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NeuML profile image David Mezzetti
David Mezzetti for NeuML

Posted on • Edited on • Originally published at neuml.hashnode.dev

Distributed embeddings cluster

#ai #llm #rag #vectordatabase

The txtai API is a web-based service backed by FastAPI. All txtai functionality is available via the API. The API can also cluster multiple embeddings indices into a single logical index to horizontally scale over multiple nodes.

This article installs the txtai API and shows an example of building an embeddings cluster.

Install dependencies

Install txtai and all dependencies. Since this article uses the API, we need to install the api extras package.

pip install txtai[api]
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Start distributed embeddings cluster

First we'll start multiple API instances that will serve as embeddings index shards. Each shard stores a subset of the indexed data and these shards work in tandem to form a single logical index.

Then we'll start the main API instance that clusters the shards together into a logical instance.

The API instances are all started in the background.

import os os.chdir("/content")
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writable: true # Embeddings settings embeddings: path: sentence-transformers/nli-mpnet-base-v2
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# Embeddings cluster cluster: shards: - http://127.0.0.1:8001 - http://127.0.0.1:8002
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# Start embeddings shards CONFIG=index.yml nohup uvicorn --port 8001 "txtai.api:app" &> shard-1.log & CONFIG=index.yml nohup uvicorn --port 8002 "txtai.api:app" &> shard-2.log & # Start main instance CONFIG=cluster.yml nohup uvicorn --port 8000 "txtai.api:app" &> main.log & # Wait for startup sleep 90
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Python

Let's first try the cluster out directly in Python. The code below aggregates the two shards into a single cluster and executes actions against the cluster.

from txtai.api import Cluster cluster = Cluster({"shards": ["http://127.0.0.1:8001", "http://127.0.0.1:8002"]}) data = [ "US tops 5 million confirmed virus cases", "Canada's last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg", "Beijing mobilises invasion craft along coast as Taiwan tensions escalate", "The National Park Service warns against sacrificing slower friends in a bear attack", "Maine man wins $1M from $25 lottery ticket", "Make huge profits without work, earn up to $100,000 a day", ] # Index data cluster.add([{"id": x, "text": row} for x, row in enumerate(data)]) cluster.index() # Test search result = cluster.search("feel good story", 1)[0] print("Query: feel good story\nResult:", result["text"])
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Query: feel good story Result: Maine man wins $1M from $25 lottery ticket
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JavaScript

Next let's try to run the same code above via the API using JavaScript.

npm install txtai
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For this example, we'll clone the txtai.js project to import the example build configuration.

git clone https://github.com/neuml/txtai.js
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Run cluster.js

The following script is a JavaScript version of the logic above

import {Embeddings} from "txtai"; import {sprintf} from "sprintf-js"; const run = async () => { try { let embeddings = new Embeddings(process.argv[2]); let data = ["US tops 5 million confirmed virus cases", "Canada's last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg", "Beijing mobilises invasion craft along coast as Taiwan tensions escalate", "The National Park Service warns against sacrificing slower friends in a bear attack", "Maine man wins $1M from $25 lottery ticket", "Make huge profits without work, earn up to $100,000 a day"]; console.log(); console.log("Querying an Embeddings cluster"); console.log(sprintf("%-20s %s", "Query", "Best Match")); console.log("-".repeat(50)); for (let query of ["feel good story", "climate change", "public health story", "war", "wildlife", "asia", "lucky", "dishonest junk"]) { let results = await embeddings.search(query, 1); if (results && results.length > 0) { let result = results[0].text; console.log(sprintf("%-20s %s", query, result)); } } } catch (e) { console.trace(e); } }; run();
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Build and run cluster.js 

cd txtai.js/examples/node npm install npm run build
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Next lets run the code against the main cluster URL

node dist/cluster.js http://127.0.0.1:8000
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Querying an Embeddings cluster Query Best Match -------------------------------------------------- feel good story Maine man wins $1M from $25 lottery ticket climate change Canada's last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg health US tops 5 million confirmed virus cases war Beijing mobilises invasion craft along coast as Taiwan tensions escalate wildlife The National Park Service warns against sacrificing slower friends in a bear attack asia Beijing mobilises invasion craft along coast as Taiwan tensions escalate lucky Maine man wins $1M from $25 lottery ticket dishonest junk Make huge profits without work, earn up to $100,000 a day
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The JavaScript program is showing the same results as the Python code above. This is running a clustered query against both nodes in the cluster and aggregating the results together.

Queries can be run against each individual shard to see what the queries independently return.

node dist/cluster.js http://127.0.0.1:8001
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Querying an Embeddings cluster Query Best Match -------------------------------------------------- feel good story Maine man wins $1M from $25 lottery ticket climate change Beijing mobilises invasion craft along coast as Taiwan tensions escalate public health story US tops 5 million confirmed virus cases war Beijing mobilises invasion craft along coast as Taiwan tensions escalate wildlife Beijing mobilises invasion craft along coast as Taiwan tensions escalate asia Beijing mobilises invasion craft along coast as Taiwan tensions escalate lucky Maine man wins $1M from $25 lottery ticket dishonest junk US tops 5 million confirmed virus cases
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node dist/cluster.js http://127.0.0.1:8002
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Querying an Embeddings cluster Query Best Match -------------------------------------------------- feel good story Make huge profits without work, earn up to $100,000 a day climate change Canada's last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg public health story The National Park Service warns against sacrificing slower friends in a bear attack war The National Park Service warns against sacrificing slower friends in a bear attack wildlife The National Park Service warns against sacrificing slower friends in a bear attack asia The National Park Service warns against sacrificing slower friends in a bear attack lucky The National Park Service warns against sacrificing slower friends in a bear attack dishonest junk Make huge profits without work, earn up to $100,000 a day
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Note the differences. The section below runs a count against the full cluster and each shard to show the count of records in each.

curl http://127.0.0.1:8000/count printf "\n" curl http://127.0.0.1:8001/count printf "\n" curl http://127.0.0.1:8002/count
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6 3 3
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This article showed how a distributed embeddings cluster can be created with txtai. This example can be further scaled out on Kubernetes with StatefulSets, which will be covered in a future tutorial.

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