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