Custom Chat Completion
Integrate generative LLMs. Generate structured responses, summarize long text files, translate content, and build intelligent chatbots.
Generative AI & Embedding Engine
Build next-gen tools with the AI Studio API
Connect your business workflows to advanced generative models. Generate vector embeddings, execute semantic searches, translate languages, and run customized inference models using our secure AI API portal.
- dsi-core-llm-3
- 1536 dims
- Zero
AI Studio Gateway
Online (v3.0)
POST /v3/models/chat-completion HTTP/1.1
Host: console.devsecit.com
Authorization: Bearer DSI_AI_...
Content-Type: application/json
// Request Payload
{
"model": "dsi-core-llm-3",
"messages": [{ "role": "user", "content": "Summarize white label API benefits." }]
}Enterprise GPU ClusterSalted API Token Authentication
Platform overview
Platform Overview & Business Architecture
The DEV SEC IT AI Studio API provides a high-performance gateway to advanced generative artificial intelligence models. Building, deploying, and maintaining private GPU server clusters for large language models, image generators, and vector embedding engines is a significant resource burden. AI Studio solves this problem by offering scalable, low-latency API access to optimized open-source and proprietary models.
Using our API, developers can integrate chat interfaces, automate content creation, generate high-dimensional vector embeddings, compile code, translate languages, and build semantic search tools. The platform uses load-balanced clusters of enterprise-grade GPUs to handle variable workloads, ensuring fast response times even during periods of peak traffic.
Data privacy is a core design pillar. Customer data, training inputs, and model request histories processed through the AI Studio API are kept strictly private. They are never used to train public models or shared with third parties, helping you meet enterprise compliance requirements.
Capabilities
AI Studio Features
Vector Embeddings Engine
Generate high-dimensional vector representations of text strings. Perfect for semantic searches, clustering, and recommendation engines.
Private Fine-Tuning
Fine-tune base models with your proprietary dataset. Deploy isolated model checkpoints that belong solely to your organization.
Semantic & Vector Search
Build intelligent document retrieval systems (RAG). Index data, query vector indexes, and return context-aware answers.
Technical reference
API Reference & Technical Endpoints
The AI Studio API communicates via secure HTTPS. Below are the request and response details for completing chat sessions.
CHAT COMPLETIONS
POST https://console.devsecit.com/api/v3/models/chat-completion
Header "Authorization: Bearer YOUR_AI_STUDIO_KEY"
Header "Content-Type: application/json"
{
"model": "dsi-core-llm-3",
"messages": [
{ "role": "system", "content": "You are a helpful software engineering assistant." },
{ "role": "user", "content": "Explain vector embeddings in one sentence." }
],
"temperature": 0.3,
"max_tokens": 150
}{
"id": "chatcmpl_9874102x",
"object": "chat.completion",
"created": 1724018290,
"model": "dsi-core-llm-3",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Vector embeddings represent text as high-dimensional numerical vectors, allowing machines to analyze semantic relationships and meanings."
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 28,
"completion_tokens": 24,
"total_tokens": 52
}
}How it works
Understanding Vector Embeddings
Vector embeddings are fundamental to modern AI systems. The embedding process translates text into arrays of floating-point numbers:
Text Processing
Your database documents are split into smaller chunks (e.g., paragraphs).
Embedding Generation
The text chunks are sent to our embedding API, returning a 1536-dimensional float vector.
Vector Storage
These numerical representations are indexed in a vector database (such as Pgvector, Pinecone, or Qdrant).
Semantic Querying
To perform searches, convert the user's search query into a vector and use cosine similarity to retrieve documents with the closest matching vector parameters.
Security & compliance
Security, Model Isolation & Compliance
Deploying generative AI workloads requires maintaining strict security standards:
Strict Data Privacy
Input payloads are encrypted in transit and at rest. Your text prompts are never stored on persistent disks or used for model training.
Secure Model Hosting
Fine-tuned checkpoints are encrypted and stored in isolated storage buckets, preventing unauthorized access.
Rate Limiting Protection
The gateway manages call volumes to protect GPU clusters, queuing requests during peak traffic times.
Detailed Usage Metrics
Track tokens consumed, request latency, and billing details for each model endpoint.
Deployments
Enterprise Use Cases
Retrieval-Augmented Generation (RAG)
Build intelligent Q&A bots that search your internal databases and files to provide accurate, context-aware answers.
Automated Content Operations
Automate content tasks like translating documentation, translating user reviews, generating product descriptions, and moderating community posts.
Code Curation & Analysis
Analyze application scripts to identify security issues, check code quality, generate unit tests, and translate scripts across languages.
FAQ & Troubleshooting Guide
Frequently asked
Which base models are available in AI Studio?
We support a range of models, including Llama 3, Mistral, customized developer helper models, and our proprietary high-efficiency model `dsi-core-llm-3`.
Are user prompts used to train base models?
No. We enforce a zero-data-retention policy for API calls. Your inputs are discarded immediately after processing.
How do we fine-tune a model on our data?
Upload your training dataset in JSONL format, select the base model, and call the fine-tuning endpoint to start the process on our GPU cluster.
Does the API support token streaming?
Yes. You can set the `stream` parameter to `true` in your chat completion calls to receive tokens in real time via Server-Sent Events (SSE).
Get started
Build Generative Workflows
Get your developer credentials, configure LLM chat completion endpoints, and deploy embeddings models.