Release new docs

v2.3.x/site/en/integrations/integrate_with_langchain.md

@@ -67,7 +67,7 @@ Once the documents are ready, we need to convert them into vector embeddings and
 
 ```python
 # Set up an embedding model to covert document chunks into vector embeddings.
-embeddings = OpenAIEmbeddings(model="ada")
+embeddings = OpenAIEmbeddings()
 
 # Set up a vector store used to save the vector embeddings. Here we use Milvus as the vector store.
 vector_store = Milvus.from_documents(
@@ -117,4 +117,4 @@ The returned results include both the **intermediate_steps** and **output_text**
   '\nWhat is Milvus? Milvus was created in 2019 with a singular goal: store, index, and manage massive embedding vectors generated by deep neural networks and other machine learning (ML) models. As a database specifically designed to handle queries over input vectors, it is capable of indexing vectors on a trillion scale. Unlike existing relational databases which mainly deal with structured data following a pre-defined pattern, Milvus is designed from the bottom-up to handle embedding vectors converted from unstructured data.',
   ' Milvus is a vector database and similarity search platform that enables users to quickly and accurately search for semantically similar vectors in an unstructured data repository. It uses modern embedding techniques to convert unstructured data to embedding vectors, and approximate nearest neighbor (ANN) search algorithms to accelerate the searching process.'],
  'output_text': ' Milvus is a vector database and similarity search platform that enables users to quickly and accurately search for semantically similar vectors in an unstructured data repository. It uses modern embedding techniques to convert unstructured data to embedding vectors, and approximate nearest neighbor (ANN) search algorithms to accelerate the searching process.\nSOURCES: https://milvus.io/docs/overview.md'}
-```
+```

version.json

@@ -1,4 +1,4 @@
 {
-    "version": "v2.2.x",
+    "version": "v2.3.x",
     "released": "yes"
 }