Docs / Getting Started / Embedding Providers Embedding Providers Generate text embeddings using 35+ providers through PraisonAI
Overview
PraisonAI supports 35+ embedding providers through LiteLLM integration, giving you access to hundreds of embedding models with a unified API.
Quick Start
python from praisonaiagents import embedding
# OpenAI (default)
result = embedding("Hello world", model="text-embedding-3-small")
# Cohere
result = embedding("Hello world", model="cohere/embed-english-v3.0")
# Voyage AI
result = embedding("Hello world", model="voyage/voyage-3")
# Azure OpenAI
result = embedding("Hello world", model="azure/text-embedding-ada-002")
print(f"Dimensions: {len(result.embeddings[0])}")
CLI Usage
bash # OpenAI
praisonai embed "Hello world" --model text-embedding-3-small
# Cohere
praisonai embed "Hello world" --model cohere/embed-english-v3.0
# Any provider
praisonai embedding "Hello world" --model voyage/voyage-3
Supported Providers
Tier 1: Major Cloud Providers
Tier 2: Specialized Embedding Providers
Provider Prefix Example Model Docs Cohere cohere/cohere/embed-english-v3.0→ Voyage AI voyage/voyage/voyage-3→ Jina AI jina_ai/jina_ai/jina-embeddings-v3→ Mistral mistral/mistral/mistral-embed→
Tier 3: Open Source & Self-Hosted
Provider Prefix Example Model Docs HuggingFace huggingface/huggingface/BAAI/bge-large-en-v1.5→ Ollama ollama/ollama/nomic-embed-text→ Infinity infinity/infinity/BAAI/bge-small-en-v1.5→ vLLM hosted_vllm/hosted_vllm/intfloat/e5-mistral-7b-instruct→ LM Studio lm_studio/lm_studio/nomic-embed-text→
Tier 4: Additional Providers
Provider Prefix Example Model Docs Together AI together_ai/together_ai/togethercomputer/m2-bert-80M-8k-retrieval→ Fireworks AI fireworks_ai/fireworks_ai/nomic-ai/nomic-embed-text-v1.5→ NVIDIA NIM nvidia_nim/nvidia_nim/NV-Embed-QA→ Databricks databricks/databricks/databricks-bge-large-en→ Snowflake snowflake/snowflake/snowflake-arctic-embed-m→ Watsonx watsonx/watsonx/ibm/slate-125m-english-rtrvr→ SambaNova sambanova/sambanova/E5-mistral-7b-instruct→ Nebius nebius/nebius/BAAI/bge-en-icl→ OVHcloud ovhcloud/ovhcloud/multilingual-e5-base→ Volcengine volcengine/volcengine/doubao-embedding→
Model Selection Guide
Provider prefixes (ollama/…, azure/…) and version tags (:v1.5, :latest) are stripped during dimension lookup, so ollama/nomic-embed-text:v1.5 sizes the same as nomic-embed-text.
By Use Case
Use Case Recommended Model Dimensions General Purpose text-embedding-3-small1536 High Quality text-embedding-3-large3072 Multilingual cohere/embed-multilingual-v3.01024 Code Search voyage/voyage-code-31024 Cost Effective cohere/embed-english-light-v3.0384 Self-Hosted ollama/nomic-embed-text768
By Dimension Size
Dimensions Models 256-512 cohere/embed-english-light-v3.0, jina_ai/jina-embeddings-v2-small-en768-1024 cohere/embed-english-v3.0, voyage/voyage-3, mistral/mistral-embed1536 text-embedding-3-small, azure/text-embedding-ada-0023072 text-embedding-3-large
Environment Variables
Each provider requires specific environment variables:
bash # OpenAI
export OPENAI_API_KEY="sk-..."
# Azure OpenAI
export AZURE_API_KEY="..."
export AZURE_API_BASE="https://your-resource.openai.azure.com"
# Cohere
export COHERE_API_KEY="..."
# Voyage AI
export VOYAGE_API_KEY="..."
# Google
export GOOGLE_API_KEY="..." # or GEMINI_API_KEY
# AWS Bedrock
export AWS_ACCESS_KEY_ID="..."
export AWS_SECRET_ACCESS_KEY="..."
export AWS_REGION_NAME="us-east-1"
API Reference
Parameters
Parameter Type Default Description inputstr or List[str] Required Text(s) to embed modelstr text-embedding-3-smallModel identifier with optional provider prefix dimensionsint None Output dimensions (if supported) encoding_formatstr floatfloat or base64timeoutfloat 600.0 Request timeout in seconds api_keystr None API key override api_basestr None API base URL override
Response
python EmbeddingResult(
embeddings=[[0.1, 0.2, ...]], # List of embedding vectors
model="text-embedding-3-small",
usage={"prompt_tokens": 4, "total_tokens": 4}
)
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