Tair Vector Store

In this notebook we are going to show a quick demo of using the TairVectorStore.

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

%pip install llama-index-vector-stores-tair

!pip install llama-index

import os
import sys
import logging
import textwrap

import warnings

warnings.filterwarnings("ignore")

# stop huggingface warnings
os.environ["TOKENIZERS_PARALLELISM"] = "false"

# Uncomment to see debug logs
# logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from llama_index.core import (
    GPTVectorStoreIndex,
    SimpleDirectoryReader,
    Document,
)
from llama_index.vector_stores.tair import TairVectorStore
from IPython.display import Markdown, display

Setup OpenAI

Lets first begin by adding the openai api key. This will allow us to access openai for embeddings and to use chatgpt.

import os

os.environ["OPENAI_API_KEY"] = "sk-<your key here>"

Download Data

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

Read in a dataset

# load documents
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
print(
    "Document ID:",
    documents[0].doc_id,
    "Document Hash:",
    documents[0].doc_hash,
)

Build index from documents

Let’s build a vector index with GPTVectorStoreIndex, using TairVectorStore as its backend. Replace tair_url with the actual url of your Tair instance.

from llama_index.core import StorageContext

tair_url = "redis://{username}:{password}@r-bp****************.redis.rds.aliyuncs.com:{port}"

vector_store = TairVectorStore(
    tair_url=tair_url, index_name="pg_essays", overwrite=True
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = GPTVectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

Query the data

Now we can use the index as knowledge base and ask questions to it.

query_engine = index.as_query_engine()
response = query_engine.query("What did the author learn?")
print(textwrap.fill(str(response), 100))

response = query_engine.query("What was a hard moment for the author?")
print(textwrap.fill(str(response), 100))

Deleting documents

To delete a document from the index, use delete method.

document_id = documents[0].doc_id
document_id

info = vector_store.client.tvs_get_index("pg_essays")
print("Number of documents", int(info["data_count"]))

vector_store.delete(document_id)

info = vector_store.client.tvs_get_index("pg_essays")
print("Number of documents", int(info["data_count"]))

Deleting index

Delete the entire index using delete_index method.

vector_store.delete_index()

print("Check index existence:", vector_store.client._index_exists())