Unfolding the Mystery of Embeddings: Generation, Usage, and Search

What are Embeddings?

In the realm of machine learning, especially in the context of natural language processing (NLP), embeddings are dense vector representations that capture the essence of the data they represent. Imagine having a word or a sentence, and instead of representing them through traditional methods (like one-hot encoding that doesn't consider semantic similarity), we transform these words or sentences into multi-dimensional vectors (embeddings). These embeddings are designed to reflect the meaning and context of the words, with similar words having similar vector representations.

What are They Used For?

The fascinating aspect of embeddings is their ability to encapsulate relationships and semantic information within their vector space, providing a fertile ground for various machine learning algorithms.

1. Text Classification: Embeddings can be used in text classification tasks like sentiment analysis or spam detection, where they provide a meaningful representation of the text data.
2. Recommendation Systems: Embeddings can model items and user preferences in recommendation systems, capturing user-item interaction dynamics within their vectors.
3. Language Translation and Text Generation: Embeddings play a critical role in machine translation, chatbots, and text generation tasks where the meaning of words and their context matter.
4. Information Retrieval: In search systems, embeddings can improve the relevance of search results by capturing the semantic similarity between queries and documents.

How Can I Generate Embeddings Through Sentence Transformer?

Sentence Transformers library provides a user-friendly approach to generate embeddings for text data. The library supports various transformer models like BERT, GPT, and RoBERTa.

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
embeddings = self.model.encode(text)

The all-mpnet-base-v2 model is fast, free, and comparable in performance to OpenAI embedding models. However, the context size is limited to 300 tokens.(Exceeding the max token sizee of 300 will cause cosine similarity to not return good results.)

How Can I Generate Embeddings Using OpenAI API?

It is also possible to use the OpenAI API to generate embeddings. The code snippet below shows how to achieve this:

response = openai.Embedding.create(input=[text], model="text-embedding-ada-002")
return response['data'][0]['embedding']

The context size for ada-002 is 2000 tokens.

How Can I Search Embeddings?

Searching in the embedding space involves measuring the similarity between vectors. The most common method is using cosine similarity, which measures the cosine of the angle between two vectors. A smaller angle corresponds to higher similarity.

Libraries like Scikit-learn provide efficient functions for this:

from sklearn.metrics.pairwise import cosine_similarity

# Assuming embeddings1 and embeddings2 are your two vectors
similarity_score = cosine_similarity(embeddings1, embeddings2)

In the above example, embeddings1 represents the embedded form of your query, and embeddings2 is the embedded text. This way, the text with the highest similarity score is the text with the closest contextual meaning to the query.

Alternatively, you can use approximate nearest neighbor search algorithms, such as those provided by libraries like FAISS, Annoy, or ElasticSearch, which offer more efficient search mechanisms for large-scale datasets.

Conclusions

Embeddings form the backbone of most modern machine learning systems, particularly in the domain of NLP. The dense vector representations encapsulate complex relationships and semantics that are leveraged across a wide array of applications, ranging from text classification to recommendation systems.