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feat(videoSearch): use XML parsing, use few shot prompting

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ItzCrazyKns
2025-07-19 17:52:14 +05:30
parent 94e6db10bb
commit cddc793915
1 changed files with 46 additions and 26 deletions
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72
src/lib/chains/videoSearchAgent.ts
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@ -3,33 +3,19 @@ import {
RunnableMap,
RunnableLambda,
} from '@langchain/core/runnables';
import { PromptTemplate } from '@langchain/core/prompts';
import { ChatPromptTemplate, 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 '../searxng';
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
import LineOutputParser from '../outputParsers/lineOutputParser';
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:
`;
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.
Output only the rephrased query wrapped in an XML <query> element. Do not include any explanation or additional text.
`;
type VideoSearchChainInput = {
chat_history: BaseMessage[];
@ -55,12 +41,46 @@ const createVideoSearchChain = (llm: BaseChatModel) => {
return input.query;
},
}),
PromptTemplate.fromTemplate(VideoSearchChainPrompt),
ChatPromptTemplate.fromMessages([
['system', videoSearchChainPrompt],
[
'user',
'<conversation>\n</conversation>\n<follow_up>\nHow does a car work?\n</follow_up>'
],
[
'assistant',
'<query>How does a car work?</query>'
],
[
'user',
'<conversation>\n</conversation>\n<follow_up>\nWhat is the theory of relativity?\n</follow_up>'
],
[
'assistant',
'<query>Theory of relativity</query>'
],
[
'user',
'<conversation>\n</conversation>\n<follow_up>\nHow does an AC work?\n</follow_up>'
],
[
'assistant',
'<query>AC working</query>'
],
[
'user',
'<conversation>{chat_history}</conversation>\n<follow_up>\n{query}\n</follow_up>'
]
]),
llm,
strParser,
RunnableLambda.from(async (input: string) => {
input = input.replace(/<think>.*?<\/think>/g, '');
const queryParser = new LineOutputParser({
key: 'query'
});
return (await queryParser.parse(input));
}),
RunnableLambda.from(async (input: string) => {
const res = await searchSearxng(input, {
engines: ['youtube'],
});
@ -92,8 +112,8 @@ const handleVideoSearch = (
input: VideoSearchChainInput,
llm: BaseChatModel,
) => {
const VideoSearchChain = createVideoSearchChain(llm);
return VideoSearchChain.invoke(input);
const videoSearchChain = createVideoSearchChain(llm);
return videoSearchChain.invoke(input);
};
export default handleVideoSearch;