Retake

The Real-Time Database for AI Data

Documentation • Website

Retake is a real-time database for AI data. By integratating your Postgres data with embedding models, Retake enables real-time search over your database.

Documents in Retake are stored in real-time indexes. These indexes stay in sync with Postgres and can be queried with keyword search, vector search, or hybrid search — a combination of the two.

To get started, run our Docker Compose file:

git clone [email protected]:getretake/retake.git
cd retake/docker
docker compose up

By default, this will start the Retake engine at http://localhost:8000 with API key retake-test-key.

Usage

Python

Install the SDK

pip install retakesearch

The core API is just a few functions.

from retakesearch import Client, Index, Database, Table, Search

client = Client(api_key="retake-test-key", url="http://localhost:8000")

database = Database(
    host="***",
    user="***",
    password="***",
    port=5432
    dbname="***"
)

columns = ["column1"]
table = Table(
    name="table_name",
    columns=columns
)

index = client.create_index("my_index")
# Note: The table must have a primary key
index.add_source(database, table)
index.vectorize({ fieldNames: columns })

# Keyword (BM25) search
query = Search().query("match", column1="my query")
response = index.search(query)

# Semantic (vector-based) search
query = Search().with_semantic("my_query", columns)
response = index.search(query)

# Neural (keyword + semantic) search
query = Search().with_neural("my_query", columns)
response = index.search(query)

print(response)

Typescript

Install the SDK

npm install retake-search

The core API is just a few functions.

import { Client, Database, Table, Search } from "retake-search"
import { withSemantic, withNeural, matchQuery } from "retake-search/helpers"

const client = new Client("retake-test-key", "http://localhost:8000")

// Replace with your database credentials
const columns = ["column_to_search"]
const database = new Database({
  host: "***",
  user: "***",
  password: "***",
  dbName: "***",
  port: 5432,
})
const table = new Table({
  table: "table_name",
  columns: columns,
})

const index = await client.createIndex("table_name")

// Note: The table must have a primary key
await index.addSource(database, table)
await index.vectorize({ fieldNames: columns })

// Keyword (BM25) search
const bm25Query = Search().query(matchQuery("column_to_search", "my query"))
index.search(bm25Query)

// Semantic (vector-based) search
const semanticQuery = Search().query(withSemantic("my query", columns))
index.search(semanticQuery)

// Neural (keyword + semantic) search
const neuralQuery = Search().query(withNeural("my query", columns))
index.search(neuralQuery)

Key Features

🔄 Always in Sync

Retake leverages logical-replication-based Change-Data-Capture (CDC) to integrate directly with Postgres. As data changes or new data arrives, Retake ensures that the indexed data is kept in sync.

🧠 Intelligent Vector Cache

Whenever data is changed in Postgres, Retake also updates the embedding/vector representation of that data behind the scenes. Vectors are automatically cached for lightning-fast query results with semantic understanding.

🚀 Low-Code SDK

Retake provides intuitive search SDKs that drop into any Python or Typescript application (other languages coming soon). The core API is just a few functions.

⚡ Open/ElasticSearch DSL Compatible

Retake enables developers to query with the full expressiveness of the OpenSearch DSL (domain-specific language).

🌐 Deployable Anywhere

Retake is deployable anywhere, from a laptop to a distributed cloud system.

How Retake Works

A detailed overview of Retake's architecture can be found in our documentation.

Contributing

For more information on how to contribute, please see our Contributing Guide.

License

Retake is licensed under the Apache-2.0 License.