diff --git a/fern/pages/text-embeddings/semantic-search-embed.mdx b/fern/pages/text-embeddings/semantic-search-embed.mdx
new file mode 100644
index 00000000..5b7610e4
--- /dev/null
+++ b/fern/pages/text-embeddings/semantic-search-embed.mdx
@@ -0,0 +1,239 @@
+---
+title: "Semantic Search with Embeddings"
+slug: "docs/semantic-search-embed"
+
+hidden: false
+description: >-
+  Examples on how to use the Embed endpoint to perform semantic search (API v1).
+image: "../../assets/images/fa074c3-cohere_docs_preview_image_1200x630_copy.jpg"  
+keywords: "vector embeddings, embeddings, natural language processing"
+
+---
+
+This section provides examples on how to use the Embed endpoint to perform semantic search.
+
+Semantic search solves the problem faced by the more traditional approach of lexical search, which is great at finding keyword matches, but struggles to capture the context or meaning of a piece of text.
+
+
+```python PYTHON
+import cohere
+import numpy as np
+co = cohere.Client(api_key="YOUR_API_KEY") # Get your free API key: https://dashboard.cohere.com/api-keys
+```
+
+The Embed endpoint takes in texts as input and returns embeddings as output.
+
+For semantic search, there are two types of documents we need to turn into embeddings.
+
+- The list of documents to search from.
+- The query that will be used to search the documents.
+
+### Step 1: Embed the documents
+We call the Embed endpoint using `co.embed()` and pass the required arguments:
+- `texts`: The list of texts
+- `model`: Here we choose `embed-english-v3.0`, which generates embeddings of size 1024
+- `input_type`: We choose `search_document` to ensure the model treats these as the documents for search
+- `embedding_types`: We choose `float` to get a float array as the output
+
+### Step 2: Embed the query
+Next, we add and embed a query. We choose `search_query` as the `input_type` to ensure the model treats this as the query (instead of documents) for search.
+
+### Step 3: Return the most similar documents
+Next, we calculate and sort similarity scores between a query and document embeddings, then display the top N most similar documents. Here, we are using the numpy library for calculating similarity using a dot product approach. 
+
+
+
+```python PYTHON
+### STEP 1: Embed the documents
+
+# Define the documents
+documents = [
+    "Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.",
+    "Finding Coffee Spots: For your caffeine fix, head to the break room's coffee machine or cross the street to the café for artisan coffee.",
+    "Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!",
+    "Working Hours Flexibility: We prioritize work-life balance. While our core hours are 9 AM to 5 PM, we offer flexibility to adjust as needed.",
+]
+
+# Embed the documents
+doc_emb = co.embed(
+    texts=documents,
+    model="embed-english-v3.0",
+    input_type="search_document",
+    embedding_types=["float"]
+).embeddings.float
+
+### STEP 2: Embed the query
+
+# Add the user query
+query = "How to connect with my teammates?"
+
+# Embed the query
+query_emb = co.embed(
+    texts=[query],
+    model="embed-english-v3.0",
+    input_type="search_query",
+    embedding_types=["float"]
+).embeddings.float
+
+### STEP 3: Return the most similar documents
+
+# Calculate similarity scores
+scores = np.dot(query_emb, np.transpose(doc_emb))[0]
+
+# Sort and filter documents based on scores
+top_n = 2
+top_doc_idxs = np.argsort(-scores)[:top_n]
+
+# Display search results
+for idx, docs_idx in enumerate(top_doc_idxs):
+    print(f"Rank: {idx+1}")
+    print(f"Document: {documents[docs_idx]}\n")
+```
+```
+Rank: 1
+Document: Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!
+
+Rank: 2
+Document: Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.
+```   
+
+
+## Content quality measure with Embed v3
+
+A standard text embeddings model is optimized for only topic similarity between a query and candidate documents. But in many real-world applications, you have redundant information with varying content quality.
+
+For instance, consider a user query of “COVID-19 Symptoms” and compare that to candidate document, “COVID-19 has many symptoms”. This document does not offer high-quality and rich information. However, with a typical embedding model, it will appear high on search results because it is highly similar to the query.
+
+
+The Embed v3 model is trained to capture both content quality and topic similarity. Through this approach, a search system can extract richer information from documents and is robust against noise.
+
+As an example below, give a query ("COVID-19 Symptoms"), the document with the highest quality ("COVID-19 symptoms can include: a high temperature or shivering...") is ranked first.
+
+Another document ("COVID-19 has many symptoms") is arguably more similar to the query based on what information it contains, yet it is ranked lower as it doesn’t contain that much information.
+
+This demonstrates how Embed v3 helps to surface high-quality documents for a given query.
+
+
+```python PYTHON
+### STEP 1: Embed the documents
+
+documents = [
+    "COVID-19 has many symptoms.",
+    "COVID-19 symptoms are bad.",
+    "COVID-19 symptoms are not nice",
+    "COVID-19 symptoms are bad. 5G capabilities include more expansive service coverage, a higher number of available connections, and lower power consumption.",
+    "COVID-19 is a disease caused by a virus. The most common symptoms are fever, chills, and sore throat, but there are a range of others.",
+    "COVID-19 symptoms can include: a high temperature or shivering (chills); a new, continuous cough; a loss or change to your sense of smell or taste; and many more",
+    "Dementia has the following symptom: Experiencing memory loss, poor judgment, and confusion.",
+    "COVID-19 has the following symptom: Experiencing memory loss, poor judgment, and confusion.",
+]
+
+# Embed the documents
+doc_emb = co.embed(
+    texts=documents,
+    model="embed-english-v3.0",
+    input_type="search_document",
+    embedding_types=["float"]
+).embeddings.float
+
+### STEP 2: Embed the query
+
+# Add the user query
+query = "COVID-19 Symptoms"
+
+# Embed the query
+query_emb = co.embed(
+    texts=[query],
+    model="embed-english-v3.0",
+    input_type="search_query",
+    embedding_types=["float"]
+).embeddings.float
+
+### STEP 3: Return the most similar documents
+
+# Calculate similarity scores
+scores = np.dot(query_emb, np.transpose(doc_emb))[0]
+
+# Sort and filter documents based on scores
+top_n = 5
+top_doc_idxs = np.argsort(-scores)[:top_n]
+
+# Display search results
+for idx, docs_idx in enumerate(top_doc_idxs):
+    print(f"Rank: {idx+1}")
+    print(f"Document: {documents[docs_idx]}\n")
+```
+```
+Rank: 1
+Document: COVID-19 symptoms can include: a high temperature or shivering (chills); a new, continuous cough; a loss or change to your sense of smell or taste; and many more
+
+Rank: 2
+Document: COVID-19 is a disease caused by a virus. The most common symptoms are fever, chills, and sore throat, but there are a range of others.
+
+Rank: 3
+Document: COVID-19 has the following symptom: Experiencing memory loss, poor judgment, and confusion.
+
+Rank: 4
+Document: COVID-19 has many symptoms.
+
+Rank: 5
+Document: COVID-19 symptoms are not nice
+```  
+
+
+## Multilingual semantic search
+
+The Embed endpoint also supports multilingual semantic search via the `embed-multilingual-...` models. This means you can perform semantic search on texts in different languages.
+
+Specifically, you can do both multilingual and cross-lingual searches using one single model.
+
+
+```python PYTHON
+### STEP 1: Embed the documents
+
+documents = [
+    "Remboursement des frais de voyage : Gérez facilement vos frais de voyage en les soumettant via notre outil financier. Les approbations sont rapides et simples.",
+    "Travailler de l'étranger : Il est possible de travailler à distance depuis un autre pays. Il suffit de coordonner avec votre responsable et de vous assurer d'être disponible pendant les heures de travail.",
+    "Avantages pour la santé et le bien-être : Nous nous soucions de votre bien-être et proposons des adhésions à des salles de sport, des cours de yoga sur site et une assurance santé complète.",
+    "Fréquence des évaluations de performance : Nous organisons des bilans informels tous les trimestres et des évaluations formelles deux fois par an.",
+]
+
+# Embed the documents
+doc_emb = co.embed(
+    texts=documents,
+    model="embed-english-v3.0",
+    input_type="search_document",
+    embedding_types=["float"]
+).embeddings.float
+
+### STEP 2: Embed the query
+
+# Add the user query
+query = "What's your remote-working policy?"
+
+# Embed the query
+query_emb = co.embed(
+    texts=[query],
+    model="embed-english-v3.0",
+    input_type="search_query",
+    embedding_types=["float"]
+).embeddings.float
+
+### STEP 3: Return the most similar documents
+
+# Calculate similarity scores
+scores = np.dot(query_emb, np.transpose(doc_emb))[0]
+
+# Sort and filter documents based on scores
+top_n = 1
+top_doc_idxs = np.argsort(-scores)[:top_n]
+
+# Display search results
+for idx, docs_idx in enumerate(top_doc_idxs):
+    print(f"Rank: {idx+1}")
+    print(f"Document: {documents[docs_idx]}\n")
+```
+```
+Rank: 1
+Document: Travailler de l'étranger : Il est possible de travailler à distance depuis un autre pays. Il suffit de coordonner avec votre responsable et de vous assurer d'être disponible pendant les heures de travail.
+```
\ No newline at end of file
diff --git a/fern/pages/v2/text-embeddings/semantic-search-embed.mdx b/fern/pages/v2/text-embeddings/semantic-search-embed.mdx
new file mode 100644
index 00000000..6b0be710
--- /dev/null
+++ b/fern/pages/v2/text-embeddings/semantic-search-embed.mdx
@@ -0,0 +1,239 @@
+---
+title: "Semantic Search with Embeddings"
+slug: "v2/docs/semantic-search-embed"
+
+hidden: false
+description: >-
+  Examples on how to use the Embed endpoint to perform semantic search (API v2).
+image: "../../../assets/images/fa074c3-cohere_docs_preview_image_1200x630_copy.jpg"  
+keywords: "vector embeddings, embeddings, natural language processing"
+
+---
+
+This section provides examples on how to use the Embed endpoint to perform semantic search.
+
+Semantic search solves the problem faced by the more traditional approach of lexical search, which is great at finding keyword matches, but struggles to capture the context or meaning of a piece of text.
+
+
+```python PYTHON
+import cohere
+import numpy as np
+co = cohere.Client(api_key="YOUR_API_KEY") # Get your free API key: https://dashboard.cohere.com/api-keys
+```
+
+The Embed endpoint takes in texts as input and returns embeddings as output.
+
+For semantic search, there are two types of documents we need to turn into embeddings.
+
+- The list of documents to search from.
+- The query that will be used to search the documents.
+
+### Step 1: Embed the documents
+We call the Embed endpoint using `co.embed()` and pass the required arguments:
+- `texts`: The list of texts
+- `model`: Here we choose `embed-english-v3.0`, which generates embeddings of size 1024
+- `input_type`: We choose `search_document` to ensure the model treats these as the documents for search
+- `embedding_types`: We choose `float` to get a float array as the output
+
+### Step 2: Embed the query
+Next, we add and embed a query. We choose `search_query` as the `input_type` to ensure the model treats this as the query (instead of documents) for search.
+
+### Step 3: Return the most similar documents
+Next, we calculate and sort similarity scores between a query and document embeddings, then display the top N most similar documents. Here, we are using the numpy library for calculating similarity using a dot product approach. 
+
+
+
+```python PYTHON
+### STEP 1: Embed the documents
+
+# Define the documents
+documents = [
+    "Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.",
+    "Finding Coffee Spots: For your caffeine fix, head to the break room's coffee machine or cross the street to the café for artisan coffee.",
+    "Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!",
+    "Working Hours Flexibility: We prioritize work-life balance. While our core hours are 9 AM to 5 PM, we offer flexibility to adjust as needed.",
+]
+
+# Embed the documents
+doc_emb = co.embed(
+    texts=documents,
+    model="embed-english-v3.0",
+    input_type="search_document",
+    embedding_types=["float"]
+).embeddings.float
+
+### STEP 2: Embed the query
+
+# Add the user query
+query = "How to connect with my teammates?"
+
+# Embed the query
+query_emb = co.embed(
+    texts=[query],
+    model="embed-english-v3.0",
+    input_type="search_query",
+    embedding_types=["float"]
+).embeddings.float
+
+### STEP 3: Return the most similar documents
+
+# Calculate similarity scores
+scores = np.dot(query_emb, np.transpose(doc_emb))[0]
+
+# Sort and filter documents based on scores
+top_n = 2
+top_doc_idxs = np.argsort(-scores)[:top_n]
+
+# Display search results
+for idx, docs_idx in enumerate(top_doc_idxs):
+    print(f"Rank: {idx+1}")
+    print(f"Document: {documents[docs_idx]}\n")
+```
+```
+Rank: 1
+Document: Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!
+
+Rank: 2
+Document: Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.
+```
+
+
+## Content quality measure with Embed v3
+
+A standard text embeddings model is optimized for only topic similarity between a query and candidate documents. But in many real-world applications, you have redundant information with varying content quality.
+
+For instance, consider a user query of “COVID-19 Symptoms” and compare that to candidate document, “COVID-19 has many symptoms”. This document does not offer high-quality and rich information. However, with a typical embedding model, it will appear high on search results because it is highly similar to the query.
+
+
+The Embed v3 model is trained to capture both content quality and topic similarity. Through this approach, a search system can extract richer information from documents and is robust against noise.
+
+As an example below, give a query ("COVID-19 Symptoms"), the document with the highest quality ("COVID-19 symptoms can include: a high temperature or shivering...") is ranked first.
+
+Another document ("COVID-19 has many symptoms") is arguably more similar to the query based on what information it contains, yet it is ranked lower as it doesn’t contain that much information.
+
+This demonstrates how Embed v3 helps to surface high-quality documents for a given query.
+
+
+```python PYTHON
+### STEP 1: Embed the documents
+
+documents = [
+    "COVID-19 has many symptoms.",
+    "COVID-19 symptoms are bad.",
+    "COVID-19 symptoms are not nice",
+    "COVID-19 symptoms are bad. 5G capabilities include more expansive service coverage, a higher number of available connections, and lower power consumption.",
+    "COVID-19 is a disease caused by a virus. The most common symptoms are fever, chills, and sore throat, but there are a range of others.",
+    "COVID-19 symptoms can include: a high temperature or shivering (chills); a new, continuous cough; a loss or change to your sense of smell or taste; and many more",
+    "Dementia has the following symptom: Experiencing memory loss, poor judgment, and confusion.",
+    "COVID-19 has the following symptom: Experiencing memory loss, poor judgment, and confusion.",
+]
+
+# Embed the documents
+doc_emb = co.embed(
+    texts=documents,
+    model="embed-english-v3.0",
+    input_type="search_document",
+    embedding_types=["float"]
+).embeddings.float
+
+### STEP 2: Embed the query
+
+# Add the user query
+query = "COVID-19 Symptoms"
+
+# Embed the query
+query_emb = co.embed(
+    texts=[query],
+    model="embed-english-v3.0",
+    input_type="search_query",
+    embedding_types=["float"]
+).embeddings.float
+
+### STEP 3: Return the most similar documents
+
+# Calculate similarity scores
+scores = np.dot(query_emb, np.transpose(doc_emb))[0]
+
+# Sort and filter documents based on scores
+top_n = 5
+top_doc_idxs = np.argsort(-scores)[:top_n]
+
+# Display search results
+for idx, docs_idx in enumerate(top_doc_idxs):
+    print(f"Rank: {idx+1}")
+    print(f"Document: {documents[docs_idx]}\n")
+```
+```
+Rank: 1
+Document: COVID-19 symptoms can include: a high temperature or shivering (chills); a new, continuous cough; a loss or change to your sense of smell or taste; and many more
+
+Rank: 2
+Document: COVID-19 is a disease caused by a virus. The most common symptoms are fever, chills, and sore throat, but there are a range of others.
+
+Rank: 3
+Document: COVID-19 has the following symptom: Experiencing memory loss, poor judgment, and confusion.
+
+Rank: 4
+Document: COVID-19 has many symptoms.
+
+Rank: 5
+Document: COVID-19 symptoms are not nice
+```
+
+
+## Multilingual semantic search
+
+The Embed endpoint also supports multilingual semantic search via the `embed-multilingual-...` models. This means you can perform semantic search on texts in different languages.
+
+Specifically, you can do both multilingual and cross-lingual searches using one single model.
+
+
+```python PYTHON
+### STEP 1: Embed the documents
+
+documents = [
+    "Remboursement des frais de voyage : Gérez facilement vos frais de voyage en les soumettant via notre outil financier. Les approbations sont rapides et simples.",
+    "Travailler de l'étranger : Il est possible de travailler à distance depuis un autre pays. Il suffit de coordonner avec votre responsable et de vous assurer d'être disponible pendant les heures de travail.",
+    "Avantages pour la santé et le bien-être : Nous nous soucions de votre bien-être et proposons des adhésions à des salles de sport, des cours de yoga sur site et une assurance santé complète.",
+    "Fréquence des évaluations de performance : Nous organisons des bilans informels tous les trimestres et des évaluations formelles deux fois par an.",
+]
+
+# Embed the documents
+doc_emb = co.embed(
+    texts=documents,
+    model="embed-english-v3.0",
+    input_type="search_document",
+    embedding_types=["float"]
+).embeddings.float
+
+### STEP 2: Embed the query
+
+# Add the user query
+query = "What's your remote-working policy?"
+
+# Embed the query
+query_emb = co.embed(
+    texts=[query],
+    model="embed-english-v3.0",
+    input_type="search_query",
+    embedding_types=["float"]
+).embeddings.float
+
+### STEP 3: Return the most similar documents
+
+# Calculate similarity scores
+scores = np.dot(query_emb, np.transpose(doc_emb))[0]
+
+# Sort and filter documents based on scores
+top_n = 1
+top_doc_idxs = np.argsort(-scores)[:top_n]
+
+# Display search results
+for idx, docs_idx in enumerate(top_doc_idxs):
+    print(f"Rank: {idx+1}")
+    print(f"Document: {documents[docs_idx]}\n")
+```
+```
+Rank: 1
+Document: Travailler de l'étranger : Il est possible de travailler à distance depuis un autre pays. Il suffit de coordonner avec votre responsable et de vous assurer d'être disponible pendant les heures de travail.
+```
\ No newline at end of file
diff --git a/fern/v1.yml b/fern/v1.yml
index fefce975..c79f587c 100644
--- a/fern/v1.yml
+++ b/fern/v1.yml
@@ -135,6 +135,8 @@ navigation:
         contents:
           - page: Introduction to Embeddings at Cohere
             path: pages/text-embeddings/embeddings.mdx
+          - page: Semantic Search with Embeddings
+            path: pages/text-embeddings/semantic-search-embed.mdx
           - page: Batch Embedding Jobs
             path: pages/text-embeddings/embed-jobs-api.mdx
           - section: Reranking
diff --git a/fern/v2.yml b/fern/v2.yml
index 0020adcf..94e91b20 100644
--- a/fern/v2.yml
+++ b/fern/v2.yml
@@ -116,6 +116,8 @@ navigation:
         contents:
           - page: Introduction to Embeddings at Cohere
             path: pages/v2/text-embeddings/embeddings.mdx
+          - page: Semantic Search with Embeddings
+            path: pages/v2/text-embeddings/semantic-search-embed.mdx
           - page: Batch Embedding Jobs
             path: pages/v2/text-embeddings/embed-jobs-api.mdx
           - section: Reranking