LlamaIndex
Transform any source into LlamaIndex TextNodes for query engines and chatbots.
Quick Start
# From any source
skill-seekers scrape --format llama-index --config configs/react.json
What You Get
- LlamaIndex TextNode objects
- Automatic text splitting with overlap
- Node relationships (parent/child)
- Metadata for filtering
Python Example
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
# Load documents
reader = SimpleDirectoryReader("output/react-llama-index/")
documents = reader.load_data()
# Create index
index = VectorStoreIndex.from_documents(documents)
# Create query engine
query_engine = index.as_query_engine()
# Query
response = query_engine.query("What are React Hooks?")
print(response)
Chat Engine
# Chat engine is accessed directly from the index
chat_engine = index.as_chat_engine()
response = chat_engine.chat("Explain useEffect")
print(response)
Next Steps
- Vector Databases - Store your index
- RAG Tutorial - Complete example