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Deploys trained SavedModels behind gRPC and REST endpoints, with hot-swappable versioning so new weights load without downtime. Built around servables, loaders, sources, and a manager, plus request batching to cut accelerator cost.
Unified metadata platform for data discovery, observability, and governance — central metadata repository, column-level lineage, and a pluggable ingestion framework with 84+ connectors. Suited for teams that need searchable data catalogs, automated lineage, and collaborative data governance.
Official collection of example notebooks and guides for building with the OpenAI API — text generation, embeddings, function calling, RAG, fine-tuning, and more. Mostly runnable Jupyter notebooks (~93%); mirrored at cookbook.openai.com.
Connects LLMs to private and domain-specific data with ingestion, indexing, and retrieval primitives for RAG and agentic apps. Centers on document parsing via LlamaParse for 90+ file formats, schema-based extraction, and composable queries.
Routes one API call across hundreds of LLMs from dozens of providers, with credits, fallbacks, pricing comparison, and data-policy controls for teams that need model choice without wiring every provider separately.
Turns documents, web pages and audio into a private, searchable knowledge base and builder for AI assistants and agents — supports wide-format ingestion, multi-model (cloud or local) execution, retrieval-augmented responses, and enterprise deployment features like RBAC and SSO.
Bring-your-own-key chat client that keeps every conversation in the local browser, never a server. One UI reaches OpenAI, Claude, Gemini, DeepSeek and a dozen more providers across web, desktop and mobile, with MCP, plugins, and one-click self-hosting.
A bring-your-own-API-key chat frontend for ChatGPT, Claude, Gemini and other models, running entirely in your browser with local storage. Adds a prompt library, plugins, model switching, and team/agent setups on top of raw provider APIs.
Ensures LLM outputs match precise, schema-defined structures by enforcing Python types, Pydantic models, JSON schemas or grammars at generation time. Provider-agnostic integrations (OpenAI, transformers, vLLM, Ollama, etc.) and function-call style mapping reduce brittle post-processing and make structured generation reliable for production pipelines.