We recently announced a suite of LangChain packages for the Google Cloud database portfolio. Each package will have up to three LangChain integrations:
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Vector stores to enable semantic search for our databases that support vectors
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Document loaders for loading and saving documents to/from your database
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Chat Message Memory to enable chains to recall previous conversations
In this blog, we deep dive into the benefits of the VectorStore from our Cloud SQL for PostgreSQL LangChain package, and see how it helps make generative AI application development easy, secure, and flexible.
Security
The Cloud SQL for PostgreSQL LangChain packages come embedded with the Cloud SQL Python connector, which makes connecting securely to your database easy. Developers get the following benefits out of the box:
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IAM authorization: Uses IAM permissions to control who or what can connect to your Cloud SQL instances
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Convenience: Removes the requirement to manage SSL certificates, configure firewall rules, or enable authorized networks
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IAM database authentication: Provides support for Cloud SQL’s automatic IAM database authentication feature
Ease of use
Connect with just instance name
Now, you no longer need to construct a connection string with your IP address or pass in a myriad of arguments to connect to your PostgreSQL instance. Instead, the instance name alone will suffice as shown below:
