#googlecloudnext25

Open-source LangChain PostgreSQL upgrades

Google Cloud contributed heavily to the library and updated LangChain Postgres at Google Cloud Next ’25. These upgrades enable all application developers to design database-backed agentic gen AI solutions utilising open source technologies.

LangChain, an open-source framework, simplifies agentic gen AI systems that use massive language models. It connects large language models (LLMs) to other data sources for more powerful and context-aware AI applications. LangChain regularly interacts with databases to efficiently manage and extract structured data. The langchain-postgres package integrates PostgreSQL databases to load documents, store chat history, and store vectors for embeddings. Connectivity is needed for LLM-powered apps to use relational data, perform semantic searches, and generate memory chatbots.

Google Cloud enhancements include enterprise-level connection pooling, faster SQL filtering with relational metadata columns, and optimised performance with asynchronous PostgreSQL drivers. It also included:

Developers can use LangChain to create vector databases with vector indexes.

Flexible database schemas for more robust and manageable applications

For better security, the LangChain vector store APIs follow the least privilege principle and clearly distinguish database setup and usage.

Some new enhancements

Improved security and connectivity

Developing secure and dependable generative AI systems requires careful consideration of how your application interacts with the data architecture. Its LangChain Postgres contributions have prioritised security and connection through several key changes.

Following the least privilege concept has been our focus. The revised API distinguishes between database schema creation and application use rights. This separation lets you restrict the application layer's database schema changes. Separating these tasks can boost AI application security and reduce the attack surface.

Maintaining a pool of database connections reduces the overhead of making new connections for each query. This stabilises your application by efficiently limiting resource utilisation and preventing thousands of idle PostgreSQL connections. It also improves speed, especially in high-throughput scenarios.

Designing schema better

The langchain-postgres package historically only allowed schemas with fixed table names and a single json metadata column to resemble vector databases. PostgreSQL's sophisticated querying features allow you to filter non-vector columns to improve vector search quality. Our LangChain postgres package modifications let you define metadata columns to combine vector search queries with SQL filters when querying your vector storage.

Use the new LangChain PostgreSQL package to turn your PostgreSQL database structure into an AI workload with a few lines of code. This eliminates data schema migration.

Features ready for production

Google Cloud introduced vector index management and first-class asynchronous driver integrations in LangChain to enable production-scale applications. Asynchronous drivers enable non-blocking I/O operations, improving performance. This helps your application grow efficiently, reduce resource consumption, and increase responsiveness to handle more concurrent requests.

LangChain may now directly create and maintain vector indexes. This lets you utilise LangChain to describe and build your entire application stack, from database schema to vector index creation, using an infrastructure-as-code technique for vector search. This end-to-end connection simplifies development and makes LangChain AI-powered apps easy to set up and manage by using asynchronous operations and vector search.

LangChain packages for Google Cloud databases were upgraded by Google Cloud. It upstreamed those changes from its packages into LangChain PostgreSQL so developers on any platform could use them. Generative AI applications increasingly rely on databases, therefore software libraries must offer high-quality database connectors to exploit your data. These databases root LLMs, provide RAG application knowledge, and fuel high-quality vector search.

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Future applications require dynamic, AI-driven experiences at unknown scale and little downtime, thus old databases are unsuitable. At Google Cloud Next 25, Google Cloud introduced new features, performance, and migration tools to help migrate MySQL workloads to Spanner, their horizontally scaled, always-on operational database.

Moving programs from MySQL to Spanner is easier.

MySQL was not designed for today's availability and scaling needs. Manual replication and sharding are risky and complicated solutions that emerge when the firm is least ready. On self-managed databases, scale planning and implementation require expensive after-market solutions. Development teams may spend months designing and testing these solutions, delaying user-facing functionality. Due to scaling costs, firms often prepare for peak usage even if they seldom utilise it.

Future apps must do more than process transactions. Dynamic pricing, collaborative ideas, real-time fraud detection, and semantic discovery require novel data storage and querying methods.

Live MySQL-Spanner migrations are easier

Enterprises struggling to extend and modernise their applications may use Spanner to safely and quickly migrate production workloads from MySQL with little disruption. They may then use Spanner's full-text search, rich graph, integrated AI, and hands-free reliability.

Spanner migration automates schema and data transfer to consolidate petabyte-sized sharded MySQL databases in days rather than months for live cutovers. Updated built-in reverse replication synchronises data from Spanner to sharded MySQL instances for near-real-time failover in a disaster, and improved data movement templates increase throughput at lower cost and allow data transformation during migration. Finally, new Terraform configurations and CLI interface enable implementation customisation.

Better latency and fewer code and query modifications

Google Cloud adds powerful relational features to Spanner that closely map to MySQL to reduce the cost and difficulty of migrating application code and queries.

MySQL's default isolation level, repeated read, balances performance and consistency. Repeated read isolation, in preview, improves Spanner's serialisable isolation. It gives MySQL developers extra options to enhance efficiency and is familiar. Most popular workloads can see a 5x latency reduction over Spanner. The inclusion of auto_increment keys, SELECT…FOR UPDATE, and over 80 new MySQL procedures dramatically reduces the adjustments needed to migrate an application to Spanner.

A recent Forrester Consulting overall Economic Impact analysis found that Spanner gave a composite company typical of the clients polled a 132% return on investment and $7.74 million in benefits over three years. This is primarily owing to Spanner's integrated, hands-free, high availability operations and elastic scalability replacing self-managed databases. Spanner's ability to reduce unexpected downtime and system maintenance allowed development teams to capitalise on new prospects without expensive re-architecture projects or new capital expenditures.

Summary