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Sweep: Ensure `predict` in the `dspy/` folder has documentation. about dspy HOT 1 OPEN

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Sweep: Ensure `predict` in the `dspy/` folder has documentation.

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dspy/dspy/predict/predict.py

Lines 3 to 123 in c7a522a

    
           from dspy.predict.parameter import Parameter 
        
           from dspy.primitives.prediction import Prediction 
        
           from dspy.signatures.field import InputField, OutputField 
        
           from dspy.signatures.signature import infer_prefix 
        
           class Predict(Parameter): 
        
               def __init__(self, signature, **config): 
        
                   self.stage = random.randbytes(8).hex() 
        
                   self.signature = signature #.signature 
        
                   self.config = config 
        
                   self.reset() 
        
                   # if the signature is a string 
        
                   if isinstance(signature, str): 
        
                       inputs, outputs = signature.split("->") 
        
                       inputs, outputs = inputs.split(","), outputs.split(",") 
        
                       inputs, outputs = [field.strip() for field in inputs], [field.strip() for field in outputs] 
        
                       assert all(len(field.split()) == 1 for field in (inputs + outputs)) 
        
                       inputs_ = ', '.join([f"`{field}`" for field in inputs]) 
        
                       outputs_ = ', '.join([f"`{field}`" for field in outputs]) 
        
                       instructions = f"""Given the fields {inputs_}, produce the fields {outputs_}.""" 
        
                       inputs = {k: InputField() for k in inputs} 
        
                       outputs = {k: OutputField() for k in outputs} 
        
                       for k, v in inputs.items(): 
        
                           v.finalize(k, infer_prefix(k)) 
        
                       for k, v in outputs.items(): 
        
                           v.finalize(k, infer_prefix(k)) 
        
                       self.signature = dsp.Template(instructions, **inputs, **outputs) 
        
               def reset(self): 
        
                   self.lm = None 
        
                   self.traces = [] 
        
                   self.train = [] 
        
                   self.demos = [] 
        
               def dump_state(self): 
        
                   state_keys = ["lm", "traces", "train", "demos"] 
        
                   return {k: getattr(self, k) for k in state_keys} 
        
               def load_state(self, state): 
        
                   for name, value in state.items(): 
        
                       setattr(self, name, value) 
        
                   import dspy 
        
                   self.demos = [dspy.Example(**x) for x in self.demos] 
        
               def __call__(self, **kwargs): 
        
                   return self.forward(**kwargs) 
        
               def forward(self, **kwargs): 
        
                   # Extract the three privileged keyword arguments. 
        
                   signature = kwargs.pop("signature", self.signature) 
        
                   demos = kwargs.pop("demos", self.demos) 
        
                   config = dict(**self.config, **kwargs.pop("config", {})) 
        
                   # Get the right LM to use. 
        
                   lm = kwargs.pop("lm", self.lm) or dsp.settings.lm 
        
                   # If temperature is 0.0 but its n > 1, set temperature to 0.7. 
        
                   temperature = config.get("temperature", None) 
        
                   temperature = lm.kwargs['temperature'] if temperature is None else temperature 
        
                   num_generations = config.get("n", None) 
        
                   num_generations = lm.kwargs['n'] if num_generations is None else num_generations 
        
                   if (temperature is None or temperature <= 0.15) and num_generations > 1: 
        
                       config["temperature"] = 0.7 
        
                       # print(f"#> Setting temperature to 0.7 since n={num_generations} and prior temperature={temperature}.") 
        
                   # All of the other kwargs are presumed to fit a prefix of the signature. 
        
                   x = dsp.Example(demos=demos, **kwargs) 
        
                   if self.lm is None: 
        
                       x, C = dsp.generate(signature, **config)(x, stage=self.stage) 
        
                   else: 
        
                       with dsp.settings.context(lm=self.lm, query_only=True): 
        
                           # print(f"using lm = {self.lm} !") 
        
                           x, C = dsp.generate(signature, **config)(x, stage=self.stage) 
        
                   completions = [] 
        
                   for c in C: 
        
                       completions.append({}) 
        
                       for field in signature.fields: 
        
                           if field.output_variable not in kwargs.keys(): 
        
                               completions[-1][field.output_variable] = getattr(c, field.output_variable) 
        
                   pred = Prediction.from_completions(completions, signature=signature) 
        
                   if dsp.settings.trace is not None: 
        
                       trace = dsp.settings.trace 
        
                       trace.append((self, {**kwargs}, pred)) 
        
                   return pred 
        
               def update_config(self, **kwargs): 
        
                   self.config = {**self.config, **kwargs} 
        
               def get_config(self): 
        
                   return self.config 
        
               def __repr__(self): 
        
                   return f"{self.__class__.__name__}({self.signature})" 
        
           # TODO: get some defaults during init from the context window? 
        
           # # TODO: FIXME: Hmm, I guess expected behavior is that contexts can 
        
           # affect exeuction. Well, we need to determine whether context dominates, __init__ demoninates, or forward dominates. 
        
           # Generally, unless overwritten, we'd see n=None, temperature=None.

dspy/README.md

Lines 1 to 45 in c7a522a

    
           current docs link: https://dspy-ai.readthedocs.io/en/latest/ 
        
           <p align="center"> 
        
             <img align="center" src="docs/images/DSPy8.png" width="460px" /> 
        
           </p> 
        
           <p align="left"> 
        
           ## DSPy: _Programming_—not prompting—Foundation Models 
        
           Paper —— **[DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://arxiv.org/abs/2310.03714)** 
        
           [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/intro.ipynb) 
        
           **DSPy** is the framework for solving advanced tasks with language models (LMs) and retrieval models (RMs). **DSPy** unifies techniques for **prompting** and **fine-tuning** LMs — and approaches for **reasoning**, **self-improvement**, and **augmentation with retrieval and tools**. All of these are expressed through modules that compose and learn. 
        
           To make this possible: 
        
           - **DSPy** provides **composable and declarative modules** for instructing LMs in a familiar Pythonic syntax. It upgrades "prompting techniques" like chain-of-thought and self-reflection from hand-adapted _string manipulation tricks_ into truly modular _generalized operations that learn to adapt to your task_. 
        
           - **DSPy** introduces an **automatic compiler that teaches LMs** how to conduct the declarative steps in your program. Specifically, the **DSPy compiler** will internally _trace_ your program and then **craft high-quality prompts for large LMs (or train automatic finetunes for small LMs)** to teach them the steps of your task. 
        
           The **DSPy compiler** _bootstraps_ prompts and finetunes from minimal data **without needing manual labels for the intermediate steps** in your program. Instead of brittle "prompt engineering" with hacky string manipulation, you can explore a systematic space of modular and trainable pieces. 
        
           For complex tasks, **DSPy** can routinely teach powerful models like `GPT-3.5` and local models like `T5-base` or `Llama2-13b` to be much more reliable at tasks. **DSPy** will compile the _same program_ into different few-shot prompts and/or finetunes for each LM. 
        
           If you want to see **DSPy** in action, **[open our intro tutorial notebook](intro.ipynb)**. 
        
           ### Table of Contents 
        
           1. **[Installation](#1-installation)** 
        
           1. **[Framework Syntax](#2-syntax-youre-in-charge-of-the-workflowits-free-form-python-code)** 
        
           1. **[Compiling: Two Powerful Concepts](#3-two-powerful-concepts-signatures--teleprompters)** 
        
           1. **[Tutorials & Documentation](#4-documentation--tutorials)** 
        
           1. **[FAQ: Is DSPy right for me?](#5-faq-is-dspy-right-for-me)** 
        
           ### Analogy to Neural Networks 
        
           When we build neural networks, we don't write manual _for-loops_ over lists of _hand-tuned_ floats. Instead, you might use a framework like [PyTorch](https://pytorch.org/) to compose declarative layers (e.g., `Convolution` or `Dropout`) and then use optimizers (e.g., SGD or Adam) to learn the parameters of the network.

dspy/dspy/predict/__init__.py

Lines 1 to 7 in c7a522a

    
           from .predict import Predict 
        
           from .chain_of_thought import ChainOfThought 
        
           from .multi_chain_comparison import MultiChainComparison 
        
           from .chain_of_thought_with_hint import ChainOfThoughtWithHint 
        
           from .react import ReAct 
        
           from .aggregation import majority 
        
           from .program_of_thought import ProgramOfThought

dspy/docs/modules/modules.md

Lines 1 to 20 in c7a522a

    
           # dspy.Modules Documentation 
        
           This documentation provides an overview of the DSPy Modules. 
        
           ## DSPy Modules 
        
           | Module | Jump To | 
        
           | --- | --- | 
        
           | Predict | [Predict Section](#dspypredict) | 
        
           | Retrieve | [Retrieve Section](#dspyretrieve) | 
        
           | ChainOfThought | [ChainOfThought Section](#dspychainofthought) | 
        
           | ChainOfThoughtWithHint | [ChainOfThoughtWithHint Section](#dspychainofthoughtwithhint) | 
        
           | MultiChainComparison | [MultiChainComparison Section](#dspymultichaincomparison) | 
        
           | ReAct | [ReAct Section](#dspyreact) | 
        
           | Assertion Helpers | [Assertion Helpers Section](#dspyassertionhelpers) | 
        
           ## dspy.Predict 
        
           ### Constructor

Step 2: ⌨️ Coding

Modify docs/modules/modules.md ✓ ee3fc12 Edit

Modify docs/modules/modules.md with contents:
• Start by adding a brief introduction to the `predict` module. This should explain what the module is and what it is used for. This information can be drawn from the README.md file.
• Next, describe the code in the `predict` module. This should include a description of the `Predict` class and its methods. Refer to the `dspy/predict/predict.py` file for this information.
• Then, explain the philosophical usage/purpose of `predict`. This should include an explanation of how the `predict` module fits into the overall framework of DSPy and how it contributes to the goal of programming with foundation models. This information can be drawn from the README.md file.
• Finally, provide examples of how to use the `predict` module. These examples can be drawn from the jupyter notebooks. Make sure to explain what each example is doing and how it demonstrates the use of the `predict` module.

--- 
+++ 
@@ -1,4 +1,6 @@
-# dspy.Modules Documentation
+## Predict
+
+The `Predict` module is a core component of DSPy that encapsulates the task-oriented capacities of language models within an accessible interface. It is designed to facilitate the making of predictions and generations based on provided input as part of a broader declarative programming model. The `Predict` module leverages language compositions that signal an intent to generate specific kinds of information or complete complex tasks, effectively bridging the gap between human-level task descriptions and machine-executable code.# dspy.Modules Documentation
 
 This documentation provides an overview of the DSPy Modules.
 
@@ -16,6 +18,10 @@
 
 ## dspy.Predict
 
+### Introduction
+
+The `Predict` class is integral to the DSPy predictive module, responsible for handling predictions and generative tasks. It is constructed with flexibility in mind, allowing users to define the input-output schema through a `signature` and configure the predictive behavior with additional options. The class instills the power of language models into routine programming practices.
+
 ### Constructor
 
 The constructor initializes the `Predict` class and sets up its attributes, taking in the `signature` and additional config options. If the `signature` is a string, it processes the input and output fields, generates instructions, and creates a template for the specified `signature` type.
@@ -41,7 +47,27 @@
 This handler is used to bypass assertions and suggestions. When used, both assertions and suggestions will become no-operations (noops).
 
 #### `bypass_suggest_handler(func)`
-
+### Examples
+
+#### Example 1: Basic Prediction
+
+```python
+# Define the signature of the task
+class MyTaskSignature(dspy.Signature):
+    input1 = dspy.InputField(desc='A description of input1')
+    output1 = dspy.OutputField(desc='Expected output from the prediction')
+
+# Initialize the Predict module with the defined signature
+predictor = dspy.Predict(MyTaskSignature)
+
+# Call the Predict module
+result = predictor(input1='sample input')
+print(f"Predicted output: {result.output1}")
+```
+
+In this example, the `Predict` module is instantiated with a user-defined signature, and a prediction is made through a straightforward call using sample input. It demonstrates the ease of use of `Predict` for task executions based on varying inputs.### Philosophy and Usage
+
+In DSPy, the philosophy around 'predict' entails harnessing the declarative power of language models to execute well-defined tasks within a broader computational context. The 'Predict' module, in essence, transforms these declarative expressions into performative actions that a language model can understand and execute, making it a philosophical cornerstone for high-level abstraction of foundation models. It aligns closely with the framework's goal of making foundation models programmable while retaining flexibility and adaptability to various task specifications and domains.
 This handler is used to bypass suggestions only. If a suggestion fails, it will be logged but not raised. If an assertion fails, it will be raised.
 
 #### `bypass_assert_handler(func)`

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Sweep: Ensure `predict` in the `dspy/` folder has documentation. about dspy HOT 1 OPEN

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🚀 Here's the PR! #48

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	from dspy.predict.parameter import Parameter
	from dspy.primitives.prediction import Prediction
	from dspy.signatures.field import InputField, OutputField
	from dspy.signatures.signature import infer_prefix


	class Predict(Parameter):
	def __init__(self, signature, **config):
	self.stage = random.randbytes(8).hex()
	self.signature = signature #.signature
	self.config = config
	self.reset()

	# if the signature is a string
	if isinstance(signature, str):
	inputs, outputs = signature.split("->")
	inputs, outputs = inputs.split(","), outputs.split(",")
	inputs, outputs = [field.strip() for field in inputs], [field.strip() for field in outputs]

	assert all(len(field.split()) == 1 for field in (inputs + outputs))

	inputs_ = ', '.join([f"`{field}`" for field in inputs])
	outputs_ = ', '.join([f"`{field}`" for field in outputs])

	instructions = f"""Given the fields {inputs_}, produce the fields {outputs_}."""

	inputs = {k: InputField() for k in inputs}
	outputs = {k: OutputField() for k in outputs}

	for k, v in inputs.items():
	v.finalize(k, infer_prefix(k))

	for k, v in outputs.items():
	v.finalize(k, infer_prefix(k))

	self.signature = dsp.Template(instructions, inputs, outputs)


	def reset(self):
	self.lm = None
	self.traces = []
	self.train = []
	self.demos = []

	def dump_state(self):
	state_keys = ["lm", "traces", "train", "demos"]
	return {k: getattr(self, k) for k in state_keys}

	def load_state(self, state):
	for name, value in state.items():
	setattr(self, name, value)

	import dspy
	self.demos = [dspy.Example(**x) for x in self.demos]

	def __call__(self, **kwargs):
	return self.forward(**kwargs)

	def forward(self, **kwargs):
	# Extract the three privileged keyword arguments.
	signature = kwargs.pop("signature", self.signature)
	demos = kwargs.pop("demos", self.demos)
	config = dict(self.config, kwargs.pop("config", {}))

	# Get the right LM to use.
	lm = kwargs.pop("lm", self.lm) or dsp.settings.lm

	# If temperature is 0.0 but its n > 1, set temperature to 0.7.
	temperature = config.get("temperature", None)
	temperature = lm.kwargs['temperature'] if temperature is None else temperature

	num_generations = config.get("n", None)
	num_generations = lm.kwargs['n'] if num_generations is None else num_generations

	if (temperature is None or temperature <= 0.15) and num_generations > 1:
	config["temperature"] = 0.7
	# print(f"#> Setting temperature to 0.7 since n={num_generations} and prior temperature={temperature}.")

	# All of the other kwargs are presumed to fit a prefix of the signature.

	x = dsp.Example(demos=demos, **kwargs)

	if self.lm is None:
	x, C = dsp.generate(signature, **config)(x, stage=self.stage)
	else:
	with dsp.settings.context(lm=self.lm, query_only=True):
	# print(f"using lm = {self.lm} !")
	x, C = dsp.generate(signature, **config)(x, stage=self.stage)

	completions = []

	for c in C:
	completions.append({})
	for field in signature.fields:
	if field.output_variable not in kwargs.keys():
	completions[-1][field.output_variable] = getattr(c, field.output_variable)

	pred = Prediction.from_completions(completions, signature=signature)

	if dsp.settings.trace is not None:
	trace = dsp.settings.trace
	trace.append((self, {**kwargs}, pred))

	return pred

	def update_config(self, **kwargs):
	self.config = {self.config, kwargs}

	def get_config(self):
	return self.config

	def __repr__(self):
	return f"{self.__class__.__name__}({self.signature})"



	# TODO: get some defaults during init from the context window?
	# # TODO: FIXME: Hmm, I guess expected behavior is that contexts can
	# affect exeuction. Well, we need to determine whether context dominates, __init__ demoninates, or forward dominates.
	# Generally, unless overwritten, we'd see n=None, temperature=None.

	current docs link: https://dspy-ai.readthedocs.io/en/latest/

	<p align="center">
	<img align="center" src="docs/images/DSPy8.png" width="460px" />
	</p>
	<p align="left">


	## DSPy: _Programming_—not prompting—Foundation Models

	Paper —— [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://arxiv.org/abs/2310.03714)

	[<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/intro.ipynb)


	DSPy is the framework for solving advanced tasks with language models (LMs) and retrieval models (RMs). DSPy unifies techniques for prompting and fine-tuning LMs — and approaches for reasoning, self-improvement, and augmentation with retrieval and tools. All of these are expressed through modules that compose and learn.

	To make this possible:

	- DSPy provides composable and declarative modules for instructing LMs in a familiar Pythonic syntax. It upgrades "prompting techniques" like chain-of-thought and self-reflection from hand-adapted _string manipulation tricks_ into truly modular _generalized operations that learn to adapt to your task_.

	- DSPy introduces an automatic compiler that teaches LMs how to conduct the declarative steps in your program. Specifically, the DSPy compiler will internally _trace_ your program and then craft high-quality prompts for large LMs (or train automatic finetunes for small LMs) to teach them the steps of your task.

	The DSPy compiler _bootstraps_ prompts and finetunes from minimal data without needing manual labels for the intermediate steps in your program. Instead of brittle "prompt engineering" with hacky string manipulation, you can explore a systematic space of modular and trainable pieces.

	For complex tasks, DSPy can routinely teach powerful models like `GPT-3.5` and local models like `T5-base` or `Llama2-13b` to be much more reliable at tasks. DSPy will compile the _same program_ into different few-shot prompts and/or finetunes for each LM.


	If you want to see DSPy in action, [open our intro tutorial notebook](intro.ipynb).


	### Table of Contents


	1. [Installation](#1-installation)
	1. [Framework Syntax](#2-syntax-youre-in-charge-of-the-workflowits-free-form-python-code)
	1. [Compiling: Two Powerful Concepts](#3-two-powerful-concepts-signatures--teleprompters)
	1. [Tutorials & Documentation](#4-documentation--tutorials)
	1. [FAQ: Is DSPy right for me?](#5-faq-is-dspy-right-for-me)



	### Analogy to Neural Networks

	When we build neural networks, we don't write manual _for-loops_ over lists of _hand-tuned_ floats. Instead, you might use a framework like [PyTorch](https://pytorch.org/) to compose declarative layers (e.g., `Convolution` or `Dropout`) and then use optimizers (e.g., SGD or Adam) to learn the parameters of the network.

	from .predict import Predict
	from .chain_of_thought import ChainOfThought
	from .multi_chain_comparison import MultiChainComparison
	from .chain_of_thought_with_hint import ChainOfThoughtWithHint
	from .react import ReAct
	from .aggregation import majority
	from .program_of_thought import ProgramOfThought

	# dspy.Modules Documentation

	This documentation provides an overview of the DSPy Modules.

	## DSPy Modules

	\| Module \| Jump To \|
	\| --- \| --- \|
	\| Predict \| [Predict Section](#dspypredict) \|
	\| Retrieve \| [Retrieve Section](#dspyretrieve) \|
	\| ChainOfThought \| [ChainOfThought Section](#dspychainofthought) \|
	\| ChainOfThoughtWithHint \| [ChainOfThoughtWithHint Section](#dspychainofthoughtwithhint) \|
	\| MultiChainComparison \| [MultiChainComparison Section](#dspymultichaincomparison) \|
	\| ReAct \| [ReAct Section](#dspyreact) \|
	\| Assertion Helpers \| [Assertion Helpers Section](#dspyassertionhelpers) \|

	## dspy.Predict

	### Constructor