Esperanto 🌐

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.

Why Esperanto? 🚀

🪶 Ultra-Lightweight Architecture

• Direct HTTP Communication: All providers communicate directly via HTTP APIs using httpx - no bulky vendor SDKs required
• Minimal Dependencies: Unlike LangChain and similar frameworks, Esperanto has a tiny footprint with zero overhead layers
• Production-Ready Performance: Direct API calls mean faster response times and lower memory usage

🔄 True Provider Flexibility

• Standardized Responses: Switch between any provider (OpenAI ↔ Anthropic ↔ Google ↔ etc.) without changing a single line of code
• Consistent Interface: Same methods, same response objects, same patterns across all 15+ providers
• Future-Proof: Add new providers or change existing ones without refactoring your application

⚡ Perfect for Production

• Prototyping to Production: Start experimenting and deploy the same code to production
• No Vendor Lock-in: Test different providers, optimize costs, and maintain flexibility
• Enterprise-Ready: Direct HTTP calls, standardized error handling, and comprehensive async support

Whether you're building a quick prototype or a production application serving millions of requests, Esperanto gives you the performance of direct API calls with the convenience of a unified interface.

Features ✨

• Unified Interface: Work with multiple LLM providers using a consistent API
• Provider Support:
  • OpenAI (GPT-4o, o1, o3, o4, Whisper, TTS)
  • OpenAI-Compatible (LM Studio, Ollama, vLLM, custom endpoints)
  • Anthropic (Claude models)
  • OpenRouter (Access to multiple models)
  • xAI (Grok)
  • Perplexity (Sonar models)
  • Groq (Mixtral, Llama, Whisper)
  • Google GenAI (Gemini LLM, Text To Speech, Embedding with native task optimization)
  • Vertex AI (Google Cloud, LLM, Embedding, TTS)
  • Ollama (Local deployment multiple models)
  • Transformers (Universal local models - Qwen, CrossEncoder, BAAI, Jina, Mixedbread)
  • ElevenLabs (Text-to-Speech, Speech-to-Text)
  • Azure OpenAI (Chat, Embedding)
  • Mistral (Mistral Large, Small, Embedding, etc.)
  • DeepSeek (deepseek-chat)
  • Voyage (Embeddings, Reranking)
  • Jina (Advanced embedding models with task optimization, Reranking)
• Embedding Support: Multiple embedding providers for vector representations
• Reranking Support: Universal reranking interface for improving search relevance
• 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
• Reranking Providers Documentation
• Speech-to-Text Providers Documentation
• Text-to-Speech Providers Documentation

Installation 🚀

Install Esperanto using pip:

pip install esperanto

Optional Dependencies

Transformers Provider

If you plan to use the transformers provider, install with the transformers extra:

pip install "esperanto[transformers]"

This installs:

• transformers - Core Hugging Face library
• torch - PyTorch framework
• tokenizers - Fast tokenization
• sentence-transformers - CrossEncoder support
• scikit-learn - Advanced embedding features
• numpy - Numerical computations

LangChain Integration

If you plan to use any of the .to_langchain() methods, you need to install the correct LangChain SDKs manually:

# Core LangChain dependencies (required)
pip install "langchain>=0.3.8,<0.4.0" "langchain-core>=0.3.29,<0.4.0"

# Provider-specific LangChain packages (install only what you need)
pip install "langchain-openai>=0.2.9"
pip install "langchain-anthropic>=0.3.0"
pip install "langchain-google-genai>=2.1.2"
pip install "langchain-ollama>=0.2.0"
pip install "langchain-groq>=0.2.1"
pip install "langchain_mistralai>=0.2.1"
pip install "langchain_deepseek>=0.1.3"
pip install "langchain-google-vertexai>=2.0.24"

Provider Support Matrix

Provider	LLM Support	Embedding Support	Reranking Support	Speech-to-Text	Text-to-Speech	JSON Mode
OpenAI	✅	✅	❌	✅	✅	✅
OpenAI-Compatible	✅	❌	❌	❌	✅	⚠️*
Anthropic	✅	❌	❌	❌	❌	✅
Groq	✅	❌	❌	✅	❌	✅
Google (GenAI)	✅	✅	❌	❌	✅	✅
Vertex AI	✅	✅	❌	❌	✅	❌
Ollama	✅	✅	❌	❌	❌	❌
Perplexity	✅	❌	❌	❌	❌	✅
Transformers	❌	✅	✅	❌	❌	❌
ElevenLabs	❌	❌	❌	✅	✅	❌
Azure OpenAI	✅	✅	❌	❌	❌	✅
Mistral	✅	✅	❌	❌	❌	✅
DeepSeek	✅	❌	❌	❌	❌	✅
Voyage	❌	✅	✅	❌	❌	❌
Jina	❌	✅	✅	❌	❌	❌
xAI	✅	❌	❌	❌	❌	✅
OpenRouter	✅	❌	❌	❌	❌	✅

*⚠️ OpenAI-Compatible: JSON mode support depends on the specific endpoint implementation

Quick Start 🏃‍♂️

You can use Esperanto in two ways: directly with provider-specific classes or through the AI Factory.

Using 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', 'openai-compatible', 'anthropic', 'google', 'groq', 'ollama', 'openrouter', 'xai', 'perplexity', 'azure', 'mistral', 'deepseek'],
#     'embedding': ['openai', 'google', 'ollama', 'vertex', 'transformers', 'voyage', 'mistral', 'azure', 'jina'],
#     'reranker': ['jina', 'voyage', 'transformers'],
#     'speech_to_text': ['openai', 'groq', 'elevenlabs'],
#     'text_to_speech': ['openai', 'elevenlabs', 'google', 'vertex', 'openai-compatible']
# }

# Create model instances
model = AIFactory.create_language(
    "openai", 
    "gpt-3.5-turbo",
    config={"structured": {"type": "json"}}
)  # Language model
embedder = AIFactory.create_embedding("openai", "text-embedding-3-small")  # Embedding model
reranker = AIFactory.create_reranker("transformers", "cross-encoder/ms-marco-MiniLM-L-6-v2")  # Universal reranker 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)

Using Provider-Specific Classes

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 format

Standardized Responses

All providers in Esperanto return standardized response objects, making it easy to work with different models without changing your code.

LLM Responses

from esperanto.factory import AIFactory

model = AIFactory.create_language(
    "openai", 
    "gpt-3.5-turbo",
    config={"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
print(response.content)          # Shortcut for response.choices[0].message.content

# For streaming responses
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)

Embedding Responses

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

Reranking Responses

from esperanto.factory import AIFactory

reranker = AIFactory.create_reranker("transformers", "BAAI/bge-reranker-base")
query = "What is machine learning?"
documents = [
    "Machine learning is a subset of artificial intelligence.",
    "The weather is nice today.",
    "Python is a programming language used in ML."
]

# All reranking responses follow this structure
response = reranker.rerank(query, documents, top_k=2)
print(response.results[0].document)          # Highest ranked document
print(response.results[0].relevance_score)   # Normalized 0-1 relevance score
print(response.results[0].index)             # Original document index
print(response.model)                        # The model used

Task-Aware Embeddings 🎯

Esperanto supports advanced task-aware embeddings that optimize vector representations for specific use cases. This works across all embedding providers through a universal interface:

from esperanto.factory import AIFactory
from esperanto.common_types.task_type import EmbeddingTaskType

# Task-optimized embeddings work with ANY provider
model = AIFactory.create_embedding(
    provider="jina",  # Also works with: "openai", "google", "transformers", etc.
    model_name="jina-embeddings-v3",
    config={
        "task_type": EmbeddingTaskType.RETRIEVAL_QUERY,  # Optimize for search queries
        "late_chunking": True,                           # Better long-context handling
        "output_dimensions": 512                         # Control vector size
    }
)

# Generate optimized embeddings
query = "What is machine learning?"
embeddings = model.embed([query])

Universal Task Types:

• RETRIEVAL_QUERY - Optimize for search queries
• RETRIEVAL_DOCUMENT - Optimize for document storage
• SIMILARITY - General text similarity
• CLASSIFICATION - Text classification tasks
• CLUSTERING - Document clustering
• CODE_RETRIEVAL - Code search optimization
• QUESTION_ANSWERING - Optimize for Q&A tasks
• FACT_VERIFICATION - Optimize for fact checking

Provider Support:

• Jina: Native API support for all features
• Google: Native task type translation to Gemini API
• OpenAI: Task optimization via intelligent text prefixes
• Transformers: Local emulation with task-specific processing
• Others: Graceful degradation with consistent interface

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

Provider Configuration 🔧

OpenAI

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
)

OpenAI-Compatible Endpoints

Use any OpenAI-compatible endpoint (LM Studio, Ollama, vLLM, custom deployments) with the same interface:

from esperanto.factory import AIFactory

# Using factory config
model = AIFactory.create_language(
    "openai-compatible",
    "your-model-name",  # Use any model name supported by your endpoint
    config={
        "base_url": "http://localhost:1234/v1",  # Your endpoint URL (required)
        "api_key": "your-api-key"                # Your API key (optional)
    }
)

# Or set environment variables
# OPENAI_COMPATIBLE_BASE_URL=http://localhost:1234/v1
# OPENAI_COMPATIBLE_API_KEY=your-api-key  # Optional for endpoints that don't require auth
model = AIFactory.create_language("openai-compatible", "your-model-name")

# Works with any OpenAI-compatible endpoint
messages = [{"role": "user", "content": "Hello!"}]
response = model.chat_complete(messages)
print(response.content)

# Streaming support
for chunk in model.chat_complete(messages, stream=True):
    print(chunk.choices[0].delta.content, end="", flush=True)

Common Use Cases:

• LM Studio: Local model serving with GUI
• Ollama: ollama serve with OpenAI compatibility
• vLLM: High-performance inference server
• Custom Deployments: Any server implementing OpenAI chat completions API

Features:

• ✅ Streaming: Real-time response streaming
• ✅ Pass-through Model Names: Use any model name your endpoint supports
• ✅ Graceful Degradation: Automatically handles varying feature support
• ✅ Error Handling: Clear error messages for troubleshooting
• ⚠️ JSON Mode: Depends on endpoint implementation

Perplexity

Perplexity uses an OpenAI-compatible API but includes additional parameters for controlling search behavior.

from esperanto.providers.llm.perplexity import PerplexityLanguageModel

model = PerplexityLanguageModel(
    api_key="your-api-key",  # Or set PERPLEXITY_API_KEY env var
    model_name="llama-3-sonar-large-32k-online", # Recommended default
    temperature=0.7,         # Optional
    max_tokens=850,         # Optional
    streaming=False,        # Optional
    top_p=0.9,             # Optional
    structured={"type": "json"}, # Optional, for JSON output

    # Perplexity-specific parameters
    search_domain_filter=["example.com", "-excluded.com"], # Optional, limit search domains
    return_images=False,             # Optional, include images in search results
    return_related_questions=True,  # Optional, return related questions
    search_recency_filter="week",    # Optional, filter search by time ('day', 'week', 'month', 'year')
    web_search_options={"search_context_size": "high"} # Optional, control search context ('low', 'medium', 'high')
)

Streaming Responses 🌊

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)

Structured Output 📊

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 format

LangChain Integration 🔗

Convert 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)

Documentation 📚

You can find the documentation for Esperanto in the docs directory.

There is also a cool beginner's tutorial in the tutorial directory.

Contributing 🤝

We welcome contributions! Please see our Contributing Guidelines for details on how to get started.

License 📄

This project is licensed under the MIT License - see the LICENSE file for details.

Development 🛠️

1. Clone the repository:

git clone https://github.com/lfnovo/esperanto.git
cd esperanto

2. Install dependencies:

pip install -r requirements.txt

3. Run tests:

pytest