{"adoption": {"forks": 291, "observed_at": "2026-08-28T04:05:59.563732+00:00", "stars": 1962}, "canonical_url": "https://ross.abutalabs.com/products/cortex-ai-super-rag", "card": {"archived": false, "artifact_type": "application", "description": "CORTEX RAG is an enterprise retrieval and knowledge assistant that helps teams find accurate answers from company data with citations, permission-aware retrieval, and fast deployment", "domain": ["large-language-models", "artificial-intelligence", "pdf"], "enriched": true, "function": ["rag", "search-engine", "llm-inference", "chatbot", "pdf", "nlp"], "health_score": 74, "homepage": null, "language": "Python", "license": "MIT", "license_family": "permissive", "maturity": "active", "member_repos": ["SaiAkhil066/CORTEX-AI-SUPER-RAG"], "name": "SaiAkhil066/CORTEX-AI-SUPER-RAG", "platform": ["python", "cross-platform", "self-hosted"], "pushed_at": "2026-06-25T05:00:22+00:00", "repo": "SaiAkhil066/CORTEX-AI-SUPER-RAG", "stars": 1962, "tags": ["rag-fusion", "graphrag", "corrective-rag", "ollama", "streamlit", "local-llm", "knowledge-graph", "citations", "privacy-first", "retrieval-augmented-generation", "natural-language-processing"], "topics": [], "urls": [], "use_cases": ["ask questions about pdf documents with citations", "build a local rag chatbot without api keys", "chat with company documents privately offline", "retrieve answers from documents using knowledge graphs", "run a document question answering system with ollama", "improve rag recall with query fusion and reranking"], "what_it_is": "CORTEX RAG is a local-first, agentic retrieval-augmented generation application that lets users upload PDFs and ask questions with cited answers, running entirely on their machine via Ollama. It combines nine RAG techniques including contextual retrieval, RAG-Fusion with reciprocal rank fusion, GraphRAG knowledge graphs, and corrective RAG chunk grading.", "when_to_avoid": ["you need a managed cloud RAG service with enterprise SSO", "you have no hardware to run local LLMs", "you need non-PDF enterprise connectors out of the box"], "when_to_choose": ["you need private, on-machine document Q&A with no cloud upload", "you want advanced RAG techniques like GraphRAG and CRAG in one pipeline", "you want cited answers from PDFs using local LLMs"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/cortex-ai-super-rag", "repo": "SaiAkhil066/CORTEX-AI-SUPER-RAG", "role": "main", "score": 61}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:05:59.563732+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T03:05:34.667456+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "803c6b38d3daa58d98568b3ab1a0ccdee631dbcff24b7e332c8e105ceb6ab0b8", "fetched_at": "2026-08-28T04:05:59.563732+00:00", "kind": "readme", "missing": false, "url": "https://github.com/SaiAkhil066/CORTEX-AI-SUPER-RAG"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:05:59.563732+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T03:05:34.667456+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "803c6b38d3daa58d98568b3ab1a0ccdee631dbcff24b7e332c8e105ceb6ab0b8", "fetched_at": "2026-08-28T04:05:59.563732+00:00", "kind": "readme", "missing": false, "url": "https://github.com/SaiAkhil066/CORTEX-AI-SUPER-RAG"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T03:05:34.667456+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "803c6b38d3daa58d98568b3ab1a0ccdee631dbcff24b7e332c8e105ceb6ab0b8", "fetched_at": "2026-08-28T04:05:59.563732+00:00", "kind": "readme", "missing": false, "url": "https://github.com/SaiAkhil066/CORTEX-AI-SUPER-RAG"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:05:59.563732+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:05:59.563732+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:05:59.563732+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T03:05:34.667456+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "803c6b38d3daa58d98568b3ab1a0ccdee631dbcff24b7e332c8e105ceb6ab0b8", "fetched_at": "2026-08-28T04:05:59.563732+00:00", "kind": "readme", "missing": false, "url": "https://github.com/SaiAkhil066/CORTEX-AI-SUPER-RAG"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:05:59.563732+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:05:59.563732+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T03:05:34.667456+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "803c6b38d3daa58d98568b3ab1a0ccdee631dbcff24b7e332c8e105ceb6ab0b8", "fetched_at": "2026-08-28T04:05:59.563732+00:00", "kind": "readme", "missing": false, "url": "https://github.com/SaiAkhil066/CORTEX-AI-SUPER-RAG"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:05:59.563732+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:05:59.563732+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:05:59.563732+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T03:05:34.667456+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "803c6b38d3daa58d98568b3ab1a0ccdee631dbcff24b7e332c8e105ceb6ab0b8", "fetched_at": "2026-08-28T04:05:59.563732+00:00", "kind": "readme", "missing": false, "url": "https://github.com/SaiAkhil066/CORTEX-AI-SUPER-RAG"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:05:59.563732+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:05:59.563732+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T03:05:34.667456+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "803c6b38d3daa58d98568b3ab1a0ccdee631dbcff24b7e332c8e105ceb6ab0b8", "fetched_at": "2026-08-28T04:05:59.563732+00:00", "kind": "readme", "missing": false, "url": "https://github.com/SaiAkhil066/CORTEX-AI-SUPER-RAG"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T03:05:34.667456+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "803c6b38d3daa58d98568b3ab1a0ccdee631dbcff24b7e332c8e105ceb6ab0b8", "fetched_at": "2026-08-28T04:05:59.563732+00:00", "kind": "readme", "missing": false, "url": "https://github.com/SaiAkhil066/CORTEX-AI-SUPER-RAG"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T03:05:34.667456+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "803c6b38d3daa58d98568b3ab1a0ccdee631dbcff24b7e332c8e105ceb6ab0b8", "fetched_at": "2026-08-28T04:05:59.563732+00:00", "kind": "readme", "missing": false, "url": "https://github.com/SaiAkhil066/CORTEX-AI-SUPER-RAG"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T03:05:34.667456+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "803c6b38d3daa58d98568b3ab1a0ccdee631dbcff24b7e332c8e105ceb6ab0b8", "fetched_at": "2026-08-28T04:05:59.563732+00:00", "kind": "readme", "missing": false, "url": "https://github.com/SaiAkhil066/CORTEX-AI-SUPER-RAG"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 89, "longevity": 41, "rhythm": 35}, "computed_at": "2026-09-02T17:46:02.011165+00:00", "flags": ["no_releases"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 580, "days_push": 69, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 61, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}