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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.
Give AI agents semantic memory with hybrid search and knowledge graph support.
Access live NPM package data for version checks and dependency risk reviews.
Provides AI agents with local search, business context retrieval, and summarization prompts.
Build MCP agent workflows with NestJS, LangGraph, and flexible model orchestration.
Turn codebases into structural graphs for efficient AI-assisted code exploration.