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Mar 23, 2025
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docs: update the readme to note Google provided integrations #173

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docs: update the readme to note Google provided integrations
dishaprakash Mar 20, 2025
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Change format into a table of features
dishaprakash Mar 21, 2025
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dishaprakash Mar 21, 2025
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dishaprakash Mar 21, 2025
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22 changes: 22 additions & 0 deletions README.md
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Expand Up @@ -83,3 +83,25 @@ print(chat_history.messages)
### Vectorstore

See example for the [PGVector vectorstore here](https://github.com/langchain-ai/langchain-postgres/blob/main/examples/vectorstore.ipynb)

## Google Cloud Integrations

[Google Cloud](https://python.langchain.com/docs/integrations/providers/google/) provides Vector Store, Chat Message History, and Data Loader integrations for [AlloyDB](https://cloud.google.com/alloydb) and [Cloud SQL](https://cloud.google.com/sql) for PostgreSQL databases via the following PyPi packages:

* [`langchain-google-alloydb-pg`](https://github.com/googleapis/langchain-google-alloydb-pg-python)

* [`langchain-google-cloud-sql-pg`](https://github.com/googleapis/langchain-google-cloud-sql-pg-python)

Using the Google Cloud provided integrations provides the following benefits:
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Using the Google Cloud provided integrations provides the following benefits:
Using the Google Cloud integrations provides the following benefits:

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Changed


- **Enhanced security**: Easily and securely connect to Google Cloud databases utilizing IAM for authorization and database authentication without needing to manage SSL certificates, configure firewall rules, or enable authorized networks.
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Consider dropping marketing language from technical docs. (words like "easily" aren't great for technical docs)

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- **Enhanced security**: Easily and securely connect to Google Cloud databases utilizing IAM for authorization and database authentication without needing to manage SSL certificates, configure firewall rules, or enable authorized networks.
- **Enhanced Security**: Securely connect to Google Cloud databases utilizing IAM for authorization and database authentication without needing to manage SSL certificates, configure firewall rules, or enable authorized networks.

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Sure, I've revised it

- **Simplified and Secure Connections:** Connect to Google Cloud databases effortlessly using the instance name instead of complex connection strings. The integrations creates a secure connection pool that can be easily shared across your application using the `engine` object.

Learn how to [migrate a `PGVector` vector store to `AlloyDBVectorStore`](https://github.com/googleapis/langchain-google-alloydb-pg-python/blob/main/samples/migrations/migrate_pgvector_to_alloydb.md) to gain the following benefits:
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The marketing language is very strong here. I think it's fair to have a summary of which features are supported by each implementation. Right now, this is just reading as a recommendation to migrate off PGVector to alloydbvectorstore, which I don't think is appropriate.

Perhaps a table listing in column the features, packages on the row, and information about whether the feature is supported or not?

Is database agnostic filtering implemented? If not, could you specify that.

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I've revised it into a tabular form, and yes db agnostic filtering has been implemented now (and mentioned in the table).


- **Simplified management**: a single table contains data corresponding to a single collection, making it easier to query, update, and maintain.
- **Schema flexibility**: allow users to add tables into any database schema.
- **Improved performance**: using a single-table schema can lead to faster query execution, especially for large collections.
- **Improved metadata handling**: store metadata in columns instead of JSON, resulting in significant performance improvements.
- **Clear separation**: clearly separate table and extension creation, allowing for distinct permissions and streamlined workflows.
- **Better integration with AlloyDB**: take advantage of AlloyDB's advanced indexing and scalability capabilities.