Par GPT is a Python framework that lets developers execute multiple GPT-3 or GPT-4 prompts concurrently. It handles task distribution, rate limiting, error retries, and result aggregation, enabling scalable LLM-based workflows.
AI Platforms & Frameworks
Coding Assistance
Workflow Automation
#API management
#parallel processing
#OpenAI-Integration
#latency reduction
#Rate Limiting
#error handling
#batch requests
#dynamic inputs
#data aggregation
#Developer Tools
#GTP-3
#GTP-4
#Windows support
#macOS support
#Linux Support
#customizable concurrency
#Automation Tools
#data serialization
#Prompt Templates
#reliable request handling
...
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Par GPT
Par GPT is a Python framework that lets developers execute multiple GPT-3 or GPT-4 prompts concurrently. It handles task distribution, rate limiting, error retries, and result aggregation, enabling scalable LLM-based workflows.
AI Platforms & Frameworks
Coding Assistance
Workflow Automation
#API management
#parallel processing
#OpenAI-Integration
#latency reduction
#Rate Limiting
#error handling
#batch requests
#dynamic inputs
#data aggregation
#Developer Tools
#GTP-3
#GTP-4
#Windows support
#macOS support
#Linux Support
#customizable concurrency
#Automation Tools
#data serialization
#Prompt Templates
#reliable request handling
...
Featured
What is Par GPT?
Par GPT provides a simple interface to dispatch large volumes of OpenAI GPT calls in parallel, optimizing API usage and reducing end-to-end latency. Developers define prompt tasks, and Par GPT automatically manages subprocess workers, enforces rate limits, retries failed requests, and consolidates outputs into structured results. It supports customization of worker counts, timeouts, and concurrency controls across Windows, macOS, and Linux platforms.
Who will use Par GPT?
- Python developers
- Data scientists
- AI researchers
- Machine learning engineers
- Software teams using GPT APIs
How to use the Par GPT?
- Step1: Install via pip: pip install par_gpt
- Step2: Set your OpenAI API key in environment variables
- Step3: Import ParGPTManager from par_gpt in Python
- Step4: Create a list of prompt tasks with messages or templates
- Step5: Initialize ParGPTManager with concurrency settings
- Step6: Call manager.run(tasks) to execute parallel GPT calls
- Step7: Collect and process the aggregated results
Platform
- mac
- windows
- linux
Par GPT's Core Features & Benefits
The Core Features
- Parallel execution of GPT-3/GPT-4 calls
- Configurable concurrency and rate limits
- Automatic retry and error handling
- Result aggregation into JSON or CSV
- Support for prompt templates and dynamic inputs
The Benefits
- Dramatically faster batch processing
- Improved API throughput and utilization
- Reduced latency for large-scale prompts
- Simplified code for parallel tasks
- Robust handling of API errors and timeouts
Par GPT's Main Use Cases & Applications
- Generating large-scale customer support responses
- Batch classification of text inputs
- Data augmentation for training sets
- Bulk content summarization
- Mass prompt-based research and analysis
FAQs of Par GPT
Par GPT Company Information
Par GPT Reviews
5/5
Par GPT's Main Competitors and alternatives?
- LangChain
- AsyncGPT
- BatchGPT
- GPT-Parallelizer
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