What is an Embedding?
An embedding is a numerical representation of data (text, images, etc.) as a vector of floating-point numbers. Embeddings capture semantic meaning, allowing similar concepts to have similar vector representations.
Why Embeddings Matter
Embeddings enable:
Semantic similarity searchClustering related contentEfficient storage and retrievalCross-modal comparisonsMachine learning inputsHow Embeddings Work
The embedding process:
1. Input text is processed by an embedding model
2. Model outputs a fixed-size vector (e.g., 1536 dimensions)
3. Vector captures semantic meaning of the input
4. Similar texts produce similar vectors
Embedding Models
Popular embedding models:
**OpenAI text-embedding-3**: High quality, easy API**Cohere Embed**: Multilingual support**Voyage AI**: Domain-specific options**sentence-transformers**: Open-source models**E5/BGE**: Open-source, competitive qualityEmbedding Dimensions
Common embedding sizes:
384 dimensions (small models)768 dimensions (BERT-sized)1536 dimensions (OpenAI)3072 dimensions (large models)Higher dimensions generally capture more nuance but require more storage.
Similarity Metrics
Measuring embedding similarity:
**Cosine Similarity**: Most common, ignores magnitude**Dot Product**: Includes magnitude information**Euclidean Distance**: Geometric distanceUse Cases in Agent Memory
Embeddings enable memory operations:
Find memories similar to current contextCluster related conversationsDetect duplicate informationMatch user queries to stored knowledgeBuild semantic search over historyBest Practices
Tips for working with embeddings:
Use the same model for indexing and queryingConsider domain-specific models for specialized contentNormalize vectors for cosine similarityChunk long texts appropriatelyMonitor embedding quality over time