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RAG Pipeline Deployment

Deploy enterprise RAG pipelines to query private data securely using vector databases, semantic chunking, embedding models, and role-based security controls.

By VertApp Team
RAG Pipeline Deployment

RAG Pipeline Deployment

Ground your AI models in your own private data using state-of-the-art Retrieval-Augmented Generation.

Large Language Models (LLMs) trained on open web data lack knowledge of internal corporate documentation. Training custom base models or fine-tuning existing ones is expensive, slow, and forces you to retrain whenever data changes. Retrieval-Augmented Generation (RAG) resolves this by fetching relevant factual passages from your private database and feeding them directly into the LLM prompt during inference.

Analogy: The Open-Book Exam

Imagine a student taking a complex law exam. A standard LLM operates closed-book, relying strictly on memorized parameters that can decay or hallucinate details. A RAG pipeline turns the exam into an open-book test. When a question arises, an automated librarian searches your private archive, locates the three exact relevant clauses, and places them directly on the student's desk to form an accurate answer based on verified facts.

What's Included

1. Vector Database Setup

A vector database indexes and searches high-dimensional vector embeddings, representing text by semantic meaning rather than basic keyword matching.

  • Database Infrastructure: Deploy managed vector stores (Pinecone, Qdrant, Milvus) or open-source database extensions like PostgreSQL with pgvector to balance maintenance overhead against data governance requirements.
  • Indexing Algorithms: Implement Hierarchical Navigable Small World (HNSW) or Inverted File (IVF) graph indexes to enable sub-100 millisecond vector similarity searches across collections containing millions of records.
  • Metadata Tagging & Filtering: Attach operational metadata tags (such as department: engineering, access_level: restricted, or created_at: 2026-03-01) to filter search spaces prior to running cosine similarity or dot-product math.

2. Chunking & Embedding Strategies

Raw documents must be split into structured text chunks and converted into numerical vectors before storage.

  • Chunking Techniques:
    • Fixed-size Chunking: Divides document streams into exact token lengths (e.g., 512 tokens with 50-token sliding overlaps to prevent context loss across boundaries).
    • Semantic Chunking: Parses documents along structural boundaries like markdown headings, code blocks, or natural topic transitions.
  • Embedding Models: Select dense representation models (e.g., text-embedding-3-small, bge-large-en-v1.5, or cohere-embed-v3) that capture domain terminology into 768-dimensional or 1536-dimensional vector spaces.
  • Concrete Example: When processing a 120-page aircraft maintenance manual, fixed-size chunking might cut a safety warning in half between consecutive blocks. Semantic chunking identifies the warning header and keeps the full 350-word procedure intact in a single chunk, preventing dangerous gaps in model context.

3. Security & Compliance

Exposing confidential enterprise repositories to internal or external AI interfaces requires strict access controls.

  • Identity & Access Management (IAM): Integrate vector query layers with enterprise SSO providers like Okta or Azure AD. Apply Role-Based Access Control (RBAC) so users only retrieve documents they have explicit permissions to view in source systems like Sharepoint or Jira.
  • Data Isolation & Encryption: Enforce AES-256 encryption for stored vector indices and TLS 1.3 for data in transit. Multi-tenant environments require tenant isolation via dedicated collections or PostgreSQL Row-Level Security (RLS).
  • Audit Logging: Maintain immutable query logs recording user identities, prompt parameters, retrieved document IDs, and system outputs for SOC 2 Type II, HIPAA, and GDPR compliance audits.