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feat(app): remove old search agent

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2025-11-22 22:22:34 +05:30
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137
src/lib/prompts/webSearch.ts
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import { BaseMessageLike } from '@langchain/core/messages';
export const webSearchRetrieverPrompt = `
You are an AI question rephraser. You will be given a conversation and a follow-up question, you will have to rephrase the follow up question so it is a standalone question and can be used by another LLM to search the web for information to answer it.
If it is a simple writing task or a greeting (unless the greeting contains a question after it) like Hi, Hello, How are you, etc. than a question then you need to return \`not_needed\` as the response (This is because the LLM won't need to search the web for finding information on this topic).
If the user asks some question from some URL or wants you to summarize a PDF or a webpage (via URL) you need to return the links inside the \`links\` XML block and the question inside the \`question\` XML block. If the user wants to you to summarize the webpage or the PDF you need to return \`summarize\` inside the \`question\` XML block in place of a question and the link to summarize in the \`links\` XML block.
You must always return the rephrased question inside the \`question\` XML block, if there are no links in the follow-up question then don't insert a \`links\` XML block in your response.
**Note**: All user messages are individual entities and should be treated as such do not mix conversations.
`;
export const webSearchRetrieverFewShots: BaseMessageLike[] = [
[
'user',
`<conversation>
</conversation>
<query>
What is the capital of France
</query>`,
],
[
'assistant',
`<question>
Capital of france
</question>`,
],
[
'user',
`<conversation>
</conversation>
<query>
Hi, how are you?
</query>`,
],
[
'assistant',
`<question>
not_needed
</question>`,
],
[
'user',
`<conversation>
</conversation>
<query>
What is Docker?
</query>`,
],
[
'assistant',
`<question>
What is Docker
</question>`,
],
[
'user',
`<conversation>
</conversation>
<query>
Can you tell me what is X from https://example.com
</query>`,
],
[
'assistant',
`<question>
What is X?
</question>
<links>
https://example.com
</links>`,
],
[
'user',
`<conversation>
</conversation>
<query>
Summarize the content from https://example.com
</query>`,
],
[
'assistant',
`<question>
summarize
</question>
<links>
https://example.com
</links>`,
],
];
export const webSearchResponsePrompt = `
You are Perplexica, an AI model skilled in web search and crafting detailed, engaging, and well-structured answers. You excel at summarizing web pages and extracting relevant information to create professional, blog-style responses.
Your task is to provide answers that are:
- **Informative and relevant**: Thoroughly address the user's query using the given context.
- **Well-structured**: Include clear headings and subheadings, and use a professional tone to present information concisely and logically.
- **Engaging and detailed**: Write responses that read like a high-quality blog post, including extra details and relevant insights.
- **Cited and credible**: Use inline citations with [number] notation to refer to the context source(s) for each fact or detail included.
- **Explanatory and Comprehensive**: Strive to explain the topic in depth, offering detailed analysis, insights, and clarifications wherever applicable.
### Formatting Instructions
- **Structure**: Use a well-organized format with proper headings (e.g., "## Example heading 1" or "## Example heading 2"). Present information in paragraphs or concise bullet points where appropriate.
- **Tone and Style**: Maintain a neutral, journalistic tone with engaging narrative flow. Write as though you're crafting an in-depth article for a professional audience.
- **Markdown Usage**: Format your response with Markdown for clarity. Use headings, subheadings, bold text, and italicized words as needed to enhance readability.
- **Length and Depth**: Provide comprehensive coverage of the topic. Avoid superficial responses and strive for depth without unnecessary repetition. Expand on technical or complex topics to make them easier to understand for a general audience.
- **No main heading/title**: Start your response directly with the introduction unless asked to provide a specific title.
- **Conclusion or Summary**: Include a concluding paragraph that synthesizes the provided information or suggests potential next steps, where appropriate.
### Citation Requirements
- Cite every single fact, statement, or sentence using [number] notation corresponding to the source from the provided \`context\`.
- Integrate citations naturally at the end of sentences or clauses as appropriate. For example, "The Eiffel Tower is one of the most visited landmarks in the world[1]."
- Ensure that **every sentence in your response includes at least one citation**, even when information is inferred or connected to general knowledge available in the provided context.
- Use multiple sources for a single detail if applicable, such as, "Paris is a cultural hub, attracting millions of visitors annually[1][2]."
- Always prioritize credibility and accuracy by linking all statements back to their respective context sources.
- Avoid citing unsupported assumptions or personal interpretations; if no source supports a statement, clearly indicate the limitation.
### Special Instructions
- If the query involves technical, historical, or complex topics, provide detailed background and explanatory sections to ensure clarity.
- If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
### User instructions
These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
{systemInstructions}
### Example Output
- Begin with a brief introduction summarizing the event or query topic.
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
- Provide explanations or historical context as needed to enhance understanding.
- End with a conclusion or overall perspective if relevant.
<context>
{context}
</context>
Current date & time in ISO format (UTC timezone) is: {date}.
`;

17
src/lib/prompts/writingAssistant.ts
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export const writingAssistantPrompt = `
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are currently set on focus mode 'Writing Assistant', this means you will be helping the user write a response to a given query.
Since you are a writing assistant, you would not perform web searches. If you think you lack information to answer the query, you can ask the user for more information or suggest them to switch to a different focus mode.
You will be shared a context that can contain information from files user has uploaded to get answers from. You will have to generate answers upon that.
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
### User instructions
These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
{systemInstructions}
<context>
{context}
</context>
`;

59
src/lib/search/index.ts
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import MetaSearchAgent from '@/lib/search/metaSearchAgent';
import prompts from '../prompts';
export const searchHandlers: Record<string, MetaSearchAgent> = {
webSearch: new MetaSearchAgent({
activeEngines: [],
queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
responsePrompt: prompts.webSearchResponsePrompt,
queryGeneratorFewShots: prompts.webSearchRetrieverFewShots,
rerank: true,
rerankThreshold: 0.3,
searchWeb: true,
}),
academicSearch: new MetaSearchAgent({
activeEngines: ['arxiv', 'google scholar', 'pubmed'],
queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
responsePrompt: prompts.webSearchResponsePrompt,
queryGeneratorFewShots: prompts.webSearchRetrieverFewShots,
rerank: true,
rerankThreshold: 0,
searchWeb: true,
}),
writingAssistant: new MetaSearchAgent({
activeEngines: [],
queryGeneratorPrompt: '',
queryGeneratorFewShots: [],
responsePrompt: prompts.writingAssistantPrompt,
rerank: true,
rerankThreshold: 0,
searchWeb: false,
}),
wolframAlphaSearch: new MetaSearchAgent({
activeEngines: ['wolframalpha'],
queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
responsePrompt: prompts.webSearchResponsePrompt,
queryGeneratorFewShots: prompts.webSearchRetrieverFewShots,
rerank: false,
rerankThreshold: 0,
searchWeb: true,
}),
youtubeSearch: new MetaSearchAgent({
activeEngines: ['youtube'],
queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
responsePrompt: prompts.webSearchResponsePrompt,
queryGeneratorFewShots: prompts.webSearchRetrieverFewShots,
rerank: true,
rerankThreshold: 0.3,
searchWeb: true,
}),
redditSearch: new MetaSearchAgent({
activeEngines: ['reddit'],
queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
responsePrompt: prompts.webSearchResponsePrompt,
queryGeneratorFewShots: prompts.webSearchRetrieverFewShots,
rerank: true,
rerankThreshold: 0.3,
searchWeb: true,
}),
};

514
src/lib/search/metaSearchAgent.ts
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import { ChatOpenAI } from '@langchain/openai';
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
import type { Embeddings } from '@langchain/core/embeddings';
import {
ChatPromptTemplate,
MessagesPlaceholder,
PromptTemplate,
} from '@langchain/core/prompts';
import {
RunnableLambda,
RunnableMap,
RunnableSequence,
} from '@langchain/core/runnables';
import { BaseMessage, BaseMessageLike } from '@langchain/core/messages';
import { StringOutputParser } from '@langchain/core/output_parsers';
import LineListOutputParser from '../outputParsers/listLineOutputParser';
import LineOutputParser from '../outputParsers/lineOutputParser';
import { getDocumentsFromLinks } from '../utils/documents';
import { Document } from '@langchain/core/documents';
import { searchSearxng } from '../searxng';
import path from 'node:path';
import fs from 'node:fs';
import computeSimilarity from '../utils/computeSimilarity';
import formatChatHistoryAsString from '../utils/formatHistory';
import eventEmitter from 'events';
import { StreamEvent } from '@langchain/core/tracers/log_stream';
export interface MetaSearchAgentType {
searchAndAnswer: (
message: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
fileIds: string[],
systemInstructions: string,
) => Promise<eventEmitter>;
}
interface Config {
searchWeb: boolean;
rerank: boolean;
rerankThreshold: number;
queryGeneratorPrompt: string;
queryGeneratorFewShots: BaseMessageLike[];
responsePrompt: string;
activeEngines: string[];
}
type BasicChainInput = {
chat_history: BaseMessage[];
query: string;
};
class MetaSearchAgent implements MetaSearchAgentType {
private config: Config;
private strParser = new StringOutputParser();
constructor(config: Config) {
this.config = config;
}
private async createSearchRetrieverChain(llm: BaseChatModel) {
(llm as unknown as ChatOpenAI).temperature = 0;
return RunnableSequence.from([
ChatPromptTemplate.fromMessages([
['system', this.config.queryGeneratorPrompt],
...this.config.queryGeneratorFewShots,
[
'user',
`
<conversation>
{chat_history}
</conversation>
<query>
{query}
</query>
`,
],
]),
llm,
this.strParser,
RunnableLambda.from(async (input: string) => {
const linksOutputParser = new LineListOutputParser({
key: 'links',
});
const questionOutputParser = new LineOutputParser({
key: 'question',
});
const links = await linksOutputParser.parse(input);
let question = (await questionOutputParser.parse(input)) ?? input;
if (question === 'not_needed') {
return { query: '', docs: [] };
}
if (links.length > 0) {
if (question.length === 0) {
question = 'summarize';
}
let docs: Document[] = [];
const linkDocs = await getDocumentsFromLinks({ links });
const docGroups: Document[] = [];
linkDocs.map((doc) => {
const URLDocExists = docGroups.find(
(d) =>
d.metadata.url === doc.metadata.url &&
d.metadata.totalDocs < 10,
);
if (!URLDocExists) {
docGroups.push({
...doc,
metadata: {
...doc.metadata,
totalDocs: 1,
},
});
}
const docIndex = docGroups.findIndex(
(d) =>
d.metadata.url === doc.metadata.url &&
d.metadata.totalDocs < 10,
);
if (docIndex !== -1) {
docGroups[docIndex].pageContent =
docGroups[docIndex].pageContent + `\n\n` + doc.pageContent;
docGroups[docIndex].metadata.totalDocs += 1;
}
});
await Promise.all(
docGroups.map(async (doc) => {
const res = await llm.invoke(`
You are a web search summarizer, tasked with summarizing a piece of text retrieved from a web search. Your job is to summarize the
text into a detailed, 2-4 paragraph explanation that captures the main ideas and provides a comprehensive answer to the query.
If the query is \"summarize\", you should provide a detailed summary of the text. If the query is a specific question, you should answer it in the summary.
- **Journalistic tone**: The summary should sound professional and journalistic, not too casual or vague.
- **Thorough and detailed**: Ensure that every key point from the text is captured and that the summary directly answers the query.
- **Not too lengthy, but detailed**: The summary should be informative but not excessively long. Focus on providing detailed information in a concise format.
The text will be shared inside the \`text\` XML tag, and the query inside the \`query\` XML tag.
<example>
1. \`<text>
Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers.
It was first released in 2013 and is developed by Docker, Inc. Docker is designed to make it easier to create, deploy, and run applications
by using containers.
</text>
<query>
What is Docker and how does it work?
</query>
Response:
Docker is a revolutionary platform-as-a-service product developed by Docker, Inc., that uses container technology to make application
deployment more efficient. It allows developers to package their software with all necessary dependencies, making it easier to run in
any environment. Released in 2013, Docker has transformed the way applications are built, deployed, and managed.
\`
2. \`<text>
The theory of relativity, or simply relativity, encompasses two interrelated theories of Albert Einstein: special relativity and general
relativity. However, the word "relativity" is sometimes used in reference to Galilean invariance. The term "theory of relativity" was based
on the expression "relative theory" used by Max Planck in 1906. The theory of relativity usually encompasses two interrelated theories by
Albert Einstein: special relativity and general relativity. Special relativity applies to all physical phenomena in the absence of gravity.
General relativity explains the law of gravitation and its relation to other forces of nature. It applies to the cosmological and astrophysical
realm, including astronomy.
</text>
<query>
summarize
</query>
Response:
The theory of relativity, developed by Albert Einstein, encompasses two main theories: special relativity and general relativity. Special
relativity applies to all physical phenomena in the absence of gravity, while general relativity explains the law of gravitation and its
relation to other forces of nature. The theory of relativity is based on the concept of "relative theory," as introduced by Max Planck in
1906. It is a fundamental theory in physics that has revolutionized our understanding of the universe.
\`
</example>
Everything below is the actual data you will be working with. Good luck!
<query>
${question}
</query>
<text>
${doc.pageContent}
</text>
Make sure to answer the query in the summary.
`);
const document = new Document({
pageContent: res.content as string,
metadata: {
title: doc.metadata.title,
url: doc.metadata.url,
},
});
docs.push(document);
}),
);
return { query: question, docs: docs };
} else {
question = question.replace(/<think>.*?<\/think>/g, '');
const res = await searchSearxng(question, {
language: 'en',
engines: this.config.activeEngines,
});
const documents = res.results.map(
(result) =>
new Document({
pageContent:
result.content ||
(this.config.activeEngines.includes('youtube')
? result.title
: '') /* Todo: Implement transcript grabbing using Youtubei (source: https://www.npmjs.com/package/youtubei) */,
metadata: {
title: result.title,
url: result.url,
...(result.img_src && { img_src: result.img_src }),
},
}),
);
return { query: question, docs: documents };
}
}),
]);
}
private async createAnsweringChain(
llm: BaseChatModel,
fileIds: string[],
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
systemInstructions: string,
) {
return RunnableSequence.from([
RunnableMap.from({
systemInstructions: () => systemInstructions,
query: (input: BasicChainInput) => input.query,
chat_history: (input: BasicChainInput) => input.chat_history,
date: () => new Date().toISOString(),
context: RunnableLambda.from(async (input: BasicChainInput) => {
const processedHistory = formatChatHistoryAsString(
input.chat_history,
);
let docs: Document[] | null = null;
let query = input.query;
if (this.config.searchWeb) {
const searchRetrieverChain =
await this.createSearchRetrieverChain(llm);
const searchRetrieverResult = await searchRetrieverChain.invoke({
chat_history: processedHistory,
query,
});
query = searchRetrieverResult.query;
docs = searchRetrieverResult.docs;
}
const sortedDocs = await this.rerankDocs(
query,
docs ?? [],
fileIds,
embeddings,
optimizationMode,
);
return sortedDocs;
})
.withConfig({
runName: 'FinalSourceRetriever',
})
.pipe(this.processDocs),
}),
ChatPromptTemplate.fromMessages([
['system', this.config.responsePrompt],
new MessagesPlaceholder('chat_history'),
['user', '{query}'],
]),
llm,
this.strParser,
]).withConfig({
runName: 'FinalResponseGenerator',
});
}
private async rerankDocs(
query: string,
docs: Document[],
fileIds: string[],
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) {
if (docs.length === 0 && fileIds.length === 0) {
return docs;
}
const filesData = fileIds
.map((file) => {
const filePath = path.join(process.cwd(), 'uploads', file);
const contentPath = filePath + '-extracted.json';
const embeddingsPath = filePath + '-embeddings.json';
const content = JSON.parse(fs.readFileSync(contentPath, 'utf8'));
const embeddings = JSON.parse(fs.readFileSync(embeddingsPath, 'utf8'));
const fileSimilaritySearchObject = content.contents.map(
(c: string, i: number) => {
return {
fileName: content.title,
content: c,
embeddings: embeddings.embeddings[i],
};
},
);
return fileSimilaritySearchObject;
})
.flat();
if (query.toLocaleLowerCase() === 'summarize') {
return docs.slice(0, 15);
}
const docsWithContent = docs.filter(
(doc) => doc.pageContent && doc.pageContent.length > 0,
);
if (optimizationMode === 'speed' || this.config.rerank === false) {
if (filesData.length > 0) {
const [queryEmbedding] = await Promise.all([
embeddings.embedQuery(query),
]);
const fileDocs = filesData.map((fileData) => {
return new Document({
pageContent: fileData.content,
metadata: {
title: fileData.fileName,
url: `File`,
},
});
});
const similarity = filesData.map((fileData, i) => {
const sim = computeSimilarity(queryEmbedding, fileData.embeddings);
return {
index: i,
similarity: sim,
};
});
let sortedDocs = similarity
.filter(
(sim) => sim.similarity > (this.config.rerankThreshold ?? 0.3),
)
.sort((a, b) => b.similarity - a.similarity)
.slice(0, 15)
.map((sim) => fileDocs[sim.index]);
sortedDocs =
docsWithContent.length > 0 ? sortedDocs.slice(0, 8) : sortedDocs;
return [
...sortedDocs,
...docsWithContent.slice(0, 15 - sortedDocs.length),
];
} else {
return docsWithContent.slice(0, 15);
}
} else if (optimizationMode === 'balanced') {
const [docEmbeddings, queryEmbedding] = await Promise.all([
embeddings.embedDocuments(
docsWithContent.map((doc) => doc.pageContent),
),
embeddings.embedQuery(query),
]);
docsWithContent.push(
...filesData.map((fileData) => {
return new Document({
pageContent: fileData.content,
metadata: {
title: fileData.fileName,
url: `File`,
},
});
}),
);
docEmbeddings.push(...filesData.map((fileData) => fileData.embeddings));
const similarity = docEmbeddings.map((docEmbedding, i) => {
const sim = computeSimilarity(queryEmbedding, docEmbedding);
return {
index: i,
similarity: sim,
};
});
const sortedDocs = similarity
.filter((sim) => sim.similarity > (this.config.rerankThreshold ?? 0.3))
.sort((a, b) => b.similarity - a.similarity)
.slice(0, 15)
.map((sim) => docsWithContent[sim.index]);
return sortedDocs;
}
return [];
}
private processDocs(docs: Document[]) {
return docs
.map(
(_, index) =>
`${index + 1}. ${docs[index].metadata.title} ${docs[index].pageContent}`,
)
.join('\n');
}
private async handleStream(
stream: AsyncGenerator<StreamEvent, any, any>,
emitter: eventEmitter,
) {
for await (const event of stream) {
if (
event.event === 'on_chain_end' &&
event.name === 'FinalSourceRetriever'
) {
emitter.emit(
'data',
JSON.stringify({ type: 'sources', data: event.data.output }),
);
}
if (
event.event === 'on_chain_stream' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit(
'data',
JSON.stringify({ type: 'response', data: event.data.chunk }),
);
}
if (
event.event === 'on_chain_end' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit('end');
}
}
}
async searchAndAnswer(
message: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
fileIds: string[],
systemInstructions: string,
) {
const emitter = new eventEmitter();
const answeringChain = await this.createAnsweringChain(
llm,
fileIds,
embeddings,
optimizationMode,
systemInstructions,
);
const stream = answeringChain.streamEvents(
{
chat_history: history,
query: message,
},
{
version: 'v1',
},
);
this.handleStream(stream, emitter);
return emitter;
}
}
export default MetaSearchAgent;