Files
Perplexica/src/lib/agents/search/index.ts
2025-12-23 17:15:32 +05:30

171 lines
4.5 KiB
TypeScript

import { ResearcherOutput, SearchAgentInput } from './types';
import SessionManager from '@/lib/session';
import { classify } from './classifier';
import Researcher from './researcher';
import { getWriterPrompt } from '@/lib/prompts/search/writer';
import { WidgetExecutor } from './widgets';
import db from '@/lib/db';
import { chats, messages } from '@/lib/db/schema';
import { and, eq, gt } from 'drizzle-orm';
import { TextBlock } from '@/lib/types';
class SearchAgent {
async searchAsync(session: SessionManager, input: SearchAgentInput) {
const exists = await db.query.messages.findFirst({
where: and(
eq(messages.chatId, input.chatId),
eq(messages.messageId, input.messageId),
),
});
if (!exists) {
await db.insert(messages).values({
chatId: input.chatId,
messageId: input.messageId,
backendId: session.id,
query: input.followUp,
createdAt: new Date().toISOString(),
status: 'answering',
responseBlocks: [],
});
} else {
await db
.delete(messages)
.where(
and(eq(messages.chatId, input.chatId), gt(messages.id, exists.id)),
)
.execute();
await db
.update(messages)
.set({
status: 'answering',
backendId: session.id,
responseBlocks: [],
})
.where(
and(
eq(messages.chatId, input.chatId),
eq(messages.messageId, input.messageId),
),
)
.execute();
}
const classification = await classify({
chatHistory: input.chatHistory,
enabledSources: input.config.sources,
query: input.followUp,
llm: input.config.llm,
});
const widgetPromise = WidgetExecutor.executeAll({
classification,
chatHistory: input.chatHistory,
followUp: input.followUp,
llm: input.config.llm,
}).then((widgetOutputs) => {
widgetOutputs.forEach((o) => {
session.emitBlock({
id: crypto.randomUUID(),
type: 'widget',
data: {
widgetType: o.type,
params: o.data,
},
});
});
return widgetOutputs;
});
let searchPromise: Promise<ResearcherOutput> | null = null;
if (!classification.classification.skipSearch) {
const researcher = new Researcher();
searchPromise = researcher.research(session, {
chatHistory: input.chatHistory,
followUp: input.followUp,
classification: classification,
config: input.config,
});
}
const [widgetOutputs, searchResults] = await Promise.all([
widgetPromise,
searchPromise,
]);
session.emit('data', {
type: 'researchComplete',
});
const finalContext =
searchResults?.searchFindings
.map(
(f, index) =>
`<result index=${index + 1} title=${f.metadata.title}>${f.content}</result>`,
)
.join('\n') || '';
const widgetContext = widgetOutputs
.map((o) => {
return `<result>${o.llmContext}</result>`;
})
.join('\n-------------\n');
const finalContextWithWidgets = `<search_results note="These are the search results and assistant can cite these">\n${finalContext}\n</search_results>\n<widgets_result noteForAssistant="Its output is already showed to the user, assistant can use this information to answer the query but do not CITE this as a souce">\n${widgetContext}\n</widgets_result>`;
const writerPrompt = getWriterPrompt(finalContextWithWidgets);
const answerStream = input.config.llm.streamText({
messages: [
{
role: 'system',
content: writerPrompt,
},
...input.chatHistory,
{
role: 'user',
content: input.followUp,
},
],
});
const block: TextBlock = {
id: crypto.randomUUID(),
type: 'text',
data: '',
};
session.emitBlock(block);
for await (const chunk of answerStream) {
block.data += chunk.contentChunk;
session.updateBlock(block.id, [
{
op: 'replace',
path: '/data',
value: block.data,
},
]);
}
session.emit('end', {});
await db
.update(messages)
.set({
status: 'completed',
responseBlocks: session.getAllBlocks(),
})
.where(
and(
eq(messages.chatId, input.chatId),
eq(messages.messageId, input.messageId),
),
)
.execute();
}
}
export default SearchAgent;