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Adding output parse in ConversationChain #4098

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82 changes: 49 additions & 33 deletions packages/components/nodes/chains/ConversationChain/ConversationChain.ts
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
import { ConversationChain } from 'langchain/chains'
import { ConversationChain, LLMChain } from 'langchain/chains'
import {
ChatPromptTemplate,
HumanMessagePromptTemplate,
Expand All @@ -8,12 +8,12 @@ import {
PromptTemplate
} from '@langchain/core/prompts'
import { RunnableSequence } from '@langchain/core/runnables'
import { StringOutputParser } from '@langchain/core/output_parsers'
import { BaseLLMOutputParser, BaseOutputParser, StringOutputParser } from '@langchain/core/output_parsers'
import { BaseChatModel } from '@langchain/core/language_models/chat_models'
import { HumanMessage } from '@langchain/core/messages'
import { ConsoleCallbackHandler as LCConsoleCallbackHandler } from '@langchain/core/tracers/console'
import { checkInputs, Moderation, streamResponse } from '../../moderation/Moderation'
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
import { formatResponse, injectOutputParser } from '../../outputparsers/OutputParserHelpers'
import { addImagesToMessages, llmSupportsVision } from '../../../src/multiModalUtils'
import { ChatOpenAI } from '../../chatmodels/ChatOpenAI/FlowiseChatOpenAI'
import {
Expand All @@ -28,6 +28,7 @@ import {
} from '../../../src/Interface'
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
import { getBaseClasses, handleEscapeCharacters, transformBracesWithColon } from '../../../src/utils'
import { BaseLanguageModel, BaseLanguageModelCallOptions } from '@langchain/core/language_models/base'

let systemMessage = `The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.`
const inputKey = 'input'
Expand Down Expand Up @@ -81,6 +82,12 @@ class ConversationChain_Chains implements INode {
optional: true,
list: true
},*/
{
label: 'Output Parser',
name: 'outputParser',
type: 'BaseLLMOutputParser',
optional: true
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
Expand Down Expand Up @@ -121,7 +128,6 @@ class ConversationChain_Chains implements INode {

if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the LLM chain
input = await checkInputs(moderations, input)
} catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500))
Expand All @@ -145,9 +151,9 @@ class ConversationChain_Chains implements INode {
if (shouldStreamResponse) {
const handler = new CustomChainHandler(sseStreamer, chatId)
callbacks.push(handler)
res = await chain.invoke({ input }, { callbacks })
res = await chain.invoke({ input }, { callbacks }) as string;
} else {
res = await chain.invoke({ input }, { callbacks })
res = await chain.invoke({ input }, { callbacks }) as string;
}

await memory.addChatMessages(
Expand Down Expand Up @@ -180,7 +186,6 @@ const prepareChatPrompt = (nodeData: INodeData, humanImageMessages: MessageConte
const humanPrompt = chatPromptTemplate.promptMessages[chatPromptTemplate.promptMessages.length - 1]
const messages = [sysPrompt, new MessagesPlaceholder(memory.memoryKey ?? 'chat_history'), humanPrompt]

// OpenAI works better when separate images into standalone human messages
if (model instanceof ChatOpenAI && humanImageMessages.length) {
messages.push(new HumanMessage({ content: [...humanImageMessages] }))
} else if (humanImageMessages.length) {
Expand Down Expand Up @@ -211,7 +216,6 @@ const prepareChatPrompt = (nodeData: INodeData, humanImageMessages: MessageConte
HumanMessagePromptTemplate.fromTemplate(`{${inputKey}}`)
]

// OpenAI works better when separate images into standalone human messages
if (model instanceof ChatOpenAI && humanImageMessages.length) {
messages.push(new HumanMessage({ content: [...humanImageMessages] }))
} else if (humanImageMessages.length) {
Expand All @@ -225,53 +229,65 @@ const prepareChatPrompt = (nodeData: INodeData, humanImageMessages: MessageConte
}

const prepareChain = async (nodeData: INodeData, options: ICommonObject, sessionId?: string) => {
let model = nodeData.inputs?.model as BaseChatModel
const memory = nodeData.inputs?.memory as FlowiseMemory
const memoryKey = memory.memoryKey ?? 'chat_history'
const prependMessages = options?.prependMessages
let model = nodeData.inputs?.model as BaseChatModel;
const memory = nodeData.inputs?.memory as FlowiseMemory;
const memoryKey = memory.memoryKey ?? 'chat_history';
const prependMessages = options?.prependMessages;

let messageContent: MessageContentImageUrl[] = []
let messageContent: MessageContentImageUrl[] = [];
if (llmSupportsVision(model)) {
messageContent = await addImagesToMessages(nodeData, options, model.multiModalOption)
const visionChatModel = model as IVisionChatModal
messageContent = await addImagesToMessages(nodeData, options, model.multiModalOption);
const visionChatModel = model as IVisionChatModal;
if (messageContent?.length) {
visionChatModel.setVisionModel()
visionChatModel.setVisionModel();
} else {
// revert to previous values if image upload is empty
visionChatModel.revertToOriginalModel()
visionChatModel.revertToOriginalModel();
}
}

const chatPrompt = prepareChatPrompt(nodeData, messageContent)
let promptVariables = {}
const promptValuesRaw = (chatPrompt as any).promptValues
let chatPrompt = prepareChatPrompt(nodeData, messageContent);
let promptVariables = {};
const promptValuesRaw = (chatPrompt as any).promptValues;
if (promptValuesRaw) {
const promptValues = handleEscapeCharacters(promptValuesRaw, true)
const promptValues = handleEscapeCharacters(promptValuesRaw, true);
for (const val in promptValues) {
promptVariables = {
...promptVariables,
[val]: () => {
return promptValues[val]
}
}
[val]: () => promptValues[val]
};
}
}

const providedOutputParser = nodeData.inputs?.outputParser as BaseLLMOutputParser;
const finalOutputParser = providedOutputParser ? providedOutputParser : new StringOutputParser();
if (providedOutputParser) {
const dummyChain = { prompt: chatPrompt } as unknown as LLMChain<string | object | BaseLanguageModel<any, BaseLanguageModelCallOptions>>;
let promptValues = (chatPrompt as any).promptValues || {};
promptValues = injectOutputParser(
providedOutputParser as unknown as BaseOutputParser<unknown>,
dummyChain,
promptValues
);
(chatPrompt as any).promptValues = promptValues;
(chatPrompt as any).promptValues = promptValues;
}

const conversationChain = RunnableSequence.from([
{
[inputKey]: (input: { input: string }) => input.input,
[memoryKey]: async () => {
const history = await memory.getChatMessages(sessionId, true, prependMessages)
return history
const history = await memory.getChatMessages(sessionId, true, prependMessages);
return history;
},
...promptVariables
},
prepareChatPrompt(nodeData, messageContent),
chatPrompt,
model,
new StringOutputParser()
])
finalOutputParser
]);

return conversationChain;
};

return conversationChain
}

module.exports = { nodeClass: ConversationChain_Chains }