Chain of Thoughts

Set chain_of_thought=True on any ChatConfig to make the model reason step by step before answering. The feature works identically across all five provider kits — OpenAI, Anthropic, Google, Ollama, and HuggingFace — with zero adapter changes.

How it works

When chain_of_thought=True, the kit creates a non-mutating copy of the active RactoPrompt and appends the following constraint to the [CONSTRAINTS] section of the compiled system prompt:

“Before answering, reason through the problem step by step. State each reasoning step clearly and explicitly, then conclude with your final answer.”

The constraint is added last, after all caller-defined rules, so it never overrides your own constraints. The original RactoPrompt object is never mutated.

Basic usage

from ractogateway import openai_developer_kit as gpt, RactoPrompt

prompt = RactoPrompt(
    role="You are a maths tutor.",
    aim="Solve the problem the student gives you.",
    constraints=["Show every calculation step.", "Use plain English."],
    tone="Patient and encouraging",
    output_format="text",
)
kit = gpt.Chat(model="gpt-4o", default_prompt=prompt)

response = kit.chat(gpt.ChatConfig(
    user_message="What is 17 × 23?",
    chain_of_thought=True,
))
print(response.content)
# "Step 1: Break 17 × 23 into (17 × 20) + (17 × 3).
#  Step 2: 17 × 20 = 340.
#  Step 3: 17 × 3 = 51.
#  Step 4: 340 + 51 = 391.
#  Answer: 391."

Works with every kit

Swap gpt for any other kit — the API is identical:

from ractogateway import anthropic_developer_kit as claude
from ractogateway import google_developer_kit as gemini
from ractogateway import ollama_developer_kit as local
from ractogateway import huggingface_developer_kit as hf

config = claude.ChatConfig(user_message="Explain recursion.", chain_of_thought=True)
response = claude.Chat(model="claude-opus-4-6", default_prompt=prompt).chat(config)

Works with streaming

for chunk in kit.stream(gpt.ChatConfig(
    user_message="Explain the travelling salesman problem.",
    chain_of_thought=True,
    temperature=0.4,
)):
    print(chunk.delta.text, end="", flush=True)

Tips

Goal	Setting
Richer, more expressive reasoning	Raise temperature to 0.3–0.7
Long multi-step reasoning chains	Increase max_tokens (default 4096)
Structured final answer only	Add output_format=YourModel to the prompt; CoT reasons internally then outputs JSON
Disable for a specific call	Simply omit chain_of_thought (default False)

ChatConfig field reference

Field	Type	Default	Description
chain_of_thought	bool	False	Inject CoT reasoning instruction
temperature	float	0.0	Raise for richer reasoning traces
max_tokens	int	4096	Increase for long reasoning chains

Implementation details

The helper lives in src/ractogateway/_cot.py:

from ractogateway._cot import apply_chain_of_thought

modified_prompt = apply_chain_of_thought(original_prompt)
# Returns a new RactoPrompt; original_prompt is unchanged

Each kit calls apply_chain_of_thought immediately after resolving the active prompt (in chat(), achat(), stream(), and astream()), so the constraint is always present in the compiled system prompt that the adapter receives.