DashVector Vector Store

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

!pip install llama-index dashvector

import logging
import sys
import os

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

Creating a DashVector Collection

import dashvector

api_key = os.environ["DASHVECTOR_API_KEY"]
endpoint = os.environ["DASHVECTOR_ENDPOINT"]

client = dashvector.Client(api_key=api_key, endpoint=endpoint)

# delete if needed
# client.delete("quickstart")

# dimensions are for text-embedding-ada-002
client.create("quickstart", dimension=1536)

dashvector_collection = client.get("quickstart")

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 documents, build the DashVectorStore and VectorStoreIndex

from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores import DashVectorStore
from IPython.display import Markdown, display

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

from llama_index.storage.storage_context import StorageContext

vector_store = DashVectorStore(dashvector_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_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>"))

The author worked on writing and programming outside of school. They wrote short stories and tried writing programs on the IBM 1401 computer. They also built a microcomputer and started programming on it, writing simple games and a word processor.