From text to task: Constrained generation for structured extraction in R1

By Mikiko Bazeley|2/1/2025

What is constrained generation and why is it useful

Constrained generation is a technique in natural language processing (NLP) where language models are guided to produce text that adheres to specific predefined rules or structures. This approach is particularly useful in applications requiring structured outputs, such as generating code, creating formatted documents, or producing data in formats like JSON. By enforcing constraints during the text generation process, models can ensure outputs that are not only coherent but also conform to the desired structure, enhancing both the utility and reliability of the generated content.

What we’ll cover

• How constrained generation works
  • Guiding model token selection
  • Constrained decoding for structured outputs
• Reasoning models and structured extraction
  • The role of constrained generation in reasoning models
  • Fireworks' JSON mode for reasoning models
• Examples of constrained generation in action
  • Structured Q&A with reasoning
  • Healthcare records with AI-driven summaries
  • Computer system specifications with structured recommendations
• Conclusion
  • Why structured generation improves AI reliability
  • Future applications and best practices

How does it work

The process of constrained generation involves manipulating a model's token generation to restrict its next-token predictions to only those that do not violate the required output structure. This can be achieved through various methods, such as constrained decoding, where the model's output is directed to follow specific patterns or formats. For instance, in structured generation tasks, constrained decoding can simplify the next-token prediction space, accelerating generation by allowing some token generation steps to be skipped. Additionally, by focusing only on generating the necessary parts of the output and bypassing boilerplate sections, the overall efficiency of the generation process is improved.

Implementing constrained generation not only enhances the quality of the output by ensuring adherence to desired formats but may also improve performance. By reducing the complexity of the generation task and narrowing down the prediction space, the model can generate outputs more quickly and with greater accuracy. This efficiency gain is particularly beneficial in applications where rapid and reliable generation of structured text is crucial.

Constrained generation in reasoning models

In the context of reasoning models, such as the recently released DeepSeek R1, constrained generation plays a pivotal role in ensuring that outputs adhere to specific formats and structures. The DeepSeek R1 model exemplifies this by incorporating a unique mechanism: it generates a reasoning process enclosed within <think> and </think> tokens, followed by a JSON-formatted output. This structured approach allows the model to transparently display its thought process before presenting the final result. Notably, the JSON schema applies exclusively to the JSON section that follows the <think> tags, ensuring that the reasoning process and the final output are clearly delineated and properly formatted. The caller can employ simple output parsing to separate the reasoning section from the structured output.

Example 1: Simple Q&A

In this section, we'll demonstrate how to utilize the DeepSeek R1 reasoning model in JSON mode using the Fireworks API.

# Import necessary libraries
import json
import re
from pydantic import BaseModel
from openai import OpenAI
import os

# Initialize the Fireworks client
client = OpenAI(
    base_url="https://api.fireworks.ai/inference/v1",
    api_key=os.getenv("FIREWORKS_API_KEY"),
)


# Define the output schema using Pydantic
class QAResult(BaseModel):
    question: str
    answer: str


# Prepare the user input
user_input = "Who wrote 'Pride and Prejudice'?"

# Construct the messages payload
messages = [{"role": "user", "content": user_input}]

# Make the API call to DeepSeek R1
response = client.chat.completions.create(
    model="accounts/fireworks/models/deepseek-r1",
    messages=messages,
    response_format={"type": "json_object", "schema": QAResult.model_json_schema()},
    max_tokens=1000,  # Adjust as needed to prevent truncation
)

# Extract the content of the response
response_content = response.choices[0].message.content
print(f"Response content: {response_content}")

# Use regular expressions to extract the reasoning and JSON parts.
# The reasoning is enclosed within <think>...</think> tags,
# and the JSON part follows the </think> tag.
reasoning_match = re.search(r"<think>(.*?)</think>", response_content, re.DOTALL)
json_match = re.search(r"</think>\s*(\{.*\})", response_content, re.DOTALL)

# Extract reasoning
reasoning = reasoning_match.group(1).strip()

# Extract JSON string
json_str = json_match.group(1).strip()

# Directly parse the JSON string into a Pydantic model
qa_result = QAResult.model_validate_json(json_str)

# Output the extracted reasoning and the parsed Pydantic model
print(f"\nReasoning: {reasoning}")
print(f"\nQAResult: {qa_result}")

How it works

1. User Input: A question is submitted to the model (e.g., "Who wrote Pride and Prejudice?").
2. Constrained Generation: The model first produces its reasoning, enclosed in <think>...</think>, ensuring a structured explanation before providing the answer.
3. Schema Enforcement: The JSON-formatted response follows a Pydantic-defined schema, ensuring structured data.
4. Parsing and Validation: The reasoning section and JSON output are extracted separately, maintaining cleanly structured and machine-readable responses.

Let’s show another example, this time for a health care use case.

Example 2: Structured healthcare data generation with reasoning JSON mode

This example showcases how to generate structured healthcare records using Fireworks AI’s DeepSeek R1 model in Reasoning JSON Mode. By combining structured output with an explanation of the model’s thought process, this approach enhances transparency and reliability in AI-generated medical documentation.

The workflow consists of the following steps:

• Defining a Pydantic Schema – Ensures consistency in patient records by specifying required fields.
• Making an API Request – Generates structured healthcare data tailored to a given medical scenario.
• Extracting Model Reasoning – Captures the model’s thought process within <think>...</think> tags to provide insights into diagnoses and treatments.
• Validating and Parsing JSON Output – Uses Pydantic to ensure compliance with the predefined schema.

This structured approach enables seamless integration of AI-generated healthcare data into medical systems, making it valuable for clinical documentation, decision support, and automated reporting.

# Import necessary libraries
import json
import re
from pydantic import BaseModel
from openai import OpenAI
import os

# Load environment variables
from dotenv import load_dotenv
load_dotenv()

# Initialize the Fireworks client with API Key from environment variables
client = OpenAI(
    base_url="https://api.fireworks.ai/inference/v1",
    api_key=os.getenv("FIREWORKS_API_KEY"),
)

# Define the structured output schema for a healthcare patient record
class PatientRecord(BaseModel):
    name: str
    age: int
    gender: str
    diagnosis: str
    treatment: str
    medications: str
    notes: str

# Prepare the user input for generating a structured healthcare record
user_input = "Generate a structured healthcare record with reasoning for a patient diagnosed with hypertension."

# Construct the messages payload
messages = [{"role": "user", "content": user_input}]

# Make the API call to DeepSeek R1 using Reasoning JSON Mode
response = client.chat.completions.create(
    model="accounts/fireworks/models/deepseek-r1",
    messages=messages,
    response_format={"type": "json_object", "schema": PatientRecord.model_json_schema()},
    max_tokens=3000,  # Adjust max tokens to prevent truncation
)

# Extract the content of the response
response_content = response.choices[0].message.content
print(f"Response content: {response_content}")

# Use regular expressions to extract the reasoning and JSON parts.
# The reasoning is enclosed within <think>...</think> tags,
# and the JSON part follows the </think> tag.
reasoning_match = re.search(r"<think>(.*?)</think>", response_content, re.DOTALL)
json_match = re.search(r"</think>\s*(\{.*\})", response_content, re.DOTALL)

# Extract reasoning if present
reasoning = reasoning_match.group(1).strip() if reasoning_match else "No reasoning provided."

# Extract JSON string if present
json_str = json_match.group(1).strip() if json_match else "{}"

# Parse the JSON string into a Pydantic model
patient_record = PatientRecord.model_validate_json(json_str)

# Output the extracted reasoning and the parsed patient record
print("\n🧠 Model Reasoning:")
print(reasoning)

print("\n📋 Structured Patient Record (JSON Data):")
print(patient_record.model_dump_json(indent=4))

Output

When the script is run, the outputs would be similar to the following:

The response from R1

<think>
Okay, the user wants a structured healthcare record for a patient with hypertension, including reasoning. Let me start by recalling what a standard healthcare record includes. Typically, there's patient demographics, medical history, presenting complaints, physical exam findings, lab results, diagnosis, management plan, and follow-up. Plus, they want reasoning, so I should explain why each part is included.

First, demographics. John Doe, 58, male, African American. Wait, African Americans have higher hypertension risk, so that's relevant. Also, 10-year history of hypertension, non-compliant with meds—that's important for management. Family history matters because his father had hypertension and died of stroke. Lifestyle factors: sedentary, high-salt diet, smoking. Those are modifiable risk factors. Okay, so addressing those in the plan makes sense.

Presenting complaints: headache, fatigue, chest discomfort. Headaches and fatigue are common in hypertension. Chest pain could indicate something more serious like cardiac issues, so I need to note that as a possible differential diagnosis, maybe order an ECG. Shortness of breath too—could be heart failure or other complications.

Vitals: BP 168/100 mmHg, which is stage 2 hypertension. Elevated heart rate—maybe due to anxiety or compensation. BMI 30 is obese, which is a risk factor. Waist circumference and waist-hip ratio also indicate central obesity, adding to cardiovascular risk.

Physical exam: JVP normal, so maybe no fluid overload. Lungs clear, no edema—so no signs of heart failure yet. Fundoscopy with AV nicking and arteriolar narrowing—those are hypertensive retinopathy signs. Good to include that as it shows end-organ damage.

Labs: Basic metabolic panel, lipid profile, urinalysis. Elevated creatinine suggests possible kidney impairment—hypertension can cause that. LDL high, HDL low, which are cardiovascular risks. Urinalysis shows proteinuria, indicating kidney damage. Microalbuminuria is an early sign. So, these labs support the diagnosis and show complications.

Diagnosis: Essential hypertension, stage 2. Obesity and hyperlipidemia as comorbidities. Hypertensive retinopathy and CKD stage 3a as complications. Differential diagnoses like secondary hypertension? Maybe check for that with aldosterone, renin levels. Pheochromocytoma less likely but mentioned.

Management plan: Pharmacological—ACE inhibitor (lisinopril) because it's good in African Americans and for kidney protection. Thiazide diuretic (HCTZ) for added effect. Statin for cholesterol. Non-pharmacological: Lifestyle changes—diet (DASH), exercise, smoking cessation. Education on med compliance. Follow-up in 4 weeks. Referral to nephrology for CKD and nutritionist for diet.

Reasoning: Why ACEi? Because in African Americans, they respond better to ACEi or CCBs. Also, renal protection. Thiazide for synergy. Statin for ASCVD risk. Lifestyle changes crucial. Early CKD needs specialist input. Follow-up to adjust meds and monitor.

Follow-up plan: Adjust meds if BP not controlled. Check labs again. 24-hour BP monitoring if needed. Patient engagement important to ensure compliance.

Wait, should I mention differential diagnoses reasoning more? Like why secondary hypertension is considered? Maybe check for resistant hypertension causes. Labs like thyroid, cortisol? Maybe add those to the lab tests for secondary causes. But the user might not want too much detail. They asked for structured record with reasoning, so maybe keep it concise but cover key points.

Also, avoid mentioning lab tests for secondary causes in the management unless necessary. But since differentials include secondary HTN, maybe just the aldosterone/renin ratio and catecholamines. TSH for thyroid issues. That's covered in the lab tests.

Okay, making sure the structure is clear and each section has reasoning. Combine all into sections as per the example. Does it flow well? Maybe check if all essential parts are included. Yes, seems comprehensive. Need to make sure the reasoning is integrated to show clinical decision-making. That should cover the user's request.
</think>

The reasoning

Reasoning:
Okay, the user wants a structured healthcare record for a patient with hypertension, including reasoning. Let me start by recalling what a standard healthcare record includes. Typically, there's patient demographics, medical history, presenting complaints, physical exam findings, lab results, diagnosis, management plan, and follow-up. Plus, they want reasoning, so I should explain why each part is included.

First, demographics. John Doe, 58, male, African American. Wait, African Americans have higher hypertension risk, so that's relevant. Also, 10-year history of hypertension, non-compliant with meds—that's important for management. Family history matters because his father had hypertension and died of stroke. Lifestyle factors: sedentary, high-salt diet, smoking. Those are modifiable risk factors. Okay, so addressing those in the plan makes sense.

Presenting complaints: headache, fatigue, chest discomfort. Headaches and fatigue are common in hypertension. Chest pain could indicate something more serious like cardiac issues, so I need to note that as a possible differential diagnosis, maybe order an ECG. Shortness of breath too—could be heart failure or other complications.

Vitals: BP 168/100 mmHg, which is stage 2 hypertension. Elevated heart rate—maybe due to anxiety or compensation. BMI 30 is obese, which is a risk factor. Waist circumference and waist-hip ratio also indicate central obesity, adding to cardiovascular risk.

Physical exam: JVP normal, so maybe no fluid overload. Lungs clear, no edema—so no signs of heart failure yet. Fundoscopy with AV nicking and arteriolar narrowing—those are hypertensive retinopathy signs. Good to include that as it shows end-organ damage.

Labs: Basic metabolic panel, lipid profile, urinalysis. Elevated creatinine suggests possible kidney impairment—hypertension can cause that. LDL high, HDL low, which are cardiovascular risks. Urinalysis shows proteinuria, indicating kidney damage. Microalbuminuria is an early sign. So, these labs support the diagnosis and show complications.

Diagnosis: Essential hypertension, stage 2. Obesity and hyperlipidemia as comorbidities. Hypertensive retinopathy and CKD stage 3a as complications. Differential diagnoses like secondary hypertension? Maybe check for that with aldosterone, renin levels. Pheochromocytoma less likely but mentioned.

Management plan: Pharmacological—ACE inhibitor (lisinopril) because it's good in African Americans and for kidney protection. Thiazide diuretic (HCTZ) for added effect. Statin for cholesterol. Non-pharmacological: Lifestyle changes—diet (DASH), exercise, smoking cessation. Education on med compliance. Follow-up in 4 weeks. Referral to nephrology for CKD and nutritionist for diet.

Reasoning: Why ACEi? Because in African Americans, they respond better to ACEi or CCBs. Also, renal protection. Thiazide for synergy. Statin for ASCVD risk. Lifestyle changes crucial. Early CKD needs specialist input. Follow-up to adjust meds and monitor.

Follow-up plan: Adjust meds if BP not controlled. Check labs again. 24-hour BP monitoring if needed. Patient engagement important to ensure compliance.

Wait, should I mention differential diagnoses reasoning more? Like why secondary hypertension is considered? Maybe check for resistant hypertension causes. Labs like thyroid, cortisol? Maybe add those to the lab tests for secondary causes. But the user might not want too much detail. They asked for structured record with reasoning, so maybe keep it concise but cover key points.

Also, avoid mentioning lab tests for secondary causes in the management unless necessary. But since differentials include secondary HTN, maybe just the aldosterone/renin ratio and catecholamines. TSH for thyroid issues. That's covered in the lab tests.

Okay, making sure the structure is clear and each section has reasoning. Combine all into sections as per the example. Does it flow well? Maybe check if all essential parts are included. Yes, seems comprehensive. Need to make sure the reasoning is integrated to show clinical decision-making. That should cover the user's request.

The parsed JSON data

Patient Record (JSON Data):
{
    "name": "John Doe",
    "age": 58,
    "gender": "Male",
    "diagnosis": "Hypertension (Stage 2)",
    "treatment": "Lisinopril (10 mg daily), Hydrochlorothiazide (12.5 mg daily)",
    "medications": "",
    "notes": " "
}

Example 3: Generating structured computer system specifications with reasoning JSON mode

In this last example, we show how to generate structured computer system specifications using DeepSeek R1 in Reasoning JSON Mode. By leveraging structured output and model reasoning, this method ensures clear and consistent AI-generated hardware recommendations.

The process involves:

• Defining a Pydantic Schema – Enforces structured specifications for computer components.
• Making an API Request – Generates PC configurations based on user requirements.
• Extracting Model Reasoning – Explains why specific components were selected within <think>...</think> tags.
• Validating and Parsing JSON Output – Ensures the structured response adheres to expected hardware constraints.

This approach is particularly useful for system builders, procurement tools, and recommendation engines that require AI-driven hardware configurations with explainable decision-making.

# Import necessary libraries
import json
import re
import os
from pydantic import BaseModel, Field
from dotenv import load_dotenv
from openai import OpenAI

# Load environment variables from .env file
load_dotenv()

# Initialize the Fireworks client
client = OpenAI(
    base_url="https://api.fireworks.ai/inference/v1",
    api_key=os.getenv("FIREWORKS_API_KEY"),
)

# Define the output schema using Pydantic
class Processor(BaseModel):
    brand: str = Field(pattern="^(Intel|AMD)$")
    model: str
    cores: int = Field(ge=2, le=32)
    clock_speed_ghz: float = Field(ge=1.0, le=6.0)

class Memory(BaseModel):
    size_gb: int = Field(ge=4, le=256)
    type: str = Field(pattern="^(DDR4|DDR5)$")

class Storage(BaseModel):
    type: str = Field(pattern="^(SSD|HDD|NVMe SSD)$")
    capacity_tb: float = Field(ge=0.5, le=10)

class GPU(BaseModel):
    brand: str = Field(pattern="^(NVIDIA|AMD)$")
    model: str

class ComputerSpec(BaseModel):
    processor: Processor
    memory: Memory
    storage: Storage
    gpu: GPU
    motherboard: str = Field(pattern="^(ATX|Micro-ATX|Mini-ITX)$")
    cooling: str = Field(pattern="^(Air-cooled|Liquid-cooled)$")
    networking: str = Field(pattern="^(WiFi 6|WiFi 6E|Gigabit Ethernet|2.5G Ethernet)$")
    ports: list[str] = Field(min_items=1, max_items=5)
    os: str = Field(pattern="^(Windows 11|macOS Sonoma|Ubuntu 22.04|Fedora 38)$")

# Prepare user input
user_input = "Generate a structured gaming PC specification with an Intel Core i9, 64GB DDR5 RAM, and an NVIDIA RTX 4090."

# Construct messages payload
messages = [{"role": "user", "content": user_input}]

# Make the API call to DeepSeek R1
response = client.chat.completions.create(
    model="accounts/fireworks/models/deepseek-r1",
    messages=messages,
    response_format={"type": "json_object", "schema": ComputerSpec.model_json_schema()},
    max_tokens=2000,  # Adjust token limit
)

# Extract the response content
response_content = response.choices[0].message.content
print("Raw Response Content:\n", response_content)

# Extract the reasoning part enclosed in <think>...</think> tags
reasoning_match = re.search(r"<think>(.*?)</think>", response_content, re.DOTALL)
reasoning = reasoning_match.group(1).strip() if reasoning_match else "No reasoning provided."

# Extract the JSON part that follows after the reasoning
json_match = re.search(r"</think>\s*(\{.*\})", response_content, re.DOTALL)
json_str = json_match.group(1).strip() if json_match else "{}"

# Parse the JSON response into the Pydantic model
computer_spec = ComputerSpec.model_validate_json(json_str)

# Output the extracted reasoning and structured data
print("\nReasoning:\n", reasoning)
print("\nComputer Specification (JSON Data):")
print(computer_spec.model_dump_json(indent=4))

Output

When the example above is run, you should see output similar to:

The response from R1

<think>
Okay, the user wants a high-performance gaming PC with an AMD Ryzen 9, 64GB DDR5 RAM, and an NVIDIA RTX 3090 GPU. Let me start by breaking down each component. 

First, the CPU. AMD Ryzen 9 series has several options. The latest would be the 7000 series, like the Ryzen 9 7950X. That's 16 cores, which is overkill for gaming but great for multitasking and future-proofing. Some users might prefer the 7900X if they want a balance, but since the user specified Ryzen 9, the top-tier model seems suitable.

Next, the GPU: RTX 3090. Wait, NVIDIA has newer models like the 40 series. The 3090 is still powerful, but I should check if the user specifically wants the 3090. Maybe they have a reason, like availability or specific use cases. So stick with the 3090, but note that a 4080 or 4090 might offer better performance. Also, the 3090 has 24GB VRAM, which is great for gaming at high resolutions or content creation.

RAM: 64GB DDR5. That's a lot for gaming. Most games don't need more than 32GB, but maybe the user is into streaming, video editing, or heavy multitasking. So need to pick a high-speed DDR5, like 6000MHz. CL latency matters; lower CL is better. Maybe a 6000MHz CL30 or CL32 kit. Also, ensure it's AMD EXPO certified for Ryzen compatibility.

Motherboard: X670E chipset to support the Ryzen 7000 series. Need PCIe 5.0 for future GPUs and NVMe. Look for good VRM cooling, multiple M.2 slots, and built-in Wi-Fi 6E if possible. Brands like ASUS, MSI, or Gigabyte.

Storage: For gaming, NVMe SSD is a must. A 2TB PCIe 4.0 drive like Samsung 980 Pro or WD Black SN850X. Maybe a 4TB if budget allows. Also, secondary storage could be a larger SATA SSD or HDD, but the user might prefer all NVMe for speed.

Power Supply: RTX 3090 is power-hungry. A 850W PSU minimum, but 1000W to be safe, especially with a high-end CPU and room for overclocking. 80+ Platinum efficiency. Modular for cable management. Brands like Corsair, EVGA, or Seasonic.

Cooling: Ryzen 9 can run hot. An AIO liquid cooler with 360mm radiator would be best. Arctic Liquid Freezer II or Corsair iCUE H150i. Good thermal paste also important.

Case: Needs good airflow. Lian Li PC-O11 Dynamic or Corsair 5000D. Make sure it fits the 360mm radiator and the GPU length. Plenty of fan mounts for airflow.

Extras: Maybe RGB for aesthetics, Windows 11 Pro, and a monitor recommendation. If the user is using a 3090, a 4K 144Hz monitor would pair well. Peripherals like mechanical keyboard and gaming mouse.

Wait, the user didn't mention a budget, but high-performance implies top-tier parts. Check for possible bottlenecks. 64GB RAM might be overkill, but the user specified it. Maybe note that 32GB is enough for gaming, but 64GB is for other tasks. Also, ensure DDR5 is dual-channel and dual-rank for optimal performance.

Double-check compatibility: CPU socket AM5 for Ryzen 7000, motherboard chipset X670E, and ensure the PSU has enough PCIe connectors for the GPU. Case dimensions for all components.

Alternative components if certain parts are unavailable. Mention possible upgrades like adding more storage later. Also, highlight the reasoning behind each choice to help the user understand the build's balance.

Make sure the total wattage is covered by the PSU. RTX 3090 can peak around 450W, Ryzen 9 7950X up to 230W, other components adding maybe 100W. Total around 800W at peak, so 1000W PSU gives headroom for overclocking and efficiency.

Final check: No incompatible parts, all components fit in the case, adequate cooling for both CPU and GPU, and high-speed storage for quick load times.
</think>
{"processor": "AMD Ryzen 9 7950X", "memory": "64GB Corsair Dominator Platinum RGB DDR5 6000MHz (CL30)", "storage": "Primary: 2TB Samsung 990 Pro NVMe SSD (PCIe 4.0) | Secondary: 4TB Seagate BarraCuda HDD", "gpu": "NVIDIA GeForce RTX 3090 Founders Edition (24GB GDDR6X)", "motherboard": "ASUS ROG Crosshair X670E Extreme (Wi-Fi 6E, PCIe 5.0)", "cooling": "NZXT Kraken Z73 360mm AIO Liquid Cooler", "networking": "Onboard 10G LAN + Wi-Fi 6E", "ports": "USB4, Thunderbolt 4, 12x USB 3.2 Gen 2", "os": "Windows 11 Pro 64-bit" }

The reasoning

Reasoning:
Okay, the user wants a high-performance gaming PC with an AMD Ryzen 9, 64GB DDR5 RAM, and an NVIDIA RTX 3090 GPU. Let me start by breaking down each component. 

First, the CPU. AMD Ryzen 9 series has several options. The latest would be the 7000 series, like the Ryzen 9 7950X. That's 16 cores, which is overkill for gaming but great for multitasking and future-proofing. Some users might prefer the 7900X if they want a balance, but since the user specified Ryzen 9, the top-tier model seems suitable.

Next, the GPU: RTX 3090. Wait, NVIDIA has newer models like the 40 series. The 3090 is still powerful, but I should check if the user specifically wants the 3090. Maybe they have a reason, like availability or specific use cases. So stick with the 3090, but note that a 4080 or 4090 might offer better performance. Also, the 3090 has 24GB VRAM, which is great for gaming at high resolutions or content creation.

RAM: 64GB DDR5. That's a lot for gaming. Most games don't need more than 32GB, but maybe the user is into streaming, video editing, or heavy multitasking. So need to pick a high-speed DDR5, like 6000MHz. CL latency matters; lower CL is better. Maybe a 6000MHz CL30 or CL32 kit. Also, ensure it's AMD EXPO certified for Ryzen compatibility.

Motherboard: X670E chipset to support the Ryzen 7000 series. Need PCIe 5.0 for future GPUs and NVMe. Look for good VRM cooling, multiple M.2 slots, and built-in Wi-Fi 6E if possible. Brands like ASUS, MSI, or Gigabyte.

Storage: For gaming, NVMe SSD is a must. A 2TB PCIe 4.0 drive like Samsung 980 Pro or WD Black SN850X. Maybe a 4TB if budget allows. Also, secondary storage could be a larger SATA SSD or HDD, but the user might prefer all NVMe for speed.

Power Supply: RTX 3090 is power-hungry. A 850W PSU minimum, but 1000W to be safe, especially with a high-end CPU and room for overclocking. 80+ Platinum efficiency. Modular for cable management. Brands like Corsair, EVGA, or Seasonic.

Cooling: Ryzen 9 can run hot. An AIO liquid cooler with 360mm radiator would be best. Arctic Liquid Freezer II or Corsair iCUE H150i. Good thermal paste also important.

Case: Needs good airflow. Lian Li PC-O11 Dynamic or Corsair 5000D. Make sure it fits the 360mm radiator and the GPU length. Plenty of fan mounts for airflow.

Extras: Maybe RGB for aesthetics, Windows 11 Pro, and a monitor recommendation. If the user is using a 3090, a 4K 144Hz monitor would pair well. Peripherals like mechanical keyboard and gaming mouse.

Wait, the user didn't mention a budget, but high-performance implies top-tier parts. Check for possible bottlenecks. 64GB RAM might be overkill, but the user specified it. Maybe note that 32GB is enough for gaming, but 64GB is for other tasks. Also, ensure DDR5 is dual-channel and dual-rank for optimal performance.

Double-check compatibility: CPU socket AM5 for Ryzen 7000, motherboard chipset X670E, and ensure the PSU has enough PCIe connectors for the GPU. Case dimensions for all components.

Alternative components if certain parts are unavailable. Mention possible upgrades like adding more storage later. Also, highlight the reasoning behind each choice to help the user understand the build's balance.

Make sure the total wattage is covered by the PSU. RTX 3090 can peak around 450W, Ryzen 9 7950X up to 230W, other components adding maybe 100W. Total around 800W at peak, so 1000W PSU gives headroom for overclocking and efficiency.

Final check: No incompatible parts, all components fit in the case, adequate cooling for both CPU and GPU, and high-speed storage for quick load times.

The parsed JSON data

Computer Specification (JSON Data):
{
    "processor": "AMD Ryzen 9 7950X",
    "memory": "64GB Corsair Dominator Platinum RGB DDR5 6000MHz (CL30)",
    "storage": "Primary: 2TB Samsung 990 Pro NVMe SSD (PCIe 4.0) | Secondary: 4TB Seagate BarraCuda HDD",
    "gpu": "NVIDIA GeForce RTX 3090 Founders Edition (24GB GDDR6X)",
    "motherboard": "ASUS ROG Crosshair X670E Extreme (Wi-Fi 6E, PCIe 5.0)",
    "cooling": "NZXT Kraken Z73 360mm AIO Liquid Cooler",
    "networking": "Onboard 10G LAN + Wi-Fi 6E",
    "ports": "USB4, Thunderbolt 4, 12x USB 3.2 Gen 2",
    "os": "Windows 11 Pro 64-bit"
}

Additional resources

For more details on using constrained reasoning in Fireworks, check out our documentation on structured response modes, where we hope to publish additional examples soon.

Conclusion

To conclude, constrained generation in reasoning models like DeepSeek R1 enables AI to produce structured, interpretable, and machine-readable outputs across a wide range of applications. By enforcing predefined formats through JSON mode, grammar-based constraints, and reasoning-enhanced responses, we can ensure reliability, transparency, and consistency in AI-generated content. Whether it’s structured Q&A, healthcare documentation, or computer system recommendations, this approach not only improves the accuracy of outputs but also enhances their usability in real-world systems. As AI continues to evolve, structured generation techniques will play a crucial role in making model outputs more actionable, verifiable, and seamlessly integrable into existing workflows.

Why Fireworks AI

Fireworks AI is an enterprise scale LLM inference engine. Today, several AI-enabled developer experiences built on the Fireworks Inference platform are serving millions of developers.

Fireworks lightning fast serving stack enables enterprises to build mission critical Generative AI Applications that are super low latency. With methods like prompt caching, speculative API, we guarantee high throughput performance with low total cost of offering (TCO) in addition to bringing best of the open-source LLMs on the same day of the launch.

If you have more questions, join our community and tag a Fireworks AI team member or drop a note to discuss building with LLMs from prototype to production.