From 6eafdc8f0537bdc1091d0f60b68983bf661b7a62 Mon Sep 17 00:00:00 2001
From: JP Hwang <me@jphwang.com>
Date: Thu, 1 Aug 2024 11:50:09 +0100
Subject: [PATCH] Add missing ts examples for multi-target vector search

---
 .../code/howto/search.multi-target-v3.ts      | 145 ++++++++++++++----
 developers/weaviate/search/multi-vector.md    |  34 +++-
 2 files changed, 146 insertions(+), 33 deletions(-)

diff --git a/_includes/code/howto/search.multi-target-v3.ts b/_includes/code/howto/search.multi-target-v3.ts
index 0f778b40f8..54f78e0d06 100644
--- a/_includes/code/howto/search.multi-target-v3.ts
+++ b/_includes/code/howto/search.multi-target-v3.ts
@@ -6,50 +6,139 @@
 // ===== INSTANTIATION-COMMON =====
 // ================================
 
+import weaviate from 'weaviate-client';
 
-
-import weaviate from 'weaviate-client'
-
-const client = await weaviate.connectToLocal(
- {
-   headers: { 'X-Openai-Api-Key': process.env.OPENAI_API_KEY || '',  }
-})
+const client = await weaviate.connectToLocal({
+  headers: { 'X-Openai-Api-Key': process.env.OPENAI_API_KEY || '' },
+});
 
 // ========================
 // ===== Basic search =====
 // ========================
 
+// START-ANY
+var jeopardy, result;
+
+// END-ANY
+
 // START MultiBasic
-const jeopardy = client.collections.get('Jeopardy_Tiny_Dataset');
+jeopardy = client.collections.get('JeopardyTiny');
 
-const result = await jeopardy.query.nearText('animals in movies', {
+result = await jeopardy.query.nearText('a wild animal', {
   limit: 2,
-  targetVector: jeopardy.multiTargetVector.sum(['jeopardy_questions_vector', 'jeopardy_answers_vector']),
-})
+  // highlight-start
+  targetVector: ['jeopardy_questions_vector', 'jeopardy_answers_vector'],
+  // highlight-end
+  returnMetadata: ['distance'],
+});
 
-result.objects.forEach(item => {
-  console.log(JSON.stringify(item.properties, null, 2))
-})
+result.objects.forEach((item) => {
+  console.log(JSON.stringify(item.properties, null, 2));
+  console.log(JSON.stringify(item.metadata.distance, null, 2));
+});
 // END MultiBasic
 
+// ========================
+// ===== Specify query vectors =====
+// ========================
+
+var resp = await jeopardy.query.fetchObjects({
+  limit: 2,
+  includeVector: true,
+});
+
+var v1 = resp.objects[0].vectors.jeopardy_questions_vector;
+var v2 = resp.objects[0].vectors.jeopardy_answers_vector;
+
+// START MultiTargetNearVector
+jeopardy = client.collections.get('JeopardyTiny');
+
+result = await jeopardy.query.nearVector({
+  // highlight-start
+  'jeopardy_questions_vector': v1,
+  'jeopardy_answers_vector': v2
+  // highlight-end
+}, {
+  limit: 2,
+  // highlight-start
+  targetVector: ['jeopardy_questions_vector', 'jeopardy_answers_vector'],
+  // highlight-end
+  returnMetadata: ['distance'],
+});
+
+result.objects.forEach((item) => {
+  console.log(JSON.stringify(item.properties, null, 2));
+  console.log(JSON.stringify(item.metadata.distance, null, 2));
+});
+// END MultiTargetNearVector
+
+// ========================
+// ===== Simple join strategy =====
+// ========================
+
+// START MultiTargetWithSimpleJoin
+jeopardy = client.collections.get('JeopardyTiny');
+
+result = await jeopardy.query.nearText('a wild animal', {
+  limit: 2,
+  // highlight-start
+  targetVector: jeopardy.multiTargetVector.average(['jeopardy_questions_vector', 'jeopardy_answers_vector']),
+  // highlight-end
+  returnMetadata: ['distance'],
+});
+
+result.objects.forEach((item) => {
+  console.log(JSON.stringify(item.properties, null, 2));
+  console.log(JSON.stringify(item.metadata.distance, null, 2));
+});
+// END MultiTargetWithSimpleJoin
 
 // ========================
-// ===== Set Weights =====
+// ===== Set Manual Weights =====
 // ========================
 
-// START MultiWeights
-const jeopardy = client.collections.get('Jeopardy_Tiny_Dataset');
+// START MultiTargetManualWeights
+jeopardy = client.collections.get('JeopardyTiny');
 
-const result = await jeopardy.query.nearText('animals in movies', {
- limit: 2,
- targetVector: jeopardy.multiTargetVector.manualWeights(
-  {
-    'jeopardy_questions_vector': .5,
-    'jeopardy_answers_vector': 10
+result = await jeopardy.query.nearText('a wild animal', {
+  limit: 2,
+  // highlight-start
+  targetVector: jeopardy.multiTargetVector.manualWeights({
+    jeopardy_questions_vector: 10,
+    jeopardy_answers_vector: 50,
   }),
-})
+  // highlight-end
+  returnMetadata: ['distance'],
+});
+
+result.objects.forEach((item) => {
+  console.log(JSON.stringify(item.properties, null, 2));
+  console.log(JSON.stringify(item.metadata.distance, null, 2));
+});
+// END MultiTargetManualWeights
+
+// ========================
+// ===== Relative Score =====
+// ========================
+
+// START MultiTargetRelativeScore
+jeopardy = client.collections.get('JeopardyTiny');
+
+result = await jeopardy.query.nearText('a wild animal', {
+  limit: 2,
+  // highlight-start
+  targetVector: jeopardy.multiTargetVector.relativeScore({
+    jeopardy_questions_vector: 10,
+    jeopardy_answers_vector: 10,
+  }),
+  // highlight-end
+  returnMetadata: ['distance'],
+});
+
+result.objects.forEach((item) => {
+  console.log(JSON.stringify(item.properties, null, 2));
+  console.log(JSON.stringify(item.metadata.distance, null, 2));
+});
+// END MultiTargetRelativeScore
 
-result.objects.forEach(item => {
- console.log(JSON.stringify(item.properties, null, 2))
-})
-// END MultiWeights
+client.close();
diff --git a/developers/weaviate/search/multi-vector.md b/developers/weaviate/search/multi-vector.md
index 7012fb7cf7..4a5ffe0045 100644
--- a/developers/weaviate/search/multi-vector.md
+++ b/developers/weaviate/search/multi-vector.md
@@ -59,7 +59,7 @@ Specify target vectors as a list/array of named vectors. The default join strate
   text={GoCode}
   startMarker="// START MultiBasic"
   endMarker="// END MultiBasic"
-  language="python"
+  language="go"
 />
 <details>
   <summary>Complete code</summary>
@@ -67,7 +67,7 @@ Specify target vectors as a list/array of named vectors. The default join strate
   text={GoCode}
   startMarker="// START BasicFull"
   endMarker="// END BasicFull"
-  language="python"
+  language="go"
 />
 </details>
 </TabItem>
@@ -89,9 +89,9 @@ Specify query vectors as a dictionary/map of names and vectors.
 <TabItem value="ts" label="JS/TS Client v3">
 <FilteredTextBlock
   text={TSCodeV3}
-  startMarker="// START MultiWeights"
-  endMarker="// END MultiWeights"
-  language="python"
+  startMarker="// START MultiTargetNearVector"
+  endMarker="// END MultiTargetNearVector"
+  language="ts"
 />
 </TabItem>
 </Tabs>
@@ -113,6 +113,14 @@ The `sum`, `average`, `minimum` join strategies only require the name of the str
   language="python"
 />
 </TabItem>
+<TabItem value="ts" label="JS/TS Client v3">
+<FilteredTextBlock
+  text={TSCodeV3}
+  startMarker="// START MultiTargetWithSimpleJoin"
+  endMarker="// END MultiTargetWithSimpleJoin"
+  language="ts"
+/>
+</TabItem>
 </Tabs>
 
 ## Weight raw vector distances
@@ -137,6 +145,14 @@ For a more detailed explanation of how scores are normalized, see the blog post
   language="python"
 />
 </TabItem>
+<TabItem value="ts" label="JS/TS Client v3">
+<FilteredTextBlock
+  text={TSCodeV3}
+  startMarker="// START MultiTargetManualWeights"
+  endMarker="// END MultiTargetManualWeights"
+  language="ts"
+/>
+</TabItem>
 </Tabs>
 
 ## Weight normalized vector distances
@@ -159,6 +175,14 @@ Each distance is normalized against other results for that target vector. Each n
   language="python"
 />
 </TabItem>
+<TabItem value="ts" label="JS/TS Client v3">
+<FilteredTextBlock
+  text={TSCodeV3}
+  startMarker="// START MultiTargetRelativeScore"
+  endMarker="// END MultiTargetRelativeScore"
+  language="ts"
+/>
+</TabItem>
 </Tabs>
 
 ## Related pages