feat(agent): Improve relevance checking and preview content handling in web search agent.

Better groq support.

src/lib/agents/webSearchAgent.ts

@@ -181,15 +181,25 @@ export class WebSearchAgent {
         }),
       );
 
-      // Sort by relevance score and take top 12 results
+      let previewContents: PreviewContent[] = [];
-      const previewContents: PreviewContent[] = resultsWithSimilarity
+      // Always take the top 3 results for preview content
+      previewContents.push(...filteredResults.slice(0, 3)
+        .map((result) => ({
+          title: result.title || 'Untitled',
+          snippet: result.content || '',
+          url: result.url,
+        }))
+      );
+
+      // Sort by relevance score and take top 12 results for a total of 15
+      previewContents.push(...resultsWithSimilarity.slice(3)
         .sort((a, b) => b.similarity - a.similarity)
         .slice(0, 12)
         .map(({ result }) => ({
           title: result.title || 'Untitled',
           snippet: result.content || '',
           url: result.url,
-        }));
+        })));
 
       console.log(
         `Extracted preview content from ${previewContents.length} search results for analysis`,
@@ -306,9 +316,7 @@ export class WebSearchAgent {
         });
 
         // Summarize the top 2 search results
-        for (const result of resultsWithSimilarity
+        for (const result of previewContents) {
-          .slice(0, 12)
-          .map((r) => r.result)) {
           if (this.signal.aborted) {
             console.warn('Search operation aborted by signal');
             break; // Exit if the operation is aborted

src/lib/providers/groq.ts

@@ -1,91 +1,91 @@
 import { ChatOpenAI } from '@langchain/openai';
 import { getGroqApiKey } from '../config';
 import { ChatModel } from '.';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
 
 export const PROVIDER_INFO = {
   key: 'groq',
   displayName: 'Groq',
 };
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
 
-const groqChatModels: Record<string, string>[] = [
+interface GroqModel {
-  {
+  id: string;
-    displayName: 'Gemma2 9B IT',
+  object: string;
-    key: 'gemma2-9b-it',
+  created: number;
-  },
+  owned_by: string;
-  {
+  active: boolean;
-    displayName: 'Llama 3.3 70B Versatile',
+  context_window: number;
-    key: 'llama-3.3-70b-versatile',
+  max_completion_tokens: number;
-  },
+}
-  {
+
-    displayName: 'Llama 3.1 8B Instant',
+interface GroqModelsResponse {
-    key: 'llama-3.1-8b-instant',
+  object: string;
-  },
+  data: GroqModel[];
-  {
+}
-    displayName: 'Llama3 70B 8192',
+
-    key: 'llama3-70b-8192',
+const generateDisplayName = (modelId: string, ownedBy: string): string => {
-  },
+  // Handle special cases for better display names
-  {
+  const modelMap: Record<string, string> = {
-    displayName: 'Llama3 8B 8192',
+    'gemma2-9b-it': 'Gemma2 9B IT',
-    key: 'llama3-8b-8192',
+    'llama-3.3-70b-versatile': 'Llama 3.3 70B Versatile',
-  },
+    'llama-3.1-8b-instant': 'Llama 3.1 8B Instant',
-  {
+    'llama3-70b-8192': 'Llama3 70B 8192',
-    displayName: 'Mixtral 8x7B 32768',
+    'llama3-8b-8192': 'Llama3 8B 8192',
-    key: 'mixtral-8x7b-32768',
+    'mixtral-8x7b-32768': 'Mixtral 8x7B 32768',
-  },
+    'qwen-qwq-32b': 'Qwen QWQ 32B',
-  {
+    'mistral-saba-24b': 'Mistral Saba 24B',
-    displayName: 'Qwen QWQ 32B (Preview)',
+    'deepseek-r1-distill-llama-70b': 'DeepSeek R1 Distill Llama 70B',
-    key: 'qwen-qwq-32b',
+    'deepseek-r1-distill-qwen-32b': 'DeepSeek R1 Distill Qwen 32B',
-  },
+  };
-  {
+
-    displayName: 'Mistral Saba 24B (Preview)',
+  // Return mapped name if available
-    key: 'mistral-saba-24b',
+  if (modelMap[modelId]) {
-  },
+    return modelMap[modelId];
-  {
+  }
-    displayName: 'Qwen 2.5 Coder 32B (Preview)',
+
-    key: 'qwen-2.5-coder-32b',
+  // Generate display name from model ID
-  },
+  let displayName = modelId
-  {
+    .replace(/[-_]/g, ' ')
-    displayName: 'Qwen 2.5 32B (Preview)',
+    .split(' ')
-    key: 'qwen-2.5-32b',
+    .map(word => word.charAt(0).toUpperCase() + word.slice(1))
-  },
+    .join(' ');
-  {
+
-    displayName: 'DeepSeek R1 Distill Qwen 32B (Preview)',
+  // Add owner info for certain models
-    key: 'deepseek-r1-distill-qwen-32b',
+  if (modelId.includes('meta-llama/')) {
-  },
+    displayName = displayName.replace('Meta Llama/', '');
-  {
+  }
-    displayName: 'DeepSeek R1 Distill Llama 70B (Preview)',
+
-    key: 'deepseek-r1-distill-llama-70b',
+  return displayName;
-  },
+};
-  {
+
-    displayName: 'Llama 3.3 70B SpecDec (Preview)',
+const fetchGroqModels = async (apiKey: string): Promise<GroqModel[]> => {
-    key: 'llama-3.3-70b-specdec',
+  try {
-  },
+    const response = await fetch('https://api.groq.com/openai/v1/models', {
-  {
+      headers: {
-    displayName: 'Llama 3.2 1B Preview (Preview)',
+        'Authorization': `Bearer ${apiKey}`,
-    key: 'llama-3.2-1b-preview',
+        'Content-Type': 'application/json',
-  },
+      },
-  {
+    });
-    displayName: 'Llama 3.2 3B Preview (Preview)',
+
-    key: 'llama-3.2-3b-preview',
+    if (!response.ok) {
-  },
+      throw new Error(`Failed to fetch models: ${response.status} ${response.statusText}`);
-  {
+    }
-    displayName: 'Llama 3.2 11B Vision Preview (Preview)',
+
-    key: 'llama-3.2-11b-vision-preview',
+    const data: GroqModelsResponse = await response.json();
-  },
+    
-  {
+    // Filter for active chat completion models (exclude audio/whisper models)
-    displayName: 'Llama 3.2 90B Vision Preview (Preview)',
+    return data.data.filter(model => 
-    key: 'llama-3.2-90b-vision-preview',
+      model.active && 
-  },
+      !model.id.includes('whisper') &&
-  /* {
+      !model.id.includes('tts') &&
-    displayName: 'Llama 4 Maverick 17B 128E Instruct (Preview)',
+      !model.id.includes('guard') &&
-    key: 'meta-llama/llama-4-maverick-17b-128e-instruct',
+      !model.id.includes('prompt-guard')
-  }, */
+    );
-  {
+  } catch (error) {
-    displayName: 'Llama 4 Scout 17B 16E Instruct (Preview)',
+    console.error('Error fetching Groq models:', error);
-    key: 'meta-llama/llama-4-scout-17b-16e-instruct',
+    return [];
-  },
+  }
-];
+};
 
 export const loadGroqChatModels = async () => {
   const groqApiKey = getGroqApiKey();
@@ -95,12 +95,15 @@ export const loadGroqChatModels = async () => {
   try {
     const chatModels: Record<string, ChatModel> = {};
 
-    groqChatModels.forEach((model) => {
+    // Fetch available models from Groq API
-      chatModels[model.key] = {
+    const availableModels = await fetchGroqModels(groqApiKey);
-        displayName: model.displayName,
+
+    availableModels.forEach((model) => {
+      chatModels[model.id] = {
+        displayName: generateDisplayName(model.id, model.owned_by),
         model: new ChatOpenAI({
           openAIApiKey: groqApiKey,
-          modelName: model.key,
+          modelName: model.id,
           // temperature: 0.7,
           configuration: {
             baseURL: 'https://api.groq.com/openai/v1',

src/lib/utils/analyzePreviewContent.ts

@@ -83,9 +83,8 @@ Snippet: ${content.snippet}
 
 # Instructions
 - Analyze the provided search result previews (titles + snippets), and chat history context to determine if they collectively contain enough information to provide a complete and accurate answer to the Task Query
-- You must make a binary decision: either the preview content is sufficient OR it is not sufficient
+- If the preview content can provide a complete answer to the Task Query, consider it sufficient
-- If the preview content can provide a complete answer to the Task Query, set isSufficient to true
+- If the preview content lacks important details, requires deeper analysis, or cannot fully answer the Task Query, consider it insufficient
-- If the preview content lacks important details, requires deeper analysis, or cannot fully answer the Task Query, set isSufficient to false and provide a specific reason
 - Be specific in your reasoning when the content is not sufficient
 - The original query is provided for additional context, only use it for clarification of overall expectations and intent. You do **not** need to answer the original query directly or completely
 
@@ -103,11 +102,6 @@ ${taskQuery}
 
 # Search Result Previews to Analyze:
 ${formattedPreviewContent}
-
-# Response Format
-You must return a JSON object with:
-- isSufficient: boolean indicating whether preview content is sufficient
-- reason: string explaining why full content analysis is required (only if isSufficient is false)
 `,
       { signal },
     );

src/lib/utils/summarizeWebContent.ts

@@ -1,5 +1,6 @@
 import { Document } from '@langchain/core/documents';
 import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { z } from 'zod';
 import { formatDateForLLM } from '../utils';
 import { getWebContent } from './documents';
 import { removeThinkingBlocks } from './contentUtils';
@@ -10,6 +11,16 @@ export type SummarizeResult = {
   notRelevantReason?: string;
 };
 
+// Zod schema for structured relevance check output
+const RelevanceCheckSchema = z.object({
+  relevant: z
+    .boolean()
+    .describe('Whether the content is relevant to the user query'),
+  reason: z
+    .string()
+    .describe('Brief explanation of why content is or isn\'t relevant'),
+});
+
 export const summarizeWebContent = async (
   url: string,
   query: string,
@@ -37,55 +48,49 @@ export const summarizeWebContent = async (
           `Short content detected (${contentToAnalyze.length} chars) for URL: ${url}, checking relevance only`,
         );
 
-        const relevancePrompt = `${systemPrompt}You are a content relevance checker. Your task is to determine if the given content is relevant to the user's query.
+        try {
+          // Create structured LLM with Zod schema
+          const structuredLLM = llm.withStructuredOutput(RelevanceCheckSchema);
+
+          const relevanceResult = await structuredLLM.invoke(
+            `${systemPrompt}You are a content relevance checker. Your task is to determine if the given content is relevant to the user's query.
 
 # Instructions
 - Analyze the content to determine if it contains information relevant to the user's query
 - You do not need to provide a full answer to the query in order to be relevant, partial answers are acceptable
-- Respond with valid JSON in the following format:
+- Provide a brief explanation of your reasoning
-{
-  "relevant": true/false,
-  "reason": "brief explanation of why content is or isn't relevant"
-}
 
 Today's date is ${formatDateForLLM(new Date())}
 
 Here is the query you need to answer: ${query}
 
 Here is the content to analyze:
-${contentToAnalyze}`;
+${contentToAnalyze}`,
+            { signal }
+          );
 
-        try {
+          if (!relevanceResult) {
-          const result = await llm.invoke(relevancePrompt, { signal });
+            console.error(`No relevance result returned for URL ${url}`);
-          const responseText = removeThinkingBlocks(result.content as string).trim();
-          
-          try {
-            const parsedResponse = JSON.parse(responseText);
-            
-            if (parsedResponse.relevant === true) {
-              console.log(`Short content for URL "${url}" is relevant: ${parsedResponse.reason}`);
-              return {
-                document: new Document({
-                  pageContent: content.pageContent,
-                  metadata: {
-                    ...content.metadata,
-                    url: url,
-                    processingType: 'short-content',
-                  },
-                }),
-                notRelevantReason: undefined,
-              };
-            } else {
-              console.log(`Short content for URL "${url}" is not relevant: ${parsedResponse.reason}`);
-              return {
-                document: null,
-                notRelevantReason: parsedResponse.reason || 'Content not relevant to query',
-              };
-            }
-          } catch (parseError) {
-            console.error(`Error parsing JSON response for URL ${url}:`, parseError);
-            console.error(`Raw response:`, responseText);
             // Fall through to full summarization as fallback
+          } else if (relevanceResult.relevant) {
+            console.log(`Short content for URL "${url}" is relevant: ${relevanceResult.reason}`);
+            return {
+              document: new Document({
+                pageContent: content.pageContent,
+                metadata: {
+                  ...content.metadata,
+                  url: url,
+                  processingType: 'short-content',
+                },
+              }),
+              notRelevantReason: undefined,
+            };
+          } else {
+            console.log(`Short content for URL "${url}" is not relevant: ${relevanceResult.reason}`);
+            return {
+              document: null,
+              notRelevantReason: relevanceResult.reason || 'Content not relevant to query',
+            };
           }
         } catch (error) {
           console.error(`Error checking relevance for short content from URL ${url}:`, error);