generative-ai

Generates text, images, and video content using Gemini models (2.0, 2.5, 3.0 families) via the Vertex AI API, supporting simultaneous processing of text, images, audio, and video inputs in a single request. The implementation uses the google.generativeai SDK or Vertex AI client libraries to marshal multimodal payloads directly to Google's managed inference endpoints, with automatic batching and streaming response handling for long-form outputs.

Enables Gemini models to invoke external tools and APIs by declaring function schemas (JSON Schema format) that the model learns to call autonomously. The implementation uses Vertex AI's function calling API which accepts tool definitions, validates model-generated function calls against the schema, and returns structured call directives that applications execute and feed back to the model for multi-turn tool use chains. Supports native bindings for Google Cloud services (BigQuery, Firestore, Cloud Functions) and arbitrary REST APIs.

Translates natural language questions into SQL queries that execute against BigQuery or other databases, enabling non-technical users to analyze data. The implementation uses Gemini to understand the question, inspect database schema, generate SQL, and execute queries with automatic result formatting. Integrates with Looker for visualization and supports follow-up questions with context preservation.

Deploys open-source models (Llama, Gemma, Mistral) on Vertex AI using Model Garden, which provides pre-configured serving containers (TGI, vLLM, PyTorch) and automatic scaling. The implementation handles model downloading, container orchestration, and endpoint management without requiring custom deployment code. Supports both batch and real-time serving with configurable hardware (GPUs, TPUs).

Automatically optimizes prompts to improve model performance on specific tasks using Vertex AI's Prompt Optimizer (VAPO). The implementation takes a task description and initial prompt, generates variations, evaluates them against metrics, and iteratively refines the prompt. Uses Gemini to generate prompt variations and another model instance to evaluate quality, creating a feedback loop that improves performance without manual iteration.

Provides speech-to-text (ASR) and text-to-speech (TTS) capabilities using Vertex AI's Chirp3 speech models. Chirp3 supports 99+ languages, handles accented speech and background noise, and integrates with Gemini for end-to-end voice applications. The implementation accepts audio streams or files, transcribes to text, and optionally synthesizes responses back to speech with custom voice profiles.

Implements RAG by combining Vertex AI's Vector Search 2.0 (managed ANN retrieval) with Gemini models to ground responses in external knowledge. The architecture uses Vertex AI's RAG Engine which manages corpus ingestion, chunking, embedding generation (via Gecko or custom embeddings), and retrieval, then passes retrieved documents to Gemini with automatic context window management. Supports multimodal RAG where both text and images are embedded and retrieved together.

Provides secure, isolated execution environments for agents to run Python and JavaScript code generated by Gemini models. The Agent Engine uses containerized sandboxes (one per execution) with resource limits (CPU, memory, timeout), automatic dependency installation, and output capture. Agents can iteratively generate code, execute it, observe results, and refine based on feedback — enabling complex multi-step reasoning tasks like data analysis, mathematical problem-solving, and system design.

Enables coordination of multiple specialized agents working on complex tasks through Vertex AI's Agent Development Kit (ADK) and Memory Bank. Agents communicate through a shared memory layer that persists conversation history, intermediate results, and task state across agent boundaries. The orchestration layer routes tasks to appropriate agents based on capability, manages context passing between agents, and implements hierarchical task decomposition where parent agents delegate to child agents.

Constrains Gemini model outputs to conform to specified JSON schemas, ensuring structured, predictable responses suitable for downstream processing. The implementation uses Vertex AI's controlled generation feature which accepts a JSON Schema definition and modifies the model's token sampling to only generate valid schema-conforming outputs. Supports nested objects, arrays, enums, and type validation without requiring post-processing or retry logic.

Provides real-time, bidirectional streaming of multimodal inputs (audio, video, text) to Gemini models via WebSocket connections, enabling low-latency interactive applications. The Multimodal Live API accepts continuous audio/video streams, processes them incrementally, and returns streaming text responses with minimal buffering. Supports voice-to-voice conversations, real-time video analysis, and interactive tutoring applications without request-response round-trip delays.

Processes large documents (PDFs, Word docs, web pages) by intelligently chunking them into semantically coherent segments, extracting metadata, and preparing them for RAG or analysis. The implementation uses Vertex AI's document processing capabilities which parse document structure (headings, tables, lists), preserve layout information, and generate embeddings for each chunk. Supports OCR for scanned documents and automatic language detection.

Enables customization of Gemini models through supervised fine-tuning (SFT) on labeled examples or reinforcement learning from human feedback (RLHF) using Vertex AI's training infrastructure. The implementation accepts training datasets in JSON format, manages distributed training across TPU/GPU clusters, and produces task-specific model checkpoints deployable on Vertex AI. Supports both full model fine-tuning and parameter-efficient methods (LoRA).

Evaluates Gemini model outputs against multiple dimensions (accuracy, safety, coherence, factuality) using Vertex AI's Gen AI Evaluation Service. The implementation runs models on test datasets, compares outputs against reference answers or rubrics, and generates evaluation reports with pass/fail metrics. Supports both automated metrics (BLEU, ROUGE, semantic similarity) and LLM-as-judge evaluation where another model scores outputs.