{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HCIT3CN3V7EFHQCG3XGJAHTALG","short_pith_number":"pith:HCIT3CN3","schema_version":"1.0","canonical_sha256":"38913d89bbafc853c046ddcc901e605999eefdd589b7af1574dabefad089abe3","source":{"kind":"arxiv","id":"2502.02464","version":3},"attestation_state":"computed","paper":{"title":"Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Abdelrahman Abdallah, Adam Jatowt, Bhawna Piryani, Jamshid Mozafari, Mohammed Ali","submitted_at":"2025-02-04T16:33:25Z","abstract_excerpt":"Retrieval, re-ranking, and retrieval-augmented generation (RAG) are critical components of modern applications in information retrieval, question answering, or knowledge-based text generation. However, existing solutions are often fragmented, lacking a unified framework that easily integrates these essential processes. The absence of a standardized implementation, coupled with the complexity of retrieval and re-ranking workflows, makes it challenging for researchers to compare and evaluate different approaches in a consistent environment. While existing toolkits such as Rerankers and RankLLM p"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2502.02464","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-02-04T16:33:25Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"0411a4ed1b56c1064e5abd5b1182c228c5b5ef0a05de9db1eba5a1c8784c3e5f","abstract_canon_sha256":"d55a34ae2cb8a93942cac8b941e8d115f9b18b6ecdc88060b2eee3bc8f690011"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:17:15.865389Z","signature_b64":"DuKhaUSmB/HY3a5Lh0Jn1ExjcA1DkVnqpwaE20BpDv8yMbbpTU7Gdoi5P8RLSk0Ryes7u7CflKeURfh7TMcbAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38913d89bbafc853c046ddcc901e605999eefdd589b7af1574dabefad089abe3","last_reissued_at":"2026-07-05T10:17:15.864815Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:17:15.864815Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Abdelrahman Abdallah, Adam Jatowt, Bhawna Piryani, Jamshid Mozafari, Mohammed Ali","submitted_at":"2025-02-04T16:33:25Z","abstract_excerpt":"Retrieval, re-ranking, and retrieval-augmented generation (RAG) are critical components of modern applications in information retrieval, question answering, or knowledge-based text generation. However, existing solutions are often fragmented, lacking a unified framework that easily integrates these essential processes. The absence of a standardized implementation, coupled with the complexity of retrieval and re-ranking workflows, makes it challenging for researchers to compare and evaluate different approaches in a consistent environment. While existing toolkits such as Rerankers and RankLLM p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.02464","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.02464/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2502.02464","created_at":"2026-07-05T10:17:15.864883+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.02464v3","created_at":"2026-07-05T10:17:15.864883+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.02464","created_at":"2026-07-05T10:17:15.864883+00:00"},{"alias_kind":"pith_short_12","alias_value":"HCIT3CN3V7EF","created_at":"2026-07-05T10:17:15.864883+00:00"},{"alias_kind":"pith_short_16","alias_value":"HCIT3CN3V7EFHQCG","created_at":"2026-07-05T10:17:15.864883+00:00"},{"alias_kind":"pith_short_8","alias_value":"HCIT3CN3","created_at":"2026-07-05T10:17:15.864883+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2507.13334","citing_title":"A Survey of Context Engineering for Large Language Models","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.03676","citing_title":"Are LLM-Based Retrievers Worth Their Cost? An Empirical Study of Efficiency, Robustness, and Reasoning Overhead","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07220","citing_title":"HIVE: Query, Hypothesize, Verify An LLM Framework for Multimodal Reasoning-Intensive Retrieval","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07201","citing_title":"BRIDGE: Multimodal-to-Text Retrieval via Reinforcement-Learned Query Alignment","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07079","citing_title":"MARVEL: Multimodal Adaptive Reasoning-intensiVe Expand-rerank and retrievaL","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05766","citing_title":"The LLM Effect on IR Benchmarks: A Meta-Analysis of Effectiveness, Baselines, and Contamination","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HCIT3CN3V7EFHQCG3XGJAHTALG","json":"https://pith.science/pith/HCIT3CN3V7EFHQCG3XGJAHTALG.json","graph_json":"https://pith.science/api/pith-number/HCIT3CN3V7EFHQCG3XGJAHTALG/graph.json","events_json":"https://pith.science/api/pith-number/HCIT3CN3V7EFHQCG3XGJAHTALG/events.json","paper":"https://pith.science/paper/HCIT3CN3"},"agent_actions":{"view_html":"https://pith.science/pith/HCIT3CN3V7EFHQCG3XGJAHTALG","download_json":"https://pith.science/pith/HCIT3CN3V7EFHQCG3XGJAHTALG.json","view_paper":"https://pith.science/paper/HCIT3CN3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.02464&json=true","fetch_graph":"https://pith.science/api/pith-number/HCIT3CN3V7EFHQCG3XGJAHTALG/graph.json","fetch_events":"https://pith.science/api/pith-number/HCIT3CN3V7EFHQCG3XGJAHTALG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HCIT3CN3V7EFHQCG3XGJAHTALG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HCIT3CN3V7EFHQCG3XGJAHTALG/action/storage_attestation","attest_author":"https://pith.science/pith/HCIT3CN3V7EFHQCG3XGJAHTALG/action/author_attestation","sign_citation":"https://pith.science/pith/HCIT3CN3V7EFHQCG3XGJAHTALG/action/citation_signature","submit_replication":"https://pith.science/pith/HCIT3CN3V7EFHQCG3XGJAHTALG/action/replication_record"}},"created_at":"2026-07-05T10:17:15.864883+00:00","updated_at":"2026-07-05T10:17:15.864883+00:00"}