Principle:Togethercomputer Together python Embedding Generation
Overview
Embedding Generation is the mechanism for computing dense vector representations of text for semantic similarity, retrieval, clustering, and classification tasks using the Together Python SDK.
Description
Embedding generation converts text strings into fixed-dimensional floating-point vectors that capture semantic meaning. Similar texts produce vectors with high cosine similarity, enabling a wide range of downstream applications. The Together Python SDK exposes this capability through the Embeddings.create() method, which sends text to a hosted embedding model and returns dense vector representations.
These embeddings are the foundation for:
- Semantic search -- Finding documents that are semantically similar to a query, even without keyword overlap
- Vector database indexing -- Storing embeddings in vector databases (e.g., Pinecone, Weaviate, Chroma) for efficient nearest-neighbor retrieval
- RAG (Retrieval-Augmented Generation) -- Retrieving relevant context from a knowledge base to augment LLM generation
- Clustering -- Grouping similar documents by computing pairwise embedding distances
- Classification -- Using embeddings as feature vectors for downstream classifiers
Usage
Use embedding generation when you need vector representations of text for similarity comparison, vector database indexing, or retrieval in RAG systems. The typical workflow is:
- Preprocess text inputs (see Principle:Togethercomputer_Together_python_Text_Preprocessing)
- Call
client.embeddings.create()with the text and model name - Store the resulting vectors in a vector database or use them directly for similarity computations
- Optionally rerank results using Principle:Togethercomputer_Together_python_Document_Reranking
Theoretical Basis
Text embeddings map variable-length text to fixed-dimensional vector spaces where geometric proximity (cosine similarity, dot product) approximates semantic similarity. The key theoretical foundations are:
- Contrastive learning -- Embedding models are trained with contrastive objectives (e.g., InfoNCE loss) that pull semantically similar texts together and push dissimilar texts apart in embedding space. This creates a vector space where distance correlates with semantic relatedness.
- Bi-encoder architecture -- Embedding models use a bi-encoder approach where each text is encoded independently. This enables efficient batch processing and pre-computation of document embeddings, making retrieval scalable to millions of documents.
- Dimensionality and information density -- Embeddings compress the full semantic content of text into a fixed number of dimensions (e.g., 768 or 1024). Higher-dimensional embeddings can capture more nuanced distinctions but require more storage and computation.
- Similarity metrics -- The most common similarity metrics for text embeddings are:
- Cosine similarity -- Measures the angle between vectors, invariant to magnitude. Ranges from -1 to 1.
- Dot product -- Measures both direction and magnitude. Useful when embedding magnitude carries meaning.
- Euclidean distance -- Measures straight-line distance. Less common for text embeddings.
- Token limit considerations -- Models have a maximum input token limit. Text exceeding this limit is truncated, potentially losing semantic content. Chunking long documents into smaller segments ensures complete semantic coverage.
Metadata
| Property | Value |
|---|---|
| Principle | Embedding Generation |
| Domain | NLP, Information_Retrieval, RAG |
| Workflow | Embeddings_And_Reranking |
| Related Concepts | Vector Similarity, Bi-Encoder, Contrastive Learning, Semantic Search |
| Implementation | Implementation:Togethercomputer_Together_python_Embeddings_Create |