ai-tools

AI Tools Examples

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

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