mirror of
https://github.com/ItzCrazyKns/Perplexica.git
synced 2025-07-20 23:48:31 +00:00
feat(metaSearchAgent): implement structured outputs
This commit is contained in:
ItzCrazyKns
@ -24,6 +24,7 @@ import computeSimilarity from '../utils/computeSimilarity';
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import formatChatHistoryAsString from '../utils/formatHistory';
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import eventEmitter from 'events';
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import { StreamEvent } from '@langchain/core/tracers/log_stream';
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import { z } from 'zod';
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export interface MetaSearchAgentType {
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searchAndAnswer: (
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@ -52,6 +53,17 @@ type BasicChainInput = {
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query: string;
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};
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const retrieverLLMOutputSchema = z.object({
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query: z.string().describe('The query to search the web for.'),
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links: z
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.array(z.string())
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.describe('The links to search/summarize if present'),
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searchRequired: z
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.boolean()
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.describe('Wether there is a need to search the web'),
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searchMode: z.enum(['', 'normal', 'news']).describe('The search mode.'),
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});
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class MetaSearchAgent implements MetaSearchAgentType {
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private config: Config;
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private strParser = new StringOutputParser();
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@ -62,73 +74,71 @@ class MetaSearchAgent implements MetaSearchAgentType {
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private async createSearchRetrieverChain(llm: BaseChatModel) {
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(llm as unknown as ChatOpenAI).temperature = 0;
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return RunnableSequence.from([
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PromptTemplate.fromTemplate(this.config.queryGeneratorPrompt),
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llm,
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this.strParser,
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RunnableLambda.from(async (input: string) => {
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const linksOutputParser = new LineListOutputParser({
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key: 'links',
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});
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Object.assign(
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Object.create(Object.getPrototypeOf(llm)),
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llm,
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).withStructuredOutput(retrieverLLMOutputSchema, {
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...(llm.metadata?.['model-type'] === 'groq'
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? {
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method: 'json-object',
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}
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: {}),
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}),
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RunnableLambda.from(
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async (input: z.infer<typeof retrieverLLMOutputSchema>) => {
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let question = input.query;
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const links = input.links;
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const questionOutputParser = new LineOutputParser({
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key: 'question',
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});
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const links = await linksOutputParser.parse(input);
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let question = this.config.summarizer
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? await questionOutputParser.parse(input)
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: input;
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if (question === 'not_needed') {
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return { query: '', docs: [] };
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}
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if (links.length > 0) {
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if (question.length === 0) {
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question = 'summarize';
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if (!input.searchRequired) {
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return { query: '', docs: [] };
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}
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let docs: Document[] = [];
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const linkDocs = await getDocumentsFromLinks({ links });
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const docGroups: Document[] = [];
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linkDocs.map((doc) => {
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const URLDocExists = docGroups.find(
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(d) =>
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d.metadata.url === doc.metadata.url &&
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d.metadata.totalDocs < 10,
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);
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if (!URLDocExists) {
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docGroups.push({
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...doc,
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metadata: {
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...doc.metadata,
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totalDocs: 1,
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},
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});
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if (links.length > 0) {
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if (question.length === 0) {
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question = 'summarize';
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}
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const docIndex = docGroups.findIndex(
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(d) =>
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d.metadata.url === doc.metadata.url &&
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d.metadata.totalDocs < 10,
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);
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let docs: Document[] = [];
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if (docIndex !== -1) {
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docGroups[docIndex].pageContent =
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docGroups[docIndex].pageContent + `\n\n` + doc.pageContent;
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docGroups[docIndex].metadata.totalDocs += 1;
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}
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});
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const linkDocs = await getDocumentsFromLinks({ links });
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await Promise.all(
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docGroups.map(async (doc) => {
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const res = await llm.invoke(`
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const docGroups: Document[] = [];
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linkDocs.map((doc) => {
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const URLDocExists = docGroups.find(
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(d) =>
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d.metadata.url === doc.metadata.url &&
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d.metadata.totalDocs < 10,
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);
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if (!URLDocExists) {
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docGroups.push({
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...doc,
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metadata: {
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...doc.metadata,
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totalDocs: 1,
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},
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});
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}
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const docIndex = docGroups.findIndex(
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(d) =>
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d.metadata.url === doc.metadata.url &&
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d.metadata.totalDocs < 10,
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);
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if (docIndex !== -1) {
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docGroups[docIndex].pageContent =
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docGroups[docIndex].pageContent + `\n\n` + doc.pageContent;
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docGroups[docIndex].metadata.totalDocs += 1;
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}
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});
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await Promise.all(
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docGroups.map(async (doc) => {
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const res = await llm.invoke(`
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You are a web search summarizer, tasked with summarizing a piece of text retrieved from a web search. Your job is to summarize the
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text into a detailed, 2-4 paragraph explanation that captures the main ideas and provides a comprehensive answer to the query.
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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.
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@ -189,46 +199,50 @@ class MetaSearchAgent implements MetaSearchAgentType {
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Make sure to answer the query in the summary.
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`);
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const document = new Document({
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pageContent: res.content as string,
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metadata: {
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title: doc.metadata.title,
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url: doc.metadata.url,
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},
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});
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const document = new Document({
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pageContent: res.content as string,
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metadata: {
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title: doc.metadata.title,
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url: doc.metadata.url,
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},
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});
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docs.push(document);
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}),
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);
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return { query: question, docs: docs };
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} else {
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question = question.replace(/<think>.*?<\/think>/g, '');
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const res = await searchSearxng(question, {
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language: 'en',
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engines: this.config.activeEngines,
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});
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const documents = res.results.map(
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(result) =>
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new Document({
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pageContent:
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result.content ||
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(this.config.activeEngines.includes('youtube')
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? result.title
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: '') /* Todo: Implement transcript grabbing using Youtubei (source: https://www.npmjs.com/package/youtubei) */,
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metadata: {
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title: result.title,
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url: result.url,
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...(result.img_src && { img_src: result.img_src }),
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},
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docs.push(document);
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}),
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);
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);
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return { query: question, docs: documents };
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}
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}),
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return { query: question, docs: docs };
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} else {
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question = question.replace(/<think>.*?<\/think>/g, '');
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const res = await searchSearxng(question, {
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language: 'en',
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engines:
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input.searchMode === 'normal'
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? this.config.activeEngines
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: ['bing news'],
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});
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const documents = res.results.map(
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(result) =>
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new Document({
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pageContent:
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result.content ||
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(this.config.activeEngines.includes('youtube')
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? result.title
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: '') /* Todo: Implement transcript grabbing using Youtubei (source: https://www.npmjs.com/package/youtubei) */,
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metadata: {
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title: result.title,
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url: result.url,
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...(result.img_src && { img_src: result.img_src }),
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},
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}),
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);
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return { query: question, docs: documents };
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}
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},
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),
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]);
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}
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