A dense float vector (e.g. 768-3072 dims) that encodes the semantic content of text, images, or code. Nearby vectors mean similar meaning, measured by cosine similarity.
How it works
An embedding model transforms various types of input, like text or images, into a high-dimensional vector space. Each input is represented as a dense float vector, typically ranging from 768 to 3072 dimensions. The model learns to place semantically similar inputs close together in this space. For instance, if you input the phrases 'dog' and 'puppy,' the model generates vectors that are near each other, reflecting their similar meanings. Once you have these vectors, you store them in a vector database like pgvector, Pinecone, or Qdrant, which is optimized for fast retrieval.
When you need to find similar items, you perform an approximate nearest-neighbor search using algorithms such as Hierarchical Navigable Small World (HNSW) or Inverted File (IVF). These algorithms quickly identify which stored vectors are closest to the query vector based on a similarity measure, often cosine similarity. For example, if you query with 'canine,' the system retrieves vectors for 'dog,' 'puppy,' and 'wolf,' all of which are semantically related and nearby in the vector space.
This approach allows for a more nuanced understanding of relationships between concepts. Without embeddings, you would rely on keyword matching, which would miss out on the underlying meanings. By embedding the data, you enable the system to understand context and intent, leading to more relevant and accurate results.
- Input data is transformed into a vector.
- Store vectors in a vector database.
- Use approximate nearest-neighbor for retrieval.
- Combine with keyword search for best results.
- Choose embeddings for semantic understanding.
The software-engineering analogue
You can think of embeddings as a sophisticated version of a hash table or a full-text index. In traditional indexing, a hash matches exact keys, while a full-text index retrieves documents based on keyword presence. However, embeddings go beyond this by matching Meaning. For instance, a search for 'car' can return results for 'automobile' even though they share no characters. This semantic matching allows for a richer and more flexible search experience.
The key difference here is that while hashes and indexes operate on explicit terms, embeddings operate on the underlying semantics of the content. This means that embeddings can recognize synonyms and paraphrases, which traditional indexing methods cannot. This semantic understanding is what sets embeddings apart and allows for more intuitive retrieval.
Why not the obvious alternative
A newcomer might first consider using traditional keyword search methods like BM25 for retrieving relevant documents. While BM25 excels at finding documents that contain exact terms, it struggles with paraphrases and synonyms. For example, a search for 'automobile' might miss relevant documents that only contain the word 'car.' This limitation arises because keyword searches rely solely on the presence of specific words, ignoring the broader context and intent behind the search.
In contrast, embeddings capture the semantic relationships between words and phrases, allowing for a more comprehensive understanding of user intent. However, relying solely on embeddings can also lead to challenges, particularly in cases where precision is crucial. This is why many systems implement a hybrid approach that combines both keyword search and embeddings. By doing so, they can use the strengths of both methods: keywords for exact matches and embeddings for capturing intent and meaning.
When to use which
Use embeddings when you need to perform semantic search, retrieve answers from a knowledge base (RAG), deduplicate content, cluster similar items, or make recommendations based on user preferences. Embeddings shine when understanding the meaning behind the words is critical. However, for tasks that require exact matches, such as finding specific IDs, names, or rare tokens, traditional keyword search methods are more effective. In these cases, embeddings may blur the precision needed to retrieve the exact results.
When deciding which method to use, consider the trade-offs involved. Embeddings can provide richer contextual understanding but may introduce some noise in results due to their inherent nature of focusing on meaning rather than exact matches. On the other hand, keyword search offers precision at the cost of missing out on related concepts. Balancing these approaches based on your specific use case will yield the best results.
In production: when it breaks
Why it happens. This issue often arises due to drift in the embedding space, where the model generating the embeddings has become misaligned with the data it is processing. This misalignment can occur due to changes in the underlying data distribution or updates to the model that are not reflected in the embeddings stored in the database.
- Step 1: Measure the cosine similarity between the embeddings of recent queries and their expected results to identify any significant deviations.
- Step 2: Log the distribution of embeddings over time to check for shifts or drifts that correlate with the drop in accuracy.
- Step 3: A/B test the current embedding model against a previous version to see if the accuracy improves with the older model.
What it delivers
The use of embeddings enables retrieval and grouping based on meaning rather than just keywords. This capability forms the backbone of many modern applications, including Retrieval-Augmented Generation (RAG) systems and any feature that requires finding similar items. By understanding the semantic relationships between inputs, you can significantly enhance user experience and improve the relevance of search results.