Documentation Index
Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
Use this file to discover all available pages before exploring further.
Overview
PraisonAI supports 35+ embedding providers through LiteLLM integration, giving you access to hundreds of embedding models with a unified API.
Quick Start
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
# 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
| Provider | Prefix | Example Model | Docs |
|---|
| OpenAI | openai/ or none | text-embedding-3-small | → |
| Azure OpenAI | azure/ | azure/text-embedding-ada-002 | → |
| Google Vertex AI | vertex_ai/ | vertex_ai/textembedding-gecko | → |
| Google Gemini | gemini/ | gemini/text-embedding-004 | → |
| AWS Bedrock | bedrock/ | bedrock/amazon.titan-embed-text-v1 | → |
| Azure AI | azure_ai/ | azure_ai/Cohere-embed-v3-english | → |
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
By Use Case
| Use Case | Recommended Model | Dimensions |
|---|
| General Purpose | text-embedding-3-small | 1536 |
| High Quality | text-embedding-3-large | 3072 |
| Multilingual | cohere/embed-multilingual-v3.0 | 1024 |
| Code Search | voyage/voyage-code-3 | 1024 |
| Cost Effective | cohere/embed-english-light-v3.0 | 384 |
| Self-Hosted | ollama/nomic-embed-text | 768 |
By Dimension Size
| Dimensions | Models |
|---|
| 256-512 | cohere/embed-english-light-v3.0, jina_ai/jina-embeddings-v2-small-en |
| 768-1024 | cohere/embed-english-v3.0, voyage/voyage-3, mistral/mistral-embed |
| 1536 | text-embedding-3-small, azure/text-embedding-ada-002 |
| 3072 | text-embedding-3-large |
Environment Variables
Each provider requires specific environment variables:
# 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 |
|---|
input | str or List[str] | Required | Text(s) to embed |
model | str | text-embedding-3-small | Model identifier with optional provider prefix |
dimensions | int | None | Output dimensions (if supported) |
encoding_format | str | float | float or base64 |
timeout | float | 600.0 | Request timeout in seconds |
api_key | str | None | API key override |
api_base | str | None | API base URL override |
Response
EmbeddingResult(
embeddings=[[0.1, 0.2, ...]], # List of embedding vectors
model="text-embedding-3-small",
usage={"prompt_tokens": 4, "total_tokens": 4}
)