{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-google-generativeai","slug":"pypi-google-generativeai","name":"google-generativeai","type":"repo","url":"https://github.com/google/generative-ai-python","page_url":"https://unfragile.ai/pypi-google-generativeai","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-google-generativeai__cap_0","uri":"capability://text.generation.language.multi.modal.generative.text.completion.with.streaming","name":"multi-modal generative text completion with streaming","description":"Generates text responses from prompts containing text, images, audio, and video inputs using Google's Gemini models. Implements streaming via server-sent events (SSE) for real-time token delivery, with automatic batching of multimodal content into a unified request payload. Supports both synchronous blocking calls and asynchronous streaming for integration into event-driven architectures.","intents":["I need to send an image and text prompt to Gemini and get back generated text in real-time","I want to build a chatbot that handles mixed media inputs (text + images) without managing raw HTTP details","I need to process video frames and audio alongside text for comprehensive AI analysis"],"best_for":["Python developers building multimodal AI applications","Teams integrating Google Gemini into existing Python backends","Rapid prototypers who want high-level abstractions over raw REST APIs"],"limitations":["Streaming responses are SSE-based and require persistent HTTP connections; incompatible with serverless functions with strict timeout constraints","No built-in request batching across multiple independent prompts — each call is atomic","Audio and video inputs require pre-processing into base64 or URI format; no direct file streaming","Response streaming cannot be paused/resumed mid-generation without losing context"],"requires":["Python 3.9+","google-generativeai package (latest version)","Valid Google API key with Gemini API access","Network connectivity to Google's generative-ai.googleapis.com endpoints"],"input_types":["text (string)","images (PIL Image, base64, URI)","audio (base64-encoded WAV/MP3)","video (base64-encoded MP4/WebM)"],"output_types":["text (streamed or buffered)","structured metadata (finish_reason, usage tokens)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-google-generativeai__cap_1","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.binding","name":"function calling with schema-based tool binding","description":"Enables models to invoke external functions by declaring a schema of available tools upfront and letting the model decide when/how to call them. Implements automatic serialization of function signatures into JSON Schema format, with built-in validation of model-generated function calls against declared schemas. Supports both single-turn tool invocation and multi-turn agentic loops where the model can chain multiple function calls.","intents":["I want the model to decide when to call my custom Python functions based on the user's request","I need to build an agent that can call multiple APIs (weather, calculator, database) in sequence","I want type-safe function calling where the model's output is validated against my function signatures before execution"],"best_for":["Developers building AI agents with deterministic tool dependencies","Teams integrating Gemini into existing Python codebases with established function libraries","Builders who need structured function invocation without managing JSON Schema manually"],"limitations":["Schema generation is automatic but limited to Python type hints; complex nested types or union types may require manual schema overrides","No built-in function execution — developers must implement the actual function dispatch logic after the model returns a tool call","Tool calling is not guaranteed; models may ignore tool declarations if they decide to answer directly","Maximum of ~128 tools per request due to context window constraints; no tool prioritization or filtering mechanism"],"requires":["Python 3.9+","google-generativeai package","Valid Google API key","Python functions with type annotations (for automatic schema inference)"],"input_types":["Python function objects with type hints","Manual JSON Schema definitions","Tool descriptions (docstrings)"],"output_types":["FunctionCall objects with name and arguments","Structured tool invocation metadata"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-google-generativeai__cap_10","uri":"capability://text.generation.language.system.instruction.customization.with.role.based.prompting","name":"system instruction customization with role-based prompting","description":"Allows setting system-level instructions that define the model's behavior, tone, and constraints across all turns in a conversation. System instructions are passed as a separate parameter distinct from user messages, enabling role-based prompting (e.g., 'You are a helpful assistant', 'You are a code reviewer'). Instructions are applied consistently across multi-turn conversations without requiring repetition in each user message.","intents":["I want the model to behave as a specific persona or role throughout a conversation","I need to enforce consistent constraints (e.g., 'respond in JSON only') across all responses","I want to set the tone and style of responses without repeating instructions in each message"],"best_for":["Developers building role-based chatbots or assistants","Teams implementing domain-specific AI agents (code reviewer, customer support, etc.)","Builders who want consistent behavior across multi-turn conversations"],"limitations":["System instructions consume tokens from the context window; very long instructions reduce available space for user input","Model adherence to system instructions is not guaranteed; complex or conflicting instructions may be ignored","No instruction versioning or A/B testing support; changing instructions requires code changes","System instructions are not validated; malformed or contradictory instructions may cause unexpected behavior","No audit trail of which system instructions were used for a given response"],"requires":["Python 3.9+","google-generativeai package","Valid Google API key"],"input_types":["system instruction text (string)"],"output_types":["model responses following the system instructions"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-google-generativeai__cap_11","uri":"capability://automation.workflow.rate.limiting.and.quota.management.with.automatic.backoff","name":"rate limiting and quota management with automatic backoff","description":"Implements client-side rate limiting and quota management to prevent exceeding API rate limits and quota thresholds. Automatically backs off and retries requests when rate limit errors are encountered, with exponential backoff strategy and configurable retry parameters. Tracks quota usage across requests and provides methods to check remaining quota before submitting new requests.","intents":["I want to submit many requests without hitting rate limit errors","I need to know how much quota I have remaining before submitting a large batch","I want automatic retry logic for transient failures without implementing it myself"],"best_for":["Developers building high-volume API consumers","Teams processing large batches with strict rate limit compliance","Builders who want transparent quota management without manual tracking"],"limitations":["Rate limiting is client-side only; no coordination across multiple client instances or processes","Quota tracking is approximate; actual quota usage may differ due to API-side calculations","Backoff strategy is fixed (exponential); no customization of retry behavior","No quota reset time prediction; developers must manually check quota status","Rate limiting does not account for different rate limits across different API endpoints"],"requires":["Python 3.9+","google-generativeai package","Valid Google API key"],"input_types":["retry configuration (max retries, backoff multiplier)","quota check requests"],"output_types":["successful response after retries","quota status (remaining requests, reset time)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-google-generativeai__cap_2","uri":"capability://memory.knowledge.conversation.history.management.with.automatic.context.windowing","name":"conversation history management with automatic context windowing","description":"Maintains a stateful conversation history across multiple turns, automatically managing token limits by truncating or summarizing older messages when context window is exceeded. Implements a simple list-based history structure where each message is tagged with role (user/model) and content, with built-in methods to append new messages and retrieve the full conversation for re-submission to the API.","intents":["I want to build a multi-turn chatbot without manually tracking conversation state","I need to ensure my conversation doesn't exceed the model's context window limits","I want to persist conversation history across sessions without implementing my own database"],"best_for":["Developers building simple chatbot prototypes","Teams wanting quick conversation management without external state stores","Rapid prototyping where in-memory history is acceptable"],"limitations":["History is in-memory only; no persistence across process restarts without manual serialization","No automatic summarization — when context window is exceeded, older messages are simply dropped rather than compressed","No built-in deduplication or filtering of repeated messages","Single conversation per client instance; no multi-conversation multiplexing","No access control or privacy masking for sensitive conversation content"],"requires":["Python 3.9+","google-generativeai package","Valid Google API key"],"input_types":["text messages","multimodal content (images, audio, video)"],"output_types":["conversation history list","serialized conversation (JSON)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-google-generativeai__cap_3","uri":"capability://data.processing.analysis.embedding.generation.with.semantic.vector.output","name":"embedding generation with semantic vector output","description":"Converts text or multimodal content into high-dimensional dense vector embeddings suitable for semantic search, clustering, or similarity comparison. Uses Google's embedding models (e.g., embedding-001) which produce 768-dimensional vectors optimized for semantic relevance. Supports batch embedding of multiple texts in a single API call, with automatic chunking for large inputs.","intents":["I need to convert documents into vectors for semantic search over a knowledge base","I want to find similar texts by comparing their embeddings","I need to build a RAG system where I embed user queries and documents for retrieval"],"best_for":["Developers building semantic search or RAG systems","Teams implementing document similarity or clustering pipelines","Builders needing embeddings without managing separate embedding services"],"limitations":["Embedding models are fixed (embedding-001); no fine-tuning or custom model selection","Batch size is limited to ~100 texts per request; larger batches require manual chunking","Embeddings are not cached; repeated embedding of the same text incurs API costs each time","No built-in vector storage or indexing — embeddings must be stored externally (Pinecone, Weaviate, etc.)","Embedding dimensions are fixed at 768; no option for lower-dimensional projections"],"requires":["Python 3.9+","google-generativeai package","Valid Google API key with Embedding API access","Text input (strings or lists of strings)"],"input_types":["text (string or list of strings)","multimodal content (images + text)"],"output_types":["numpy arrays or lists of floats (768-dimensional vectors)","embedding metadata (model name, token count)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-google-generativeai__cap_4","uri":"capability://safety.moderation.content.safety.filtering.with.configurable.safety.thresholds","name":"content safety filtering with configurable safety thresholds","description":"Filters generated content based on safety categories (hate speech, sexual content, violence, harassment) with configurable threshold levels (BLOCK_NONE, BLOCK_ONLY_HIGH, BLOCK_MEDIUM_AND_ABOVE, BLOCK_LOW_AND_ABOVE). Safety filters are applied server-side by the Gemini API, with client-side configuration passed as request parameters. Blocked responses return a safety_ratings object indicating which categories triggered the block.","intents":["I need to ensure generated content meets my application's safety standards","I want to adjust safety filtering based on my use case (e.g., stricter for children's apps)","I need to understand why a response was blocked and what safety categories were triggered"],"best_for":["Teams building public-facing AI applications with safety requirements","Developers in regulated industries (healthcare, finance) needing content moderation","Builders who want server-side safety enforcement without implementing custom filters"],"limitations":["Safety filtering is applied server-side only; no client-side pre-filtering of user inputs","Threshold levels are coarse-grained (4 levels); no fine-grained per-category control","Blocked responses are not retried automatically; developers must handle retry logic manually","Safety ratings are returned only for blocked content; no granular scores for non-blocked responses","No ability to whitelist specific content or override safety decisions"],"requires":["Python 3.9+","google-generativeai package","Valid Google API key"],"input_types":["safety threshold configuration (enum)","text prompts and content"],"output_types":["safety_ratings object with category scores","blocked/unblocked response status"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-google-generativeai__cap_5","uri":"capability://tool.use.integration.model.capability.introspection.and.version.management","name":"model capability introspection and version management","description":"Provides runtime access to model metadata including supported input types, context window size, maximum output tokens, and available features (function calling, vision, etc.). Implements a model registry that can be queried to list all available models and their capabilities without hardcoding model names. Supports model versioning with automatic fallback to stable versions if a specific version is unavailable.","intents":["I want to check if a model supports function calling before attempting to use it","I need to dynamically select the best model based on available capabilities","I want to list all available Gemini models and their context window sizes"],"best_for":["Developers building model-agnostic AI applications","Teams managing multiple model versions and needing runtime capability detection","Builders who want to avoid hardcoding model names and versions"],"limitations":["Model metadata is fetched from the API on first call; no local caching by default","Capability information is limited to high-level features; no granular performance metrics or cost data","Model availability is region-dependent; introspection does not account for regional restrictions","No deprecation warnings or migration guidance when models are sunset"],"requires":["Python 3.9+","google-generativeai package","Valid Google API key"],"input_types":["model name (string or None for default)"],"output_types":["Model object with metadata attributes","list of available models"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-google-generativeai__cap_6","uri":"capability://automation.workflow.batch.processing.with.asynchronous.request.submission","name":"batch processing with asynchronous request submission","description":"Submits multiple generation requests asynchronously and collects results without blocking on individual responses. Implements async/await patterns using Python's asyncio library, enabling concurrent API calls with configurable concurrency limits. Batch results are returned as a list or async generator, allowing streaming processing of large batches without loading all results into memory.","intents":["I need to process 1000 documents through Gemini without waiting for each one sequentially","I want to parallelize API calls while respecting rate limits","I need to stream batch results as they complete rather than waiting for all to finish"],"best_for":["Developers processing large document collections or datasets","Teams building data pipelines that integrate Gemini","Builders needing efficient API utilization with rate limit awareness"],"limitations":["Concurrency is limited by API rate limits; no built-in backoff or retry logic for rate limit errors","Batch results are not persisted; if the process crashes, all in-flight requests are lost","No built-in error handling or partial failure recovery; a single failed request does not automatically retry","Async implementation requires asyncio event loop; not compatible with synchronous-only codebases without threading","No progress tracking or ETA estimation for large batches"],"requires":["Python 3.9+","google-generativeai package","asyncio event loop (Python 3.7+)","Valid Google API key"],"input_types":["list of prompts or requests","concurrency limit (integer)"],"output_types":["list of responses","async generator of responses"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-google-generativeai__cap_7","uri":"capability://data.processing.analysis.file.upload.and.caching.for.multimodal.content","name":"file upload and caching for multimodal content","description":"Uploads files (documents, images, audio, video) to Google's servers and returns file references that can be reused across multiple API calls without re-uploading. Implements automatic MIME type detection and chunked upload for large files. Cached files are stored server-side for a configurable TTL (time-to-live), reducing bandwidth and API costs for repeated use of the same content.","intents":["I want to upload a large PDF once and reference it in multiple API calls","I need to analyze the same image across different prompts without re-uploading","I want to reduce API costs by caching frequently-used documents"],"best_for":["Developers processing large files or documents","Teams analyzing the same content with multiple prompts","Builders optimizing API costs by leveraging server-side caching"],"limitations":["File caching is server-side only; no local caching or offline access","TTL for cached files is fixed (typically 24-48 hours); no option to extend or make permanent","File size limits apply (e.g., 2GB for video); no streaming upload for very large files","Uploaded files are associated with the API key; no fine-grained access control or sharing","No built-in file versioning or deletion API; expired files are automatically cleaned up"],"requires":["Python 3.9+","google-generativeai package","Valid Google API key","File path or file-like object (bytes)"],"input_types":["file paths (string)","file-like objects (bytes)","MIME type (auto-detected or manual)"],"output_types":["File object with URI reference","file metadata (size, MIME type, creation time)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-google-generativeai__cap_8","uri":"capability://data.processing.analysis.response.formatting.with.structured.output.schemas","name":"response formatting with structured output schemas","description":"Constrains model output to follow a specified JSON schema, ensuring responses are valid, parseable structured data. Implements schema-based output formatting via a response_schema parameter that accepts JSON Schema definitions or Pydantic models. The model is instructed to generate output matching the schema, with server-side validation to reject non-conforming responses.","intents":["I want the model to always return JSON in a specific format (e.g., {name, age, email})","I need to extract structured data from text without parsing unstructured output","I want to use Pydantic models to define and validate response schemas"],"best_for":["Developers building data extraction pipelines","Teams integrating Gemini into systems expecting structured outputs","Builders who want type-safe responses without post-processing"],"limitations":["Schema enforcement is best-effort; complex schemas may not be perfectly adhered to","No automatic retry if output does not match schema; developers must implement retry logic","Schema validation is server-side; no client-side pre-validation of schema definitions","Pydantic model support requires additional dependencies (pydantic package)","Nested schemas with deep nesting may exceed context limits or cause performance degradation"],"requires":["Python 3.9+","google-generativeai package","Valid Google API key","JSON Schema definition or Pydantic model"],"input_types":["JSON Schema (dict or string)","Pydantic BaseModel class"],"output_types":["JSON string matching schema","Parsed Python dict or Pydantic model instance"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-google-generativeai__cap_9","uri":"capability://data.processing.analysis.token.counting.and.cost.estimation","name":"token counting and cost estimation","description":"Estimates the number of tokens a prompt will consume before sending it to the API, enabling cost prediction and budget management. Implements token counting via the same tokenizer used by the model, with support for multimodal content (images, audio, video). Provides both prompt token count and estimated completion token count based on model behavior patterns.","intents":["I want to estimate API costs before submitting a large batch of requests","I need to check if my prompt fits within the context window before sending it","I want to optimize prompts to reduce token consumption and API costs"],"best_for":["Developers managing API budgets or cost-sensitive applications","Teams processing large volumes of requests and needing cost forecasting","Builders optimizing prompt efficiency"],"limitations":["Token counting is approximate for multimodal content; actual token usage may vary slightly","Completion token estimation is based on statistical models; actual completion length may differ","Token counting does not account for function calling overhead or safety filtering","No historical token usage data or cost tracking; developers must implement their own logging","Token counting API calls themselves consume quota; no free tier for token estimation"],"requires":["Python 3.9+","google-generativeai package","Valid Google API key"],"input_types":["text prompts","multimodal content (images, audio, video)","conversation history"],"output_types":["token count (integer)","cost estimate (float, if pricing data is available)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"low","permissions":["Python 3.9+","google-generativeai package (latest version)","Valid Google API key with Gemini API access","Network connectivity to Google's generative-ai.googleapis.com endpoints","google-generativeai package","Valid Google API key","Python functions with type annotations (for automatic schema inference)","Valid Google API key with Embedding API access","Text input (strings or lists of strings)","asyncio event loop (Python 3.7+)"],"failure_modes":["Streaming responses are SSE-based and require persistent HTTP connections; incompatible with serverless functions with strict timeout constraints","No built-in request batching across multiple independent prompts — each call is atomic","Audio and video inputs require pre-processing into base64 or URI format; no direct file streaming","Response streaming cannot be paused/resumed mid-generation without losing context","Schema generation is automatic but limited to Python type hints; complex nested types or union types may require manual schema overrides","No built-in function execution — developers must implement the actual function dispatch logic after the model returns a tool call","Tool calling is not guaranteed; models may ignore tool declarations if they decide to answer directly","Maximum of ~128 tools per request due to context window constraints; no tool prioritization or filtering mechanism","System instructions consume tokens from the context window; very long instructions reduce available space for user input","Model adherence to system instructions is not guaranteed; complex or conflicting instructions may be ignored","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":1,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.295Z","last_scraped_at":"2026-05-03T15:20:10.823Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-google-generativeai","compare_url":"https://unfragile.ai/compare?artifact=pypi-google-generativeai"}},"signature":"woo4Oj7xaB6hQ3Rc7lPYcvikdcNqMtfLseb3K83Iqbf8zZNNHSPpaWcjXbIX+IuSIWe2eKHUfo+bJeou01dgAg==","signedAt":"2026-06-17T12:22:41.952Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-google-generativeai","artifact":"https://unfragile.ai/pypi-google-generativeai","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-google-generativeai","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"}}