AI RAG Implementation

Service Summary

PES plans and implements Retrieval-Augmented Generation (RAG) systems that let businesses query their institutional knowledge using natural language — with cited, verifiable answers. Our RAG architecture covers the full pipeline: document ingestion, text chunking, embedding generation, vector database storage, retrieval, and LLM-augmented response generation — all secured to CSF 2.0 and ISO 27001 standards.

Note: The RAG architecture and implementation strategies are recommendations based on current AI/ML best practices. Every implementation is tailored to your document corpus, compliance requirements, and LLM/model preferences.

Document Ingestion

Collect documents from multiple sources — PDFs, Word docs, wikis, SharePoint, email archives, support tickets. Content is extracted, cleaned, and chunked into semantically meaningful segments using strategies like recursive character splitting (LangChain) or semantic chunking (LlamaIndex). Supported chunk sizes: 256–1024 tokens with configurable overlap.

Sources — S3, SharePoint, Confluence, file systems, email archives
Formats — PDF, DOCX, TXT, HTML, Markdown, CSV
Chunking — Recursive split, semantic split, fixed-size with overlap
Pipeline — Unstructured.io, LangChain document loaders, custom extractors

Embedding & Vector Database

Document chunks are transformed into vector embeddings using models like OpenAI text-embedding-3-small, Cohere embed, or open-source models (BGE, Instructor). Embeddings are stored in a vector database with HNSW indexes for fast approximate nearest neighbor search.

Embedding Models — text-embedding-3 (OpenAI), Cohere Embed, BGE, Instructor-XL
Vector Databases — pgvector (PostgreSQL), Pinecone, Weaviate, Chroma, Qdrant
Index Type — HNSW with cosine or dot-product distance
Metadata Filtering — Document source, date, category stored alongside vectors

Retrieval & Generation

User queries are embedded using the same model as the ingestion pipeline. The system retrieves the top-k most semantically relevant document chunks (k=3–10), passes them as context to the LLM along with the user's question, and generates a grounded response with citations.

Retrieval Strategy — Hybrid search (vector + keyword BM25), re-ranking with Cohere/Cross-encoders
LLM Integration — GPT-4, Claude, Llama via LangChain/LlamaIndex orchestration
Prompt Engineering — System prompt with citation format, context window management, hallucination guardrails
Response Format — Cited answer with source document references

Security & Compliance

All RAG systems are secured with encryption at rest and transit, role-based access to document sources, and audit logging for every query. PES aligns deployments with NIST AI Risk Management Framework and ISO 27001 controls.

Data Encryption — TLS 1.2+ for all API calls, AES-256 for vector data at rest
Access Control — Document-level permissions, RBAC on query endpoints, metadata-based filtering
Audit Trail — Every query logged with user, timestamp, retrieved context, and generated response
Compliance — NIST AI RMF, ISO 27001, GDPR, SOC 2 alignment

Local RAG Implementation Options

For businesses with sensitive data or regulatory requirements that prohibit cloud-based AI services, PES deploys fully local, private RAG systems. Below are three verified local implementation stacks — no internet API calls, no data exfiltration risk, all running entirely within your infrastructure.

Option 1: Ollama + LlamaIndex + Chroma

Fully local, zero cloud dependency. Ollama runs open-source LLMs (Llama 3, Mistral, Gemma) locally on GPU or CPU. LlamaIndex handles document ingestion, chunking, and RAG orchestration. Chroma serves as the in-memory or persistent vector store.

Component	Role	License
Ollama	Local LLM inference server	MIT
LlamaIndex	RAG pipeline orchestration	MIT
Chroma	Vector database (HNSW)	Apache 2.0

Option 2: LM Studio + LangChain + pgvector

Desktop-friendly, ideal for Windows and macOS teams. LM Studio runs GGUF-quantized models locally with a local REST API. LangChain orchestrates the RAG pipeline with pgvector as the PostgreSQL-backed vector store.

Component	Role	License
LM Studio	Local LLM runtime	Free tier
LangChain	RAG orchestration	MIT
pgvector	PostgreSQL vector extension	PostgreSQL

Option 3: Hugging Face Transformers + FAISS

Python-native, no external services. Load models directly from HuggingFace Hub or local disk. FAISS provides vector indexing optimized for large-scale similarity search.

Component	Role	License
Transformers	Model loading (LLMs + embeddings)	Apache 2.0
FAISS	Vector similarity search	MIT
SentenceTransformers	Embedding generation	Apache 2.0

Document Types Used as Source for Vector Database

PDFs — policies, manuals, regulatory documents
Word documents — SOPs, training materials
Internal wikis and knowledge bases
Customer support tickets and email archives
Meeting transcripts
Intranet and SharePoint content

Benefits for Local Companies

Customer support teams resolve tickets faster by retrieving relevant documentation instantly
Compliance teams can ask "show me all policies related to data retention" and get cited answers
New employees onboard faster by asking questions in natural language against company knowledge
Sales teams access product specs, pricing, and competitive intelligence in seconds

Implementation Plan

Phase 1

Use Case Discovery — Weeks 1–2

Identify business questions, inventory document sources, classify data sensitivity. CSF: Identify ISO: A.8

Phase 2

Architecture Design — Weeks 3–5

Vector database selection (pgvector, Pinecone, Weaviate), embedding model, LLM selection. CSF: Govern ISO: A.5

Phase 3

Pipeline Development — Weeks 6–9

Document ingestion, chunking, embedding generation, retrieval tuning, prompt engineering. CSF: Protect ISO: A.12

Phase 4

Security Review — Weeks 10–11

Data classification, role-based access, encryption, audit logging. CSF: Detect ISO: A.8, A.12

Phase 5

Deployment — Weeks 12–13

Production deployment, feedback loop, ongoing knowledge base updates. CSF: Respond ISO: A.16

Workflow Diagram — RAG Pipeline

flowchart LR;A[Document Sources]-->B[Ingestion Pipeline];B-->C[Text Chunking];C-->D[Embedding Generation];D-->E[Vector Database];F[User Query]-->G[Query Embedding];G-->E;E-->H[Retrieved Context];H-->I[LLM Generation];I-->J[Response + Citations]

Implementation Timeline

Phase	Activity	Duration	CSF 2.0	ISO 27001
1	Use Case Discovery	Weeks 1–2	Identify	A.8
2	Architecture Design	Weeks 3–5	Govern	A.5
3	Pipeline Development	Weeks 6–9	Protect	A.12
4	Security Review	Weeks 10–11	Detect	A.8, A.12
5	Deployment	Weeks 12–13	Respond	A.16

Why Businesses Will Benefit

AI implementation without governance is a liability. PES builds RAG systems that are secure, auditable, and grounded in your actual data — not hallucinated answers. Our CSF 2.0 and ISO 27001 alignment ensures your vector database, embedding pipeline, and LLM integration meet enterprise compliance standards.