Add langchain-oracledb documentation

reference/packages.yml

@@ -773,3 +773,8 @@ packages:
   path: libs/voyageai
   downloads: 55000
   downloads_updated_at: "2025-11-10T00:06:22.543193+00:00"
+- name: langchain-oracledb
+  name_title: Oracle AI Vector Search
+  repo: oracle/langchain-oracle
+  downloads: 700
+  downloads_updated_at: "2025-11-25T00:06:22.543193+00:00"

src/oss/python/integrations/document_loaders/oracleadb_loader.mdx

@@ -4,21 +4,24 @@ title: Oracle Autonomous Database
 
 Oracle Autonomous Database is a cloud database that uses machine learning to automate database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs.
 
-This notebook covers how to load documents from Oracle Autonomous Database, the loader supports connection with connection string or tns configuration.
+This notebook covers how to load documents from Oracle Autonomous Database.
 
 ## Prerequisites
 
-1. Install `python-oracledb`: `pip install oracledb`
-   1. See [Installing `python-oracledb`](https://python-oracledb.readthedocs.io/en/latest/user_guide/installation.html).
+A database that `python-oracledb`'s default `'Thin'` mode can connected to. This is true of Oracle Autonomous Database, see [`python-oracledb` Architecture](https://python-oracledb.readthedocs.io/en/latest/user_guide/introduction.html#architecture).
 
-## Instructions
+You'll need to install `langchain-oracledb` to use this integration.
+
+The `python-oracledb` driver is installed automatically as a dependency of langchain-oracledb.
 
 ```python
-pip install oracledb
+# python -m pip install -U langchain-oracledb
 ```
 
+## Instructions
+
 ```python
-from langchain_community.document_loaders import OracleAutonomousDatabaseLoader
+from langchain_oracledb.document_loaders import OracleAutonomousDatabaseLoader
 from settings import s
 ```
 
@@ -35,7 +38,7 @@ doc_loader_1 = OracleAutonomousDatabaseLoader(
     config_dir=s.CONFIG_DIR,
     wallet_location=s.WALLET_LOCATION,
     wallet_password=s.PASSWORD,
-    tns_name=s.TNS_NAME,
+    dsn=s.DSN,
 )
 doc_1 = doc_loader_1.load()
 
@@ -44,7 +47,7 @@ doc_loader_2 = OracleAutonomousDatabaseLoader(
     user=s.USERNAME,
     password=s.PASSWORD,
     schema=s.SCHEMA,
-    connection_string=s.CONNECTION_STRING,
+    dsn=s.DSN,
     wallet_location=s.WALLET_LOCATION,
     wallet_password=s.PASSWORD,
 )
@@ -62,7 +65,7 @@ doc_loader_3 = OracleAutonomousDatabaseLoader(
     password=s.PASSWORD,
     schema=s.SCHEMA,
     config_dir=s.CONFIG_DIR,
-    tns_name=s.TNS_NAME,
+    dsn=s.DSN,
     parameters=["Direct Sales"],
 )
 doc_3 = doc_loader_3.load()
@@ -72,7 +75,7 @@ doc_loader_4 = OracleAutonomousDatabaseLoader(
     user=s.USERNAME,
     password=s.PASSWORD,
     schema=s.SCHEMA,
-    connection_string=s.CONNECTION_STRING,
+    dsn=s.DSN,
     parameters=["Direct Sales"],
 )
 doc_4 = doc_loader_4.load()

src/oss/python/integrations/document_loaders/oracleai.mdx

@@ -28,10 +28,12 @@ If you are just starting with Oracle Database, consider exploring the [free Orac
 
 ### Prerequisites
 
-Please install Oracle Python Client driver to use LangChain with Oracle AI Vector Search.
+You'll need to install `langchain-oracledb` to use this integration.
+
+The `python-oracledb` driver is installed automatically as a dependency of langchain-oracledb.
 
 ```python
-# pip install oracledb
+# python -m pip install -U langchain-oracledb
 ```
 
 ### Connect to Oracle Database
@@ -43,17 +45,13 @@ 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>"
+dsn = "<hostname>:<port>/<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)
+connection = oracledb.connect(user=username, password=password, dsn=dsn)
+print("Connection successful!")
 ```
 
 Now let's create a table and insert some sample docs to test.
@@ -102,10 +100,10 @@ Users have the flexibility to load documents from either the Oracle Database, a
 
 A significant advantage of utilizing OracleDocLoader is its capability to process over 150 distinct file formats, eliminating the need for multiple loaders for different document types. For a complete list of the supported formats, please refer to the [Oracle Text Supported Document Formats](https://docs.oracle.com/en/database/oracle/oracle-database/23/ccref/oracle-text-supported-document-formats.html).
 
-Below is a sample code snippet that demonstrates how to use OracleDocLoader
+Below is a sample code snippet that demonstrates how to use `OracleDocLoader`:
 
 ```python
-from langchain_community.document_loaders.oracleai import OracleDocLoader
+from langchain_oracledb.document_loaders.oracleai import OracleDocLoader
 from langchain_core.documents import Document
 
 """
@@ -141,7 +139,7 @@ The documents may vary in size, ranging from small to very large. Users often pr
 Below is a sample code illustrating how to implement this:
 
 ```python
-from langchain_community.document_loaders.oracleai import OracleTextSplitter
+from langchain_oracledb.document_loaders.oracleai import OracleTextSplitter
 from langchain_core.documents import Document
 
 """

src/oss/python/integrations/providers/oracleai.mdx

@@ -1,5 +1,5 @@
 ---
-title: OracleAI Vector Search
+title: Oracle AI Vector Search
 ---
 
 Oracle AI Vector Search is designed for Artificial Intelligence (AI) workloads that allows you to query data based on semantics, rather than keywords.
@@ -8,59 +8,58 @@ This is not only powerful but also significantly more effective because you don'
 
 In addition, your vectors can benefit from all of Oracle Database’s most powerful features, like the following:
 
- * [Partitioning Support](https://www.oracle.com/database/technologies/partitioning.html)
- * [Real Application Clusters scalability](https://www.oracle.com/database/real-application-clusters/)
- * [Exadata smart scans](https://www.oracle.com/database/technologies/exadata/software/smartscan/)
- * [Shard processing across geographically distributed databases](https://www.oracle.com/database/distributed-database/)
- * [Transactions](https://docs.oracle.com/en/database/oracle/oracle-database/23/cncpt/transactions.html)
- * [Parallel SQL](https://docs.oracle.com/en/database/oracle/oracle-database/21/vldbg/parallel-exec-intro.html#GUID-D28717E4-0F77-44F5-BB4E-234C31D4E4BA)
- * [Disaster recovery](https://www.oracle.com/database/data-guard/)
- * [Security](https://www.oracle.com/security/database-security/)
- * [Oracle Machine Learning](https://www.oracle.com/artificial-intelligence/database-machine-learning/)
- * [Oracle Graph Database](https://www.oracle.com/database/integrated-graph-database/)
- * [Oracle Spatial and Graph](https://www.oracle.com/database/spatial/)
- * [Oracle Blockchain](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_blockchain_table.html#GUID-B469E277-978E-4378-A8C1-26D3FF96C9A6)
- * [JSON](https://docs.oracle.com/en/database/oracle/oracle-database/23/adjsn/json-in-oracle-database.html)
-
+* [Partitioning Support](https://www.oracle.com/database/technologies/partitioning.html)
+* [Real Application Clusters scalability](https://www.oracle.com/database/real-application-clusters/)
+* [Exadata smart scans](https://www.oracle.com/database/technologies/exadata/software/smartscan/)
+* [Shard processing across geographically distributed databases](https://www.oracle.com/database/distributed-database/)
+* [Transactions](https://docs.oracle.com/en/database/oracle/oracle-database/23/cncpt/transactions.html)
+* [Parallel SQL](https://docs.oracle.com/en/database/oracle/oracle-database/21/vldbg/parallel-exec-intro.html#GUID-D28717E4-0F77-44F5-BB4E-234C31D4E4BA)
+* [Disaster recovery](https://www.oracle.com/database/data-guard/)
+* [Security](https://www.oracle.com/security/database-security/)
+* [Oracle Machine Learning](https://www.oracle.com/artificial-intelligence/database-machine-learning/)
+* [Oracle Graph Database](https://www.oracle.com/database/integrated-graph-database/)
+* [Oracle Spatial and Graph](https://www.oracle.com/database/spatial/)
+* [Oracle Blockchain](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_blockchain_table.html#GUID-B469E277-978E-4378-A8C1-26D3FF96C9A6)
+* [JSON](https://docs.oracle.com/en/database/oracle/oracle-database/23/adjsn/json-in-oracle-database.html)
 
 ## Document loaders
 
 Please check the [usage example](/oss/integrations/document_loaders/oracleai).
 
 ```python
-from langchain_community.document_loaders.oracleai import OracleDocLoader
+from langchain_oracledb.document_loaders.oracleai import OracleDocLoader
 ```
 
 ## Text Splitter
 
 Please check the [usage example](/oss/integrations/document_loaders/oracleai).
 
 ```python
-from langchain_community.document_loaders.oracleai import OracleTextSplitter
+from langchain_oracledb.document_loaders.oracleai import OracleTextSplitter
 ```
 
 ## Embeddings
 
 Please check the [usage example](/oss/integrations/text_embedding/oracleai).
 
 ```python
-from langchain_community.embeddings.oracleai import OracleEmbeddings
+from langchain_oracledb.embeddings.oracleai import OracleEmbeddings
 ```
 
 ## Summary
 
 Please check the [usage example](/oss/integrations/tools/oracleai).
 
 ```python
-from langchain_community.utilities.oracleai import OracleSummary
+from langchain_oracledb.utilities.oracleai import OracleSummary
 ```
 
 ## Vector Store
 
 Please check the [usage example](/oss/integrations/vectorstores/oracle).
 
 ```python
-from langchain_community.vectorstores.oraclevs import OracleVS
+from langchain_oracledb.vectorstores.oraclevs import OracleVS
 ```
 
 ## End to End Demo

src/oss/python/integrations/text_embedding/oracleai.mdx

@@ -28,10 +28,12 @@ If you are just starting with Oracle Database, consider exploring the [free Orac
 
 ### Prerequisites
 
-Ensure you have the Oracle Python Client driver installed to facilitate the integration of LangChain with Oracle AI Vector Search.
+You'll need to install `langchain-oracledb` to use this integration.
+
+The `python-oracledb` driver is installed automatically as a dependency of langchain-oracledb.
 
 ```python
-# pip install oracledb
+# python -m pip install -U langchain-oracledb
 ```
 
 ### Connect to Oracle Database
@@ -43,17 +45,13 @@ import sys
 
 import oracledb
 
-# Update the following variables with your Oracle database credentials and connection details
+# Please update with your username, password, hostname, port and service_name
 username = "<username>"
 password = "<password>"
-dsn = "<hostname>/<service_name>"
+dsn = "<hostname>:<port>/<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)
+connection = oracledb.connect(user=username, password=password, dsn=dsn)
+print("Connection successful!")
 ```
 
 For embedding generation, several provider options are available to users, including embedding generation within the database and third-party services such as OcigenAI, Hugging Face, and OpenAI. Users opting for third-party providers must establish credentials that include the requisite authentication information. Alternatively, if users select 'database' as their provider, they are required to load an ONNX model into the Oracle Database to facilitate embeddings.
@@ -69,7 +67,7 @@ A significant advantage of utilizing an ONNX model directly within Oracle is the
 Below is the example code to upload an ONNX model into Oracle Database:
 
 ```python
-from langchain_community.embeddings.oracleai import OracleEmbeddings
+from langchain_oracledb.embeddings.oracleai import OracleEmbeddings
 
 # Update the directory and file names for your ONNX model
 # make sure that you have onnx file in the system
@@ -144,7 +142,7 @@ proxy = "<proxy>"
 The following sample code will show how to generate embeddings:
 
 ```python
-from langchain_community.embeddings.oracleai import OracleEmbeddings
+from langchain_oracledb.embeddings.oracleai import OracleEmbeddings
 from langchain_core.documents import Document
 
 """

src/oss/python/integrations/tools/oracleai.mdx

@@ -28,32 +28,28 @@ If you are just starting with Oracle Database, consider exploring the [free Orac
 
 ### Prerequisites
 
-Please install Oracle Python Client driver to use LangChain with Oracle AI Vector Search.
+You'll need to install `langchain-oracledb` to use this integration.
+
+The `python-oracledb` driver is installed automatically as a dependency of langchain-oracledb.
 
 ```python
-# pip install oracledb
+# python -m pip install -U langchain-oracledb
 ```
 
 ### Connect to Oracle Database
 
 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.
 
 ```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!")
 ```
 
 ### Generate Summary
@@ -70,7 +66,7 @@ proxy = "<proxy>"
 The following sample code will show how to generate summary:
 
 ```python
-from langchain_community.utilities.oracleai import OracleSummary
+from langchain_oracledb.utilities.oracleai import OracleSummary
 from langchain_core.documents import Document
 
 """

src/oss/python/integrations/vectorstores/index.mdx

@@ -666,6 +666,7 @@ vector_store = QdrantVectorStore(
 | [`Weaviate`](/oss/integrations/vectorstores/weaviate) | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
 | [`SQLServer`](/oss/integrations/vectorstores/sqlserver) | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
 | [`ZeusDB`](/oss/integrations/vectorstores/zeusdb) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ |
+| [`Oracle AI Vector Search`](/oss/integrations/vectorstores/oracle) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ |
 
 ## All vector stores
 
@@ -787,5 +788,6 @@ vector_store = QdrantVectorStore(
 <Card title="Zep Cloud" icon="link" href="/oss/integrations/vectorstores/zep_cloud" arrow="true" cta="View guide"/>
 <Card title="ZeusDB" icon="link" href="/oss/integrations/vectorstores/zeusdb" arrow="true" cta="View guide"/>
 <Card title="Zilliz" icon="link" href="/oss/integrations/vectorstores/zilliz" arrow="true" cta="View guide"/>
+<Card title="Oracle AI Vector Search" icon="link" href="/oss/integrations/vectorstores/oracle" arrow="true" cta="View guide"/>
 </Columns>