Awadb Vector Store

If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

python

%pip install llama-index-embeddings-huggingface
%pip install llama-index-vector-stores-awadb

python

!pip install llama-index

Creating an Awadb index

python

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

python

from llama_index.core import (
    SimpleDirectoryReader,
    VectorStoreIndex,
    StorageContext,
)
from IPython.display import Markdown, display
import openai

openai.api_key = ""

Download Data

python

!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

python

# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()

python

from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.vector_stores.awadb 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)

index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, embed_model=embed_model
)

Query Index

python

# 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?")

python

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

python

# 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?"
)

python

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