sashabaranov/go-openai
Go Openai
This project is a Go library that provides a programmatic interface for interacting with generative AI services. It serves as a comprehensive software development kit for integrating large language models into applications, enabling developers to perform tasks such as text and chat completion, image generation, and audio transcription.
The library distinguishes itself through a unified infrastructure designed for robust network communication and service management. It features structured request mapping and error normalization to ensure type-safe interactions and simplified debugging. Furthermore, it includes specialized support for stateful AI agents, persistent conversation threads, and function calling, allowing for complex, multi-turn interactions. The client also provides flexible configuration options, including specific support for mapping deployments across different cloud environments.
Beyond core text generation, the project covers a broad range of generative AI capabilities. This includes tools for semantic embedding generation and vector search, content safety screening, and administrative management of fine-tuning jobs and custom model versions. It also handles media-heavy workflows, such as file management for assistant context and automated processing of audio and visual assets.
The library is designed for direct integration into Go applications, providing a consistent client initialization pattern for managing API credentials and service endpoints.
Features
- Generative AI Clients - Provides a programmatic interface for accessing cloud-based AI models for text, embedding, and moderation tasks.
- Generative AI APIs - Provides a programmatic interface for integrating large language models to generate text, chat, and code.
- Text Generation Interfaces - Provides programmatic interfaces for invoking language models to perform text transformation, editing, and completion tasks.
- API Client Libraries - Offers a comprehensive Go library for interacting with AI services, including chat, image, and audio capabilities.
- AI Assistants - Provides management for custom AI assistants, including configuration of models, instructions, and specialized tools.
- Chat Completion Services - Provides interfaces for chat and text completion with support for streaming responses.
- Conversation Threads - Manages persistent conversation threads to maintain stateful interactions between users and AI models.
- LLM Integration Frameworks - Provides an SDK for integrating large language models with support for streaming and function calling.
- Message Management - Maintains interaction history and context by managing individual messages within conversation threads.
- Vector Embeddings - Converts input text into dense numerical vector representations for semantic similarity searches.
- SDK Client Initialization - Provides a consistent pattern for initializing service clients with API keys and provider-specific configurations.
- HTTP Request Abstractions - Provides a unified interface for network communication, managing authentication, headers, and connection pooling across services.
- Image Generation - Generates original visual content from descriptive text prompts with configurable parameters.
- Model Fine-Tuning - Manages the training and customization of specialized model versions on proprietary datasets.
- AI and Machine Learning - OpenAI API client.
- LLM Applications - Unofficial Go client library for OpenAI API services.
- Language SDKs - Go language client for interacting with language model APIs.
- Semantic Search Engines - Converts text into vector embeddings to enable semantic similarity comparisons and information retrieval.
- Tool Use and Function Calling - Enables language models to invoke external software tools via defined JSON schemas.
- Audio Processing - Converts spoken audio to text and generates synthetic speech from text input.
- Response Streaming - Processes incremental data streams from remote services for real-time response handling.
- Server-Sent Events - Processes incremental data chunks from long-running network requests to enable real-time UI updates.
- Cloud API Request Execution - Executes authenticated API requests with support for custom headers and multipart file uploads.
- Text-to-Image Generators - Integrates image generation capabilities to create visual assets from descriptive text prompts.
- Image Editing - Modifies existing images using text prompts and masks via generative AI.
- Assistant Context Integrations - Links external documents to AI assistants to provide necessary context for retrieval and analysis.
- API Error Handling - Parses and normalizes service error responses into structured objects for debugging.
- Media Automation - Automates the transcription of audio files and the generation of synthetic speech for multimedia applications.
- Content Moderation - Analyzes text input against safety guidelines to detect and flag prohibited content.
- Type-Safe - Ensures type-safe communication by mapping language-specific data structures to JSON payloads.
- Model Selection Policies - Configures the specific language model to use for generation tasks.
- Model Fine-Tuning - Initiates and monitors training jobs for custom model versions.
- Vector Similarity Search - Computes dot products between embedding vectors to quantify semantic relationships.
- Remote File Managers - Manages the lifecycle of files stored on remote AI provider infrastructure.
- Vector Storage Management - Configures and manages storage containers and file batches for vector indexing.
- Error Handling Utilities - Normalizes remote service error responses into unified, type-safe objects to simplify debugging and application-level error handling.
- Service Configuration - Configures clients for cloud-hosted instances with endpoint URLs and authentication credentials.
Star history
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