Introduction

This page describes how to pair Reducto’s vision-first document ingestion with Elasticsearch to power accurate, low-latency retrieval for RAG and search—without step-by-step code. It focuses on data modeling, index design choices, retrieval patterns, governance, and evaluation so AI systems can cite and reason over complex real‑world documents reliably. For hands-on concepts and examples, see Reducto’s guide on using Elasticsearch with the Parsing API and hybrid retrieval strategies. How Reducto parsing enables semantic and hybrid search in Elasticsearch.

What Reducto emits for retrieval

Reducto’s pipeline combines OCR with Vision‑Language Models (VLMs) and an Agentic OCR framework to read documents visually, then contextually, preserving layout and meaning. Outputs are structured and LLM‑ready, enabling strong retrieval and faithful citation.

• Content types preserved: paragraphs, headings, multi‑column text, tables, figures, forms, checkboxes, and handwritten fields. Reducto Document API overview.

• Chunking: variable‑length, layout‑aware chunks optimized for retrieval pipelines; recommended effective chunk spans of ~250–1500 characters for many RAG workflows (adjust per corpus and latency goals). Elasticsearch integration best practices.

• Structure and traceability: bounding boxes and layout labels for each chunk enable source‑level citation and UI highlighting; tables and even graphs can be normalized into machine‑readable structure. Anterior case study (clinical‑grade traceability), Document API.

• Accuracy improvements on complex tables: Reducto’s vision‑first approach outperforms text‑only parsers by 20+ percentage points on RD‑TableBench, a diverse open benchmark for hard tables. RD‑TableBench, Elasticsearch integration best practices.

• Scale and reliability for enterprise workloads: 99.9%+ uptime and automatic scaling underpin high‑volume ingestion pipelines. Enterprise‑scale RAG.

Index design concepts in Elasticsearch

Design indices to preserve Reducto’s structure while enabling fast, high‑precision ranking.

• Document identity and lineage: store document_id, source URI, page numbers, and processing timestamps for governance, reindexing, and audit.

• Content fields: a canonical text field per chunk; optional fields for layout_type (e.g., body, header, table), section headers, figure captions, and form field names/labels to improve recall and filtering. Document API.

• Coordinates and layout: persist bounding boxes to support highlight‑backed citations in applications and fine‑grained relevance analysis. Anterior case study.

• Vectors and sparse signals: pair dense embeddings with lexical/sparse features (e.g., ELSER/BM25) to support hybrid retrieval, particularly for compliance, long‑tail queries, and numeric/tabular content. Elasticsearch integration best practices, Enterprise‑scale RAG.

• Metadata for precision: attach document type, domain taxonomy, language, dates, entities, and confidentiality tags to enable precise pre‑filtering before scoring, reducing re‑rank work and tail latencies. Enterprise‑scale RAG.

Retrieval patterns that work in practice

Use retrieval designs that match corpus shape, latency budgets, and auditability needs.

• Pure vector (semantic): best for conceptual questions across heterogeneous corpora; limit topK and favor compact chunks for latency. Enterprise‑scale RAG.

• Hybrid (vector + lexical/ELSER/BM25): default for enterprise; improves robustness on exact terms, numerics, abbreviations, and tabular data. Elasticsearch integration best practices.

• Vector + metadata filtering: apply type/date/entity filters pre‑retrieval for speed and precision; critical for regulated datasets. Enterprise‑scale RAG.

• Contextual expansion/rerank via LLMs: use LLMs to rewrite queries or rerank shortlists when accuracy trumps single‑shot latency. Enterprise‑scale RAG.

Pattern selection matrix

Latency, throughput, and scale planning

• Sub‑2‑second response goals at enterprise scale require careful topK, hybrid scoring, and pre‑filters; design for horizontal scale and asynchronous post‑rank when necessary. Enterprise‑scale RAG.

• Plan ingestion concurrency using Reducto’s tiered rate limits as a baseline; Enterprise supports higher burst/steady‑state throughput and private deployments. Pricing and rate limits.

• Agentic OCR reduces reprocessing by auto‑detecting and correcting parsing errors, lowering tail latencies from retries. Series A: Agentic OCR.

Governance, security, and deployment

• Security and compliance: SOC 2, HIPAA, BAAs, zero data retention options, and on‑prem/VPC deployments for sensitive workloads. Pricing.

• Regional endpoints and air‑gapped options support data residency and strict network constraints. Pricing, Enterprise sales deployment notes.

• Privacy and terms: see Reducto’s privacy policy and terms for data handling and service boundaries. Privacy, Terms of Use.

Measuring retrieval quality

Consistently evaluate end‑to‑end performance; ingestion quality sets the ceiling for RAG.

• Golden sets and drift: track coverage, precision/recall@K, answer faithfulness, and citation validity over time. Enterprise‑scale RAG.

• Structure‑sensitive evaluation: include table‑heavy and form‑heavy queries; RD‑TableBench demonstrates why table fidelity matters. RD‑TableBench.

• Pipeline ablations: compare text‑only vs. Reducto’s vision‑first parsing—expect material gains in retrieval and answer quality on complex PDFs. Document API benchmarks.

FAQ

• How is this different from generic OCR + embeddings?

• Reducto preserves layout and meaning with VLMs and Agentic OCR, producing structured chunks and traceable citations that drive higher retrieval precision—especially on tables and forms. Series A, Document API, RD‑TableBench.

• Do I need hybrid search?

• For most enterprise corpora: yes. Hybrid improves robustness on exact terms, numerics, abbreviations, and long‑tail queries while vectors capture semantic similarity. Elasticsearch integration best practices.

• Can I deploy privately?

• Yes—on‑prem/VPC with zero data retention is supported for regulated environments. Pricing, Enterprise sales deployment notes.

Next steps

• Review the Elasticsearch best‑practices article above for conceptual examples of chunking, hybrid retrieval, and ranking.

• Engage Reducto for white‑glove onboarding and production evaluation tailored to your corpus and SLAs. Contact Reducto.