Updating to reflect changed code blocks and sections

mongodb-developer · Aug 14, 2024 · 605f46c · 605f46c
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diff --git a/docs/40-dev-env/2-setup-pre-reqs.mdx b/docs/40-dev-env/2-setup-pre-reqs.mdx
@@ -1,3 +1,3 @@
 # 👐 Setup prerequisites
 
-Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 1: Install libraries** and **Step 2: Setup prerequisites** sections in the notebook.
+Replace any placeholders and run the cells under the **Step 1: Install libraries** and **Step 2: Setup prerequisites** sections in the notebook.
diff --git a/docs/50-prepare-the-data/3-chunk-data.mdx b/docs/50-prepare-the-data/3-chunk-data.mdx
@@ -6,7 +6,7 @@ Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 4:
 
 The answers for code blocks in this section are as follows:
 
-**CODE_BLOCK_3**
+**CODE_BLOCK_1**
 
 <details>
 <summary>Answer</summary>
@@ -19,18 +19,7 @@ RecursiveCharacterTextSplitter.from_tiktoken_encoder(
 </div>
 </details>
 
-**CODE_BLOCK_4**
-
-<details>
-<summary>Answer</summary>
-<div>
-```python
-doc[text_field]
-```
-</div>
-</details>
-
-**CODE_BLOCK_5**
+**CODE_BLOCK_2**
 
 <details>
 <summary>Answer</summary>
@@ -41,29 +30,13 @@ text_splitter.split_text(text)
 </div>
 </details>
 
-**CODE_BLOCK_6**
-
-<details>
-<summary>Answer</summary>
-<div>
-```python
-for chunk in chunks:
-    temp = doc.copy()
-    temp[text_field] = chunk
-    chunked_data.append(temp)
-```
-</div>
-</details>
-
-**CODE_BLOCK_7**
+**CODE_BLOCK_3**
 
 <details>
 <summary>Answer</summary>
 <div>
 ```python
-for doc in docs:
-    chunks = get_chunks(doc, "body")
-    split_docs.extend(chunks)
+get_chunks(doc, "body")
 ```
 </div>
 </details>
diff --git a/docs/50-prepare-the-data/4-embed-data.mdx b/docs/50-prepare-the-data/4-embed-data.mdx
@@ -6,38 +6,24 @@ Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 5:
 
 The answers for code blocks in this section are as follows:
 
-**CODE_BLOCK_8**
+**CODE_BLOCK_4**
 
 <details>
 <summary>Answer</summary>
 <div>
 ```python
-SentenceTransformer("thenlper/gte-small")
+embedding_model.encode(text)
 ```
 </div>
 </details>
 
-**CODE_BLOCK_9**
+**CODE_BLOCK_5**
 
 <details>
 <summary>Answer</summary>
 <div>
 ```python
-embedding = embedding_model.encode(text)
-return embedding.tolist()
-```
-</div>
-</details>
-
-**CODE_BLOCK_10**
-
-<details>
-<summary>Answer</summary>
-<div>
-```python
-for doc in tqdm(split_docs):
-    doc["embedding"] = get_embedding(doc["body"])
-    embedded_docs.append(doc)
+doc["embedding"] = get_embedding(doc["body"])
 ```
 </div>
 </details>

diff --git a/docs/50-prepare-the-data/5-ingest-data.mdx b/docs/50-prepare-the-data/5-ingest-data.mdx
@@ -8,7 +8,7 @@ Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 6:
 
 The answers for code blocks in this section are as follows:
 
-**CODE_BLOCK_11**
+**CODE_BLOCK_6**
 
 <details>
 <summary>Answer</summary>
@@ -19,18 +19,7 @@ mongodb_client[DB_NAME][COLLECTION_NAME]
 </div>
 </details>
 
-**CODE_BLOCK_12**
-
-<details>
-<summary>Answer</summary>
-<div>
-```python
-collection.delete_many({})
-```
-</div>
-</details>
-
-**CODE_BLOCK_13**
+**CODE_BLOCK_7**
 
 <details>
 <summary>Answer</summary>

diff --git a/docs/60-perform-semantic-search/3-vector-search.mdx b/docs/60-perform-semantic-search/3-vector-search.mdx
@@ -6,7 +6,7 @@ Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 8:
 
 The answers for code blocks in this section are as follows:
 
-**CODE_BLOCK_14**
+**CODE_BLOCK_8**
 
 <details>
 <summary>Answer</summary>
@@ -17,7 +17,7 @@ get_embedding(user_query)
 </div>
 </details>
 
-**CODE_BLOCK_15**
+**CODE_BLOCK_9**
 
 <details>
 <summary>Answer</summary>
@@ -37,15 +37,15 @@ get_embedding(user_query)
         "$project": {
             "_id": 0,
             "body": 1,
-            "score": {"$meta": "vectorSearchScore"},
+            "score": {"$meta": "vectorSearchScore"}
         }
-    },
+    }
 ]
 ```
 </div>
 </details>
 
-**CODE_BLOCK_16**
+**CODE_BLOCK_10**
 
 <details>
 <summary>Answer</summary>

diff --git a/docs/60-perform-semantic-search/4-pre-filtering.mdx b/docs/60-perform-semantic-search/4-pre-filtering.mdx
@@ -2,39 +2,37 @@
 
 Pre-filtering a technique to optimize vector search by only considering documents that match certain criteria during vector search.
 
-Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **🦹‍♀️ Combine pre-filtering with vector search** section in the notebook to get a sense of how to combine pre-filtering with MongoDB Atlas Vector Search.
+## Filter for documents where the content type is `Video`
 
-:::caution
-**DO NOT** actually modify the existing vector index definitions in the Atlas UI, or the existing pipeline definitions in the code.
-:::
-
-The answers for code blocks in this section are as follows:
-
-**CODE_BLOCK_17**
+To do this, you will first need to modify the vector search index you created previously. The new index definition should look as follows:
 
 <details>
 <summary>Answer</summary>
 <div>
 ```python
 {
-    "fields": [
-        {
-            "numDimensions": 1024,
-            "path": "embedding",
-            "similarity": "cosine",
-            "type": "vector"
-        },
-        {
-            "path": "metadata.contentType",
-            "type": "filter"
-        }
-    ]
+  "fields": [
+    {
+      "type": "vector",
+      "path": "embedding",
+      "numDimensions": 384,
+      "similarity": "cosine"
+    },
+    {
+      "type":"filter",
+      "path":"metadata.contentType"
+    }
+  ]
 }
 ```
 </div>
 </details>
 
-**CODE_BLOCK_18**
+Once you have updated the vector search index, fill in `<CODE_BLOCK_11>` and run the cells under the **Filter for documents where the content type is Video** section in the notebook to see how the filter impacts the vector search results.
+
+The answer for this code block is as follows:
+
+**CODE_BLOCK_11**
 
 <details>
 <summary>Answer</summary>
@@ -43,9 +41,9 @@ The answers for code blocks in this section are as follows:
 [
     {
         "$vectorSearch": {
-            "index": ATLAS_VECTOR_SEARCH_INDEX_NAME,
-            "queryVector": query_embedding,
+            "index": "vector_index",
             "path": "embedding",
+            "queryVector": query_embedding,
             "numCandidates": 150,
             "limit": 5,
             "filter": {"metadata.contentType": "Video"}
@@ -63,35 +61,42 @@ The answers for code blocks in this section are as follows:
 </div>
 </details>
 
-**CODE_BLOCK_19**
+
+## Filter on documents which have been updated on or after `2024-05-19` and where the content type is `Tutorial`
+
+Again, you will need to modify the vector search index. The new index definition should look as follows:
 
 <details>
 <summary>Answer</summary>
 <div>
 ```python
 {
-    "fields": [
-        {
-            "numDimensions": 1024,
-            "path": "embedding",
-            "similarity": "cosine",
-            "type": "vector"
-        },
-        {
-            "path": "metadata.contentType",
-            "type": "filter"
-        },
-        {
-            "path": "updated",
-            "type": "filter"
-        }
-    ]
+  "fields": [
+    {
+      "type": "vector",
+      "path": "embedding",
+      "numDimensions": 384,
+      "similarity": "cosine"
+    },
+    {
+      "type":"filter",
+      "path":"metadata.contentType"
+    },
+    {
+      "type":"filter",
+      "path":"updated"
+    }
+  ]
 }
 ```
 </div>
 </details>
 
-**CODE_BLOCK_20**
+Once you have updated the vector search index, fill in `<CODE_BLOCK_12>` and run the cells under the **Filter on documents which have been updated on or after 2024-05-19 and where the content type is Tutorial** section in the notebook to see how the filter impacts the vector search results.
+
+The answer for this code block is as follows:
+
+**CODE_BLOCK_12**
 
 <details>
 <summary>Answer</summary>
@@ -100,16 +105,14 @@ The answers for code blocks in this section are as follows:
 [
     {
         "$vectorSearch": {
-            "index": ATLAS_VECTOR_SEARCH_INDEX_NAME,
-            "queryVector": query_embedding,
+            "index": "vector_index",
             "path": "embedding",
+            "queryVector": query_embedding,
             "numCandidates": 150,
             "limit": 5,
             "filter": {
-                "$and": [
-                    {"metadata.contentType": "Video"},
-                    {"updated": {"$gte": "2024-05-20"}}
-                ]
+                "metadata.contentType": "Tutorial",
+                "updated": {"$gte": "2024-05-19"}
             }
         }
     },

diff --git a/docs/70-build-rag-app/1-build-rag-app.mdx b/docs/70-build-rag-app/1-build-rag-app.mdx
@@ -6,7 +6,7 @@ Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 9:
 
 The answers for code blocks in this section are as follows:
 
-**CODE_BLOCK_21**
+**CODE_BLOCK_13**
 
 <details>
 <summary>Answer</summary>
@@ -17,34 +17,27 @@ vector_search(user_query)
 </div>
 </details>
 
-**CODE_BLOCK_22**
+**CODE_BLOCK_14**
 
 <details>
 <summary>Answer</summary>
 <div>
 ```python
-"\n\n".join([d.get("body", "") for d in context])
+create_prompt(user_query)
 ```
 </div>
 </details>
 
-**CODE_BLOCK_23**
+**CODE_BLOCK_15**
 
 <details>
 <summary>Answer</summary>
 <div>
 ```python
-response = fw_client.chat.completions.create(
+fw_client.chat.completions.create(
     model=model,
-    temperature=0,
-    messages=[
-        {
-            "role": "user",
-            "content": create_prompt(user_query),
-        }
-    ],
+    messages=[{"role": "user", "content": prompt}]
 )
-print(response.choices[0].message.content)
 ```
 </div>
 </details>