Reasoning Models

import GlomaxGPT from "GlomaxGPT";

const client = new GlomaxGPT();

// Medium effort (default)
const response = await client.responses.create({
  model: "glomaxgpt-think",
  reasoning: { effort: "medium" },
  input: "Prove that the square root of 2 is irrational.",
});

console.log(response.output_text);
console.log(`Reasoning tokens: ${response.usage.output_tokens_details.reasoning_tokens}`);

// High effort for a hard problem
const hardProblem = await client.responses.create({
  model: "glomaxgpt-think",
  reasoning: { effort: "high" },
  input: "Solve: Find all integer solutions to x^3 + y^3 = z^3 + w^3 where 1 ≤ x, y, z, w ≤ 1000.",
});

console.log(hardProblem.output_text);

from GlomaxGPT import GlomaxGPT

client = GlomaxGPT()

response = client.responses.create(
    model="glomaxgpt-think",
    reasoning={"effort": "high"},
    input="""Prove by induction that for all positive integers n:

    1² + 2² + 3² + ... + n² = n(n+1)(2n+1)/6

Write a complete, rigorous proof including:
1. Base case
2. Inductive hypothesis
3. Inductive step with algebraic manipulation
4. Conclusion"""
)

print(response.output_text)

from GlomaxGPT import GlomaxGPT

client = GlomaxGPT()

response = client.responses.create(
    model="glomaxgpt-think",
    reasoning={"effort": "high"},
    input="""A company produces two products, A and B.
- Product A requires 2 hours of labor and 1 kg of material; profit = $40
- Product B requires 1 hour of labor and 3 kg of material; profit = $30
- Available: 100 hours of labor, 150 kg of material

Formulate and solve this as a linear programming problem.
Find the optimal production quantities to maximize profit.
Include the feasible region analysis and corner point method."""
)

print(response.output_text)

from GlomaxGPT import GlomaxGPT

client = GlomaxGPT()

buggy_code = """
def find_duplicates(nums):
    seen = set()
    duplicates = []
    for i in range(len(nums)):
        for j in range(i, len(nums)):  # Bug here
            if nums[i] == nums[j]:
                duplicates.append(nums[i])
    return list(set(duplicates))

# Expected: find_duplicates([1,3,4,2,2]) == [2]
# Actual: returns [1, 2, 3, 4]
print(find_duplicates([1, 3, 4, 2, 2]))
"""

response = client.responses.create(
    model="glomaxgpt-think-mini",
    reasoning={"effort": "medium"},
    input=f"""Find and fix all bugs in this Python function. Explain each bug clearly.

```python
{buggy_code}
```

Provide:
1. List of all bugs found with explanations
2. Fixed version with comments
3. Test cases that would catch these bugs"""
)

print(response.output_text)

from GlomaxGPT import GlomaxGPT

client = GlomaxGPT()

code_to_review = """
class UserCache:
    def __init__(self):
        self.cache = {}
    
    def get_user(self, user_id):
        if user_id not in self.cache:
            self.cache[user_id] = self._fetch_from_db(user_id)
        return self.cache[user_id]
    
    def _fetch_from_db(self, user_id):
        # Simulate DB query
        return {"id": user_id, "name": "User " + str(user_id)}
    
    def invalidate(self, user_id):
        del self.cache[user_id]
"""

response = client.responses.create(
    model="glomaxgpt-think",
    reasoning={"effort": "medium"},
    input=f"""Review this Python class for correctness, performance, and production readiness.

```python
{code_to_review}
```

Analyze:
1. Thread safety issues
2. Memory leak risks
3. Missing error handling
4. Performance concerns
5. Cache invalidation edge cases

Provide a production-ready rewrite addressing all issues."""
)

print(response.output_text)