Tag
Explore by tags
Distributed search and analytics engine and vector database built on Lucene that enables near-real-time full-text and vector search, indexing, and analytics over large datasets. Provides vector embeddings support, REST APIs, RAG-friendly features, and deployment options including Elastic Cloud and Docker.
Turns model definitions into a shared layer across training and inference stacks, covering text, vision, audio, video, and multimodal models. Pipelines, Trainer, and generation APIs make pretrained models usable without locking teams to one framework.
Runs approximate nearest-neighbor search over billions of vector embeddings, separating compute from storage so reads and writes scale independently. Offers HNSW, IVF, DiskANN, and GPU CAGRA indexes plus hybrid dense+sparse and BM25 retrieval.
Provides a hosted or self-hosted Postgres platform that exposes database, auth, realtime subscriptions, file storage, serverless functions, and auto-generated REST/GraphQL APIs. Includes an AI & vector/embeddings toolkit and modular client libraries for building web, mobile and AI applications without stitching multiple vendors.
Self-hostable personal “AI second brain” that turns web pages and documents into a searchable knowledge base, builds custom agents and automations, and connects to local or cloud LLMs with multi-platform access.
Provides cleaned, per-language snapshots of Wikipedia articles (id, url, title, text) packaged as Hugging Face dataset configs (Parquet). Covers 300+ language configs and dated dumps — useful for language modeling, multilingual NLP, retrieval, and RAG pipelines.
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.
Framework for building multi-channel AI assistants that autonomously plan tasks, invoke tools/skills, and keep long-term memory; supports many LLM providers and channels (WeChat, Feishu, QQ, web) for local or server 24/7 deployment.
Build, run, and monitor LLM agents across one stack: an open framework for chaining models and tools, LangGraph for stateful agent orchestration, and LangSmith for tracing, evaluation, and deployment in production.
Build LLM-powered agents and applications from modular components: provider-agnostic model abstractions, tool integrations, retrievers for RAG, and agent orchestration primitives. Suited for prototyping and production agent workflows; requires developer wiring and dependency management.
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.
Runs an agentic RAG loop over scientific papers: searches literature, gathers and re-ranks evidence chunks, then answers with in-text citations. Adds metadata-aware embeddings, retraction checks, and contradiction detection across full PDFs.