Overview Generate embeddings for text using any of LiteLLM’s supported embedding providers. Returns responses in OpenAI format.
Function Signature def embedding ( model : str , input : Union[ str , List[ str ]] = [], # Optional params dimensions : Optional[ int ] = None , encoding_format : Optional[ str ] = None , timeout : float = 600 , # API configuration api_base : Optional[ str ] = None , api_version : Optional[ str ] = None , api_key : Optional[ str ] = None , api_type : Optional[ str ] = None , # LiteLLM specific caching : bool = False , user : Optional[ str ] = None , custom_llm_provider : Optional[ str ] = None , ** kwargs ) -> EmbeddingResponse
Parameters Required Parameters The embedding model to use. Examples:
text-embedding-3-small (OpenAI)text-embedding-ada-002 (OpenAI)amazon.titan-embed-text-v1 (Bedrock)textembedding-gecko@003 (Vertex AI)embed-english-v3.0 (Cohere) input
Union[str, List[str]]
required
Input text to embed. Can be a single string or array of strings. # Single string input = "The quick brown fox" # Multiple strings input = [ "First text" , "Second text" , "Third text" ]
Optional Parameters Number of dimensions for the output embeddings. Only supported by some models (e.g., text-embedding-3 and later). dimensions = 512 # Reduce from default 1536
Format to return embeddings in. Options:
"float": Array of floats"base64": Base64 encoded string Request timeout in seconds (default 10 minutes).
Unique identifier for your end-user, for abuse monitoring.
API Configuration API key for the provider. If not provided, uses environment variables.
Base URL for the API endpoint.
API version to use (provider-specific).
API type (e.g., “azure” for Azure OpenAI).
LiteLLM Specific Override the provider detection. Example: custom_llm_provider="bedrock"
Additional metadata to tag the request.
Response EmbeddingResponse Object type, always “list”.
List of embedding objects. Object type, always “embedding”.
The embedding vector as an array of floats.
Index of the embedding in the list.
Model used for embeddings.
Token usage information. Number of tokens in the input.
Usage Examples Basic Embedding import litellm response = litellm.embedding( model = "text-embedding-3-small" , input = "The quick brown fox jumps over the lazy dog" ) print (response.data[ 0 ].embedding) # [0.123, -0.456, ...] print ( len (response.data[ 0 ].embedding)) # 1536
Batch Embeddings import litellm texts = [ "First document to embed" , "Second document to embed" , "Third document to embed" ] response = litellm.embedding( model = "text-embedding-3-small" , input = texts ) for i, embedding_obj in enumerate (response.data): print ( f "Document { i } : { len (embedding_obj.embedding) } dimensions" )
Async Embeddings import litellm import asyncio async def main (): response = await litellm.aembedding( model = "text-embedding-3-small" , input = "Async embedding example" ) print (response.data[ 0 ].embedding) asyncio.run(main())
Custom Dimensions import litellm # Reduce embedding dimensions for smaller storage response = litellm.embedding( model = "text-embedding-3-small" , input = "Sample text" , dimensions = 512 # Instead of default 1536 ) print ( len (response.data[ 0 ].embedding)) # 512
Multiple Providers import litellm # OpenAI response = litellm.embedding( model = "text-embedding-3-small" , input = "Hello world" ) # Cohere response = litellm.embedding( model = "embed-english-v3.0" , input = "Hello world" ) # AWS Bedrock response = litellm.embedding( model = "amazon.titan-embed-text-v1" , input = "Hello world" ) # Azure OpenAI response = litellm.embedding( model = "azure/text-embedding-ada-002" , input = "Hello world" , api_key = "your-azure-key" , api_base = "https://your-endpoint.openai.azure.com/" , api_version = "2024-02-01" ) # Vertex AI response = litellm.embedding( model = "textembedding-gecko@003" , input = "Hello world" )
Semantic Search Example import litellm import numpy as np def cosine_similarity ( a , b ): return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)) # Documents to search documents = [ "Python is a programming language" , "JavaScript is used for web development" , "Machine learning is a subset of AI" ] # Get embeddings for all documents response = litellm.embedding( model = "text-embedding-3-small" , input = documents ) doc_embeddings = [item.embedding for item in response.data] # Query query = "What is Python?" query_response = litellm.embedding( model = "text-embedding-3-small" , input = query ) query_embedding = query_response.data[ 0 ].embedding # Find most similar document similarities = [ cosine_similarity(query_embedding, doc_emb) for doc_emb in doc_embeddings ] most_similar_idx = np.argmax(similarities) print ( f "Most similar: { documents[most_similar_idx] } " ) print ( f "Similarity: { similarities[most_similar_idx] :.4f} " )
Provider-Specific Examples import litellm response = litellm.embedding( model = "embed-english-v3.0" , input = "Sample text" , input_type = "search_document" # or "search_query", "classification" )
Vertex AI Multimodal Embeddings import litellm response = litellm.embedding( model = "multimodalembedding@001" , input = "Sample text" )
Error Handling import litellm from litellm import AuthenticationError, RateLimitError try : response = litellm.embedding( model = "text-embedding-3-small" , input = "Sample text" ) except AuthenticationError as e: print ( f "Authentication failed: { e } " ) except RateLimitError as e: print ( f "Rate limit exceeded: { e } " ) except Exception as e: print ( f "An error occurred: { e } " )
Supported Providers LiteLLM supports embeddings from:
OpenAI : text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002Azure OpenAI : All OpenAI embedding modelsCohere : embed-english-v3.0, embed-multilingual-v3.0AWS Bedrock : amazon.titan-embed-text-v1, cohere.embed-*Google Vertex AI : textembedding-gecko, text-embedding-004Hugging Face : All embedding modelsVoyage AI : voyage-2, voyage-code-2Together AI : togethercomputer/m2-bert-80M-*And many more!
See Embedding Providers for the complete list.