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Python toolkit for NLP, multimodal LLM, agent, and knowledge graph research
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "npcpy" yet — see the docs or source repo.
Using npcpy, write a Python example that loads local CSV text data, cleans it, trains a basic text classification model, and outputs accuracy plus sample predictions.
Runnable Python code including data loading, preprocessing, model training, and evaluation output.
Based on npcpy, design a multimodal question-answering example that takes an image path and question text, runs model inference, and returns the answer with intermediate processing steps.
An example script showing joint image-text processing and a description of the result structure.
Use npcpy to write a Python script that extracts entities and relations from technical documents and formats the results into JSON suitable for knowledge graph import.
An executable script, entity-relation extraction logic, and sample normalized knowledge graph JSON output.
Control a real browser with natural language for navigation and routine web tasks.
Build a codebase knowledge graph for instant code navigation and analysis.
Give AI agents semantic memory with hybrid search and knowledge graph support.
Build and explore a neuroscience-inspired knowledge graph for search and reasoning.
Access live NPM package data for version checks and dependency risk reviews.
Build MCP agent workflows with NestJS, LangGraph, and flexible model orchestration.