Resources
This resource list is structured so it can grow into a curated library. Prefer official documentation and primary sources when making implementation decisions.
Research library
Search by title, author, publisher, or topic. Primary sources and publisher records are distinguished from reader-supplied references that still require canonical-link or bibliographic verification.
43 references
Foundational paper that introduced the RAG formulation combining parametric and non-parametric memory.
Broad survey of RAG methods, enhancement strategies, applications, and evaluation.
Introduces graph-based indexing and community summaries for corpus-level sensemaking questions.
v1: January 21, 2025; v2: September 4, 2025; current v3: September 29, 2025.
Taxonomy of graph-based knowledge organization, retrieval, and integration for customized LLM systems.
Current v4 submitted April 1, 2026.
Survey of agent cardinality, control structures, autonomy, knowledge representation, applications, and open challenges.
v1: March 11, 2025; v2: March 17, 2025.
Reviews retrieval, generation, integration, benchmarks, applications, and future knowledge-oriented RAG research.
First edition. ISBN-13: 9781835089507.
Practical coverage of ingestion, chunking, metadata, indexes, retrieval, reranking, agents, tracing, evaluation, and deployment.
Accessible overview of RAG, its benefits, indexing, retrieval, augmentation, and generation.
Official documentation distinguishing local, global, DRIFT, and basic search methods.
Official description of GraphRAG indexing methods and generated knowledge artifacts.
Evaluation model covering context relevance, groundedness, and answer relevance.
Reader-supplied evaluation overview. Confirm details against current framework documentation.
Comparison-oriented secondary source; verify features in each framework's official documentation.
Reader-supplied comparison. Use as orientation, not as a substitute for official documentation.
Progressive explanation of agentic retrieval patterns for different experience levels.
Reader-supplied conference reference concerning iterative retrieval and self-correction.
Secondary taxonomy useful for orientation; validate implementation details with papers and official repositories.
Overview of corrective retrieval patterns. Verify product-specific behavior in current official documentation.
Practical secondary guidance on choosing chunk boundaries and sizes.
Product documentation reference. Confirm the exact page and behavior in the current RAGFlow release.
Community guidance; test recommendations against your own corpus and evaluation set.
Secondary comparison that should be validated through corpus-specific experiments.
Introductory definition and overview of common chunking approaches.
Reader-supplied article; pair with current Unstructured and vector database documentation.
Selection criteria should be tested against workload, governance, scale, and operational constraints.
Community comparison; confirm current pgvector behavior and benchmark with representative data.
Secondary comparison of approximate-nearest-neighbor indexing approaches.
Decision-oriented secondary source; validate model limits, pricing, and latency with current provider data.
Useful framing: prompting, retrieval, and fine-tuning solve different problems and can be combined.
Secondary decision guide; avoid treating vendor-specific cost or performance claims as universal.
Community perspectives can reveal practical concerns but are not authoritative evidence.
Vendor-authored comparison; cross-check each framework against its official documentation.
Secondary comparison. Framework APIs change quickly, so verify current capabilities.
Reader-supplied source not reviewed in detail. Confirm claims with official documentation.
Comparison source; distinguish orchestration, agent runtime, and data-framework responsibilities.
General comparison; verify deployment, licensing, and feature claims from primary sources.
Implementation-oriented secondary source; pair with GraphRAG papers and official project documentation.
Vendor perspective on combining GraphRAG workflows with graph database infrastructure.
A useful hypothesis for investigation, not a general conclusion. Retrieval and reasoning must be evaluated separately.
High-stakes domain guidance must be checked against applicable law, security review, and institutional policy.
Use-case survey; validate expected benefits with pilots and domain-specific success metrics.
Secondary use-case overview; avoid treating projected outcomes as guaranteed.
Bibliographic details and canonical publication location require confirmation before scholarly citation.
Working resources
Use these sources while implementing and evaluating systems. Product features and APIs can change, so check current official documentation.
Official documentation for Dify apps, workflows, knowledge bases, and deployment.
Official docs for LangChain, LangGraph, retrieval, agents, and integrations.
Official docs for data connectors, indexes, retrievers, workflows, and evaluation.
Official docs for building component-based AI pipelines and RAG systems.
Foundational RAG paper by Lewis et al. introducing retrieval-augmented generation for knowledge-intensive tasks.
Benchmark suite for information retrieval evaluation across diverse datasets.
Large-scale dataset commonly used in passage retrieval research. Verify task fit before use.
Open-source RAG evaluation framework repository.
Examples for building retrieval workflows with OpenAI APIs. Verify current API details in official docs.
Community hub for models, datasets, spaces, and retrieval-related discussions.
Official vector database documentation for collections, filtering, search, and deployment.
Official documentation for vector and hybrid search with Weaviate.
Official documentation for Milvus vector database concepts, operations, and search.
Official documentation for LLM tracing, evaluation, prompt management, and observability.
Official Phoenix documentation for observability, tracing, and LLM evaluation workflows.
Official documentation for RAG evaluation metrics and workflows.
Official documentation for document parsing and preprocessing before RAG indexing.
Official documentation for managed vector search and RAG-oriented retrieval patterns.