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-vector-stores-postgres

!pip install llama-index

Running the following cell will install Postgres with PGVector in Colab.

!sudo apt update
!echo | sudo apt install -y postgresql-common
!echo | sudo /usr/share/postgresql-common/pgdg/apt.postgresql.org.sh
!echo | sudo apt install postgresql-15-pgvector
!sudo service postgresql start
!sudo -u postgres psql -c "ALTER USER postgres PASSWORD 'password';"
!sudo -u postgres psql -c "CREATE DATABASE vector_db;"

# 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.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.postgres import PGVectorStore
import textwrap

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"] = "sk-..."

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'

--2025-07-18 16:24:08--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 75042 (73K) [text/plain]
Saving to: ‘data/paul_graham/paul_graham_essay.txt’

data/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.04s

2025-07-18 16:24:09 (1.62 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]

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: 370b5f28-a362-429b-ad6d-a931b30e00a0

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. The example below constructs a PGVectorStore with a HNSW index with m = 16, ef_construction = 64, and ef_search = 40, with the vector_cosine_ops method.

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
    hnsw_kwargs={
        "hnsw_m": 16,
        "hnsw_ef_construction": 64,
        "hnsw_ef_search": 40,
        "hnsw_dist_method": "vector_cosine_ops",
    },
)

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()

/Users/loganmarkewich/giant_change/llama_index/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
Parsing nodes: 100%|██████████| 1/1 [00:00<00:00,  7.66it/s]
Generating embeddings: 100%|██████████| 22/22 [00:01<00:00, 18.30it/s]

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 essays, programming, creating microcomputers, developing software,
giving talks, and starting a startup.

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

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

AI was in the air in the mid 1980s, and two things that influenced the desire to work on it were a
novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called
Mike, and a PBS documentary that showed Terry Winograd using SHRDLU.

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
    hnsw_kwargs={
        "hnsw_m": 16,
        "hnsw_ef_construction": 64,
        "hnsw_ef_search": 40,
        "hnsw_dist_method": "vector_cosine_ops",
    },
)

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 essays, programming, creating microcomputers, developing software,
giving talks, and starting a startup.

Hybrid Search

To enable hybrid search, you need to:

1. pass in hybrid_search=True when constructing the PGVectorStore (and optionally configure text_search_config with the desired language)
2. pass in vector_store_query_mode="hybrid" when constructing the query engine (this config is passed to the retriever under the hood). You can also optionally set the sparse_top_k to configure how many results we should obtain from sparse text search (default is using the same value as similarity_top_k).

from sqlalchemy import make_url

url = make_url(connection_string)
hybrid_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_hybrid_search",
    embed_dim=1536,  # openai embedding dimension
    hybrid_search=True,
    text_search_config="english",
    hnsw_kwargs={
        "hnsw_m": 16,
        "hnsw_ef_construction": 64,
        "hnsw_ef_search": 40,
        "hnsw_dist_method": "vector_cosine_ops",
    },
)

storage_context = StorageContext.from_defaults(
    vector_store=hybrid_vector_store
)
hybrid_index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

hybrid_query_engine = hybrid_index.as_query_engine(
    vector_store_query_mode="hybrid", sparse_top_k=2
)
hybrid_response = hybrid_query_engine.query(
    "Who does Paul Graham think of with the word schtick"
)

print(hybrid_response)

Roy Lichtenstein

Improving hybrid search with QueryFusionRetriever

Since the scores for text search and vector search are calculated differently, the nodes that were found only by text search will have a much lower score.

You can often improve hybrid search performance by using QueryFusionRetriever, which makes better use of the mutual information to rank the nodes.

from llama_index.core.response_synthesizers import CompactAndRefine
from llama_index.core.retrievers import QueryFusionRetriever
from llama_index.core.query_engine import RetrieverQueryEngine

vector_retriever = hybrid_index.as_retriever(
    vector_store_query_mode="default",
    similarity_top_k=5,
)
text_retriever = hybrid_index.as_retriever(
    vector_store_query_mode="sparse",
    similarity_top_k=5,  # interchangeable with sparse_top_k in this context
)
retriever = QueryFusionRetriever(
    [vector_retriever, text_retriever],
    similarity_top_k=5,
    num_queries=1,  # set this to 1 to disable query generation
    mode="relative_score",
    use_async=False,
)

response_synthesizer = CompactAndRefine()
query_engine = RetrieverQueryEngine(
    retriever=retriever,
    response_synthesizer=response_synthesizer,
)

response = query_engine.query(
    "Who does Paul Graham think of with the word schtick, and why?"
)
print(response)

Paul Graham thinks of Roy Lichtenstein when using the word "schtick" because Lichtenstein's distinctive signature style in his paintings immediately identifies his work as his own.

Metadata filters

PGVectorStore supports storing metadata in nodes, and filtering based on that metadata during the retrieval step.

Download git commits dataset

!mkdir -p 'data/git_commits/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/csv/commit_history.csv' -O 'data/git_commits/commit_history.csv'

import csv

with open("data/git_commits/commit_history.csv", "r") as f:
    commits = list(csv.DictReader(f))

print(commits[0])
print(len(commits))

{'commit': '44e41c12ab25e36c202f58e068ced262eadc8d16', 'author': 'Lakshmi Narayanan Sreethar<lakshmi@timescale.com>', 'date': 'Tue Sep 5 21:03:21 2023 +0530', 'change summary': 'Fix segfault in set_integer_now_func', 'change details': 'When an invalid function oid is passed to set_integer_now_func, it finds out that the function oid is invalid but before throwing the error, it calls ReleaseSysCache on an invalid tuple causing a segfault. Fixed that by removing the invalid call to ReleaseSysCache.  Fixes #6037 '}
4167

Add nodes with custom metadata

# Create TextNode for each of the first 100 commits
from llama_index.core.schema import TextNode
from datetime import datetime
import re

nodes = []
dates = set()
authors = set()
for commit in commits[:100]:
    author_email = commit["author"].split("<")[1][:-1]
    commit_date = datetime.strptime(
        commit["date"], "%a %b %d %H:%M:%S %Y %z"
    ).strftime("%Y-%m-%d")
    commit_text = commit["change summary"]
    if commit["change details"]:
        commit_text += "\n\n" + commit["change details"]
    fixes = re.findall(r"#(\d+)", commit_text, re.IGNORECASE)
    nodes.append(
        TextNode(
            text=commit_text,
            metadata={
                "commit_date": commit_date,
                "author": author_email,
                "fixes": fixes,
            },
        )
    )
    dates.add(commit_date)
    authors.add(author_email)

print(nodes[0])
print(min(dates), "to", max(dates))
print(authors)

Node ID: 50fdca05-b1ce-41d8-b771-b13aa3ad7df0
Text: Fix segfault in set_integer_now_func  When an invalid function
oid is passed to set_integer_now_func, it finds out that the function
oid is invalid but before throwing the error, it calls ReleaseSysCache
on an invalid tuple causing a segfault. Fixed that by removing the
invalid call to ReleaseSysCache.  Fixes #6037
2023-03-22 to 2023-09-05
{'dmitry@timescale.com', 'jguthrie@timescale.com', 'lakshmi@timescale.com', 'rafia.sabih@gmail.com', 'sven@timescale.com', 'konstantina@timescale.com', 'engel@sero-systems.de', 'nikhil@timescale.com', '36882414+akuzm@users.noreply.github.com', 'mats@timescale.com', 'satish.8483@gmail.com', 'fabriziomello@gmail.com', 'me@noctarius.com', 'erik@timescale.com', 'jan@timescale.com'}

vector_store = PGVectorStore.from_params(
    database=db_name,
    host=url.host,
    password=url.password,
    port=url.port,
    user=url.username,
    table_name="metadata_filter_demo3",
    embed_dim=1536,  # openai embedding dimension
    hnsw_kwargs={
        "hnsw_m": 16,
        "hnsw_ef_construction": 64,
        "hnsw_ef_search": 40,
        "hnsw_dist_method": "vector_cosine_ops",
    },
)

index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
index.insert_nodes(nodes)

print(index.as_query_engine().query("How did Lakshmi fix the segfault?"))

Lakshmi fixed the segfault by removing the invalid call to ReleaseSysCache that was causing the issue.

Apply metadata filters

Now we can filter by commit author or by date when retrieving nodes.

from llama_index.core.vector_stores.types import (
    MetadataFilter,
    MetadataFilters,
)

filters = MetadataFilters(
    filters=[
        MetadataFilter(key="author", value="mats@timescale.com"),
        MetadataFilter(key="author", value="sven@timescale.com"),
    ],
    condition="or",
)

retriever = index.as_retriever(
    similarity_top_k=10,
    filters=filters,
)

retrieved_nodes = retriever.retrieve("What is this software project about?")

for node in retrieved_nodes:
    print(node.node.metadata)

{'commit_date': '2023-08-07', 'author': 'mats@timescale.com', 'fixes': []}
{'commit_date': '2023-08-27', 'author': 'sven@timescale.com', 'fixes': []}
{'commit_date': '2023-07-13', 'author': 'mats@timescale.com', 'fixes': []}
{'commit_date': '2023-08-07', 'author': 'sven@timescale.com', 'fixes': []}
{'commit_date': '2023-08-30', 'author': 'sven@timescale.com', 'fixes': []}
{'commit_date': '2023-08-15', 'author': 'sven@timescale.com', 'fixes': []}
{'commit_date': '2023-08-23', 'author': 'sven@timescale.com', 'fixes': []}
{'commit_date': '2023-08-10', 'author': 'mats@timescale.com', 'fixes': []}
{'commit_date': '2023-07-25', 'author': 'mats@timescale.com', 'fixes': ['5892']}
{'commit_date': '2023-08-21', 'author': 'sven@timescale.com', 'fixes': []}

filters = MetadataFilters(
    filters=[
        MetadataFilter(key="commit_date", value="2023-08-15", operator=">="),
        MetadataFilter(key="commit_date", value="2023-08-25", operator="<="),
    ],
    condition="and",
)

retriever = index.as_retriever(
    similarity_top_k=10,
    filters=filters,
)

retrieved_nodes = retriever.retrieve("What is this software project about?")

for node in retrieved_nodes:
    print(node.node.metadata)

{'commit_date': '2023-08-23', 'author': 'erik@timescale.com', 'fixes': []}
{'commit_date': '2023-08-17', 'author': 'konstantina@timescale.com', 'fixes': []}
{'commit_date': '2023-08-15', 'author': '36882414+akuzm@users.noreply.github.com', 'fixes': []}
{'commit_date': '2023-08-15', 'author': '36882414+akuzm@users.noreply.github.com', 'fixes': []}
{'commit_date': '2023-08-24', 'author': 'lakshmi@timescale.com', 'fixes': []}
{'commit_date': '2023-08-15', 'author': 'sven@timescale.com', 'fixes': []}
{'commit_date': '2023-08-23', 'author': 'sven@timescale.com', 'fixes': []}
{'commit_date': '2023-08-21', 'author': 'sven@timescale.com', 'fixes': []}
{'commit_date': '2023-08-20', 'author': 'sven@timescale.com', 'fixes': []}
{'commit_date': '2023-08-21', 'author': 'sven@timescale.com', 'fixes': []}

Apply nested filters

In the above examples, we combined multiple filters using AND or OR. We can also combine multiple sets of filters.

e.g. in SQL:

WHERE (commit_date >= '2023-08-01' AND commit_date <= '2023-08-15') AND (author = 'mats@timescale.com' OR author = 'sven@timescale.com')

filters = MetadataFilters(
    filters=[
        MetadataFilters(
            filters=[
                MetadataFilter(
                    key="commit_date", value="2023-08-01", operator=">="
                ),
                MetadataFilter(
                    key="commit_date", value="2023-08-15", operator="<="
                ),
            ],
            condition="and",
        ),
        MetadataFilters(
            filters=[
                MetadataFilter(key="author", value="mats@timescale.com"),
                MetadataFilter(key="author", value="sven@timescale.com"),
            ],
            condition="or",
        ),
    ],
    condition="and",
)

retriever = index.as_retriever(
    similarity_top_k=10,
    filters=filters,
)

retrieved_nodes = retriever.retrieve("What is this software project about?")

for node in retrieved_nodes:
    print(node.node.metadata)

{'commit_date': '2023-08-07', 'author': 'mats@timescale.com', 'fixes': []}
{'commit_date': '2023-08-07', 'author': 'sven@timescale.com', 'fixes': []}
{'commit_date': '2023-08-15', 'author': 'sven@timescale.com', 'fixes': []}
{'commit_date': '2023-08-10', 'author': 'mats@timescale.com', 'fixes': []}

The above can be simplified by using the IN operator. PGVectorStore supports in, nin, and contains for comparing an element with a list.

filters = MetadataFilters(
    filters=[
        MetadataFilter(key="commit_date", value="2023-08-01", operator=">="),
        MetadataFilter(key="commit_date", value="2023-08-15", operator="<="),
        MetadataFilter(
            key="author",
            value=["mats@timescale.com", "sven@timescale.com"],
            operator="in",
        ),
    ],
    condition="and",
)

retriever = index.as_retriever(
    similarity_top_k=10,
    filters=filters,
)

retrieved_nodes = retriever.retrieve("What is this software project about?")

for node in retrieved_nodes:
    print(node.node.metadata)

{'commit_date': '2023-08-07', 'author': 'mats@timescale.com', 'fixes': []}
{'commit_date': '2023-08-07', 'author': 'sven@timescale.com', 'fixes': []}
{'commit_date': '2023-08-15', 'author': 'sven@timescale.com', 'fixes': []}
{'commit_date': '2023-08-10', 'author': 'mats@timescale.com', 'fixes': []}

# Same thing, with NOT IN
filters = MetadataFilters(
    filters=[
        MetadataFilter(key="commit_date", value="2023-08-01", operator=">="),
        MetadataFilter(key="commit_date", value="2023-08-15", operator="<="),
        MetadataFilter(
            key="author",
            value=["mats@timescale.com", "sven@timescale.com"],
            operator="nin",
        ),
    ],
    condition="and",
)

retriever = index.as_retriever(
    similarity_top_k=10,
    filters=filters,
)

retrieved_nodes = retriever.retrieve("What is this software project about?")

for node in retrieved_nodes:
    print(node.node.metadata)

{'commit_date': '2023-08-09', 'author': 'me@noctarius.com', 'fixes': ['5805']}
{'commit_date': '2023-08-15', 'author': '36882414+akuzm@users.noreply.github.com', 'fixes': []}
{'commit_date': '2023-08-15', 'author': '36882414+akuzm@users.noreply.github.com', 'fixes': []}
{'commit_date': '2023-08-11', 'author': '36882414+akuzm@users.noreply.github.com', 'fixes': []}
{'commit_date': '2023-08-03', 'author': 'dmitry@timescale.com', 'fixes': []}
{'commit_date': '2023-08-09', 'author': 'konstantina@timescale.com', 'fixes': ['5923', '5680', '5774', '5786', '5906', '5912']}
{'commit_date': '2023-08-03', 'author': 'dmitry@timescale.com', 'fixes': ['5908']}
{'commit_date': '2023-08-01', 'author': 'nikhil@timescale.com', 'fixes': []}
{'commit_date': '2023-08-10', 'author': 'konstantina@timescale.com', 'fixes': []}
{'commit_date': '2023-08-10', 'author': '36882414+akuzm@users.noreply.github.com', 'fixes': []}

# CONTAINS
filters = MetadataFilters(
    filters=[
        MetadataFilter(key="fixes", value="5680", operator="contains"),
    ]
)

retriever = index.as_retriever(
    similarity_top_k=10,
    filters=filters,
)

retrieved_nodes = retriever.retrieve("How did these commits fix the issue?")
for node in retrieved_nodes:
    print(node.node.metadata)

{'commit_date': '2023-08-09', 'author': 'konstantina@timescale.com', 'fixes': ['5923', '5680', '5774', '5786', '5906', '5912']}

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="hybrid",
    similarity_top_k=5,
    vector_store_kwargs={"ivfflat_probes": 10},
)

HNSW EF Search

Specify the size of the dynamic candidate list for search (40 by default)

retriever = index.as_retriever(
    vector_store_query_mode="hybrid",
    similarity_top_k=5,
    vector_store_kwargs={"hnsw_ef_search": 300},
)