Embedding Models
Usage
from litellm import embedding
import os
os.environ['OPENAI_API_KEY'] = ""
response = embedding('text-embedding-ada-002', input=["good morning from litellm"])
OpenAI Embedding Models
Model Name | Function Call | Required OS Variables |
---|---|---|
text-embedding-ada-002 | embedding('text-embedding-ada-002', input) | os.environ['OPENAI_API_KEY'] |
Azure OpenAI Embedding Models
Model Name | Function Call | Required OS Variables |
---|---|---|
text-embedding-ada-002 | embedding('embedding-model-deployment', input=input, custom_llm_provider="azure") | os.environ['AZURE_API_KEY'] ,os.environ['AZURE_API_BASE'] ,os.environ['AZURE_API_VERSION'] |
Cohere Embedding Models
https://docs.cohere.com/reference/embed
from litellm import embedding
import os
os.environ['COHERE_API_KEY'] = ""
response = embedding('embed-english-v2.0', input=["good morning from litellm"])
Model Name | Function Call | Required OS Variables |
---|---|---|
embed-english-v2.0 | embedding('embed-english-v2.0', input=input) | os.environ['COHERE_API_KEY'] |
embed-english-light-v2.0 | embedding('embed-english-light-v2.0', input=input) | os.environ['COHERE_API_KEY'] |
embed-multilingual-v2.0 | embedding('embed-multilingual-v2.0', input=input) | os.environ['COHERE_API_KEY'] |
HuggingFace Embedding Models
LiteLLM supports all Feature-Extraction Embedding models: https://huggingface.co/models?pipeline_tag=feature-extraction
from litellm import embedding
import os
os.environ['HUGGINGFACE_API_KEY'] = ""
response = embedding(
model='huggingface/microsoft/codebert-base',
input=["good morning from litellm"]
)
Model Name | Function Call | Required OS Variables |
---|---|---|
microsoft/codebert-base | embedding('huggingface/microsoft/codebert-base', input=input) | os.environ['HUGGINGFACE_API_KEY'] |
BAAI/bge-large-zh | embedding('huggingface/BAAI/bge-large-zh', input=input) | os.environ['HUGGINGFACE_API_KEY'] |
any-hf-embedding-model | embedding('huggingface/hf-embedding-model', input=input) | os.environ['HUGGINGFACE_API_KEY'] |