AI Applications

Power Your RAG Systems
with Real-Time Web Data

Retrieval-augmented generation is only as good as your data. Spider keeps your knowledge base current by crawling documentation, websites, and knowledge sources—so your AI always has the latest information.

Get Started Read the Guide

The Challenge

LLMs hallucinate when they lack current information
Documentation changes frequently and goes stale
Manual data updates don't scale
Building reliable crawlers is a distraction from core product

The Spider Solution

Ground your AI in real, current web data
Webhook delivery for real-time updates
Incremental updates—only fetch what changed
Native integrations with LangChain & LlamaIndex

Features for RAG

Vector-Ready Output

Clean markdown with proper chunking for embedding models. Optimized for semantic search.

Incremental Crawling

Only fetch pages that changed since your last crawl. Save time and reduce costs.

Batch Processing

Process multiple URLs in a single request. Efficient bulk data collection.

Low Latency

Fast crawling means fresher data. Get results in milliseconds, not minutes.

Source Attribution

Every chunk includes source URL and metadata for proper citations.

Webhook Delivery

Push new content directly to your vector database via webhooks.

LangChain Integration

Use Spider as a LangChain document loader Python

from langchain_community.document_loaders import SpiderLoader

# Load documents from a website
loader = SpiderLoader(
    url="https://docs.example.com",
    api_key="your-api-key",
    mode="crawl",  # or "scrape" for single page
)

documents = loader.load()

# Documents are ready for your vector store
for doc in documents:
    print(doc.page_content[:100])
    print(doc.metadata["source"])