{"id":18710811,"url":"https://github.com/sayamalt/content-engine","last_synced_at":"2026-05-18T09:31:30.879Z","repository":{"id":246733932,"uuid":"822203566","full_name":"SayamAlt/Content-Engine","owner":"SayamAlt","description":"Successfully designed and developed a system which analyzes and compares multiple PDF documents, specifically identifying and highlighting their differences.","archived":false,"fork":false,"pushed_at":"2024-06-30T15:25:11.000Z","size":12423,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-09-10T12:21:47.130Z","etag":null,"topics":["chromadb","content-engineering","langchain-python","llm","rag"],"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/SayamAlt.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":"2024-06-30T15:12:58.000Z","updated_at":"2025-03-25T15:02:39.000Z","dependencies_parsed_at":null,"dependency_job_id":"b720b35a-41e4-4eec-b955-5ad8b9877ca4","html_url":"https://github.com/SayamAlt/Content-Engine","commit_stats":null,"previous_names":["sayamalt/content-engine"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/SayamAlt/Content-Engine","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayamAlt%2FContent-Engine","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayamAlt%2FContent-Engine/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayamAlt%2FContent-Engine/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayamAlt%2FContent-Engine/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SayamAlt","download_url":"https://codeload.github.com/SayamAlt/Content-Engine/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayamAlt%2FContent-Engine/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33172576,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-18T09:27:30.708Z","status":"ssl_error","status_checked_at":"2026-05-18T09:27:28.300Z","response_time":71,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["chromadb","content-engineering","langchain-python","llm","rag"],"created_at":"2024-11-07T12:35:44.977Z","updated_at":"2026-05-18T09:31:30.857Z","avatar_url":"https://github.com/SayamAlt.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Content Engine Documentation\n\n### Overview\n\nThe Content Engine is designed to analyze and compare multiple PDF documents using Retrieval Augmented Generation (RAG) techniques. It integrates a backend framework, vector store, embedding model, and local language model (LLM), along with a Streamlit frontend for user interaction.\n\n### 1. Setup\n#### a) Backend Framework\n\u003col type='a'\u003e\n  \u003cli\u003eLangChain\n  A powerful toolkit for building LLM applications with a focus on retrieval-augmented generation.\n  Installation instructions: pip install langchain\u003c/li\u003e\n  \u003cli\u003eFrontend Framework\nStreamlit\nAn open-source app framework for creating interactive web applications.\nInstallation instructions: pip install streamlit\u003c/li\u003e\n  \u003cli\u003eVector Store\nChromaDB\nChosen for its efficient management and querying of embeddings.\nSetup instructions:\npip install chromadb\n\u003c/li\u003e\n  \u003cli\u003eEmbedding Model\nSentence Transformer\nLocal embedding model to generate embeddings from PDF content.\nInstallation:\npip install sentence-transformers\u003c/li\u003e\n  \u003cli\u003eLocal Language Model (LLM)\nHugging Face Transformers\nIntegration of a local instance for processing and generating insights.\nInstallation:\n  pip install transformers\u003c/li\u003e\n\u003c/ol\u003e\n\n### 2. Initialization\n\n#### Data Preparation\nDownload and preprocess the three provided PDF documents (Alphabet Inc., Tesla Inc., Uber Technologies Inc.).\n\n#### Parsing Documents\nUse PyMuPDF or PyPDF2 to extract text and structure from PDFs.\n\n#### Generating Vectors\nUtilize Sentence Transformer to create embeddings for document content.\n\n#### Storing in Vector Store\nImplement functions to persist embeddings into ChromaDB vector store.\n\n### 3. Development\n#### Configuring Query Engine\nDefine retrieval tasks based on document embeddings using ChromaDB.\n\n#### Integrating LLM\nSet up a local instance of a Large Language Model (LLM) for contextual insights.\n\n#### Developing Chatbot Interface\nUse Streamlit to create a user-friendly interface for querying and displaying comparative insights from documents.\n\n### 3. Usage\n\u003cul\u003e\n  \u003cli\u003eClone the repository:\n\ngit clone https://github.com/yourusername/content-engine.git\ncd content-engine\n\u003c/li\u003e\n \u003cli\u003eInstall dependencies:\npip install -r requirements.txt\u003c/li\u003e\n\u003cli\u003eRun the Streamlit app:\nstreamlit run content_engine.py\u003c/li\u003e\n\u003c/ul\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsayamalt%2Fcontent-engine","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsayamalt%2Fcontent-engine","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsayamalt%2Fcontent-engine/lists"}