabilities.

Cohere provides three models catering to monolingual and multilingual tasks, which comprise an English model equipped with 4096-dimensional embeddings.

2. Google- Available on Eden AI

The Vertex AI text-embeddings API, powered by Generative AI, allows for the swift creation of text embeddings. These embeddings operate imperceptibly in the background, serving to enhance your Google search, provide tailored shopping recommendations, or suggest a new music group on your favorite streaming platform, depending on your musical preferences.

The Vertex AI produces embeddings with an output dimension of 768.

3. OpenAI- Available on Eden AI

OpenAI highly recommends its second-generation text-embedding model, ada-002 for outstanding outcomes across numerous applications. With 1536-dimensional embeddings, it excels in performance, affordability and user-friendliness.

In three prominent benchmarks, said embeddings outdo competitors by boasting an impressive 20% improvement in code search. Neural networks inspired by GPT-3 power this fresh endpoint, efficiently mapping text and code into high-dimensional vectors through “embedding.”

Commonly utilized for tasks such as text similarity, search and code search, these models enable top-notch results.

Pricing Structure for Embedding API Providers

Eden AI offers a user-friendly platform for evaluating pricing information from diverse API providers and monitoring price changes over time. As a result, keeping up-to-date with the latest pricing is crucial. The pricing chart below outlines the rates for smaller quantities for November 2023, as well as you can get discounts for potentially large volumes.

Check the current prices on Eden AI

How Eden AI can help you?

Eden AI is the future of AI usage in companies: our app allows you to call multiple AI APIs.

  • Centralized and fully monitored billing on Eden AI for Embedding APIs
  • Unified API for all providers: simple and standard to use, quick switch between providers, access to the specific features of each provider
  • Standardized response format: the JSON output format is the same for all suppliers thanks to Eden AI’s standardization work. The response elements are also standardized thanks to Eden AI’s powerful matching algorithms.
  • The best Artificial Intelligence APIs in the market are available: big cloud providers (Google, AWS, Microsoft, and more specialized engines)
  • Data protection: Eden AI will not store or use any data. Possibility to filter to use only GDPR engines.

You can see Eden AI documentation here.

Next step in your project

The Eden AI team can help you with your Embedding integration project. This can be done by :

  • Organizing a product demo and a discussion to understand your needs better. You can book a time slot on this link: Contact
  • By testing the public version of Eden AI for free: however, not all providers are available on this version. Some are only available on the Enterprise version.
  • By benefiting from the support and advice of a team of experts to find the optimal combination of providers according to the specifics of your needs
  • Having the possibility to integrate on a third-party platform: we can quickly develop connectors.

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