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LlamaIndex Python Documentation
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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

Section titled “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'

Loading documents

Section titled “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: 56e70c8c-0fb7-4250-99be-b953d0185a01

Create the Database

Section titled “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

Section titled “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()
Parsing nodes:   0%|          | 0/1 [00:00<?, ?it/s]






Generating embeddings:   0%|          | 0/22 [00:00<?, ?it/s]




2025-09-11 16:47:21,725 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"

Query the index

Section titled “Query the index”

We can now ask questions using our index.

response = query_engine.query("What did the author do?")
2025-09-11 16:47:30,412 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-09-11 16:47:31,665 - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
print(textwrap.fill(str(response), 100))
The author worked on writing essays, programming, building microcomputers, predicting rocket
heights, developing a word processor, and giving talks on starting a startup.
response = query_engine.query("What happened in the mid 1980s?")
2025-09-11 16:47:37,531 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-09-11 16:47:38,352 - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
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

Section titled “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.
Section titled “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

Section titled “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

Section titled “Metadata filters”

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

Download git commits dataset

Section titled “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<[email protected]>', '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

Section titled “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: 9c2c2f17-d763-4ce8-bb02-83cb176008e4
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
{'[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]'}
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)
2025-09-11 16:48:11,383 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
print(index.as_query_engine().query("How did Lakshmi fix the segfault?"))
2025-09-11 16:48:15,149 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-09-11 16:48:15,687 - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"




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

Apply metadata filters

Section titled “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="[email protected]"),
        MetadataFilter(key="author", value="[email protected]"),
    ],
    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)
2025-09-11 16:48:31,673 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"




{'commit_date': '2023-08-07', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-27', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-07-13', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-07', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-30', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-15', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-23', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-10', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-07-25', 'author': '[email protected]', 'fixes': ['5892']}
{'commit_date': '2023-08-21', 'author': '[email protected]', '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)
2025-09-11 16:48:40,347 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"




{'commit_date': '2023-08-23', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-17', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-15', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-15', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-24', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-15', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-23', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-21', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-20', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-21', 'author': '[email protected]', 'fixes': []}

Apply nested filters

Section titled “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 = '[email protected]' OR author = '[email protected]')
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="[email protected]"),
                MetadataFilter(key="author", value="[email protected]"),
            ],
            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)
2025-09-11 16:48:45,021 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"




{'commit_date': '2023-08-07', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-07', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-15', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-10', 'author': '[email protected]', '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=["[email protected]", "[email protected]"],
            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)
2025-09-11 16:48:49,129 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"




{'commit_date': '2023-08-07', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-07', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-15', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-10', 'author': '[email protected]', '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=["[email protected]", "[email protected]"],
            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)
2025-09-11 16:48:51,587 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"




{'commit_date': '2023-08-09', 'author': '[email protected]', 'fixes': ['5805']}
{'commit_date': '2023-08-15', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-15', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-11', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-09', 'author': '[email protected]', 'fixes': ['5923', '5680', '5774', '5786', '5906', '5912']}
{'commit_date': '2023-08-03', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-03', 'author': '[email protected]', 'fixes': ['5908']}
{'commit_date': '2023-08-01', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-10', 'author': '[email protected]', 'fixes': []}
{'commit_date': '2023-08-10', 'author': '[email protected]', '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)
2025-09-11 16:48:56,822 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"




{'commit_date': '2023-08-09', 'author': '[email protected]', 'fixes': ['5923', '5680', '5774', '5786', '5906', '5912']}

Customize queries

Section titled “Customize queries”

It is possible to build more complex queries such as joining other tables. This is done by setting the customize_query_fn argument with your function. First, lets create a user table and populate it.

from sqlalchemy import (
    Table,
    MetaData,
    Column,
    String,
    Integer,
    create_engine,
    insert,
)


engine = create_engine(url=connection_string + "/" + db_name)


metadata = MetaData()


user_table = Table(
    "user",
    metadata,
    Column("id", Integer, primary_key=True, autoincrement=True),
    Column("name", String, nullable=False),
    Column("email", String, nullable=False),
)


user_table.drop(engine, checkfirst=True)
user_table.create(engine)


with engine.begin() as conn:
    stmt = insert(user_table)
    conn.execute(
        stmt, [{"name": "Konstantina", "email": "[email protected]"}]
    )

Then, we can create a query customization function and instantiate PGVectorStore with customize_query_fn.

from typing import Any
from sqlalchemy import Select




def customize_query(query: Select, table_class: Any, **kwargs: Any) -> Select:
    # Join the user table on the email addresses and add the name column to the select statement
    return query.add_columns(user_table.c.name).join(
        user_table,
        user_table.c.email == table_class.metadata_["author"].astext,
    )




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",
    },
    customize_query_fn=customize_query,
)
index = VectorStoreIndex.from_vector_store(vector_store=vector_store)

We can then query the vector store and retrieve any additional field added to the select statement in a dictionary named custom_fields in the node metadata.

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)
2025-09-11 17:06:43,812 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"




{'commit_date': '2023-08-09', 'author': '[email protected]', 'fixes': ['5923', '5680', '5774', '5786', '5906', '5912'], 'custom_fields': {'name': 'Konstantina'}}

PgVector Query Options

Section titled “PgVector Query Options”

IVFFlat Probes

Section titled “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},
)
Section titled “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},
)