UEL (Upsonic Expression Language)

Build powerful AI chains with intuitive composition patterns. The UEL (Upsonic Expression Language) provides a declarative way to compose AI components into sophisticated chains. Without UEL, building complex AI workflows requires verbose code with manual data passing and error handling. With UEL, you can:

Overview

UEL can be used with minimal configuration or with extensive customization to suit your specific needs. The system provides a robust foundation for AI-powered applications with built-in support for various advanced features.

Key Features

Chain Components Elegantly - Use the pipe operator (|) for readable, maintainable code
Execute Operations in Parallel - Maximize performance and reduce latency
Build Dynamic Chains - Adapt based on input conditions
Manage Conversation History - Seamlessly handle context across interactions
Integrate Tools and Structured Outputs - Minimal boilerplate required
Visualize Your Chains - Understand complex workflows at a glance
Async Support - Full async/await support for all operations

Example

Basic Example

from upsonic.uel import ChatPromptTemplate, StrOutputParser
from upsonic.models import infer_model

# Create a simple chain with output parser
chain = (
    ChatPromptTemplate.from_template("Tell me about {topic}") 
    | infer_model("anthropic/claude-sonnet-4-5")
    | StrOutputParser()
)

# Execute the chain
result = chain.invoke({"topic": "quantum computing"})
print(result)

When using infer_model() without specifying a model, it defaults to "openai/gpt-4o". Make sure you have the appropriate API key set in your environment.

Advanced Example with Memory

from upsonic.uel import ChatPromptTemplate, StrOutputParser
from upsonic.models import infer_model

# Create model with memory
model = infer_model("anthropic/claude-sonnet-4-5").add_memory(history=True)

# Create chain with conversation history and output parser
chain = (
    ChatPromptTemplate.from_messages([
        ("system", "You are a helpful assistant."),
        ("placeholder", {"variable_name": "chat_history"}),
        ("human", "{input}")
    ])
    | model
    | StrOutputParser()
)

# First interaction
response1 = chain.invoke({
    "input": "My name is Alice",
    "chat_history": []
})
print(response1)

# Second interaction - model remembers context
response2 = chain.invoke({
    "input": "What's my name?",
    "chat_history": [
        ("human", "My name is Alice"),
        ("ai", response1)
    ]
})
print(response2)  # Output: Your name is Alice

Attributes - Comprehensive guide to all UEL configuration options
Parallel Execution - Execute multiple chains simultaneously
Conditional Routing - Route execution based on conditions
Custom Chains - Create custom chain functions
RAG Patterns - Build RAG systems with UEL
Visualization - Visualize your chains
Async Execution - Use async/await with chains

GET STARTED

CONCEPTS

STARTING AN AGENT PROJECT

READY TO USE SNIPPETS

DEPLOYMENT

FURTHER READINGS

UEL (Upsonic Expression Language)

Overview

Key Features

Example

Basic Example

Advanced Example with Memory

Navigation

​Overview

​Key Features

​Example

​Basic Example

​Advanced Example with Memory

​Navigation

Overview

Key Features

Example

Basic Example

Advanced Example with Memory

Navigation