{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-googlecloudplatform--generative-ai","slug":"googlecloudplatform--generative-ai","name":"generative-ai","type":"agent","url":"https://docs.cloud.google.com/gemini-enterprise-agent-platform/","page_url":"https://unfragile.ai/googlecloudplatform--generative-ai","categories":["ai-agents"],"tags":["agents","gcp","gemini","gemini-api","gen-ai","generative-ai","google","google-cloud","google-gemini","langchain","large-language-models","llm","vertex-ai","vertex-ai-gemini-api","vertexai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-googlecloudplatform--generative-ai__cap_0","uri":"capability://text.generation.language.multimodal.gemini.text.image.video.generation","name":"multimodal-gemini-text-image-video-generation","description":"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.","intents":["Generate text responses from mixed image and text prompts without separate API calls","Process video frames and extract insights in a single multimodal inference pass","Build chatbots that understand and respond to images, PDFs, and audio simultaneously","Create content generation pipelines that accept any combination of input modalities"],"best_for":["Teams building document understanding applications on GCP","Developers creating multimodal chatbots and assistants","Data analysts processing mixed-media datasets with natural language queries"],"limitations":["Video input limited to 1 hour maximum duration per request","Image resolution capped at 20MB per image; video at 2GB per file","Streaming responses not available for all model variants (Flash Lite has reduced streaming support)","No local inference — all processing requires GCP project and API authentication"],"requires":["Google Cloud project with Vertex AI API enabled","Python 3.9+ with google-cloud-aiplatform SDK or google-generativeai library","Valid service account credentials or OAuth2 authentication","Gemini model access (gemini-2.0-flash, gemini-2.5-pro, or gemini-3-flash-preview)"],"input_types":["text (UTF-8 strings)","image (JPEG, PNG, WebP, GIF)","video (MP4, MPEG, MOV, AVI, FLV, MKV, WEBM, WMV)","audio (WAV, MP3, AIFF, AAC, OGG, FLAC)"],"output_types":["text (streaming or batch)","structured JSON (with schema constraints)","function call directives (for tool use)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-googlecloudplatform--generative-ai__cap_1","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.binding","name":"function-calling-with-schema-based-tool-binding","description":"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.","intents":["Build agents that autonomously call APIs to fetch real-time data (weather, stock prices, database queries)","Create code execution agents that generate and validate function calls before execution","Implement multi-step workflows where the model chains tool calls across different services","Enable structured data extraction by having the model call validation functions that enforce schemas"],"best_for":["Teams building autonomous agents on Vertex AI","Developers creating API-driven chatbots that need real-time data","Organizations integrating Gemini with existing microservice architectures"],"limitations":["Function schemas limited to JSON Schema draft 7 — no OpenAPI 3.0 or GraphQL schema auto-conversion","No built-in retry logic for failed function calls — applications must implement their own retry handlers","Parallel function calling supported but sequential execution order not guaranteed if model generates multiple calls","Tool definitions must be re-declared per request; no persistent tool registry across sessions"],"requires":["Vertex AI API enabled with appropriate IAM roles (aiplatform.user)","Python 3.9+ with google-cloud-aiplatform SDK","JSON Schema definitions for each tool (can be auto-generated from Python type hints)","Implementation of tool execution handlers in application code"],"input_types":["JSON Schema object definitions","Python function signatures (auto-converted to schema)","OpenAPI-style parameter descriptions"],"output_types":["Structured function call objects with parameters","Tool execution results (any JSON-serializable type)","Multi-turn conversation with interleaved tool calls"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-googlecloudplatform--generative-ai__cap_10","uri":"capability://data.processing.analysis.data.analytics.api.with.natural.language.to.sql","name":"data-analytics-api-with-natural-language-to-sql","description":"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.","intents":["Enable business users to query databases without SQL knowledge","Build chatbots that answer data questions by generating and executing queries","Create self-service analytics interfaces for non-technical stakeholders","Automate report generation by translating business questions to SQL"],"best_for":["Organizations with non-technical users needing data access","Teams building self-service analytics platforms","Enterprises implementing natural language interfaces to data warehouses"],"limitations":["SQL generation accuracy depends on schema clarity; ambiguous column names cause errors","Complex queries (CTEs, window functions, subqueries) may be generated incorrectly","No support for cross-database queries; limited to single BigQuery project","Query execution cost is billed to BigQuery; no cost controls or query optimization","Schema changes require re-indexing; no automatic schema update detection"],"requires":["Vertex AI Data Analytics API enabled","BigQuery dataset with accessible schema","Python 3.9+ with google-cloud-bigquery SDK","IAM permissions for BigQuery query execution","Database schema documentation or comments for clarity"],"input_types":["Natural language questions (UTF-8 text)","Database schema (auto-discovered from BigQuery)","Optional context (previous queries, filters)"],"output_types":["Generated SQL queries","Query results (tables, aggregations)","Visualizations (via Looker integration)","Natural language summaries of results"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-googlecloudplatform--generative-ai__cap_11","uri":"capability://automation.workflow.open.model.deployment.with.model.garden","name":"open-model-deployment-with-model-garden","description":"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).","intents":["Deploy open-source models without managing infrastructure or containers","Run models on custom hardware (A100 GPUs, TPUs) for cost optimization","Create multi-model endpoints serving different models for A/B testing","Fine-tune open models on Vertex AI using managed training infrastructure"],"best_for":["Teams preferring open-source models over proprietary APIs","Organizations with cost constraints requiring GPU-efficient inference","Developers needing full model control and customization"],"limitations":["Open models generally have lower quality than Gemini for complex reasoning tasks","Deployment requires GPU/TPU quota; CPU-only serving is slow and expensive","Model Garden provides limited customization — custom serving logic requires container modifications","Scaling open models to high throughput requires significant infrastructure investment","No built-in monitoring or observability; requires separate logging/tracing setup"],"requires":["Vertex AI Model Garden enabled in GCP project","GPU or TPU quota (A100 GPUs recommended for Llama 70B)","Python 3.9+ with google-cloud-aiplatform SDK","Sufficient GCP budget for compute resources","Understanding of model serving frameworks (TGI, vLLM)"],"input_types":["Model identifiers (e.g., 'meta-llama/Llama-2-70b-hf')","Custom model weights (HuggingFace format)","Serving configuration (batch size, quantization)"],"output_types":["Deployed model endpoints (REST API)","Batch prediction results","Model serving metrics (latency, throughput)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-googlecloudplatform--generative-ai__cap_12","uri":"capability://text.generation.language.prompt.optimization.with.vapo","name":"prompt-optimization-with-vapo","description":"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.","intents":["Improve model performance on specific tasks without fine-tuning","Automatically generate high-quality prompts for new tasks","Find optimal prompt structure for different model variants","Reduce manual prompt engineering effort for production systems"],"best_for":["Teams with limited prompt engineering expertise","Organizations needing rapid iteration on model performance","Developers optimizing prompts for cost and quality trade-offs"],"limitations":["Optimization cost scales with number of iterations; each iteration requires multiple model calls","Optimized prompts may be task-specific and not generalize to similar tasks","No guarantees of convergence; optimization may plateau without improvement","Evaluation metrics must be defined upfront; no automatic metric discovery"],"requires":["Vertex AI Prompt Optimizer enabled","Task description and initial prompt","Evaluation dataset with reference outputs or quality rubrics","Python 3.9+ with google-cloud-aiplatform SDK"],"input_types":["Task description (natural language)","Initial prompt template","Evaluation dataset (JSONL with inputs and reference outputs)","Evaluation metrics (accuracy, F1, semantic similarity)"],"output_types":["Optimized prompt template","Performance metrics before/after optimization","Prompt variation history with evaluation scores"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-googlecloudplatform--generative-ai__cap_13","uri":"capability://text.generation.language.speech.recognition.and.synthesis.with.chirp3","name":"speech-recognition-and-synthesis-with-chirp3","description":"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.","intents":["Build voice assistants that understand and respond in natural language","Transcribe audio recordings with high accuracy across multiple languages","Create multilingual voice interfaces without language-specific models","Implement voice-based data entry and command systems"],"best_for":["Teams building voice-first applications","Organizations needing multilingual speech support","Developers creating accessible interfaces for users with visual impairments"],"limitations":["ASR accuracy varies by language and audio quality; noisy audio may have >10% error rate","TTS latency is 500-2000ms depending on text length; not suitable for real-time conversation","Custom voice profiles require voice samples for training; no zero-shot voice cloning","Streaming ASR has higher latency than batch processing; not suitable for real-time transcription"],"requires":["Vertex AI Speech Services enabled in GCP project","Audio input (microphone, file, or stream)","Python 3.9+ with google-cloud-speech SDK","Supported audio formats (WAV, MP3, OGG, FLAC, AIFF)"],"input_types":["Audio files (WAV, MP3, OGG, FLAC, AIFF)","Audio streams (real-time from microphone)","Text for synthesis (UTF-8)"],"output_types":["Transcribed text with confidence scores","Audio files (MP3, WAV) for TTS output","Language detection results"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-googlecloudplatform--generative-ai__cap_2","uri":"capability://memory.knowledge.retrieval.augmented.generation.with.vector.search","name":"retrieval-augmented-generation-with-vector-search","description":"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.","intents":["Build knowledge-grounded chatbots that cite sources from internal documents or knowledge bases","Create question-answering systems over large document collections without fine-tuning","Implement semantic search over multimodal corpora (PDFs, images, videos) with natural language queries","Reduce hallucinations by constraining model responses to retrieved facts from authoritative sources"],"best_for":["Enterprise teams with large document repositories needing semantic search","Organizations building customer support chatbots grounded in help documentation","Teams implementing compliance-critical applications requiring source attribution"],"limitations":["Vector Search 2.0 requires pre-indexed corpus — real-time document additions have 5-10 minute indexing latency","Embedding generation costs scale with corpus size; large corpora (>1M documents) require careful chunking strategy","No native support for hybrid search (keyword + semantic) — requires separate BM25 index or post-retrieval filtering","Context window limitations mean retrieved documents are truncated if corpus is very large; requires intelligent ranking"],"requires":["Vertex AI Vector Search enabled in GCP project","Document corpus in supported formats (PDF, TXT, HTML, DOCX) or raw text","Embedding model access (Gecko embeddings included; custom embeddings require separate model deployment)","Python 3.9+ with google-cloud-aiplatform SDK","Sufficient quota for embedding generation (billed per 1000 embeddings)"],"input_types":["PDF documents","Text files (TXT, Markdown)","Web pages (HTML)","Images (JPEG, PNG)","Structured data (JSON, CSV with text fields)"],"output_types":["Retrieved document chunks with relevance scores","Grounded text responses with source citations","Structured data with provenance metadata"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-googlecloudplatform--generative-ai__cap_3","uri":"capability://code.generation.editing.agent.engine.with.code.execution.sandboxes","name":"agent-engine-with-code-execution-sandboxes","description":"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.","intents":["Build data analysis agents that write and execute pandas/numpy code to explore datasets","Create mathematical reasoning agents that generate and test code solutions","Implement autonomous debugging agents that generate test cases and refine code","Enable agents to interact with external APIs by generating and executing HTTP client code"],"best_for":["Data science teams building autonomous analysis workflows","Developers creating agents for complex reasoning tasks requiring code execution","Organizations needing agents that can validate their own outputs through testing"],"limitations":["Execution timeout capped at 60 seconds per code block — long-running computations must be chunked","Sandboxes are ephemeral; no persistent state between code blocks unless explicitly serialized","Network access limited to whitelisted GCP services and public APIs — no arbitrary outbound connections","Python and JavaScript only; no support for compiled languages or system-level operations","Memory limit of 512MB per sandbox — large dataset processing requires streaming or chunking"],"requires":["Vertex AI Agent Engine enabled in GCP project","Python 3.9+ with google-cloud-aiplatform SDK","Gemini model with code generation capability (2.0-flash or later)","IAM permissions for aiplatform.agent.execute","Datasets or APIs that agents will access must be accessible from GCP network"],"input_types":["Natural language task descriptions","Dataset files (CSV, JSON, Parquet)","API endpoints and authentication credentials","Code snippets for agents to refine or debug"],"output_types":["Executed code results (stdout, stderr)","Generated visualizations (matplotlib, plotly)","Structured analysis results (JSON, DataFrames)","Test results and validation reports"],"categories":["code-generation-editing","planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-googlecloudplatform--generative-ai__cap_4","uri":"capability://planning.reasoning.multi.agent.orchestration.with.memory.bank","name":"multi-agent-orchestration-with-memory-bank","description":"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.","intents":["Decompose complex tasks (e.g., financial analysis) into specialized sub-tasks handled by different agents","Implement multi-agent debate or consensus mechanisms where agents propose solutions and critique each other","Build workflows where agents hand off work sequentially (e.g., data collection → analysis → reporting)","Create self-healing systems where monitoring agents detect issues and trigger remediation agents"],"best_for":["Enterprise teams building complex autonomous workflows","Organizations implementing multi-stage decision-making systems","Teams needing agents with different expertise (financial, technical, compliance) to collaborate"],"limitations":["Memory Bank has no built-in garbage collection — long-running systems accumulate state that must be manually pruned","Agent communication is asynchronous; no real-time synchronization between agents on shared state","No native deadlock detection — circular agent dependencies can cause infinite loops","Scaling beyond 10-15 concurrent agents requires custom load balancing; ADK provides no built-in orchestration for large agent pools"],"requires":["Vertex AI Agent Development Kit (ADK) installed (Python package)","Gemini models with function calling capability","Firestore or Cloud Datastore for Memory Bank persistence","Python 3.9+ with google-cloud-aiplatform SDK","IAM permissions for Firestore write/read operations"],"input_types":["Task descriptions in natural language","Agent capability definitions (JSON schema)","Initial context and constraints","External data sources (APIs, databases)"],"output_types":["Final task results with execution trace","Intermediate agent outputs and reasoning","Memory Bank state snapshots","Agent interaction logs for debugging"],"categories":["planning-reasoning","automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-googlecloudplatform--generative-ai__cap_5","uri":"capability://data.processing.analysis.controlled.generation.with.json.schema.constraints","name":"controlled-generation-with-json-schema-constraints","description":"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.","intents":["Extract structured data from unstructured text with guaranteed schema compliance","Generate API responses that conform to OpenAPI specifications without manual validation","Create form-filling agents that produce valid JSON matching database schemas","Implement reliable data pipelines where downstream systems expect strict schema compliance"],"best_for":["Data extraction pipelines requiring 100% schema compliance","API backends using Gemini for request/response generation","Teams building form-filling or data entry automation"],"limitations":["Schema complexity is limited — deeply nested schemas (>10 levels) may cause generation slowdown","Enum values must be pre-defined; dynamic enum generation not supported","No support for conditional schemas (e.g., 'if type=A then require field X') — requires post-processing","Schema constraints add ~50-100ms latency to generation due to token sampling modifications"],"requires":["Vertex AI API with controlled generation support (Gemini 2.0-flash or later)","JSON Schema definition for output format (draft 7 compatible)","Python 3.9+ with google-cloud-aiplatform SDK","Understanding of JSON Schema syntax for schema definition"],"input_types":["JSON Schema object definitions","Text or multimodal prompts to constrain"],"output_types":["JSON objects conforming to specified schema","Guaranteed valid structured data (no malformed JSON)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-googlecloudplatform--generative-ai__cap_6","uri":"capability://text.generation.language.live.multimodal.streaming.with.websocket.api","name":"live-multimodal-streaming-with-websocket-api","description":"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.","intents":["Build voice assistants with sub-second response latency for natural conversation","Create real-time video analysis applications (e.g., live sports commentary, security monitoring)","Implement interactive tutoring systems where students receive immediate feedback","Enable live translation or transcription services with streaming output"],"best_for":["Teams building conversational AI applications requiring <500ms latency","Developers creating real-time video analysis or monitoring systems","Organizations implementing interactive voice response (IVR) systems"],"limitations":["WebSocket connections have 30-minute timeout; long-running sessions require reconnection logic","Audio input limited to 16kHz PCM or Opus codec; video limited to 1080p 30fps","Streaming responses may have variable latency (100-2000ms) depending on model load","No persistent session state across WebSocket reconnections — applications must manage context","Requires stable network connection; packet loss or high latency degrades experience"],"requires":["Vertex AI Multimodal Live API enabled in GCP project","WebSocket client library (Python: websockets, JavaScript: ws)","Audio capture capability (microphone access for browser or audio device for server)","Gemini model with streaming support (2.0-flash or later)","Network connectivity with low latency to GCP endpoints"],"input_types":["Audio stream (PCM 16-bit 16kHz or Opus codec)","Video stream (H.264 or VP9 codec, 1080p max)","Text messages (UTF-8)","Interleaved audio/video/text in single stream"],"output_types":["Streaming text responses (chunked UTF-8)","Audio output (optional, requires separate TTS integration)","Metadata (confidence scores, detected intents)"],"categories":["text-generation-language","image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-googlecloudplatform--generative-ai__cap_7","uri":"capability://data.processing.analysis.document.processing.with.intelligent.chunking","name":"document-processing-with-intelligent-chunking","description":"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.","intents":["Ingest large PDF documents into RAG systems with semantically meaningful chunks","Extract structured data from documents (tables, forms) while preserving context","Build document understanding pipelines that preserve document layout and hierarchy","Process multilingual documents with automatic language detection and translation"],"best_for":["Organizations processing large document repositories (contracts, regulations, manuals)","Teams building document-based RAG systems","Enterprises needing to extract structured data from unstructured documents"],"limitations":["OCR accuracy depends on document quality; scanned documents with poor resolution may have >5% error rate","Chunking strategy is fixed (semantic boundaries); no custom chunking logic per document type","Large documents (>500 pages) may timeout; requires pagination or pre-splitting","Table extraction preserves structure but may lose complex formatting (merged cells, nested tables)","Processing cost scales with document size; large batches require careful quota management"],"requires":["Vertex AI Document AI enabled in GCP project","Documents in supported formats (PDF, DOCX, PPTX, HTML, TXT, images)","Python 3.9+ with google-cloud-documentai SDK","Sufficient quota for document processing (billed per page)"],"input_types":["PDF files (text and scanned)","Microsoft Office documents (DOCX, PPTX)","Web pages (HTML)","Images (JPEG, PNG, TIFF)","Plain text files"],"output_types":["Extracted text with structure metadata","Semantic chunks with embeddings","Structured data (tables, forms as JSON)","Document layout information (bounding boxes, page numbers)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-googlecloudplatform--generative-ai__cap_8","uri":"capability://code.generation.editing.fine.tuning.with.supervised.and.reinforcement.learning","name":"fine-tuning-with-supervised-and-reinforcement-learning","description":"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).","intents":["Adapt Gemini to domain-specific language and terminology (legal, medical, financial)","Improve model performance on specialized tasks with labeled training data","Implement RLHF to align model outputs with human preferences or organizational values","Create custom models for specific use cases without building from scratch"],"best_for":["Organizations with large labeled datasets (>1000 examples) for specific domains","Teams needing to align models with organizational policies or values","Enterprises requiring custom models for competitive advantage"],"limitations":["Minimum dataset size of 100 examples for SFT; smaller datasets may overfit","Fine-tuning cost is high (~$100-1000+ per training run depending on model size and data)","Training time ranges from 1-24 hours depending on dataset size; no real-time training feedback","Fine-tuned models are not automatically updated when base Gemini models are updated","RLHF requires human annotation of preference pairs; no automated preference generation"],"requires":["Vertex AI Training enabled in GCP project","Training dataset in JSONL format with input-output pairs","Python 3.9+ with google-cloud-aiplatform SDK","Sufficient quota for TPU/GPU training (requires quota increase request)","Budget for training costs (varies by model size and dataset)"],"input_types":["JSONL files with {instruction, output} pairs for SFT","JSONL files with {prompt, chosen, rejected} for RLHF","CSV files (auto-converted to JSONL)"],"output_types":["Fine-tuned model checkpoint (deployable on Vertex AI)","Training metrics (loss, accuracy, perplexity)","Model evaluation results on held-out test set"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-googlecloudplatform--generative-ai__cap_9","uri":"capability://data.processing.analysis.model.evaluation.with.automated.metrics","name":"model-evaluation-with-automated-metrics","description":"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.","intents":["Measure model quality improvements from fine-tuning or prompt changes","Detect regressions before deploying new model versions to production","Compare different model variants (2.0-flash vs 2.5-pro) on specific tasks","Generate evaluation reports for compliance or stakeholder review"],"best_for":["Teams iterating on model performance with quantitative metrics","Organizations requiring evaluation reports for compliance or audits","Developers comparing model variants before production deployment"],"limitations":["Automated metrics (BLEU, ROUGE) are poor proxies for semantic quality; LLM-as-judge is more reliable but slower","Evaluation requires reference answers or rubrics; unsupervised evaluation not supported","LLM-as-judge evaluation cost scales with test set size (billed per evaluation)","No support for custom evaluation metrics without implementing custom code"],"requires":["Vertex AI Gen AI Evaluation Service enabled","Test dataset with inputs and reference outputs (JSONL format)","Python 3.9+ with google-cloud-aiplatform SDK","Evaluation rubrics or reference answers for comparison"],"input_types":["Test dataset (JSONL with input/reference pairs)","Evaluation rubrics (natural language descriptions of quality criteria)","Model predictions (generated by running models on test set)"],"output_types":["Evaluation metrics (accuracy, F1, BLEU, ROUGE, semantic similarity)","Per-example evaluation results with explanations","Evaluation reports (HTML or JSON)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Google Cloud project with Vertex AI API enabled","Python 3.9+ with google-cloud-aiplatform SDK or google-generativeai library","Valid service account credentials or OAuth2 authentication","Gemini model access (gemini-2.0-flash, gemini-2.5-pro, or gemini-3-flash-preview)","Vertex AI API enabled with appropriate IAM roles (aiplatform.user)","Python 3.9+ with google-cloud-aiplatform SDK","JSON Schema definitions for each tool (can be auto-generated from Python type hints)","Implementation of tool execution handlers in application code","Vertex AI Data Analytics API enabled","BigQuery dataset with accessible schema"],"failure_modes":["Video input limited to 1 hour maximum duration per request","Image resolution capped at 20MB per image; video at 2GB per file","Streaming responses not available for all model variants (Flash Lite has reduced streaming support)","No local inference — all processing requires GCP project and API authentication","Function schemas limited to JSON Schema draft 7 — no OpenAPI 3.0 or GraphQL schema auto-conversion","No built-in retry logic for failed function calls — applications must implement their own retry handlers","Parallel function calling supported but sequential execution order not guaranteed if model generates multiple calls","Tool definitions must be re-declared per request; no persistent tool registry across sessions","SQL generation accuracy depends on schema clarity; ambiguous column names cause errors","Complex queries (CTEs, window functions, subqueries) may be generated incorrectly","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.744025679365779,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:21.998Z","last_commit":"2026-05-01T22:39:18Z"},"community":{"stars":16781,"forks":4182,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=googlecloudplatform--generative-ai","compare_url":"https://unfragile.ai/compare?artifact=googlecloudplatform--generative-ai"}},"signature":"9tSyRhq2GF8s/Y8AVzOz7CmTGnQb9gl8VCPNgDhJtcqO6wb0RQYN9I5tSWnmAH9BO9KKN6p1qxGT1fLw7COcAw==","signedAt":"2026-06-22T08:24:36.474Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/googlecloudplatform--generative-ai","artifact":"https://unfragile.ai/googlecloudplatform--generative-ai","verify":"https://unfragile.ai/api/v1/verify?slug=googlecloudplatform--generative-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}