pgvecto.rs

Firstly, you will probably need to install dependencies :

%pip install llama-index-vector-stores-pgvecto-rs

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

Then start the pgvecto.rs server as the official document suggests:

!docker run --name pgvecto-rs-demo -e POSTGRES_PASSWORD=mysecretpassword -p 5432:5432 -d tensorchord/pgvecto-rs:latest

Setup the logger.

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

from pgvecto_rs.sdk import PGVectoRs

URL = "postgresql+psycopg://{username}:{password}@{host}:{port}/{db_name}".format(
    port=os.getenv("DB_PORT", "5432"),
    host=os.getenv("DB_HOST", "localhost"),
    username=os.getenv("DB_USER", "postgres"),
    password=os.getenv("DB_PASS", "mysecretpassword"),
    db_name=os.getenv("DB_NAME", "postgres"),
)

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

Setup OpenAI

import os

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

Load documents, build the PGVectoRsStore and VectorStoreIndex

from IPython.display import Markdown, display

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.vector_stores.pgvecto_rs import PGVectoRsStore

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'

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

# initialize without metadata filter
from llama_index.core import StorageContext

vector_store = PGVectoRsStore(client=client)
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?")

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"

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

The author, growing up, worked on writing and programming. They wrote short stories and also tried writing programs on an IBM 1401 computer. They later got a microcomputer and started programming more extensively, writing simple games and a word processor.