ai-tools

AI Tools Examples

Examples showing how to integrate Reader with AI frameworks, LLMs, and vector stores.

Prerequisites

Start Ulixee Cloud in a separate terminal:

npx @ulixee/cloud start

Examples

LLM Summarization

Scrape webpages and summarize with LLMs.

Example	Description	API Key Required
openai-summary.ts	Summarize with GPT	OPENAI_API_KEY
anthropic-summary.ts	Summarize with Claude	ANTHROPIC_API_KEY
vercel-ai-stream.ts	Streaming summary with Vercel AI SDK	OPENAI_API_KEY

export OPENAI_API_KEY="sk-..."
npx tsx ai-tools/openai-summary.ts https://example.com

export ANTHROPIC_API_KEY="sk-ant-..."
npx tsx ai-tools/anthropic-summary.ts https://example.com

RAG Frameworks

Load scraped content into RAG frameworks for retrieval-augmented generation.

Example	Description
langchain-loader.ts	Custom LangChain document loader
llamaindex-loader.ts	LlamaIndex document loader

npx tsx ai-tools/langchain-loader.ts
npx tsx ai-tools/llamaindex-loader.ts

Vector Stores

Scrape and ingest content directly into vector databases for semantic search.

Example	Description	API Keys Required
pinecone-ingest.ts	Ingest into Pinecone	PINECONE_API_KEY, OPENAI_API_KEY
qdrant-ingest.ts	Ingest into Qdrant	OPENAI_API_KEY, optionally QDRANT_URL

# Pinecone
export PINECONE_API_KEY="..."
export OPENAI_API_KEY="sk-..."
npx tsx ai-tools/pinecone-ingest.ts

# Qdrant (local)
docker run -p 6333:6333 qdrant/qdrant
export OPENAI_API_KEY="sk-..."
npx tsx ai-tools/qdrant-ingest.ts

Tips

• Use markdown format for LLM input (cleaner than HTML)
• Truncate content if it exceeds token limits
• For production, consider chunking large documents before embedding