LLM Integration¶

Introduction¶

Syntera Marketplace's LLM Integration component is an enterprise-grade platform for deploying, managing, and accessing open-source large language models (LLMs) and AI agents. This documentation provides comprehensive guidance for efficiently utilizing our platform to integrate powerful AI capabilities into your applications through our API endpoints.

Platform Overview¶

Syntera Marketplace enables organizations to leverage state-of-the-art AI models through our API endpoints. Our platform distinguishes itself through:

Key Features¶

🤖 Multiple Model Support
Access to various open-source and proprietary models
Easy model switching through configuration
Automatic resource scaling for complex requests
Cost-effective resource allocation
🔄 Resource Management
Dynamic resource allocation based on request complexity
Automatic scaling to dedicated machines for heavy workloads
Cost optimization through resource sharing
Performance monitoring and optimization
🛠️ API Integration
Simple REST API endpoints
Consistent response format
Webhook support for async operations
Streaming response handling

Platform Architecture¶

LLM Inference Service - User Experience

Available Models¶

Language Models (LLMs)¶

Model Category	Description	Use Cases
Instruction-tuned LLMs	Models optimized for following specific instructions	Chatbots, virtual assistants, content generation
Base LLMs	Foundation models with broader capabilities	Research, fine-tuning projects, creative applications
Specialized LLMs	Domain-specific models (legal, financial, medical, etc.)	Industry-specific applications requiring domain expertise

Multimodal Models¶

Models capable of processing both text and other data types:

Text-to-Image: Generate images from textual descriptions
Image Understanding: Extract information and insights from visual content
Audio Processing: Transcription, translation, and audio generation capabilities

Getting Started¶

Prerequisites¶

API key from Syntera Marketplace
Basic understanding of REST APIs
Infrastructure requirements based on your use case

Quick Start¶

Get your API key:
Register at mar-syntera.ai
Navigate to API Keys section
Generate a new API key

Make your first request:

curl -X POST "https://mar-syntera.ai/v1/completions" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "your-selected-model",
    "prompt": "What is artificial intelligence?",
    "max_tokens": 256,
    "temperature": 0.7
  }'

Python example:

import requests
import json

API_KEY = "your-api-key"
API_URL = "https://mar-syntera.ai/v1/completions"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

data = {
    "model": "your-selected-model",
    "prompt": "What is artificial intelligence?",
    "max_tokens": 256,
    "temperature": 0.7
}

response = requests.post(API_URL, headers=headers, json=data)
result = response.json()
print(result["choices"][0]["text"])

Model Configuration¶

Basic Parameters¶

Parameter	Type	Description	Typical Range
max_tokens	Integer	Controls output length	128-4096
temperature	Float	Randomness/creativity level	0.0-1.0
top_p	Float	Nucleus sampling parameter	0.0-1.0
top_k	Integer	Limits token selection	1-100
repetition_penalty	Float	Reduces repetition	1.0-2.0

Resource Management¶

Our platform automatically manages resources based on your request:

Standard Requests: Processed on shared model instances
Complex Requests: Automatically routed to dedicated instances
Resource Scaling: Dynamic allocation based on:
Request complexity
Token count
Concurrent requests
Available resources

Cost Optimization¶

Shared Resources: Lower cost for standard requests
Dedicated Resources: Higher cost for complex requests
Automatic Scaling: Pay only for what you use
Resource Monitoring: Track usage and costs

API Reference¶

Authentication¶

All API requests require an API key for authentication:

curl -X POST "https://mar-syntera.ai/v1/completions" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "your-selected-model",
    "prompt": "Once upon a time"
  }'

Endpoints¶

Text Completion¶

response = requests.post(
    "https://mar-syntera.ai/v1/completions",
    headers={
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    },
    json={
        "model": "your-selected-model",
        "prompt": "Input text to complete",
        "max_tokens": 256,
        "temperature": 0.7,
        "top_p": 0.9,
        "stop": ["\n", "END"]
    }
)

Chat Completion¶

response = requests.post(
    "https://mar-syntera.ai/v1/chat/completions",
    headers={
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    },
    json={
        "model": "your-selected-model",
        "messages": [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Hello, how are you?"}
        ],
        "max_tokens": 256,
        "temperature": 0.7
    }
)

Security & Compliance¶

Data Privacy¶

Data Isolation: All processing occurs within our secure infrastructure
No External Data Transfer: Models do not send data outside our environment
No Training on User Data: Your inputs are not used to train or improve models
Data Retention Controls: Configure retention policies for logs and requests

Authentication & Authorization¶

API Key Management: Rotate, revoke, and manage access keys
Role-Based Access Control: Define user roles and permissions
IP Restrictions: Limit API access to specific IP ranges
Usage Auditing: Track and monitor all API requests

Rate Limits & Performance¶

Default Rate Limits¶

Tier	Requests/min	Requests/hour	Concurrent
Standard	60	3,000	5
Professional	300	15,000	20
Enterprise	Custom	Custom	Custom

Performance Optimization¶

Request Batching: Combine multiple requests for efficiency
Streaming Responses: Get partial results as they're generated
Resource Monitoring: Track resource usage and costs
Automatic Scaling: Platform handles resource allocation

Troubleshooting¶

Common Issues¶

Issue	Possible Causes	Resolution
Slow Response Times	Complex requests, high load	Consider request optimization
Rate Limit Errors	Exceeding request quotas	Implement rate limiting
Authentication Failures	Invalid API key	Verify key, regenerate if necessary
Resource Allocation	Complex request requiring dedicated resources	Monitor costs and optimize requests
Output Quality Issues	Suboptimal parameters	Refine parameters and prompt design

Use Case Examples¶

Content Generation¶

response = requests.post(
    "https://mar-syntera.ai/v1/completions",
    headers={
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    },
    json={
        "model": "content-generator-v1",
        "prompt": "Write a product description for a wireless ergonomic keyboard:",
        "max_tokens": 200,
        "temperature": 0.7
    }
)

Customer Support Chatbot¶

response = requests.post(
    "https://mar-syntera.ai/v1/chat/completions",
    headers={
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    },
    json={
        "model": "support-assistant-v2",
        "messages": [
            {"role": "system", "content": "You are a helpful customer support assistant."},
            {"role": "user", "content": "I'm having trouble connecting my device to WiFi."}
        ],
        "max_tokens": 150,
        "temperature": 0.3
    }
)

Best Practices¶

Request Optimization¶

Be Specific: Clear, detailed prompts produce better results
Context Matters: Provide relevant background information
Format Instructions: Specify desired output format
Examples Help: Few-shot examples improve consistency
System Messages: Use system messages to set behavior expectations

Resource Management¶

Monitor Usage: Track resource consumption and costs
Optimize Requests: Balance complexity and resource usage
Batch When Possible: Combine similar requests
Use Streaming: For long-running requests

FAQs¶

General Questions¶

Q: How does resource allocation work?
A: Our platform automatically manages resources. Standard requests use shared resources, while complex requests are automatically routed to dedicated instances with appropriate resource allocation.

Q: How are costs calculated?
A: Costs are based on resource usage. Standard requests on shared resources are more cost-effective, while complex requests requiring dedicated resources have higher costs.

Q: How do I optimize my requests?
A: Consider request complexity, use appropriate parameters, and monitor resource usage. Our platform provides tools to help optimize your requests.

Technical Questions¶

Q: What happens when a request is too complex?
A: The platform automatically routes complex requests to dedicated resources, ensuring reliable processing while maintaining performance for other users.

Q: How can I monitor resource usage?
A: Use our monitoring dashboard to track resource consumption, costs, and performance metrics for your requests.

Q: Can I control resource allocation?
A: While the platform automatically manages resources, you can optimize your requests to better control resource usage and costs.

Last updated: May 11, 2025