mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-28 17:01:00 +03:00
Chore/refractor (#4454)
* markdown files and env examples cleanup * components update * update jsonlines description * server refractor * update telemetry * add execute custom node * add ui refractor * add username and password authenticate * correctly retrieve past images in agentflowv2 * disable e2e temporarily * add existing username and password authenticate * update migration to default workspace * update todo * blob storage migrating * throw error on agent tool call error * add missing execution import * add referral * chore: add error message when importData is undefined * migrate api keys to db * fix: data too long for column executionData * migrate api keys from json to db at init * add info on account setup * update docstore missing fields --------- Co-authored-by: chungyau97 <chungyau97@gmail.com>
This commit is contained in:
Henry Heng
@@ -0,0 +1,165 @@
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import { RunCollectorCallbackHandler } from '@langchain/core/tracers/run_collector'
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import { Run } from '@langchain/core/tracers/base'
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import { EvaluationRunner } from './EvaluationRunner'
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import { encoding_for_model, get_encoding } from '@dqbd/tiktoken'
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export class EvaluationRunTracer extends RunCollectorCallbackHandler {
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evaluationRunId: string
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model: string
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constructor(id: string) {
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super()
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this.evaluationRunId = id
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}
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async persistRun(run: Run): Promise<void> {
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return super.persistRun(run)
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}
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countPromptTokens = (encoding: any, run: Run): number => {
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let promptTokenCount = 0
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if (encoding) {
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if (run.inputs?.messages?.length > 0 && run.inputs?.messages[0]?.length > 0) {
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run.inputs.messages[0].map((message: any) => {
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let content = message.content
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? message.content
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: message.SystemMessage?.content
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? message.SystemMessage.content
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: message.HumanMessage?.content
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? message.HumanMessage.content
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: message.AIMessage?.content
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? message.AIMessage.content
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: undefined
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promptTokenCount += content ? encoding.encode(content).length : 0
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})
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}
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if (run.inputs?.prompts?.length > 0) {
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const content = run.inputs.prompts[0]
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promptTokenCount += content ? encoding.encode(content).length : 0
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}
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}
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return promptTokenCount
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}
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countCompletionTokens = (encoding: any, run: Run): number => {
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let completionTokenCount = 0
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if (encoding) {
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if (run.outputs?.generations?.length > 0 && run.outputs?.generations[0]?.length > 0) {
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run.outputs?.generations[0].map((chunk: any) => {
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let content = chunk.text ? chunk.text : chunk.message?.content ? chunk.message?.content : undefined
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completionTokenCount += content ? encoding.encode(content).length : 0
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})
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}
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}
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return completionTokenCount
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}
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extractModelName = (run: Run): string => {
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return (
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(run?.serialized as any)?.kwargs?.model ||
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(run?.serialized as any)?.kwargs?.model_name ||
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(run?.extra as any)?.metadata?.ls_model_name ||
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(run?.extra as any)?.metadata?.fw_model_name
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)
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}
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onLLMEnd?(run: Run): void | Promise<void> {
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if (run.name) {
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let provider = run.name
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if (provider === 'BedrockChat') {
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provider = 'awsChatBedrock'
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}
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EvaluationRunner.addMetrics(
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this.evaluationRunId,
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JSON.stringify({
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provider: provider
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})
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)
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}
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let model = this.extractModelName(run)
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if (run.outputs?.llmOutput?.tokenUsage) {
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const tokenUsage = run.outputs?.llmOutput?.tokenUsage
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if (tokenUsage) {
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const metric = {
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completionTokens: tokenUsage.completionTokens,
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promptTokens: tokenUsage.promptTokens,
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model: model,
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totalTokens: tokenUsage.totalTokens
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}
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EvaluationRunner.addMetrics(this.evaluationRunId, JSON.stringify(metric))
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}
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} else if (
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run.outputs?.generations?.length > 0 &&
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run.outputs?.generations[0].length > 0 &&
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run.outputs?.generations[0][0]?.message?.usage_metadata?.total_tokens
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) {
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const usage_metadata = run.outputs?.generations[0][0]?.message?.usage_metadata
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if (usage_metadata) {
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const metric = {
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completionTokens: usage_metadata.output_tokens,
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promptTokens: usage_metadata.input_tokens,
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model: model || this.model,
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totalTokens: usage_metadata.total_tokens
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}
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EvaluationRunner.addMetrics(this.evaluationRunId, JSON.stringify(metric))
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}
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} else {
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let encoding: any = undefined
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let promptInputTokens = 0
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let completionTokenCount = 0
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try {
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encoding = encoding_for_model(model as any)
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promptInputTokens = this.countPromptTokens(encoding, run)
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completionTokenCount = this.countCompletionTokens(encoding, run)
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} catch (e) {
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try {
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// as tiktoken will fail for non openai models, assume that is 'cl100k_base'
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encoding = get_encoding('cl100k_base')
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promptInputTokens = this.countPromptTokens(encoding, run)
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completionTokenCount = this.countCompletionTokens(encoding, run)
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} catch (e) {
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// stay silent
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}
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}
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const metric = {
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completionTokens: completionTokenCount,
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promptTokens: promptInputTokens,
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model: model,
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totalTokens: promptInputTokens + completionTokenCount
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}
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EvaluationRunner.addMetrics(this.evaluationRunId, JSON.stringify(metric))
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//cleanup
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this.model = ''
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}
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}
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async onRunUpdate(run: Run): Promise<void> {
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const json = {
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[run.run_type]: elapsed(run)
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}
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let metric = JSON.stringify(json)
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if (metric) {
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EvaluationRunner.addMetrics(this.evaluationRunId, metric)
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}
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if (run.run_type === 'llm') {
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let model = this.extractModelName(run)
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if (model) {
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EvaluationRunner.addMetrics(this.evaluationRunId, JSON.stringify({ model: model }))
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this.model = model
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}
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// OpenAI non streaming models
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const estimatedTokenUsage = run.outputs?.llmOutput?.estimatedTokenUsage
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if (estimatedTokenUsage && typeof estimatedTokenUsage === 'object' && Object.keys(estimatedTokenUsage).length > 0) {
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EvaluationRunner.addMetrics(this.evaluationRunId, estimatedTokenUsage)
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}
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}
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}
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}
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function elapsed(run: Run) {
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if (!run.end_time) return ''
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const elapsed = run.end_time - run.start_time
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return `${elapsed.toFixed(2)}`
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}
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@@ -0,0 +1,186 @@
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import { ChatMessage, LLMEndEvent, LLMStartEvent, LLMStreamEvent, MessageContentTextDetail, RetrievalEndEvent, Settings } from 'llamaindex'
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import { EvaluationRunner } from './EvaluationRunner'
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import { additionalCallbacks, ICommonObject, INodeData } from '../src'
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import { RetrievalStartEvent } from 'llamaindex/dist/type/llm/types'
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import { AgentEndEvent, AgentStartEvent } from 'llamaindex/dist/type/agent/types'
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import { encoding_for_model } from '@dqbd/tiktoken'
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import { MessageContent } from '@langchain/core/messages'
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export class EvaluationRunTracerLlama {
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evaluationRunId: string
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static cbInit = false
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static startTimes = new Map<string, number>()
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static models = new Map<string, string>()
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static tokenCounts = new Map<string, number>()
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constructor(id: string) {
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this.evaluationRunId = id
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EvaluationRunTracerLlama.constructCallBacks()
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}
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static constructCallBacks = () => {
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if (!EvaluationRunTracerLlama.cbInit) {
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Settings.callbackManager.on('llm-start', (event: LLMStartEvent) => {
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const evalID = (event as any).reason.parent?.caller?.evaluationRunId || (event as any).reason.caller?.evaluationRunId
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if (!evalID) return
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const model = (event as any).reason?.caller?.model
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if (model) {
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EvaluationRunTracerLlama.models.set(evalID, model)
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try {
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const encoding = encoding_for_model(model)
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if (encoding) {
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const { messages } = event.detail.payload
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let tokenCount = messages.reduce((count: number, message: ChatMessage) => {
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return count + encoding.encode(extractText(message.content)).length
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}, 0)
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EvaluationRunTracerLlama.tokenCounts.set(evalID + '_promptTokens', tokenCount)
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EvaluationRunTracerLlama.tokenCounts.set(evalID + '_outputTokens', 0)
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}
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} catch (e) {
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// catch the error and continue to work.
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}
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}
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EvaluationRunTracerLlama.startTimes.set(evalID + '_llm', event.timeStamp)
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})
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Settings.callbackManager.on('llm-end', (event: LLMEndEvent) => {
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this.calculateAndSetMetrics(event, 'llm')
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})
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Settings.callbackManager.on('llm-stream', (event: LLMStreamEvent) => {
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const evalID = (event as any).reason.parent?.caller?.evaluationRunId || (event as any).reason.caller?.evaluationRunId
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if (!evalID) return
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const { chunk } = event.detail.payload
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const { delta } = chunk
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const model = (event as any).reason?.caller?.model
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try {
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const encoding = encoding_for_model(model)
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if (encoding) {
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let tokenCount = EvaluationRunTracerLlama.tokenCounts.get(evalID + '_outputTokens') || 0
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tokenCount += encoding.encode(extractText(delta)).length
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EvaluationRunTracerLlama.tokenCounts.set(evalID + '_outputTokens', tokenCount)
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}
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} catch (e) {
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// catch the error and continue to work.
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}
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})
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Settings.callbackManager.on('retrieve-start', (event: RetrievalStartEvent) => {
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const evalID = (event as any).reason.parent?.caller?.evaluationRunId || (event as any).reason.caller?.evaluationRunId
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if (evalID) {
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EvaluationRunTracerLlama.startTimes.set(evalID + '_retriever', event.timeStamp)
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}
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})
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Settings.callbackManager.on('retrieve-end', (event: RetrievalEndEvent) => {
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this.calculateAndSetMetrics(event, 'retriever')
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})
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Settings.callbackManager.on('agent-start', (event: AgentStartEvent) => {
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const evalID = (event as any).reason.parent?.caller?.evaluationRunId || (event as any).reason.caller?.evaluationRunId
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if (evalID) {
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EvaluationRunTracerLlama.startTimes.set(evalID + '_agent', event.timeStamp)
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}
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})
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Settings.callbackManager.on('agent-end', (event: AgentEndEvent) => {
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this.calculateAndSetMetrics(event, 'agent')
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})
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EvaluationRunTracerLlama.cbInit = true
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}
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}
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private static calculateAndSetMetrics(event: any, label: string) {
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const evalID = event.reason.parent?.caller?.evaluationRunId || event.reason.caller?.evaluationRunId
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if (!evalID) return
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const startTime = EvaluationRunTracerLlama.startTimes.get(evalID + '_' + label) as number
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let model =
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(event as any).reason?.caller?.model || (event as any).reason?.caller?.llm?.model || EvaluationRunTracerLlama.models.get(evalID)
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if (event.detail.payload?.response?.message && model) {
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try {
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const encoding = encoding_for_model(model)
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if (encoding) {
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let tokenCount = EvaluationRunTracerLlama.tokenCounts.get(evalID + '_outputTokens') || 0
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tokenCount += encoding.encode(event.detail.payload.response?.message?.content || '').length
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EvaluationRunTracerLlama.tokenCounts.set(evalID + '_outputTokens', tokenCount)
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}
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} catch (e) {
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// catch the error and continue to work.
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}
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}
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// Anthropic
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if (event.detail?.payload?.response?.raw?.usage) {
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const usage = event.detail.payload.response.raw.usage
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if (usage.output_tokens) {
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const metric = {
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completionTokens: usage.output_tokens,
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promptTokens: usage.input_tokens,
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model: model,
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totalTokens: usage.input_tokens + usage.output_tokens
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}
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EvaluationRunner.addMetrics(evalID, JSON.stringify(metric))
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} else if (usage.completion_tokens) {
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const metric = {
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completionTokens: usage.completion_tokens,
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promptTokens: usage.prompt_tokens,
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model: model,
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totalTokens: usage.total_tokens
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}
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EvaluationRunner.addMetrics(evalID, JSON.stringify(metric))
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}
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} else if (event.detail?.payload?.response?.raw['amazon-bedrock-invocationMetrics']) {
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const usage = event.detail?.payload?.response?.raw['amazon-bedrock-invocationMetrics']
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const metric = {
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completionTokens: usage.outputTokenCount,
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promptTokens: usage.inputTokenCount,
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model: event.detail?.payload?.response?.raw.model,
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totalTokens: usage.inputTokenCount + usage.outputTokenCount
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}
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EvaluationRunner.addMetrics(evalID, JSON.stringify(metric))
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} else {
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const metric = {
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[label]: (event.timeStamp - startTime).toFixed(2),
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completionTokens: EvaluationRunTracerLlama.tokenCounts.get(evalID + '_outputTokens'),
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promptTokens: EvaluationRunTracerLlama.tokenCounts.get(evalID + '_promptTokens'),
|
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model: model || EvaluationRunTracerLlama.models.get(evalID) || '',
|
||||
totalTokens:
|
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(EvaluationRunTracerLlama.tokenCounts.get(evalID + '_outputTokens') || 0) +
|
||||
(EvaluationRunTracerLlama.tokenCounts.get(evalID + '_promptTokens') || 0)
|
||||
}
|
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EvaluationRunner.addMetrics(evalID, JSON.stringify(metric))
|
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}
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|
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//cleanup
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EvaluationRunTracerLlama.startTimes.delete(evalID + '_' + label)
|
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EvaluationRunTracerLlama.startTimes.delete(evalID + '_outputTokens')
|
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EvaluationRunTracerLlama.startTimes.delete(evalID + '_promptTokens')
|
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EvaluationRunTracerLlama.models.delete(evalID)
|
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}
|
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|
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static async injectEvaluationMetadata(nodeData: INodeData, options: ICommonObject, callerObj: any) {
|
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if (options.evaluationRunId && callerObj) {
|
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// these are needed for evaluation runs
|
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options.llamaIndex = true
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await additionalCallbacks(nodeData, options)
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Object.defineProperty(callerObj, 'evaluationRunId', {
|
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enumerable: true,
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configurable: true,
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||||
writable: true,
|
||||
value: options.evaluationRunId
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// from https://github.com/run-llama/LlamaIndexTS/blob/main/packages/core/src/llm/utils.ts
|
||||
export function extractText(message: MessageContent): string {
|
||||
if (typeof message !== 'string' && !Array.isArray(message)) {
|
||||
console.warn('extractText called with non-MessageContent message, this is likely a bug.')
|
||||
return `${message}`
|
||||
} else if (typeof message !== 'string' && Array.isArray(message)) {
|
||||
// message is of type MessageContentDetail[] - retrieve just the text parts and concatenate them
|
||||
// so we can pass them to the context generator
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||||
return message
|
||||
.filter((c): c is MessageContentTextDetail => c.type === 'text')
|
||||
.map((c) => c.text)
|
||||
.join('\n\n')
|
||||
} else {
|
||||
return message
|
||||
}
|
||||
}
|
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@@ -0,0 +1,172 @@
|
||||
import axios from 'axios'
|
||||
import { v4 as uuidv4 } from 'uuid'
|
||||
import { ICommonObject } from '../src'
|
||||
|
||||
import { getModelConfigByModelName, MODEL_TYPE } from '../src/modelLoader'
|
||||
|
||||
export class EvaluationRunner {
|
||||
static metrics = new Map<string, string[]>()
|
||||
static async getAndDeleteMetrics(id: string) {
|
||||
const val = EvaluationRunner.metrics.get(id)
|
||||
if (val) {
|
||||
try {
|
||||
//first lets get the provider and model
|
||||
let selectedModel = undefined
|
||||
let selectedProvider = undefined
|
||||
if (val && val.length > 0) {
|
||||
let modelName = ''
|
||||
let providerName = ''
|
||||
for (let i = 0; i < val.length; i++) {
|
||||
const metric = val[i]
|
||||
if (typeof metric === 'object') {
|
||||
modelName = metric['model']
|
||||
providerName = metric['provider']
|
||||
} else {
|
||||
modelName = JSON.parse(metric)['model']
|
||||
providerName = JSON.parse(metric)['provider']
|
||||
}
|
||||
|
||||
if (modelName) {
|
||||
selectedModel = modelName
|
||||
}
|
||||
if (providerName) {
|
||||
selectedProvider = providerName
|
||||
}
|
||||
}
|
||||
}
|
||||
let modelConfig = await getModelConfigByModelName(MODEL_TYPE.CHAT, selectedProvider, selectedModel)
|
||||
if (modelConfig) {
|
||||
val.push(JSON.stringify({ cost_values: modelConfig }))
|
||||
} else {
|
||||
modelConfig = await getModelConfigByModelName(MODEL_TYPE.LLM, selectedProvider, selectedModel)
|
||||
if (modelConfig) {
|
||||
val.push(JSON.stringify({ cost_values: modelConfig }))
|
||||
}
|
||||
}
|
||||
} catch (error) {
|
||||
//stay silent
|
||||
}
|
||||
}
|
||||
EvaluationRunner.metrics.delete(id)
|
||||
return val
|
||||
}
|
||||
|
||||
static addMetrics(id: string, metric: string) {
|
||||
if (EvaluationRunner.metrics.has(id)) {
|
||||
EvaluationRunner.metrics.get(id)?.push(metric)
|
||||
} else {
|
||||
EvaluationRunner.metrics.set(id, [metric])
|
||||
}
|
||||
}
|
||||
|
||||
baseURL = ''
|
||||
|
||||
constructor(baseURL: string) {
|
||||
this.baseURL = baseURL
|
||||
}
|
||||
|
||||
getChatflowApiKey(chatflowId: string, apiKeys: { chatflowId: string; apiKey: string }[] = []) {
|
||||
return apiKeys.find((item) => item.chatflowId === chatflowId)?.apiKey || ''
|
||||
}
|
||||
|
||||
public async runEvaluations(data: ICommonObject) {
|
||||
const chatflowIds = JSON.parse(data.chatflowId)
|
||||
const returnData: ICommonObject = {}
|
||||
returnData.evaluationId = data.evaluationId
|
||||
returnData.runDate = new Date()
|
||||
returnData.rows = []
|
||||
for (let i = 0; i < data.dataset.rows.length; i++) {
|
||||
returnData.rows.push({
|
||||
input: data.dataset.rows[i].input,
|
||||
expectedOutput: data.dataset.rows[i].output,
|
||||
itemNo: data.dataset.rows[i].sequenceNo,
|
||||
evaluations: [],
|
||||
status: 'pending'
|
||||
})
|
||||
}
|
||||
for (let i = 0; i < chatflowIds.length; i++) {
|
||||
const chatflowId = chatflowIds[i]
|
||||
await this.evaluateChatflow(chatflowId, this.getChatflowApiKey(chatflowId, data.apiKeys), data, returnData)
|
||||
}
|
||||
return returnData
|
||||
}
|
||||
|
||||
async evaluateChatflow(chatflowId: string, apiKey: string, data: any, returnData: any) {
|
||||
for (let i = 0; i < data.dataset.rows.length; i++) {
|
||||
const item = data.dataset.rows[i]
|
||||
const uuid = uuidv4()
|
||||
|
||||
const headers: any = {
|
||||
'X-Request-ID': uuid,
|
||||
'X-Flowise-Evaluation': 'true'
|
||||
}
|
||||
if (apiKey) {
|
||||
headers['Authorization'] = `Bearer ${apiKey}`
|
||||
}
|
||||
let axiosConfig = {
|
||||
headers: headers
|
||||
}
|
||||
let startTime = performance.now()
|
||||
const runData: any = {}
|
||||
runData.chatflowId = chatflowId
|
||||
runData.startTime = startTime
|
||||
const postData: any = { question: item.input, evaluationRunId: uuid, evaluation: true }
|
||||
if (data.sessionId) {
|
||||
postData.overrideConfig = { sessionId: data.sessionId }
|
||||
}
|
||||
try {
|
||||
let response = await axios.post(`${this.baseURL}/api/v1/prediction/${chatflowId}`, postData, axiosConfig)
|
||||
const endTime = performance.now()
|
||||
const timeTaken = (endTime - startTime).toFixed(2)
|
||||
if (response?.data?.metrics) {
|
||||
runData.metrics = response.data.metrics
|
||||
runData.metrics.push({
|
||||
apiLatency: timeTaken
|
||||
})
|
||||
} else {
|
||||
runData.metrics = [
|
||||
{
|
||||
apiLatency: timeTaken
|
||||
}
|
||||
]
|
||||
}
|
||||
runData.status = 'complete'
|
||||
let resultText = ''
|
||||
if (response.data.text) resultText = response.data.text
|
||||
else if (response.data.json) resultText = '```json\n' + JSON.stringify(response.data.json, null, 2)
|
||||
else resultText = JSON.stringify(response.data, null, 2)
|
||||
|
||||
runData.actualOutput = resultText
|
||||
runData.latency = timeTaken
|
||||
runData.error = ''
|
||||
} catch (error: any) {
|
||||
runData.status = 'error'
|
||||
runData.actualOutput = ''
|
||||
runData.error = error?.response?.data?.message
|
||||
? error.response.data.message
|
||||
: error?.message
|
||||
? error.message
|
||||
: 'Unknown error'
|
||||
try {
|
||||
if (runData.error.indexOf('-') > -1) {
|
||||
// if there is a dash, remove all content before
|
||||
runData.error = 'Error: ' + runData.error.substr(runData.error.indexOf('-') + 1).trim()
|
||||
}
|
||||
} catch (error) {
|
||||
//stay silent
|
||||
}
|
||||
const endTime = performance.now()
|
||||
const timeTaken = (endTime - startTime).toFixed(2)
|
||||
runData.metrics = [
|
||||
{
|
||||
apiLatency: timeTaken
|
||||
}
|
||||
]
|
||||
runData.latency = timeTaken
|
||||
}
|
||||
runData.uuid = uuid
|
||||
returnData.rows[i].evaluations.push(runData)
|
||||
}
|
||||
return returnData
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user