{"id":20317325,"url":"https://github.com/mahikshith/langchain_rag_document_app","last_synced_at":"2025-03-04T09:16:06.882Z","repository":{"id":250901629,"uuid":"835743456","full_name":"mahikshith/Langchain_RAG_document_app","owner":"mahikshith","description":"used gemini-pro as base LLM","archived":false,"fork":false,"pushed_at":"2024-07-30T15:03:54.000Z","size":6900,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-14T13:16:32.165Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","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/mahikshith.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":"2024-07-30T12:52:22.000Z","updated_at":"2024-07-30T15:03:58.000Z","dependencies_parsed_at":"2024-07-30T18:58:26.416Z","dependency_job_id":"bfcbd250-8fb6-4d9f-a128-8b287c9d3677","html_url":"https://github.com/mahikshith/Langchain_RAG_document_app","commit_stats":null,"previous_names":["mahikshith/langchain_rag_document_app"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mahikshith%2FLangchain_RAG_document_app","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mahikshith%2FLangchain_RAG_document_app/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mahikshith%2FLangchain_RAG_document_app/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mahikshith%2FLangchain_RAG_document_app/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mahikshith","download_url":"https://codeload.github.com/mahikshith/Langchain_RAG_document_app/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241818890,"owners_count":20025210,"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":[],"created_at":"2024-11-14T18:30:59.551Z","updated_at":"2025-03-04T09:16:06.863Z","avatar_url":"https://github.com/mahikshith.png","language":"Python","readme":"# Langchain_RAG_app\n \nRetrival augumented generation :\n\nRetrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response.  \n\nWhich means we can use the power LLMs to make it do something outside it's training knowledege.\n\nIn this exmaple we use GEMINI pro as think tank , FAISS as vector store to generate and store embeddings.\n\nUser query (based on the pdf) is taken as input and we retive the embeddings from the vector store and use it as context for the LLM to generate the answer.\n\nThis approach helps in improving the accuracy and relevance of the generated response.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmahikshith%2Flangchain_rag_document_app","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmahikshith%2Flangchain_rag_document_app","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmahikshith%2Flangchain_rag_document_app/lists"}