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| 1 | +/* |
| 2 | + * Copyright 2024 New Relic Corporation. All rights reserved. |
| 3 | + * SPDX-License-Identifier: Apache-2.0 |
| 4 | + */ |
| 5 | + |
| 6 | +'use strict' |
| 7 | + |
| 8 | +const tap = require('tap') |
| 9 | +const helper = require('../../lib/agent_helper') |
| 10 | +// load the assertSegments assertion |
| 11 | +require('../../lib/metrics_helper') |
| 12 | +const { version: pkgVersion } = require('@langchain/core/package.json') |
| 13 | +const createOpenAIMockServer = require('../openai/mock-server') |
| 14 | +const { filterLangchainEvents, filterLangchainEventsByType } = require('./common') |
| 15 | +const { DESTINATIONS } = require('../../../lib/config/attribute-filter') |
| 16 | +const params = require('../../lib/params') |
| 17 | +const { Document } = require('@langchain/core/documents') |
| 18 | + |
| 19 | +const config = { |
| 20 | + ai_monitoring: { |
| 21 | + enabled: true |
| 22 | + }, |
| 23 | + feature_flag: { |
| 24 | + langchain_instrumentation: true |
| 25 | + } |
| 26 | +} |
| 27 | + |
| 28 | +tap.test('Langchain instrumentation - vectorstore', (t) => { |
| 29 | + t.autoend() |
| 30 | + |
| 31 | + t.beforeEach(async (t) => { |
| 32 | + const { host, port, server } = await createOpenAIMockServer() |
| 33 | + t.context.server = server |
| 34 | + t.context.agent = helper.instrumentMockedAgent(config) |
| 35 | + const { OpenAIEmbeddings } = require('@langchain/openai') |
| 36 | + |
| 37 | + const { Client } = require('@elastic/elasticsearch') |
| 38 | + const clientArgs = { |
| 39 | + client: new Client({ |
| 40 | + node: `http://${params.elastic_host}:${params.elastic_port}` |
| 41 | + }) |
| 42 | + } |
| 43 | + const { ElasticVectorSearch } = require('@langchain/community/vectorstores/elasticsearch') |
| 44 | + |
| 45 | + t.context.embedding = new OpenAIEmbeddings({ |
| 46 | + openAIApiKey: 'fake-key', |
| 47 | + configuration: { |
| 48 | + baseURL: `http://${host}:${port}` |
| 49 | + } |
| 50 | + }) |
| 51 | + const docs = [ |
| 52 | + new Document({ |
| 53 | + metadata: { id: '2' }, |
| 54 | + pageContent: 'This is an embedding test.' |
| 55 | + }) |
| 56 | + ] |
| 57 | + const vectorStore = new ElasticVectorSearch(t.context.embedding, clientArgs) |
| 58 | + await vectorStore.deleteIfExists() |
| 59 | + await vectorStore.addDocuments(docs) |
| 60 | + t.context.vs = vectorStore |
| 61 | + }) |
| 62 | + |
| 63 | + t.afterEach(async (t) => { |
| 64 | + t.context?.server?.close() |
| 65 | + helper.unloadAgent(t.context.agent) |
| 66 | + // bust the require-cache so it can re-instrument |
| 67 | + Object.keys(require.cache).forEach((key) => { |
| 68 | + if ( |
| 69 | + key.includes('@langchain/core') || |
| 70 | + key.includes('openai') || |
| 71 | + key.includes('@elastic') || |
| 72 | + key.includes('@langchain/community') |
| 73 | + ) { |
| 74 | + delete require.cache[key] |
| 75 | + } |
| 76 | + }) |
| 77 | + }) |
| 78 | + |
| 79 | + t.test('should create vectorstore events for every similarity search call', (t) => { |
| 80 | + const { agent, vs } = t.context |
| 81 | + |
| 82 | + helper.runInNamedTransaction(agent, async (tx) => { |
| 83 | + await vs.similaritySearch('This is an embedding test.', 1) |
| 84 | + |
| 85 | + const events = agent.customEventAggregator.events.toArray() |
| 86 | + t.equal(events.length, 3, 'should create 3 events') |
| 87 | + |
| 88 | + const langchainEvents = events.filter((event) => { |
| 89 | + const [, chainEvent] = event |
| 90 | + return chainEvent.vendor === 'langchain' |
| 91 | + }) |
| 92 | + |
| 93 | + t.equal(langchainEvents.length, 2, 'should create 2 langchain events') |
| 94 | + |
| 95 | + tx.end() |
| 96 | + t.end() |
| 97 | + }) |
| 98 | + }) |
| 99 | + |
| 100 | + t.test('should create span on successful vectorstore create', (t) => { |
| 101 | + const { agent, vs } = t.context |
| 102 | + helper.runInTransaction(agent, async (tx) => { |
| 103 | + const result = await vs.similaritySearch('This is an embedding test.', 1) |
| 104 | + t.ok(result) |
| 105 | + t.assertSegments(tx.trace.root, ['Llm/vectorstore/Langchain/similaritySearch'], { |
| 106 | + exact: false |
| 107 | + }) |
| 108 | + tx.end() |
| 109 | + t.end() |
| 110 | + }) |
| 111 | + }) |
| 112 | + |
| 113 | + t.test('should increment tracking metric for each langchain vectorstore event', (t) => { |
| 114 | + const { agent, vs } = t.context |
| 115 | + |
| 116 | + helper.runInTransaction(agent, async (tx) => { |
| 117 | + await vs.similaritySearch('This is an embedding test.', 1) |
| 118 | + |
| 119 | + const metrics = agent.metrics.getOrCreateMetric( |
| 120 | + `Supportability/Nodejs/ML/Langchain/${pkgVersion}` |
| 121 | + ) |
| 122 | + t.equal(metrics.callCount > 0, true) |
| 123 | + |
| 124 | + tx.end() |
| 125 | + t.end() |
| 126 | + }) |
| 127 | + }) |
| 128 | + |
| 129 | + t.test( |
| 130 | + 'should create vectorstore events for every similarity search call with embeddings', |
| 131 | + (t) => { |
| 132 | + const { agent, vs } = t.context |
| 133 | + |
| 134 | + helper.runInNamedTransaction(agent, async (tx) => { |
| 135 | + await vs.similaritySearch('This is an embedding test.', 1) |
| 136 | + |
| 137 | + const events = agent.customEventAggregator.events.toArray() |
| 138 | + const langchainEvents = filterLangchainEvents(events) |
| 139 | + |
| 140 | + const vectorSearchResultEvents = filterLangchainEventsByType( |
| 141 | + langchainEvents, |
| 142 | + 'LlmVectorSearchResult' |
| 143 | + ) |
| 144 | + |
| 145 | + const vectorSearchEvents = filterLangchainEventsByType(langchainEvents, 'LlmVectorSearch') |
| 146 | + |
| 147 | + t.langchainVectorSearch({ |
| 148 | + tx, |
| 149 | + vectorSearch: vectorSearchEvents[0], |
| 150 | + responseDocumentSize: 1 |
| 151 | + }) |
| 152 | + t.langchainVectorSearchResult({ |
| 153 | + tx, |
| 154 | + vectorSearchResult: vectorSearchResultEvents, |
| 155 | + vectorSearchId: vectorSearchEvents[0][1].id |
| 156 | + }) |
| 157 | + |
| 158 | + tx.end() |
| 159 | + t.end() |
| 160 | + }) |
| 161 | + } |
| 162 | + ) |
| 163 | + |
| 164 | + t.test( |
| 165 | + 'should create only vectorstore search event for similarity search call with embeddings and invalid metadata filter', |
| 166 | + (t) => { |
| 167 | + const { agent, vs } = t.context |
| 168 | + |
| 169 | + helper.runInNamedTransaction(agent, async (tx) => { |
| 170 | + // search for documents with invalid filter |
| 171 | + await vs.similaritySearch('This is an embedding test.', 1, { |
| 172 | + a: 'some filter' |
| 173 | + }) |
| 174 | + |
| 175 | + const events = agent.customEventAggregator.events.toArray() |
| 176 | + const langchainEvents = filterLangchainEvents(events) |
| 177 | + |
| 178 | + const vectorSearchResultEvents = filterLangchainEventsByType( |
| 179 | + langchainEvents, |
| 180 | + 'LlmVectorSearchResult' |
| 181 | + ) |
| 182 | + |
| 183 | + const vectorSearchEvents = filterLangchainEventsByType(langchainEvents, 'LlmVectorSearch') |
| 184 | + |
| 185 | + // there are no documents in vector store with that filter |
| 186 | + t.equal(vectorSearchResultEvents.length, 0, 'should have 0 events') |
| 187 | + t.langchainVectorSearch({ |
| 188 | + tx, |
| 189 | + vectorSearch: vectorSearchEvents[0], |
| 190 | + responseDocumentSize: 0 |
| 191 | + }) |
| 192 | + |
| 193 | + tx.end() |
| 194 | + t.end() |
| 195 | + }) |
| 196 | + } |
| 197 | + ) |
| 198 | + |
| 199 | + t.test('should not create vectorstore events when not in a transaction', async (t) => { |
| 200 | + const { agent, vs } = t.context |
| 201 | + |
| 202 | + await vs.similaritySearch('This is an embedding test.', 1) |
| 203 | + |
| 204 | + const events = agent.customEventAggregator.events.toArray() |
| 205 | + t.equal(events.length, 0, 'should not create vectorstore events') |
| 206 | + t.end() |
| 207 | + }) |
| 208 | + |
| 209 | + t.test('should add llm attribute to transaction', (t) => { |
| 210 | + const { agent, vs } = t.context |
| 211 | + |
| 212 | + helper.runInTransaction(agent, async (tx) => { |
| 213 | + await vs.similaritySearch('This is an embedding test.', 1) |
| 214 | + |
| 215 | + const attributes = tx.trace.attributes.get(DESTINATIONS.TRANS_EVENT) |
| 216 | + t.equal(attributes.llm, true) |
| 217 | + |
| 218 | + tx.end() |
| 219 | + t.end() |
| 220 | + }) |
| 221 | + }) |
| 222 | + |
| 223 | + t.test('should create error events', (t) => { |
| 224 | + const { agent, vs } = t.context |
| 225 | + |
| 226 | + helper.runInNamedTransaction(agent, async (tx) => { |
| 227 | + try { |
| 228 | + await vs.similaritySearch('Embedding not allowed.', 1) |
| 229 | + } catch (error) { |
| 230 | + t.ok(error) |
| 231 | + } |
| 232 | + |
| 233 | + const events = agent.customEventAggregator.events.toArray() |
| 234 | + // Only LlmEmbedding and LlmVectorSearch events will be created |
| 235 | + // LangChainVectorSearchResult event won't be created since there was an error |
| 236 | + t.equal(events.length, 2, 'should create 2 events') |
| 237 | + |
| 238 | + const langchainEvents = events.filter((event) => { |
| 239 | + const [, chainEvent] = event |
| 240 | + return chainEvent.vendor === 'langchain' |
| 241 | + }) |
| 242 | + |
| 243 | + t.equal(langchainEvents.length, 1, 'should create 1 langchain vectorsearch event') |
| 244 | + t.equal(langchainEvents[0][1].error, true) |
| 245 | + |
| 246 | + // But, we should also get two error events: 1xLLM and 1xLangChain |
| 247 | + const exceptions = tx.exceptions |
| 248 | + for (const e of exceptions) { |
| 249 | + const str = Object.prototype.toString.call(e.customAttributes) |
| 250 | + t.equal(str, '[object LlmErrorMessage]') |
| 251 | + } |
| 252 | + |
| 253 | + tx.end() |
| 254 | + t.end() |
| 255 | + }) |
| 256 | + }) |
| 257 | +}) |
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