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.

Download Data

!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 Data

# 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
)

Query Index

# 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?")

display(Markdown(f"<b>{response}</b>"))

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?"
)

display(Markdown(f"<b>{response}</b>"))

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.