Timescale Vector Store (PostgreSQL)¶

This notebook shows how to use the Postgres vector store TimescaleVector to store and query vector embeddings.

What is Timescale Vector?¶

Timescale Vector is PostgreSQL++ for AI applications.

Timescale Vector enables you to efficiently store and query millions of vector embeddings in PostgreSQL.

• Enhances pgvector with faster and more accurate similarity search on millions of vectors via DiskANN inspired indexing algorithm.
• Enables fast time-based vector search via automatic time-based partitioning and indexing.
• Provides a familiar SQL interface for querying vector embeddings and relational data.

Timescale Vector scales with you from POC to production:

• Simplifies operations by enabling you to store relational metadata, vector embeddings, and time-series data in a single database.
• Benefits from rock-solid PostgreSQL foundation with enterprise-grade feature liked streaming backups and replication, high-availability and row-level security.
• Enables a worry-free experience with enterprise-grade security and compliance.

How to use Timescale Vector¶

Timescale Vector is available on Timescale, the cloud PostgreSQL platform. (There is no self-hosted version at this time.)

LlamaIndex users get a 90-day free trial for Timescale Vector.

• To get started, signup to Timescale, create a new database and follow this notebook!
• See the Timescale Vector explainer blog for details and performance benchmarks.
• See the installation instructions for more details on using Timescale Vector in python.

0. Setup¶

Let's import everything we'll need for this notebook.

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

In [ ]:

%pip install llama-index-embeddings-openai
%pip install llama-index-vector-stores-timescalevector

%pip install llama-index-embeddings-openai %pip install llama-index-vector-stores-timescalevector

In [ ]:

!pip install llama-index

!pip install llama-index

In [ ]:

# 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))

import timescale_vector
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.timescalevector import TimescaleVectorStore
from llama_index.core.vector_stores import VectorStoreQuery, MetadataFilters
import textwrap
import openai

# 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)) import timescale_vector from llama_index.core import SimpleDirectoryReader, StorageContext from llama_index.core import VectorStoreIndex from llama_index.vector_stores.timescalevector import TimescaleVectorStore from llama_index.core.vector_stores import VectorStoreQuery, MetadataFilters import textwrap import openai

Setup OpenAI API Key¶

To create embeddings for documents loaded into the index, let's configure your OpenAI API key:

In [ ]:

# Get openAI api key by reading local .env file
# The .env file should contain a line starting with `OPENAI_API_KEY=sk-`
import os
from dotenv import load_dotenv, find_dotenv

_ = load_dotenv(find_dotenv())

# OR set it explicitly
# import os
# os.environ["OPENAI_API_KEY"] = "<your key>"
openai.api_key = os.environ["OPENAI_API_KEY"]

# Get openAI api key by reading local .env file # The .env file should contain a line starting with `OPENAI_API_KEY=sk-` import os from dotenv import load_dotenv, find_dotenv _ = load_dotenv(find_dotenv()) # OR set it explicitly # import os # os.environ["OPENAI_API_KEY"] = "" openai.api_key = os.environ["OPENAI_API_KEY"]

Create a PostgreSQL database and get a Timescale service URL¶

You need a service url to connect to your Timescale database instance.

First, launch a new cloud database in Timescale (sign up for free using the link above).

To connect to your cloud PostgreSQL database, you'll need your service URI, which can be found in the cheatsheet or .env file you downloaded after creating a new database.

The URI will look something like this: postgres://tsdbadmin:<password>@<id>.tsdb.cloud.timescale.com:<port>/tsdb?sslmode=require

In [ ]:

# Get the service url by reading local .env file
# The .env file should contain a line starting with `TIMESCALE_SERVICE_URL=postgresql://`
import os
from dotenv import load_dotenv, find_dotenv

_ = load_dotenv(find_dotenv())

TIMESCALE_SERVICE_URL = os.environ["TIMESCALE_SERVICE_URL"]

# OR set it explicitly
# TIMESCALE_SERVICE_URL = "postgres://tsdbadmin:<password>@<id>.tsdb.cloud.timescale.com:<port>/tsdb?sslmode=require"

# Get the service url by reading local .env file # The .env file should contain a line starting with `TIMESCALE_SERVICE_URL=postgresql://` import os from dotenv import load_dotenv, find_dotenv _ = load_dotenv(find_dotenv()) TIMESCALE_SERVICE_URL = os.environ["TIMESCALE_SERVICE_URL"] # OR set it explicitly # TIMESCALE_SERVICE_URL = "postgres://tsdbadmin:@.tsdb.cloud.timescale.com:/tsdb?sslmode=require"

1. Simple Similarity Search with Timescale Vector¶

Download Data¶

In [ ]:

!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'

!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¶

For this example, we'll use a SimpleDirectoryReader to load the documents stored in the paul_graham_essay directory.

The SimpleDirectoryReader is one of LlamaIndex's most commonly used data connectors to read one or multiple files from a directory.

In [ ]:

# load sample data from the data directory using a SimpleDirectoryReader
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
print("Document ID:", documents[0].doc_id)

# load sample data from the data directory using a SimpleDirectoryReader documents = SimpleDirectoryReader("./data/paul_graham").load_data() print("Document ID:", documents[0].doc_id)

Document ID: 740ce1a1-4d95-40cc-b7f7-6d2874620a53

Create a VectorStore Index with the TimescaleVectorStore¶

Next, to perform a similarity search, we first create a TimescaleVector vector store to store our vector embeddings from the essay content. TimescaleVectorStore takes a few arguments, namely the service_url which we loaded above, along with a table_name which we will be the name of the table that the vectors are stored in.

Then we create a Vector Store Index on the documents backed by Timescale using the previously documents.

In [ ]:

# Create a TimescaleVectorStore to store the documents
vector_store = TimescaleVectorStore.from_params(
    service_url=TIMESCALE_SERVICE_URL,
    table_name="paul_graham_essay",
)

# Create a new VectorStoreIndex using the TimescaleVectorStore
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

# Create a TimescaleVectorStore to store the documents vector_store = TimescaleVectorStore.from_params( service_url=TIMESCALE_SERVICE_URL, table_name="paul_graham_essay", ) # Create a new VectorStoreIndex using the TimescaleVectorStore storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex.from_documents( documents, storage_context=storage_context )

Query the index¶

Now that we've indexed the documents in our VectorStore, we can ask questions about our documents in the index by using the default query_engine.

Note you can also configure the query engine to configure the top_k most similar results returned, as well as metadata filters to filter the results by. See the configure standard query setting section for more details.

In [ ]:

query_engine = index.as_query_engine()
response = query_engine.query("Did the author work at YC?")

query_engine = index.as_query_engine() response = query_engine.query("Did the author work at YC?")

In [ ]:

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

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

Yes, the author did work at YC.

In [ ]:

response = query_engine.query("What did the author work on before college?")

response = query_engine.query("What did the author work on before college?")

In [ ]:

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

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

Before college, the author worked on writing and programming. They wrote short stories and also
tried programming on the IBM 1401 computer using an early version of Fortran.

Querying existing index¶

In the example above, we created a new Timescale Vector vectorstore and index from documents we loaded. Next we'll look at how to query an existing index. All we need is the service URI and the table name we want to access.

In [ ]:

vector_store = TimescaleVectorStore.from_params(
    service_url=TIMESCALE_SERVICE_URL,
    table_name="paul_graham_essay",
)

index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do before YC?")

vector_store = TimescaleVectorStore.from_params( service_url=TIMESCALE_SERVICE_URL, table_name="paul_graham_essay", ) index = VectorStoreIndex.from_vector_store(vector_store=vector_store) query_engine = index.as_query_engine() response = query_engine.query("What did the author do before YC?")

In [ ]:

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

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

Before YC, the author wrote all of YC's internal software in Arc. They also worked on HN and had
three projects: writing essays, working on YC, and working in Arc. However, they gradually stopped
working on Arc due to time constraints and the increasing dependence on it for infrastructure.

2. Using ANN search indexes to speed up queries¶

(Note: These indexes are ANN indexes, and differ from the index concept in LlamaIndex)

You can speed up similarity queries by creating an index on the embedding column. You should only do this once you have ingested a large part of your data.

Timescale Vector supports the following indexes:

• timescale_vector_index: a disk-ann inspired graph index for fast similarity search (default).
• pgvector's HNSW index: a hierarchical navigable small world graph index for fast similarity search.
• pgvector's IVFFLAT index: an inverted file index for fast similarity search.

Important note: In PostgreSQL, each table can only have one index on a particular column. So if you'd like to test the performance of different index types, you can do so either by (1) creating multiple tables with different indexes, (2) creating multiple vector columns in the same table and creating different indexes on each column, or (3) by dropping and recreating the index on the same column and comparing results.

In [ ]:

# Instantiate the TimescaleVectorStore from part 1
vector_store = TimescaleVectorStore.from_params(
    service_url=TIMESCALE_SERVICE_URL,
    table_name="paul_graham_essay",
)

# Instantiate the TimescaleVectorStore from part 1 vector_store = TimescaleVectorStore.from_params( service_url=TIMESCALE_SERVICE_URL, table_name="paul_graham_essay", )

Using the create_index() function without additional arguments will create a timescale_vector (DiskANN) index by default, using the default parameters.

In [ ]:

# Create a timescale vector index (DiskANN)
vector_store.create_index()

# Create a timescale vector index (DiskANN) vector_store.create_index()

You can also specify the parameters for the index. See the Timescale Vector documentation for a full discussion of the different parameters and their effects on performance.

In [ ]:

# drop old index
vector_store.drop_index()

# create new timescale vector index (DiskANN) with specified parameters
vector_store.create_index("tsv", max_alpha=1.0, num_neighbors=50)

# drop old index vector_store.drop_index() # create new timescale vector index (DiskANN) with specified parameters vector_store.create_index("tsv", max_alpha=1.0, num_neighbors=50)

Timescale Vector also supports HNSW and ivfflat indexes:

In [ ]:

vector_store.drop_index()

# Create an HNSW index
# Note: You don't need to specify m and ef_construction parameters as we set smart defaults.
vector_store.create_index("hnsw", m=16, ef_construction=64)

vector_store.drop_index() # Create an HNSW index # Note: You don't need to specify m and ef_construction parameters as we set smart defaults. vector_store.create_index("hnsw", m=16, ef_construction=64)

In [ ]:

# Create an IVFFLAT index
# Note: You don't need to specify num_lists and num_records parameters as we set smart defaults.
vector_store.drop_index()
vector_store.create_index("ivfflat", num_lists=20, num_records=1000)

# Create an IVFFLAT index # Note: You don't need to specify num_lists and num_records parameters as we set smart defaults. vector_store.drop_index() vector_store.create_index("ivfflat", num_lists=20, num_records=1000)

We recommend using timescale-vector or HNSW indexes in general.

In [ ]:

# drop the ivfflat index
vector_store.drop_index()
# Create a timescale vector index (DiskANN)
vector_store.create_index()

# drop the ivfflat index vector_store.drop_index() # Create a timescale vector index (DiskANN) vector_store.create_index()

3. Similarity Search with time-based filtering¶

A key use case for Timescale Vector is efficient time-based vector search. Timescale Vector enables this by automatically partitioning vectors (and associated metadata) by time. This allows you to efficiently query vectors by both similarity to a query vector and time.

Time-based vector search functionality is helpful for applications like:

• Storing and retrieving LLM response history (e.g. chatbots)
• Finding the most recent embeddings that are similar to a query vector (e.g recent news).
• Constraining similarity search to a relevant time range (e.g asking time-based questions about a knowledge base)

To illustrate how to use TimescaleVector's time-based vector search functionality, we'll use the git log history for TimescaleDB as a sample dataset and ask questions about it. Each git commit entry has a timestamp associated with it, as well as natural language message and other metadata (e.g author, commit hash etc).

We'll illustrate how to create nodes with a time-based uuid and how run similarity searches with time range filters using the TimescaleVector vectorstore.

Extract content and metadata from git log CSV file¶

First lets load in the git log csv file into a new collection in our PostgreSQL database named timescale_commits.

Note: Since this is a demo, we will only work with the first 1000 records. In practice, you can load as many records as you want.

In [ ]:

import pandas as pd
from pathlib import Path

file_path = Path("../data/csv/commit_history.csv")
# Read the CSV file into a DataFrame
df = pd.read_csv(file_path)

# Light data cleaning on CSV
df.dropna(inplace=True)
df = df.astype(str)
df = df[:1000]

import pandas as pd from pathlib import Path file_path = Path("../data/csv/commit_history.csv") # Read the CSV file into a DataFrame df = pd.read_csv(file_path) # Light data cleaning on CSV df.dropna(inplace=True) df = df.astype(str) df = df[:1000]

In [ ]:

# Take a look at the data in the csv (optional)
df.head()

# Take a look at the data in the csv (optional) df.head()

We'll define a helper funciton to create a uuid for a node and associated vector embedding based on its timestamp. We'll use this function to create a uuid for each git log entry.

Important note: If you are working with documents/nodes and want the current date and time associated with vector for time-based search, you can skip this step. A uuid will be automatically generated when the nodes are added to the table in Timescale Vector by default. In our case, because we want the uuid to be based on the timestamp in the past, we need to create the uuids manually.

In [ ]:

from timescale_vector import client


# Function to take in a date string in the past and return a uuid v1
def create_uuid(date_string: str):
    if date_string is None:
        return None
    time_format = "%a %b %d %H:%M:%S %Y %z"
    datetime_obj = datetime.strptime(date_string, time_format)
    uuid = client.uuid_from_time(datetime_obj)
    return str(uuid)

from timescale_vector import client # Function to take in a date string in the past and return a uuid v1 def create_uuid(date_string: str): if date_string is None: return None time_format = "%a %b %d %H:%M:%S %Y %z" datetime_obj = datetime.strptime(date_string, time_format) uuid = client.uuid_from_time(datetime_obj) return str(uuid)

In [ ]:

# Helper functions
from typing import List, Tuple


# Helper function to split name and email given an author string consisting of Name Lastname <email>
def split_name(input_string: str) -> Tuple[str, str]:
    if input_string is None:
        return None, None
    start = input_string.find("<")
    end = input_string.find(">")
    name = input_string[:start].strip()
    return name


from datetime import datetime, timedelta


def create_date(input_string: str) -> datetime:
    if input_string is None:
        return None
    # Define a dictionary to map month abbreviations to their numerical equivalents
    month_dict = {
        "Jan": "01",
        "Feb": "02",
        "Mar": "03",
        "Apr": "04",
        "May": "05",
        "Jun": "06",
        "Jul": "07",
        "Aug": "08",
        "Sep": "09",
        "Oct": "10",
        "Nov": "11",
        "Dec": "12",
    }

    # Split the input string into its components
    components = input_string.split()
    # Extract relevant information
    day = components[2]
    month = month_dict[components[1]]
    year = components[4]
    time = components[3]
    timezone_offset_minutes = int(
        components[5]
    )  # Convert the offset to minutes
    timezone_hours = timezone_offset_minutes // 60  # Calculate the hours
    timezone_minutes = (
        timezone_offset_minutes % 60
    )  # Calculate the remaining minutes
    # Create a formatted string for the timestamptz in PostgreSQL format
    timestamp_tz_str = (
        f"{year}-{month}-{day} {time}+{timezone_hours:02}{timezone_minutes:02}"
    )
    return timestamp_tz_str

# Helper functions from typing import List, Tuple # Helper function to split name and email given an author string consisting of Name Lastname def split_name(input_string: str) -> Tuple[str, str]: if input_string is None: return None, None start = input_string.find("<") end = input_string.find(">") name = input_string[:start].strip() return name from datetime import datetime, timedelta def create_date(input_string: str) -> datetime: if input_string is None: return None # Define a dictionary to map month abbreviations to their numerical equivalents month_dict = { "Jan": "01", "Feb": "02", "Mar": "03", "Apr": "04", "May": "05", "Jun": "06", "Jul": "07", "Aug": "08", "Sep": "09", "Oct": "10", "Nov": "11", "Dec": "12", } # Split the input string into its components components = input_string.split() # Extract relevant information day = components[2] month = month_dict[components[1]] year = components[4] time = components[3] timezone_offset_minutes = int( components[5] ) # Convert the offset to minutes timezone_hours = timezone_offset_minutes // 60 # Calculate the hours timezone_minutes = ( timezone_offset_minutes % 60 ) # Calculate the remaining minutes # Create a formatted string for the timestamptz in PostgreSQL format timestamp_tz_str = ( f"{year}-{month}-{day} {time}+{timezone_hours:02}{timezone_minutes:02}" ) return timestamp_tz_str

Next, we'll define a function to create a TextNode for each git log entry. We'll use the helper function create_uuid() we defined above to create a uuid for each node based on its timestampe. And we'll use the helper functions create_date() and split_name() above to extract relevant metadata from the git log entry and add them to the node.

In [ ]:

from llama_index.core.schema import TextNode, NodeRelationship, RelatedNodeInfo


# Create a Node object from a single row of data
def create_node(row):
    record = row.to_dict()
    record_name = split_name(record["author"])
    record_content = (
        str(record["date"])
        + " "
        + record_name
        + " "
        + str(record["change summary"])
        + " "
        + str(record["change details"])
    )
    # Can change to TextNode as needed
    node = TextNode(
        id_=create_uuid(record["date"]),
        text=record_content,
        metadata={
            "commit": record["commit"],
            "author": record_name,
            "date": create_date(record["date"]),
        },
    )
    return node

from llama_index.core.schema import TextNode, NodeRelationship, RelatedNodeInfo # Create a Node object from a single row of data def create_node(row): record = row.to_dict() record_name = split_name(record["author"]) record_content = ( str(record["date"]) + " " + record_name + " " + str(record["change summary"]) + " " + str(record["change details"]) ) # Can change to TextNode as needed node = TextNode( id_=create_uuid(record["date"]), text=record_content, metadata={ "commit": record["commit"], "author": record_name, "date": create_date(record["date"]), }, ) return node

In [ ]:

nodes = [create_node(row) for _, row in df.iterrows()]

nodes = [create_node(row) for _, row in df.iterrows()]

Next we'll create vector embeddings of the content of each node so that we can perform similarity search on the text associated with each node. We'll use the OpenAIEmbedding model to create the embeddings.

In [ ]:

# Create embeddings for nodes
from llama_index.embeddings.openai import OpenAIEmbedding

embedding_model = OpenAIEmbedding()

for node in nodes:
    node_embedding = embedding_model.get_text_embedding(
        node.get_content(metadata_mode="all")
    )
    node.embedding = node_embedding

# Create embeddings for nodes from llama_index.embeddings.openai import OpenAIEmbedding embedding_model = OpenAIEmbedding() for node in nodes: node_embedding = embedding_model.get_text_embedding( node.get_content(metadata_mode="all") ) node.embedding = node_embedding

Let's examine the first node in our collection to see what it looks like.

In [ ]:

print(nodes[0].get_content(metadata_mode="all"))

print(nodes[0].get_content(metadata_mode="all"))

commit: 44e41c12ab25e36c202f58e068ced262eadc8d16
author: Lakshmi Narayanan Sreethar
date: 2023-09-5 21:03:21+0850

Tue Sep 5 21:03:21 2023 +0530 Lakshmi Narayanan Sreethar 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

In [ ]:

print(nodes[0].get_embedding())

print(nodes[0].get_embedding())

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Load documents and metadata into TimescaleVector vectorstore¶

Now that we have prepared our nodes and added embeddings to them, let's add them into our TimescaleVector vectorstore.

We'll create a Timescale Vector instance from the list of nodes we created.

First, we'll define a collection name, which will be the name of our table in the PostgreSQL database.

We'll also define a time delta, which we pass to the time_partition_interval argument, which will be used to as the interval for partitioning the data by time. Each partition will consist of data for the specified length of time. We'll use 7 days for simplicity, but you can pick whatever value make sense for your use case -- for example if you query recent vectors frequently you might want to use a smaller time delta like 1 day, or if you query vectors over a decade long time period then you might want to use a larger time delta like 6 months or 1 year.

Then we'll add the nodes to the Timescale Vector vectorstore.

In [ ]:

# Create a timescale vector store and add the newly created nodes to it
ts_vector_store = TimescaleVectorStore.from_params(
    service_url=TIMESCALE_SERVICE_URL,
    table_name="li_commit_history",
    time_partition_interval=timedelta(days=7),
)
_ = ts_vector_store.add(nodes)

# Create a timescale vector store and add the newly created nodes to it ts_vector_store = TimescaleVectorStore.from_params( service_url=TIMESCALE_SERVICE_URL, table_name="li_commit_history", time_partition_interval=timedelta(days=7), ) _ = ts_vector_store.add(nodes)

Querying vectors by time and similarity¶

Now that we have loaded our documents into TimescaleVector, we can query them by time and similarity.

TimescaleVector provides multiple methods for querying vectors by doing similarity search with time-based filtering Let's take a look at each method below.

First we define a query string and get the vector embedding for the query string.

In [ ]:

# Define query and generate embedding for it
query_str = "What's new with TimescaleDB functions?"
embed_model = OpenAIEmbedding()
query_embedding = embed_model.get_query_embedding(query_str)

# Define query and generate embedding for it query_str = "What's new with TimescaleDB functions?" embed_model = OpenAIEmbedding() query_embedding = embed_model.get_query_embedding(query_str)

Then we set some variables which we'll use in our time filters.

In [ ]:

# Time filter variables for query
start_dt = datetime(
    2023, 8, 1, 22, 10, 35
)  # Start date = 1 August 2023, 22:10:35
end_dt = datetime(
    2023, 8, 30, 22, 10, 35
)  # End date = 30 August 2023, 22:10:35
td = timedelta(days=7)  # Time delta = 7 days

# Time filter variables for query start_dt = datetime( 2023, 8, 1, 22, 10, 35 ) # Start date = 1 August 2023, 22:10:35 end_dt = datetime( 2023, 8, 30, 22, 10, 35 ) # End date = 30 August 2023, 22:10:35 td = timedelta(days=7) # Time delta = 7 days

Method 1: Filter within a provided start date and end date.

In [ ]:

# Query the vector database
vector_store_query = VectorStoreQuery(
    query_embedding=query_embedding, similarity_top_k=5
)

# return most similar vectors to query between start date and end date date range
# returns a VectorStoreQueryResult object
query_result = ts_vector_store.query(
    vector_store_query, start_date=start_dt, end_date=end_dt
)
query_result

# Query the vector database vector_store_query = VectorStoreQuery( query_embedding=query_embedding, similarity_top_k=5 ) # return most similar vectors to query between start date and end date date range # returns a VectorStoreQueryResult object query_result = ts_vector_store.query( vector_store_query, start_date=start_dt, end_date=end_dt ) query_result

Out[ ]:

VectorStoreQueryResult(nodes=[TextNode(id_='22747180-31f1-11ee-bd8e-101e36c28c91', embedding=None, metadata={'commit': ' 7aeed663b9c0f337b530fd6cad47704a51a9b2ec', 'author': 'Dmitry Simonenko', 'date': '2023-08-3 14:30:23+0500'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='3273f20a98f02c75847896b929888b05e8751ae5e258d7feb8605bd5290ef8ca', text='Thu Aug 3 14:30:23 2023 +0300 Dmitry Simonenko Feature flags for TimescaleDB features This PR adds several GUCs which allow to enable/disable major timescaledb features:  - enable_hypertable_create - enable_hypertable_compression - enable_cagg_create - enable_policy_create ', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='faa8ea00-4686-11ee-b933-c2c7df407c25', embedding=None, metadata={'commit': ' e4facda540286b0affba47ccc63959fefe2a7b26', 'author': 'Sven Klemm', 'date': '2023-08-29 18:13:24+0320'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='6f45ab1cccf673ddf75c625983b6cf2f4a66bbf865a4c1c65025997a470f3bb3', text='Tue Aug 29 18:13:24 2023 +0200 Sven Klemm Add compatibility layer for _timescaledb_internal functions With timescaledb 2.12 all the functions present in _timescaledb_internal were moved into the _timescaledb_functions schema to improve schema security. This patch adds a compatibility layer so external callers of these internal functions will not break and allow for more flexibility when migrating. ', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='d7080180-40d2-11ee-af6f-f43e81a0925a', embedding=None, metadata={'commit': ' cf04496e4b4237440274eb25e4e02472fc4e06fc', 'author': 'Sven Klemm', 'date': '2023-08-22 12:01:19+0320'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='d5a20dc83ae04f44aa901ba2f654e80ca68cb21f6a313bd91afcd91e404b471e', text='Tue Aug 22 12:01:19 2023 +0200 Sven Klemm Move utility functions to _timescaledb_functions schema To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - generate_uuid() - get_git_commit() - get_os_info() - tsl_loaded() ', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='01b10780-4649-11ee-a375-5719b2881af3', embedding=None, metadata={'commit': ' a9751ccd5eb030026d7b975d22753f5964972389', 'author': 'Sven Klemm', 'date': '2023-08-29 10:49:47+0320'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='8fde14d147def41808d82bf2ffa35e1e0ed78b0331962907cee856af34a34e44', text='Tue Aug 29 10:49:47 2023 +0200 Sven Klemm Move partitioning functions to _timescaledb_functions schema To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - get_partition_for_key(val anyelement) - get_partition_hash(val anyelement) ', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='e7ba7f80-36af-11ee-9479-6c18a6a65db1', embedding=None, metadata={'commit': ' 44eab9cf9bef34274c88efd37a750eaa74cd8044', 'author': 'Konstantina Skovola', 'date': '2023-08-9 15:26:03+0500'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='f0db9c719928ecc16653bbf0a44f1eaeb221ac79dae16fd36710044d1561dbfa', text='Wed Aug 9 15:26:03 2023 +0300 Konstantina Skovola Release 2.11.2 This release contains bug fixes since the 2.11.1 release. We recommend that you upgrade at the next available opportunity.  **Features** * #5923 Feature flags for TimescaleDB features  **Bugfixes** * #5680 Fix DISTINCT query with JOIN on multiple segmentby columns * #5774 Fixed two bugs in decompression sorted merge code * #5786 Ensure pg_config --cppflags are passed * #5906 Fix quoting owners in sql scripts. * #5912 Fix crash in 1-step integer policy creation  **Thanks** * @mrksngl for submitting a PR to fix extension upgrade scripts * @ericdevries for reporting an issue with DISTINCT queries using segmentby columns of compressed hypertable ', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n')], similarities=[0.18141598590707553, 0.1821951378700205, 0.1948705199438009, 0.19657938500765504, 0.19664154042725346], ids=[UUID('22747180-31f1-11ee-bd8e-101e36c28c91'), UUID('faa8ea00-4686-11ee-b933-c2c7df407c25'), UUID('d7080180-40d2-11ee-af6f-f43e81a0925a'), UUID('01b10780-4649-11ee-a375-5719b2881af3'), UUID('e7ba7f80-36af-11ee-9479-6c18a6a65db1')])

Let's inspect the nodes that were returned from the similarity search:

In [ ]:

# for each node in the query result, print the node metadata date
for node in query_result.nodes:
    print("-" * 80)
    print(node.metadata["date"])
    print(node.get_content(metadata_mode="all"))

# for each node in the query result, print the node metadata date for node in query_result.nodes: print("-" * 80) print(node.metadata["date"]) print(node.get_content(metadata_mode="all"))

--------------------------------------------------------------------------------
2023-08-3 14:30:23+0500
commit:  7aeed663b9c0f337b530fd6cad47704a51a9b2ec
author: Dmitry Simonenko
date: 2023-08-3 14:30:23+0500

Thu Aug 3 14:30:23 2023 +0300 Dmitry Simonenko Feature flags for TimescaleDB features This PR adds several GUCs which allow to enable/disable major timescaledb features:  - enable_hypertable_create - enable_hypertable_compression - enable_cagg_create - enable_policy_create
--------------------------------------------------------------------------------
2023-08-29 18:13:24+0320
commit:  e4facda540286b0affba47ccc63959fefe2a7b26
author: Sven Klemm
date: 2023-08-29 18:13:24+0320

Tue Aug 29 18:13:24 2023 +0200 Sven Klemm Add compatibility layer for _timescaledb_internal functions With timescaledb 2.12 all the functions present in _timescaledb_internal were moved into the _timescaledb_functions schema to improve schema security. This patch adds a compatibility layer so external callers of these internal functions will not break and allow for more flexibility when migrating.
--------------------------------------------------------------------------------
2023-08-22 12:01:19+0320
commit:  cf04496e4b4237440274eb25e4e02472fc4e06fc
author: Sven Klemm
date: 2023-08-22 12:01:19+0320

Tue Aug 22 12:01:19 2023 +0200 Sven Klemm Move utility functions to _timescaledb_functions schema To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - generate_uuid() - get_git_commit() - get_os_info() - tsl_loaded()
--------------------------------------------------------------------------------
2023-08-29 10:49:47+0320
commit:  a9751ccd5eb030026d7b975d22753f5964972389
author: Sven Klemm
date: 2023-08-29 10:49:47+0320

Tue Aug 29 10:49:47 2023 +0200 Sven Klemm Move partitioning functions to _timescaledb_functions schema To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - get_partition_for_key(val anyelement) - get_partition_hash(val anyelement)
--------------------------------------------------------------------------------
2023-08-9 15:26:03+0500
commit:  44eab9cf9bef34274c88efd37a750eaa74cd8044
author: Konstantina Skovola
date: 2023-08-9 15:26:03+0500

Wed Aug 9 15:26:03 2023 +0300 Konstantina Skovola Release 2.11.2 This release contains bug fixes since the 2.11.1 release. We recommend that you upgrade at the next available opportunity.  **Features** * #5923 Feature flags for TimescaleDB features  **Bugfixes** * #5680 Fix DISTINCT query with JOIN on multiple segmentby columns * #5774 Fixed two bugs in decompression sorted merge code * #5786 Ensure pg_config --cppflags are passed * #5906 Fix quoting owners in sql scripts. * #5912 Fix crash in 1-step integer policy creation  **Thanks** * @mrksngl for submitting a PR to fix extension upgrade scripts * @ericdevries for reporting an issue with DISTINCT queries using segmentby columns of compressed hypertable

Note how the query only returns results within the specified date range.

Method 2: Filter within a provided start date, and a time delta later.

In [ ]:

vector_store_query = VectorStoreQuery(
    query_embedding=query_embedding, similarity_top_k=5
)

# return most similar vectors to query from start date and a time delta later
query_result = ts_vector_store.query(
    vector_store_query, start_date=start_dt, time_delta=td
)

for node in query_result.nodes:
    print("-" * 80)
    print(node.metadata["date"])
    print(node.get_content(metadata_mode="all"))

vector_store_query = VectorStoreQuery( query_embedding=query_embedding, similarity_top_k=5 ) # return most similar vectors to query from start date and a time delta later query_result = ts_vector_store.query( vector_store_query, start_date=start_dt, time_delta=td ) for node in query_result.nodes: print("-" * 80) print(node.metadata["date"]) print(node.get_content(metadata_mode="all"))

--------------------------------------------------------------------------------
2023-08-3 14:30:23+0500
commit:  7aeed663b9c0f337b530fd6cad47704a51a9b2ec
author: Dmitry Simonenko
date: 2023-08-3 14:30:23+0500

Thu Aug 3 14:30:23 2023 +0300 Dmitry Simonenko Feature flags for TimescaleDB features This PR adds several GUCs which allow to enable/disable major timescaledb features:  - enable_hypertable_create - enable_hypertable_compression - enable_cagg_create - enable_policy_create
--------------------------------------------------------------------------------
2023-08-7 19:49:47+-500
commit:  5bba74a2ec083728f8e93e09d03d102568fd72b5
author: Fabrízio de Royes Mello
date: 2023-08-7 19:49:47+-500

Mon Aug 7 19:49:47 2023 -0300 Fabrízio de Royes Mello Relax strong table lock when refreshing a CAGG When refreshing a Continuous Aggregate we take a table lock on _timescaledb_catalog.continuous_aggs_invalidation_threshold when processing the invalidation logs (the first transaction of the refresh Continuous Aggregate procedure). It means that even two different Continuous Aggregates over two different hypertables will wait each other in the first phase of the refreshing procedure. Also it lead to problems when a pg_dump is running because it take an AccessShareLock on tables so Continuous Aggregate refresh execution will wait until the pg_dump finish.  Improved it by relaxing the strong table-level lock to a row-level lock so now the Continuous Aggregate refresh procedure can be executed in multiple sessions with less locks.  Fix #3554
--------------------------------------------------------------------------------
2023-08-3 14:36:39+0500
commit:  2863daf3df83c63ee36c0cf7b66c522da5b4e127
author: Dmitry Simonenko
date: 2023-08-3 14:36:39+0500

Thu Aug 3 14:36:39 2023 +0300 Dmitry Simonenko Support CREATE INDEX ONLY ON main table This PR adds support for CREATE INDEX ONLY ON clause which allows to create index only on the main table excluding chunks.  Fix #5908
--------------------------------------------------------------------------------
2023-08-2 20:24:14+0140
commit:  3af0d282ea71d9a8f27159a6171e9516e62ec9cb
author: Lakshmi Narayanan Sreethar
date: 2023-08-2 20:24:14+0140

Wed Aug 2 20:24:14 2023 +0100 Lakshmi Narayanan Sreethar PG16: ExecInsertIndexTuples requires additional parameter PG16 adds a new boolean parameter to the ExecInsertIndexTuples function to denote if the index is a BRIN index, which is then used to determine if the index update can be skipped. The fix also removes the INDEX_ATTR_BITMAP_ALL enum value.  Adapt these changes by updating the compat function to accomodate the new parameter added to the ExecInsertIndexTuples function and using an alternative for the removed INDEX_ATTR_BITMAP_ALL enum value.  postgres/postgres@19d8e23
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2023-08-7 16:36:17+0500
commit:  373c55662ca5f8a2993abf9b2aa7f5f4006b3229
author: Konstantina Skovola
date: 2023-08-7 16:36:17+0500

Mon Aug 7 16:36:17 2023 +0300 Konstantina Skovola Fix ordered append for partially compressed chunks In the exclusive presence of partially compressed chunks, this optimization was not applied because no pathkeys were supplied. Additionally, this patch makes sure that if applicable, the `enable_decompression_sorted_merge` optimization is chosen for the path, since it is more beneficial due to the ability to push down the sort below DecompressChunk.

Once again, notice how only nodes between the start date (1 August) and the defined time delta later (7 days later) are returned.

Method 3: Filter within a provided end date and a time delta earlier.

In [ ]:

vector_store_query = VectorStoreQuery(
    query_embedding=query_embedding, similarity_top_k=5
)

# return most similar vectors to query from end date and a time delta earlier
query_result = ts_vector_store.query(
    vector_store_query, end_date=end_dt, time_delta=td
)

for node in query_result.nodes:
    print("-" * 80)
    print(node.metadata["date"])
    print(node.get_content(metadata_mode="all"))

vector_store_query = VectorStoreQuery( query_embedding=query_embedding, similarity_top_k=5 ) # return most similar vectors to query from end date and a time delta earlier query_result = ts_vector_store.query( vector_store_query, end_date=end_dt, time_delta=td ) for node in query_result.nodes: print("-" * 80) print(node.metadata["date"]) print(node.get_content(metadata_mode="all"))

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2023-08-29 18:13:24+0320
commit:  e4facda540286b0affba47ccc63959fefe2a7b26
author: Sven Klemm
date: 2023-08-29 18:13:24+0320

Tue Aug 29 18:13:24 2023 +0200 Sven Klemm Add compatibility layer for _timescaledb_internal functions With timescaledb 2.12 all the functions present in _timescaledb_internal were moved into the _timescaledb_functions schema to improve schema security. This patch adds a compatibility layer so external callers of these internal functions will not break and allow for more flexibility when migrating.
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2023-08-29 10:49:47+0320
commit:  a9751ccd5eb030026d7b975d22753f5964972389
author: Sven Klemm
date: 2023-08-29 10:49:47+0320

Tue Aug 29 10:49:47 2023 +0200 Sven Klemm Move partitioning functions to _timescaledb_functions schema To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - get_partition_for_key(val anyelement) - get_partition_hash(val anyelement)
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2023-08-28 23:26:23+0320
commit:  b2a91494a11d8b82849b6f11f9ea6dc26ef8a8cb
author: Sven Klemm
date: 2023-08-28 23:26:23+0320

Mon Aug 28 23:26:23 2023 +0200 Sven Klemm Move ddl_internal functions to _timescaledb_functions schema To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - chunk_constraint_add_table_constraint(_timescaledb_catalog.chunk_constraint) - chunk_drop_replica(regclass,name) - chunk_index_clone(oid) - chunk_index_replace(oid,oid) - create_chunk_replica_table(regclass,name) - drop_stale_chunks(name,integer[]) - health() - hypertable_constraint_add_table_fk_constraint(name,name,name,integer) - process_ddl_event() - wait_subscription_sync(name,name,integer,numeric)
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2023-08-29 14:47:57+0320
commit:  08231c8aacd17152f315ad36d95c031fb46073aa
author: Jan Nidzwetzki
date: 2023-08-29 14:47:57+0320

Tue Aug 29 14:47:57 2023 +0200 Jan Nidzwetzki Export is_decompress_chunk_path / is_gapfill_path This patch adds the 'ts_' prefix to the function names of is_decompress_chunk_path and is_gapfill_path and makes them available for use by other parts of TimescaleDB.
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2023-08-28 15:32:54+0320
commit:  6576d969b319dac8e7fd08a9cf4cfc8197b34d1d
author: Sven Klemm
date: 2023-08-28 15:32:54+0320

Mon Aug 28 15:32:54 2023 +0200 Sven Klemm Move log invalidation functions to _timescaledb_functions schema To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - cagg_watermark(integer) - cagg_watermark_materialized(integer) - hypertable_invalidation_log_delete(integer) - invalidation_cagg_log_add_entry(integer,bigint,bigint) - invalidation_hyper_log_add_entry(integer,bigint,bigint) - invalidation_process_cagg_log(integer,integer,regtype,bigint,bigint,integer[],bigint[],bigint[]) - invalidation_process_cagg_log(integer,integer,regtype,bigint,bigint,integer[],bigint[],bigint[],text[]) - invalidation_process_hypertable_log(integer,integer,regtype,integer[],bigint[],bigint[]) - invalidation_process_hypertable_log(integer,integer,regtype,integer[],bigint[],bigint[],text[]) - materialization_invalidation_log_delete(integer)

The main takeaway is that in each result above, only vectors within the specified time range are returned. These queries are very efficient as they only need to search the relevant partitions.

4. Using TimescaleVector store as a Retriever and Query engine¶

Now that we've explored basic similarity search and similarity search with time-based filters, let's look at how to these features of Timescale Vector with LLamaIndex's retriever and query engine.

First we'll look at how to use TimescaleVector as a retriever, specifically a Vector Store Retriever.

To constrain the nodes retrieved to a relevant time-range, we can use TimescaleVector's time filters. We simply pass the time filter parameters as vector_strored_kwargs when creating the retriever.

In [ ]:

from llama_index.core import VectorStoreIndex
from llama_index.core import StorageContext

index = VectorStoreIndex.from_vector_store(ts_vector_store)
retriever = index.as_retriever(
    vector_store_kwargs=({"start_date": start_dt, "time_delta": td})
)
retriever.retrieve("What's new with TimescaleDB functions?")

from llama_index.core import VectorStoreIndex from llama_index.core import StorageContext index = VectorStoreIndex.from_vector_store(ts_vector_store) retriever = index.as_retriever( vector_store_kwargs=({"start_date": start_dt, "time_delta": td}) ) retriever.retrieve("What's new with TimescaleDB functions?")

Out[ ]:

[NodeWithScore(node=TextNode(id_='22747180-31f1-11ee-bd8e-101e36c28c91', embedding=None, metadata={'commit': ' 7aeed663b9c0f337b530fd6cad47704a51a9b2ec', 'author': 'Dmitry Simonenko', 'date': '2023-08-3 14:30:23+0500'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='3273f20a98f02c75847896b929888b05e8751ae5e258d7feb8605bd5290ef8ca', text='Thu Aug 3 14:30:23 2023 +0300 Dmitry Simonenko Feature flags for TimescaleDB features This PR adds several GUCs which allow to enable/disable major timescaledb features:  - enable_hypertable_create - enable_hypertable_compression - enable_cagg_create - enable_policy_create ', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.1813839050209377),
 NodeWithScore(node=TextNode(id_='b5583780-3574-11ee-871a-5a8c45d660c8', embedding=None, metadata={'commit': ' 5bba74a2ec083728f8e93e09d03d102568fd72b5', 'author': 'Fabrízio de Royes Mello', 'date': '2023-08-7 19:49:47+-500'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='ec25a09b9dd34ed2650aefc2ce71e1b11fa471ffc43683715de788d202c6cdc8', text='Mon Aug 7 19:49:47 2023 -0300 Fabrízio de Royes Mello Relax strong table lock when refreshing a CAGG When refreshing a Continuous Aggregate we take a table lock on _timescaledb_catalog.continuous_aggs_invalidation_threshold when processing the invalidation logs (the first transaction of the refresh Continuous Aggregate procedure). It means that even two different Continuous Aggregates over two different hypertables will wait each other in the first phase of the refreshing procedure. Also it lead to problems when a pg_dump is running because it take an AccessShareLock on tables so Continuous Aggregate refresh execution will wait until the pg_dump finish.  Improved it by relaxing the strong table-level lock to a row-level lock so now the Continuous Aggregate refresh procedure can be executed in multiple sessions with less locks.  Fix #3554 ', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.23511557892997959)]

Next we'll look at how to use TimescaleVector as a query engine.

Once again, we use TimescaleVector's time filters to constrain the search to a relevant time range by passing our time filter parameters as vector_strored_kwargs when creating the query engine.

In [ ]:

index = VectorStoreIndex.from_vector_store(ts_vector_store)
query_engine = index.as_query_engine(
    vector_store_kwargs=({"start_date": start_dt, "end_date": end_dt})
)

# query_str = "What's new with TimescaleDB? List 3 new features"
query_str = (
    "What's new with TimescaleDB functions? When were these changes made and"
    " by whom?"
)
response = query_engine.query(query_str)
print(str(response))

index = VectorStoreIndex.from_vector_store(ts_vector_store) query_engine = index.as_query_engine( vector_store_kwargs=({"start_date": start_dt, "end_date": end_dt}) ) # query_str = "What's new with TimescaleDB? List 3 new features" query_str = ( "What's new with TimescaleDB functions? When were these changes made and" " by whom?" ) response = query_engine.query(query_str) print(str(response))

TimescaleDB functions have undergone changes recently. These changes were made by Sven Klemm on August 29, 2023. The changes involve adding a compatibility layer for _timescaledb_internal functions. This layer ensures that external callers of these internal functions will not break and allows for more flexibility when migrating.