Results for “dolly-dataset”

Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script).

Build synthetic datasets and data generation pipelines using the Data Designer library.

Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.

SNLI-VE: Visual Entailment Dataset

DSPy: declarative LM programs, auto-optimize prompts, RAG.

Document datasets, variables, sources, and merge keys for replication

DSPy: declarative LM programs, auto-optimize prompts, RAG.

查找 Dify 的 DSL 语法、API 端点、SDK 用法和 CLI 命令等参考信息。

COYO-700M: Image-Text Pair Dataset

DSPy: declarative LM programs, auto-optimize prompts, RAG.

Fetch dataset metadata, paginate rows, search text, apply filters, and download parquet URLs from the Hugging Face Dataset Viewer API.

Build synthetic datasets and data generation pipelines using the Data Designer library.

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

Simplify molecular cheminformatics with a Pythonic wrapper around RDKit for SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, and parallel processing.

Ecto database toolkit reference for Elixir. Covers schemas, changesets with validation, composable query DSL, joins, preloading, aggregates, migrations, transactions with Ecto.Multi, and embedded schemas.

Researches authoritative APIs and data sources for a project, producing per-API docs, a data catalog, and an integration blueprint for a React + Supabase + Vercel + AI stack.

nuScenes: A Multimodal Dataset for Autonomous Driving

Discovers and uses scientific datasets, models, blog posts, and interactive demos from a curated catalog for AI/ML work in domains like biology, chemistry, physics, and genomics.

COCO: Microsoft COCO: Common Objects in Context

No Robots: A Dataset of Personally Written Instructions

Biblioteca interativa de visualização de dados científicos e estatísticos para Python. Use ao criar gráficos, plots ou visualizações incluindo scatter plots, gráficos de linhas, gráficos de barras, heatmaps, plots 3D, mapas geográficos, distribuições estatísticas, gráficos financeiros e dashboards. Suporta visualizações rápidas (Plotly Express) e personalização refinada (graph objects). Gera HTML interativo ou imagens estáticas (PNG, PDF, SVG).

Detects the project stack and generates production-ready multi-stage Dockerfiles with compose services, .dockerignore, non-root users, health checks, layer caching, and optional dev hot-reload profiles.

You are an expert in dlt, the open-source Python library for building data pipelines. You help developers load data from any API, file, or database into warehouses and lakes using simple Python decorators — with automatic schema inference, incremental loading, and built-in data contracts. dlt is the "requests library for data pipelines."

Deploy a Data Pipeline to Fabric

Patterns for H5AD-backed Shiny apps with Excel fallback, groovy data in .uns, scVI QC validation, Leiden clustering merge, per-donor tissue extraction

Create and review Cloudflare Durable Objects. Use when building stateful coordination (chat rooms, multiplayer games, booking systems), implementing RPC methods, SQLite storage, alarms, WebSockets, or reviewing DO code for best practices. Covers Workers integration, wrangler config, and testing with Vitest. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

Builds interactive data apps and ML demos with Streamlit, using pure Python widgets and charts.

Dolphins: Multimodal Language Model for Driving

Program LM pipelines with DSPy — define Signatures (input/output fields), build Modules (ChainOfThought, ReAct, Predict), compose them into Programs, then optimize with Teleprompters (BootstrapFewShot, MIPRO, COPRO) to auto-improve prompts from examples.

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming. Use when you need to build complex AI systems, program LMs declaratively, optimize prompts automatically, create modular AI pipelines, or build RAG systems and agents.

Use when data operation or statistical analysis tasks do not need full program implementation, and the agent should operate Insyra through CLI/REPL, .isr scripts, or DSL workflows, including environment workflows, reproducible command pipelines, and command selection guidance.

Manage Cloudflare D1 SQLite databases and KV key-value stores with setup commands, TypeScript usage patterns, and a decision matrix for choosing between them.

Uruchamianie i wdrażanie platformy Soneta (enova365, Triva) w kontenerach. Używaj gdy użytkownik: (1) stawia środowisko (server + web) na obrazach Soneta przez `docker compose` albo na Apple `container` / Container Desktop; (2) tworzy bazę danych w kontenerze (usługa init z `dbmgr create`, `--demo`, `--recreate`, licencja, konwersja); (3) wdraża na Kubernetes przez Helm (`helm repo add soneta`, `values.yaml`, `dblist`, `adminMode`); (4) wybiera wersję obrazów (tagi z Docker Hub `soneta/*` lub `registry.soneta.pl`), architekturę (alpine/arm64/amd64), logowanie do registry; (5) potrzebuje SQL Servera — zewnętrznego (`host.docker.internal` / `host.containers.internal`) albo jako kontener `mssql`; (6) rozwiązuje problemy startu stacku (kolejność, port zajęty, brak DNS między usługami w Apple container, zły host-alias). Słowa kluczowe: docker compose, docker-compose.yaml, apple container, Container Desktop, helm, kubernetes, obraz, tag, wersja, mssql, dbmgr, x-init, server.standard, web.standard.

Discover and use DFlow documentation, Agent CLI, Trading API, Metadata API, Proof KYC, prediction markets, and the hosted DFlow docs MCP. Use before implementing DFlow features or when field-level endpoint details are needed.

DSPy: declarative LM programs, auto-optimize prompts, RAG.