This comprehensive guide presents a detailed implementation of a self-questioning reasoning framework based on the arXiv paper 2502.05078v1, which emphasizes stream-of-consciousness thinking patterns. The implementation leverages DSPy for structured prompting and optimization, LiteLLM for language model integration, and Pydantic for robust data modeling. The resulting Jupyter notebook provides a flexible system that emulates human-like exploratory reasoning with complete traceability of thought processes.
DSPy represents a significant advancement in how developers interact with large language models. Unlike traditional prompting frameworks, DSPy introduces a declarative programming model that separates the specification of what a language model should do from how it accomplishes that task[2]. This separation allows developers to define high-level signatures that describe inputs and outputs while letting DSPy handle the complex optimization of prompts and few-shot examples.
DSPy's architecture revolves around modules that can be composed into sophisticated pipelines. The core module, Predict, serves as the foundation for language model interactions, managing prompts, demonstrations, and language model calls[4]. More specialized modules like ChainOfThought build upon this foundation to enable specific reasoning patterns[4]. This modular approach means that different reasoning strategies can be implemented and optimized independently.
What truly distinguishes DSPy is its optimization capabilities through "teleprompters," which can automatically generate and refine demonstrations based on example data[4]. These optimizers can systematically explore different prompting strategies and select those that perform best on defined metrics[8]. This capability is crucial for implementing complex reasoning patterns that require careful calibration of instructions and examples.
LiteLLM provides a standardized interface for interacting with various language models, making it easier to switch between different providers without changing application code[7]. While our implementation will default to OpenAI's GPT-4o, LiteLLM's abstraction layer ensures compatibility with other models.
Integration with LiteLLM involves simple API calls that mirror OpenAI's interface, with support for streaming responses, function calling, and other advanced features[7]. This compatibility ensures that sophisticated reasoning patterns can be implemented consistently across different language models.
Pydantic provides a reliable foundation for defining data structures in Python through type annotations[3]. By extending BaseModel, we can create structured representations of both the inputs to our reasoning system and the outputs it generates[9]. This approach ensures that data flowing through the system adheres to expected formats and contains all required fields.
For our implementation, Pydantic models will capture both the structural aspects of reasoning (inputs, intermediate steps, conclusions) and the constraints that ensure valid reasoning patterns[9]. This structured approach allows for better traceability and validation of the reasoning process.
The paper outlines a reasoning approach characterized by extensive self-questioning and thorough exploration. The method values depth over speed, embracing uncertainty and revision while maintaining a natural, conversational internal monologue. This approach mirrors human cognitive processes, where thoughts evolve through continuous questioning and reflection.
Four key principles guide this framework:
- Exploration over conclusion, prioritizing thorough investigation rather than rushing to answers
- Depth of reasoning, engaging in extensive contemplation through natural thought patterns
- Realistic thinking processes that acknowledge uncertainty and dead ends
- Persistence in exploring complex problems until natural resolution
The output format involves two main components: the extensive internal monologue (contemplation) and the eventual conclusion (final answer) if one emerges naturally from the exploration. This structure ensures transparency in the reasoning process while providing a clear summary of findings.
Our implementation will combine DSPy's structured prompting with LiteLLM's model accessibility and Pydantic's data validation to create a comprehensive reasoning framework. The architecture consists of several key components:
- Pydantic models defining the structure of inputs, reasoning steps, and outputs
- DSPy signatures and modules implementing the reasoning pattern
- DSPy optimizers for refining the reasoning process
- LiteLLM integration for flexible model selection
- Tracing mechanisms for capturing intermediate reasoning steps
The Jupyter notebook will implement this architecture with comprehensive documentation and examples, allowing users to understand and extend the reasoning framework for various applications.
We'll start by defining Pydantic models to structure the reasoning process:
from typing import List, Optional, Dict, Any
from pydantic import BaseModel, Field
class ReasoningQuery(BaseModel):
"""Model for the initial query to the reasoning system."""
question: str = Field(..., description="The question or problem to reason about")
context: Optional[str] = Field(None, description="Optional context information")
max_steps: int = Field(1000, description="Maximum number of reasoning steps")
class ReasoningStep(BaseModel):
"""Model for individual steps in the reasoning process."""
step_number: int = Field(..., description="Sequential number of this step")
thought: str = Field(..., description="The current thought or consideration")
questions: List[str] = Field(default_factory=list, description="Questions raised during this step")
revisions: List[str] = Field(default_factory=list, description="Revisions to previous thinking")
class ContemplatioNResult(BaseModel):
"""Model for the complete contemplation process."""
steps: List[ReasoningStep] = Field(..., description="All steps in the reasoning process")
class ReasoningResponse(BaseModel):
"""Model for the complete response from the reasoning system."""
query: ReasoningQuery = Field(..., description="The original query")
contemplation: ContemplatioNResult = Field(..., description="The full contemplation process")
final_answer: Optional[str] = Field(None, description="The final answer if one was reached")
metadata: Dict[str, Any] = Field(default_factory=dict, description="Additional metadata about the reasoning process")These models provide a structured representation of the reasoning process, from the initial query through each step of contemplation to the final answer. The models ensure that all necessary information is captured and validated, making it easier to trace and analyze the reasoning process[3][9].
Next, we'll define DSPy signatures and modules to implement the self-questioning reasoning pattern:
import dspy
class Contemplator(dspy.Signature):
"""Signature for the contemplation process."""
question = dspy.InputField(description="The question or problem to reason about")
context = dspy.InputField(description="Optional context information")
thoughts = dspy.OutputField(description="Extensive internal monologue exploring the question")
class FinalAnswerGenerator(dspy.Signature):
"""Signature for generating a final answer based on contemplation."""
question = dspy.InputField(description="The original question")
thoughts = dspy.InputField(description="The extensive internal monologue")
final_answer = dspy.OutputField(description="Clear, concise summary of findings, or indication that no conclusion was reached")
class SelfQuestioningReasoner(dspy.Module):
"""Module implementing the self-questioning reasoning pattern."""
def __init__(self):
super().__init__()
self.contemplator = dspy.ChainOfThought(Contemplator)
self.answer_generator = dspy.Predict(FinalAnswerGenerator)
def forward(self, question, context=None):
"""Perform self-questioning reasoning on the given question."""
contemplation = self.contemplator(question=question, context=context or "")
result = self.answer_generator(question=question, thoughts=contemplation.thoughts)
return dspy.Prediction(
thoughts=contemplation.thoughts,
final_answer=result.final_answer
)This implementation uses DSPy's ChainOfThought module to generate extensive contemplation and a separate Predict module to distill that contemplation into a final answer when appropriate[4]. The modular design allows for independent optimization of each component.
To enhance the quality of the reasoning process, we'll use DSPy's optimization capabilities:
from dspy.teleprompt import BootstrapFewShot
def optimize_reasoner(reasoner, examples):
"""Optimize the reasoner using bootstrap few-shot learning."""
optimizer = BootstrapFewShot(metric=reasoning_quality_metric)
optimized_reasoner = optimizer.compile(reasoner, examples)
return optimized_reasoner
def reasoning_quality_metric(example, prediction):
"""Metric for evaluating reasoning quality."""
# Implementation would assess depth, self-questioning, and natural progression
# This is a placeholder for a more sophisticated evaluation
depth_score = len(prediction.thoughts) / 1000 # Normalize by expected length
question_count = prediction.thoughts.count("?") / 20 # Normalize by expected questions
return (depth_score + question_count) / 2This optimization process uses DSPy's BootstrapFewShot optimizer to generate and refine demonstrations that improve the reasoning quality[8]. The custom metric evaluates aspects like depth of reasoning and frequency of self-questioning, though a real implementation would use more sophisticated measures.
To provide flexible model selection while defaulting to GPT-4o, we'll integrate with LiteLLM:
import litellm
from openinference.instrumentation.litellm import LiteLLMInstrumentor
def configure_litellm(model="gpt-4o", api_key=None):
"""Configure LiteLLM with the specified model and API key."""
if api_key:
os.environ["OPENAI_API_KEY"] = api_key
# Enable tracing for LiteLLM
tracer_provider = None # Would be configured in a real implementation
LiteLLMInstrumentor().instrument(tracer_provider=tracer_provider)
# Configure DSPy to use LiteLLM
litellm_model = litellm.completion
dspy.settings.configure(lm=litellm_model)
return litellm_model
def get_available_models():
"""Return a list of available models through LiteLLM."""
# In a real implementation, this would query LiteLLM for available models
return [
"gpt-4o",
"gpt-4o-mini",
"gpt-3.5-turbo",
# Other models would be listed here
]This integration enables tracing of LiteLLM calls for better debugging and analysis[5]. The configuration function allows easy switching between different models while maintaining consistent interfaces.
To capture and visualize the reasoning process, we'll implement tracing mechanisms:
from openinference.instrumentation.dspy import DSPyInstrumentor
import json
def enable_tracing():
"""Enable tracing for DSPy."""
tracer_provider = None # Would be configured in a real implementation
DSPyInstrumentor().instrument(tracer_provider=tracer_provider)
def export_trace(reasoning_response):
"""Export the reasoning trace to a structured format."""
return {
"query": reasoning_response.query.dict(),
"steps": [step.dict() for step in reasoning_response.contemplation.steps],
"final_answer": reasoning_response.final_answer,
"metadata": reasoning_response.metadata
}
def visualize_reasoning(reasoning_response):
"""Visualize the reasoning process."""
# This would generate a visualization of the reasoning steps
# Implementation would depend on the desired visualization format
passThis tracing implementation leverages OpenInference instrumentation for DSPy[5], allowing capture of detailed information about each step in the reasoning process. The exported trace can be used for analysis or visualization.
The complete implementation combines these components into a cohesive system:
def create_reasoner(model="gpt-4o", api_key=None, optimize=True, examples=None):
"""Create and optionally optimize a self-questioning reasoner."""
configure_litellm(model=model, api_key=api_key)
enable_tracing()
reasoner = SelfQuestioningReasoner()
if optimize and examples:
reasoner = optimize_reasoner(reasoner, examples)
return reasoner
def reason(reasoner, question, context=None, max_steps=1000):
"""Apply the reasoning process to a question."""
query = ReasoningQuery(
question=question,
context=context,
max_steps=max_steps
)
dspy_result = reasoner(question=question, context=context or "")
# Parse the thoughts into individual steps
steps = []
raw_thoughts = dspy_result.thoughts
# This parsing would be more sophisticated in a real implementation
# Here we're using a simple heuristic to separate steps
thought_segments = raw_thoughts.split("\n\n")
for i, segment in enumerate(thought_segments):
questions = [line for line in segment.split("\n") if "?" in line]
revisions = [line for line in segment.split("\n") if "Actually" in line or "Instead" in line]
steps.append(ReasoningStep(
step_number=i+1,
thought=segment,
questions=questions,
revisions=revisions
))
contemplation = ContemplatioNResult(steps=steps)
response = ReasoningResponse(
query=query,
contemplation=contemplation,
final_answer=dspy_result.final_answer,
metadata={
"model": dspy.settings.lm.model_name if hasattr(dspy.settings.lm, "model_name") else "unknown",
"step_count": len(steps),
"timestamp": datetime.now().isoformat()
}
)
return responseThis implementation creates a complete reasoning system that can be easily configured, optimized, and applied to diverse questions. The structured output captures the entire reasoning process for analysis and visualization.
# Initialize the reasoner with GPT-4o
reasoner = create_reasoner(model="gpt-4o", api_key="your_api_key")
# Apply the reasoning process to a question
response = reason(
reasoner,
question="What are the ethical implications of artificial general intelligence?",
context="Consider perspectives from various philosophical traditions."
)
# Extract the final answer
if response.final_answer:
print(f"Final Answer: {response.final_answer}")
else:
print("No definitive conclusion was reached.")
# Export the reasoning trace for analysis
trace = export_trace(response)
with open("reasoning_trace.json", "w") as f:
json.dump(trace, f, indent=2)# Define example reasoning tasks
examples = [
dspy.Example(
question="Is democracy the best form of government?",
context="Consider historical outcomes and philosophical ideals.",
thoughts="...", # Extensive demonstration of good reasoning
final_answer="..." # Clear summary of findings
),
# Additional examples would be included here
]
# Create an optimized reasoner
optimized_reasoner = create_reasoner(
model="gpt-4o-mini",
optimize=True,
examples=examples
)
# Apply the optimized reasoner to a new question
response = reason(
optimized_reasoner,
question="What role should technology play in education?"
)models = ["gpt-4o", "gpt-4o-mini", "gpt-3.5-turbo"]
question = "How might quantum computing affect cryptography?"
results = {}
for model in models:
model_reasoner = create_reasoner(model=model)
results[model] = reason(model_reasoner, question=question)
# Compare depth of reasoning
for model, response in results.items():
print(f"Model: {model}")
print(f"Steps: {len(response.contemplation.steps)}")
print(f"Questions raised: {sum(len(step.questions) for step in response.contemplation.steps)}")
print(f"Final answer length: {len(response.final_answer) if response.final_answer else 0}")
print()The implementation presented in this guide demonstrates how modern AI tools can be combined to create sophisticated reasoning frameworks. By leveraging DSPy's structured prompting and optimization capabilities, LiteLLM's flexible model access, and Pydantic's robust data modeling, we've created a system that can emulate human-like exploratory reasoning while maintaining full traceability.
This approach has several key advantages over traditional prompting:
- The structured nature of DSPy allows for systematic optimization of reasoning patterns
- Pydantic models ensure consistency and validity throughout the reasoning process
- LiteLLM integration provides flexibility in model selection
- Comprehensive tracing captures the full reasoning process for analysis
The self-questioning reasoning framework implemented here has applications across various domains where thorough, transparent thinking is valuable, including:
- Complex decision-making in business and policy contexts
- Educational tools for teaching critical thinking
- Research assistants for exploring complex topics
- Ethical analysis of difficult questions
Future improvements could include more sophisticated parsing of reasoning steps, advanced visualization tools, and integration with retrieval systems for grounding reasoning in specific documents or knowledge bases[2][4]. The modular nature of the implementation allows for these enhancements while maintaining the core reasoning approach.
By implementing this framework as a Jupyter notebook with comprehensive documentation, we've created a tool that is both immediately useful and easily extensible to diverse reasoning tasks and domains.