{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-marker-inc-korea--autorag","slug":"marker-inc-korea--autorag","name":"AutoRAG","type":"framework","url":"https://marker-inc-korea.github.io/AutoRAG/","page_url":"https://unfragile.ai/marker-inc-korea--autorag","categories":["rag-knowledge"],"tags":["analysis","automl","benchmarking","document-parser","embeddings","evaluation","llm","llm-evaluation","llm-ops","open-source","ops","optimization","pipeline","python","qa","rag","rag-evaluation","retrieval-augmented-generation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-marker-inc-korea--autorag__cap_0","uri":"capability://automation.workflow.yaml.driven.rag.pipeline.configuration.with.multi.module.trial.orchestration","name":"yaml-driven rag pipeline configuration with multi-module trial orchestration","description":"AutoRAG uses a declarative YAML configuration system that defines a sequence of Node Lines, where each node contains multiple competing modules with different parameter combinations. The Evaluator class orchestrates trials by parsing the YAML config, instantiating all module variants, and systematically testing each combination against evaluation metrics. This enables AutoML-style hyperparameter search across the entire RAG pipeline without code changes.","intents":["I want to test 50+ different retrieval strategies and reranker combinations without writing custom code","I need to find the optimal embedding model, chunk size, and reranker for my specific dataset","I want to automate the process of comparing BM25 vs semantic search vs hybrid retrieval"],"best_for":["ML engineers optimizing RAG systems for production","teams with domain-specific documents needing empirical pipeline tuning","researchers benchmarking RAG configurations across datasets"],"limitations":["YAML configuration complexity grows exponentially with module combinations; 5 modules × 4 parameter sets = 20 trials per node","No built-in distributed trial execution — all trials run sequentially on single machine by default","Configuration validation happens at runtime, not parse time, so invalid module names only fail during evaluation"],"requires":["Python 3.9+","YAML configuration file defining node structure and module parameters","QA dataset in parquet format with query, retrieval_gt, and answer columns","Corpus dataset in parquet format with doc_id and contents columns"],"input_types":["YAML configuration files","Parquet datasets (QA pairs and document corpus)","Module parameter specifications (strings, integers, floats, lists)"],"output_types":["Trial results with per-module metric scores","Best module selection per node based on strategy","Optimized pipeline configuration YAML"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-marker-inc-korea--autorag__cap_1","uri":"capability://planning.reasoning.multi.stage.rag.pipeline.evaluation.with.pluggable.node.types","name":"multi-stage rag pipeline evaluation with pluggable node types","description":"AutoRAG implements a modular node architecture where each stage of the RAG pipeline (query expansion, retrieval, reranking, filtering, augmentation, compression, prompt generation) is represented as a distinct Node type. Each node contains multiple module implementations that can be swapped and evaluated independently. The framework uses a NodeLine abstraction to chain these nodes sequentially, enabling evaluation of the full pipeline end-to-end while tracking which module combination produces the best results.","intents":["I want to evaluate whether query expansion improves retrieval for my domain","I need to test different reranking strategies (BM25, semantic, LLM-based) and measure their impact on final answer quality","I want to compare passage filtering approaches and see which reduces hallucination most effectively"],"best_for":["RAG practitioners experimenting with multi-stage pipeline architectures","teams needing to isolate which pipeline stage is the bottleneck","researchers studying the impact of individual RAG components on QA performance"],"limitations":["Node execution is strictly sequential — no parallel branching or conditional routing within a pipeline","Module outputs must conform to expected schemas (e.g., reranker expects list of passages, returns ranked list); custom output formats require wrapper modules","Adding new node types requires extending the framework's node registry and implementing required interfaces"],"requires":["Python 3.9+","Node type implementations for each pipeline stage (provided: QueryExpansion, Retrieval, PassageReranker, PassageFilter, PassageAugmenter, PromptMaker, PassageCompressor)","Module implementations for each node type (e.g., BM25Retrieval, UPRRetrieval, MonoT5Reranker)"],"input_types":["Query strings","Document corpus with embeddings","Passage lists from previous nodes","LLM responses"],"output_types":["Expanded queries (QueryExpansion node)","Retrieved passages with scores (Retrieval node)","Reranked passages (PassageReranker node)","Filtered passages (PassageFilter node)","Augmented passages (PassageAugmenter node)","Generated prompts (PromptMaker node)","Compressed passages (PassageCompressor node)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-marker-inc-korea--autorag__cap_10","uri":"capability://memory.knowledge.passage.augmentation.with.context.enrichment.and.metadata.injection","name":"passage augmentation with context enrichment and metadata injection","description":"AutoRAG's PassageAugmenter node type enables testing of multiple augmentation strategies to enrich retrieved passages with additional context or metadata. Augmentation modules can add related passages, metadata, summaries, or external knowledge to each passage before generation. The framework evaluates which augmentation strategy improves answer quality or reduces hallucination, enabling optimization of context richness.","intents":["I want to add related passages or document summaries to each retrieved passage to provide richer context","I need to inject metadata (source, date, confidence) into passages to help the LLM make better decisions","I want to test whether augmenting passages with external knowledge (Wikipedia, knowledge graphs) improves answer quality"],"best_for":["RAG teams working with sparse or incomplete documents","practitioners optimizing context richness for complex reasoning","researchers studying the impact of context augmentation on generation"],"limitations":["Augmentation increases context length — may exceed LLM token limits or increase latency","Augmentation quality depends on the augmentation strategy — poor augmentation can introduce noise","No automatic augmentation selection based on passage characteristics — all passages use the same augmentation","Augmentation requires additional data sources (related passages, metadata, external knowledge) which may not be available"],"requires":["Python 3.9+","PassageAugmenter module implementations (provided: RelatedPassageAugmenter, MetadataAugmenter, or custom)","Retrieved or reranked passages from previous nodes","Additional data sources for augmentation (related passages, metadata, external knowledge)"],"input_types":["Query (string)","Passages (list of passage objects with text and metadata)","Augmentation configuration (strategy, data sources)"],"output_types":["Augmented passages (list, with additional context or metadata)","Augmentation metadata (source of augmentation, confidence)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-marker-inc-korea--autorag__cap_11","uri":"capability://data.processing.analysis.passage.compression.with.extractive.and.abstractive.summarization.strategies","name":"passage compression with extractive and abstractive summarization strategies","description":"AutoRAG's PassageCompressor node type enables testing of multiple compression strategies (extractive summarization, abstractive summarization, key-phrase extraction) to reduce passage length while preserving relevant information. Compression modules take passages and return compressed versions, reducing context length and latency while maintaining answer quality. The framework evaluates which compression strategy balances context preservation with efficiency.","intents":["I want to compress long passages to fit within LLM token limits without losing critical information","I need to reduce generation latency by compressing passages before sending to the LLM","I want to test whether extractive or abstractive compression preserves answer quality better"],"best_for":["RAG teams working with long documents or token-limited LLMs","practitioners optimizing for latency and cost in production systems","researchers studying the impact of passage compression on generation"],"limitations":["Compression is lossy — important information may be removed, reducing answer quality","Abstractive compression adds latency — requires LLM calls for each passage","Compression quality depends on the strategy — extractive methods may miss key information, abstractive methods may hallucinate","No automatic compression ratio tuning — users must manually set compression targets"],"requires":["Python 3.9+","PassageCompressor module implementations (provided: ExtractiveCompressor, AbstractiveCompressor, or custom)","Retrieved or reranked passages from previous nodes","Compression configuration (target length, strategy)"],"input_types":["Passages (list of passage objects with text)","Compression configuration (target length, strategy, LLM model if abstractive)"],"output_types":["Compressed passages (list, shorter versions of input)","Compression metadata (original length, compressed length, compression ratio)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-marker-inc-korea--autorag__cap_12","uri":"capability://search.retrieval.retrieval.with.multiple.search.strategies.and.vector.database.backends","name":"retrieval with multiple search strategies and vector database backends","description":"AutoRAG's Retrieval node type enables testing of multiple retrieval strategies (BM25, semantic search, hybrid retrieval, dense passage retrieval) as distinct modules. Each retrieval module queries the vector database or search index and returns ranked passages. The framework evaluates which retrieval strategy produces the best retrieval F1 or downstream answer quality, enabling optimization of the retrieval stage independent of other pipeline components.","intents":["I want to test whether BM25 or semantic search retrieves better passages for my domain","I need to evaluate hybrid retrieval (combining BM25 and semantic search) vs. single-strategy retrieval","I want to see which retrieval strategy (dense passage retrieval, sparse retrieval, hybrid) maximizes downstream answer quality"],"best_for":["RAG teams optimizing retrieval quality for their domain","practitioners evaluating different retrieval strategies without manual implementation","researchers studying the impact of retrieval strategy on downstream QA performance"],"limitations":["Retrieval evaluation requires ground truth retrieval annotations — cannot evaluate on queries without retrieval_gt","Retrieval strategy selection is static — all queries use the same strategy; no adaptive routing","Hybrid retrieval requires tuning combination weights — no automatic weight optimization","Retrieval latency varies by strategy — semantic search is slower than BM25; no automatic latency-accuracy trade-off"],"requires":["Python 3.9+","Retrieval module implementations (provided: BM25Retrieval, UPRRetrieval, HybridRetrieval, or custom)","Indexed vector database with embeddings","QA dataset with retrieval_gt (ground truth retrieved passage IDs) for evaluation"],"input_types":["Query (string)","Vector database index","Retrieval configuration (top_k, similarity threshold, strategy parameters)"],"output_types":["Retrieved passages (list of passage objects with text, doc_id, score)","Retrieval scores per passage","Retrieval metrics (F1, precision, recall)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-marker-inc-korea--autorag__cap_13","uri":"capability://automation.workflow.end.to.end.rag.pipeline.evaluation.and.trial.orchestration","name":"end-to-end rag pipeline evaluation and trial orchestration","description":"AutoRAG's Evaluator class orchestrates the entire evaluation workflow: loading the YAML configuration, instantiating all module variants, ingesting the corpus into the vector database, executing trials (running each module combination through the full pipeline), computing metrics, and selecting the best module per node. The framework manages trial execution, result storage, and final pipeline selection, enabling fully automated RAG optimization without manual intervention.","intents":["I want to run a complete RAG evaluation from configuration to final pipeline selection without manual steps","I need to test 100+ module combinations and automatically select the best pipeline","I want to see the full evaluation results (metrics, best modules, final pipeline) in a structured format"],"best_for":["ML engineers automating RAG optimization workflows","teams running RAG evaluations on a schedule or in CI/CD pipelines","researchers benchmarking RAG configurations across multiple datasets"],"limitations":["Trial execution is sequential by default — no built-in parallelization; large evaluations can take hours or days","Evaluator state is not persisted — interrupting an evaluation requires restarting from the beginning","No automatic resource management — large evaluations may exhaust memory or API quotas without warnings","Result storage is file-based (parquet, JSON) — no built-in database integration for large-scale tracking"],"requires":["Python 3.9+","YAML configuration file with node definitions and module parameters","QA and corpus datasets in parquet format","Vector database and embedding model configuration","All module implementations referenced in the configuration"],"input_types":["YAML configuration file","QA dataset (parquet)","Corpus dataset (parquet)","Vector database configuration","Embedding model configuration"],"output_types":["Trial results (per-module metrics)","Best module selection per node","Optimized pipeline configuration (YAML)","Evaluation summary report"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-marker-inc-korea--autorag__cap_14","uri":"capability://tool.use.integration.api.server.deployment.with.rest.endpoints.for.optimized.rag.pipelines","name":"api server deployment with rest endpoints for optimized rag pipelines","description":"AutoRAG provides an API server deployment option that exposes the optimized RAG pipeline as REST endpoints. After evaluation completes and the best pipeline is selected, users can deploy the pipeline as a web service with endpoints for querying. The API server handles request routing, passage retrieval, reranking, generation, and response formatting, enabling production deployment of optimized RAG systems.","intents":["I want to deploy my optimized RAG pipeline as a REST API for production use","I need to expose my RAG system to external applications via HTTP endpoints","I want to monitor and log RAG queries and responses for debugging and analytics"],"best_for":["teams deploying RAG systems to production","practitioners integrating RAG into larger applications via APIs","organizations needing REST endpoints for RAG queries"],"limitations":["API server is single-instance by default — no built-in load balancing or horizontal scaling","No authentication or rate limiting — requires external API gateway for production security","Response latency depends on pipeline complexity — multi-stage pipelines with LLM generation can be slow","No built-in caching — repeated queries are re-evaluated; no query result caching"],"requires":["Python 3.9+","Optimized RAG pipeline configuration from evaluation","Vector database and embedding model deployed and accessible","LLM API access (OpenAI, Anthropic, or local model) for generation","Web framework (FastAPI or similar) for API server"],"input_types":["HTTP POST requests with query parameter","Optimized pipeline configuration"],"output_types":["HTTP JSON responses with generated answer, retrieved passages, and metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-marker-inc-korea--autorag__cap_15","uri":"capability://tool.use.integration.web.interface.for.interactive.rag.pipeline.testing.and.visualization","name":"web interface for interactive rag pipeline testing and visualization","description":"AutoRAG provides a web interface for interactive testing and visualization of RAG pipelines. Users can submit queries through the web UI, see retrieved passages, reranked results, and generated answers in real-time. The interface displays pipeline execution details (which modules were used, scores, latencies) and enables debugging of pipeline behavior without code or API calls.","intents":["I want to test my optimized RAG pipeline interactively without writing code or API calls","I need to visualize which passages were retrieved and how they were reranked for a given query","I want to debug pipeline behavior by seeing intermediate results (retrieved passages, reranked passages, generated answer)"],"best_for":["non-technical stakeholders testing RAG pipelines","practitioners debugging pipeline behavior","teams demonstrating RAG capabilities to stakeholders"],"limitations":["Web interface is read-only — cannot modify pipeline configuration or retrain models","No multi-user support or authentication — not suitable for shared production environments","Visualization is limited to text and scores — no advanced analytics or metric dashboards","Performance depends on pipeline latency — slow pipelines result in slow UI responses"],"requires":["Python 3.9+","Optimized RAG pipeline configuration from evaluation","Web framework (Streamlit, Gradio, or similar) for UI","Vector database and embedding model deployed and accessible"],"input_types":["User queries (text input in web form)"],"output_types":["Generated answer (text)","Retrieved passages (list with scores)","Reranked passages (list with new scores)","Pipeline execution details (module names, latencies)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-marker-inc-korea--autorag__cap_2","uri":"capability://data.processing.analysis.synthetic.qa.dataset.generation.with.llm.based.question.synthesis.and.filtering","name":"synthetic qa dataset generation with llm-based question synthesis and filtering","description":"AutoRAG's Data Creation component generates synthetic question-answer pairs from raw documents using LLMs to synthesize questions and applying rule-based filters (e.g., dontknow_filter_rule_based) to remove low-quality pairs. The framework parses documents using pluggable parsers (langchain_parse, llamaparse), chunks them via chunkers (llama_index_chunk, langchain_chunk), and generates QA pairs with configurable LLM prompts. Filtering rules remove questions the LLM cannot answer reliably, producing a clean qa.parquet dataset with query-answer pairs and retrieval ground truth.","intents":["I have 1000 domain documents but no labeled QA pairs — I need to generate a benchmark dataset automatically","I want to create evaluation data that reflects real user questions for my knowledge base","I need to filter out low-quality synthetic questions that the LLM generated with low confidence"],"best_for":["teams building RAG systems without existing QA datasets","domain experts with raw documents but no annotation budget","researchers creating benchmarks for new RAG techniques"],"limitations":["Synthetic QA quality depends heavily on LLM capability — weaker models produce noisier datasets requiring more aggressive filtering","Filtering rules are heuristic-based and may remove valid questions or keep invalid ones; no learned filtering model","Generated questions may not reflect actual user query distribution or domain-specific phrasing patterns","LLM API costs scale linearly with document volume; generating QA for 100K documents can be expensive"],"requires":["Python 3.9+","Raw documents in supported formats (PDF, HTML, Markdown, etc.)","LLM API access (OpenAI, Anthropic, or local model) for question generation","Configured parser (langchain_parse or llamaparse) and chunker (llama_index_chunk or langchain_chunk)"],"input_types":["Raw documents (PDF, HTML, Markdown, plain text)","Chunked passages with text content","LLM model configuration and prompts"],"output_types":["qa.parquet: Parquet file with columns [query, answer, retrieval_gt, metadata]","corpus.parquet: Parquet file with columns [doc_id, contents, metadata]"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-marker-inc-korea--autorag__cap_3","uri":"capability://data.processing.analysis.multi.metric.rag.evaluation.with.strategy.based.module.selection","name":"multi-metric rag evaluation with strategy-based module selection","description":"AutoRAG evaluates RAG pipeline modules using multiple metrics (retrieval_f1, bleu, rouge, sem_score, etc.) and selects the best module per node based on a configurable strategy (e.g., mean, weighted_sum, max). The Evaluator class computes metrics for each module variant, stores results, and applies the strategy to rank modules. This enables optimization toward different objectives (e.g., maximize retrieval accuracy vs. maximize answer quality) without re-running trials.","intents":["I want to optimize for retrieval F1 score, not just answer BLEU — which retriever works best for my data?","I need to balance multiple metrics (retrieval precision, answer relevance, latency) — which module combination is best?","I want to see the full metric breakdown for each module variant to make informed decisions"],"best_for":["RAG teams with multiple optimization objectives (accuracy, latency, cost)","researchers studying metric correlation in RAG systems","practitioners needing transparency into which metrics drive module selection"],"limitations":["Metric computation adds overhead — evaluating 20 module variants × 5 metrics on 1000 queries can take hours","Strategy selection is static per node — cannot dynamically adjust strategy based on metric distributions","Some metrics require reference answers (BLEU, ROUGE) — cannot evaluate on queries without ground truth","Metric implementations may differ from standard definitions; no validation against external benchmarks"],"requires":["Python 3.9+","QA dataset with query, answer, and retrieval_gt columns","Metric implementations (provided: retrieval_f1, bleu, rouge, sem_score, etc.)","Strategy definition (mean, weighted_sum, max, or custom)"],"input_types":["Module outputs (retrieved passages, reranked passages, generated answers)","Ground truth answers and retrieval ground truth","Metric configuration (metric names, weights for weighted strategies)"],"output_types":["Per-module metric scores (dict with metric names as keys)","Ranked module list based on strategy","Best module selection per node","Metric comparison reports"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-marker-inc-korea--autorag__cap_4","uri":"capability://memory.knowledge.vector.database.integration.with.pluggable.embedding.models.and.multi.backend.support","name":"vector database integration with pluggable embedding models and multi-backend support","description":"AutoRAG abstracts vector database operations through a configurable embedding and vector store layer. The framework supports multiple vector databases (Chroma, Weaviate, Pinecone, Milvus, etc.) and embedding models (OpenAI, Hugging Face, local models) via a unified interface. During evaluation, the Evaluator ingests the corpus into the configured vector DB using the specified embedding model, enabling retrieval modules to query the same indexed data across all trials.","intents":["I want to test different embedding models (OpenAI vs. Hugging Face vs. local) and see which retrieves best for my domain","I need to use a specific vector database (Pinecone for production, Chroma for local development) without changing my RAG pipeline code","I want to evaluate retrieval performance with different embedding dimensions and similarity metrics"],"best_for":["teams evaluating embedding models for their domain","practitioners migrating between vector databases","researchers studying the impact of embedding quality on RAG performance"],"limitations":["Embedding model selection is global per evaluation run — cannot test multiple embedding models in parallel within a single trial","Vector DB ingestion is a one-time operation per evaluation; re-indexing with different embeddings requires re-running the entire evaluation","Some vector databases have API rate limits or cost implications for large corpus sizes; no built-in cost estimation","Custom embedding models require implementing the embedding interface; no automatic model discovery"],"requires":["Python 3.9+","Vector database client library (chroma, weaviate, pinecone, milvus, etc.)","Embedding model API access or local model weights (OpenAI API key, Hugging Face model, etc.)","Vector database configuration (connection string, credentials, index name)"],"input_types":["Corpus dataset with doc_id and contents columns","Embedding model configuration (model name, API key, dimension)","Vector database configuration (backend type, connection parameters)"],"output_types":["Indexed vector database with embedded passages","Retrieval results from vector similarity search","Embedding metadata (model name, dimension, similarity metric)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-marker-inc-korea--autorag__cap_5","uri":"capability://data.processing.analysis.document.parsing.and.intelligent.chunking.with.multiple.backend.support","name":"document parsing and intelligent chunking with multiple backend support","description":"AutoRAG's Data Creation component includes pluggable parsers (langchain_parse, llamaparse) that convert raw documents (PDF, HTML, Markdown) into structured text, and chunkers (llama_index_chunk, langchain_chunk) that split parsed content into semantically coherent passages. The framework handles document preprocessing, metadata extraction, and chunk size configuration, producing a corpus.parquet dataset with doc_id and contents columns ready for embedding and retrieval evaluation.","intents":["I have 500 PDF documents with mixed layouts — I need to parse them into clean text without manual preprocessing","I want to test different chunk sizes (256, 512, 1024 tokens) and see which improves retrieval accuracy","I need to preserve document structure (headings, tables) during parsing to maintain semantic coherence"],"best_for":["teams building RAG systems from unstructured documents","practitioners optimizing chunk size for domain-specific content","researchers studying the impact of parsing quality on RAG performance"],"limitations":["Parser quality varies by document type — PDFs with complex layouts may require manual cleanup","Chunking is document-agnostic — no semantic awareness of document structure; may split mid-sentence or mid-table","No built-in deduplication — duplicate passages across documents are not detected or merged","Metadata extraction is limited — only doc_id and contents are preserved; custom metadata requires manual post-processing"],"requires":["Python 3.9+","Raw documents in supported formats (PDF, HTML, Markdown, plain text)","Parser library (langchain or llamaparse) with API access if using cloud-based parsing","Chunker library (llama_index or langchain) with configurable chunk size and overlap"],"input_types":["Raw documents (PDF, HTML, Markdown, plain text files)","Chunk size configuration (tokens or characters)","Overlap configuration (for sliding window chunking)"],"output_types":["Parsed text (cleaned, structured)","Chunked passages with doc_id and contents","corpus.parquet: Parquet file with columns [doc_id, contents, metadata]"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-marker-inc-korea--autorag__cap_6","uri":"capability://text.generation.language.query.expansion.with.multiple.expansion.strategies.and.module.variants","name":"query expansion with multiple expansion strategies and module variants","description":"AutoRAG's QueryExpansion node type enables testing of multiple query expansion strategies (e.g., multi-query expansion, hypothetical document embeddings, query decomposition) as distinct modules. Each expansion module takes a user query and generates multiple related queries or reformulations, which are then passed to retrieval modules. The framework evaluates which expansion strategy (or no expansion) produces the best retrieval results, enabling data-driven decisions about query preprocessing.","intents":["I want to test whether multi-query expansion improves retrieval for ambiguous user queries","I need to evaluate query decomposition for complex, multi-hop questions","I want to see if query expansion helps or hurts retrieval latency and accuracy for my domain"],"best_for":["RAG teams working with ambiguous or complex user queries","practitioners optimizing for multi-hop reasoning tasks","researchers studying query reformulation techniques"],"limitations":["Query expansion increases latency — each expanded query requires a separate retrieval call","Expansion quality depends on LLM capability — weak models may generate irrelevant queries","No automatic evaluation of expansion quality — only end-to-end retrieval metrics are measured","Expansion strategies are query-agnostic — no adaptation based on query complexity or domain"],"requires":["Python 3.9+","QueryExpansion module implementations (provided: MultiQueryExpansion, HyDE, QueryDecomposition, or custom)","LLM API access for expansion (OpenAI, Anthropic, or local model)"],"input_types":["User query (string)","LLM model configuration","Expansion strategy parameters"],"output_types":["List of expanded queries (strings)","Original query (if no expansion)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-marker-inc-korea--autorag__cap_7","uri":"capability://search.retrieval.passage.reranking.with.multiple.ranking.models.and.scoring.strategies","name":"passage reranking with multiple ranking models and scoring strategies","description":"AutoRAG's PassageReranker node type enables testing of multiple reranking strategies (BM25-based, semantic similarity, LLM-based, learned ranking models) as distinct modules. Each reranker takes a list of retrieved passages and a query, scores them, and returns a reranked list. The framework evaluates which reranking strategy produces the best retrieval F1 or downstream answer quality, enabling optimization of the retrieval-to-generation pipeline.","intents":["I want to test whether LLM-based reranking (MonoT5, RankGPT) improves answer quality over semantic reranking","I need to evaluate the trade-off between reranking latency and retrieval accuracy","I want to see if combining multiple rerankers (ensemble) outperforms single rerankers"],"best_for":["RAG teams optimizing retrieval quality for generation","practitioners balancing latency vs. accuracy in multi-stage pipelines","researchers studying the impact of reranking on downstream QA performance"],"limitations":["LLM-based reranking adds significant latency — scoring 100 passages with an LLM can take seconds","Reranker quality depends on model training data — domain-specific rerankers may outperform general models","No automatic reranker selection based on query complexity — all queries use the same reranker","Ensemble reranking requires custom module implementation; no built-in ensemble support"],"requires":["Python 3.9+","PassageReranker module implementations (provided: BM25Reranker, MonoT5Reranker, RankGPTReranker, or custom)","Retrieved passages from retrieval node","LLM API access for LLM-based rerankers"],"input_types":["Query (string)","Retrieved passages (list of passage objects with text and metadata)","Reranker model configuration"],"output_types":["Reranked passages (list, sorted by relevance score)","Relevance scores per passage"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-marker-inc-korea--autorag__cap_8","uri":"capability://safety.moderation.passage.filtering.with.rule.based.and.learned.filtering.strategies","name":"passage filtering with rule-based and learned filtering strategies","description":"AutoRAG's PassageFilter node type enables testing of multiple filtering strategies (rule-based, similarity-based, LLM-based) to remove irrelevant or low-confidence passages before generation. Each filter module takes a list of passages and returns a filtered subset based on configurable criteria (e.g., similarity threshold, LLM confidence). The framework evaluates which filtering strategy reduces hallucination or improves answer quality without removing necessary context.","intents":["I want to filter out low-confidence passages to reduce hallucination in generated answers","I need to test different similarity thresholds and see which maximizes answer quality","I want to evaluate whether LLM-based filtering (asking the LLM to judge passage relevance) helps or hurts"],"best_for":["RAG teams struggling with hallucination or irrelevant context","practitioners optimizing context window usage for cost and latency","researchers studying the impact of context quality on generation"],"limitations":["Filtering is irreversible — removed passages cannot be recovered if they were needed","Rule-based filtering is brittle — thresholds may not generalize across domains or query types","LLM-based filtering adds latency and cost — filtering 50 passages with an LLM can be expensive","No automatic threshold tuning — users must manually set similarity or confidence thresholds"],"requires":["Python 3.9+","PassageFilter module implementations (provided: SimilarityFilter, LLMFilter, or custom)","Retrieved or reranked passages from previous nodes","Filter configuration (thresholds, criteria)"],"input_types":["Query (string)","Passages (list of passage objects with text, scores, metadata)","Filter configuration (thresholds, LLM model, criteria)"],"output_types":["Filtered passages (list, subset of input)","Filter decisions per passage (kept/removed)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-marker-inc-korea--autorag__cap_9","uri":"capability://text.generation.language.prompt.template.optimization.with.llm.based.generation.and.answer.quality.evaluation","name":"prompt template optimization with llm-based generation and answer quality evaluation","description":"AutoRAG's PromptMaker and Generator nodes enable testing of multiple prompt templates and generation strategies. The PromptMaker node constructs prompts from passages and queries using configurable templates, and the Generator node sends prompts to LLMs and evaluates generated answers against ground truth. The framework measures answer quality using metrics (BLEU, ROUGE, semantic similarity) and selects the best prompt template or generation strategy, enabling optimization of the generation stage.","intents":["I want to test different prompt templates (zero-shot, few-shot, chain-of-thought) and see which produces better answers","I need to optimize the prompt structure for my domain (e.g., medical QA vs. technical documentation)","I want to evaluate whether adding instructions or examples to the prompt improves answer quality"],"best_for":["RAG teams optimizing generation quality for their domain","practitioners experimenting with prompt engineering at scale","researchers studying the impact of prompt design on LLM performance"],"limitations":["Prompt optimization is LLM-specific — templates optimized for GPT-4 may not work well for Llama","Generation evaluation requires ground truth answers — cannot evaluate on queries without reference answers","Prompt templates are static — no dynamic adaptation based on query or passage characteristics","LLM API costs scale with number of templates and evaluation queries; 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