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refactor: Update Langchain to 0.1.41 & add support for Claude 3 (#8825)

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Signed-off-by: Oleg Ivaniv <me@olegivaniv.com>
Co-authored-by: Michael Kret <michael.k@radency.com>
This commit is contained in:
oleg
2024-03-07 11:36:36 +01:00
committed by GitHub
parent c6f6254c0e
commit 0f7ae3f50a
76 changed files with 908 additions and 451 deletions
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25
packages/@n8n/nodes-langchain/nodes/chains/ChainLLM/ChainLlm.node.ts
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@@ -8,19 +8,19 @@ import type {
INodeTypeDescription,
} from 'n8n-workflow';
import type { BaseLanguageModel } from 'langchain/base_language';
import type { BaseLanguageModel } from '@langchain/core/language_models/base';
import {
AIMessagePromptTemplate,
PromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
ChatPromptTemplate,
} from 'langchain/prompts';
import type { BaseOutputParser } from 'langchain/schema/output_parser';
} from '@langchain/core/prompts';
import type { BaseOutputParser } from '@langchain/core/output_parsers';
import { CombiningOutputParser } from 'langchain/output_parsers';
import { LLMChain } from 'langchain/chains';
import type { BaseChatModel } from 'langchain/chat_models/base';
import { HumanMessage } from 'langchain/schema';
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
import { HumanMessage } from '@langchain/core/messages';
import { getTemplateNoticeField } from '../../../utils/sharedFields';
import {
getOptionalOutputParsers,
@@ -92,6 +92,7 @@ async function getChainPromptTemplate(
llm: BaseLanguageModel | BaseChatModel,
messages?: MessagesTemplate[],
formatInstructions?: string,
query?: string,
) {
const queryTemplate = new PromptTemplate({
template: `{query}${formatInstructions ? '\n{formatInstructions}' : ''}`,
@@ -129,7 +130,15 @@ async function getChainPromptTemplate(
}),
);
parsedMessages.push(new HumanMessagePromptTemplate(queryTemplate));
const lastMessage = parsedMessages[parsedMessages.length - 1];
// If the last message is a human message and it has an array of content, we need to add the query to the last message
if (lastMessage instanceof HumanMessage && Array.isArray(lastMessage.content)) {
const humanMessage = new HumanMessagePromptTemplate(queryTemplate);
const test = await humanMessage.format({ query });
lastMessage.content.push({ text: test.content.toString(), type: 'text' });
} else {
parsedMessages.push(new HumanMessagePromptTemplate(queryTemplate));
}
return ChatPromptTemplate.fromMessages(parsedMessages);
}
@@ -146,6 +155,7 @@ async function createSimpleLLMChain(
llm,
prompt,
});
const response = (await chain.call({
query,
signal: context.getExecutionCancelSignal(),
@@ -167,6 +177,8 @@ async function getChain(
itemIndex,
llm,
messages,
undefined,
query,
);
// If there are no output parsers, create a simple LLM chain and execute the query
@@ -187,6 +199,7 @@ async function getChain(
llm,
messages,
formatInstructions,
query,
);
const chain = prompt.pipe(llm).pipe(combinedOutputParser);