diff --git a/autogen/agentchat/contrib/retrieve_user_proxy_agent.py b/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
index 10b70e0e9720..b247d7a158f6 100644
--- a/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
+++ b/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
@@ -178,7 +178,7 @@ def __init__(
                     vector db. Default is None, SentenceTransformer with the given `embedding_model`
                     will be used. If you want to use OpenAI, Cohere, HuggingFace or other embedding
                     functions, you can pass it here,
-                    follow the examples in `https://docs.trychroma.com/embeddings`.
+                    follow the examples in `https://docs.trychroma.com/guides/embeddings`.
                 - `customized_prompt` (Optional, str) - the customized prompt for the retrieve chat.
                     Default is None.
                 - `customized_answer_prefix` (Optional, str) - the customized answer prefix for the
diff --git a/autogen/retrieve_utils.py b/autogen/retrieve_utils.py
index 9393903ec86c..4fb53c7c9600 100644
--- a/autogen/retrieve_utils.py
+++ b/autogen/retrieve_utils.py
@@ -365,7 +365,7 @@ def create_vector_db_from_dir(
             embedding_function is not None.
         embedding_function (Optional, Callable): the embedding function to use. Default is None, SentenceTransformer with
             the given `embedding_model` will be used. If you want to use OpenAI, Cohere, HuggingFace or other embedding
-            functions, you can pass it here, follow the examples in `https://docs.trychroma.com/embeddings`.
+            functions, you can pass it here, follow the examples in `https://docs.trychroma.com/guides/embeddings`.
         custom_text_split_function (Optional, Callable): a custom function to split a string into a list of strings.
             Default is None, will use the default function in `autogen.retrieve_utils.split_text_to_chunks`.
         custom_text_types (Optional, List[str]): a list of file types to be processed. Default is TEXT_FORMATS.
@@ -448,7 +448,7 @@ def query_vector_db(
             embedding_function is not None.
         embedding_function (Optional, Callable): the embedding function to use. Default is None, SentenceTransformer with
             the given `embedding_model` will be used. If you want to use OpenAI, Cohere, HuggingFace or other embedding
-            functions, you can pass it here, follow the examples in `https://docs.trychroma.com/embeddings`.
+            functions, you can pass it here, follow the examples in `https://docs.trychroma.com/guides/embeddings`.
 
     Returns:
 
diff --git a/website/blog/2023-10-18-RetrieveChat/index.mdx b/website/blog/2023-10-18-RetrieveChat/index.mdx
index d3ad6aff3287..10fcd8f0ed4e 100644
--- a/website/blog/2023-10-18-RetrieveChat/index.mdx
+++ b/website/blog/2023-10-18-RetrieveChat/index.mdx
@@ -176,7 +176,7 @@ huggingface_ef = embedding_functions.HuggingFaceEmbeddingFunction(
 )
 ```
 
-More examples can be found [here](https://docs.trychroma.com/embeddings).
+More examples can be found [here](https://docs.trychroma.com/guides/embeddings).
 
 ### Customizing Text Split Function
 Before we can store the documents into a vector database, we need to split the texts into chunks. Although