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Concepts Agent 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:

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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.

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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

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Example

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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("openai/gpt-4o")
    | 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.

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Advanced Example with Memory

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

# Create model with memory
model = infer_model("openai/gpt-4o").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