feat(agents): embed docs & query together

Embed documents and query together to reduce the time taken for retrieving the sources ~1 seconds.

README.md

@@ -30,9 +30,9 @@ Perplexica is an open-source AI-powered searching tool or an AI-powered search e
   - **Copilot Mode:** (In development) Boosts search by generating different queries to find more relevant internet sources. Like normal search instead of just using the context by SearxNG, it visits the top matches and tries to find relevant sources to the user's query directly from the page.
   - **Normal Mode:** Processes your query and performs a web search.
 - **Focus Modes:** Special modes to better answer specific types of questions. Perplexica currently has 6 focus modes:
-  -  **All Mode:** Searches the entire web to find the best results.
-  -  **Writing Assistant Mode:** Helpful for writing tasks that does not require searching the web.
-  -  **Academic Search Mode:** Finds articles and papers, ideal for academic research.
+  - **All Mode:** Searches the entire web to find the best results.
+  - **Writing Assistant Mode:** Helpful for writing tasks that does not require searching the web.
+  - **Academic Search Mode:** Finds articles and papers, ideal for academic research.
   - **YouTube Search Mode:** Finds YouTube videos based on the search query.
   - **Wolfram Alpha Search Mode:** Answers queries that need calculations or data analysis using Wolfram Alpha.
   - **Reddit Search Mode:** Searches Reddit for discussions and opinions related to the query.

docker-compose.yaml

@@ -9,6 +9,7 @@ services:
       - 4000:8080
     networks:
       - perplexica-network
+
   perplexica-backend:
     build:
       context: .

src/agents/academicSearchAgent.ts

@@ -166,11 +166,10 @@ const createBasicAcademicSearchAnsweringChain = (
       (doc) => doc.pageContent && doc.pageContent.length > 0,
     );
 
-    const docEmbeddings = await embeddings.embedDocuments(
-      docsWithContent.map((doc) => doc.pageContent),
-    );
-
-    const queryEmbedding = await embeddings.embedQuery(query);
+    const [docEmbeddings, queryEmbedding] = await Promise.all([
+      embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)),
+      embeddings.embedQuery(query),
+    ]);
 
     const similarity = docEmbeddings.map((docEmbedding, i) => {
       const sim = computeSimilarity(queryEmbedding, docEmbedding);

src/agents/redditSearchAgent.ts

@@ -161,11 +161,10 @@ const createBasicRedditSearchAnsweringChain = (
       (doc) => doc.pageContent && doc.pageContent.length > 0,
     );
 
-    const docEmbeddings = await embeddings.embedDocuments(
-      docsWithContent.map((doc) => doc.pageContent),
-    );
-
-    const queryEmbedding = await embeddings.embedQuery(query);
+    const [docEmbeddings, queryEmbedding] = await Promise.all([
+      embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)),
+      embeddings.embedQuery(query),
+    ]);
 
     const similarity = docEmbeddings.map((docEmbedding, i) => {
       const sim = computeSimilarity(queryEmbedding, docEmbedding);

src/agents/webSearchAgent.ts

@@ -159,11 +159,10 @@ const createBasicWebSearchAnsweringChain = (
       (doc) => doc.pageContent && doc.pageContent.length > 0,
     );
 
-    const docEmbeddings = await embeddings.embedDocuments(
-      docsWithContent.map((doc) => doc.pageContent),
-    );
-
-    const queryEmbedding = await embeddings.embedQuery(query);
+    const [docEmbeddings, queryEmbedding] = await Promise.all([
+      embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)),
+      embeddings.embedQuery(query),
+    ]);
 
     const similarity = docEmbeddings.map((docEmbedding, i) => {
       const sim = computeSimilarity(queryEmbedding, docEmbedding);

src/agents/youtubeSearchAgent.ts

@@ -161,11 +161,10 @@ const createBasicYoutubeSearchAnsweringChain = (
       (doc) => doc.pageContent && doc.pageContent.length > 0,
     );
 
-    const docEmbeddings = await embeddings.embedDocuments(
-      docsWithContent.map((doc) => doc.pageContent),
-    );
-
-    const queryEmbedding = await embeddings.embedQuery(query);
+    const [docEmbeddings, queryEmbedding] = await Promise.all([
+      embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)),
+      embeddings.embedQuery(query),
+    ]);
 
     const similarity = docEmbeddings.map((docEmbedding, i) => {
       const sim = computeSimilarity(queryEmbedding, docEmbedding);