Building Multi-Tenant RAG Applications With PostgreSQL: Choosing the Right Approach

If you’re building a retrieval-augmented generation (RAG) app with PostgreSQL and pgvector, you’ll probably run into the problem of handling multi-tenancy. This article explains how to pick the right approach to handle multi-tenancy for your use case.

What Is Multi-Tenancy?

Multi-tenancy is like an apartment building for software. Just as one building houses multiple tenants (families or individuals), a multi-tenant application serves multiple customers or organizations using a single instance of the software.

Multi-tenancy serves multiple "tenants" independently and securely—thereby preventing accidental or unauthorized cross-referencing of private information between different users. This means designing a system that not only understands and retrieves information effectively but also strictly adheres to user-specific data boundaries. 

Multi-Tenant Applications: Pros and Cons 

Multi-tenancy in RAG applications is vital for several key reasons, all of which deliver benefits:

• Data isolation: This allows multiple tenants to use the same RAG system while keeping their data separate and secure. This is crucial for maintaining privacy and confidentiality.
• Scalability: A multi-tenant architecture enables the system to efficiently serve many users or organizations without needing separate deployments for each, leading to better resource utilization and cost-effectiveness.
• Customization: Different tenants often have unique needs. Multi-tenancy allows for customization of the RAG system for each tenant, such as using tenant-specific knowledge bases or fine-tuning models for particular domains.
• Compliance: In many industries, regulatory requirements mandate strict data separation. Multi-tenancy helps meet these compliance needs by ensuring data from different tenants doesn't mix.
• Efficient updates: With a multi-tenant system, updates and improvements can be rolled out to all tenants simultaneously, ensuring everyone benefits from the latest features and security patches.
• Cost-effectiveness: Sharing infrastructure and resources across tenants can significantly reduce operational costs compared to maintaining separate systems for each user or organization.
• Consistent performance: A well-designed multi-tenant RAG system can provide more consistent performance across all tenants, as resources are dynamically allocated based on usage.

To build for long-term performance, flexibility, and efficiency, it’s also helpful to keep in mind the potential challenges of multi-tenant architectures:

• A multi-tenant environment allows multiple access points for users, which can increase the threat of a security breach.
• Serving multiple clients in one instance of an application or database adds an extra level of complexity to the codebase and database maintenance.
• Backup and restoration are more complex, so not all providers offer reliable restoration services.
• The ability to offer tenant-specific customizations is limited, and balancing the shared codebase with unique tenant requirements is often necessary.
• A technical problem on the provider’s end may affect all tenants simultaneously. This may apply to uptime, system upgrades, and other global processes.

PostgreSQL Benefits for Multi-Tenant RAG Apps

PostgreSQL, enhanced with the pgvector extension (the popular open-source extension for vector handling in PostgreSQL), offers a robust solution for implementing multi-tenant RAG apps. Its ability to efficiently store and search vector embeddings alongside traditional data types makes it an ideal choice for organizations looking to leverage their existing infrastructure. 

Here are the reasons why PostgreSQL is a good fit for multi-tenant RAG applications:

1. Built-in full-text search: PostgreSQL has robust full-text search capabilities, which are crucial for efficient retrieval in RAG systems.
2. JSON support: PostgreSQL handles JSON data natively, allowing flexible storage of documents and metadata.
3. Vector extensions: extensions like pgvector enable vector similarity searches, essential for embedding-based retrieval.
4. Row-level security: this feature allows fine-grained access control, crucial for multi-tenant setups to ensure data isolation.
5. Scalability: PostgreSQL can handle large datasets and concurrent users, important for growing multi-tenant applications.
6. ACID compliance: atomicity, consistency, isolation, and durability (ACID) compliance ensures data integrity and consistency across transactions.
7. Extensibility: custom functions and extensions can be added to tailor the database to specific RAG needs.
8. Cost-effective: as an open-source solution, it can be more cost-effective than some cloud-based alternatives.

Using PostgreSQL for multi-tenant RAG applications also gives you the advantage of Timescale Cloud’s stack of open-source extensions to easily build and scale RAG, search, and agents applications. In addition to pgvector, this stack includes pgvectorscale (which builds on pgvector for enhanced performance and scale) and pgai (which brings embedding creation and large language model completions to the database, giving more PostgreSQL developers the skills of AI engineers). Both extensions complement pgvector and rely on its capabilities.

Implementing RAG with PostgreSQL

Implementing RAG with PostgreSQL involves a multi-step process that leverages the database's vector storage capabilities. The workflow typically includes ingesting and chunking data, converting text into vector embeddings using an embedding model, and storing these vectors in PostgreSQL using pgvector. 

When a user query is received, the system retrieves the most relevant data from the vector database based on similarity search. This retrieved information is then combined with the user's question and any additional context to create a comprehensive prompt for the large language model (LLM). The LLM processes this enriched prompt and generates a response, which is then returned to the user, providing a more accurate and contextually relevant answer.

Strategies for Dealing With Multi-Tenancy for RAG in PostgreSQL

To pick the right strategy for your multi-tenant RAG application with PostgreSQL, consider your requirements (and your users’ or customers’ requirements) for shared resources, data separation, customization, scalability, and of course, costs. 

PostgreSQL offers four levels of multi-tenancy implementation—table, schema, logical database, and database service—each suitable for distinct use case scenarios and each with its pros and cons. Here’s a comparative overview of each level.  

1. Table-level multi-tenancy gives each tenant its own table. This is simple but may lead to data isolation concerns. Table-level isolation works well for simple apps with shared data.
2. Schema-level separation gives each tenant its own schema in the same logical database. It offers better isolation with minimal operational and cost overhead, balancing isolation and efficiency for most use cases.
3. Logical database separation gives each tenant its own logical database in a database instance. Separation at the logical database level provides stronger separation but increases complexity. Logical databases suit clients needing stricter separation.
4. Database service separation gives each tenant their own database service. Service-level separation is ideal for high-security scenarios or clients with unique needs. It offers the highest isolation but at the cost of increased resources and management overhead.

Conclusion

By carefully considering the optimal use cases, pros, and cons of each multi-tenancy approach and aligning them with your application's needs, you can create a scalable, secure, and performant RAG system in PostgreSQL. As RAG technologies continue to evolve, PostgreSQL's extensibility and strong community support ensure that it will remain an adaptable platform for building sophisticated multi-tenant AI applications. 

Additionally, PostgreSQL on Timescale Cloud allows you to store your relational, time series, events, semi-structured, and vector data in one place. This removes the operational complexity of managing a separate vector database. It can deliver performance, rich capabilities, and user experience equal to or better than a specialized tool.

Create a free account to try Timescale Cloud's open-source AI stack today (including pgvector, pgai, and pgvectorscale).