V98 Connection Algorithm

Comprehensive connection to V98Store API supporting 475+ models with health checks and OpenAI-compatible endpoints

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V98 Connection Algorithm

Comprehensive connection algorithm for V98Store API providing access to 475+ AI models across multiple providers.

Overview

The V98 Connection Algorithm provides a unified interface to connect to V98Store's API gateway, supporting Claude, GPT, Gemini, GLM, O-series, and Codex models through OpenAI-compatible endpoints.

Features

✅ 475+ Models: Auto-discovery and categorization
✅ Model Categories: Claude (21), GPT (100), Gemini (31), GLM (16), O-series (23), Codex (1)
✅ Health Monitoring: Real-time API health checks
✅ OpenAI Compatible: Standard chat completions endpoint
✅ Model Filtering: Search and filter by model name patterns

API Configuration

Base URL: https://v98store.com/v1
API Key: Environment variable V98_API_KEY or passed as parameter

Headers:
{
    "Authorization": "Bearer {api_key}",
    "Content-Type": "application/json"
}

Supported Actions

1. Connect

Test connection and discover available models.

result = v98_algo.execute({"action": "connect"})
# Returns: 475 models with categorization

2. List Models

List all models with optional filtering.

result = v98_algo.execute({
    "action": "list_models",
    "filter_by": "claude"  # Optional filter
})
# Returns: Filtered model list

3. Chat

Send OpenAI-compatible chat completion requests.

result = v98_algo.execute({
    "action": "chat",
    "model": "claude-opus-4-6",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Your prompt here"}
    ],
    "temperature": 0.7,
    "max_tokens": 2000
})

4. Health

Check API health and response times.

result = v98_algo.execute({"action": "health"})
# Returns: Health status and response time

Usage Examples

Basic Connection

from core.algorithms.algorithm_manager import AlgorithmManager

manager = AlgorithmManager(auto_scan=True)
v98 = manager.get_algorithm("V98Connection")

# Test connection
result = v98.execute({"action": "connect"})
print(f"Total models: {result.data['total_models']}")
print(f"Categories: {result.data['categories']}")

List Specific Models

# Find all Codex models
result = v98.execute({
    "action": "list_models",
    "filter_by": "codex"
})

print(f"Codex models: {result.data['models']}")

Chat Completion

# Use GPT-5.1-Codex for code generation
result = v98.execute({
    "action": "chat",
    "model": "gpt-5.1-codex",
    "messages": [
        {"role": "user", "content": "Write a binary search function in Python"}
    ],
    "temperature": 0.3,
    "max_tokens": 1000
})

print(result.data['response'])

Model Categories

Category	Count	Key Models
Claude	21	claude-opus-4-6, claude-sonnet-3-5
GPT	100	gpt-4o, gpt-5.1-codex
Gemini	31	gemini-pro, gemini-1.5-pro
GLM	16	glm-4.6, glm-4.6v
O-series	23	o1-preview, o3-mini
Codex	1	gpt-5.1-codex

Best Practices

Use Environment Variables: Store API key in V98_API_KEY
Cache Model Lists: Call connect once and cache results
Monitor Health: Periodic health checks for production systems
Rate Limiting: Implement token bucket for high-volume usage
Error Handling: Always check result.status before using data

Error Handling

result = v98.execute({"action": "chat", "model": "gpt-4o", ...})

if result.status == "success":
    print(result.data['response'])
elif result.status == "error":
    print(f"Error: {result.error}")

Performance

Connection Test: ~884ms average
Model Discovery: ~1s for 475 models
Chat Response: 2-10s depending on model and tokens
Health Check: <1s

Integration

3-AI Orchestrator

# Used by ThreeAIOrchestrator for:
# - claude-opus-4-6 (Primary Lead)
# - gpt-5.1-codex (Code Reviewer)
# - glm-4.6v (Consultant)

Workflow Integration

# Add to algorithm chain
workflow = [
    "V98Connection",
    "CodeGenerator",
    "TestWriter"
]

Troubleshooting

Issue	Solution
Connection timeout	Check network, verify API key
401 Unauthorized	Validate V98_API_KEY environment variable
Model not found	Run `list_models` to see available options
Rate limit hit	Implement exponential backoff

Related Algorithms

AICodingConnection: Alternative API with Anthropic format
ThreeAIOrchestrator: Multi-model orchestration
SmartModelRouter: Intelligent model selection

File Location

D:\Antigravity\Dive AI\core\algorithms\operational\v98_connection.py

Version

v2.0 - Enhanced with full model support and categorization

Repository: duclm1x1/Dive-Ai
Commit: 20ba150

Last updated: 27 days ago
Created: 27 days ago

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