RAG pipelines that show evidence—and still admit model limits
Index documents responsibly, retrieve with hybrid search, and qualify answers in UI—knowing RAG reduces but does not remove hallucinations and citations are not proof.
Retrieval & Evidence Studio
Hybrid Dense Semantic & BM25 Sparse Search
Hybrid RetrievalCombining high-dimensional vector embeddings with lexical BM25 keyword matching via Reciprocal Rank Fusion (RRF) for maximum search recall.
RAG Retrieval, Reranking & Grounded Evidence Observatory
Inspect how Digital Elliptical architects production RAG pipelines around hybrid dense-sparse search, cross-encoder reranking, multi-tenant row-level ACLs, and automated incremental index lifecycles.
Enterprise Knowledge Base with Hybrid Dense-Sparse Search
Fusing pgvector cosine distance embeddings with PostgreSQL tsvector BM25 full-text keyword matching using Reciprocal Rank Fusion (RRF) for 40% higher recall.
RRF_Score = 1 / (60 + dense_rank) + 1 / (60 + sparse_rank)
Combines semantic conceptual matching with exact technical keyword precision.
Async database query executing vector and text searches in parallel CTEs
User query pre-processed with synonym expansion and typo correction
Top-20 fused candidate chunks passed directly to reranking tier
-- PostgreSQL Hybrid Search CTE with RRF
WITH dense_results AS (
SELECT id, content, metadata, ROW_NUMBER() OVER () AS dense_rank
FROM documents
WHERE tenant_id = $1
ORDER BY embedding <=> $2::vector
LIMIT 50
),
sparse_results AS (
SELECT id, content, metadata, ROW_NUMBER() OVER () AS sparse_rank
FROM documents
WHERE tenant_id = $1 AND search_vector @@ plainto_tsquery('english', $3)
ORDER BY ts_rank_cd(search_vector, plainto_tsquery('english', $3)) DESC
LIMIT 50
)
SELECT COALESCE(d.id, s.id) AS id, COALESCE(d.content, s.content) AS content,
(COALESCE(1.0 / (60 + d.dense_rank), 0.0) + COALESCE(1.0 / (60 + s.sparse_rank), 0.0)) AS rrf_score
FROM dense_results d
FULL OUTER JOIN sparse_results s ON d.id = s.id
ORDER BY rrf_score DESC
LIMIT 20;// Fusion Score Normalization Contract
// Combines dense semantic semantic distance with exact BM25 keyword matchingProduction RAG Pipeline & Verifiable Grounding Topology
A structured breakdown of how document chunking, hybrid vector/BM25 retrieval, cross-encoder reranking, and grounded citation synthesis coordinate.
Document Ingestion & Chunking Plane
Parsing PDFs and markdown docs, computing SHA-256 chunk hash diffs, and generating 1536-dimensional dense vector embeddings.
Hybrid Storage & Vector Indexing
Storing chunk text, dense vectors with pgvector HNSW indexing, and tsvector sparse inverted indexes with tenant row-level ACLs.
Hybrid Retrieval & RRF Fusion Engine
Executing dense semantic search and BM25 full-text keyword search in parallel CTEs, merging candidates via Reciprocal Rank Fusion.
Cross-Encoder Reranking Tier
Scoring top-50 candidate passages through Cohere Rerank v3 cross-encoders, filtering low-scoring noise and compressing prompt context.
Grounded Synthesis & Verifiable Citations
Synthesizing answers strictly grounded in verified excerpts, rendering interactive inline citation links, and qualifying confidence.
When RAG Pipeline Architecture Fits
- Your application answers questions over evolving private enterprise documents (PDFs, knowledge bases, manuals) where knowledge updates daily.
- Users demand verifiable source citations and verbatim paragraph links to audit the source of every generated answer.
- Your queries contain specific technical identifiers, part numbers, or exact acronyms requiring lexical BM25 matching alongside semantic vector search.
- You need strict multi-tenant data isolation and row-level access control enforced directly at the vector query layer.
When Fine-Tuning, Claude or LangGraph Fits Better
- Your domain requires learning specialized grammar, stylistic tone, or deep structural domain habits rather than factual lookup (choose Model Fine-Tuning).
- You are processing a single self-contained 200,000 token document where direct full-context ingestion is feasible (choose Claude API).
- You need cyclical multi-agent graphs with persistent conversational state checkpointing (choose LangChain / LangGraph).
Production RAG Pipeline Best Practices
Reciprocal Rank Fusion (RRF)
Combining dense vector similarity with BM25 keyword matching via RRF to eliminate single-retriever failure modes and maximize search recall.
Cross-Encoder Precision Reranking
Scoring candidate passages through Cohere Rerank to filter out irrelevant chunks and ensure only the highest-quality context reaches the LLM.
Row-Level ACL Enforcement
Embedding tenant IDs and access group arrays directly into vector tables, enforcing hard WHERE clauses to guarantee zero cross-tenant data leaks.
Automated Chunk Eviction
Computing SHA-256 hash diffs on document changes to re-embed only modified paragraphs and immediately purge deleted content from vector indexes.
Discuss Your Enterprise RAG Architecture
Design hybrid dense-sparse vector pipelines, configure cross-encoder reranking, enforce multi-tenant row-level ACLs, and implement verifiable citation UIs with our AI architects.
Related Technical Proof & Service Capabilities
Services & solutions
rag-knowledge-base-developmentRelated insights
ai-automationFrequently Asked Questions About Production RAG Architecture
Does RAG guarantee truthful answers?
No. RAG improves grounding when retrieval is good, but models can still misread or overgeneralize chunks.
Do citations prove an answer is correct?
No. Citations show provenance for review—they are not automatic correctness proof.