A Distributed RAG System with Kafka, ChromaDB, and gRPC

At JoLoMo LLC, we are building an advanced Retrieval-Augmented Generation (RAG) system designed for high-performance document ingestion, storage, and intelligent querying. Our architecture is already structured for distributed scaling, allowing it to handle large workloads across multiple servers while ensuring efficient processing.

Feel free to check out the code on GitHub:
https://github.com/JoeLorenzoMontano/gRPC-Kafka-VectorDb-Ollama

Current Tech Stack and Architecture

Our system leverages a combination of event-driven messaging, vector storage, and high-performance communication protocols to ensure scalability, reliability, and speed.

• Kafka (3-node Kraft cluster): Our messaging backbone uses a 3-node Kafka cluster with Kraft mode, eliminating the need for Zookeeper. This setup ensures high availability, fault tolerance, and the ability to process large-scale document ingestion while remaining distributed across multiple machines.
• ChromaDB: We use ChromaDB as our vector database, storing document embeddings for fast semantic search and retrieval. It is deployed as a network-accessible service, ensuring that multiple nodes can access and retrieve data efficiently.
• gRPC-based document service: Our document management and embedding services are built using gRPC, providing low-latency, high-throughput communication between different components.
• Ollama for embeddings: We use Ollama to generate high-quality document embeddings, allowing us to process and store documents in a way that enables advanced semantic search capabilities.
• Docker-based deployment: Our services run in Docker containers, with plans to move toward Kubernetes for improved orchestration and scaling.

Why We Chose gRPC Over REST

A key architectural decision in our system was choosing gRPC over REST for inter-service communication. While REST is widely used for API-based systems, gRPC provides several advantages that align with our scalability and performance goals:

1. High Performance: gRPC uses HTTP/2 instead of HTTP/1.1, enabling multiplexed requests, reducing latency, and making it ideal for real-time document processing.
2. Efficient Streaming: Unlike REST, which requires workarounds for streaming large files, gRPC natively supports bidirectional streaming, making it well-suited for handling large documents efficiently.
3. Compact Binary Serialization: Instead of JSON (which REST APIs typically use), gRPC relies on Protocol Buffers (protobufs), which are smaller, faster, and more efficient. This reduces network overhead when transferring large amounts of data.
4. Stronger Typing & Auto-Generated Code: gRPC enforces strict data contracts, reducing errors and allowing for auto-generated client and server code, making it easier to maintain the API structure as the system evolves.
5. Built-in Load Balancing & Authentication: Features like automatic load balancing and built-in authentication mechanisms make it more resilient in a distributed environment compared to REST.

By using gRPC, our document ingestion, retrieval, and processing pipeline operates with higher efficiency, lower latency, and better support for streaming workloads, all of which are critical for handling large-scale RAG applications.

Designed for Distributed Workloads

The Kafka cluster (3-node Kraft setup) plays a crucial role in distributing workloads efficiently. By using Kafka as an event-driven backbone, our system can:

• Scale horizontally by adding more consumers without disrupting the flow of data.
• Ensure document processing remains asynchronous, reducing the risk of bottlenecks.
• Handle failures gracefully, as messages persist in the Kafka log until fully processed.

In parallel, ChromaDB is deployed as a centralized vector database, allowing multiple document processing nodes to store and retrieve embeddings efficiently. Each document’s text is converted into embeddings, indexed, and made available for fast semantic search.

Future Improvements and Scalability Plans

While our system is already structured for multi-server deployment, we are still in an early phase of development with plans for further enhancements:

• Improved Load Balancing: Adding more Kafka brokers and ChromaDB instances for better fault tolerance and load distribution.
• Advanced Query Optimization: Optimizing vector search with improved indexing strategies to ensure even faster retrieval.
• Integration with External Services: Connecting with platforms like Slack, Notion, and enterprise knowledge bases to make document retrieval more seamless.
• Multi-Tenant Support: Expanding our infrastructure to support multi-tenant deployments, enabling secure, isolated document processing for multiple clients.
• Kubernetes Deployment: Transitioning from a Docker-based setup to Kubernetes orchestration, allowing for automated scaling and better resource management.

Looking Ahead

At JoLoMo LLC, we are committed to advancing RAG-based AI solutions and enhancing intelligent document processing. Our current system is already architected for scalability, distributed workloads, and real-time performance, but we recognize that there is room for further optimization and expansion.

As we refine our architecture and expand its capabilities, we are excited about the potential for this system to power AI-driven search, research assistance, and knowledge retrieval applications at scale. Stay tuned for more updates as we continue to push the boundaries of distributed AI-driven document management.