91 lines
2.5 KiB
TypeScript
91 lines
2.5 KiB
TypeScript
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import {
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RunnableSequence,
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RunnableMap,
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RunnableLambda,
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} from '@langchain/core/runnables';
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import { PromptTemplate } from '@langchain/core/prompts';
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import formatChatHistoryAsString from '../utils/formatHistory';
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import { BaseMessage } from '@langchain/core/messages';
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import { StringOutputParser } from '@langchain/core/output_parsers';
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import { searchSearxng } from '../lib/searxng';
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import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
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const VideoSearchChainPrompt = `
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You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search Youtube for videos.
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You need to make sure the rephrased question agrees with the conversation and is relevant to the conversation.
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Example:
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1. Follow up question: How does a car work?
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Rephrased: How does a car work?
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2. Follow up question: What is the theory of relativity?
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Rephrased: What is theory of relativity
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3. Follow up question: How does an AC work?
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Rephrased: How does an AC work
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Conversation:
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{chat_history}
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Follow up question: {query}
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Rephrased question:
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`;
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type VideoSearchChainInput = {
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chat_history: BaseMessage[];
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query: string;
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};
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const strParser = new StringOutputParser();
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const createVideoSearchChain = (llm: BaseChatModel) => {
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return RunnableSequence.from([
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RunnableMap.from({
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chat_history: (input: VideoSearchChainInput) => {
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return formatChatHistoryAsString(input.chat_history);
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},
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query: (input: VideoSearchChainInput) => {
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return input.query;
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},
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}),
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PromptTemplate.fromTemplate(VideoSearchChainPrompt),
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llm,
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strParser,
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RunnableLambda.from(async (input: string) => {
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const res = await searchSearxng(input, {
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engines: ['youtube'],
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});
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const videos = [];
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res.results.forEach((result) => {
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if (
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result.thumbnail &&
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result.url &&
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result.title &&
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result.iframe_src
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) {
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videos.push({
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img_src: result.thumbnail,
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url: result.url,
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title: result.title,
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iframe_src: result.iframe_src,
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});
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}
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});
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return videos.slice(0, 10);
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}),
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]);
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};
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const handleVideoSearch = (
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input: VideoSearchChainInput,
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llm: BaseChatModel,
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) => {
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const VideoSearchChain = createVideoSearchChain(llm);
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return VideoSearchChain.invoke(input);
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};
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export default handleVideoSearch;
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