A highly scalable, secure, and privacy-focused Retrieval-Augmented Generation (RAG) pipeline leveraging AWS Serverless architecture, local embedding models, and large LLMs.
Designed for cost-efficiency and infinite scaling. The document ingestion pipeline is triggered by AWS S3 events and processed asynchronously using AWS Lambda functions, completely eliminating idle server costs.
To ensure maximum data security and compliance, the pipeline utilizes local embedding models (e.g., nomic-embed-text) via self-hosted Ollama instances. Sensitive enterprise data never leaves your VPC during the vectorization process.
Chunks are indexed in a high-performance Vector Database. When queries are made, semantic search retrieves the most relevant context, which is then fed into large self-hosted LLMs (like LLaMA 3.1) to generate highly accurate and context-aware responses.
Event-driven, serverless compute for cost-effective and highly scalable data ingestion pipelines.
Orchestrating the RAG logic, from document loading and recursive chunking to prompt templating.
Self-hosting models like LLaMA 3.1 and nomic-embed-text to guarantee privacy and reduce ongoing API costs.
High-performance backend framework exposing the RAG capabilities as robust RESTful APIs.
Watch how our Serverless RAG pipeline securely ingests documents and answers queries in real-time.
We build secure, custom Retrieval-Augmented Generation architectures tailored to your enterprise data.
Ready to leverage your enterprise data with absolute privacy and limitless scalability? Let's build your custom RAG solution.
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