Embeddings in Search and Information Retrieval
Embeddings are numerical vector representations of content such as text, images, or audio. They encode the meaning of the content into a list of numbers so computers can compare items by semantic similarity. In practice, embeddings allow a system to retrieve information based on meaning and context instead of relying only on keyword matches.
Basic Idea
An embedding converts a piece of content into a vector, which is simply a list of numbers.
Example:
- Text: "The cat is sleeping on the sofa."
- Embedding vector (simplified example): [0.21, -0.44, 0.73, 0.11, ...]
The vector may contain hundreds or thousands of numbers. These numbers represent semantic features learned by a machine learning model. Texts with similar meaning produce vectors that are close together in this high dimensional space.
Why Search Uses Embeddings
Traditional search engines rely on keywords, inverted indexes, and term frequency statistics. Embedding-based search works differently.
Steps:
- Convert all documents into embeddings.
- Convert the search query into an embedding.
- Compare the query vector with document vectors.
- Retrieve the closest vectors.
Similarity is usually measured using cosine similarity, dot product, or Euclidean distance. The closer the vectors, the more similar the meaning.
Example Documents:
- "How to train a dog"
- "Dog training guide"
- "Cooking pasta"
User query: "how do I teach my dog commands"
Keyword search may struggle. Embedding search understands the semantic similarity and retrieves:
- Dog training guide
- How to train a dog
Even though the exact words differ.
What the Embedding Space Represents
Imagine a very large coordinate space. Similar ideas cluster together.
Example clusters:
- Animals: dog, puppy, canine
- Finance: stocks, trading, portfolio
Documents that discuss similar topics sit close to each other in this space.
How Embeddings Are Generated
A neural network trained on large datasets produces embeddings. Common models include OpenAI embedding models, BERT-based models, Sentence Transformers, Cohere embeddings, and Gemini embeddings. The model reads the text and produces the vector.
Example pipeline:
- Text → Embedding Model → Vector
Where Embeddings Are Used
Embeddings power many modern retrieval systems. Examples include semantic search, vector databases, and retrieval augmented generation.
Vector databases store embeddings for fast similarity search. Examples include:
- Pinecone
- Weaviate
- Qdrant
- Milvus
- PostgreSQL with pgvector
In retrieval augmented generation, the process is:
- User query is converted into an embedding.
- Relevant documents are retrieved.
- The documents are passed to a language model to produce an answer.
Why Embeddings Matter for AI Search
Embeddings allow systems to understand paraphrases, retrieve conceptually related information, and connect different vocabulary that means the same thing. This makes modern AI search context aware.
Simple Mental Model
Meaning becomes coordinates in a semantic map. Search becomes finding the nearest meaning on that map.