{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-ruc-nlpir--flashrag","slug":"ruc-nlpir--flashrag","name":"FlashRAG","type":"repo","url":"https://arxiv.org/abs/2405.13576","page_url":"https://unfragile.ai/ruc-nlpir--flashrag","categories":["productivity"],"tags":["benchmark","datasets","large-language-models","retrieval-augmented-generation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"github-ruc-nlpir--flashrag__cap_0","uri":"capability://automation.workflow.configuration.driven.component.factory.instantiation","name":"configuration-driven component factory instantiation","description":"FlashRAG uses a layered Config class that merges YAML configuration files with runtime dictionaries, then factory functions (get_retriever, get_generator, get_refiner, get_reranker, get_judger, get_dataset) dynamically instantiate components based on resolved config parameters. This eliminates hard-coded component selection and enables swapping implementations via config without code changes. The factory pattern integrates with a central utils.py module that resolves model paths and handles dependency injection across the entire RAG pipeline.","intents":["I want to swap between different retriever implementations (dense, sparse, neural sparse) by changing a config file, not rewriting code","I need to run multiple RAG experiments with different component combinations without creating separate scripts","I want to version-control my RAG pipeline configuration separately from implementation code"],"best_for":["RAG researchers running systematic ablation studies across component combinations","teams building reproducible RAG benchmarks with standardized configurations","developers prototyping new RAG methods without modifying core framework code"],"limitations":["Config merging adds ~50-100ms overhead per experiment initialization","Factory pattern requires explicit component registration — custom components need boilerplate factory methods","YAML schema validation is minimal — invalid configs may fail at runtime rather than config load time"],"requires":["Python 3.9+","PyYAML library for config parsing","Component implementations must inherit from base classes (Retriever, Generator, etc.)"],"input_types":["YAML configuration files","Python dictionaries with config overrides","model identifiers and paths"],"output_types":["instantiated component objects (Retriever, Generator, Refiner, Reranker, Judger, Dataset)","configured pipeline ready for execution"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruc-nlpir--flashrag__cap_1","uri":"capability://search.retrieval.multi.index.retrieval.with.dense.sparse.and.neural.sparse.backends","name":"multi-index retrieval with dense, sparse, and neural-sparse backends","description":"FlashRAG's retriever system (flashrag/retriever/) supports three distinct indexing strategies: Faiss for dense vector retrieval, BM25s/Pyserini for sparse lexical matching, and Seismic for neural-sparse hybrid retrieval. The index_builder.py module handles corpus preprocessing (Wikipedia extraction, token/sentence/recursive/word-based chunking) and index construction. Retrievers can be composed via multi-retriever patterns and reranked using CrossEncoderReranker, enabling hybrid retrieval pipelines that combine complementary signals (semantic similarity + keyword matching + neural sparsity).","intents":["I want to combine dense semantic search with sparse BM25 matching to improve recall on both semantic and keyword-based queries","I need to build retrieval indexes from large Wikipedia corpora with configurable chunking strategies (token-level, sentence-level, recursive)","I want to rerank retrieved documents using a cross-encoder model to improve precision without modifying the underlying retrieval index"],"best_for":["researchers comparing retrieval strategies (dense vs sparse vs hybrid) on standardized benchmarks","teams building production RAG systems requiring high recall across diverse query types","developers optimizing retrieval latency-accuracy tradeoffs with multiple index backends"],"limitations":["Maintaining multiple indexes increases storage overhead by 2-3x compared to single-index approaches","Reranking adds 50-200ms latency per query depending on cross-encoder model size and retrieved document count","Neural-sparse (Seismic) requires specialized model training — not suitable for out-of-the-box use without domain-specific data","Index building for large corpora (Wikipedia) can take hours; incremental index updates not supported"],"requires":["Python 3.9+","Faiss library for dense indexing","BM25s or Pyserini for sparse indexing","Sentence-transformers for embedding generation","Cross-encoder models (e.g., ms-marco-MiniLM-L-12-v2) for reranking","Sufficient disk space for multiple indexes (typically 5-50GB per corpus)"],"input_types":["raw text corpus (Wikipedia dumps, documents)","pre-chunked documents in JSONL format","queries (strings)","embedding models (HuggingFace model identifiers)"],"output_types":["Faiss index files (.index)","BM25 index files (.pkl)","retrieved document lists with scores","reranked document lists with cross-encoder scores"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruc-nlpir--flashrag__cap_10","uri":"capability://automation.workflow.web.based.ui.for.configuration.and.evaluation","name":"web-based ui for configuration and evaluation","description":"FlashRAG provides a Gradio-based web interface (webui/interface.py) that enables non-technical users to configure RAG experiments, run evaluations, and visualize results without writing code. The UI exposes configuration options for component selection, hyperparameter tuning, and dataset selection. Users can upload custom datasets, run experiments, and view results in a browser. This democratizes RAG research by removing the need to write Python scripts for experiment execution.","intents":["I want to run RAG experiments without writing Python code by using a web interface","I need to visualize evaluation results and compare multiple method runs","I want to share RAG experiments with non-technical stakeholders via a web UI"],"best_for":["non-technical users exploring RAG methods without coding","teams sharing RAG experiments across organization","researchers prototyping RAG configurations interactively"],"limitations":["Web UI is limited to pre-configured components — custom components require code modification","No built-in user authentication — not suitable for multi-user production deployments","Gradio UI is single-threaded — concurrent experiments may queue or timeout","Limited visualization options — complex analysis requires exporting results and using external tools"],"requires":["Python 3.9+","Gradio library","Web browser for UI access","Configured FlashRAG components and datasets"],"input_types":["component selections (dropdown menus)","hyperparameter values (text inputs, sliders)","dataset selection (dropdown)","custom dataset upload (file upload)"],"output_types":["evaluation results (table with metrics)","result visualizations (charts, plots)","downloadable result files (CSV, JSON)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruc-nlpir--flashrag__cap_11","uri":"capability://automation.workflow.command.line.interface.for.batch.experiment.execution","name":"command-line interface for batch experiment execution","description":"FlashRAG provides a command-line interface (run_exp.py) that enables batch execution of RAG experiments specified in YAML configuration files. Users can run multiple experiments sequentially or in parallel by specifying config files and output directories. The CLI integrates with the configuration system and factory functions to instantiate components and execute pipelines. This enables reproducible, version-controlled experiment execution suitable for continuous evaluation and benchmarking.","intents":["I want to run 10 different RAG method configurations on 5 datasets and collect results in a single command","I need to execute RAG experiments in batch mode on a cluster or cloud infrastructure","I want to version-control my experiment configurations and reproduce results months later"],"best_for":["researchers running systematic ablation studies and benchmarks","teams executing RAG experiments on cloud infrastructure or clusters","developers automating RAG evaluation in CI/CD pipelines"],"limitations":["CLI is synchronous — no built-in support for distributed execution across multiple machines","Error handling is basic — failed experiments may not be retried automatically","No built-in experiment tracking or result versioning — results must be manually organized","Limited progress reporting — long-running experiments provide minimal status updates"],"requires":["Python 3.9+","YAML configuration files specifying experiments","Configured FlashRAG components and datasets","Sufficient compute resources (GPU for generation, CPU for retrieval)"],"input_types":["config file path (YAML)","output directory for results","optional: number of parallel workers"],"output_types":["evaluation results (JSON/CSV files)","experiment logs (text files)","metadata (execution time, resource usage)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruc-nlpir--flashrag__cap_12","uri":"capability://text.generation.language.prompt.template.management.with.variable.substitution","name":"prompt template management with variable substitution","description":"FlashRAG's generator system includes prompt template management that enables defining prompts with variable placeholders (e.g., {query}, {context}, {examples}) that are filled at generation time. Templates can be specified in configuration files or code, and different templates can be used for different models or tasks. This abstraction enables researchers to experiment with prompt variations without modifying pipeline code, facilitating systematic study of prompt engineering impact on RAG quality.","intents":["I want to test 5 different prompt templates on the same RAG pipeline to see which produces better answers","I need to use different prompts for different LLMs (e.g., GPT-4 vs Llama 2) without code changes","I want to add few-shot examples to my prompts and measure their impact on generation quality"],"best_for":["researchers studying prompt engineering impact on RAG quality","teams optimizing prompts for specific LLMs and tasks","developers experimenting with different prompt strategies"],"limitations":["Template syntax is basic — no advanced templating features (conditionals, loops)","No built-in prompt optimization — requires manual tuning or external tools","Template effectiveness varies significantly by model — requires per-model tuning","No automatic few-shot example selection — examples must be manually specified"],"requires":["Python 3.9+","Prompt template strings with variable placeholders","Variable values at generation time (query, context, etc.)"],"input_types":["template string with {variable} placeholders","variable values (dictionary)","model identifier (for model-specific templates)"],"output_types":["filled prompt (string)","generated answer (string)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruc-nlpir--flashrag__cap_13","uri":"capability://image.visual.multimodal.generation.support.for.image.and.text.outputs","name":"multimodal generation support for image and text outputs","description":"FlashRAG's generator system includes support for multimodal generation that can produce both text and image outputs. The multimodal generation framework (flashrag/generator/) integrates with vision-language models and image generation APIs. This enables RAG systems to generate richer responses that combine text explanations with relevant images, improving user experience for visual queries. Multimodal generation follows the same component abstraction as text generation, enabling seamless integration into RAG pipelines.","intents":["I want to generate answers that include both text explanations and relevant images for visual queries","I need to retrieve images from a corpus and include them in generated responses","I want to use vision-language models to generate image descriptions alongside text answers"],"best_for":["teams building RAG systems for visual domains (product search, image-based QA)","researchers studying multimodal RAG on vision-language tasks","developers creating richer user experiences with image + text responses"],"limitations":["Multimodal generation is less mature than text generation — fewer models and methods available","Image retrieval and generation add significant latency (1-5 seconds per query)","Evaluation of multimodal outputs is challenging — no standard metrics for image quality","Requires vision-language models with larger memory footprint than text-only models"],"requires":["Python 3.9+","Vision-language model (e.g., CLIP, LLaVA, GPT-4V)","Image corpus or image generation API (e.g., DALL-E, Stable Diffusion)","GPU with sufficient VRAM for vision-language models (12GB+)"],"input_types":["query (string)","retrieved documents (text and/or images)","image generation prompt (optional)"],"output_types":["generated text (string)","generated or retrieved images (image files or URLs)","multimodal response combining text and images"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruc-nlpir--flashrag__cap_14","uri":"capability://data.processing.analysis.index.building.and.management.for.large.scale.corpora","name":"index building and management for large-scale corpora","description":"FlashRAG's index_builder.py module provides utilities for building and managing retrieval indexes from large corpora. It handles index construction for Faiss (dense), BM25s/Pyserini (sparse), and Seismic (neural-sparse) backends, with support for incremental updates and index statistics. The builder integrates with corpus preprocessing to ensure consistent chunking and metadata handling. Index management includes loading, saving, and querying indexes with configurable batch sizes for memory efficiency.","intents":["I want to build a Faiss index from a 1M-document corpus without running out of GPU memory","I need to update an existing BM25 index with new documents without rebuilding from scratch","I want to compare index statistics (size, query latency) across different backends"],"best_for":["teams building retrieval systems for large-scale corpora (Wikipedia, web crawls)","researchers studying indexing strategies and their impact on retrieval performance","developers optimizing index size and query latency tradeoffs"],"limitations":["Index building for large corpora is time-consuming (2-4 hours for Wikipedia on single machine)","Incremental index updates are not supported for all backends — may require full rebuild","Index size can be large (5-50GB for Wikipedia) — requires significant disk space","No built-in index compression — indexes cannot be easily shared or deployed"],"requires":["Python 3.9+","Faiss library (for dense indexing)","BM25s or Pyserini (for sparse indexing)","Sufficient disk space for indexes (5-50GB)","GPU with sufficient VRAM for dense index building (8GB+)"],"input_types":["corpus in JSONL format ({id, text, metadata})","embedding model (for dense indexing)","index backend (Faiss, BM25s, Seismic)","index parameters (chunk size, batch size)"],"output_types":["index files (.index for Faiss, .pkl for BM25s)","index metadata (statistics, build time, size)","index configuration (for reproducibility)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruc-nlpir--flashrag__cap_2","uri":"capability://planning.reasoning.23.implemented.rag.algorithms.across.4.pipeline.architectures","name":"23 implemented rag algorithms across 4 pipeline architectures","description":"FlashRAG implements 23 distinct RAG methods (including 7 reasoning-based variants) orchestrated through 4 pipeline types: Sequential (linear retrieval→generation), Conditional (branching based on query classification), Branching (parallel retrieval paths), and Loop (iterative refinement). Each method is implemented as a pipeline composition using base classes in flashrag/pipeline/ (Pipeline, SequentialPipeline, ConditionalPipeline, BranchingPipeline, LoopPipeline). Methods include standard RAG, Self-RAG, Corrective-RAG, Multi-hop reasoning, and others. The pipeline system enables researchers to implement new RAG variants by composing existing components without reimplementing retrieval or generation logic.","intents":["I want to benchmark my custom RAG method against 22 other established methods on standardized datasets","I need to implement a new RAG variant that combines conditional retrieval routing with iterative refinement without building from scratch","I want to understand how different pipeline architectures (sequential vs conditional vs branching) affect retrieval quality and latency"],"best_for":["RAG researchers publishing papers comparing algorithm performance on standardized benchmarks","teams implementing production RAG systems and needing reference implementations of established methods","developers building custom RAG variants by composing existing pipeline patterns"],"limitations":["Each method requires specific component configurations (e.g., Self-RAG requires a judger component) — not all methods work with all component combinations","Iterative methods (Loop pipelines) can add 2-5x latency compared to single-pass Sequential pipelines","Reasoning-based methods require LLMs capable of chain-of-thought reasoning — performance varies significantly by model","No automatic method selection — developers must manually choose which method to use for their use case"],"requires":["Python 3.9+","Retriever and generator components configured and instantiated","For reasoning methods: LLM with chain-of-thought capability (GPT-4, Claude, Llama 2 70B+)","For conditional methods: Judger/classifier component for query routing","Benchmark datasets in FlashRAG format (JSONL with {id, question, golden_answers, metadata})"],"input_types":["queries (strings)","retrieved documents (lists of text)","LLM responses (strings)","pipeline configuration (method name, component parameters)"],"output_types":["generated answers (strings)","intermediate reasoning traces (for reasoning-based methods)","retrieval decisions and routing paths (for conditional/branching methods)","evaluation metrics (EM, F1, BLEU, ROUGE)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruc-nlpir--flashrag__cap_3","uri":"capability://data.processing.analysis.unified.benchmark.dataset.management.with.36.pre.processed.datasets","name":"unified benchmark dataset management with 36 pre-processed datasets","description":"FlashRAG provides 36 pre-processed benchmark datasets in unified JSONL format with standardized schema ({id, question, golden_answers, metadata}). The Dataset class (flashrag/dataset/) handles loading, splitting, and iteration. The get_dataset() utility function in flashrag/utils/utils.py provides single-line dataset access. Datasets span multiple domains (QA, retrieval, reasoning) and are hosted on HuggingFace and ModelScope. This standardization eliminates dataset preprocessing overhead and enables researchers to focus on algorithm development rather than data wrangling.","intents":["I want to run my RAG method on 10 different benchmark datasets without writing custom data loaders for each","I need to split a dataset into train/val/test with consistent random seeds for reproducible experiments","I want to compare my method's performance across multiple domains (open-domain QA, multi-hop reasoning, retrieval) using standardized evaluation"],"best_for":["RAG researchers publishing papers with results on multiple standardized benchmarks","teams evaluating RAG systems across diverse query types and domains","developers prototyping RAG methods and needing quick access to evaluation data"],"limitations":["Datasets are fixed — no support for adding custom datasets without modifying the codebase","Dataset schema is rigid ({id, question, golden_answers, metadata}) — custom fields require preprocessing","Some datasets are small (< 1000 examples) — may not be suitable for training retrieval models","No built-in data augmentation or synthetic data generation"],"requires":["Python 3.9+","HuggingFace datasets library","Internet connection to download datasets from HuggingFace/ModelScope (first run only)","~10-50GB disk space for all 36 datasets"],"input_types":["dataset name (string identifier)","split specification (train/val/test)","batch size for iteration"],"output_types":["Dataset objects with __getitem__ and __len__ methods","individual items with {id, question, golden_answers, metadata}","train/val/test splits"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruc-nlpir--flashrag__cap_4","uri":"capability://data.processing.analysis.corpus.preprocessing.with.configurable.chunking.strategies","name":"corpus preprocessing with configurable chunking strategies","description":"FlashRAG provides corpus preprocessing utilities (scripts/preprocess_wiki.py, scripts/chunk_doc_corpus.py) that handle Wikipedia extraction and document chunking with 4 configurable strategies: token-based (fixed token count), sentence-based (split on sentence boundaries), recursive (hierarchical chunking), and word-based (fixed word count). Preprocessing outputs standardized JSONL format compatible with index builders. This modular approach enables researchers to experiment with chunking strategies' impact on retrieval performance without reimplementing preprocessing logic.","intents":["I want to test how different chunk sizes (128 tokens vs 512 tokens) affect retrieval accuracy on my corpus","I need to preprocess a Wikipedia dump into a retrieval corpus with sentence-level chunks to preserve semantic boundaries","I want to apply recursive chunking to hierarchical documents (papers with sections) to maintain document structure"],"best_for":["researchers studying chunking strategy impact on RAG performance","teams building retrieval indexes from raw document collections","developers optimizing retrieval latency by tuning chunk size"],"limitations":["Preprocessing large corpora (Wikipedia) takes 2-4 hours on single machine — no distributed preprocessing","Chunking strategies are fixed — custom chunking logic requires script modification","No overlap between chunks — may lose context at chunk boundaries","Sentence-based chunking requires language-specific tokenizers — non-English languages may have poor sentence detection"],"requires":["Python 3.9+","Wikipedia dump (for Wikipedia preprocessing) or raw documents","NLTK or spaCy for sentence tokenization","Sufficient disk space for output JSONL (typically 2-3x raw corpus size)"],"input_types":["raw Wikipedia XML dump or document files","chunking strategy (token/sentence/recursive/word)","chunk size parameters (token count, word count, overlap)"],"output_types":["JSONL files with {id, text, metadata} format","chunk statistics (average chunk size, chunk count)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruc-nlpir--flashrag__cap_5","uri":"capability://text.generation.language.multi.backend.text.generation.with.huggingface.vllm.fastchat.and.openai","name":"multi-backend text generation with huggingface, vllm, fastchat, and openai","description":"FlashRAG's generator system (flashrag/generator/generator.py) abstracts text generation across 4 backend types: HuggingFace (local transformers), vLLM (optimized local inference), FastChat (distributed inference), and OpenAI (API-based). The VLLMGenerator, HFGenerator, FastChatGenerator, and OpenAIGenerator classes implement a unified interface with configurable prompt templates, temperature, max_tokens, and other hyperparameters. This abstraction enables researchers to swap generation backends without changing pipeline code, facilitating comparison of model size/latency/cost tradeoffs.","intents":["I want to compare generation quality between a local Llama 2 7B model (vLLM) and GPT-4 (OpenAI API) without rewriting my pipeline","I need to optimize generation latency by switching from HuggingFace to vLLM without code changes","I want to run distributed generation across multiple GPUs using FastChat while keeping the same pipeline code"],"best_for":["researchers comparing generation models (open-source vs proprietary, different sizes) on RAG tasks","teams optimizing generation latency and cost by testing multiple backends","developers building RAG systems that need to support multiple LLM providers"],"limitations":["vLLM and FastChat require GPU hardware — not suitable for CPU-only environments","OpenAI backend requires API key and incurs per-token costs — expensive for large-scale evaluation","Prompt template format varies by model — may require manual tuning for different LLMs","No built-in prompt optimization or few-shot example selection"],"requires":["Python 3.9+","For HuggingFace: transformers library, model weights (local or HuggingFace Hub)","For vLLM: vLLM library, CUDA 11.8+, GPU with 8GB+ VRAM","For FastChat: FastChat library, distributed setup","For OpenAI: API key and account with billing"],"input_types":["prompt (string or template with variables)","generation parameters (temperature, max_tokens, top_p)","model identifier (HuggingFace model ID or OpenAI model name)"],"output_types":["generated text (string)","generation metadata (tokens used, latency, cost for OpenAI)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruc-nlpir--flashrag__cap_6","uri":"capability://data.processing.analysis.context.refinement.and.compression.with.llmlingua.and.similar.methods","name":"context refinement and compression with llmlingua and similar methods","description":"FlashRAG's refiner system (flashrag/refiner/) implements context compression and refinement methods that reduce retrieved context size before passing to the generator. The LLMLinguaRefiner uses token importance scoring to compress context while preserving key information. Refiners operate as pipeline components that take retrieved documents and output compressed context, reducing generation latency and cost without sacrificing answer quality. This enables RAG systems to handle larger retrieved document sets within token budget constraints.","intents":["I want to reduce the number of tokens passed to the generator by 50% while maintaining answer quality","I need to compress retrieved documents to fit within the context window of smaller LLMs (e.g., Llama 2 7B)","I want to prioritize the most relevant parts of retrieved documents to improve generation efficiency"],"best_for":["teams optimizing generation cost by reducing context size","developers using smaller LLMs with limited context windows","researchers studying context compression impact on RAG quality"],"limitations":["Compression adds 100-500ms latency per query depending on context size","Aggressive compression (>70%) may lose important context and reduce answer quality","LLMLingua requires a separate language model for importance scoring — adds model inference overhead","Compression effectiveness varies by domain — may require tuning for specific use cases"],"requires":["Python 3.9+","LLMLingua library (for LLMLinguaRefiner)","Language model for importance scoring (e.g., DistilBERT)","Retrieved documents in text format"],"input_types":["retrieved documents (list of strings)","compression ratio (target percentage of original size)","query (optional, for query-aware compression)"],"output_types":["compressed context (string)","compression metadata (original size, compressed size, compression ratio)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruc-nlpir--flashrag__cap_7","uri":"capability://planning.reasoning.query.classification.and.routing.with.judger.components","name":"query classification and routing with judger components","description":"FlashRAG's judger system (flashrag/judger/) implements query classification and routing logic that determines which retrieval/generation strategy to use for each query. The SKRJudger and similar components classify queries (e.g., simple vs complex, single-hop vs multi-hop) and route them to appropriate pipeline branches. Judgers integrate with ConditionalPipeline to enable adaptive RAG workflows where different queries follow different retrieval-generation paths. This enables RAG systems to optimize for query-specific characteristics rather than using a one-size-fits-all approach.","intents":["I want to route simple factual queries to fast BM25 retrieval and complex reasoning queries to dense retrieval + multi-hop reasoning","I need to classify queries as requiring single-hop or multi-hop reasoning and apply different generation strategies","I want to detect out-of-domain queries and handle them differently than in-domain queries"],"best_for":["teams building adaptive RAG systems that optimize for query characteristics","researchers studying query-aware retrieval strategy selection","developers implementing conditional RAG pipelines with query routing"],"limitations":["Query classification adds 50-200ms latency per query","Classification accuracy depends on training data — may perform poorly on out-of-distribution queries","Requires labeled training data for supervised classification — unsupervised methods may be less accurate","No automatic strategy selection — developers must manually define routing rules"],"requires":["Python 3.9+","Classifier model (SKRJudger uses a trained classifier)","Query classification labels or rules","Multiple retrieval/generation strategies configured for different query types"],"input_types":["query (string)","query features (optional, for feature-based classification)"],"output_types":["query classification (category/label)","routing decision (which pipeline branch to execute)","confidence score (optional)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruc-nlpir--flashrag__cap_8","uri":"capability://automation.workflow.sequential.and.conditional.pipeline.orchestration","name":"sequential and conditional pipeline orchestration","description":"FlashRAG's pipeline system (flashrag/pipeline/pipeline.py, sequential_pipeline.py, active_pipeline.py) provides base Pipeline class and concrete implementations: SequentialPipeline executes components in linear order (retrieve → refine → rerank → generate), ConditionalPipeline branches execution based on judger decisions, BranchingPipeline runs multiple retrieval paths in parallel, and LoopPipeline iterates until convergence. Each pipeline type composes retrievers, generators, refiners, rerankers, and judgers into directed acyclic graphs (DAGs). This abstraction enables researchers to implement complex RAG workflows without managing component orchestration manually.","intents":["I want to build a RAG pipeline that retrieves documents, compresses them, reranks them, and generates an answer in sequence","I need to implement a conditional pipeline that routes simple queries to fast BM25 and complex queries to dense retrieval","I want to run multiple retrieval strategies in parallel and merge results before generation"],"best_for":["researchers implementing complex RAG workflows with multiple components","teams building production RAG systems with conditional logic and optimization","developers prototyping new RAG architectures without managing orchestration manually"],"limitations":["Pipeline execution is synchronous — no built-in parallelization across sequential steps","LoopPipeline convergence criteria must be manually defined — no automatic stopping condition detection","Debugging complex pipelines (especially conditional/branching) can be difficult — limited logging/tracing","No built-in caching of intermediate results — repeated pipeline runs recompute all steps"],"requires":["Python 3.9+","Configured retriever, generator, and optional refiner/reranker/judger components","Pipeline configuration specifying component connections and parameters"],"input_types":["query (string)","pipeline configuration (component names, connections, parameters)"],"output_types":["generated answer (string)","pipeline execution trace (intermediate results, component outputs)","metadata (latency, component execution times)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruc-nlpir--flashrag__cap_9","uri":"capability://data.processing.analysis.evaluation.metrics.and.scoring.with.em.f1.bleu.rouge","name":"evaluation metrics and scoring with em, f1, bleu, rouge","description":"FlashRAG's evaluation system (flashrag/evaluation/) implements standard metrics for RAG evaluation: Exact Match (EM), F1 score, BLEU, and ROUGE. The evaluation process compares generated answers against golden answers from benchmark datasets and computes aggregate scores. Metrics can be computed at item level (per-query) or corpus level (average across all queries). 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