RAG61 articles

RAG

Articles

Open-Source Document Parsing and Layout Analysis Engines in Production: Comparing Docling, Marker, MinerU, and Unstructured
Production retrieval-augmented generation (RAG) and document agent systems often fail before a single vector embedding is calculated or an LLM prompt is constructed. The breakdown occurs at the document ingestion boundary. Standard text extraction utilities, such as pypdf or raw pdfminer, extract text streams sequentially without spatial awareness. When processing multi-column academic papers, financial reports with multi-header tables, or technical manuals with complex formulas, these naive ext
August 28, 20261 min
Vector Databases and Semantic Search Engines in Production: Comparing Qdrant, Milvus, Weaviate, and pgvector Architecture, Filtered Index Traversal, Quantization Memory Footprints, and Serving Economics
Vector Databases and Semantic Search Engines in Production: Comparing Qdrant, Milvus, Weaviate, and pgvector Architecture, Filtered Index Traversal, Quantization Memory Footprints, and Serving Economics In production retrieval-augmented generation (RAG) and agentic workflows, semantic search is rarely a simple vector similarity calculation. Production vector search requires sub-10ms query latency under heavy concurrency, high-throughput ingestion, zero downtime during index rebuilds, precise me
August 28, 20261 min
Agentic Web Search and Retrieval APIs in Production: Comparing Tavily, Exa, Brave Search API, and Firecrawl Architecture, Neural Reranking, Markdown Extraction, and Serving Economics
Autonomous artificial intelligence agents and retrieval-augmented generation (RAG) pipelines rely on live web data to ground their reasoning, verify factual assertions, and inspect external systems. However, traditional human-oriented search APIs such as Google Custom Search or Bing Web Search were engineered for browser display rather than direct language model consumption. They return paginated link lists, raw HTML payloads laden with boilerplate, tracking scripts, navigation bars, and cookie
August 28, 20261 min
Reranking Models and Late-Interaction Frameworks in Production RAG: Comparing Cohere Rerank 3.5, BGE-Reranker-v2, FlashRank, and ColBERTv2 Architecture, Cross-Encoder Latency, MaxSim Compression, and Retrieval Economics
Reranking Models and Late-Interaction Frameworks in Production RAG: Comparing Cohere Rerank 3.5, BGE-Reranker-v2, FlashRank, and ColBERTv2 Architecture, Cross-Encoder Latency, MaxSim Compression, and Retrieval Economics In production Retrieval-Augmented Generation (RAG), relying solely on first-stage bi-encoder dense embeddings or lexical BM25 search creates a structural information bottleneck. First-stage retrieval compresses an entire document passage into a single fixed-dimensional vector (s
August 28, 20261 min
LLM Evaluation and Automated Testing Frameworks in Production: Comparing Ragas, DeepEval, Promptfoo, and TruLens Architecture, Synthetic Testsets, LLM-as-a-Judge Economics, and CI/CD Pipelines
Evaluating large language model applications in production requires transitioning from subjective manual spot-checking to automated, reproducible testing pipelines. Unlike traditional software unit tests where deterministic inputs produce exact expected outputs, LLM-driven applications exhibit non-deterministic generation, complex multi-hop retrieval dynamics, and open-ended tool interactions. Deploying updates to prompts, retrieval parameters, embedding models, or base LLM checkpoints introduc
August 28, 20261 min
Open-Source Embedding Models and Serving Frameworks in Production: Comparing BGE-M3, NV-Embed-v2, GTE-Qwen2, and ModernBERT-Embed Architecture, Matryoshka Projections, Context Scaling, and Serving Economics
Open-Source Embedding Models and Serving Frameworks in Production: Comparing BGE-M3, NV-Embed-v2, GTE-Qwen2, and ModernBERT-Embed Architecture, Matryoshka Projections, Context Scaling, and Serving Economics (TEI vs. Triton vs. vLLM) Text embeddings form the indexing and retrieval foundation for production Retrieval-Augmented Generation (RAG), semantic search, and agentic memory systems. While early production architectures relied almost exclusively on closed commercial APIs (such as OpenAI's te
August 28, 20261 min
Context Reranking Engines in Production RAG: Comparing Cohere Rerank, BGE-Reranker-v2, FlashRank, and ColBERT Late Interaction
Context Reranking Engines in Production RAG: Comparing Cohere Rerank, BGE-Reranker-v2, FlashRank, and ColBERT Late Interaction Standard Retrieval-Augmented Generation (RAG) pipelines frequently face a fundamental retrieval bottleneck: single-vector bi-encoders compress variable-length documents into a single dense embedding. While dense vector search enables high-throughput approximate nearest neighbor (ANN) retrieval across millions of documents, it discards token-level interactions. This comp
August 28, 20261 min
Chunking Strategies in Production RAG: Comparing Fixed-Size, Semantic Chunking, Late Chunking, and Contextual Retrieval
In production Retrieval-Augmented Generation (RAG) pipelines, the chunking strategy determines the theoretical ceiling of retrieval quality. Splitting documents into discrete text spans transforms continuous discourse into isolated segments. When chunks are indexed in isolation, critical context disappears: pronoun antecedents lose their referents, domain-specific acronyms lose their definitions, and propositions spanning arbitrary token boundaries become fragmented. Selecting an appropriate ch
August 27, 20261 min
Document Parsing Engines in Production RAG: Comparing Docling, Marker, MinerU, and Unstructured Architecture, Layout Analysis, Table Extraction, and Serving Economics
Retrieval-Augmented Generation (RAG) systems frequently fail not in their embedding models or vector databases, but at the initial document ingestion layer. When enterprise documents (including financial 10-K filings, clinical trial reports, technical manuals, and multi-column academic papers) are parsed using legacy text scrapers such as basic pypdf or pdfminer, structural semantics are lost. Multi-column reading orders interleave across paragraphs, header and footer noise pollutes vector indic
August 27, 20261 min
GraphRAG Frameworks in Production: Comparing Microsoft GraphRAG, LightRAG, Fast-GraphRAG, and Neo4j Hybrid Architectures
Standard dense vector retrieval fails on two specific query topologies: global corpus sense-making and multi-hop associative entity traversal. Standard vector search relies on flat chunk embeddings (cosine similarity over top-k chunks), which isolates information into disconnected fragments. If a query requires connecting entity A to entity C through intermediate entity B across documents, or synthesizing thematic patterns across an entire million-token repository, vector databases return disjoi
August 27, 20261 min
Embedding Models and Rerankers in Production: Comparing Dense Bi-Encoders, ColBERT Late Interaction, Cross-Encoders, and Serving Architectures
Production retrieval-augmented generation systems depend heavily on the retrieval pipeline that precedes generation. While frontier language models receive the bulk of engineering attention, the accuracy, latency, and operational cost of a RAG application are often determined by the interplay between embedding models and rerankers. Deploying search infrastructure requires navigating three distinct architectural paradigms: dense bi-encoders, multi-vector late interaction engines, and cross-encod
August 27, 20261 min
GraphRAG Frameworks and Architectures in Production: Comparing Microsoft GraphRAG, LightRAG, Neo4j GenAI, and Kùzu
Standard dense retrieval-augmented generation (RAG) relies on vector embeddings to retrieve top-k chunks based on cosine similarity. While effective for point-lookup queries against localized text segments, dense vector search breaks down under two common production workloads: multi-hop relational reasoning across disconnected documents and global corpus-wide summarization. When answering questions that require traversing relationship paths across disparate data points, or synthesizing broad th
August 27, 20261 min
LLM Evaluation Frameworks and CI/CD Quality Gates in Production: Comparing DeepEval, Ragas, Promptfoo, and TruLens
Moving large language model applications from exploratory prototypes to production systems requires automated quality validation. Relying on manual inspection or unstructured testing introduces regression risk across model updates, prompt edits, and retrieval modifications. Automated evaluation frameworks address this by converting probabilistic model outputs into measurable, repeatable software assertions. While traditional unit testing relies on deterministic assertions, production LLM testin
August 26, 20261 min
Prompt Compression and Context Pruning Engines in Production: Comparing LLMLingua-2, LongLLMLingua, Selective-Context, and RECOMP
Prompt Compression and Context Pruning Engines in Production: Comparing LLMLingua-2, LongLLMLingua, Selective-Context, and RECOMP Every non-obvious claim below links to a source. Benchmarks are from the papers as cited; the comparative numbers are taken directly from the LLMLingua-2 paper and the RECOMP paper, not synthesized from prose. The context window paradox is real: modern LLMs accept 128k to 1M tokens, but API cost scales linearly with input length, attention compute scales quadratical
August 26, 20261 min
Vector Databases in Production RAG: Comparing Qdrant, Milvus, Weaviate, and pgvector Architecture, HNSW vs DiskANN, Filter Pipelines, Quantization, and Serving Economics
In production Retrieval-Augmented Generation (RAG) systems, vector retrieval is rarely a simple nearest-neighbor lookup across unconstrained float32 embeddings. Real-world enterprise workloads require strict multi-tenant metadata filtering, low p99 latency under concurrent load, high write throughput for continuous ingestion, and strict memory budgeting. While synthetic benchmarks frequently test unconstrained recall on static datasets, production teams encounter severe operational divergence a
August 26, 20261 min
Document Parsing Engines in Production RAG: Comparing Docling, MinerU, Marker, and Unstructured Architecture, Table Structure Recognition, Reading Order Recovery, and Ingestion Economics
Document parsing remains one of the primary failure modes in enterprise Retrieval-Augmented Generation (RAG) pipelines. While modern embedding models and vector databases offer sub-millisecond retrieval across millions of dense vectors, downstream generation quality remains bounded by the structural fidelity of upstream document ingestion. Naive text extractors like PyPDF or basic PDFMiner strip away structural metadata, flattening multi-column text into interleaved sentences, shredding table ro
August 26, 20261 min
Beyond Naive RAG: Production Comparison of Self-RAG, CRAG, and Adaptive-RAG for Enterprise LLMs
Beyond Naive RAG: Production Comparison of Self-RAG, CRAG, and Adaptive-RAG for Enterprise LLMs As retrieval-augmented generation (RAG) matures from prototype to production, teams face a critical choice: which advanced RAG variant best balances accuracy, latency, and operational complexity? Three leading approaches—Self-RAG, Corrective RAG (CRAG), and Adaptive-RAG—offer distinct trade-offs for enterprise deployment. Architectural Overview Self-RAG: Learning to Reflect Self-RAG (Asai et al.
August 26, 20261 min
Vector Databases in Production: Comparing Qdrant, Milvus, Weaviate, and pgvector Architecture, Indexing Overhead, Filtered Search, and Serving Economics
Deploying vector search in production requires navigating fundamental trade-offs across storage topology, indexing latency, memory allocation, and metadata filtering overhead. As retrieval-augmented generation (RAG), multimodal search, and agentic memory architectures scale beyond tens of millions of embeddings, database selection determines whether inference latency remains bounded or collapses under complex filtering constraints. The current vector infrastructure ecosystem divides into two pr
August 26, 20261 min
Document Chunking Strategies in Production RAG: Comparing Recursive Splitting, Semantic Clustering, Late Chunking, and Contextual Retrieval Architecture, Boundary Preservation, and Retrieval Precision
In retrieval-augmented generation (RAG) systems, document chunking dictates the upper bound of downstream retrieval quality and generation fidelity. Naive document fragmentation breaks cross-sentence references, severs entity co-occurrences, and discards global discourse structure. Conversely, oversized chunks inject extraneous noise into context windows, diluting the dense embedding representation and wasting generation budget. Production architectures have moved beyond static character counti
August 25, 20261 min
Graph RAG in Production: Comparing Microsoft GraphRAG, LightRAG, Fast GraphRAG, and HippoRAG Architecture, Indexing Overhead, and Multi-Hop Retrieval Economics
Standard vector retrieval-augmented generation (RAG) relies on dense semantic embeddings to map text chunks into a shared vector space. While effective for localized semantic lookups, dense retrieval degrades on two distinct query classes: corpus-wide global summarization (such as identifying overarching themes across an entire document store) and complex multi-hop associative queries (connecting entity A to entity D through multiple intermediary relationships across disparate documents). Graph
August 25, 20261 min