{"data":{"slug":"giovannipasq-agentic-rag-for-dummies","name":"agentic-rag-for-dummies","tagline":"A modular Agentic RAG built with LangGraph for learning Retrieval-Augmented Generation Agents","github_url":"https://github.com/GiovanniPasq/agentic-rag-for-dummies","owner":"GiovanniPasq","repo":"agentic-rag-for-dummies","owner_avatar_url":"https://avatars.githubusercontent.com/u/33225259?v=4","primary_language":"Jupyter Notebook","stars":4188,"forks":552,"topics":["agent","agentic-ai","agentic-rag","agents","ai-agents","bm25","generative-ai","gradio","langchain","langgraph","llm","ollama","qdrant","rag","rag-agents","rag-chatbot","rag-pipeline","retrieval-augmented-generation","retrieval-augmented-generation-rag"],"archived":false,"github_pushed_at":"2026-08-30T10:19:02+00:00","maintenance_label":"Active","stars_delta_30d":295,"url":"https://www.graphcanon.com/tools/giovannipasq-agentic-rag-for-dummies","markdown_url":"https://www.graphcanon.com/tools/giovannipasq-agentic-rag-for-dummies.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/giovannipasq-agentic-rag-for-dummies","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=giovannipasq-agentic-rag-for-dummies","description":"A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.","homepage_url":null,"license":"MIT","open_issues":0,"watchers":26,"ai_summary":"This repository offers a guide and codebase to build an agentic retrieval-augmented generation system using LangGraph and Ollama models, aimed at simplifying the process of setting up such agents.","readme_excerpt":"# Install Ollama from https://ollama.com\nollama pull granite4.1:8b\n```\n\n```python\nfrom langchain_ollama import ChatOllama\n\nllm = ChatOllama(model=\"granite4.1:8b\", temperature=0, seed=42)\n```\n> ⚠️ For reliable tool calling and instruction following, prefer models **8B+**. Smaller models may ignore retrieval instructions or hallucinate. See [Troubleshooting](#troubleshooting).\n\n---\n\n---\n\n## Installation & Usage\n\nSample pdf files can be found here: [javascript](https://www.tutorialspoint.com/javascript/javascript_tutorial.pdf), [blockchain](https://blockchain-observatory.ec.europa.eu/document/download/1063effa-59cc-4df4-aeee-d2cf94f69178_en?filename=Blockchain_For_Beginners_A_EUBOF_Guide.pdf), [fortinet](https://www.commoncriteriaportal.org/files/epfiles/Fortinet%20FortiGate_EAL4_ST_V1.5.pdf(320893)_TMP.pdf).\n\n---\n\n### Option 3: Docker Deployment\n\nSee [`project/README.md`](./project/README.md#Docker-Deployment) for full Docker instructions and system requirements.","github_created_at":"2025-10-13T18:51:41+00:00","created_at":"2026-07-15T11:17:10.161246+00:00","updated_at":"2026-09-20T05:14:36.204813+00:00","categories":[{"slug":"ai-agents","name":"AI Agents","url":"https://www.graphcanon.com/categories/ai-agents","markdown_url":"https://www.graphcanon.com/categories/ai-agents.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/ai-agents"},{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"}],"tags":[{"slug":"agent","name":"agent"},{"slug":"agentic-ai","name":"agentic-ai"},{"slug":"bm25","name":"bm25"},{"slug":"gradio","name":"gradio"},{"slug":"langchain","name":"langchain"},{"slug":"llm","name":"llm"},{"slug":"ollama","name":"ollama"},{"slug":"rag","name":"rag"}],"trust":{"provenance":{"is_fork":false,"github_id":1075623145,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-20T05:14:34.400Z","maintenance":{"label":"Active","score":82,"methodology":"github_public_v1","releases_90d":0,"days_since_push":20,"last_release_at":"2026-06-21T11:53:13Z","stars_delta_30d":295,"open_issues_delta_30d":0},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":2,"high_count":0,"last_scan_at":"2026-07-15T11:17:11.750Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-20T05:14:35.440Z"},"languages":{"value":["jupyter notebook"],"source":"github.language","observed_at":"2026-09-20T05:14:35.440Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-09-20T05:14:35.440Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When aiming to quickly develop a retrieval-augmented generation agent, thanks to its streamlined setup with LangGraph.","For projects needing detailed instruction-following from tools, given preference for larger models that ensure reliable performance."],"when_not_to_use":["If smaller language model sizes are required as they might ignore retrieval instructions or hallucinate details.","Projects sensitive about Docker and system requirements must carefully review the outlined conditions for deployment."],"source":"enrich:decision_facts","observed_at":"2026-07-17T12:20:41.338Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Agentic RAG for Dummies simplifies the setup of retrieval-augmented generation agents using LangGraph and Ollama models."}]}}