feat(agent): Start agent speed optimizations.

src/lib/utils/analyzePreviewContent.ts

@@ -0,0 +1,128 @@
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { BaseMessage } from '@langchain/core/messages';
+import LineOutputParser from '../outputParsers/lineOutputParser';
+import { formatDateForLLM } from '../utils';
+
+export type PreviewAnalysisResult = {
+  isSufficient: boolean;
+  reason?: string;
+};
+
+export type PreviewContent = {
+  title: string;
+  snippet: string;
+  url: string;
+};
+
+export const analyzePreviewContent = async (
+  previewContents: PreviewContent[],
+  query: string,
+  chatHistory: BaseMessage[],
+  llm: BaseChatModel,
+  systemInstructions: string,
+  signal: AbortSignal,
+): Promise<PreviewAnalysisResult> => {
+  try {
+    console.log(`Analyzing preview content for query: "${query}"`);
+    console.log(`Preview content being analyzed:`, previewContents.map(content => ({
+      title: content.title,
+      snippet: content.snippet.substring(0, 100) + '...',
+      url: content.url
+    })));
+
+    // Format preview content for analysis
+    const formattedPreviewContent = previewContents
+      .map((content, index) => 
+        `Source ${index + 1}:
+Title: ${content.title}
+Snippet: ${content.snippet}
+URL: ${content.url}
+---`
+      )
+      .join('\n\n');
+
+    // Format chat history for context
+    const formattedChatHistory = chatHistory
+      .slice(-10) // Only include last 10 messages for context
+      .map((message, index) => 
+        `${message._getType()}: ${message.content}`
+      )
+      .join('\n');
+
+    const systemPrompt = systemInstructions
+      ? `${systemInstructions}\n\n`
+      : '';
+
+    console.log(`Invoking LLM for preview content analysis`);
+    
+    const analysisResponse = await llm.invoke(
+      `${systemPrompt}You are a preview content analyzer, tasked with determining if search result snippets contain sufficient information to answer a user's query.
+
+# Instructions
+- Analyze the provided search result previews (titles + snippets) to determine if they collectively contain enough information to provide a complete and accurate answer to the user's query
+- Consider the chat history context when making your decision
+- 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 query, respond with "sufficient"
+- If the preview content lacks important details, requires deeper analysis, or cannot fully answer the query, respond with "not_needed: [specific reason why full content analysis is required]"
+- Be specific in your reasoning when the content is not sufficient
+- Consider query complexity: simple factual questions may be answerable from snippets, while complex research questions typically need full content
+- Consider information completeness: if key details are missing from the snippets that would be needed for a complete answer, full analysis is required
+- Output your decision inside a \`decision\` XML tag
+
+Today's date is ${formatDateForLLM(new Date())}
+
+# Chat History Context:
+${formattedChatHistory ? formattedChatHistory : 'No previous conversation context'}
+
+# User Query:
+${query}
+
+# Search Result Previews to Analyze:
+${formattedPreviewContent}
+      `,
+      { signal },
+    );
+
+    if (!analysisResponse || !analysisResponse.content) {
+      console.error('No analysis response returned from LLM');
+      return { 
+        isSufficient: false, 
+        reason: 'No analysis response returned from LLM - falling back to full content processing' 
+      };
+    }
+
+    const decisionParser = new LineOutputParser({ key: 'decision' });
+    const decision = await decisionParser.parse(
+      analysisResponse.content as string,
+    );
+
+    console.log(`LLM decision response:`, decision);
+
+    if (decision.toLowerCase().trim() === 'sufficient') {
+      console.log('Preview content determined to be sufficient for answering the query');
+      return { isSufficient: true };
+    } else if (decision.toLowerCase().startsWith('not_needed')) {
+      // Extract the reason from the "not_needed" response
+      const reason = decision.startsWith('not_needed') 
+        ? decision.substring('not_needed:'.length).trim()
+        : 'Preview content insufficient for complete answer';
+        
+      console.log(`Preview content determined to be insufficient. Reason: ${reason}`);
+      return { isSufficient: false, reason };
+    } else {
+      // Default to not sufficient if unclear response
+      console.log(`Unclear LLM response, defaulting to insufficient: ${decision}`);
+      return { 
+        isSufficient: false, 
+        reason: 'Unclear analysis response - falling back to full content processing' 
+      };
+    }
+
+  } catch (error) {
+    console.error('Error analyzing preview content:', error);
+    return { 
+      isSufficient: false, 
+      reason: `Error during preview analysis: ${error instanceof Error ? error.message : 'Unknown error'} - falling back to full content processing` 
+    };
+  }
+};