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Functional API

The Functional API

Welcome to your first Synalinks lesson! In this tutorial, you will learn how to build AI applications using the Functional API - the most intuitive and recommended approach for creating programs.

What is Synalinks?

Synalinks is a framework for building AI applications powered by Large Language Models (LLMs). Think of it like building with LEGO blocks - you connect different pieces (called "modules") together to create something useful.

Core Concepts

1. Programs and Modules

A Program in Synalinks is like a recipe - it defines the steps your AI application will follow. Each step is performed by a Module, which is a reusable building block.

graph LR
    Input --> Module1[Module 1] --> Module2[Module 2] --> Output

2. Data Models

Data flows through your program in structured formats called DataModels. Think of them as blueprints that define what information looks like:

class Query(synalinks.DataModel):
    query: str = synalinks.Field(description="The user's question")

3. The Functional API

The Functional API lets you build programs by:

  1. Creating an Input placeholder
  2. Passing it through modules (like connecting pipes)
  3. Wrapping everything in a Program
# Step 1: Define where data enters
inputs = synalinks.Input(data_model=Query)

# Step 2: Pass through a module (Generator uses an LLM to create output)
outputs = await synalinks.Generator(
    data_model=Answer,
    language_model=language_model,
)(inputs)

# Step 3: Create the program
program = synalinks.Program(inputs=inputs, outputs=outputs)

Complete Example

Here's a complete Chain of Thought program that shows reasoning before answering:

import asyncio
from dotenv import load_dotenv
import synalinks

# Define input and output data models
class Query(synalinks.DataModel):
    query: str = synalinks.Field(description="The user query")

class AnswerWithThinking(synalinks.DataModel):
    thinking: str = synalinks.Field(description="Your step by step thinking")
    answer: str = synalinks.Field(description="The correct answer")

async def main():
    load_dotenv()

    language_model = synalinks.LanguageModel(model="openai/gpt-4.1")

    # Build with Functional API
    inputs = synalinks.Input(data_model=Query)
    outputs = await synalinks.Generator(
        data_model=AnswerWithThinking,
        language_model=language_model,
    )(inputs)

    program = synalinks.Program(
        inputs=inputs,
        outputs=outputs,
        name="chain_of_thought",
        description="Useful to answer in a step by step manner.",
    )

    # Run the program
    result = await program(Query(query="What are the key aspects of human cognition?"))
    print(f"Thinking: {result['thinking']}")
    print(f"Answer: {result['answer']}")

asyncio.run(main())

Key Takeaways

  • Functional API: Build programs by connecting modules like pipes - data flows from Input through modules to create outputs.
  • Three Steps: (1) Create an Input, (2) Pass through modules, (3) Wrap in a Program.
  • Generator Module: The core module that uses an LLM to transform input data into structured output matching your data model.
  • Reusable Programs: Once built, programs can be called like functions with your input data model.

Program Visualization

chain_of_thought

API References

AnswerWithThinking

Bases: DataModel

The output from our program - reasoning + final answer.

By asking the LLM to show its thinking, we get better answers. This is called "Chain of Thought" prompting.

Source code in examples/1a_functional_api.py 
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class AnswerWithThinking(synalinks.DataModel):
    """The output from our program - reasoning + final answer.

    By asking the LLM to show its thinking, we get better answers.
    This is called "Chain of Thought" prompting.
    """

    thinking: str = synalinks.Field(
        description="Your step by step thinking",
    )
    answer: str = synalinks.Field(
        description="The correct answer",
    )

Query

Bases: DataModel

The input to our program - a user's question.

Source code in examples/1a_functional_api.py 
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class Query(synalinks.DataModel):
    """The input to our program - a user's question."""

    query: str = synalinks.Field(
        description="The user query",
    )