# Esperanto 🌐
[](https://badge.fury.io/py/esperanto)
[](https://pypi.org/project/esperanto/)
[](https://github.com/lfnovo/esperanto)
[](https://pypi.org/project/esperanto/)
[](https://opensource.org/licenses/MIT)
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) APIs by offering a consistent interface while maintaining provider-specific optimizations.
## Features ✨
- **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](https://github.com/lfnovo/esperanto/blob/main/docs/llm.md)
- [Embedding Providers Documentation](https://github.com/lfnovo/esperanto/blob/main/docs/embedding.md)
- [Speech-to-Text Providers Documentation](https://github.com/lfnovo/esperanto/blob/main/docs/speech_to_text.md)
- [Text-to-Speech Providers Documentation](https://github.com/lfnovo/esperanto/blob/main/docs/text_to_speech.md)
## Installation 🚀
Install Esperanto using pip:
```bash
pip install esperanto
```
For specific providers, install with their extras:
```bash
# 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 all providers
pip install "esperanto[all]"
```
## Provider Support Matrix
| Provider | LLM Support | Embedding Support | Speech-to-Text | Text-to-Speech | JSON Mode |
|------------|-------------|------------------|----------------|----------------|-----------|
| OpenAI | ✅ | ✅ | ✅ | ✅ | ✅ |
| Anthropic | ✅ | ❌ | ❌ | ❌ | ✅ |
| Groq | ✅ | ❌ | ✅ | ❌ | ✅ |
| Google (GenAI) | ✅ | ✅ | ❌ | ✅ | ✅ |
| Vertex AI | ✅ | ✅ | ❌ | ❌ | ❌ |
| Ollama | ✅ | ✅ | ❌ | ❌ | ❌ |
| ElevenLabs | ❌ | ❌ | ❌ | ✅ | ❌ |
## Quick Start 🏃♂️
You can use Esperanto in two ways: directly with provider-specific classes or through the AI Factory.
### Using AI Factory
```python
from esperanto.factory import AIFactory
# Create an LLM instance
model = AIFactory.create_llm("openai", "gpt-3.5-turbo")
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
model = AIFactory.create_embedding("openai", "text-embedding-3-small")
texts = ["Hello, world!", "Another text"]
embeddings = model.embed(texts)
```
## Standardized Responses
All providers in Esperanto return standardized response objects, making it easy to work with different models without changing your code.
### LLM Responses
```python
from esperanto.factory import AIFactory
model = AIFactory.create_llm("openai", "gpt-3.5-turbo")
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 text
```
### Embedding Responses
```python
from 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 information
```
The 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
## Links 🔗
- **Documentation**: [GitHub Documentation](https://github.com/lfnovo/esperanto#readme)
- **Source Code**: [GitHub Repository](https://github.com/lfnovo/esperanto)
- **Issue Tracker**: [GitHub Issues](https://github.com/lfnovo/esperanto/issues)
## License 📄
This project is licensed under the MIT License - see the [LICENSE](https://github.com/lfnovo/esperanto/blob/main/LICENSE) file for details.
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"description": "# Esperanto \ud83c\udf10\n\n[](https://badge.fury.io/py/esperanto)\n[](https://pypi.org/project/esperanto/)\n[](https://github.com/lfnovo/esperanto)\n[](https://pypi.org/project/esperanto/)\n[](https://opensource.org/licenses/MIT)\n\nEsperanto 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) APIs by offering a consistent interface while maintaining provider-specific optimizations.\n\n## Features \u2728\n\n- **Unified Interface**: Work with multiple LLM providers using a consistent API\n- **Provider Support**:\n - OpenAI (GPT-4, GPT-3.5, o1, Whisper, TTS)\n - Anthropic (Claude 3)\n - OpenRouter (Access to multiple models)\n - xAI (Grok)\n - Groq (Mixtral, Llama, Whisper)\n - Google GenAI (Gemini LLM, Text To Speech, Embedding)\n - Vertex AI (Google Cloud)\n - Ollama (Local deployment)\n - ElevenLabs (Text-to-Speech)\n\n- **Embedding Support**: Multiple embedding providers for vector representations\n- **Speech-to-Text Support**: Transcribe audio using multiple providers\n- **Text-to-Speech Support**: Generate speech using multiple providers\n- **Async Support**: Both synchronous and asynchronous API calls\n- **Streaming**: Support for streaming responses\n- **Structured Output**: JSON output formatting (where supported)\n- **LangChain Integration**: Easy conversion to LangChain chat models\n\nFor detailed information about our providers, check out:\n- [LLM Providers Documentation](https://github.com/lfnovo/esperanto/blob/main/docs/llm.md)\n- [Embedding Providers Documentation](https://github.com/lfnovo/esperanto/blob/main/docs/embedding.md)\n- [Speech-to-Text Providers Documentation](https://github.com/lfnovo/esperanto/blob/main/docs/speech_to_text.md)\n- [Text-to-Speech Providers Documentation](https://github.com/lfnovo/esperanto/blob/main/docs/text_to_speech.md)\n\n## Installation \ud83d\ude80\n\nInstall Esperanto using pip:\n\n```bash\npip install esperanto\n```\n\nFor specific providers, install with their extras:\n\n```bash\n# For OpenAI support\npip install \"esperanto[openai]\"\n\n# For Anthropic support\npip install \"esperanto[anthropic]\"\n\n# For Google (GenAI) support\npip install \"esperanto[google]\"\n\n# For Vertex AI support\npip install \"esperanto[vertex]\"\n\n# For Groq support\npip install \"esperanto[groq]\"\n\n# For Ollama support\npip install \"esperanto[ollama]\"\n\n# For all providers\npip install \"esperanto[all]\"\n```\n\n## Provider Support Matrix\n\n| Provider | LLM Support | Embedding Support | Speech-to-Text | Text-to-Speech | JSON Mode |\n|------------|-------------|------------------|----------------|----------------|-----------|\n| OpenAI | \u2705 | \u2705 | \u2705 | \u2705 | \u2705 |\n| Anthropic | \u2705 | \u274c | \u274c | \u274c | \u2705 |\n| Groq | \u2705 | \u274c | \u2705 | \u274c | \u2705 |\n| Google (GenAI) | \u2705 | \u2705 | \u274c | \u2705 | \u2705 |\n| Vertex AI | \u2705 | \u2705 | \u274c | \u274c | \u274c |\n| Ollama | \u2705 | \u2705 | \u274c | \u274c | \u274c |\n| ElevenLabs | \u274c | \u274c | \u274c | \u2705 | \u274c |\n\n## Quick Start \ud83c\udfc3\u200d\u2642\ufe0f\n\nYou can use Esperanto in two ways: directly with provider-specific classes or through the AI Factory.\n\n### Using AI Factory\n\n```python\nfrom esperanto.factory import AIFactory\n\n# Create an LLM instance\nmodel = AIFactory.create_llm(\"openai\", \"gpt-3.5-turbo\")\nmessages = [\n {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n {\"role\": \"user\", \"content\": \"What's the capital of France?\"},\n]\nresponse = model.chat_complete(messages)\n\n# Create an embedding instance\nmodel = AIFactory.create_embedding(\"openai\", \"text-embedding-3-small\")\ntexts = [\"Hello, world!\", \"Another text\"]\nembeddings = model.embed(texts)\n```\n\n## Standardized Responses\n\nAll providers in Esperanto return standardized response objects, making it easy to work with different models without changing your code.\n\n### LLM Responses\n\n```python\nfrom esperanto.factory import AIFactory\n\nmodel = AIFactory.create_llm(\"openai\", \"gpt-3.5-turbo\")\nmessages = [{\"role\": \"user\", \"content\": \"Hello!\"}]\n\n# All LLM responses follow this structure\nresponse = model.chat_complete(messages)\nprint(response.choices[0].message.content) # The actual response text\nprint(response.choices[0].message.role) # 'assistant'\nprint(response.model) # The model used\nprint(response.usage.total_tokens) # Token usage information\n\n# For streaming responses\nfor chunk in model.chat_complete(messages):\n print(chunk.choices[0].delta.content) # Partial response text\n```\n\n### Embedding Responses\n\n```python\nfrom esperanto.factory import AIFactory\n\nmodel = AIFactory.create_embedding(\"openai\", \"text-embedding-3-small\")\ntexts = [\"Hello, world!\", \"Another text\"]\n\n# All embedding responses follow this structure\nresponse = model.embed(texts)\nprint(response.data[0].embedding) # Vector for first text\nprint(response.data[0].index) # Index of the text (0)\nprint(response.model) # The model used\nprint(response.usage.total_tokens) # Token usage information\n```\n\nThe standardized response objects ensure consistency across different providers, making it easy to:\n- Switch between providers without changing your application code\n- Handle responses in a uniform way\n- Access common attributes like token usage and model information\n\n## Links \ud83d\udd17\n\n- **Documentation**: [GitHub Documentation](https://github.com/lfnovo/esperanto#readme)\n- **Source Code**: [GitHub Repository](https://github.com/lfnovo/esperanto)\n- **Issue Tracker**: [GitHub Issues](https://github.com/lfnovo/esperanto/issues)\n\n## License \ud83d\udcc4\n\nThis project is licensed under the MIT License - see the [LICENSE](https://github.com/lfnovo/esperanto/blob/main/LICENSE) file for details.\n",
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