Relyt

Firstly, you will probably need to install dependencies :

python

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

python

%pip install llama-index "pgvecto_rs[sdk]"

Then start the relyt as the official document:

Setup the logger.

python

import logging
import os
import sys

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

Creating a pgvecto_rs client

python

from pgvecto_rs.sdk import PGVectoRs

URL = "postgresql+psycopg://{username}:{password}@{host}:{port}/{db_name}".format(
    port=os.getenv("RELYT_PORT", "5432"),
    host=os.getenv("RELYT_HOST", "localhost"),
    username=os.getenv("RELYT_USER", "postgres"),
    password=os.getenv("RELYT_PASS", "mysecretpassword"),
    db_name=os.getenv("RELYT_NAME", "postgres"),
)

client = PGVectoRs(
    db_url=URL,
    collection_name="example",
    dimension=1536,  # Using OpenAI’s text-embedding-ada-002
)

Setup OpenAI

python

import os

os.environ["OPENAI_API_KEY"] = "sk-..."

Load documents, build the PGVectoRsStore and VectorStoreIndex

python

from IPython.display import Markdown, display

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.vector_stores.relyt import RelytVectorStore

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'

python

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

python

# initialize without metadata filter
from llama_index.core import StorageContext

vector_store = RelytVectorStore(client=client)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

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>"))