Esperanto is a powerful Python library that provides a unified interface for interacting with various Large Language Model (LLM) providers. It simplifies the process of working with different AI models (LLMs, Embedders, Transcribers, and TTS) APIs by offering a consistent interface while maintaining provider-specific optimizations.
- Unified Interface: Work with multiple LLM providers using a consistent API
- Provider Support:
- OpenAI (GPT-4, GPT-3.5, o1, Whisper, TTS)
- Anthropic (Claude 3)
- OpenRouter (Access to multiple models)
- xAI (Grok)
- Groq (Mixtral, Llama, Whisper)
- Google GenAI (Gemini LLM, Text To Speech, Embedding)
- Vertex AI (Google Cloud)
- Ollama (Local deployment)
- ElevenLabs (Text-to-Speech)
- Embedding Support: Multiple embedding providers for vector representations
- Speech-to-Text Support: Transcribe audio using multiple providers
- Text-to-Speech Support: Generate speech using multiple providers
- Async Support: Both synchronous and asynchronous API calls
- Streaming: Support for streaming responses
- Structured Output: JSON output formatting (where supported)
- LangChain Integration: Easy conversion to LangChain chat models
For detailed information about our providers, check out:
- LLM Providers Documentation
- Embedding Providers Documentation
- Speech-to-Text Providers Documentation
- Text-to-Speech Providers Documentation
Install Esperanto using pip:
pip install esperantoFor specific providers, install with their extras:
# For OpenAI support
pip install "esperanto[openai]"
# For Anthropic support
pip install "esperanto[anthropic]"
# For Google (GenAI) support
pip install "esperanto[google]"
# For Vertex AI support
pip install "esperanto[vertex]"
# For Groq support
pip install "esperanto[groq]"
# For Ollama support
pip install "esperanto[ollama]"
# For ElevenLabs support
pip install "esperanto[elevenlabs]"
# For Google TTS support
pip install "esperanto[googletts]"
# For all providers
pip install "esperanto[all]"| Provider | LLM Support | Embedding Support | Speech-to-Text | Text-to-Speech | JSON Mode |
|---|---|---|---|---|---|
| OpenAI | ✅ | ✅ | ✅ | ✅ | ✅ |
| Anthropic | ✅ | ❌ | ❌ | ❌ | ✅ |
| Groq | ✅ | ❌ | ✅ | ❌ | ✅ |
| Google (GenAI) | ✅ | ✅ | ❌ | ✅ | ✅ |
| Vertex AI | ✅ | ✅ | ❌ | ❌ | ❌ |
| Ollama | ✅ | ✅ | ❌ | ❌ | ❌ |
| ElevenLabs | ❌ | ❌ | ❌ | ✅ | ❌ |
You can use Esperanto in two ways: directly with provider-specific classes or through the AI Factory.
The AI Factory provides a convenient way to create model instances and discover available providers:
from esperanto.factory import AIFactory
# Get available providers for each model type
providers = AIFactory.get_available_providers()
print(providers)
# Output:
# {
# 'language': ['openai', 'anthropic', 'google', 'groq', 'ollama', 'openrouter', 'xai'],
# 'embedding': ['openai', 'google', 'ollama', 'vertex'],
# 'speech_to_text': ['openai', 'groq'],
# 'text_to_speech': ['openai', 'elevenlabs', 'google']
# }
# Create model instances
model = AIFactory.create_language(
"openai",
"gpt-3.5-turbo",
structured={"type": "json"}
) # Language model
embedder = AIFactory.create_embedding("openai", "text-embedding-3-small") # Embedding model
transcriber = AIFactory.create_speech_to_text("openai", "whisper-1") # Speech-to-text model
speaker = AIFactory.create_text_to_speech("openai", "tts-1") # Text-to-speech model
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What's the capital of France?"},
]
response = model.chat_complete(messages)
# Create an embedding instance
texts = ["Hello, world!", "Another text"]
# Synchronous usage
embeddings = embedder.embed(texts)
# Async usage
embeddings = await embedder.aembed(texts)Here's a simple example to get you started:
from esperanto.providers.llm.openai import OpenAILanguageModel
from esperanto.providers.llm.anthropic import AnthropicLanguageModel
# Initialize a provider with structured output
model = OpenAILanguageModel(
api_key="your-api-key",
model_name="gpt-4", # Optional, defaults to gpt-4
structured={"type": "json"} # Optional, for JSON output
)
# Simple chat completion
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "List three colors in JSON format"}
]
# Synchronous call
response = model.chat_complete(messages)
print(response.choices[0].message.content) # Will be in JSON format
# Async call
async def get_response():
response = await model.achat_complete(messages)
print(response.choices[0].message.content) # Will be in JSON formatAll providers in Esperanto return standardized response objects, making it easy to work with different models without changing your code.
from esperanto.factory import AIFactory
model = AIFactory.create_language(
"openai",
"gpt-3.5-turbo",
structured={"type": "json"}
)
messages = [{"role": "user", "content": "Hello!"}]
# All LLM responses follow this structure
response = model.chat_complete(messages)
print(response.choices[0].message.content) # The actual response text
print(response.choices[0].message.role) # 'assistant'
print(response.model) # The model used
print(response.usage.total_tokens) # Token usage information
# For streaming responses
for chunk in model.chat_complete(messages):
print(chunk.choices[0].delta.content) # Partial response textfrom esperanto.factory import AIFactory
model = AIFactory.create_embedding("openai", "text-embedding-3-small")
texts = ["Hello, world!", "Another text"]
# All embedding responses follow this structure
response = model.embed(texts)
print(response.data[0].embedding) # Vector for first text
print(response.data[0].index) # Index of the text (0)
print(response.model) # The model used
print(response.usage.total_tokens) # Token usage informationThe standardized response objects ensure consistency across different providers, making it easy to:
- Switch between providers without changing your application code
- Handle responses in a uniform way
- Access common attributes like token usage and model information
from esperanto.providers.llm.openai import OpenAILanguageModel
model = OpenAILanguageModel(
api_key="your-api-key", # Or set OPENAI_API_KEY env var
model_name="gpt-4", # Optional
temperature=0.7, # Optional
max_tokens=850, # Optional
streaming=False, # Optional
top_p=0.9, # Optional
structured={"type": "json"}, # Optional, for JSON output
base_url=None, # Optional, for custom endpoint
organization=None # Optional, for org-specific API
)Enable streaming to receive responses token by token:
# Enable streaming
model = OpenAILanguageModel(api_key="your-api-key", streaming=True)
# Synchronous streaming
for chunk in model.chat_complete(messages):
print(chunk.choices[0].delta.content, end="", flush=True)
# Async streaming
async for chunk in model.achat_complete(messages):
print(chunk.choices[0].delta.content, end="", flush=True)Request JSON-formatted responses (supported by OpenAI and some OpenRouter models):
model = OpenAILanguageModel(
api_key="your-api-key", # or use ENV
structured={"type": "json"}
)
messages = [
{"role": "user", "content": "List three European capitals as JSON"}
]
response = model.chat_complete(messages)
# Response will be in JSON formatConvert any provider to a LangChain chat model:
model = OpenAILanguageModel(api_key="your-api-key")
langchain_model = model.to_langchain()
# Use with LangChain
from langchain.chains import ConversationChain
chain = ConversationChain(llm=langchain_model)We welcome contributions! Please see our Contributing Guidelines for details on how to get started.
This project is licensed under the MIT License - see the LICENSE file for details.
- Clone the repository:
git clone https://github.com/lfnovo/esperanto.git
cd esperanto- Install dependencies:
pip install -r requirements.txt- Run tests:
pytest