Python

import os
from tinfoil import TinfoilAI

# Configure client for DeepSeek V3.1 Terminus
client = TinfoilAI(
    api_key=os.getenv("TINFOIL_API_KEY")
)

# Example: Multi-step agentic workflow
response = client.chat.completions.create(
    model="deepseek-v31-terminus",
    messages=[
        {
            "role": "user",
            "content": "Write a Python function to validate email addresses using regex, then test it with several examples and debug any issues."
        }
    ]
)

print(response.choices[0].message.content)

import os
from tinfoil import TinfoilAI

# Configure client for DeepSeek R1
client = TinfoilAI(
    api_key=os.getenv("TINFOIL_API_KEY")
)

# Example: Complex reasoning task
response = client.chat.completions.create(
    model="deepseek-r1-0528",
    messages=[
        {
            "role": "user",
            "content": "Solve this step by step: If a train travels 120 miles in 2 hours, and then increases its speed by 25% for the next 3 hours, how far does it travel in total?"
        }
    ]
)

print(response.choices[0].message.content)

import os
from tinfoil import TinfoilAI

# Configure client for Mistral Small 3.1 24B
client = TinfoilAI(
    api_key=os.getenv("TINFOIL_API_KEY")
)

# Example: Multilingual conversation
response = client.chat.completions.create(
    model="mistral-small-3-1-24b",
    messages=[
        {
            "role": "user",
            "content": "Explain the concept of machine learning in both English and French."
        }
    ]
)

print(response.choices[0].message.content)

import os
from tinfoil import TinfoilAI

# Configure client for Llama 3.3 70B
client = TinfoilAI(
    api_key=os.getenv("TINFOIL_API_KEY")
)

# Example: Conversational AI
response = client.chat.completions.create(
    model="llama3-3-70b",
    messages=[
        {
            "role": "user",
            "content": "What are the key differences between renewable and non-renewable energy sources?"
        }
    ]
)

print(response.choices[0].message.content)

import os
from tinfoil import TinfoilAI

# Configure client for GPT-OSS 120B
client = TinfoilAI(
    api_key=os.getenv("TINFOIL_API_KEY")
)

# Example: Advanced reasoning with configurable effort levels
response = client.chat.completions.create(
    model="gpt-oss-120b",
    messages=[
        {
            "role": "user",
            "content": "Analyze the trade-offs between different database architectures for a high-traffic e-commerce platform."
        }
    ]
)

print(response.choices[0].message.content)

import os
from tinfoil import TinfoilAI

# Configure client for Qwen3 Coder 480B
client = TinfoilAI(
    api_key=os.getenv("TINFOIL_API_KEY")
)

# Example: Repository-scale code understanding and agentic coding
response = client.chat.completions.create(
    model="qwen3-coder-480b",
    messages=[
        {
            "role": "user",
            "content": "Review this codebase and suggest architectural improvements for better modularity and testability."
        }
    ]
)

print(response.choices[0].message.content)

import os
from tinfoil import TinfoilAI

# Configure client for Qwen 2.5 72B
client = TinfoilAI(
    api_key=os.getenv("TINFOIL_API_KEY")
)

# Example: Code generation and analysis
response = client.chat.completions.create(
    model="qwen2-5-72b",
    messages=[
        {
            "role": "user",
            "content": "Write a Python function to calculate the Fibonacci sequence up to n terms, then explain how it works."
        }
    ]
)

print(response.choices[0].message.content)

import os
from tinfoil import TinfoilAI

# Configure client for Whisper Large V3 Turbo
client = TinfoilAI(
    api_key=os.getenv("TINFOIL_API_KEY")
)

# Example: Audio transcription
with open("meeting_recording.mp3", "rb") as audio_file:
    transcription = client.audio.transcriptions.create(
        file=audio_file,
        model="whisper-large-v3-turbo",
        language="en",  # Optional: specify language for better accuracy
        prompt="This is a business meeting discussing quarterly results"  # Optional: provide context
    )

print("Transcription:", transcription.text)

import os
from tinfoil import TinfoilAI

# Configure client for Kokoro TTS
client = TinfoilAI(
    api_key=os.getenv("TINFOIL_API_KEY")
)

# Example: Text-to-speech with different voices
text_to_speak = "Welcome to Tinfoil's secure AI platform. Your data remains private and protected."

# Single voice
response = client.audio.speech.create(
    model="kokoro",
    voice="af_sky",
    input=text_to_speak,
    response_format="mp3"
)
response.write_to_file("speech_single.mp3")

# Combined voices for richer sound
response = client.audio.speech.create(
    model="kokoro",
    voice="af_sky+af_bella",
    input=text_to_speak,
    response_format="mp3"
)
response.write_to_file("speech_combined.mp3")

print("Speech files generated successfully!")

import os
from tinfoil import TinfoilAI
import numpy as np

# Configure client for Nomic Embed Text
client = TinfoilAI(
    api_key=os.getenv("TINFOIL_API_KEY")
)

# Example: Generate embeddings for similarity search
documents = [
    "Artificial intelligence is transforming modern technology.",
    "Machine learning enables computers to learn from data.",
    "The weather today is sunny and warm.",
    "Deep learning uses neural networks with multiple layers."
]

# Generate embeddings for all documents
embeddings = []
for doc in documents:
    response = client.embeddings.create(
        model="nomic-embed-text",
        input=doc
    )
    embeddings.append(response.data[0].embedding)

# Calculate similarity between first two documents
emb1 = np.array(embeddings[0])
emb2 = np.array(embeddings[1])
similarity = np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2))

print(f"Similarity between first two AI-related documents: {similarity:.3f}")
print(f"Embedding dimension: {len(embeddings[0])}")