Timescale Vector
PostgreSQL++ for AI Applications.
- Signup for Timescale
Vector:
Get 90 days free to try Timescale Vector on the Timescale cloud data
platform. There is no self-managed version at this time.
- Documentation: Learn the
key features of Timescale Vector and how to use them.
- Getting Started
Tutorial:
Learn how to use Timescale Vector for semantic search on a real-world
dataset.
- Learn
more:
Learn more about Timescale Vector, how it works and why we built it.
If you prefer to use an LLM development or data framework, see Timescale
Vector’s integrations with
LangChain
and
LlamaIndex
Install
To install the main library use:
pip install timescale_vector
We also use dotenv
in our examples for passing around secrets and
keys. You can install that with:
pip install python-dotenv
If you run into installation errors related to the psycopg2 package, you
will need to install some prerequisites. The timescale-vector package
explicitly depends on psycopg2 (the non-binary version). This adheres to
the advice provided by
psycopg2.
Building psycopg from source requires a few prerequisites to be
installed.
Make sure these are installed before trying to
pip install timescale_vector
.
Basic usage
First, import all the necessary libraries:
from dotenv import load_dotenv, find_dotenv
import os
from timescale_vector import client
import uuid
from datetime import datetime, timedelta
Load up your PostgreSQL credentials. Safest way is with a .env file:
_ = load_dotenv(find_dotenv(), override=True)
service_url = os.environ['TIMESCALE_SERVICE_URL']
Next, create the client. In this tutorial, we will use the sync client.
But we have an async client as well (with an identical interface that
uses async functions).
The client constructor takes three required arguments:
name | description |
---|
service_url | Timescale service URL / connection string |
table_name | Name of the table to use for storing the embeddings. Think of this as the collection name |
num_dimensions | Number of dimensions in the vector |
vec = client.Sync(service_url, "my_data", 2)
Next, create the tables for the collection:
vec.create_tables()
Next, insert some data. The data record contains:
- A UUID to uniquely identify the embedding
- A JSON blob of metadata about the embedding
- The text the embedding represents
- The embedding itself
Because this data includes UUIDs which become primary keys, we ingest
with upserts.
vec.upsert([\
(uuid.uuid1(), {"animal": "fox"}, "the brown fox", [1.0,1.3]),\
(uuid.uuid1(), {"animal": "fox", "action":"jump"}, "jumped over the", [1.0,10.8]),\
])
You can now create a vector index to speed up similarity search:
vec.create_embedding_index(client.TimescaleVectorIndex())
Now, you can query for similar items:
vec.search([1.0, 9.0])
[[UUID('45ecb666-0f15-11ef-8d89-e666703872d0'),
{'action': 'jump', 'animal': 'fox'},
'jumped over the',
array([ 1. , 10.8], dtype=float32),
0.00016793422934946456],
[UUID('45ecb350-0f15-11ef-8d89-e666703872d0'),
{'animal': 'fox'},
'the brown fox',
array([1. , 1.3], dtype=float32),
0.14489260377438218]]
There are many search options which we will cover below in the
Advanced search
section.
As one example, we will return one item using a similarity search
constrained by a metadata filter.
vec.search([1.0, 9.0], limit=1, filter={"action": "jump"})
[[UUID('45ecb666-0f15-11ef-8d89-e666703872d0'),
{'action': 'jump', 'animal': 'fox'},
'jumped over the',
array([ 1. , 10.8], dtype=float32),
0.00016793422934946456]]
The returned records contain 5 fields:
name | description |
---|
id | The UUID of the record |
metadata | The JSON metadata associated with the record |
contents | the text content that was embedded |
embedding | The vector embedding |
distance | The distance between the query embedding and the vector |
You can access the fields by simply using the record as a dictionary
keyed on the field name:
records = vec.search([1.0, 9.0], limit=1, filter={"action": "jump"})
(records[0]["id"],records[0]["metadata"], records[0]["contents"], records[0]["embedding"], records[0]["distance"])
(UUID('45ecb666-0f15-11ef-8d89-e666703872d0'),
{'action': 'jump', 'animal': 'fox'},
'jumped over the',
array([ 1. , 10.8], dtype=float32),
0.00016793422934946456)
You can delete by ID:
vec.delete_by_ids([records[0]["id"]])
Or you can delete by metadata filters:
vec.delete_by_metadata({"action": "jump"})
To delete all records use:
vec.delete_all()
Advanced usage
In this section, we will go into more detail about our feature. We will
cover:
- Search filter options - how to narrow your search by additional
constraints
- Indexing - how to speed up your similarity queries
- Time-based partitioning - how to optimize similarity queries that
filter on time
- Setting different distance types to use in distance calculations
Search options
The search
function is very versatile and allows you to search for the
right vector in a wide variety of ways. We’ll describe the search option
in 3 parts:
- We’ll cover basic similarity search.
- Then, we’ll describe how to filter your search based on the
associated metadata.
- Finally, we’ll talk about filtering on time when time-partitioning
is enabled.
Let’s use the following data for our example:
vec.upsert([\
(uuid.uuid1(), {"animal":"fox", "action": "sit", "times":1}, "the brown fox", [1.0,1.3]),\
(uuid.uuid1(), {"animal":"fox", "action": "jump", "times":100}, "jumped over the", [1.0,10.8]),\
])
The basic query looks like:
vec.search([1.0, 9.0])
[[UUID('4d629b54-0f15-11ef-8d89-e666703872d0'),
{'times': 100, 'action': 'jump', 'animal': 'fox'},
'jumped over the',
array([ 1. , 10.8], dtype=float32),
0.00016793422934946456],
[UUID('4d629a50-0f15-11ef-8d89-e666703872d0'),
{'times': 1, 'action': 'sit', 'animal': 'fox'},
'the brown fox',
array([1. , 1.3], dtype=float32),
0.14489260377438218]]
You could provide a limit for the number of items returned:
vec.search([1.0, 9.0], limit=1)
[[UUID('4d629b54-0f15-11ef-8d89-e666703872d0'),
{'times': 100, 'action': 'jump', 'animal': 'fox'},
'jumped over the',
array([ 1. , 10.8], dtype=float32),
0.00016793422934946456]]
Narrowing your search by metadata
We have two main ways to filter results by metadata: - filters
for
equality matches on metadata. - predicates
for complex conditions on
metadata.
Filters are more likely to be performant but are more limited in what
they can express, so we suggest using those if your use case allows it.
Filters
You could specify a match on the metadata as a dictionary where all keys
have to match the provided values (keys not in the filter are
unconstrained):
vec.search([1.0, 9.0], limit=1, filter={"action": "sit"})
[[UUID('4d629a50-0f15-11ef-8d89-e666703872d0'),
{'times': 1, 'action': 'sit', 'animal': 'fox'},
'the brown fox',
array([1. , 1.3], dtype=float32),
0.14489260377438218]]
You can also specify a list of filter dictionaries, where an item is
returned if it matches any dict:
vec.search([1.0, 9.0], limit=2, filter=[{"action": "jump"}, {"animal": "fox"}])
[[UUID('4d629b54-0f15-11ef-8d89-e666703872d0'),
{'times': 100, 'action': 'jump', 'animal': 'fox'},
'jumped over the',
array([ 1. , 10.8], dtype=float32),
0.00016793422934946456],
[UUID('4d629a50-0f15-11ef-8d89-e666703872d0'),
{'times': 1, 'action': 'sit', 'animal': 'fox'},
'the brown fox',
array([1. , 1.3], dtype=float32),
0.14489260377438218]]
Predicates
Predicates allow for more complex search conditions. For example, you
could use greater than and less than conditions on numeric values.
vec.search([1.0, 9.0], limit=2, predicates=client.Predicates("times", ">", 1))
[[UUID('4d629b54-0f15-11ef-8d89-e666703872d0'),
{'times': 100, 'action': 'jump', 'animal': 'fox'},
'jumped over the',
array([ 1. , 10.8], dtype=float32),
0.00016793422934946456]]
Predicates
objects are defined by the name of the metadata key, an operator, and a
value.
The supported operators are: ==
, !=
, <
, <=
, >
, >=
The type of the values determines the type of comparison to perform. For
example, passing in "Sam"
(a string) will do a string comparison while
a 10
(an int) will perform an integer comparison while a 10.0
(float) will do a float comparison. It is important to note that using a
value of "10"
will do a string comparison as well so it’s important to
use the right type. Supported Python types are: str
, int
, and
float
. One more example with a string comparison:
vec.search([1.0, 9.0], limit=2, predicates=client.Predicates("action", "==", "jump"))
[[UUID('4d629b54-0f15-11ef-8d89-e666703872d0'),
{'times': 100, 'action': 'jump', 'animal': 'fox'},
'jumped over the',
array([ 1. , 10.8], dtype=float32),
0.00016793422934946456]]
The real power of predicates is that they can also be combined using the
&
operator (for combining predicates with AND semantics) and |
(for
combining using OR semantic). So you can do:
vec.search([1.0, 9.0], limit=2, predicates=client.Predicates("action", "==", "jump") & client.Predicates("times", ">", 1))
[[UUID('4d629b54-0f15-11ef-8d89-e666703872d0'),
{'times': 100, 'action': 'jump', 'animal': 'fox'},
'jumped over the',
array([ 1. , 10.8], dtype=float32),
0.00016793422934946456]]
Just for sanity, let’s show a case where no results are returned because
or predicates:
vec.search([1.0, 9.0], limit=2, predicates=client.Predicates("action", "==", "jump") & client.Predicates("times", "==", 1))
[]
And one more example where we define the predicates as a variable and
use grouping with parenthesis:
my_predicates = client.Predicates("action", "==", "jump") & (client.Predicates("times", "==", 1) | client.Predicates("times", ">", 1))
vec.search([1.0, 9.0], limit=2, predicates=my_predicates)
[[UUID('4d629b54-0f15-11ef-8d89-e666703872d0'),
{'times': 100, 'action': 'jump', 'animal': 'fox'},
'jumped over the',
array([ 1. , 10.8], dtype=float32),
0.00016793422934946456]]
We also have some semantic sugar for combining many predicates with AND
semantics. You can pass in multiple 3-tuples to
Predicates
:
vec.search([1.0, 9.0], limit=2, predicates=client.Predicates(("action", "==", "jump"), ("times", ">", 10)))
[[UUID('4d629b54-0f15-11ef-8d89-e666703872d0'),
{'times': 100, 'action': 'jump', 'animal': 'fox'},
'jumped over the',
array([ 1. , 10.8], dtype=float32),
0.00016793422934946456]]
Filter your search by time
When using time-partitioning
(see below). You can very efficiently
filter your search by time. Time-partitioning makes a timestamp embedded
as part of the UUID-based ID associated with an embedding. Let us first
create a collection with time partitioning and insert some data (one
item from January 2018 and another in January 2019):
tpvec = client.Sync(service_url, "time_partitioned_table", 2, time_partition_interval=timedelta(hours=6))
tpvec.create_tables()
specific_datetime = datetime(2018, 1, 1, 12, 0, 0)
tpvec.upsert([\
(client.uuid_from_time(specific_datetime), {"animal":"fox", "action": "sit", "times":1}, "the brown fox", [1.0,1.3]),\
(client.uuid_from_time(specific_datetime+timedelta(days=365)), {"animal":"fox", "action": "jump", "times":100}, "jumped over the", [1.0,10.8]),\
])
Then, you can filter using the timestamps by specifing a
uuid_time_filter
:
tpvec.search([1.0, 9.0], limit=4, uuid_time_filter=client.UUIDTimeRange(specific_datetime, specific_datetime+timedelta(days=1)))
[[UUID('95899000-ef1d-11e7-990e-7d2f7e013038'),
{'times': 1, 'action': 'sit', 'animal': 'fox'},
'the brown fox',
array([1. , 1.3], dtype=float32),
0.14489260377438218]]
A
UUIDTimeRange
can specify a start_date or end_date or both(as in the example above).
Specifying only the start_date or end_date leaves the other end
unconstrained.
tpvec.search([1.0, 9.0], limit=4, uuid_time_filter=client.UUIDTimeRange(start_date=specific_datetime))
[[UUID('0e505000-0def-11e9-8732-a154fea6fb50'),
{'times': 100, 'action': 'jump', 'animal': 'fox'},
'jumped over the',
array([ 1. , 10.8], dtype=float32),
0.00016793422934946456],
[UUID('95899000-ef1d-11e7-990e-7d2f7e013038'),
{'times': 1, 'action': 'sit', 'animal': 'fox'},
'the brown fox',
array([1. , 1.3], dtype=float32),
0.14489260377438218]]
You have the option to define the inclusivity of the start and end dates
with the start_inclusive
and end_inclusive
parameters. Setting
start_inclusive
to true results in comparisons using the >=
operator, whereas setting it to false applies the >
operator. By
default, the start date is inclusive, while the end date is exclusive.
One example:
tpvec.search([1.0, 9.0], limit=4, uuid_time_filter=client.UUIDTimeRange(start_date=specific_datetime, start_inclusive=False))
[[UUID('0e505000-0def-11e9-8732-a154fea6fb50'),
{'times': 100, 'action': 'jump', 'animal': 'fox'},
'jumped over the',
array([ 1. , 10.8], dtype=float32),
0.00016793422934946456]]
Notice how the results are different when we use the
start_inclusive=False
option because the first row has the exact
timestamp specified by start_date
.
We’ve also made it easy to integrate time filters using the filter
and
predicates
parameters described above using special reserved key names
to make it appear that the timestamps are part of your metadata. We
found this useful when integrating with other systems that just want to
specify a set of filters (often these are “auto retriever” type
systems). The reserved key names are __start_date
and __end_date
for
filters and __uuid_timestamp
for predicates. Some examples below:
tpvec.search([1.0, 9.0], limit=4, filter={ "__start_date": specific_datetime, "__end_date": specific_datetime+timedelta(days=1)})
[[UUID('95899000-ef1d-11e7-990e-7d2f7e013038'),
{'times': 1, 'action': 'sit', 'animal': 'fox'},
'the brown fox',
array([1. , 1.3], dtype=float32),
0.14489260377438218]]
tpvec.search([1.0, 9.0], limit=4,
predicates=client.Predicates("__uuid_timestamp", ">", specific_datetime) & client.Predicates("__uuid_timestamp", "<", specific_datetime+timedelta(days=1)))
[[UUID('95899000-ef1d-11e7-990e-7d2f7e013038'),
{'times': 1, 'action': 'sit', 'animal': 'fox'},
'the brown fox',
array([1. , 1.3], dtype=float32),
0.14489260377438218]]
Indexing
Indexing speeds up queries over your data. By default, we set up indexes
to query your data by the UUID and the metadata.
But to speed up similarity search based on the embeddings, you have to
create additional indexes.
Note that if performing a query without an index, you will always get an
exact result, but the query will be slow (it has to read all of the data
you store for every query). With an index, your queries will be
order-of-magnitude faster, but the results are approximate (because
there are no known indexing techniques that are exact).
Nevertheless, there are excellent approximate algorithms. There are 3
different indexing algorithms available on the Timescale platform:
Timescale Vector index, pgvector HNSW, and pgvector ivfflat. Below are
the trade-offs between these algorithms:
Algorithm | Build speed | Query speed | Need to rebuild after updates |
---|
timescale vector | Slow | Fastest | No |
pgvector hnsw | Slowest | Faster | No |
pgvector ivfflat | Fastest | Slowest | Yes |
You can see
benchmarks
on our blog.
We recommend using the Timescale Vector index for most use cases. This
can be created with:
vec.create_embedding_index(client.TimescaleVectorIndex())
Indexes are created for a particular distance metric type. So it is
important that the same distance metric is set on the client during
index creation as it is during queries. See the distance type
section
below.
Each of these indexes has a set of build-time options for controlling
the speed/accuracy trade-off when creating the index and an additional
query-time option for controlling accuracy during a particular query. We
have smart defaults for all of these options but will also describe the
details below so that you can adjust these options manually.
Timescale Vector index
The Timescale Vector index is a graph-based algorithm that uses the
DiskANN algorithm. You can read
more about it on our
blog
announcing its release.
To create this index, run:
vec.create_embedding_index(client.TimescaleVectorIndex())
The above command will create the index using smart defaults. There are
a number of parameters you could tune to adjust the accuracy/speed
trade-off.
The parameters you can set at index build time are:
Parameter name | Description | Default value |
---|
num_neighbors | Sets the maximum number of neighbors per node. Higher values increase accuracy but make the graph traversal slower. | 50 |
search_list_size | This is the S parameter used in the greedy search algorithm used during construction. Higher values improve graph quality at the cost of slower index builds. | 100 |
max_alpha | Is the alpha parameter in the algorithm. Higher values improve graph quality at the cost of slower index builds. | 1.0 |
To set these parameters, you could run:
vec.create_embedding_index(client.TimescaleVectorIndex(num_neighbors=50, search_list_size=100, max_alpha=1.0))
You can also set a parameter to control the accuracy vs. query speed
trade-off at query time. The parameter is set in the search()
function
using the query_params
argment. You can set the
search_list_size
(default: 100). This is the number of additional
candidates considered during the graph search at query time. Higher
values improve query accuracy while making the query slower.
You can specify this value during search as follows:
To drop the index, run:
vec.drop_embedding_index()
pgvector HNSW index
Pgvector provides a graph-based indexing algorithm based on the popular
HNSW algorithm.
To create this index, run:
vec.create_embedding_index(client.HNSWIndex())
The above command will create the index using smart defaults. There are
a number of parameters you could tune to adjust the accuracy/speed
trade-off.
The parameters you can set at index build time are:
Parameter name | Description | Default value |
---|
m | Represents the maximum number of connections per layer. Think of these connections as edges created for each node during graph construction. Increasing m increases accuracy but also increases index build time and size. | 16 |
ef_construction | Represents the size of the dynamic candidate list for constructing the graph. It influences the trade-off between index quality and construction speed. Increasing ef_construction enables more accurate search results at the expense of lengthier index build times. | 64 |
To set these parameters, you could run:
vec.create_embedding_index(client.HNSWIndex(m=16, ef_construction=64))
You can also set a parameter to control the accuracy vs. query speed
trade-off at query time. The parameter is set in the search()
function
using the query_params
argument. You can set the ef_search
(default:
40). This parameter specifies the size of the dynamic candidate list
used during search. Higher values improve query accuracy while making
the query slower.
You can specify this value during search as follows:
To drop the index run:
vec.drop_embedding_index()
pgvector ivfflat index
Pgvector provides a clustering-based indexing algorithm. Our blog
post
describes how it works in detail. It provides the fastest index-build
speed but the slowest query speeds of any indexing algorithm.
To create this index, run:
vec.create_embedding_index(client.IvfflatIndex())
Note: ivfflat should never be created on empty tables because it needs
to cluster data, and that only happens when an index is first created,
not when new rows are inserted or modified. Also, if your table
undergoes a lot of modifications, you will need to rebuild this index
occasionally to maintain good accuracy. See our blog
post
for details.
Pgvector ivfflat has a lists
index parameter that is automatically set
with a smart default based on the number of rows in your table. If you
know that you’ll have a different table size, you can specify the number
of records to use for calculating the lists
parameter as follows:
vec.create_embedding_index(client.IvfflatIndex(num_records=1000000))
You can also set the lists
parameter directly:
vec.create_embedding_index(client.IvfflatIndex(num_lists=100))
You can also set a parameter to control the accuracy vs. query speed
trade-off at query time. The parameter is set in the search()
function
using the query_params
argument. You can set the probes
. This
parameter specifies the number of clusters searched during a query. It
is recommended to set this parameter to sqrt(lists)
where lists is the
num_list
parameter used above during index creation. Higher values
improve query accuracy while making the query slower.
You can specify this value during search as follows:
To drop the index, run:
vec.drop_embedding_index()
Time partitioning
In many use cases where you have many embeddings, time is an important
component associated with the embeddings. For example, when embedding
news stories, you often search by time as well as similarity (e.g.,
stories related to Bitcoin in the past week or stories about Clinton in
November 2016).
Yet, traditionally, searching by two components “similarity” and “time”
is challenging for Approximate Nearest Neighbor (ANN) indexes and makes
the similarity-search index less effective.
One approach to solving this is partitioning the data by time and
creating ANN indexes on each partition individually. Then, during
search, you can:
- Step 1: filter our partitions that don’t match the time predicate.
- Step 2: perform the similarity search on all matching partitions.
- Step 3: combine all the results from each partition in step 2, rerank,
and filter out results by time.
Step 1 makes the search a lot more efficient by filtering out whole
swaths of data in one go.
Timescale-vector supports time partitioning using TimescaleDB’s
hypertables. To use this feature, simply indicate the length of time for
each partition when creating the client:
from datetime import timedelta
from datetime import datetime
vec = client.Async(service_url, "my_data_with_time_partition", 2, time_partition_interval=timedelta(hours=6))
await vec.create_tables()
Then, insert data where the IDs use UUIDs v1 and the time component of
the UUID specifies the time of the embedding. For example, to create an
embedding for the current time, simply do:
id = uuid.uuid1()
await vec.upsert([(id, {"key": "val"}, "the brown fox", [1.0, 1.2])])
To insert data for a specific time in the past, create the UUID using
our
uuid_from_time
function
specific_datetime = datetime(2018, 8, 10, 15, 30, 0)
await vec.upsert([(client.uuid_from_time(specific_datetime), {"key": "val"}, "the brown fox", [1.0, 1.2])])
You can then query the data by specifying a uuid_time_filter
in the
search call:
rec = await vec.search([1.0, 2.0], limit=4, uuid_time_filter=client.UUIDTimeRange(specific_datetime-timedelta(days=7), specific_datetime+timedelta(days=7)))
Distance metrics
By default, we use cosine distance to measure how similarly an embedding
is to a given query. In addition to cosine distance, we also support
Euclidean/L2 distance. The distance type is set when creating the client
using the distance_type
parameter. For example, to use the Euclidean
distance metric, you can create the client with:
vec = client.Sync(service_url, "my_data", 2, distance_type="euclidean")
Valid values for distance_type
are cosine
and euclidean
.
It is important to note that you should use consistent distance types on
clients that create indexes and perform queries. That is because an
index is only valid for one particular type of distance measure.
Please note the Timescale Vector index only supports cosine distance at
this time.
LangChain integration
LangChain is a popular framework for
development applications powered by LLMs. Timescale Vector has a native
LangChain integration, enabling you to use Timescale Vector as a
vectorstore and leverage all its capabilities in your applications built
with LangChain.
Here are resources about using Timescale Vector with LangChain:
LlamaIndex integration
[LlamaIndex] is a popular data framework for connecting custom data
sources to large language models (LLMs). Timescale Vector has a native
LlamaIndex integration, enabling you to use Timescale Vector as a
vectorstore and leverage all its capabilities in your applications built
with LlamaIndex.
Here are resources about using Timescale Vector with LlamaIndex:
PgVectorize
PgVectorize enables you to create vector embeddings from any data that
you already have stored in PostgreSQL. You can get more background
information in our blog
post
announcing this feature, as well as a “how we built
in”
post going into the details of the design.
To create vector embeddings, simply attach PgVectorize to any PostgreSQL
table, and it will automatically sync that table’s data with a set of
embeddings stored in Timescale Vector. For example, let’s say you have a
blog table defined in the following way:
import psycopg2
from langchain.docstore.document import Document
from langchain.text_splitter import CharacterTextSplitter
from timescale_vector import client, pgvectorizer
from langchain_openai import OpenAIEmbeddings
from langchain.vectorstores.timescalevector import TimescaleVector
from datetime import timedelta
with psycopg2.connect(service_url) as conn:
with conn.cursor() as cursor:
cursor.execute('''
CREATE TABLE IF NOT EXISTS blog (
id SERIAL PRIMARY KEY NOT NULL,
title TEXT NOT NULL,
author TEXT NOT NULL,
contents TEXT NOT NULL,
category TEXT NOT NULL,
published_time TIMESTAMPTZ NULL --NULL if not yet published
);
''')
You can insert some data as follows:
with psycopg2.connect(service_url) as conn:
with conn.cursor() as cursor:
cursor.execute('''
INSERT INTO blog (title, author, contents, category, published_time) VALUES ('First Post', 'Matvey Arye', 'some super interesting content about cats.', 'AI', '2021-01-01');
''')
Now, say you want to embed these blogs in Timescale Vector. First, you
need to define an embed_and_write
function that takes a set of blog
posts, creates the embeddings, and writes them into TimescaleVector. For
example, if using LangChain, it could look something like the following.
def get_document(blog):
text_splitter = CharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
docs = []
for chunk in text_splitter.split_text(blog['contents']):
content = f"Author {blog['author']}, title: {blog['title']}, contents:{chunk}"
metadata = {
"id": str(client.uuid_from_time(blog['published_time'])),
"blog_id": blog['id'],
"author": blog['author'],
"category": blog['category'],
"published_time": blog['published_time'].isoformat(),
}
docs.append(Document(page_content=content, metadata=metadata))
return docs
def embed_and_write(blog_instances, vectorizer):
embedding = OpenAIEmbeddings()
vector_store = TimescaleVector(
collection_name="blog_embedding",
service_url=service_url,
embedding=embedding,
time_partition_interval=timedelta(days=30),
)
metadata_for_delete = [{"blog_id": blog['locked_id']} for blog in blog_instances]
vector_store.delete_by_metadata(metadata_for_delete)
documents = []
for blog in blog_instances:
if blog['published_time'] != None:
documents.extend(get_document(blog))
if len(documents) == 0:
return
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
ids = [d.metadata["id"] for d in documents]
vector_store.add_texts(texts, metadatas, ids)
Then, all you have to do is run the following code in a scheduled job
(cron job, Lambda job, etc):
vectorizer = pgvectorizer.Vectorize(service_url, 'blog')
while vectorizer.process(embed_and_write) > 0:
pass
Every time that job runs, it will sync the table with your embeddings.
It will sync all inserts, updates, and deletes to an embeddings table
called blog_embedding
.
Now, you can simply search the embeddings as follows (again, using
LangChain in the example):
embedding = OpenAIEmbeddings()
vector_store = TimescaleVector(
collection_name="blog_embedding",
service_url=service_url,
embedding=embedding,
time_partition_interval=timedelta(days=30),
)
res = vector_store.similarity_search_with_score("Blogs about cats")
res
[(Document(page_content='Author Matvey Arye, title: First Post, contents:some super interesting content about cats.', metadata={'id': '4a784000-4bc4-11eb-979c-e8748f6439f2', 'author': 'Matvey Arye', 'blog_id': 1, 'category': 'AI', 'published_time': '2021-01-01T00:00:00+00:00'}),
0.12657619616729976)]
Development
This project is developed with nbdev. Please
see that website for the development process.