Empowering RAG Workflows with Nexla and NVIDIA NIMS: Scalable, Private, and Easy to Deploy

Retrieval-Augmented Generation (RAG) is reshaping how organizations use GenAI to access and synthesize information, supporting applications from internal knowledge sharing to customer interactions. However, RAG workflows can be complex to set up, involving intricate processes for data parsing, scalable embeddings generation, and secure, low-latency retrieval. Nexla and NVIDIA Inference Microservices (NIMS) make it easy for any organization to build and deploy RAG workflows on our SaaS platform as well as within their own infrastructure, providing scalable AI processing that keeps data private and secure.

The Challenge

As enterprises amass data across diverse systems—cloud storage, on-premises databases, and various applications like Sharepoint, Box, Shopify, Salesforce etc —they face significant challenges in extracting and transforming this data into a format suitable for GenAI. Two key tasks are essential in a RAG workflow:
Data Integrations: Ingesting unstructured and semi-structured data into a vector database
Workflow and Orchestration: Executing natural language queries over these vectors using an LLM
Organizations need a seamless way to handle both tasks while balancing performance, data security, and integration across multiple systems. Adding to this complexity is the desire to use embeddings generation, ranking, and even chat completions with high-performance models in a private, self-hosted environment.

The Solution: Nexla and NIMS

To address these challenges, Nexla has combined its powerful no-code/low-code Data Integration and Workflow orchestration  capabilities with NVIDIA NIMS, enabling enterprises to implement RAG workflows securely and efficiently. Here’s how Nexla and NIMS work together to simplify RAG workflows:

1. Data Parsing and Chunking with Nexla: Nexla’s platform enables users to parse and chunk data from various sources, such as S3, SharePoint, Box etc through a no-code/low-code environment. This streamlined approach eliminates the need for custom coding for common transformations while providing low-code options for advanced tasks like semantic chunking. Notably, unstructured data in these sources is often multi-modal, encompassing a variety of formats including text, tables, charts, graphs, and images. NVIDIA NIMS is pivotal in parsing these complex data types, leveraging powerful AI models to accurately extract, structure, and prepare this information for further downstream tasks, such as analysis, search, and generation. This integration ensures that Nexla’s platform can efficiently handle diverse and complex data types across any enterprise data workflow.
2. Embeddings Generation with NIMS: Once data is preprocessed, NVIDIA NIMS can be used within the organization’s infrastructure to generate embeddings. This private, self-hosted setup ensures data never leaves the company’s secure environment, making it ideal for industries with strict compliance requirements. NIMS supports various use cases, including embeddings generation, ranking, and chat completions.
3. Mix and Match AI Models for Optimal Performance: Nexla enables users to leverage NIMS while also integrating other LLM models to maximize performance, allowing organizations to tailor their RAG workflows based on specific business needs. By mixing and matching models, users can achieve a balance between accuracy, latency, and computational efficiency.
4. End-to-End RAG Workflow: Nexla facilitates the seamless creation of end-to-end RAG workflows. Once embeddings are generated, they can be pushed into a vector database, enabling fast and accurate natural language querying of the vectorized data. With Nexla and NIMS, these workflows can be deployed directly within an organization’s cloud, giving teams the flexibility to run powerful AI workflows privately and securely.

Implementation Process

The implementation of RAG workflows with Nexla and NIMS involves several straightforward steps. Below is an example of ingesting SEC filings into Pinecone using Nexla and NIMS.

• Data Ingestion: Using Nexla’s connectors, data is ingested from S3. Ingestion is set up on a schedule and for subsequent runs, only new files and modified files will be processed ensuring data freshness.
• Data Processing and Chunking: Nexla’s no-code/low-code interface allows users to parse, chunk, and prepare data for embeddings generation. Additionally, Nexla also integrates with AWS Textract and Unstructured.io for more advanced capabilities. Here, customers would use their own credentials in a secured manner for those services.
• Embedding Generation with NIMS: Within an organization’s private infrastructure or via Nexla’s SaaS platform, NVIDIA NIMS is available as an API, allowing users to generate embeddings efficiently while ensuring data security. Users can leverage Nexla’s low-code capabilities to easily configure API requests for embedding generation, making it simple to integrate this powerful functionality into their workflows. Nexla also supports composable data processing, enabling users to build reusable transforms through Nexla’s transformation framework for efficient, scalable embedding workflows.
  Additionally, Nexla’s native support for asynchronous execution allows the system to scale LLM computations like embedding generation across very high data volumes. Nexla also manages pipeline reliability, ensuring that individual failures (due to rate limits, data quality issues, etc.) do not impact the entire pipeline, maintaining operational resilience and robustness.
• RAG-Ready: The generated embeddings are stored in a vector database, optimized for real-time retrieval. This setup allows users to execute natural language queries on the stored embeddings, retrieving relevant data with minimal latency. Combining NIMS with other LLM models provides a fully integrated RAG workflow, supporting efficient data retrieval and insight generation for downstream applications.

Key Features

• Private and Secure AI Processing: NIMS allows for private, self-hosted model inference, ensuring data privacy and regulatory compliance.
• Seamless Workflow Deployment: Nexla simplifies RAG workflow creation, allowing users to deploy end-to-end workflows in any cloud environment.
• Flexible Model Integration: Nexla enables mixing NIMS with other LLM models, letting organizations balance performance and cost.
• Streamlined Data Preparation: Nexla’s no-code/low-code environment simplifies data parsing and chunking, enabling teams to prepare data more efficiently and focus on insights rather than infrastructure.
• Cost Optimization: By combining NIMS with various LLM models, Nexla enables users to select the best model mix to balance computational demands with budget constraints, optimizing costs effectively.

Conclusion

This approach allows organizations to tailor their RAG workflows for high performance and security, meeting their unique data processing needs with greater control and efficiency.

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