RAG Architecture

Data Engineering &
Retrieval Architecture

From raw data processing pipelines to high-performance vector search infrastructures. We engineer the complete data lifecycle for Generative AI.

Data Engineering
Validation Workflows

Automated Quality Gates

Before data touches your model, it must pass rigorous Quality Gates. We build Great Expectations-style validation pipelines that automatically quarantine bad data based on schema drift, null values, or statistical anomalies.

Validation Logic

Schema Compliance
Validates JSON structure and data types.
Completeness Check
Ensures critical fields (UUID, Timestamp) are present.
Statistical Outliers
Detects values > 3 std devs from mean.
PII Scanner
Checks for unencrypted credit cards or HKIDs.

Ingestion Health Monitor

Real-time Batch Processing

● Valid● Quarantined
Quarantine Report: B-003Log ID: err-9921

> Validating schema... OK

> Checking null constraints... OK

> Statistical Analysis... FAIL

Error: Column 'transaction_amount' contains value $9,000,000 (Mean: $500). Z-Score > 5.

> Action: Moved to DLQ (Dead Letter Queue) for manual review.

Multimodal Processing

Beyond Just Text

Modern RAG isn't limited to text. We engineer pipelines that ingest PDFs, Images, and Audio Logs. We apply specialized processing (OCR, Transcription, Vectorization) to unify disparate data formats into a single semantic index.
Raw Input
OCR / Textract
Processor
Vector DB

Transformation Logic

def process_media(input):

raw_bytes = load(input)

# Apply specialized model based on type

text = ocr_engine.extract_text(raw_bytes)

return vector_store.upsert(text, embedding)

Quality Enhancement

Pre-Processing Enrichment

Raw text is often noisy. We implement an Enrichment Pipeline using lightweight NLP models to clean, normalize, and annotate text *before* it reaches the expensive Foundation Model. This improves accuracy and reduces token costs.
Normalize
Standardize dates & formats
Extract
Identify Entities (NER)
Sanitize
Redact PII / Secrets
Step 0: Raw Input
"meeting w/ john doe on 12/05/24 abt project falcon. email: john@test.com"
Input Formatting

Structured Data Preparation

LLMs perform best with structured inputs. We engineer pipelines that transform raw database rows into context-rich Prompt Templates or optimized JSON Schemas, ensuring the model receives clean, consistent instructions.
Raw SQL Source
idcust_nametxn_dateitems
101Acme Corp2024-10-01["Server X", "Cable Y"]
LLM Payload (ChatML)
<|system|>
You are a billing assistant. Extract invoice details as JSON.
<|user|>
Context: Customer Acme Corp purchased Server X, Cable Y on 2024-10-01.
<|model|>
{
"customer": "Acme Corp",
"date": "2024-10-01",
"items": ["Server X", "Cable Y"]
}
Template Engine

We use engines like Jinja2 or Handlebars to dynamically insert data variables into prompt templates at runtime.

Token Optimization

Scripts automatically prune verbose data fields to ensure the payload stays within the context window limits.

Schema Enforcement

We define strict JSON schemas (Pydantic/Zod) to validate that input data matches the model's expected structure.

Vector Store Solutions

High-Performance Vector Infrastructure

Building a production-grade RAG system requires more than just a vector database. It requires a robust architecture for indexing, metadata management, and real-time synchronization.

Vector Architecture

High-Performance Indexing

Latency kills RAG experiences. We design Advanced Vector Architectures that scale to billions of vectors. By selecting the right Indexing Strategy (like HNSW) and implementing Sharding, we ensure sub-millisecond retrieval times even under heavy concurrency.

Performance Simulator

Dataset: 10M Vectors (768d)
Distributed Sharding
Layer 0
Base Layer (All Vectors)
Trade-off Analysis

The gold standard. Consumes more RAM but delivers lightning fast results.

Search Query
"Can I work from home?"
ResultsSimilarity Score
Remote Work Policy 2021
2021HR

"Remote work is not permitted..."

0.92
Remote Work Policy 2024
2024HR

"Employees may work remotely..."

0.91
IT Security Guidelines
2023IT

"VPN is required for remote..."

0.85
Office Floor Plan
2022Ops

"Desk layout..."

0.78
Hallucination Risk: The 2021 policy has a higher vector similarity score (0.92) than the 2024 policy (0.91). The model will likely generate an incorrect answer based on old data.
Metadata Frameworks

Search with Context

Vectors capture meaning, but they lack context (Time, Authority, Source). We implement robust Metadata Frameworks that enrich your chunks with attributes like `timestamp`, `author_role`, and `doc_category`. This enables Hybrid Search, filtering noise before the vector lookup even begins.
  • Temporal Filtering: "Only search documents from last 6 months."
  • Access Control: "Only search documents User A has permission to see."
  • Author Weighting: "Prioritize documents written by Senior Engineers."
Integration Components

Unified Knowledge Fabric

Your data lives everywhere. We build Integration Connectors that aggregate knowledge from disparate silos (DMS, Wikis, Databases) into a single, vectorized truth source. We handle the complexity of Incremental Syncing and Access Control Mapping.
Data Sources
SQL Database
Confluence / Wiki
Google Drive
Slack / Teams
Ingestion Engine
CDC & Transform
Central Store
Vector DB
Live Index
Data Maintenance

Zero-Stale Indexing

A vector store is useless if it's outdated. We implement Change Data Capture (CDC) systems that detect updates in your source (e.g., a wiki edit) and propagate them to the vector index in near real-time. We monitor Replication Lag to guarantee data freshness.

Maintenance Policies

  • • Incremental Updates: Sync only deltas, not full dumps.
  • • Garbage Collection: Auto-delete vectors for deleted docs.
  • • Re-Indexing: Scheduled optimization for HNSW graphs.

Vector Ops Monitor

Cluster: prod-index-01
Index Freshness
2 mins ago
Document Count
14.2M
QPS Load
850
Ingestion Lag (ms)
Threshold: 500ms
Retrieval Mechanisms

Advanced Retrieval Engineering

Retrieval is the "Brain" of RAG. We implement sophisticated query decomposition, hybrid search ranking, and context assembly strategies to ensure the model gets the right information, very time.

Segmentation

Intelligent Chunking

How you slice your data determines whether the model understands it. Naive splitting cuts off context. We implement Semantic and Hierarchical Chunking to ensure the retrieval engine grabs complete thoughts, not just keywords.

Semantic Chunking

Splits text based on sentence boundaries and semantic similarity. Keeps related ideas together.

Chunk 1React Digi was founded in 2017 to revolutionize enterprise AI.
Chunk 2The company is headquartered in Kowloon, Hong Kong.
Chunk 3Our flagship product, the Neuro-Gateway, allows banks to deploy local LLMs securely.
Chunk 4In 2024, we expanded operations to Singapore.
"Compare the revenue of Apple and Microsoft in 2023."
1
Sub-query 1: What was Apple's revenue in 2023?
2
Sub-query 2: What was Microsoft's revenue in 2023?
3
Synthesizer: Compare results from 1 & 2.
Query Engineering

Search Logic

Users rarely ask perfect questions. We implement Query Transformation Layers that rewrite, expand, or decompose user intent into machine-optimized search vectors.
Hybrid Search

Precision Retrieval

Vector search is great for concepts, but keyword search is better for exact matches (e.g., part numbers). We combine both (Hybrid Search) and then apply a Cross-Encoder Reranker to grade the results, ensuring the LLM gets only the most relevant facts.

Result Ranking Simulator

Query: "Current remote work policy"
Hybrid Match
Policy 2024: Hybrid work is mandatory.
Vector Score
0.88
Keyword Match
Policy 2021: Remote work allowed.
Vector Score
0.85
Vector Match
IT Guide: How to VPN.
Vector Score
0.82
Noise
Cafeteria Menu.
Vector Score
0.60

Initial Retrieval casts a wide net. Notice how 'Policy 2021' ranks high because it shares many keywords, even though it's outdated.

Context Window Manager

Model: Llama-3-8B
SYS
USR
RETRIEVED CONTEXT (2048 toks)
02560 / 4096 Used4096 Limit
Context Assembly

The Last Mile

Retrieving data is useless if it doesn't fit in the model. We engineer Prompt Templates that dynamically pack retrieved chunks into the context window, handling truncation and prioritization to prevent "Context Overflow" errors.
Source Tracking
Injecting citation IDs for every chunk.
History Management
Summarizing old chats to save tokens.