diff --git a/libs/oracledb/README.md b/libs/oracledb/README.md
index 3b2a817..e3c12ae 100644
--- a/libs/oracledb/README.md
+++ b/libs/oracledb/README.md
@@ -8,6 +8,13 @@ This package contains the LangChain integrations with [Oracle AI Vector Search](
 python -m pip install -U langchain-oracledb
 ```
 
+## Documentation
+
+- [Oracle AI Vector Search: Vector Store](https://python.langchain.com/docs/integrations/vectorstores/oracle/)
+- [Oracle AI Vector Search: Generate Summary](https://python.langchain.com/docs/integrations/tools/oracleai/)
+- [Oracle AI Vector Search: Document Processing](https://python.langchain.com/docs/integrations/document_loaders/oracleai/)
+- [Oracle AI Vector Search: Generate Embeddings](https://python.langchain.com/docs/integrations/text_embedding/oracleai/)
+
 ## Examples
 
 The following examples showcase basic usage of the components provided by `langchain-oracledb`.
@@ -16,23 +23,20 @@ Please refer to our complete demo guide [Oracle AI Vector Search End-to-End Demo
 
 ### Connect to Oracle Database
 
-Some examples below require a connection with Oracle Database through `python-oracledb`. The following sample code will show how to connect to Oracle Database. By default, `python-oracledb` runs in a ‘Thin’ mode which connects directly to Oracle Database. This mode does not need Oracle Client libraries. However, some additional functionality is available when python-oracledb uses them. Python-oracledb is said to be in ‘Thick’ mode when Oracle Client libraries are used. Both modes have comprehensive functionality supporting the Python Database API v2.0 Specification. See the following [guide](https://python-oracledb.readthedocs.io/en/latest/user_guide/appendix_a.html#featuresummary) that talks about features supported in each mode. You might want to switch to thick-mode if you are unable to use thin-mode.
+Some examples below require a connection with Oracle Database through [`python-oracledb`](https://pypi.org/project/oracledb/). The following sample code will show how to connect to Oracle Database. By default, `python-oracledb` runs in a ‘Thin’ mode which connects directly to Oracle Database. This mode does not need Oracle Client libraries. However, some additional functionality is available when python-oracledb uses them. Python-oracledb is said to be in ‘Thick’ mode when Oracle Client libraries are used. Both modes have comprehensive functionality supporting the Python Database API v2.0 Specification. See the following [guide](https://python-oracledb.readthedocs.io/en/latest/user_guide/appendix_a.html#featuresummary) that talks about features supported in each mode. You might want to switch to Thick mode if you are unable to use Thin mode. For python-oracledb installation help, see [Installing python-oracledb](https://python-oracledb.readthedocs.io/en/latest/user_guide/installation.html).
+
+Check your database connectivity:
 
 ```python
-import sys
 import oracledb
 
-# please update with your username, password, hostname and service_name
+# Please update with your username, password, hostname, port and service_name
 username = "<username>"
 password = "<password>"
-dsn = "<hostname>/<service_name>"
-
-try:
-    conn = oracledb.connect(user=username, password=password, dsn=dsn)
-    print("Connection successful!")
-except Exception as e:
-    print("Connection failed!")
-    sys.exit(1)
+dsn = "<hostname>:<port>/<service_name>"
+
+connection = oracledb.connect(user=username, password=password, dsn=dsn)
+print("Connection successful!")
 ```
 
 ### Vector Stores
@@ -196,10 +200,10 @@ embedder_params = {
 
 # using huggingface
 embedder_params = {
-    "provider": "huggingface", 
-    "credential_name": "HF_CRED", 
-    "url": "https://api-inference.huggingface.co/pipeline/feature-extraction/", 
-    "model": "sentence-transformers/all-MiniLM-L6-v2", 
+    "provider": "huggingface",
+    "credential_name": "HF_CRED",
+    "url": "https://api-inference.huggingface.co/pipeline/feature-extraction/",
+    "model": "sentence-transformers/all-MiniLM-L6-v2",
     "wait_for_model": "true"
 }
 """
@@ -215,9 +219,9 @@ embed = embedder.embed_query("Hello World!")
 print(f"Embedding generated by OracleEmbeddings: {embed}")
 ```
 
-### Utilities 
+### Utilities
 
-#### OracleSummary 
+#### OracleSummary
 
 Generate summary for your documents using `OracleSummary`. More information can be found in [Oracle AI Vector Search: Generate Summary](https://python.langchain.com/docs/integrations/tools/oracleai/) documentation.