semantic caching in use for both Google and OpenAI

Author: wjohnsto

app/src/app/page.tsx

@@ -30,6 +30,7 @@ export default function Home() {
 
   const handleSearch = async (question: string) => {
     // Replace with your API call
+    setResults(undefined);
     const response = await fetch(`/api/search?question=${question}`);
     const data: VideoSearchResult = await response.json();
     setResults(data);

package.json

@@ -23,4 +23,4 @@
     },
     "author": "",
     "license": "ISC"
-}
+}

services/video-search/src/google/search.ts

@@ -34,6 +34,20 @@ async function getAnswer(question: string, videos: VideoDocument[]) {
         location: 'google.search.getAnswer',
     });
 
+    const answer = (await questionAnswerChain.invoke({
+        question,
+        data: JSON.stringify(videos),
+    })) as string;
+
+    return answer;
+}
+
+export async function search(question: string) {
+    log.debug(`Original question: ${question}`, {
+        location: 'google.search.search',
+    });
+    const semanticQuestion = (await summarize.question(question)) as string;
+
     const haveAnswers = await answerVectorStore.checkIndexExists();
 
     if (haveAnswers) {
@@ -52,36 +66,17 @@ async function getAnswer(question: string, videos: VideoDocument[]) {
             answer: result[0],
             score: result[1],
         });
-        
+
         if (Array.isArray(result) && result.length > 0) {
-            return result[0];
+            log.debug(`Found answer: ${result[0].metadata.answer}`, {
+                location: 'google.search.getAnswer',
+                result: result[0].metadata,
+            });
+
+            return result[0].metadata;
         }
     }
 
-    const answer = (await questionAnswerChain.invoke({
-        question,
-        data: JSON.stringify(videos),
-    })) as string;
-
-    await answerVectorStore.addDocuments([
-        new Document({
-            metadata: {
-                videos,
-                answer,
-            },
-            pageContent: question,
-        }),
-    ]);
-
-    return answer;
-}
-
-export async function search(question: string) {
-    log.debug(`Original question: ${question}`, {
-        location: 'google.search.search',
-    });
-    const semanticQuestion = (await summarize.question(question)) as string;
-
     log.debug(`Semantic question: ${semanticQuestion}`, {
         location: 'google.search.search',
     });
@@ -102,8 +97,20 @@ export async function search(question: string) {
         location: 'openai.search.search',
     });
 
+    const answer = getAnswer(semanticQuestion, videos);
+
+    await answerVectorStore.addDocuments([
+        new Document({
+            metadata: {
+                videos,
+                answer,
+            },
+            pageContent: question,
+        }),
+    ]);
+
     return {
         videos,
-        answer: await getAnswer(semanticQuestion, videos),
+        answer,
     };
 }

services/video-search/src/openai/search.ts

@@ -34,30 +34,6 @@ async function getAnswer(question: string, videos: VideoDocument[]) {
         location: 'openai.search.getAnswer',
     });
 
-    const haveAnswers = await answerVectorStore.checkIndexExists();
-
-    if (haveAnswers) {
-        log.debug(`Searching for closest answer to question: ${question}`, {
-            location: 'google.search.getAnswer',
-            question,
-        });
-
-        const [result] = await answerVectorStore.similaritySearchWithScore(
-            question,
-            1,
-        );
-
-        log.debug(`Found closest answer with score: ${String(result[1])}`, {
-            location: 'google.search.getAnswer',
-            answer: result[0],
-            score: result[1],
-        });
-        
-        if (Array.isArray(result) && result.length > 0) {
-            return result[0];
-        }
-    }
-
     const answer = (await questionAnswerChain.invoke({
         question,
         data: JSON.stringify(videos),
@@ -82,6 +58,35 @@ export async function search(question: string) {
     });
     const semanticQuestion = (await summarize.question(question)) as string;
 
+    const haveAnswers = await answerVectorStore.checkIndexExists();
+
+    if (haveAnswers) {
+        log.debug(`Searching for closest answer to question: ${question}`, {
+            location: 'openai.search.getAnswer',
+            question,
+        });
+
+        const [result] = await answerVectorStore.similaritySearchWithScore(
+            question,
+            1,
+        );
+
+        log.debug(`Found closest answer with score: ${String(result[1])}`, {
+            location: 'openai.search.getAnswer',
+            answer: result[0],
+            score: result[1],
+        });
+
+        if (Array.isArray(result) && result.length > 0) {
+            log.debug(`Found answer: ${result[0].metadata.answer}`, {
+                location: 'openai.search.getAnswer',
+                result: result[0].metadata,
+            });
+
+            return result[0].metadata;
+        }
+    }
+
     log.debug(`Semantic question: ${semanticQuestion}`, {
         location: 'openai.search.search',
     });