{"id":18223058,"url":"https://github.com/samjoesilvano/multi-source-knowledge-retrieval-system","last_synced_at":"2026-05-10T05:45:12.552Z","repository":{"id":260914523,"uuid":"877898876","full_name":"SamJoeSilvano/Multi-Source-Knowledge-Retrieval-System","owner":"SamJoeSilvano","description":"An end-to-end multi-source knowledge retrieval system using LangChain, FAISS, and OpenAI embeddings. This Retrieval-Augmented Generation (RAG) pipeline intelligently searches across Wikipedia, arXiv, and custom websites, optimizing source selection and delivering precise, real-time results based on query relevance.","archived":false,"fork":false,"pushed_at":"2024-11-03T15:02:38.000Z","size":5271,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-08T01:42:37.612Z","etag":null,"topics":["ai-pipeline","document-search","faiss","information-retrieval","knowledge-retrieval","langchain","langchain-agents","langchain-tools","machine-learning","multi-source-retrieval","natural-language-processing","openai-embeddings","python","retrieval-augmented-generation","semantic-search"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SamJoeSilvano.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","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":"2024-10-24T12:41:13.000Z","updated_at":"2024-11-03T15:02:41.000Z","dependencies_parsed_at":"2024-11-03T15:31:31.619Z","dependency_job_id":"13309009-12bf-4532-8151-2087046f73dd","html_url":"https://github.com/SamJoeSilvano/Multi-Source-Knowledge-Retrieval-System","commit_stats":null,"previous_names":["samjoesilvano/multi-source-knowledge-retrieval-system"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamJoeSilvano%2FMulti-Source-Knowledge-Retrieval-System","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamJoeSilvano%2FMulti-Source-Knowledge-Retrieval-System/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamJoeSilvano%2FMulti-Source-Knowledge-Retrieval-System/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamJoeSilvano%2FMulti-Source-Knowledge-Retrieval-System/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SamJoeSilvano","download_url":"https://codeload.github.com/SamJoeSilvano/Multi-Source-Knowledge-Retrieval-System/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247761039,"owners_count":20991533,"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":["ai-pipeline","document-search","faiss","information-retrieval","knowledge-retrieval","langchain","langchain-agents","langchain-tools","machine-learning","multi-source-retrieval","natural-language-processing","openai-embeddings","python","retrieval-augmented-generation","semantic-search"],"created_at":"2024-11-04T00:03:50.890Z","updated_at":"2026-05-10T05:45:12.524Z","avatar_url":"https://github.com/SamJoeSilvano.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Multi-Source Knowledge Retrieval System\n\nA high-speed, Retrieval-Augmented Generation (RAG) pipeline that integrates multiple knowledge sources to deliver precise, relevant search results. This end-to-end system is built using LangChain and FAISS, and leverages OpenAI embeddings to perform semantic searches across platforms such as [Wikipedia](https://www.wikipedia.org/), [arXiv](https://arxiv.org/), and custom user-defined websites.\n\n## Features\n\n- **RAG Pipeline with LangChain \u0026 FAISS**: \n   Efficient semantic search using FAISS, enhanced by OpenAI embeddings, to achieve high-speed, accurate query matching.\n\n- **Multi-Source Retrieval**:\n   Seamlessly integrates Wikipedia, arXiv research papers, and custom websites for diverse data retrieval.\n\n- **Smart Agent Selection**:\n   Employs LangChain agents and tools to intelligently select the optimal source based on the query, optimizing response relevance.\n\n- **Real-Time API Access**:\n   Utilizes real-time API connections for updated and precise content retrieval across multiple repositories.\n\n## Project Overview\n\nThis project addresses the need for comprehensive information retrieval across various sources by:\n\n1. Building a retrieval-augmented generation pipeline to automate document retrieval from multiple sources.\n  \n2. Optimizing search relevance through OpenAI-powered embeddings and FAISS similarity search.\n\n3. Enabling smart decision-making on which source to use based on query context, using LangChain agents.\n\n## Technology Stack\n\n- **LangChain**: Framework for creating language model applications, utilized for agent-based source selection and tool integration.\n  \n- **FAISS (Facebook AI Similarity Search)**: Provides vector-based similarity search for efficient retrieval.\n\n- **OpenAI Embeddings**: Supports semantic search by converting queries and documents into high-dimensional embeddings for better relevance.\n\n## Installation\n\n1. **Clone the repository**:\n   ```bash\n   git clone https://github.com/your-username/multi-source-knowledge-retrieval.git\n   cd multi-source-knowledge-retrieval\n\n2. **Install dependencies**:\n   ```bash\n   pip install -r requirements.txt\n\n3. **Set up API keys**:\n-   Add your OpenAI API key and any other API credentials in an .env file:\n    ```bash\n    OPENAI_API_KEY=\"your_openai_api_key\"\n    LANGCHAIN_API_KEY=\"your_langchain_api_key\"\n    GROQ_API_KEY=\"your_groq_api_key\"\n    OTHER_API_KEYS=\"...\"\n\n## Usage\n\n1. **Run the pipeline**:\n-   Go to 'agents' folder and execute the following command to run all the cells in the jupyter file and save the output back to the same file\n    ```bash\n    jupyter nbconvert --execute --to notebook agents.ipynb\n\n2. **Make a query**:\n-   Use the terminal interface to input queries.\n-   The system will automatically select the best source and return relevant information.\n\n## Results\nThis project delivers rapid, relevant responses by leveraging the strengths of multiple knowledge sources. With FAISS, it ensures efficient vector-based matching, while LangChain agents guarantee the best source selection for every query.\n\n## Future Enhancements\n- **Additional Knowledge Sources**: Expand integration to other repositories or specific domain databases.\n- **Enhanced Customization**: User-defined filtering to limit results based on document type, date, or other attributes.\n\n## License\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE.txt) file for details.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamjoesilvano%2Fmulti-source-knowledge-retrieval-system","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsamjoesilvano%2Fmulti-source-knowledge-retrieval-system","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamjoesilvano%2Fmulti-source-knowledge-retrieval-system/lists"}