diff --git a/sources/platform/integrations/ai/langchain.md b/sources/platform/integrations/ai/langchain.md
index b5a99f3350..4d92af35ee 100644
--- a/sources/platform/integrations/ai/langchain.md
+++ b/sources/platform/integrations/ai/langchain.md
@@ -20,11 +20,11 @@ but if you prefer to use JavaScript, you can follow the same steps in the [JavaS
 
 Before we start with the integration, we need to install all dependencies:
 
-`pip install apify-client langchain langchain_community openai tiktoken`
+`pip install apify-client langchain langchain_community langchain_openai openai tiktoken`
 
 After successful installation of all dependencies, we can start writing code.
 
-First, import `os`, `VectorstoreIndexCreator`, `ApifyWrapper`, and `Document` into your source code:
+First, import all required packages:
 
 ```python
 import os
@@ -32,6 +32,8 @@ import os
 from langchain.indexes import VectorstoreIndexCreator
 from langchain_community.utilities import ApifyWrapper
 from langchain_core.document_loaders.base import Document
+from langchain_openai import OpenAI
+from langchain_openai.embeddings import OpenAIEmbeddings
 ```
 
 Find your [Apify API token](https://console.apify.com/account/integrations) and [OpenAI API key](https://platform.openai.com/account/api-keys) and initialize these into environment variable:
@@ -57,22 +59,26 @@ loader = apify.call_actor(
 )
 ```
 
-_NOTE: The Actor call function can take some time as it loads the data from LangChain documentation website._
+:::note Crawling may take some time
+
+The Actor call may take some time as it crawls the LangChain documentation website.
+
+:::
 
 Initialize the vector index from the crawled documents:
 
 ```python
-index = VectorstoreIndexCreator().from_loaders([loader])
+index = VectorstoreIndexCreator(embedding=OpenAIEmbeddings()).from_loaders([loader])
 ```
 
 And finally, query the vector index:
 
 ```python
 query = "What is LangChain?"
-result = index.query_with_sources(query)
+result = index.query_with_sources(query, llm=OpenAI())
 
-print(result["answer"])
-print(result["sources"])
+print("answer:", result["answer"])
+print("source:", result["sources"])
 ```
 
 If you want to test the whole example, you can simply create a new file, `langchain_integration.py`, and copy the whole code into it.
@@ -83,25 +89,27 @@ import os
 from langchain.indexes import VectorstoreIndexCreator
 from langchain_community.utilities import ApifyWrapper
 from langchain_core.document_loaders.base import Document
+from langchain_openai import OpenAI
+from langchain_openai.embeddings import OpenAIEmbeddings
 
 os.environ["OPENAI_API_KEY"] = "Your OpenAI API key"
 os.environ["APIFY_API_TOKEN"] = "Your Apify API token"
 
 apify = ApifyWrapper()
 
+print("Call website content crawler ...")
 loader = apify.call_actor(
     actor_id="apify/website-content-crawler",
     run_input={"startUrls": [{"url": "https://python.langchain.com/docs/get_started/introduction"}], "maxCrawlPages": 10, "crawlerType": "cheerio"},
-    dataset_mapping_function=lambda item: Document(
-        page_content=item["text"] or "", metadata={"source": item["url"]}
-    ),
+    dataset_mapping_function=lambda item: Document(page_content=item["text"] or "", metadata={"source": item["url"]}),
 )
-index = VectorstoreIndexCreator().from_loaders([loader])
+print("Compute embeddings...")
+index = VectorstoreIndexCreator(embedding=OpenAIEmbeddings()).from_loaders([loader])
 query = "What is LangChain?"
-result = index.query_with_sources(query)
+result = index.query_with_sources(query, llm=OpenAI())
 
-print(result["answer"])
-print(result["sources"])
+print("answer:", result["answer"])
+print("source:", result["sources"])
 ```
 
 To run it, you can use the following command: `python langchain_integration.py`
@@ -109,9 +117,9 @@ To run it, you can use the following command: `python langchain_integration.py`
 After running the code, you should see the following output:
 
 ```text
-LangChain is a framework for developing applications powered by language models. It provides standard, extendable interfaces, external integrations, and end-to-end implementations for off-the-shelf use. It also integrates with other LLMs, systems, and products to create a vibrant and thriving ecosystem.
+answer: LangChain is a framework for developing applications powered by language models. It provides standard, extendable interfaces, external integrations, and end-to-end implementations for off-the-shelf use. It also integrates with other LLMs, systems, and products to create a vibrant and thriving ecosystem.
 
-https://python.langchain.com
+source: https://python.langchain.com
 ```
 
 LangChain is a standard interface through which you can interact with a variety of large language models (LLMs). It provides modules you can use to build language model applications. It also provides chains and agents with memory capabilities.