googleapis/python-genai

Features

• Generative - Acts as a framework for orchestrating LLM workflows, stateful chats, and structured output enforcement.
• Generative AI Models - Produces text, code, or media by interfacing with large-scale generative AI models.
• Generative Content APIs - Provides programmatic interfaces for sending prompts to AI models and retrieving generated content.
• Multimodal Input Processing - Processes multimodal inputs including text, images, and audio within a single request.
• Multimodal Media Generation - Produces a variety of media types including text, images, and audio based on multimodal prompts.
• Chat Session Management - Provides stateful conversation management to track dialogue history across multiple turns.
• Conversation State Management - Tracks dialogue history across multiple turns to maintain context and session state during interactive chats.
• External Tool Integrations - Integrates external utilities like search engines and code execution to extend model functionality.
• Function Calling Interfaces - Provides function calling interfaces that enable models to execute external tools and APIs.
• Automatic Tool Executions - Executes external tools automatically when the language model triggers a function call.
• Generative AI Development - Provides a comprehensive SDK for building applications that integrate large language models for text, code, and media generation.
• Image Editing - Provides capabilities to generate new images from text prompts and edit existing visuals using AI.
• Tool Call Executions - Performs the actual runtime invocation of external functions based on structured model outputs.
• Multimodal Content Generation - Creates high-resolution images, videos, and text from mixed-modality prompts.
• Multimodal Content Generators - Serves as a toolkit for producing and editing text, images, audio, and video using multimodal inputs.
• Tool Calling - Connects a model to custom functions or external tools to request and receive real-world data.
• Structured Output Enforcements - Constrains model responses to specific formats using schemas or predefined values for reliable processing.
• Text Embeddings - Converts text into numerical vector representations to enable semantic searches and similarity tasks.
• Video Generation - Produces video content from text or image prompts and allows extending existing video clips.
• Model Fine-Tuning - Enables fine-tuning of base models using custom datasets to optimize performance for specific tasks.
• Supervised Fine-Tuning - Supports adapting base models through supervised training on custom datasets to create specialized model versions.
• AI Image Processing - Provides AI-powered image generation, upscaling, and segmentation with aspect ratio control.
• LLM Schema Outputs - Enforces structured output by constraining model responses to a provided JSON schema.
• Schema-Constrained Outputs - Forces model responses to conform to specific machine-readable structures using JSON schemas.
• Multimodal Search - Performs multimodal search across uploaded files and documents to provide grounded responses.
• JSON Output Constraints - Enforces a specific schema for generated responses to ensure the output is machine-readable.
• LLM Client Libraries - Ships as a comprehensive Python SDK for programmatic integration with large language models.
• Generative Video Editing - Implements generative video editing, including frame interpolation and clip extension.
• Multimodal Content Generation - Produces and edits a wide range of media types including text, images, audio, and video.
• LLM Orchestration - Orchestrates complex model interactions including stateful chat sessions, tool calling, and structured output enforcement.
• Tool-Call Dispatchers - Maps model-generated function requests to executable Python functions or external servers.
• Batch Prediction Processing - Implements batch prediction processing for high-throughput inference on large datasets.
• Code Execution Tools - Integrates built-in code execution tools for performing mathematical and algorithmic tasks.
• High Volume AI Processing - Supports high-throughput inference through asynchronous batch predictions and server-side context caching.
• Hyperparameter Configurations - Provides tools to manage and tune model hyperparameters like temperature, presence penalty, and frequency penalty.
• Context Caching - Implements context caching for prompt and conversation history to improve response speed.
• Large Language Model Fine-Tuning - Enables adaptation of base models through supervised and reinforcement training on custom datasets.
• Asynchronous Batch Processing - Provides high-throughput inference by processing large volumes of requests as asynchronous background jobs.
• LLM Fine-Tuning Toolsets - Provides a toolkit for supervised fine-tuning and parameter-efficient updates of language models.
• Model Response Streaming - Streams model-generated content incrementally to the client to improve responsiveness.
• Real-time Multimodal Sessions - Establishes low-latency multimodal connections supporting voice activity detection and audio transcription.
• Stateful Agent Orchestration - Orchestrates complex, multi-step agent workflows using state-based transitions and tool execution.
• Vector Embeddings - Provides a library for generating vector representations of text to enable semantic search.
• Semantic Vector Search - Converts text into vector embeddings to enable semantic search and grounded data retrieval.
• Content Caching Controls - Controls the storage and expiration of cached content to optimize performance and operational cost.
• Server-Side Context Caches - Stores large inputs and system instructions on the server to reduce latency and token costs.
• Input Caches - Caches large inputs and system instructions to reduce latency and token costs.
• Batch Processing Jobs - Implements batch processing jobs for handling large volumes of requests with configurable dataset inputs and outputs.
• Web Search Grounding - Grounds model responses using real-time web search results to ensure factual accuracy.
• Uploaded File Management - Manages the lifecycle of media files and documents uploaded for use in model prompts.
• AI Content Safety Filtering - Filters and sanitizes AI inputs and outputs to ensure safety and policy compliance.
• Token Count Calculation - Calculates the number of tokens in a prompt using remote APIs or local tokenizers.
• Incremental Response Streaming - Delivers model-generated content and function arguments to clients in real-time via incremental streaming.
• Agent Frameworks - Official SDK for integrating Google generative models into Python applications.

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