test: Updated langchain vectorstore tests to use a custom vectorstore to simplify the testing process (#3793)

Author: bizob2828

test/versioned/langchain-aws/package.json

@@ -3,7 +3,7 @@
   "targets": [
     {
       "name": "@langchain/aws",
-      "minSupported": "1.0.0",
+      "minSupported": "0.1.3",
       "minAgentVersion": "13.8.0"
     }
   ],
@@ -22,21 +22,7 @@
       },
       "files": [
         "runnables.test.js",
-        "runnables-streaming.test.js"
-      ]
-    },
-    {
-      "engines": {
-        "node": ">=20"
-      },
-      "comment": "because @langchain/core and @langchain/community need to be the same minor version because of peer deps, we have to test with latest",
-      "dependencies": {
-        "@langchain/aws": "latest",
-        "@langchain/core": "latest",
-        "@langchain/community": "latest",
-        "@elastic/elasticsearch": "8.13.1"
-      },
-      "files": [
+        "runnables-streaming.test.js",
         "vectorstore.test.js"
       ]
     }

test/versioned/langchain-aws/vectorstore.test.js

@@ -11,7 +11,6 @@ const { removeModules } = require('../../lib/cache-buster')
 const { runVectorstoreTests } = require('../langchain/vectorstore')
 const { Document } = require('@langchain/core/documents')
 const { FAKE_CREDENTIALS, getAiResponseServer } = require('../../lib/aws-server-stubs')
-const params = require('../../lib/params')
 const helper = require('../../lib/agent_helper')
 
 const config = {
@@ -27,19 +26,14 @@ test.beforeEach(async (ctx) => {
   ctx.nr.server = server
   ctx.nr.agent = helper.instrumentMockedAgent(config)
 
+  const { VectorStore } = require('@langchain/core/vectorstores')
+  // must pass in VectorStore to ensure it's the same version as the test
+  // and not whatever is installed in `test/versioned/langchain/`
+  const CustomVectorStore = require('../langchain/custom-vector-store')(VectorStore)
   const { BedrockEmbeddings } = require('@langchain/aws')
   const { BedrockRuntimeClient } = require('@aws-sdk/client-bedrock-runtime')
   ctx.nr.langchainCoreVersion = require('@langchain/core/package.json').version
 
-  const { Client } = require('@elastic/elasticsearch')
-  const clientArgs = {
-    client: new Client({
-      node: `http://${params.elastic_host}:${params.elastic_port}`
-    }),
-    indexName: 'test_langchain_aws_vectorstore'
-  }
-  const { ElasticVectorSearch } = require('@langchain/community/vectorstores/elasticsearch')
-
   // Create the BedrockRuntimeClient with our mock endpoint
   const bedrockClient = new BedrockRuntimeClient({
     region: 'us-east-1',
@@ -60,18 +54,16 @@ test.beforeEach(async (ctx) => {
       pageContent: 'embed text amazon token count callback response'
     })
   ]
-  const vectorStore = new ElasticVectorSearch(ctx.nr.embedding, clientArgs)
-  await vectorStore.deleteIfExists()
+  const vectorStore = new CustomVectorStore(ctx.nr.embedding)
   await vectorStore.addDocuments(docs)
   ctx.nr.vs = vectorStore
 })
 
 test.afterEach(async (ctx) => {
-  await ctx.nr?.vs?.deleteIfExists()
   ctx.nr?.server?.destroy()
   helper.unloadAgent(ctx.nr.agent)
   // bust the require-cache so it can re-instrument
-  removeModules(['@langchain/core', '@langchain/aws', '@aws-sdk', '@elastic', '@langchain/community'])
+  removeModules(['@langchain/core', '@aws-sdk'])
 })
 
 runVectorstoreTests({

test/versioned/langchain-openai/package.json

@@ -3,7 +3,7 @@
   "targets": [
     {
       "name": "@langchain/openai",
-      "minSupported": "1.0.0",
+      "minSupported": "0.2.0",
       "minAgentVersion": "11.13.0"
     }
   ],
@@ -22,23 +22,9 @@
       },
       "files": [
         "runnables.test.js",
-        "runnables-streaming.test.js"
-      ]
-    },
-     {
-      "engines": {
-        "node": ">=20"
-      },
-      "comment": "because @langchain/core and @langchain/community need to be the same minor version because of peer deps, we have to test with latest",
-      "dependencies": {
-        "@langchain/core": "latest",
-        "@langchain/community": "latest",
-        "@langchain/openai": "latest",
-        "@elastic/elasticsearch": "8.13.1"
-      },
-      "files": [
+        "runnables-streaming.test.js",
         "vectorstore.test.js"
       ]
     }
   ]
-}
+}

test/versioned/langchain-openai/vectorstore.test.js

@@ -12,7 +12,6 @@ const { removeModules } = require('../../lib/cache-buster')
 const { runVectorstoreTests } = require('../langchain/vectorstore')
 const { Document } = require('@langchain/core/documents')
 const createOpenAIMockServer = require('../openai/mock-server')
-const params = require('../../lib/params')
 const helper = require('../../lib/agent_helper')
 
 const config = {
@@ -27,18 +26,13 @@ test.beforeEach(async (ctx) => {
   ctx.nr.server = server
   ctx.nr.agent = helper.instrumentMockedAgent(config)
 
+  const { VectorStore } = require('@langchain/core/vectorstores')
+  // must pass in VectorStore to ensure it's the same version as the test
+  // and not whatever is installed in `test/versioned/langchain/`
+  const CustomVectorStore = require('../langchain/custom-vector-store')(VectorStore)
   const { OpenAIEmbeddings } = require('@langchain/openai')
   ctx.nr.langchainCoreVersion = require('@langchain/core/package.json').version
 
-  const { Client } = require('@elastic/elasticsearch')
-  const clientArgs = {
-    client: new Client({
-      node: `http://${params.elastic_host}:${params.elastic_port}`
-    }),
-    indexName: 'test_langchain_openai_vectorstore'
-  }
-  const { ElasticVectorSearch } = require('@langchain/community/vectorstores/elasticsearch')
-
   ctx.nr.embedding = new OpenAIEmbeddings({
     apiKey: 'fake-key',
     configuration: {
@@ -51,18 +45,16 @@ test.beforeEach(async (ctx) => {
       pageContent: 'This is an embedding test.'
     })
   ]
-  const vectorStore = new ElasticVectorSearch(ctx.nr.embedding, clientArgs)
-  await vectorStore.deleteIfExists()
+  const vectorStore = new CustomVectorStore(ctx.nr.embedding)
   await vectorStore.addDocuments(docs)
   ctx.nr.vs = vectorStore
 })
 
 test.afterEach(async (ctx) => {
-  await ctx.nr?.vs?.deleteIfExists()
   ctx.nr?.server?.close()
   helper.unloadAgent(ctx.nr.agent)
   // bust the require-cache so it can re-instrument
-  removeModules(['@langchain/core', 'openai', '@elastic', '@langchain/community'])
+  removeModules(['@langchain/core', 'openai'])
 })
 
 runVectorstoreTests({

test/versioned/langchain/custom-vector-store.js

@@ -0,0 +1,55 @@
+/*
+ * Copyright 2026 New Relic Corporation. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+'use strict'
+
+/**
+ * This is a function because we must pass in the appropriate `VectorStore` class
+ * from either `langchain-openai` or `langchain-aws`.
+ *
+ * @param {VectorStore} VectorStore class from `@langchain/core/vectorstore`
+ * @returns {object} CustomVectorStore to be used for testing
+ */
+module.exports = function createCustomVectorStore(VectorStore) {
+  class CustomVectorStore extends VectorStore {
+    /**
+     * @param {object} embeddings Embedding function or object (required by VectorStore)
+     * @param {object} [options] Optional config
+     */
+    constructor(embeddings, options = {}) {
+      super(embeddings, options)
+      this._documents = []
+    }
+
+    _vectorstoreType() {
+      return 'custom'
+    }
+
+    /**
+     * Add documents to the vectorstore without generating real embeddings.
+     * @param {Array<{pageContent: string, metadata?: object}>} docs documents to add
+     */
+    async addDocuments(docs) {
+      this._documents.push(...docs)
+    }
+
+    /**
+     * Perform an in-memory similarity search over stored documents.
+     * Called by the base class `similaritySearch` after embedding the query.
+     * @param {number[]} _vector unused vector prop
+     * @param {number} k score
+     * @param {object} [filter] filter query
+     * @returns {Array} results from query
+     */
+    async similaritySearchVectorWithScore(_vector, k, filter) {
+      let docs = this._documents
+      if (filter) {
+        docs = docs.filter((doc) => Object.entries(filter).every(([key, value]) => doc.metadata?.[key] === value))
+      }
+      return docs.slice(0, k).map((doc) => [doc, 1.0])
+    }
+  }
+  return CustomVectorStore
+}