feat(metaSearchAgent): implement structured outputs

src/lib/prompts/webSearch.ts

@@ -1,63 +1,41 @@
 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.
+You are an AI question rephraser. You will be given a conversation and a follow-up question; rephrase it into a standalone question that another LLM can use to search the web.
 
-There are several examples attached for your reference inside the below \`examples\` XML block
+Return ONLY a JSON object that matches this schema:
+query: string   // the standalone question (or "summarize")
+links: string[] // URLs extracted from the user query (empty if none)
+searchRequired: boolean // true if web search is needed, false for greetings/simple writing tasks
+searchMode: "" | "normal" | "news" // "" when searchRequired is false; "news" if the user asks for news/articles, otherwise "normal"
 
-<examples>
-1. Follow up question: What is the capital of France
-Rephrased question:\`
-<question>
-Capital of france
-</question>
-\`
+Rules
+- Greetings / simple writing tasks → query:"", links:[], searchRequired:false, searchMode:""
+- Summarizing a URL → query:"summarize", links:[url...], searchRequired:true, searchMode:"normal"
+- Asking for news/articles → searchMode:"news"
+
+Examples
+1. Follow-up: What is the capital of France?
+"query":"capital of France","links":[],"searchRequired":true,"searchMode":"normal"
 
 2. Hi, how are you?
-Rephrased question\`
-<question>
-not_needed
-</question>
-\`
+"query":"","links":[],"searchRequired":false,"searchMode":""
 
-3. Follow up question: What is Docker?
-Rephrased question: \`
-<question>
-What is Docker
-</question>
-\`
+3. Follow-up: What is Docker?
+"query":"what is Docker","links":[],"searchRequired":true,"searchMode":"normal"
 
-4. Follow up question: Can you tell me what is X from https://example.com
-Rephrased question: \`
-<question>
-Can you tell me what is X?
-</question>
+4. Follow-up: Can you tell me what is X from https://example.com?
+"query":"what is X","links":["https://example.com"],"searchRequired":true,"searchMode":"normal"
 
-<links>
-https://example.com
-</links>
-\`
+5. Follow-up: Summarize the content from https://example.com
+"query":"summarize","links":["https://example.com"],"searchRequired":true,"searchMode":"normal"
 
-5. Follow up question: Summarize the content from https://example.com
-Rephrased question: \`
-<question>
-summarize
-</question>
-
-<links>
-https://example.com
-</links>
-\`
-</examples>
-
-Anything below is the part of the actual conversation and you need to use conversation and the follow-up question to rephrase the follow-up question as a standalone question based on the guidelines shared above.
+6. Follow-up: Latest news about AI
+"query":"latest news about AI","links":[],"searchRequired":true,"searchMode":"news"
 
 <conversation>
 {chat_history}
 </conversation>
 
-Follow up question: {query}
+Follow-up question: {query}
 Rephrased question:
 `;
 

src/lib/search/metaSearchAgent.ts

@@ -24,6 +24,7 @@ import computeSimilarity from '../utils/computeSimilarity';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 import { StreamEvent } from '@langchain/core/tracers/log_stream';
+import { z } from 'zod';
 
 export interface MetaSearchAgentType {
   searchAndAnswer: (
@@ -52,6 +53,17 @@ type BasicChainInput = {
   query: string;
 };
 
+const retrieverLLMOutputSchema = z.object({
+  query: z.string().describe('The query to search the web for.'),
+  links: z
+    .array(z.string())
+    .describe('The links to search/summarize if present'),
+  searchRequired: z
+    .boolean()
+    .describe('Wether there is a need to search the web'),
+  searchMode: z.enum(['', 'normal', 'news']).describe('The search mode.'),
+});
+
 class MetaSearchAgent implements MetaSearchAgentType {
   private config: Config;
   private strParser = new StringOutputParser();
@@ -62,26 +74,24 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
   private async createSearchRetrieverChain(llm: BaseChatModel) {
     (llm as unknown as ChatOpenAI).temperature = 0;
-
     return RunnableSequence.from([
       PromptTemplate.fromTemplate(this.config.queryGeneratorPrompt),
+      Object.assign(
+        Object.create(Object.getPrototypeOf(llm)),
         llm,
-      this.strParser,
-      RunnableLambda.from(async (input: string) => {
-        const linksOutputParser = new LineListOutputParser({
-          key: 'links',
-        });
+      ).withStructuredOutput(retrieverLLMOutputSchema, {
+        ...(llm.metadata?.['model-type'] === 'groq'
+          ? {
+              method: 'json-object',
+            }
+          : {}),
+      }),
+      RunnableLambda.from(
+        async (input: z.infer<typeof retrieverLLMOutputSchema>) => {
+          let question = input.query;
+          const links = input.links;
 
-        const questionOutputParser = new LineOutputParser({
-          key: 'question',
-        });
-
-        const links = await linksOutputParser.parse(input);
-        let question = this.config.summarizer
-          ? await questionOutputParser.parse(input)
-          : input;
-
-        if (question === 'not_needed') {
+          if (!input.searchRequired) {
             return { query: '', docs: [] };
           }
 
@@ -207,7 +217,10 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
             const res = await searchSearxng(question, {
               language: 'en',
-            engines: this.config.activeEngines,
+              engines:
+                input.searchMode === 'normal'
+                  ? this.config.activeEngines
+                  : ['bing news'],
             });
 
             const documents = res.results.map(
@@ -228,7 +241,8 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
             return { query: question, docs: documents };
           }
-      }),
+        },
+      ),
     ]);
   }