If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.
!pip install llama-index
import logging import sys logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) from llama_index import VectorStoreIndex, SimpleDirectoryReader
INFO:numexpr.utils:Note: NumExpr detected 12 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8. Note: NumExpr detected 12 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8. INFO:numexpr.utils:NumExpr defaulting to 8 threads. NumExpr defaulting to 8 threads.
/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.7) is available. It's recommended that you update to the latest version using `pip install -U deeplake`. warnings.warn(
!mkdir -p 'data/paul_graham/' !wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'
Load documents, build the VectorStoreIndex
# load documents documents = SimpleDirectoryReader("./data/paul_graham").load_data()
index = VectorStoreIndex.from_documents(documents)
# set Logging to DEBUG for more detailed outputs query_engine = index.as_query_engine(streaming=True, similarity_top_k=1) response_stream = query_engine.query( "What did the author do growing up?", )
The author grew up writing short stories and programming on an IBM 1401. He also nagged his father to buy him a TRS-80 microcomputer, on which he wrote simple games, a program to predict how high his model rockets would fly, and a word processor. He eventually went to college to study philosophy, but found it boring and switched to AI.