{"id":14964701,"url":"https://github.com/nisaaragharia/advanced_rag","last_synced_at":"2025-04-06T19:11:29.357Z","repository":{"id":230208768,"uuid":"778768633","full_name":"NisaarAgharia/Advanced_RAG","owner":"NisaarAgharia","description":"Advanced Retrieval-Augmented Generation (RAG) through practical  notebooks, using the power of the Langchain, OpenAI GPTs ,META LLAMA3 ,Agents.","archived":false,"fork":false,"pushed_at":"2024-04-26T07:54:32.000Z","size":4173,"stargazers_count":310,"open_issues_count":1,"forks_count":61,"subscribers_count":6,"default_branch":"main","last_synced_at":"2025-04-06T19:11:24.349Z","etag":null,"topics":["agent","agents","ai","chatgpt","genai","langchain","llama3","llm","machine-learning","nlp","openai","rag","retrival-augmented","vectordb"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/NisaarAgharia.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-03-28T11:13:49.000Z","updated_at":"2025-04-06T13:01:34.000Z","dependencies_parsed_at":"2024-08-14T08:19:19.312Z","dependency_job_id":null,"html_url":"https://github.com/NisaarAgharia/Advanced_RAG","commit_stats":{"total_commits":43,"total_committers":1,"mean_commits":43.0,"dds":0.0,"last_synced_commit":"38956ae5959dad0b48d2a2931c380a405c5b1855"},"previous_names":["nisaaragharia/rag_from_scratch","nisaaragharia/advanced_rag_from_scratch"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NisaarAgharia%2FAdvanced_RAG","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NisaarAgharia%2FAdvanced_RAG/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NisaarAgharia%2FAdvanced_RAG/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NisaarAgharia%2FAdvanced_RAG/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/NisaarAgharia","download_url":"https://codeload.github.com/NisaarAgharia/Advanced_RAG/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247535517,"owners_count":20954576,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["agent","agents","ai","chatgpt","genai","langchain","llama3","llm","machine-learning","nlp","openai","rag","retrival-augmented","vectordb"],"created_at":"2024-09-24T13:33:40.008Z","updated_at":"2025-04-06T19:11:29.335Z","avatar_url":"https://github.com/NisaarAgharia.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Dive into the world of advanced language understanding with `Advanced_RAG`. These Python notebooks offer a guided tour of Retrieval-Augmented Generation (RAG) using the Langchain framework, perfect for enhancing Large Language Models (LLMs) with rich, contextual knowledge.\n\n## Architecture Flows\n### Basic RAG :\nUnderstand the journey of a query through RAG, from user input to the final generated response, all depicted in a clear, visual flow.\n![RAG_User_Flow](https://github.com/NisaarAgharia/RAG_From_Scratch/assets/22457544/dc390fc3-5c41-4c8e-b16e-268606a8f4ed)\n\n### Advanced RAG Techniques :\nExplore the intricate components that make up an advanced RAG system, from query construction to generation.\n![Advanced RAG Components](https://github.com/NisaarAgharia/RAG_From_Scratch/assets/22457544/281e8c66-a33f-485f-ad75-e8d450ccba98)\n\n### 02. Multi Query Retriever :\nGet to grips with the Multi Query Retriever structure, which enhances the retrieval process by selecting the best responses from multiple sources.\n![MQR](https://github.com/NisaarAgharia/RAG_From_Scratch/assets/22457544/5c0db3f0-59e4-4278-af6f-4120a3bb5637)\n\n### 06. Self-Reflection-RAG :\n![self-Rag](https://github.com/NisaarAgharia/Advanced_RAG/assets/22457544/2e58751b-c986-4137-8f85-9294301c3f79)\n\n### 07. Agentic RAG :\n![download](https://github.com/NisaarAgharia/Advanced_RAG/assets/22457544/4258e17e-7dfa-48da-a5b5-753b3de5d1bc)\n\n### 08. Adaptive Agentic RAG :\n![adaptive_rag_agent](https://github.com/NisaarAgharia/Advanced_RAG/assets/22457544/283a734d-bd00-4431-8982-fc5e6ce8f15c)\n\n### 09. Corrective Agentic RAG :\n![correctiveRAG](https://github.com/NisaarAgharia/Advanced_RAG/assets/22457544/68968fa8-0b0e-46ca-a80e-b30645b1e31b)\n\n### 10. LLAMA 3 Agentic RAG Local:\n![LLAMA3_AGent](https://github.com/NisaarAgharia/Advanced_RAG/assets/22457544/a9408eea-814f-416e-a8f6-aec361410719)\n\n## Notebooks Overview\nBelow is a detailed overview of each notebook present in this repository:\n\n- **01_Introduction_To_RAG.ipynb**\n  - _Basic process of building RAG app(s)_\n- **02_Query_Transformations.ipynb**\n  - _Techniques for Modifying Questions for Retrieval_\n- **03_Routing_To_Datasources.ipynb**\n  - _Create Routing Mechanism for LLM to select the correct data Source_\n- **04_Indexing_To_VectorDBs.ipynb**\n  - _Various Indexing Methods in the Vector DB_\n- **05_Retrieval_Mechanisms.ipynb**\n  - _Reranking, RaG Fusion, and other Techniques_\n- **06_Self_Reflection_Rag.ipynb**\n  - _RAG that has self-reflection / self-grading on retrieved documents and generations._\n- **07_Agentic_Rag.ipynb**\n  - _RAG that has agentic Flow on retrieved documents and generations._\n- **08_Adaptive_Agentic_Rag.ipynb**\n  - _RAG that has adaptive agentic Flow._\n- **09_Corrective_Agentic_Rag.ipynb**\n  - _RAG that has corrective agentic Flow on retrieved documents and generations._\n- **10_LLAMA_3_Rag_Agent_Local.ipynb**\n  - _LLAMA 3 8B Agent Rag that works Locally._\n\n\nEnhance your LLMs with the powerful combination of RAG and Langchain for more informed and accurate natural language generation.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnisaaragharia%2Fadvanced_rag","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnisaaragharia%2Fadvanced_rag","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnisaaragharia%2Fadvanced_rag/lists"}