{"id":23388286,"url":"https://github.com/hegdebhavya/azureai-rag","last_synced_at":"2026-04-12T05:34:17.696Z","repository":{"id":268772347,"uuid":"901548712","full_name":"hegdebhavya/AzureAI-RAG","owner":"hegdebhavya","description":"Azure OpenAI and LangChain to create a vector database and perform question-answering tasks in terminal and using streamlit.","archived":false,"fork":false,"pushed_at":"2024-12-18T19:56:43.000Z","size":21,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-08T13:22:09.442Z","etag":null,"topics":["azure","faiss-vector-database","langchain","openai","python","streamlit"],"latest_commit_sha":null,"homepage":"","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/hegdebhavya.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":"2024-12-10T21:36:40.000Z","updated_at":"2024-12-18T19:56:47.000Z","dependencies_parsed_at":"2024-12-18T20:36:11.548Z","dependency_job_id":"1f813515-4cca-49d4-afb5-2305589f616f","html_url":"https://github.com/hegdebhavya/AzureAI-RAG","commit_stats":null,"previous_names":["hegdebhavya/azureai-rag"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hegdebhavya%2FAzureAI-RAG","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hegdebhavya%2FAzureAI-RAG/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hegdebhavya%2FAzureAI-RAG/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hegdebhavya%2FAzureAI-RAG/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hegdebhavya","download_url":"https://codeload.github.com/hegdebhavya/AzureAI-RAG/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247848186,"owners_count":21006206,"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":["azure","faiss-vector-database","langchain","openai","python","streamlit"],"created_at":"2024-12-22T02:18:31.324Z","updated_at":"2025-12-30T23:05:47.896Z","avatar_url":"https://github.com/hegdebhavya.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# RAG using Azure OpenAI\n\nThis project utilizes Azure OpenAI and LangChain to create a vector database and perform question-answering tasks in the terminal and using Streamlit.\n\n## Prerequisites\n\n- Access to an Azure subscription to deploy the Azure OpenAI endpoint and the embedding model.\n\n## Steps\n\n1. **Create and activate a virtual environment:**\n\n    ```sh\n    pip install virtualenv\n    python -m venv myenv\n    myenv\\Scripts\\activate\n    ```\n\n2. **Install the necessary pip packages from the requirements file:**\n\n    ```sh\n    pip install -r requirements.txt\n    ```\n\n3. **Run the vector database script to create embeddings for the text file:** \n\n   (The embeddings will be stored in the `faiss-db` folder if successful)\n\n    ```sh\n    python vector_db_creator.py\n    ```\n\n4. **Run the main script to interact with the chatbot via the terminal:**\n\n    ```sh\n    python main.py\n    ```\n\n5. **To interact with the chatbot via the UI, run the Streamlit script using the Streamlit command:**\n\n    ```sh\n    streamlit run streamlit.py\n    ```\n\n6. **Deactivate the virtual environment:**\n\n    ```sh\n    deactivate\n    ```\n\n## Usage\n\nThis project can be used to perform question-answering tasks using a custom-trained vector database. The terminal interaction provides a command-line interface, while the Streamlit script offers a graphical user interface.\n\n\n## Contact\n\nIf you have any questions or feedback, feel free to contact me at hegdeb09@gmail.com\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhegdebhavya%2Fazureai-rag","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhegdebhavya%2Fazureai-rag","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhegdebhavya%2Fazureai-rag/lists"}