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The AzionVectorStore is used to manage and search through a collection of documents using vector embeddings, directly on Azion’s Edge Plataform using Edge SQL. This guide provides a quick overview for getting started with Azion EdgeSQL vector stores. For detailed documentation of all AzionVectorStore features and configurations head to the API reference.

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Overview

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Integration details

ClassPackage[PY support]Version
AzionVectorStore@langchain/community❌NPM - Version

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Setup

To use the AzionVectorStore vector store, you will need to install the @langchain/community package. Besides that, you will need an Azion account and a Token to use the Azion API, configuring it as environment variable AZION_TOKEN. Further information about this can be found in the Documentation. This guide will also use OpenAI embeddings, which require you to install the @langchain/openai integration package. You can also use other supported embeddings models if you wish. 
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>

<Npm2Yarn>
  azion @langchain/openai @langchain/community
</Npm2Yarn>

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Credentials

Once you’ve done this set the AZION_TOKEN environment variable: 
process.env.AZION_TOKEN = "your-api-key"
If you are using OpenAI embeddings for this guide, you’ll need to set your OpenAI key as well: 
process.env.OPENAI_API_KEY = "YOUR_API_KEY";
If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below: 
// process.env.LANGCHAIN_TRACING_V2="true"
// process.env.LANGCHAIN_API_KEY="your-api-key"

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Instantiation

import { AzionVectorStore } from "@langchain/community/vectorstores/azion_edgesql";
import { OpenAIEmbeddings } from "@langchain/openai";

const embeddings = new OpenAIEmbeddings({
  model: "text-embedding-3-small",
});

// Instantiate with the constructor if the database and table have already been created
const vectorStore = new AzionVectorStore(embeddings, { dbName: "langchain", tableName: "documents" });

// If you have not created the database and table yet, you can do so with the setupDatabase method
// await vectorStore.setupDatabase({ columns:["topic","language"], mode: "hybrid" })

// OR instantiate with the static method if the database and table have not been created yet
// const vectorStore = await AzionVectorStore.initialize(embeddingModel, { dbName: "langchain", tableName: "documents" }, { columns:[], mode: "hybrid" })

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Manage vector store

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Add items to vector store

import type { Document } from "@langchain/core/documents";

const document1: Document = {
  pageContent: "The powerhouse of the cell is the mitochondria",
  metadata: { language: "en", topic: "biology" }
};

const document2: Document = {
  pageContent: "Buildings are made out of brick",
  metadata: { language: "en", topic: "history" }
};

const document3: Document = {
  pageContent: "Mitochondria are made out of lipids",
  metadata: { language: "en", topic: "biology" }
};

const document4: Document = {
  pageContent: "The 2024 Olympics are in Paris",
  metadata: { language: "en", topic: "history" }
}

const documents = [document1, document2, document3, document4];

await vectorStore.addDocuments(documents);

Inserting chunks
Inserting chunk 0
Chunks inserted!

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Delete items from vector store

await vectorStore.delete(["4"]);

Deleted 1 items from documents

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Query vector store

Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.

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Query directly

Performing a simple similarity search can be done as follows: 
const filter = [{ operator: "=", column: "language", value: "en" }]

const hybridSearchResults = await vectorStore.azionHybridSearch("biology", {kfts:2, kvector:1,
                                      filter:[{ operator: "=", column: "language", value: "en" }]});

console.log("Hybrid Search Results")
for (const doc of hybridSearchResults) {
  console.log(`${JSON.stringify(doc)}`);
}

Hybrid Search Results
[{"pageContent":"The Australian dingo is a unique species that plays a key role in the ecosystem","metadata":{"searchtype":"fulltextsearch"},"id":"6"},-0.25748711028997995]
[{"pageContent":"The powerhouse of the cell is the mitochondria","metadata":{"searchtype":"fulltextsearch"},"id":"16"},-0.31697985337654005]
[{"pageContent":"Australia s indigenous people have inhabited the continent for over 65,000 years","metadata":{"searchtype":"similarity"},"id":"3"},0.14822345972061157]

const similaritySearchResults = await vectorStore.azionSimilaritySearch("australia", {kvector:3, filter:[{ operator: "=", column: "topic", value: "history" }]});

console.log("Similarity Search Results")
for (const doc of similaritySearchResults) {
  console.log(`${JSON.stringify(doc)}`);
}

Similarity Search Results
[{"pageContent":"Australia s indigenous people have inhabited the continent for over 65,000 years","metadata":{"searchtype":"similarity"},"id":"3"},0.4486490488052368]

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Query by turning into retriever

You can also transform the vector store into a retriever for easier usage in your chains. 
const retriever = vectorStore.asRetriever({
  // Optional filter
  filter: filter,
  k: 2,
});
await retriever.invoke("biology");

[
  Document {
    pageContent: 'Australia s indigenous people have inhabited the continent for over 65,000 years',
    metadata: { searchtype: 'similarity' },
    id: '3'
  },
  Document {
    pageContent: 'Mitochondria are made out of lipids',
    metadata: { searchtype: 'similarity' },
    id: '18'
  }
]

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Usage for retrieval-augmented generation

For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:

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API reference

For detailed documentation of all AzionVectorStore features and configurations head to the API reference.