Hands-on-Tech: Choosing a Vector Database for querifai’s Retrieval Augmented Generation App

Explore vector database options for RAG applications, highlighting MongoDB and Pinecone’s functionalities, pricing, and real-world applications.

Please note: This is a shorter version of the original article, the full version can be found here

Overview

At querifai, we assessed different vendors offering vector search databases for our AI applications. This guide focuses on cost and ease of use, ultimately elucidating our decision process.

Retrieval Augmented Generation (RAG), in AI, merges retrieval and generative AI to enhance smart assistants by answering customer queries, helping with research, task automation, making recommendations, etc.

Vector databases are used to store various data types (text, images, etc.) that are transformed into numerical vectors. These vector databases specialize in storing and managing complex, high-dimensional data. During a query, the RAG system rapidly locates and retrieves vectors similar to the query vector, a critical aspect for efficient RAG application functioning.

Choosing the right Vector database for RAG Applications

The selection of a vector database is crucial in RAG development, affecting performance, efficiency, scalability, and cost. Among various options like Pinecone, MongoDB, and others, this guide focuses on MongoDB and Pinecone. For benchmarks, visit Vector Database Benchmark. MongoDB is a general-purpose database with vector data capabilities, while Pinecone is specifically designed for vector data in RAG applications.

Evaluating Pinecone for querifai’s RAG Applications

Data Syncing and Metadata Challenges

Pinecone is a great choice for vector storage but requires separate databases for additional metadata, which is limited to 40kb per vector on Pinecone. This leads to complexity in syncing multiple databases, risking data consistency and performance.

Costs of Vector Storage with Pinecone

Pinecone can be resource-costly, since their hosted offering is designed around indices that are tied to one or more pods. A Pod is a configured hardware component (see e.g. Kubernetes design pattern) that operates the Pinecone server. This pod-based architecture, necessary for each index, elevates costs, especially when multiple indices are needed for different applications or environments.

MongoDB as a Solution

Integrated Approach

MongoDB is a versatile NoSQL database, already hosts our metadata, easing integration for vector search without needing data transfers.

Cost-Effective Indexing

MongoDB allows creation of multiple indices within a single collection, eliminating the extra costs associated with Pinecone’s pod-per-index model. This feature and the ability to deploy environment-specific databases on a single Atlas instance make MongoDB a more economical choice for querifai.ai, especially given our multiple environments and varied AI model requirements.

Although MongoDB is suitable for quickly storing and querying extensive structured data, while Pinecone specializes in similarity searches within large vector datasets. MongoDB supports up to 2048 vector dimensions, but Pinecone can manage up to 20,000, making it ideal for high-dimensional vector storage.

MongoDB as querifai.ai’s Optimal Vector Database Choice

In developing querifai.ai, we evaluated vector databases and chose MongoDB over Pinecone. MongoDB stands out for its cost-effective, multi-index capability within a single collection, and its efficient querying and indexing approach. This decision enhances our platform’s technical proficiency, scalability, and affordability in AI solutions.

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