Express YourSelf !

DSPy = Declarative Self-improving Python.

Components

1. language model — LLM that will answer our questions,

2. signature —a declaration of the program’s input and output (what task we want to solve),

 - 1. inline

 - 2. class

dspy.InputField()

List[Literal['', '', '']] = dspy.OutputField()

3. module — the prompting technique (how we want to solve the task).

 - Building blocks

 - different prompting strategies, 

  - 1. dspy.Predict

  - 2. dspy.ChainOfThought 

  - 3. dspy.ReAct (to add tools = function calling

4. Optimiser

  - 1. Automatic few-shot learning (e.g. BootstrapFewShot or BootstrapFewShotWithRandomSearch)

  - 2. Automatic instructions optimisation (e.g. MIPROv2) 

  - 3. Automatic fine-tuning (e.g, BootstrapFinetune) 

Other points

• dspy.inspect_history for logs
• Caching

# 1. updating config

dspy.configure_cache(enable_memory_cache=False, enable_disk_cache=False)

# 2. not using cache for specific module

math = dspy.Predict("question -> answer: float", cache = False)

• dspy.configure(adapter=dspy.JSONAdapter()) 

• DSPy is integrated with MLFlow (an observability tool)