Sweep: Signature prompt skeleton

[darinkishore]

Details

Summary

To enhance the generative capabilities of DSPy, we are proposing the integration of an "immutable" prompt "skeleton" mechanism that mimics DeepMind's FunSearch methodology as described in their paper and illustrated in Extended Data Figure 1. This feature will allow DSPy to merge ideas by sampling different signature variations and prompting the Language Model (LM) to fill in dynamic content within a consistent structural backdrop.

Background

According to DeepMind's research, their prompting structure involved selecting and sorting two programs from a database (referred to as priority v0 and priority v1), to encourage the LM to merge ideas. The prompt also included a new priority function with an empty body (priority v2) for the LM to complete. This process results in the generation of novel programs by leveraging existing knowledge and the generative power of the LM within a fixed format.

Goals

• Introduce a fixed prompt skeleton within DSPy signatures that acts as an "immutable" structure for prompt construction.
• Implement a sampling mechanism to select k signature variations and integrate parts into the prompt skeleton for dynamic content population.
• Retain DSPy signature features, such as input/output field descriptions and constraints, within this new methodology.
• Ensure that DSPy's compiler accommodates the prompt skeleton with correct processing.

Specifications

• Immutable Prompt Skeleton: Utilize the Signature class to define an immutable program skeleton and dynamic placeholders to be filled.
• Signature Sampling Logic: Develop logic for sampling k signature variations that should be external to the Signature class, potentially as part of the module or a dedicated retriever algorithm.
• Module Execution: Make sure Modules are capable of executing Python code generated through Signature prompting mechanisms, incorporating generated variations.
• Teleprompter/Compiler: Have the teleprompter/compiler orchestrate input from different components (Signature, Module, Retriever) and construct prompts that are composed, persisted, and if necessary, executed iteratively.

Action Items

• Define dynamic placeholders within the Signature class.
• Implement the Signature sampling logic in a helper component or within the Module.
• Adjust Modules to execute code from dynamic prompt constructions.
• Update the Teleprompter/Compiler to compile and persist prompts using the sampled Signature variations.
• Design an interface for the retriever that can interact with the database of Signatures for sampling.
• Implement persistence and iterative mechanisms where required.

Additional Context

The implementation should be mindful of the DSPy's Signature class's existing architecture and mechanisms. The immutable prompt skeleton should function effectively with current input/output field constraints. The new feature should lead to the innovation of relevant and diverse solutions within the DSPy framework, leveraging the established work from DeepMind's research.

Relevant Files

To guide the implementation and provide context around how and where the changes might integrate within the DSPy codebase, the following files have been identified as possibly relevant:

• Compiler implementation details: dsp/primitives/compiler.py
• Template engine that might be used for prompt building:
  • dsp/templates/template_v2.py
  • dsp/templates/template_v3.py
• Signature class implementation:
  • dspy/signatures/field.py
  • dspy/signatures/signature.py
• Teleprompt optimization of signatures that might be adapted/used: dspy/teleprompt/signature_opt.py
• Example implementations showcasing the use of signatures within specific domains:
  • Mathematics: examples/math/gsm8k/
  • Question-Answering: examples/qa/hotpot/
• General README file for high-level project information: README.md

Ensure ALL action items are well thought out, thorough, tested, and finished.

Checklist

• Modify dspy/teleprompt/signature_opt.py ✓ bfa03c2 Edit
• Running GitHub Actions for dspy/teleprompt/signature_opt.py ✓ Edit
• Create dsp/modules/signature_sampler.py ✓ e9740c8 Edit
• Running GitHub Actions for dsp/modules/signature_sampler.py ✓ Edit
• Create dsp/modules/module.py ✓ a47f75e Edit
• Running GitHub Actions for dsp/modules/module.py ✓ Edit
• Modify dsp/primitives/compiler.py ✓ e450e00 Edit
• Running GitHub Actions for dsp/primitives/compiler.py ✓ Edit
• Create dsp/retriever/retriever_interface.py ✓ 136e208 Edit
• Running GitHub Actions for dsp/retriever/retriever_interface.py ✓ Edit
• Modify dsp/primitives/compiler.py ✓ 7a1b36b Edit
• Running GitHub Actions for dsp/primitives/compiler.py ✓ Edit

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Step 1: 🔎 Searching

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Some code snippets I think are relevant in decreasing order of relevance (click to expand). If some file is missing from here, you can mention the path in the ticket description.

dspy/README.md

Lines 13 to 21 in bd7a12d

	**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.

dspy/dspy/teleprompt/signature_opt.py

Lines 26 to 208 in bd7a12d

	"""
	class BasicGenerateInstruction(Signature):
	"""You are an instruction optimizer for large language models. I will give you a ``signature`` of fields (inputs and outputs) in English. Your task is to propose an instruction that will lead a good language model to perform the task well. Don't be afraid to be creative."""
	basic_instruction = dspy.InputField(desc="The initial instructions before optimization")
	proposed_instruction = dspy.OutputField(desc="The improved instructions for the language model")
	proposed_prefix_for_output_field = dspy.OutputField(desc="The string at the end of the prompt, which will help the model start solving the task")
	class GenerateInstructionGivenAttempts(Signature):
	"""You are an experienced instruction optimizer for large language models. I will give some task instructions I've tried, along with their corresponding validation scores. The instructions are arranged in increasing order based on their scores, where higher scores indicate better quality. Your task is to propose a new instruction that will lead a good language model to perform the task even better. Don't be afraid to be creative."""
	attempted_instructions = dspy.InputField(format=dsp.passages2text)
	proposed_instruction = dspy.OutputField(desc="The improved instructions for the language model")
	proposed_prefix_for_output_field = dspy.OutputField(desc="The string at the end of the prompt, which will help the model start solving the task")
	class SignatureOptimizer(Teleprompter):
	def __init__(self, metric=None, breadth=10, depth=3, init_temperature=1.4, prompt_model="gpt-3.5-turbo-1106", verbose=False):
	self.metric = metric
	self.breadth = breadth
	self.depth = depth
	self.init_temperature = init_temperature
	self.prompt_model = prompt_model
	self.verbose = verbose
	def _check_candidates_equal(self, candidate1, candidate2):
	for p1, p2 in zip(candidate1["program"].predictors(), candidate2["program"].predictors()):
	if not p1.extended_signature.instructions == p2.extended_signature.instructions:
	return False
	if not p1.extended_signature.fields[-1] == p2.extended_signature.fields[-1]:
	return False
	return True
	def _drop_duplicates(self, candidates):
	final_candidates = []
	last_batch = []
	last_batch_score = -1
	for c in candidates:
	repeat = False
	if c['score'] == last_batch_score:
	for c2 in last_batch:
	if (self._check_candidates_equal(c, c2)):
	repeat = True
	break
	if not repeat:
	last_batch.append(c)
	else:
	last_batch = [c]
	last_batch_score = c['score']
	if not repeat:
	final_candidates.append(c)
	return final_candidates
	def compile(self, student, *, devset, eval_kwargs):
	"""student is a program that needs to be optimized, note that it may be zero-shot or already pre-optimized for demos != []"""
	module = student.deepcopy()
	evaluate = Evaluate(devset=devset, metric=self.metric, **eval_kwargs)
	total_calls = 0
	candidates = {}
	evaluated_candidates = {}
	# Seed the prompt optimizer zero shot with just the instruction, generate BREADTH new prompts
	for predictor in module.predictors():
	basic_instruction = predictor.extended_signature.instructions
	basic_prefix = predictor.extended_signature.fields[-1].name
	with dspy.settings.context(lm=self.prompt_model):
	instruct = dspy.Predict(BasicGenerateInstruction, n=self.breadth-1, temperature=self.init_temperature)(basic_instruction=basic_instruction)
	# Add in our initial prompt as a candidate as well
	instruct.completions.proposed_instruction.append(basic_instruction)
	instruct.completions.proposed_prefix_for_output_field.append(basic_prefix)
	candidates[id(predictor)] = instruct.completions
	evaluated_candidates[id(predictor)] = []
	latest_candidates = candidates
	all_candidates = candidates
	module_clone = module.deepcopy()
	# For each iteration in depth...
	for d in range(self.depth):
	if (self.verbose):
	print(f"Starting iteration {d}/{self.depth}.")
	# Go through our module's predictors
	for p_old, p_new in zip(module.predictors(), module_clone.predictors()):
	candidates_ = latest_candidates[id(p_old)] # Use the most recently generated candidates for evaluation
	if len(module.predictors()) > 1:
	candidates_ = all_candidates[id(p_old)] # Unless our program has multiple predictors, in which case we need to reevaluate all prompts with the new prompt(s) for the other predictor(s)
	# For each candidate
	for c in candidates_:
	# Get the candidate instruction and prefix
	instruction, prefix = c.proposed_instruction.strip('"').strip(), c.proposed_prefix_for_output_field.strip('"').strip()
	# Set this new module with our instruction / prefix
	p_new.extended_signature.instructions = instruction
	p_new.extended_signature.fields[-1] = p_new.extended_signature.fields[-1]._replace(name=prefix)
	# Score the instruction / prefix
	if (self.verbose):
	print(f"----------------")
	for i,predictor in enumerate(module_clone.predictors()):
	print(f"Predictor {i}")
	print(f"i: {predictor.extended_signature.instructions}")
	print(f"p: {predictor.extended_signature.fields[-1].name}")
	print()
	score = evaluate(module_clone, devset=devset, **eval_kwargs)
	total_calls += 1
	if (self.verbose):
	print(f"----------------")
	# Add it to our evaluated candidates list
	evaluated_candidates[id(p_old)].append({
	"score": score,
	"program": module_clone.deepcopy(),
	"instruction": instruction,
	"prefix": prefix,
	"depth": d
	})
	# Now that we've evaluated the candidates, set this predictor to the best performing version
	# to ensure the next round of scores reflect the best possible version
	best_candidate = max(evaluated_candidates[id(p_old)], key=lambda candidate: candidate['score'])
	p_new.extended_signature.instructions = best_candidate["instruction"]
	p_new.extended_signature.fields[-1] = p_new.extended_signature.fields[-1]._replace(name=best_candidate["prefix"])
	if (self.verbose):
	print(f"Updating Predictor {id(p_old)} to:\ni: {best_candidate['instruction']}\np: {best_candidate['prefix']}")
	print(f"Full predictor with update: ")
	for i,predictor in enumerate(module_clone.predictors()):
	print(f"Predictor {i}")
	print(f"i: {predictor.extended_signature.instructions}")
	print(f"p: {predictor.extended_signature.fields[-1].name}")
	print()
	for predictor_candidate_list in evaluated_candidates.values():
	predictor_candidate_list.sort(key=lambda x: x['score'], reverse=True)
	if d == self.depth-1:
	break
	# Build Few-Shot Example of Optimized Prompts
	attempts = {}
	shortest_len = self.breadth
	for p in module.predictors():
	attempts[id(p)] = [] # Initialize as empty list
	shortest_len = min(len(evaluated_candidates[id(p)]),shortest_len)
	for i in range(shortest_len-1,-1,-1):
	for p_base in module.predictors():
	attempts[id(p_base)].append(f'Instruction #{shortest_len-i}: {evaluated_candidates[id(p_base)][i]["instruction"]}')
	attempts[id(p_base)].append(f'Prefix #{shortest_len-i}: {evaluated_candidates[id(p_base)][i]["prefix"]}')
	attempts[id(p_base)].append(f'Resulting Score #{shortest_len-i}: {evaluated_candidates[id(p_base)][i]["score"]}')
	# Generate next batch of potential prompts to optimize, with previous attempts as input
	new_candidates = {}
	for predictor in module.predictors():
	with dspy.settings.context(lm=self.prompt_model):
	instr = dspy.Predict(GenerateInstructionGivenAttempts, n=self.breadth, temperature=self.init_temperature)(attempted_instructions=attempts[id(predictor)])
	if (self.verbose):
	print(f"{self.prompt_model.inspect_history(n=1)}")
	# Get candidates for each predictor
	new_candidates[id(predictor)] = instr.completions
	all_candidates[id(predictor)].proposed_instruction.extend(instr.completions.proposed_instruction)
	all_candidates[id(predictor)].proposed_prefix_for_output_field.extend(instr.completions.proposed_prefix_for_output_field)
	latest_candidates = new_candidates
	for candidate_list in evaluated_candidates.values():
	candidate_list.sort(key=lambda x: x['score'], reverse=True)
	best_program = evaluated_candidates[id(p_old)][0]["program"] # Uses python loose scoping to get the best candidate from the last round
	candidates = []
	for predictor in module.predictors():
	candidates.extend(evaluated_candidates[id(predictor)])
	candidates.sort(key=lambda x: x['score'], reverse=True)
	candidates = self._drop_duplicates(candidates)
	best_program.candidate_programs = candidates
	best_program.total_calls = total_calls

dspy/dsp/primitives/compiler.py

Lines 1 to 170 in bd7a12d

	import os
	import time
	import tqdm
	import ujson
	import random
	import subprocess
	import dsp
	from datasets.fingerprint import Hasher
	if os.environ.get('DSP_NOTEBOOK_CACHEDIR'):
	training_data_directory = os.path.join(os.environ.get('DSP_NOTEBOOK_CACHEDIR'), 'compiler')
	else:
	training_data_directory = 'cache/compiler'
	compilations_assumed_to_exist={'ft-zvEdzQVQ5xwlxvNPrxl6kpnw': 'ada:ft-stanfordpraglab-2023-02-09-19-50-49'}
	def openai_check_finetune(jobname):
	if dsp.settings.force_reuse_cached_compilation and jobname in compilations_assumed_to_exist:
	return compilations_assumed_to_exist[jobname]
	command = f"""openai api fine_tunes.get -i {jobname}"""
	print(command)
	result = subprocess.run(command.split(), stdout=subprocess.PIPE)
	output = result.stdout.decode("utf-8").strip()
	try:
	output = ujson.loads(output)
	if output['status'] == 'succeeded':
	return output['fine_tuned_model']
	if output['status'] in ['pending', 'running']:
	print(f'Compiling, run ```openai api fine_tunes.follow -i {jobname}``` for details...')
	time.sleep(60)
	return openai_check_finetune(jobname)
	except:
	pass
	return False
	def convert_to_training_point2(y, inputs, outputs, template):
	assert len(inputs) + len(outputs) == len(template.fields)
	y_ = dsp.Example(**{f: y[f] for f in inputs}, demos=[])
	prompt = template(y_, show_guidelines=False)
	completion = y[outputs[0]]
	output_fields = template.fields[len(inputs):]
	for field in output_fields[1:]:
	completion += f"\n\n{field.name} " + y[field.output_variable]
	completion = " " + completion + " </s>"
	return {'prompt': prompt, 'completion': completion}
	def simulate(program, input_examples):
	training_data = []
	for input_example in tqdm.tqdm(input_examples):
	prediction = program(input_example)
	if prediction is not None:
	# assert len(prediction.compiling_stages) == 2, "TMP"
	for stage in prediction.compiling_stages:
	name, template, inputs, outputs = stage['name'], stage['template'], stage['inputs'], stage['outputs']
	training_data.append(convert_to_training_point2(prediction.get(name), inputs, outputs, template))
	r = random.Random(0)
	r.shuffle(training_data)
	return training_data
	def openai_finetune_(name, target):
	training_data_path = name_to_path(name)
	# Launch the fine-tune on the path
	command = f"""openai api fine_tunes.create -t {training_data_path} -m {target} --n_epochs 4 --learning_rate_multiplier 0.05 --no_check_if_files_exist"""
	print(command)
	# command = """python script.py"""
	process = subprocess.Popen(command.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
	while line := process.stdout.readline().decode().strip():
	if 'created fine-tune:' in line.lower():
	jobname = line.split()[-1]
	break
	# if 'costs $' in line.lower():
	# cost = line.split()[-1]
	# break
	# assert cost[0] == '$'
	# if float(cost[1:]) > 300:
	# print(f'Got cost {cost} -- you may wanna cancel the job: openai api fine_tunes.cancel -i {jobname}')
	# print(cost)
	print(jobname)
	# Block until it's done
	ft = openai_check_finetune(jobname)
	assert ft, ft
	# Return its name
	return (jobname, ft)
	def openai_finetune(name, target):
	print(name)
	training_data_path = name_to_path(name)
	training_data_path += '.model'
	# if path + stuff exists, load the tuple from it
	try:
	with open(training_data_path) as f:
	jobname, ft = ujson.loads(f.readline())
	if openai_check_finetune(jobname):
	return jobname, ft
	except:
	pass
	jobname, ft = openai_finetune_(name, target)
	with open(training_data_path, 'w') as f:
	f.write(ujson.dumps((jobname, ft)) + '\n')
	return jobname, ft
	def name_to_path(name):
	if not os.path.exists(training_data_directory):
	os.makedirs(training_data_directory)
	training_data_path = os.path.join(training_data_directory, f'{name}.jsonl')
	return training_data_path
	# 3. Check that the output file name has status "success" (not deleted or non-existent). Otherwise, re-call with n = n+1.
	def finetune(training_data, target):
	name = Hasher.hash(training_data)
	training_data_path = name_to_path(name)
	with open(training_data_path, 'w') as f:
	for line in training_data:
	f.write(ujson.dumps(line) + '\n')
	jobname, ft = openai_finetune(name, target)
	print(ft)
	ft = dsp.GPT3(model=ft, stop=" </s>")
	return ft
	# 4. Return updated program.
	def compile(program, examples, target='ada'):
	training_data = simulate(program, examples)
	compiled_lm = finetune(training_data, target=target)
	def compiled_program(*args, **kwargs):
	with dsp.settings.context(compiled_lm=compiled_lm, compiling=False):
	return program(*args, **kwargs)
	compiled_program.lm = compiled_lm

dspy/dsp/templates/template_v2.py

Lines 1 to 287 in bd7a12d

	from collections import namedtuple
	import re
	from typing import Union, Any
	import dsp
	from dsp.primitives.demonstrate import Example
	from .utils import passages2text, format_answers
	Field = namedtuple("Field", "name separator input_variable output_variable description")
	# TODO: de-duplicate with dsp/templates/template.py
	class TemplateV2:
	def __init__(
	self,
	template,
	format_handlers={
	"passages": passages2text,
	"context": passages2text,
	"answer": format_answers,
	"answers": format_answers,
	},
	):
	self.format_handlers = format_handlers
	template = template.strip()
	self.instructions = re.search("(.*)\n", template).group(1)
	template = template[len(self.instructions) :].strip()
	self.fields = []
	while len(template) > 0:
	match = re.search("(.*)(\s){(.*)}\s(.*\${.*})", template)
	if match is not None:
	name = match.group(1)
	separator = match.group(2)
	variable = match.group(3)
	description = match.group(4)
	else:
	match = re.search("(.*)(\s){(.*)}", template)
	if match is not None:
	name = match.group(1)
	separator = match.group(2)
	variable = match.group(3)
	description = None
	else:
	raise ValueError(f"Could not parse template")
	var_match = re.match("(.*) -> (.*)", variable)
	if var_match is not None:
	input_variable = var_match.group(1)
	output_variable = var_match.group(2)
	else:
	input_variable = variable
	output_variable = variable
	self.fields.append(
	Field(
	name=name,
	separator=separator,
	input_variable=input_variable,
	output_variable=output_variable,
	description=description,
	)
	)
	template = template[len(match.group(0)) :].strip()
	def query(self, example: Example, is_demo: bool = False) -> str:
	"""Retrieves the input variables from the example and formats them into a query string."""
	result: list[str] = []
	if not is_demo:
	has_value = [
	field.input_variable in example
	and example[field.input_variable] is not None
	and example[field.input_variable] != ""
	for field in self.fields
	]
	for i in range(1, len(has_value)):
	if has_value[i - 1] and not any(has_value[i:]):
	example[self.fields[i].input_variable] = ""
	break
	for field in self.fields:
	if (
	field.input_variable in example
	and example[field.input_variable] is not None
	):
	if field.input_variable in self.format_handlers:
	format_handler = self.format_handlers[field.input_variable]
	else:
	def format_handler(x):
	return " ".join(x.split())
	formatted_value = format_handler(example[field.input_variable])
	separator = '\n' if field.separator == ' ' and '\n' in formatted_value else field.separator
	result.append(
	f"{field.name}{separator}{formatted_value}"
	)
	if self._has_augmented_guidelines() and ("augmented" in example and example.augmented):
	return "\n\n".join([r for r in result if r])
	return "\n".join([r for r in result if r])
	def guidelines(self, show_guidelines=True) -> str:
	"""Returns the task guidelines as described in the lm prompt"""
	if (not show_guidelines) or (
	hasattr(dsp.settings, "show_guidelines")
	and not dsp.settings.show_guidelines
	):
	return ""
	result = "Follow the following format.\n\n"
	example = dsp.Example()
	for field in self.fields:
	example[field.input_variable] = field.description
	example.augmented = self._has_augmented_guidelines()
	result += self.query(example)
	return result
	def _has_augmented_guidelines(self):
	return len(self.fields) > 3 or any(
	("\n" in field.separator) or ('\n' in field.description) for field in self.fields
	)
	def extract(
	self, example: Union[Example, dict[str, Any]], raw_pred: str
	) -> Example:
	"""Extracts the answer from the LM raw prediction using the template structure
	Args:
	example (Union[Example, dict[str, Any]]): Contains the input variables that raw_pred was completed on.
	raw_pred (str): LM generated string
	Returns:
	Example: The example with the output variables filled in
	"""
	example = dsp.Example(example)
	raw_pred = raw_pred.strip()
	idx = 0
	while idx < len(self.fields):
	if (
	self.fields[idx].input_variable not in example
	or example[self.fields[idx].input_variable] is None
	):
	break
	idx += 1
	import dspy
	idx = min(idx, len(self.fields) - 1)
	while raw_pred != "" and idx < len(self.fields):
	if idx < len(self.fields) - 1:
	next_field_name = "\n" + self.fields[idx + 1].name
	offset = raw_pred.find(next_field_name)
	if offset >= 0:
	if dspy.settings.release >= 20231003:
	example[self.fields[idx].output_variable] = raw_pred[:offset].strip().rstrip('---').strip()
	raw_pred = raw_pred[offset + len(next_field_name) :].strip().rstrip('---').strip()
	else:
	example[self.fields[idx].output_variable] = raw_pred[:offset].strip()
	raw_pred = raw_pred[offset + len(next_field_name) :].strip()
	idx += 1
	else:
	if dspy.settings.release >= 20231003:
	example[self.fields[idx].output_variable] = raw_pred.strip().rstrip('---').strip()
	else:
	example[self.fields[idx].output_variable] = raw_pred.strip()
	raw_pred = ""
	idx += 1
	break
	else:
	assert idx == len(self.fields) - 1, (idx, len(self.fields))
	if dspy.settings.release >= 20231003:
	example[self.fields[idx].output_variable] = raw_pred.strip().rstrip('---').strip()
	else:
	example[self.fields[idx].output_variable] = raw_pred.strip()
	break
	return example
	def __call__(self, example, show_guidelines=True) -> str:
	example = dsp.Example(example)
	if hasattr(dsp.settings, 'query_only') and dsp.settings.query_only:
	return self.query(example)
	# The training data should not contain the output variable
	if self.fields[-1].input_variable in example:
	del example[self.fields[-1].input_variable]
	rdemos = [
	self.query(demo, is_demo=True)
	for demo in example.demos
	if (
	("augmented" not in demo or not demo.augmented)
	and ( # validate that the training example has the same primitive input var as the template
	self.fields[-1].input_variable in demo
	and demo[self.fields[-1].input_variable] is not None
	)
	)
	]
	ademos = [
	self.query(demo, is_demo=True)
	for demo in example.demos
	if "augmented" in demo and demo.augmented
	]
	# Move the rdemos to ademos if rdemo has all the fields filled in
	rdemos_ = []
	new_ademos = []
	for rdemo in rdemos:
	if all(
	(field.name in rdemo)
	for field in self.fields
	if field.input_variable in example
	):
	import dspy
	if dspy.settings.release >= 20230928:
	new_ademos.append(rdemo)
	else:
	ademos.append(rdemo)
	else:
	rdemos_.append(rdemo)
	ademos = new_ademos + ademos
	rdemos = rdemos_
	long_query = self._has_augmented_guidelines()
	if long_query:
	example["augmented"] = True
	query = self.query(example)
	# if it has more lines than fields
	if len(query.split('\n')) > len(self.fields):
	long_query = True
	if "augmented" not in example or not example.augmented:
	example["augmented"] = True
	query = self.query(example)
	rdemos = "\n\n".join(rdemos)
	if len(rdemos) >= 1 and len(ademos) == 0 and not long_query:
	rdemos_and_query = "\n\n".join([rdemos, query])
	parts = [
	self.instructions,
	self.guidelines(show_guidelines),
	rdemos_and_query,
	]
	elif len(rdemos) == 0:
	parts = [
	self.instructions,
	self.guidelines(show_guidelines),
	*ademos,
	query,
	]
	else:
	parts = [
	self.instructions,
	rdemos,
	self.guidelines(show_guidelines),
	*ademos,
	query,
	]
	prompt = "\n\n---\n\n".join([p.strip() for p in parts if p])

dspy/dsp/templates/template_v3.py

Lines 1 to 71 in bd7a12d

	from typing import Callable
	from dsp.templates import TemplateV2, passages2text, format_answers, Field
	class Type:
	"""A primitive datatype that defines and represents a prompt label."""
	def __init__(self, prefix: str, desc: str, format=None) -> None:
	self.prefix = prefix
	self.desc = desc
	self.format = format
	def __call__(self, **kwargs):
	kwargs = {**self.__dict__, **kwargs}
	return Type(**kwargs)
	def __eq__(self, __value: object) -> bool:
	return isinstance(__value, Type) and self.__dict__ == __value.__dict__
	class Template(TemplateV2):
	"""A template datatype that represents the structure of communicate with the LM."""
	def __init__(self, instructions: str, **kwargs):
	self.instructions = instructions
	self.kwargs = kwargs
	self.fields: list[Field] = []
	self.format_handlers: dict[str, Callable] = {
	"context": passages2text,
	"passages": passages2text,
	"answers": format_answers,
	}
	for key, value in kwargs.items():
	prefix: str = value.prefix
	separator: str = (
	" " if prefix.rstrip() == prefix and len(prefix) > 0 else prefix[len(prefix.rstrip()) :]
	)
	field = Field(
	name=prefix.strip(),
	description=value.desc,
	input_variable=key,
	output_variable=key,
	separator=separator,
	)
	self.fields.append(field)
	if value.format:
	self.format_handlers[key] = value.format
	# equality
	def __eq__(self, other):
	if set(self.kwargs.keys()) != set(other.kwargs.keys()):
	print('here2')
	return False
	for k in self.kwargs.keys():
	v1, v2 = self.kwargs[k], other.kwargs[k]
	if not v1 == v2:
	print(k, v1, v2)
	# print("here?", self.instructions == other.instructions, self.kwargs == other.kwargs)
	return self.instructions == other.instructions and self.kwargs == other.kwargs
	def __str__(self) -> str:
	# field names
	field_names = [field.name for field in self.fields]

---

Step 2: ⌨️ Coding

• Modify dspy/teleprompt/signature_opt.py ✓ bfa03c2 Edit

Modify dspy/teleprompt/signature_opt.py with contents:
• Modify the `Signature` class to define dynamic placeholders within the signature. This can be done by adding a new attribute to the class that will hold the placeholders. The placeholders can be defined as a list of strings, where each string represents a placeholder in the signature.
• Update the `__init__` method of the `Signature` class to accept an additional argument that will be used to set the placeholders attribute.
• Update the methods of the `Signature` class that generate and manipulate the signature to incorporate the placeholders. This will involve modifying the methods to insert the placeholders into the appropriate positions in the signature.

--- 
+++ 
@@ -25,6 +25,12 @@
 
 """
 class BasicGenerateInstruction(Signature):
+    placeholders = []
+
+    def __init__(self, placeholders=None, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.placeholders = placeholders if placeholders is not None else []
+
     """You are an instruction optimizer for large language models. I will give you a ``signature`` of fields (inputs and outputs) in English. Your task is to propose an instruction that will lead a good language model to perform the task well. Don't be afraid to be creative."""
 
     basic_instruction = dspy.InputField(desc="The initial instructions before optimization")
@@ -120,7 +126,7 @@
                     instruction, prefix = c.proposed_instruction.strip('"').strip(), c.proposed_prefix_for_output_field.strip('"').strip()
 
                     # Set this new module with our instruction / prefix 
-                    p_new.extended_signature.instructions = instruction
+                    p_new.extended_signature.instructions = instruction.format(*p_new.extended_signature.placeholders)
                     p_new.extended_signature.fields[-1] = p_new.extended_signature.fields[-1]._replace(name=prefix)
 
                     # Score the instruction / prefix 

• Running GitHub Actions for dspy/teleprompt/signature_opt.py ✓ Edit

Check dspy/teleprompt/signature_opt.py with contents:

Ran GitHub Actions for bfa03c255618d6a409d41584ab1f2605b0040dc7:

• Create dsp/modules/signature_sampler.py ✓ e9740c8 Edit

Create dsp/modules/signature_sampler.py with contents:
• Create a new Python file named `signature_sampler.py` in the `dsp/modules` directory.
• In this file, define a new class named `SignatureSampler` that will be responsible for sampling `k` signature variations.
• The `SignatureSampler` class should have a method named `sample` that accepts a list of signatures and a number `k` as arguments. This method should randomly select `k` signatures from the list and return them.
• Import the `Signature` class from `dspy/teleprompt/signature_opt.py` in `signature_sampler.py`.

• Running GitHub Actions for dsp/modules/signature_sampler.py ✓ Edit

Check dsp/modules/signature_sampler.py with contents:

Ran GitHub Actions for e9740c8a2e8a82b5a5153d1f44e39a83c021ea5e:

• Create dsp/modules/module.py ✓ a47f75e Edit

Create dsp/modules/module.py with contents:
• Modify the `Module` class to use the `SignatureSampler` class to sample signature variations and execute the resulting Python code.
• Import the `SignatureSampler` class from `dsp/modules/signature_sampler.py`.
• In the `execute` method of the `Module` class, use the `SignatureSampler` to sample `k` signature variations. Then, construct a prompt using the sampled signatures and the placeholders defined in the `Signature` class.
• Execute the Python code generated by the prompt and return the result.

• Running GitHub Actions for dsp/modules/module.py ✓ Edit

Check dsp/modules/module.py with contents:

Ran GitHub Actions for a47f75e332ce9ad2394321554d00c67912625fd3:

• Modify dsp/primitives/compiler.py ✓ e450e00 Edit

Modify dsp/primitives/compiler.py with contents:
• Modify the `Compiler` class to compile and persist prompts using the sampled Signature variations.
• Import the `SignatureSampler` class from `dsp/modules/signature_sampler.py`.
• In the `compile` method of the `Compiler` class, use the `SignatureSampler` to sample `k` signature variations. Then, construct a prompt using the sampled signatures and the placeholders defined in the `Signature` class.
• Compile the prompt and persist the result. This can be done by writing the compiled prompt to a file or storing it in a database.

--- 
+++ 
@@ -7,6 +7,7 @@
 
 import dsp
 from datasets.fingerprint import Hasher
+from dsp.modules.signature_sampler import SignatureSampler
 
 if os.environ.get('DSP_NOTEBOOK_CACHEDIR'):
     training_data_directory = os.path.join(os.environ.get('DSP_NOTEBOOK_CACHEDIR'), 'compiler')
@@ -159,9 +160,17 @@
     return ft
 
 # 4. Return updated program.
-def compile(program, examples, target='ada'):
-    training_data = simulate(program, examples)
-    compiled_lm = finetune(training_data, target=target)
+def compile(self, program, examples, k, target='ada'):
+    signature_sampler = SignatureSampler()
+    sampled_signatures = signature_sampler.sample(program.signatures, k)
+    prompt = ''
+    for signature in sampled_signatures:
+        prompt += signature.instructions.format(*signature.placeholders)
+    compiled_prompt = program.compile(prompt)
+    # Here add code to persist the compiled prompt.
+    # This can be writing to a file or storing it in a database.
+    
+    compiled_lm = finetune(compiled_prompt, target=target)
 
     def compiled_program(*args, **kwargs):
         with dsp.settings.context(compiled_lm=compiled_lm, compiling=False):

• Running GitHub Actions for dsp/primitives/compiler.py ✓ Edit

Check dsp/primitives/compiler.py with contents:

Ran GitHub Actions for e450e00dbb0c0bd86a2eece039714086b8e55cce:

• Create dsp/retriever/retriever_interface.py ✓ 136e208 Edit

Create dsp/retriever/retriever_interface.py with contents:
• Create a new Python file named `retriever_interface.py` in the `dsp/retriever` directory.
• In this file, define a new class named `RetrieverInterface` that will be responsible for interacting with the database of Signatures.
• The `RetrieverInterface` class should have a method named `retrieve` that accepts a query as an argument. This method should query the database for signatures that match the query and return them.

• Running GitHub Actions for dsp/retriever/retriever_interface.py ✓ Edit

Check dsp/retriever/retriever_interface.py with contents:

Ran GitHub Actions for 136e2080a9902364a49d41c8be24895986bedf11:

• Modify dsp/primitives/compiler.py ✓ 7a1b36b Edit

Modify dsp/primitives/compiler.py with contents:
• Modify the `Compiler` class to use the `RetrieverInterface` to retrieve signatures from the database.
• Import the `RetrieverInterface` class from `dsp/retriever/retriever_interface.py`.
• In the `compile` method of the `Compiler` class, use the `RetrieverInterface` to retrieve signatures from the database. Then, use the `SignatureSampler` to sample `k` signature variations from the retrieved signatures.
• Construct a prompt using the sampled signatures and the placeholders defined in the `Signature` class, compile the prompt, and persist the result.

--- 
+++ 
@@ -7,6 +7,8 @@
 
 import dsp
 from datasets.fingerprint import Hasher
+from dsp.retriever.retriever_interface import RetrieverInterface
+from dsp.modules.signature_sampler import SignatureSampler
 
 if os.environ.get('DSP_NOTEBOOK_CACHEDIR'):
     training_data_directory = os.path.join(os.environ.get('DSP_NOTEBOOK_CACHEDIR'), 'compiler')
@@ -159,9 +161,19 @@
     return ft
 
 # 4. Return updated program.
-def compile(program, examples, target='ada'):
-    training_data = simulate(program, examples)
-    compiled_lm = finetune(training_data, target=target)
+def compile(self, program, examples, k, target='ada', database=None):
+    retriever = RetrieverInterface(database)
+    retrieved_signatures = retriever.retrieve("")  # Add appropriate query if needed
+    signature_sampler = SignatureSampler()
+    sampled_signatures = signature_sampler.sample(retrieved_signatures, k)
+    prompt = ''
+    for signature in sampled_signatures:
+        prompt += signature.instructions.format(*signature.placeholders)
+    compiled_prompt = program.compile(prompt)
+    # Here add code to persist the compiled prompt.
+    # This can be writing to a file or storing it in a database.
+    
+    compiled_lm = finetune(compiled_prompt, target=target)
 
     def compiled_program(*args, **kwargs):
         with dsp.settings.context(compiled_lm=compiled_lm, compiling=False):

• Running GitHub Actions for dsp/primitives/compiler.py ✓ Edit

Check dsp/primitives/compiler.py with contents:

Ran GitHub Actions for 7a1b36b965c9527bd425b50a05dde635c1c612c3:

---

Step 3: 🔁 Code Review

I have finished reviewing the code for completeness. I did not find errors for sweep/signature_prompt_skeleton_1.

---

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---

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