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Don't swallow connection errors in the connectivity test example #29

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Sep 8, 2025
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Don't swallow connection errors in the connectivity test example #29

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38 changes: 21 additions & 17 deletions libs/oracledb/README.md
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Expand Up @@ -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`.
Expand All @@ -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
Expand Down Expand Up @@ -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"
}
"""
Expand All @@ -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.

Expand Down