{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-infinity-emb","slug":"pypi-infinity-emb","name":"infinity-emb","type":"api","url":"https://github.com/michaelfeil/infinity","page_url":"https://unfragile.ai/pypi-infinity-emb","categories":["llm-apis"],"tags":["vector","embedding","neural","search","sentence-transformers"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-infinity-emb__cap_0","uri":"capability://data.processing.analysis.dynamic.batching.text.embedding.inference","name":"dynamic-batching-text-embedding-inference","description":"Accumulates incoming embedding requests into optimally-sized batches using a BatchHandler that balances latency and throughput, then executes batches on GPU/accelerator hardware via backend-specific inference pipelines (PyTorch, ONNX/TensorRT, CTranslate2, AWS Neuron). The system uses multi-threaded tokenization to parallelize text preprocessing while batches are formed, reducing end-to-end latency by overlapping I/O and compute.","intents":["I need to embed thousands of documents with minimal per-request latency while maximizing GPU utilization","I want to serve embeddings at high throughput without provisioning multiple inference servers","I need to balance batch size dynamically based on incoming request patterns to avoid timeout delays"],"best_for":["teams building semantic search systems with variable request volumes","developers deploying embedding services that need sub-100ms p99 latency at scale","organizations migrating from cloud embedding APIs (OpenAI, Cohere) to self-hosted inference"],"limitations":["Batching introduces variable latency — requests arriving during batch formation wait for batch completion or timeout threshold","No built-in request prioritization — all requests treated equally regardless of SLA requirements","Multi-threaded tokenization adds overhead for very small batches (< 4 requests); optimal batch size typically 32-256 depending on model"],"requires":["Python 3.9+","NVIDIA CUDA 11.8+ OR AMD ROCM 5.6+ OR CPU (slower)","PyTorch 2.0+ or ONNX Runtime 1.16+","Minimum 4GB VRAM for small models, 16GB+ for large models"],"input_types":["text (raw strings, lists of strings)","structured text with metadata"],"output_types":["dense vector embeddings (float32 arrays)","OpenAI-compatible embedding response format (JSON with usage stats)"],"categories":["data-processing-analysis","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-infinity-emb__cap_1","uri":"capability://tool.use.integration.multi.model.orchestration.single.server","name":"multi-model-orchestration-single-server","description":"Manages multiple embedding/reranking models simultaneously within a single server process using AsyncEngineArray, which routes incoming requests to the appropriate AsyncEmbeddingEngine instance based on model ID. Each model maintains its own inference pipeline, GPU memory allocation, and batch queue, enabling efficient resource sharing and model hot-swapping without server restart.","intents":["I need to serve multiple embedding models (e.g., different languages, domains) from one endpoint without running separate servers","I want to A/B test different embedding models by routing requests to different model instances","I need to load/unload models dynamically based on demand without downtime"],"best_for":["teams managing polyglot search systems with language-specific embedding models","ML engineers running model experiments that require side-by-side inference comparison","cost-conscious deployments where consolidating models reduces infrastructure overhead"],"limitations":["GPU memory is shared across all loaded models — total VRAM must accommodate all active models simultaneously","No automatic load balancing across models — each model gets its own batch queue and processing thread","Model switching adds ~50-200ms overhead if model is not already loaded in GPU memory"],"requires":["Python 3.9+","Sufficient GPU VRAM to hold all active models (typically 2-4 models per 24GB GPU)","HuggingFace model identifiers for each model to be served"],"input_types":["HTTP requests with model_id parameter","Python SDK calls specifying model name"],"output_types":["embedding vectors (format depends on model type)","reranking scores (for reranker models)","classification logits (for classification models)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-infinity-emb__cap_10","uri":"capability://code.generation.editing.python.sdk.async.embedding.engine","name":"python-sdk-async-embedding-engine","description":"Provides a Python SDK (AsyncEmbeddingEngine, AsyncEngineArray) for programmatic embedding generation without HTTP overhead, enabling direct in-process inference for Python applications. The SDK supports async/await patterns for non-blocking inference and batch operations, with automatic model loading and GPU memory management.","intents":["I want to embed documents in my Python application without running a separate server","I need to integrate embeddings into my data pipeline without HTTP latency overhead","I want to use async/await patterns for non-blocking embedding generation"],"best_for":["Python developers building RAG systems or semantic search pipelines","data engineers embedding documents during ETL without external service calls","teams avoiding HTTP overhead by embedding in-process"],"limitations":["Python-only — no support for other languages without HTTP wrapper","Requires Python process to have GPU access — can't share GPU across multiple Python processes easily","No built-in request queuing across processes — each Python process has independent batch queue","Memory management is process-local — no centralized GPU memory allocation across multiple Python instances"],"requires":["Python 3.9+","PyTorch 2.0+","asyncio event loop (Python 3.7+)","GPU with CUDA/ROCM support (or CPU fallback)"],"input_types":["text strings","lists of strings","async iterables of text"],"output_types":["numpy arrays (embeddings)","Python lists of embeddings","async generators of embeddings"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-infinity-emb__cap_11","uri":"capability://tool.use.integration.rest.api.server.fastapi","name":"rest-api-server-fastapi","description":"Implements a FastAPI-based REST server that exposes embedding, reranking, and classification models via HTTP endpoints. The server handles request routing, response formatting, error handling, and OpenAPI documentation generation, with support for both OpenAI and Cohere API formats.","intents":["I want to expose embedding models via HTTP for use from any language or framework","I need to build a microservice that other applications can call for embeddings","I want auto-generated API documentation and interactive API testing"],"best_for":["teams building microservices that need language-agnostic embedding access","organizations deploying Infinity in Kubernetes or Docker containers","developers who need OpenAPI documentation and Swagger UI for API exploration"],"limitations":["HTTP adds serialization/deserialization overhead — typically 5-20ms per request","Network latency adds to end-to-end latency — not suitable for sub-10ms latency requirements","No built-in authentication or rate limiting — requires reverse proxy (nginx, Envoy) for production","Single-threaded request handling per worker — requires multiple workers for concurrent requests"],"requires":["Python 3.9+","FastAPI 0.100+","Uvicorn or other ASGI server","HTTP client library (requests, httpx, curl, etc.)"],"input_types":["JSON request bodies","HTTP POST requests"],"output_types":["JSON responses","OpenAPI/Swagger documentation"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-infinity-emb__cap_12","uri":"capability://automation.workflow.cli.command.line.deployment","name":"cli-command-line-deployment","description":"Provides a command-line interface (infinity_emb command) for starting the embedding server with configuration via CLI arguments or environment variables. The CLI handles model loading, server startup, and configuration management, enabling one-command deployment without writing Python code.","intents":["I want to start an embedding server with a single command for quick prototyping","I need to deploy Infinity in Docker or Kubernetes using CLI configuration","I want to avoid writing Python code to configure and start the server"],"best_for":["DevOps engineers deploying Infinity in containers","developers prototyping embedding systems quickly","teams using Infrastructure-as-Code (Terraform, CloudFormation) that need CLI-based deployment"],"limitations":["CLI configuration is limited to basic options — complex setups require Python SDK or environment variables","No interactive configuration wizard — users must know all required parameters","Configuration is not persisted — must be re-specified on each restart"],"requires":["Python 3.9+ with infinity_emb package installed","Command-line shell (bash, zsh, PowerShell, etc.)"],"input_types":["CLI arguments (--model, --port, --batch-size, etc.)","environment variables (INFINITY_MODEL, INFINITY_PORT, etc.)"],"output_types":["running FastAPI server","server logs to stdout/stderr"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-infinity-emb__cap_13","uri":"capability://automation.workflow.docker.containerized.deployment","name":"docker-containerized-deployment","description":"Provides Docker images and docker-compose configuration for containerized deployment of Infinity, with pre-built images for different hardware backends (CUDA, ROCM, CPU). The Dockerfile handles dependency installation, model caching, and server startup, enabling reproducible deployments across environments.","intents":["I want to deploy Infinity in Docker for consistent environments across dev/staging/production","I need to run Infinity in Kubernetes without managing Python dependencies","I want to use docker-compose for local development with GPU support"],"best_for":["DevOps teams deploying Infinity in Kubernetes or Docker Swarm","developers using Docker for local development","organizations standardizing on containerized deployments"],"limitations":["Docker image size is large (2-5GB) due to PyTorch and model dependencies","GPU support requires nvidia-docker or Docker 19.03+ with --gpus flag","Model caching in Docker requires volume mounts or image rebuilds","No built-in health checks or graceful shutdown — requires orchestration layer (Kubernetes) for production"],"requires":["Docker 19.03+ (or 20.10+ for GPU support)","nvidia-docker or Docker with GPU support (for GPU deployments)","Sufficient disk space for image (2-5GB)"],"input_types":["Dockerfile","docker-compose.yml","environment variables"],"output_types":["running Docker container with Infinity server","exposed port (default 8000)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-infinity-emb__cap_14","uri":"capability://memory.knowledge.request.caching.embedding.deduplication","name":"request-caching-embedding-deduplication","description":"Implements a caching layer that deduplicates identical embedding requests and returns cached results, reducing redundant inference. 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This compatibility layer enables drop-in replacement of OpenAI API calls with local Infinity instances by simply changing the base URL, without modifying client code.","intents":["I want to replace OpenAI embeddings API calls with a self-hosted solution without rewriting my client code","I need to use existing OpenAI client libraries (Python, JavaScript, etc.) against my local embedding server","I want to reduce API costs by switching from cloud embeddings to self-hosted while maintaining API compatibility"],"best_for":["teams already using OpenAI API who want to migrate to self-hosted without code changes","developers building RAG systems that need cost-effective embeddings at scale","organizations with data residency requirements that can't use cloud APIs"],"limitations":["Does not support all OpenAI API features (e.g., no user/organization headers, limited error codes)","Response format is compatible but not identical — some optional fields may differ","No built-in rate limiting or quota management like OpenAI API provides"],"requires":["Python 3.9+","FastAPI 0.100+","OpenAI Python client 1.0+ (or any HTTP client for REST calls)"],"input_types":["JSON request body with 'input' (string or array of strings) and 'model' fields","HTTP POST requests"],"output_types":["JSON response with 'data' array containing embedding objects, 'model' name, and 'usage' stats"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-infinity-emb__cap_3","uri":"capability://tool.use.integration.cohere.compatible.reranking.api","name":"cohere-compatible-reranking-api","description":"Provides a REST API endpoint that implements Cohere's reranking API specification, accepting a query and list of documents, then returning relevance scores for each document. This enables using open-source reranking models (e.g., mxbai-rerank-xlarge) as a drop-in replacement for Cohere's reranking service without changing client code.","intents":["I want to use Cohere's reranking API interface but with self-hosted models to reduce costs","I need to rerank search results locally without sending queries and documents to external APIs","I want to integrate reranking into my RAG pipeline using existing Cohere client libraries"],"best_for":["teams using Cohere reranking who want to self-host for cost/privacy reasons","developers building search systems that need sub-100ms reranking latency","organizations with sensitive data that can't use cloud reranking APIs"],"limitations":["Only supports reranking models compatible with the reranker interface — not all HuggingFace models work","Reranking is computationally expensive — throughput is typically 10-100x lower than embedding throughput","No built-in caching of reranking scores — each unique query-document pair requires fresh inference"],"requires":["Python 3.9+","Reranking model from HuggingFace (e.g., mxbai-rerank-xlarge, bge-reranker-v2-m3)","Minimum 8GB VRAM for typical reranking models"],"input_types":["JSON request with 'query' (string) and 'documents' (array of strings or objects)","HTTP POST requests"],"output_types":["JSON response with 'results' array containing document indices and relevance scores"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-infinity-emb__cap_4","uri":"capability://image.visual.multimodal.clip.embedding.generation","name":"multimodal-clip-embedding-generation","description":"Generates embeddings for both text and images using CLIP-based models (e.g., openai/clip-vit-base-patch32), producing aligned vector representations in a shared embedding space. 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The system handles audio preprocessing (resampling, normalization), spectrogram generation, and inference through the embedding pipeline, enabling audio-text cross-modal retrieval.","intents":["I need to search audio files using text queries (e.g., 'dog barking')","I want to build a music recommendation system based on text descriptions","I need to find similar audio clips based on semantic meaning rather than acoustic features"],"best_for":["teams building audio search systems with text queries","developers creating multimodal RAG for audio content","organizations managing large audio libraries that need semantic search"],"limitations":["Audio preprocessing (resampling, spectrogram generation) adds significant latency — typically 5-10x slower than text embedding","Requires audio files to be in supported formats (WAV, MP3, FLAC) — may need transcoding","CLAP models are less mature than CLIP — fewer pre-trained models available and potentially lower quality"],"requires":["Python 3.9+","librosa or similar audio processing library","CLAP model from HuggingFace","Minimum 12GB VRAM for typical CLAP models"],"input_types":["audio files (WAV, MP3, FLAC)","raw audio bytes","text descriptions"],"output_types":["dense vector embeddings aligned with text embeddings","cross-modal similarity scores"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-infinity-emb__cap_6","uri":"capability://text.generation.language.text.classification.inference","name":"text-classification-inference","description":"Executes text classification models (e.g., sentiment analysis, topic classification) that produce logits or probabilities for predefined classes. The system batches classification requests and returns class predictions with confidence scores, supporting both multi-class and multi-label classification through the unified inference pipeline.","intents":["I need to classify documents into categories (e.g., spam detection, sentiment analysis) at scale","I want to run text classification models without building custom inference code","I need to batch classification requests for throughput optimization"],"best_for":["teams building content moderation systems with local inference","developers adding sentiment analysis or topic classification to RAG pipelines","organizations classifying documents without sending them to external APIs"],"limitations":["Classification models must be compatible with HuggingFace transformers — custom architectures may not work","Output format is model-specific — no standardized response schema like embeddings","No built-in threshold tuning or confidence calibration"],"requires":["Python 3.9+","HuggingFace transformers library","Classification model from HuggingFace (e.g., distilbert-base-uncased-finetuned-sst-2-english)"],"input_types":["text strings","lists of text documents"],"output_types":["class predictions (label strings)","confidence scores (logits or probabilities)","multi-label predictions (for multi-label models)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-infinity-emb__cap_7","uri":"capability://code.generation.editing.onnx.tensorrt.backend.optimization","name":"onnx-tensorrt-backend-optimization","description":"Compiles and executes models using ONNX Runtime with TensorRT optimization, converting PyTorch/HuggingFace models to ONNX format and applying GPU-specific optimizations (quantization, kernel fusion, memory optimization). This backend provides 2-10x speedup over PyTorch inference for compatible models while reducing memory footprint.","intents":["I need to reduce inference latency for embedding models by 50%+ without changing model architecture","I want to optimize GPU memory usage to fit more models on a single GPU","I need to deploy models on production hardware with strict latency SLAs"],"best_for":["teams with strict latency requirements (sub-50ms p99)","organizations running high-volume inference where 2-10x speedup justifies optimization effort","deployments on NVIDIA GPUs where TensorRT is available"],"limitations":["ONNX conversion requires manual model export — not all HuggingFace models export cleanly","TensorRT optimization is NVIDIA-specific — doesn't work on AMD, CPU, or other hardware","Quantization may reduce model accuracy by 1-5% depending on quantization level","ONNX Runtime adds ~100-200ms startup overhead for model loading and optimization"],"requires":["Python 3.9+","NVIDIA CUDA 11.8+","TensorRT 8.5+","ONNX Runtime 1.16+ with TensorRT execution provider"],"input_types":["PyTorch models","HuggingFace transformers models","Pre-exported ONNX models"],"output_types":["optimized ONNX model files","inference results (same format as PyTorch backend)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-infinity-emb__cap_8","uri":"capability://automation.workflow.ctranslate2.backend.cpu.optimization","name":"ctranslate2-backend-cpu-optimization","description":"Executes models using CTranslate2, a C++ inference engine optimized for CPU and GPU inference with support for model quantization and efficient memory management. This backend enables fast inference on CPU-only hardware and provides 5-20x speedup over PyTorch on CPU by using optimized kernels and reduced precision arithmetic.","intents":["I need to run embedding models on CPU-only hardware without GPU","I want to reduce inference latency on CPU by 10x compared to PyTorch","I need to deploy models on edge devices with limited compute resources"],"best_for":["teams deploying on CPU-only infrastructure (cost-effective for low-throughput workloads)","edge deployments where GPU is unavailable or too expensive","organizations needing CPU inference with acceptable latency (50-200ms per request)"],"limitations":["CTranslate2 requires model conversion — not all HuggingFace models are supported","CPU inference is fundamentally slower than GPU — even with optimization, typically 10-100x slower than NVIDIA GPU","Quantization to int8 may reduce accuracy by 1-3%","Limited to models that CTranslate2 supports (primarily transformers, not all architectures)"],"requires":["Python 3.9+","CTranslate2 3.0+","Converted CTranslate2 model files (requires conversion from PyTorch/HuggingFace)"],"input_types":["text strings","pre-converted CTranslate2 models"],"output_types":["inference results in same format as other backends"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-infinity-emb__cap_9","uri":"capability://automation.workflow.aws.neuron.inferentia.backend","name":"aws-neuron-inferentia-backend","description":"Executes models on AWS Inferentia and Trainium accelerators using AWS Neuron SDK, providing optimized inference on AWS-specific hardware. 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