Awadb Vector Store
If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.
!pip install llama-index
Creating an Awadb index
import logging import sys logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
Load documents, build the VectorStoreIndex
from llama_index import ( SimpleDirectoryReader, VectorStoreIndex, StorageContext, ) from IPython.display import Markdown, display import openai openai.api_key = ""
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.
!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 documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
from llama_index import ServiceContext from llama_index.embeddings import HuggingFaceEmbedding from llama_index.vector_stores import AwaDBVectorStore embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5") vector_store = AwaDBVectorStore() storage_context = StorageContext.from_defaults(vector_store=vector_store) service_context = ServiceContext.from_defaults(embed_model=embed_model) index = VectorStoreIndex.from_documents( documents, storage_context=storage_context, service_context=service_context )
# set Logging to DEBUG for more detailed outputs query_engine = index.as_query_engine() response = query_engine.query("What did the author do growing up?")
Growing up, the author wrote short stories, experimented with programming on an IBM 1401, nagged his father to buy a TRS-80 computer, wrote simple games, a program to predict how high his model rockets would fly, and a word processor. He also studied philosophy in college, switched to AI, and worked on building the infrastructure of the web. He wrote essays and published them online, had dinners for a group of friends every Thursday night, painted, and bought a building in Cambridge.
# set Logging to DEBUG for more detailed outputs query_engine = index.as_query_engine() response = query_engine.query( "What did the author do after his time at Y Combinator?" )
After his time at Y Combinator, the author wrote essays, worked on Lisp, and painted. He also visited his mother in Oregon and helped her get out of a nursing home.