{"adoption": {"forks": 637, "observed_at": "2026-08-28T04:03:44.479307+00:00", "stars": 1140}, "canonical_url": "https://ross.abutalabs.com/products/langchain-experiments", "card": {"archived": false, "artifact_type": "learning-resource", "description": "Building Apps with LLMs", "domain": ["large-language-models", "artificial-intelligence", "tutorials", "developer-tools"], "enriched": true, "function": ["agent-framework", "rag", "chatbot", "llm-inference", "prompt-engineering", "search-engine"], "health_score": 20, "homepage": "https://datalumina.com", "language": "Jupyter Notebook", "license": "MIT", "license_family": "permissive", "maturity": "experimental", "member_repos": ["daveebbelaar/langchain-experiments"], "name": "daveebbelaar/langchain-experiments", "platform": ["python", "cross-platform"], "pushed_at": "2024-02-11T11:23:23+00:00", "repo": "daveebbelaar/langchain-experiments", "stars": 1140, "tags": ["langchain", "openai", "faiss", "jupyter-notebooks", "experiments", "youtube-transcripts", "slack-bot", "example-code", "ai-agents", "retrieval-augmented-generation"], "topics": ["ai", "langchain", "langchain-python", "python", "slack-bot"], "urls": [], "use_cases": ["learn how to build apps with langchain and llms", "create a searchable database from a youtube video transcript", "run similarity search over documents with faiss", "build a question-answering bot over custom text data", "experiment with langchain agents and memory", "prototype a slack bot powered by an llm"], "what_it_is": "A collection of Jupyter Notebook experiments demonstrating how to build LLM-powered applications with the LangChain library and OpenAI models. It includes examples such as creating a searchable database from YouTube transcripts, similarity search with FAISS, and building agents and chatbots.", "when_to_avoid": ["you need a production-ready, maintained library or application", "you want a stable API rather than evolving experiment code", "you do not want to depend on OpenAI and SerpAPI keys"], "when_to_choose": ["you are learning LangChain and want runnable example notebooks", "you want to prototype LLM apps like transcript search or chatbots quickly", "you need reference code for combining OpenAI models with custom data"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/langchain-experiments", "repo": "daveebbelaar/langchain-experiments", "role": "main", "score": 30}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:03:44.479307+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T06:35:29.613001+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "16257ca3d2a2c1b0a54f45a85ac19aafb020d4a78caddef076bfde87fe9c94ca", "fetched_at": "2026-08-28T04:03:44.479307+00:00", "kind": "readme", "missing": false, "url": "https://github.com/daveebbelaar/langchain-experiments"}, {"content_hash": "e936d240375234968cf344afcd5c14221743669262c00b8c1734eafbc8dd136b", "fetched_at": "2026-08-29T12:40:41.742080+00:00", "kind": "homepage", "missing": false, "url": "https://datalumina.com"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:03:44.479307+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T06:35:29.613001+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "16257ca3d2a2c1b0a54f45a85ac19aafb020d4a78caddef076bfde87fe9c94ca", "fetched_at": "2026-08-28T04:03:44.479307+00:00", "kind": "readme", "missing": false, "url": "https://github.com/daveebbelaar/langchain-experiments"}, {"content_hash": "e936d240375234968cf344afcd5c14221743669262c00b8c1734eafbc8dd136b", "fetched_at": "2026-08-29T12:40:41.742080+00:00", "kind": "homepage", "missing": false, "url": "https://datalumina.com"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T06:35:29.613001+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "16257ca3d2a2c1b0a54f45a85ac19aafb020d4a78caddef076bfde87fe9c94ca", "fetched_at": "2026-08-28T04:03:44.479307+00:00", "kind": "readme", "missing": false, "url": "https://github.com/daveebbelaar/langchain-experiments"}, {"content_hash": "e936d240375234968cf344afcd5c14221743669262c00b8c1734eafbc8dd136b", "fetched_at": "2026-08-29T12:40:41.742080+00:00", "kind": "homepage", "missing": false, "url": "https://datalumina.com"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:03:44.479307+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:03:44.479307+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:03:44.479307+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T06:35:29.613001+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "16257ca3d2a2c1b0a54f45a85ac19aafb020d4a78caddef076bfde87fe9c94ca", "fetched_at": "2026-08-28T04:03:44.479307+00:00", "kind": "readme", "missing": false, "url": "https://github.com/daveebbelaar/langchain-experiments"}, {"content_hash": "e936d240375234968cf344afcd5c14221743669262c00b8c1734eafbc8dd136b", "fetched_at": "2026-08-29T12:40:41.742080+00:00", "kind": "homepage", "missing": false, "url": "https://datalumina.com"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:03:44.479307+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:03:44.479307+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T06:35:29.613001+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "16257ca3d2a2c1b0a54f45a85ac19aafb020d4a78caddef076bfde87fe9c94ca", "fetched_at": "2026-08-28T04:03:44.479307+00:00", "kind": "readme", "missing": false, "url": "https://github.com/daveebbelaar/langchain-experiments"}, {"content_hash": "e936d240375234968cf344afcd5c14221743669262c00b8c1734eafbc8dd136b", "fetched_at": "2026-08-29T12:40:41.742080+00:00", "kind": "homepage", "missing": false, "url": "https://datalumina.com"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:03:44.479307+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:03:44.479307+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:03:44.479307+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T06:35:29.613001+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "16257ca3d2a2c1b0a54f45a85ac19aafb020d4a78caddef076bfde87fe9c94ca", "fetched_at": "2026-08-28T04:03:44.479307+00:00", "kind": "readme", "missing": false, "url": "https://github.com/daveebbelaar/langchain-experiments"}, {"content_hash": "e936d240375234968cf344afcd5c14221743669262c00b8c1734eafbc8dd136b", "fetched_at": "2026-08-29T12:40:41.742080+00:00", "kind": "homepage", "missing": false, "url": "https://datalumina.com"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:03:44.479307+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:03:44.479307+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T06:35:29.613001+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "16257ca3d2a2c1b0a54f45a85ac19aafb020d4a78caddef076bfde87fe9c94ca", "fetched_at": "2026-08-28T04:03:44.479307+00:00", "kind": "readme", "missing": false, "url": "https://github.com/daveebbelaar/langchain-experiments"}, {"content_hash": "e936d240375234968cf344afcd5c14221743669262c00b8c1734eafbc8dd136b", "fetched_at": "2026-08-29T12:40:41.742080+00:00", "kind": "homepage", "missing": false, "url": "https://datalumina.com"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T06:35:29.613001+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "16257ca3d2a2c1b0a54f45a85ac19aafb020d4a78caddef076bfde87fe9c94ca", "fetched_at": "2026-08-28T04:03:44.479307+00:00", "kind": "readme", "missing": false, "url": "https://github.com/daveebbelaar/langchain-experiments"}, {"content_hash": "e936d240375234968cf344afcd5c14221743669262c00b8c1734eafbc8dd136b", "fetched_at": "2026-08-29T12:40:41.742080+00:00", "kind": "homepage", "missing": false, "url": "https://datalumina.com"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T06:35:29.613001+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "16257ca3d2a2c1b0a54f45a85ac19aafb020d4a78caddef076bfde87fe9c94ca", "fetched_at": "2026-08-28T04:03:44.479307+00:00", "kind": "readme", "missing": false, "url": "https://github.com/daveebbelaar/langchain-experiments"}, {"content_hash": "e936d240375234968cf344afcd5c14221743669262c00b8c1734eafbc8dd136b", "fetched_at": "2026-08-29T12:40:41.742080+00:00", "kind": "homepage", "missing": false, "url": "https://datalumina.com"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T06:35:29.613001+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "16257ca3d2a2c1b0a54f45a85ac19aafb020d4a78caddef076bfde87fe9c94ca", "fetched_at": "2026-08-28T04:03:44.479307+00:00", "kind": "readme", "missing": false, "url": "https://github.com/daveebbelaar/langchain-experiments"}, {"content_hash": "e936d240375234968cf344afcd5c14221743669262c00b8c1734eafbc8dd136b", "fetched_at": "2026-08-29T12:40:41.742080+00:00", "kind": "homepage", "missing": false, "url": "https://datalumina.com"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 87, "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": 1231, "days_push": 934, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 30, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}