Open In Colab

Postgres Vector Store#

In this notebook we are going to show how to use Postgresql and pgvector to perform vector searches in LlamaIndex

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

!pip install llama-index

# import logging
# import sys

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

from llama_index import SimpleDirectoryReader, StorageContext
from llama_index.indices.vector_store import VectorStoreIndex
from llama_index.vector_stores import PGVectorStore
import textwrap
import openai

Setup OpenAI#

The first step is to configure the openai key. It will be used to created embeddings for the documents loaded into the index

import os

os.environ["OPENAI_API_KEY"] = "<your key>"
openai.api_key = "<your key>"

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'

Loading documents#

Load the documents stored in the data/paul_graham/ using the SimpleDirectoryReader

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

Document ID: d05d1211-b9af-4b05-8da6-956e4b389467

Create the Database#

Using an existing postgres running at localhost, create the database we’ll be using.

import psycopg2

connection_string = "postgresql://postgres:password@localhost:5432"
db_name = "vector_db"
conn = psycopg2.connect(connection_string)
conn.autocommit = True

with conn.cursor() as c:
    c.execute(f"DROP DATABASE IF EXISTS {db_name}")
    c.execute(f"CREATE DATABASE {db_name}")

Create the index#

Here we create an index backed by Postgres using the documents loaded previously. PGVectorStore takes a few arguments.

from sqlalchemy import make_url

url = make_url(connection_string)
vector_store = PGVectorStore.from_params(
    database=db_name,
    host=url.host,
    password=url.password,
    port=url.port,
    user=url.username,
    table_name="paul_graham_essay",
    embed_dim=1536,  # openai embedding dimension
)

storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, show_progress=True
)
query_engine = index.as_query_engine()

Query the index#

We can now ask questions using our index.

response = query_engine.query("What did the author do?")

print(textwrap.fill(str(response), 100))

The author worked on writing and programming before college. They wrote short stories and tried
writing programs on an IBM 1401 computer. They also built a microcomputer and started programming on
it, writing simple games and a word processor. In college, the author initially planned to study
philosophy but switched to AI. They were inspired by a novel called The Moon is a Harsh Mistress and
a PBS documentary featuring Terry Winograd using SHRDLU.

response = query_engine.query("What happened in the mid 1980s?")

print(textwrap.fill(str(response), 100))

In the mid-1980s, the author spent a significant amount of time working on a book called "On Lisp"
and had obtained a contract to publish it. They were paid large amounts of money for their work,
which allowed them to save enough to go back to RISD (Rhode Island School of Design) and pay off
their college loans. They also learned valuable lessons during this time, such as the importance of
having technology companies run by product people rather than sales people, the drawbacks of editing
code by too many people, and the significance of being the "entry level" option in a competitive
market.

Querying existing index#

vector_store = PGVectorStore.from_params(
    database="vector_db",
    host="localhost",
    password="password",
    port=5432,
    user="postgres",
    table_name="paul_graham_essay",
    embed_dim=1536,  # openai embedding dimension
)

index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
query_engine = index.as_query_engine()

response = query_engine.query("What did the author do?")

print(textwrap.fill(str(response), 100))

The author worked on writing and programming before college. They wrote short stories and tried
writing programs on an IBM 1401 computer. They also built a microcomputer and started programming on
it, writing simple games and a word processor. In college, the author initially planned to study
philosophy but switched to AI due to their interest in intelligent computers. They taught themselves
AI by learning Lisp.

PgVector Query Options#

IVFFlat Probes#

Specify the number of IVFFlat probes (1 by default)

When retrieving from the index, you can specify an appropriate number of IVFFlat probes (higher is better for recall, lower is better for speed)

retriever = index.as_retriever(
    vector_store_query_mode=query_mode,
    similarity_top_k=top_k,
    vector_store_kwargs={"ivfflat_probes": 10},
)