{"adoption": {"forks": 5260, "observed_at": "2026-08-28T04:10:42.585272+00:00", "stars": 10622}, "canonical_url": "https://ross.abutalabs.com/products/all-in-rag", "card": {"archived": false, "artifact_type": "learning-resource", "description": "🔍大模型应用开发实战一：RAG 技术全栈指南，在线阅读地址：https://datawhalechina.github.io/all-in-rag/", "domain": ["large-language-models", "tutorials", "artificial-intelligence"], "enriched": true, "function": ["rag", "llm-inference", "search-engine", "nlp"], "health_score": 77, "homepage": "https://datawhalechina.github.io/all-in-rag/", "language": "Python", "license": null, "license_family": "other", "maturity": "active", "member_repos": ["datawhalechina/all-in-rag"], "name": "datawhalechina/all-in-rag", "platform": ["python", "cross-platform"], "pushed_at": "2026-07-29T11:43:11+00:00", "repo": "datawhalechina/all-in-rag", "stars": 10622, "tags": ["rag", "tutorial", "open-course", "langchain", "llama-index", "milvus", "embeddings", "vector-database", "chinese", "datawhale", "retrieval-augmented-generation"], "topics": ["embedding", "kimi-k2", "langchain", "llama-index", "llm", "milvus", "multimodal", "rag", "ai", "neo4j", "python", "deepseek"], "urls": [], "use_cases": ["learn how to build RAG applications", "understand retrieval-augmented generation from basics to advanced", "find a hands-on RAG course with code examples", "learn to use LangChain and LlamaIndex for RAG", "build a knowledge base chatbot with vector databases", "study multimodal and graph RAG techniques"], "what_it_is": "An open-source Chinese-language tutorial (Datawhale) providing a full-stack guide to Retrieval-Augmented Generation (RAG) for LLM applications, from fundamentals to advanced practice. It includes hands-on examples using tools like LangChain, LlamaIndex, Milvus, and Neo4j, readable online.", "when_to_avoid": ["you need a production-ready RAG framework or library rather than a tutorial", "you need software with a maintained license for redistribution", "you are looking for non-Chinese-language primary content (though an English README exists)"], "when_to_choose": ["you want a structured, free curriculum for learning RAG end to end", "you prefer learning by doing with Python code examples", "you want coverage of the modern RAG stack including Milvus, Neo4j, and multimodal RAG"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/all-in-rag", "repo": "datawhalechina/all-in-rag", "role": "main", "score": 61}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:10:42.585272+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-29T17:18:27.693999+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fce446204c7892bd76185582bf91c22ddf9a8c43f81ace68d5ebe365ec06f98e", "fetched_at": "2026-08-28T04:10:42.585272+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/all-in-rag"}, {"content_hash": "f54409f5c0e02489977c4061e04087265af82904f8a7046f21d9978d0c05c0b5", "fetched_at": "2026-08-29T08:17:29.927509+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/all-in-rag/"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:10:42.585272+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-29T17:18:27.693999+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fce446204c7892bd76185582bf91c22ddf9a8c43f81ace68d5ebe365ec06f98e", "fetched_at": "2026-08-28T04:10:42.585272+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/all-in-rag"}, {"content_hash": "f54409f5c0e02489977c4061e04087265af82904f8a7046f21d9978d0c05c0b5", "fetched_at": "2026-08-29T08:17:29.927509+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/all-in-rag/"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-29T17:18:27.693999+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fce446204c7892bd76185582bf91c22ddf9a8c43f81ace68d5ebe365ec06f98e", "fetched_at": "2026-08-28T04:10:42.585272+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/all-in-rag"}, {"content_hash": "f54409f5c0e02489977c4061e04087265af82904f8a7046f21d9978d0c05c0b5", "fetched_at": "2026-08-29T08:17:29.927509+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/all-in-rag/"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:10:42.585272+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:10:42.585272+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:10:42.585272+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-29T17:18:27.693999+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fce446204c7892bd76185582bf91c22ddf9a8c43f81ace68d5ebe365ec06f98e", "fetched_at": "2026-08-28T04:10:42.585272+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/all-in-rag"}, {"content_hash": "f54409f5c0e02489977c4061e04087265af82904f8a7046f21d9978d0c05c0b5", "fetched_at": "2026-08-29T08:17:29.927509+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/all-in-rag/"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:10:42.585272+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:10:42.585272+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-29T17:18:27.693999+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fce446204c7892bd76185582bf91c22ddf9a8c43f81ace68d5ebe365ec06f98e", "fetched_at": "2026-08-28T04:10:42.585272+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/all-in-rag"}, {"content_hash": "f54409f5c0e02489977c4061e04087265af82904f8a7046f21d9978d0c05c0b5", "fetched_at": "2026-08-29T08:17:29.927509+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/all-in-rag/"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:10:42.585272+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:10:42.585272+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:10:42.585272+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-29T17:18:27.693999+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fce446204c7892bd76185582bf91c22ddf9a8c43f81ace68d5ebe365ec06f98e", "fetched_at": "2026-08-28T04:10:42.585272+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/all-in-rag"}, {"content_hash": "f54409f5c0e02489977c4061e04087265af82904f8a7046f21d9978d0c05c0b5", "fetched_at": "2026-08-29T08:17:29.927509+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/all-in-rag/"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:10:42.585272+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:10:42.585272+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-29T17:18:27.693999+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fce446204c7892bd76185582bf91c22ddf9a8c43f81ace68d5ebe365ec06f98e", "fetched_at": "2026-08-28T04:10:42.585272+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/all-in-rag"}, {"content_hash": "f54409f5c0e02489977c4061e04087265af82904f8a7046f21d9978d0c05c0b5", "fetched_at": "2026-08-29T08:17:29.927509+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/all-in-rag/"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-29T17:18:27.693999+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fce446204c7892bd76185582bf91c22ddf9a8c43f81ace68d5ebe365ec06f98e", "fetched_at": "2026-08-28T04:10:42.585272+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/all-in-rag"}, {"content_hash": "f54409f5c0e02489977c4061e04087265af82904f8a7046f21d9978d0c05c0b5", "fetched_at": "2026-08-29T08:17:29.927509+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/all-in-rag/"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-29T17:18:27.693999+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fce446204c7892bd76185582bf91c22ddf9a8c43f81ace68d5ebe365ec06f98e", "fetched_at": "2026-08-28T04:10:42.585272+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/all-in-rag"}, {"content_hash": "f54409f5c0e02489977c4061e04087265af82904f8a7046f21d9978d0c05c0b5", "fetched_at": "2026-08-29T08:17:29.927509+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/all-in-rag/"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-29T17:18:27.693999+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fce446204c7892bd76185582bf91c22ddf9a8c43f81ace68d5ebe365ec06f98e", "fetched_at": "2026-08-28T04:10:42.585272+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/all-in-rag"}, {"content_hash": "f54409f5c0e02489977c4061e04087265af82904f8a7046f21d9978d0c05c0b5", "fetched_at": "2026-08-29T08:17:29.927509+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/all-in-rag/"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 95, "longevity": 32, "rhythm": 35}, "computed_at": "2026-09-02T17:46:02.011165+00:00", "flags": ["no_releases", "no_license"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 454, "days_push": 35, "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}}