feat: Add Azure AI Search vector store support for TypeScript SDK (#3549)
This commit is contained in:
G Karthik Koundinya
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GitHub
GitHub
parent
ea22e8d9cd
commit
a40314c971
@@ -98,6 +98,8 @@
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},
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"peerDependencies": {
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"@anthropic-ai/sdk": "^0.40.1",
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"@azure/identity": "^4.0.0",
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"@azure/search-documents": "^12.0.0",
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"@cloudflare/workers-types": "^4.20250504.0",
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"@google/genai": "^1.2.0",
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"@langchain/core": "^0.3.44",
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@@ -0,0 +1,56 @@
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import { Memory } from "../../src";
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import { runTests } from "../utils/test-utils";
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export async function demoAzureAISearch() {
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console.log("\n=== Testing Azure AI Search Store ===\n");
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const memory = new Memory({
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version: "v1.1",
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embedder: {
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provider: "openai",
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config: {
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apiKey: process.env.OPENAI_API_KEY || "",
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model: "text-embedding-3-small",
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},
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},
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vectorStore: {
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provider: "azure-ai-search",
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config: {
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collectionName: "memories",
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serviceName: process.env.AZURE_AI_SEARCH_SERVICE_NAME || "",
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apiKey: process.env.AZURE_AI_SEARCH_API_KEY,
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embeddingModelDims: 1536,
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compressionType: "none", // Options: "none", "scalar", "binary"
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useFloat16: false,
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hybridSearch: false,
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vectorFilterMode: "preFilter", // Options: "preFilter", "postFilter"
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},
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},
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llm: {
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provider: "openai",
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config: {
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apiKey: process.env.OPENAI_API_KEY || "",
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model: "gpt-4-turbo-preview",
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},
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},
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historyDbPath: "memory.db",
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});
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await runTests(memory);
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}
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if (require.main === module) {
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if (!process.env.AZURE_AI_SEARCH_SERVICE_NAME) {
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console.log(
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"\nSkipping Azure AI Search test - AZURE_AI_SEARCH_SERVICE_NAME not set",
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);
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console.log("Set environment variables:");
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console.log(" - AZURE_AI_SEARCH_SERVICE_NAME (required)");
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console.log(
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" - AZURE_AI_SEARCH_API_KEY (optional, uses DefaultAzureCredential if not set)",
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);
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console.log(" - OPENAI_API_KEY (required for embeddings and LLM)");
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process.exit(0);
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}
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demoAzureAISearch();
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}
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@@ -1,6 +1,7 @@
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import dotenv from "dotenv";
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import { demoMemoryStore } from "./memory";
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import { demoSupabase } from "./supabase";
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import { demoAzureAISearch } from "./azure-ai-search";
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// import { demoQdrant } from "./qdrant";
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// import { demoRedis } from "./redis";
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// import { demoPGVector } from "./pgvector";
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@@ -15,6 +16,7 @@ async function main() {
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const stores: Record<string, () => Promise<void>> = {
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// memory: demoMemoryStore,
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supabase: demoSupabase,
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"azure-ai-search": demoAzureAISearch,
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// Uncomment these as they are implemented
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// qdrant: demoQdrant,
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// redis: demoRedis,
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@@ -23,4 +23,5 @@ export * from "./vector_stores/redis";
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export * from "./vector_stores/supabase";
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export * from "./vector_stores/langchain";
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export * from "./vector_stores/vectorize";
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export * from "./vector_stores/azure_ai_search";
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export * from "./utils/factory";
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@@ -31,6 +31,7 @@ import { AzureOpenAIEmbedder } from "../embeddings/azure";
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import { LangchainLLM } from "../llms/langchain";
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import { LangchainEmbedder } from "../embeddings/langchain";
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import { LangchainVectorStore } from "../vector_stores/langchain";
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import { AzureAISearch } from "../vector_stores/azure_ai_search";
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export class EmbedderFactory {
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static create(provider: string, config: EmbeddingConfig): Embedder {
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@@ -95,6 +96,8 @@ export class VectorStoreFactory {
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return new LangchainVectorStore(config as any);
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case "vectorize":
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return new VectorizeDB(config as any);
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case "azure-ai-search":
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return new AzureAISearch(config as any);
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default:
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throw new Error(`Unsupported vector store provider: ${provider}`);
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}
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@@ -0,0 +1,646 @@
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import {
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SearchClient,
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SearchIndexClient,
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AzureKeyCredential,
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SearchIndex,
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SearchField,
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SearchFieldDataType,
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SimpleField,
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VectorSearch,
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VectorSearchProfile,
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HnswAlgorithmConfiguration,
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ScalarQuantizationCompression,
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BinaryQuantizationCompression,
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VectorizedQuery,
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} from "@azure/search-documents";
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import { DefaultAzureCredential } from "@azure/identity";
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import { VectorStore } from "./base";
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import { SearchFilters, VectorStoreConfig, VectorStoreResult } from "../types";
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/**
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* Configuration interface for Azure AI Search vector store
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*/
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interface AzureAISearchConfig extends VectorStoreConfig {
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/**
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* Azure AI Search service name (e.g., "my-search-service")
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*/
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serviceName: string;
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/**
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* Index/collection name to use
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*/
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collectionName: string;
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/**
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* API key for authentication (if not provided, uses DefaultAzureCredential)
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*/
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apiKey?: string;
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/**
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* Vector embedding dimensions
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*/
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embeddingModelDims: number;
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/**
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* Compression type: 'none', 'scalar', or 'binary'
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* @default 'none'
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*/
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compressionType?: "none" | "scalar" | "binary";
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/**
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* Use half precision (float16) instead of full precision (float32)
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* @default false
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*/
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useFloat16?: boolean;
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/**
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* Enable hybrid search (combines vector + text search)
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* @default false
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*/
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hybridSearch?: boolean;
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/**
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* Vector filter mode: 'preFilter' or 'postFilter'
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* @default 'preFilter'
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*/
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vectorFilterMode?: string;
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}
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/**
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* Azure AI Search vector store implementation
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* Supports vector search with hybrid search, compression, and filtering
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*/
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export class AzureAISearch implements VectorStore {
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private searchClient: SearchClient<any>;
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private indexClient: SearchIndexClient;
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private readonly serviceName: string;
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private readonly indexName: string;
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private readonly embeddingModelDims: number;
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private readonly compressionType: "none" | "scalar" | "binary";
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private readonly useFloat16: boolean;
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private readonly hybridSearch: boolean;
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private readonly vectorFilterMode: string;
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private readonly apiKey: string | undefined;
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constructor(config: AzureAISearchConfig) {
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this.serviceName = config.serviceName;
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this.indexName = config.collectionName;
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this.embeddingModelDims = config.embeddingModelDims;
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this.compressionType = config.compressionType || "none";
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this.useFloat16 = config.useFloat16 || false;
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this.hybridSearch = config.hybridSearch || false;
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this.vectorFilterMode = config.vectorFilterMode || "preFilter";
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this.apiKey = config.apiKey;
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const serviceEndpoint = `https://${this.serviceName}.search.windows.net`;
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// Determine authentication: API key or DefaultAzureCredential
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const credential =
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this.apiKey && this.apiKey !== "" && this.apiKey !== "your-api-key"
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? new AzureKeyCredential(this.apiKey)
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: new DefaultAzureCredential();
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// Initialize clients
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this.searchClient = new SearchClient(
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serviceEndpoint,
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this.indexName,
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credential,
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);
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this.indexClient = new SearchIndexClient(serviceEndpoint, credential);
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// Initialize the index
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this.initialize().catch(console.error);
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}
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/**
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* Initialize the Azure AI Search index if it doesn't exist
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*/
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async initialize(): Promise<void> {
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try {
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const collections = await this.listCols();
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if (!collections.includes(this.indexName)) {
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await this.createCol();
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}
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} catch (error) {
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console.error("Error initializing Azure AI Search:", error);
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throw error;
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}
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}
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/**
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* Create a new index in Azure AI Search
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*/
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private async createCol(): Promise<void> {
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// Determine vector type based on use_float16 setting
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const vectorType = this.useFloat16
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? "Collection(Edm.Half)"
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: "Collection(Edm.Single)";
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// Configure compression settings
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const compressionConfigurations: Array<
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ScalarQuantizationCompression | BinaryQuantizationCompression
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> = [];
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let compressionName: string | undefined = undefined;
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if (this.compressionType === "scalar") {
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compressionName = "myCompression";
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compressionConfigurations.push({
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kind: "scalarQuantization",
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compressionName: compressionName,
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} as ScalarQuantizationCompression);
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} else if (this.compressionType === "binary") {
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compressionName = "myCompression";
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compressionConfigurations.push({
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kind: "binaryQuantization",
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compressionName: compressionName,
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} as BinaryQuantizationCompression);
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}
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// Define index fields
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const fields: SearchField[] = [
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{
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name: "id",
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type: "Edm.String",
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key: true,
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} as SimpleField,
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{
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name: "user_id",
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type: "Edm.String",
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filterable: true,
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} as SimpleField,
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{
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name: "run_id",
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type: "Edm.String",
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filterable: true,
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} as SimpleField,
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{
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name: "agent_id",
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type: "Edm.String",
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filterable: true,
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} as SimpleField,
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{
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name: "vector",
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type: vectorType as SearchFieldDataType,
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searchable: true,
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vectorSearchDimensions: this.embeddingModelDims,
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vectorSearchProfileName: "my-vector-config",
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} as SearchField,
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{
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name: "payload",
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type: "Edm.String",
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searchable: true,
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} as SearchField,
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];
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// Configure vector search
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const vectorSearch: VectorSearch = {
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profiles: [
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{
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name: "my-vector-config",
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algorithmConfigurationName: "my-algorithms-config",
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compressionName:
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this.compressionType !== "none" ? compressionName : undefined,
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} as VectorSearchProfile,
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],
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algorithms: [
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{
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kind: "hnsw",
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name: "my-algorithms-config",
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} as HnswAlgorithmConfiguration,
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],
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compressions: compressionConfigurations,
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};
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// Create index
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const index: SearchIndex = {
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name: this.indexName,
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fields,
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vectorSearch,
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};
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await this.indexClient.createOrUpdateIndex(index);
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}
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/**
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* Generate a document for insertion
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*/
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private generateDocument(
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vector: number[],
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payload: Record<string, any>,
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id: string,
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): Record<string, any> {
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const document: Record<string, any> = {
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id,
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vector,
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payload: JSON.stringify(payload),
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};
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// Extract additional fields if they exist
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for (const field of ["user_id", "run_id", "agent_id"]) {
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if (field in payload) {
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document[field] = payload[field];
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}
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}
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return document;
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}
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/**
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* Insert vectors into the index
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*/
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async insert(
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vectors: number[][],
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ids: string[],
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payloads: Record<string, any>[],
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): Promise<void> {
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console.log(
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`Inserting ${vectors.length} vectors into index ${this.indexName}`,
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);
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const documents = vectors.map((vector, idx) =>
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this.generateDocument(vector, payloads[idx] || {}, ids[idx]),
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);
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const response = await this.searchClient.uploadDocuments(documents);
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// Check for errors
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for (const result of response.results) {
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if (!result.succeeded) {
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throw new Error(
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`Insert failed for document ${result.key}: ${result.errorMessage}`,
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);
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}
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}
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}
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/**
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* Sanitize filter keys to remove non-alphanumeric characters
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*/
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private sanitizeKey(key: string): string {
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return key.replace(/[^\w]/g, "");
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}
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/**
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* Build OData filter expression from SearchFilters
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*/
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private buildFilterExpression(filters: SearchFilters): string {
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const filterConditions: string[] = [];
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for (const [key, value] of Object.entries(filters)) {
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const safeKey = this.sanitizeKey(key);
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if (typeof value === "string") {
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// Escape single quotes in string values
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const safeValue = value.replace(/'/g, "''");
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filterConditions.push(`${safeKey} eq '${safeValue}'`);
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} else {
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filterConditions.push(`${safeKey} eq ${value}`);
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}
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}
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return filterConditions.join(" and ");
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}
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/**
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* Extract JSON from payload string
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* Handles cases where payload might have extra text
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*/
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private extractJson(payload: string): string {
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try {
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// Try to parse as-is first
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JSON.parse(payload);
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return payload;
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} catch {
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// If that fails, try to extract JSON object
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const match = payload.match(/\{.*\}/s);
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return match ? match[0] : payload;
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}
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}
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/**
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* Search for similar vectors
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*/
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async search(
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query: number[],
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limit: number = 5,
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filters?: SearchFilters,
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): Promise<VectorStoreResult[]> {
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const filterExpression = filters
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? this.buildFilterExpression(filters)
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: undefined;
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const vectorQuery: VectorizedQuery<any> = {
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kind: "vector",
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vector: query,
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kNearestNeighborsCount: limit,
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fields: ["vector"],
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};
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let searchResults;
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if (this.hybridSearch) {
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// Hybrid search: combines vector + text search
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searchResults = await this.searchClient.search("*", {
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vectorSearchOptions: {
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queries: [vectorQuery],
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filterMode: this.vectorFilterMode as any,
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},
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filter: filterExpression,
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top: limit,
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searchFields: ["payload"],
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});
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} else {
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// Pure vector search
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searchResults = await this.searchClient.search("*", {
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vectorSearchOptions: {
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queries: [vectorQuery],
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filterMode: this.vectorFilterMode as any,
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},
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filter: filterExpression,
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top: limit,
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});
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}
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const results: VectorStoreResult[] = [];
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for await (const result of searchResults.results) {
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const payloadStr = result.document.payload as string;
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const payload = JSON.parse(this.extractJson(payloadStr));
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results.push({
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id: result.document.id as string,
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score: result.score,
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payload,
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});
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}
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return results;
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}
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/**
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* Delete a vector by ID
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*/
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async delete(vectorId: string): Promise<void> {
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const response = await this.searchClient.deleteDocuments([
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{ id: vectorId },
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]);
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for (const result of response.results) {
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if (!result.succeeded) {
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throw new Error(
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`Delete failed for document ${vectorId}: ${result.errorMessage}`,
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);
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}
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}
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console.log(
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`Deleted document with ID '${vectorId}' from index '${this.indexName}'.`,
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);
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}
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/**
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* Update a vector and its payload
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*/
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async update(
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vectorId: string,
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vector: number[],
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payload: Record<string, any>,
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): Promise<void> {
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const document: Record<string, any> = { id: vectorId };
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if (vector) {
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document.vector = vector;
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}
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if (payload) {
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document.payload = JSON.stringify(payload);
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// Extract additional fields
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for (const field of ["user_id", "run_id", "agent_id"]) {
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if (field in payload) {
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document[field] = payload[field];
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}
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}
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}
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const response = await this.searchClient.mergeOrUploadDocuments([document]);
|
||||
|
||||
for (const result of response.results) {
|
||||
if (!result.succeeded) {
|
||||
throw new Error(
|
||||
`Update failed for document ${vectorId}: ${result.errorMessage}`,
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Retrieve a vector by ID
|
||||
*/
|
||||
async get(vectorId: string): Promise<VectorStoreResult | null> {
|
||||
try {
|
||||
const result = await this.searchClient.getDocument(vectorId);
|
||||
const payloadStr = result.payload as string;
|
||||
const payload = JSON.parse(this.extractJson(payloadStr));
|
||||
|
||||
return {
|
||||
id: result.id as string,
|
||||
payload,
|
||||
};
|
||||
} catch (error: any) {
|
||||
// Return null if document not found
|
||||
if (error?.statusCode === 404) {
|
||||
return null;
|
||||
}
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* List all collections (indexes)
|
||||
*/
|
||||
private async listCols(): Promise<string[]> {
|
||||
const names: string[] = [];
|
||||
|
||||
for await (const index of this.indexClient.listIndexes()) {
|
||||
names.push(index.name);
|
||||
}
|
||||
|
||||
return names;
|
||||
}
|
||||
|
||||
/**
|
||||
* Delete the index
|
||||
*/
|
||||
async deleteCol(): Promise<void> {
|
||||
await this.indexClient.deleteIndex(this.indexName);
|
||||
}
|
||||
|
||||
/**
|
||||
* Get information about the index
|
||||
*/
|
||||
private async colInfo(): Promise<{ name: string; fields: SearchField[] }> {
|
||||
const index = await this.indexClient.getIndex(this.indexName);
|
||||
return {
|
||||
name: index.name,
|
||||
fields: index.fields,
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* List all vectors in the index
|
||||
*/
|
||||
async list(
|
||||
filters?: SearchFilters,
|
||||
limit: number = 100,
|
||||
): Promise<[VectorStoreResult[], number]> {
|
||||
const filterExpression = filters
|
||||
? this.buildFilterExpression(filters)
|
||||
: undefined;
|
||||
|
||||
const searchResults = await this.searchClient.search("*", {
|
||||
filter: filterExpression,
|
||||
top: limit,
|
||||
});
|
||||
|
||||
const results: VectorStoreResult[] = [];
|
||||
|
||||
for await (const result of searchResults.results) {
|
||||
const payloadStr = result.document.payload as string;
|
||||
const payload = JSON.parse(this.extractJson(payloadStr));
|
||||
|
||||
results.push({
|
||||
id: result.document.id as string,
|
||||
score: result.score,
|
||||
payload,
|
||||
});
|
||||
}
|
||||
|
||||
return [results, results.length];
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate a random user ID
|
||||
*/
|
||||
private generateUUID(): string {
|
||||
return "xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx".replace(
|
||||
/[xy]/g,
|
||||
function (c) {
|
||||
const r = (Math.random() * 16) | 0;
|
||||
const v = c === "x" ? r : (r & 0x3) | 0x8;
|
||||
return v.toString(16);
|
||||
},
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Get user ID from memory_migrations collection
|
||||
* Required by VectorStore interface
|
||||
*/
|
||||
async getUserId(): Promise<string> {
|
||||
try {
|
||||
// Check if memory_migrations index exists
|
||||
const collections = await this.listCols();
|
||||
const migrationIndexExists = collections.includes("memory_migrations");
|
||||
|
||||
if (!migrationIndexExists) {
|
||||
// Create memory_migrations index
|
||||
const migrationIndex: SearchIndex = {
|
||||
name: "memory_migrations",
|
||||
fields: [
|
||||
{
|
||||
name: "id",
|
||||
type: "Edm.String",
|
||||
key: true,
|
||||
} as SimpleField,
|
||||
{
|
||||
name: "user_id",
|
||||
type: "Edm.String",
|
||||
searchable: false,
|
||||
filterable: true,
|
||||
} as SimpleField,
|
||||
],
|
||||
};
|
||||
await this.indexClient.createOrUpdateIndex(migrationIndex);
|
||||
}
|
||||
|
||||
// Try to get existing user_id
|
||||
const searchResults = await this.searchClient.search("*", {
|
||||
top: 1,
|
||||
});
|
||||
|
||||
for await (const result of searchResults.results) {
|
||||
const userId = result.document.user_id as string;
|
||||
if (userId) {
|
||||
return userId;
|
||||
}
|
||||
}
|
||||
|
||||
// Generate a random user_id if none exists
|
||||
const randomUserId =
|
||||
Math.random().toString(36).substring(2, 15) +
|
||||
Math.random().toString(36).substring(2, 15);
|
||||
|
||||
await this.searchClient.uploadDocuments([
|
||||
{
|
||||
id: this.generateUUID(),
|
||||
user_id: randomUserId,
|
||||
},
|
||||
]);
|
||||
|
||||
return randomUserId;
|
||||
} catch (error) {
|
||||
console.error("Error getting user ID:", error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Set user ID in memory_migrations collection
|
||||
* Required by VectorStore interface
|
||||
*/
|
||||
async setUserId(userId: string): Promise<void> {
|
||||
try {
|
||||
// Get existing point ID or generate new one
|
||||
const searchResults = await this.searchClient.search("*", {
|
||||
top: 1,
|
||||
});
|
||||
|
||||
let pointId = this.generateUUID();
|
||||
|
||||
for await (const result of searchResults.results) {
|
||||
pointId = result.document.id as string;
|
||||
break;
|
||||
}
|
||||
|
||||
await this.searchClient.mergeOrUploadDocuments([
|
||||
{
|
||||
id: pointId,
|
||||
user_id: userId,
|
||||
},
|
||||
]);
|
||||
} catch (error) {
|
||||
console.error("Error setting user ID:", error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Reset the index by deleting and recreating it
|
||||
*/
|
||||
async reset(): Promise<void> {
|
||||
console.log(`Resetting index ${this.indexName}...`);
|
||||
|
||||
try {
|
||||
// Delete the index
|
||||
await this.deleteCol();
|
||||
|
||||
// Recreate the index
|
||||
await this.createCol();
|
||||
} catch (error) {
|
||||
console.error(`Error resetting index ${this.indexName}:`, error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,44 @@
|
||||
/// <reference types="jest" />
|
||||
import { VectorStoreFactory } from "../src/utils/factory";
|
||||
import { AzureAISearch } from "../src/vector_stores/azure_ai_search";
|
||||
|
||||
describe("VectorStoreFactory", () => {
|
||||
describe("create", () => {
|
||||
it("should create Azure AI Search vector store", () => {
|
||||
const config = {
|
||||
collectionName: "test-memories",
|
||||
serviceName: "test-service",
|
||||
apiKey: "test-api-key",
|
||||
embeddingModelDims: 1536,
|
||||
compressionType: "none" as const,
|
||||
useFloat16: false,
|
||||
hybridSearch: false,
|
||||
vectorFilterMode: "preFilter" as const,
|
||||
};
|
||||
|
||||
const vectorStore = VectorStoreFactory.create("azure-ai-search", config);
|
||||
|
||||
expect(vectorStore).toBeInstanceOf(AzureAISearch);
|
||||
});
|
||||
|
||||
it("should create memory vector store", () => {
|
||||
const config = {
|
||||
collectionName: "test-memories",
|
||||
dimension: 1536,
|
||||
};
|
||||
|
||||
const vectorStore = VectorStoreFactory.create("memory", config);
|
||||
|
||||
expect(vectorStore).toBeDefined();
|
||||
expect(vectorStore.constructor.name).toBe("MemoryVectorStore");
|
||||
});
|
||||
|
||||
it("should throw error for unsupported provider", () => {
|
||||
const config = {};
|
||||
|
||||
expect(() => {
|
||||
VectorStoreFactory.create("unsupported-provider", config);
|
||||
}).toThrow("Unsupported vector store provider: unsupported-provider");
|
||||
});
|
||||
});
|
||||
});
|
||||
Reference in New Issue
Block a user