What is Embeddings API?
Embedding, commonly referred to as text embeddings in NLP (Natural Language Processing), is the act of expressing words or phrases in a high-dimensional numerical vector space. The underlying meaning and semantic connections between words in a text corpus are captured via embeddings. The idea is to map related words to close spots, and unrelated ones to far-off points.
Since they require numerical input, traditional machine learning algorithms and models usually have trouble handling raw text directly. By maintaining semantic similarity while translating words or phrases to high-dimensional vectors, text embeddings solve this problem. In many NLP applications, including text categorization, sentiment analysis, machine translation, and question-answering systems, embeddings are employed.
Embeddings APIs Use Cases
You can use embeddings in numerous fields, here are some examples of common use cases:
1. E-commerce: Embeddings enhance product recommendations by understanding the relationships between products and user preferences, leading to more accurate suggestions.
2. Social Media Analytics: Embeddings aid in analyzing sentiments and trends in social media posts, enabling brands to understand customer opinions and tailor their strategies accordingly.
3. Financial Services: Embeddings can be used in analyzing financial news sentiment, customer interactions, and detecting unusual patterns for fraud prevention.
4. Academic Research: Embeddings assist researchers in analyzing and summarizing academic papers, identifying research trends, and exploring relationships between scientific articles.
5. Search Engines: Embeddings help search engines understand the context and meaning of user queries, leading to more relevant search results. By representing both queries and documents as embeddings, search engines can match semantically similar content.
6. Recommendation Systems: Embeddings enable recommendation systems to identify patterns in user preferences. For instance, movie recommendations can be improved by understanding the underlying similarities between movies and user preferences.
Best Embeddings APIs on the market
While comparing Embeddings APIs, it is crucial to consider different aspects, among others, cost security and privacy. Embeddings experts at Eden AI tested, compared, and used many Embeddings APIs of the market. Here are some actors that perform well (in alphabetical order):
- Clarifai
- Cohere
- NLPCloud
- OpenAI
1. Clarifai
Language production in LLM is made possible by Cohere's embedding model which relies on two foundational models: Cohere and OpenAI. Their basic model provides embeddings with an output dimension of 1024.
2. Cohere - Available on Eden AI
Cohere's embedding API excels at processing short texts with under 512 tokens. It employs an approach inspired by Reimers and Gurevych, creating contextualized embeddings for each token and averaging them to ensure even concise texts have comprehensive representations.
For longer texts exceeding the 512-token limit, the API truncates input to fit the maximum context length, accommodating varied text lengths while leveraging its potent embedding capabilities.
Cohere offers three models for monolingual and multilingual tasks, including an English model with 4096-dimensional embeddings.
3. Google - Available on Eden AI
With the Vertex AI text-embeddings API powered by Generative AI, you can swiftly create text embeddings. These embeddings work seamlessly behind the scenes, whether they're enhancing your Google search, providing personalized shopping recommendations, or suggesting a new music band on your preferred streaming platform according to your music tastes.
The Vertex AI generates embeddings with an output dimension of 768.
4. NLPCloud
NLP Cloud offers an embeddings API based on Multilingual Mpnet Base v2 that enables you to extract embeddings right out of the box with 768-dimensional embeddings.The reaction time (latency) for this model is excellent. You have the option of using the pre-trained model, creating your own custom model, or uploading one yourself. Locally testing embeddings is one thing, but employing them dependably in production is quite another. You may easily accomplish both with NLP Cloud.
5. OpenAI - Available on Eden AI
OpenAI strongly recommends their second-gen text-embedding model, ada-002, for top-notch results in various applications. With 1536-dimensional embeddings, it excels in performance, cost-effectiveness, and user-friendliness.
In three prominent benchmarks, these embeddings surpass competitors, boasting a significant 20% improvement in code search. This new endpoint, powered by neural networks inspired by GPT-3, efficiently maps text and code into high-dimensional vectors through "embedding."
These models are commonly used for tasks like text similarity, search, and code search.
Performance variations of Embeddings API
Embeddings API performance can vary depending on a number of variables, including the technology used by the provider, the underlying algorithms, the amount of the dataset, the server architecture, and network latency. Listed below are a few typical performance discrepancies between several Embeddings APIs:
- Language Support: Performance might vary based on the languages the embeddings support. Some APIs provide embeddings for a wide range of languages, while others might focus on specific languages.
- Data Size and Quality: The size and quality of the training data used to create the embeddings significantly impact their performance. Models trained on larger and more diverse datasets generally offer better embeddings.
- Task Complexity: Complex tasks like sentiment analysis might require embeddings with deep semantic understanding, while simpler tasks like keyword extraction might benefit from more basic embeddings.
Why choose Eden AI to manage your Embeddings APIs
Companies and developers from a wide range of industries (Social Media, Retail, Health, Finances, Law, etc.) use Eden AI’s unique API to easily integrate Embeddings tasks in their cloud-based applications, without having to build their own solutions.
Eden AI offers multiple AI APIs on its platform among several technologies: Text-to-Speech, Language Detection, Sentiment Analysis, Face Recognition, Question Answering, Data Anonymization, Speech Recognition, and so forth.
We want our users to have access to multiple Embeddings engines and manage them in one place so they can reach high performance, optimize cost and cover all their needs. There are many reasons for using multiple APIs :
- Fallback provider is the ABCs: You need to set up a provider API that is requested if and only if the main Embeddings API does not perform well (or is down). You can use confidence score returned or other methods to check provider accuracy.
- Performance optimization: After the testing phase, you will be able to build a mapping of provider’s to optimise performance by selecting the right provider for each field (one provider for payer, one for dates, one for amount, etc.) Each data that you need to process will then be sent to the Best Embeddings API.
- Cost - Performance ratio optimization: You can choose the cheapest Embeddings provider that performs well for your data.
- Combine multiple AI APIs: This approach is required if you look for extremely high accuracy. The combination leads to higher costs but allows your AI service to be safe and accurate because Embeddings APIs will validate and invalidate each other for each piece of data.
How Eden AI can help you?
Eden AI has been made for multiple AI APIs use. Eden AI is the future of AI usage in companies.
- Centralized and fully monitored billing on Eden AI for all Embeddings 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 Embeddings integration project. This can be done by :
- Organizing a product demo and a discussion to better understand your needs. 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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