{"id":29913816,"url":"https://github.com/gardner/nais","last_synced_at":"2025-08-02T02:15:30.475Z","repository":{"id":292636021,"uuid":"981482695","full_name":"gardner/nais","owner":"gardner","description":"Example code for RAG search","archived":false,"fork":false,"pushed_at":"2025-05-13T00:14:30.000Z","size":5796,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-08-01T22:20:35.248Z","etag":null,"topics":["rag"],"latest_commit_sha":null,"homepage":"https://colab.research.google.com/github/gardner/nais/blob/master/chat_engine_react.ipynb","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# NAIS RAG Tutorial\n\n## Get a free Google Gemini API key\n\nhttps://ai.google.dev/gemini-api/docs/api-key\n\n## Let's start here:\n\nhttps://docs.llamaindex.ai/en/stable/examples/chat_engine/chat_engine_react/\n\n##\n\nThis is a [LlamaIndex](https://www.llamaindex.ai/) simple agentic RAG project using [Agent Workflows](https://docs.llamaindex.ai/en/stable/examples/agent/agent_workflow_basic/).\n\n## Getting Started\n\n### Install `uv`:\n\nhttps://docs.astral.sh/uv/getting-started/installation/\n\nThen, setup the environment with uv:\n\n```shell\nuv sync\n```\n\nThen check the parameters that have been pre-configured in the `.env` file in this directory.\nMake sure you have set the `GOOGLE_API_KEY` for the LLM.\n\nSecond, generate the embeddings of the documents in the `./data` directory:\n\n```shell\nuv run generate\n```\n\nThird, run the development server:\n\n```shell\nuv run fastapi dev\n```\n\nThen open [http://localhost:8000](http://localhost:8000) with your browser to start the chat UI.\n\nTo start the app optimized for **production**, run:\n\n```\nuv run fastapi run\n```\n\n## Configure LLM and Embedding Model\n\nYou can configure [LLM model](https://docs.llamaindex.ai/en/stable/module_guides/models/llms) and [embedding model](https://docs.llamaindex.ai/en/stable/module_guides/models/embeddings) in [settings.py](app/settings.py).\n\n## Use Case\n\nWe have prepared an [example workflow](./app/workflow.py) for the agentic RAG use case, where you can ask questions about the example documents in the [./data](./data) directory.\n\nYou can start by sending an request on the [chat UI](http://localhost:8000) or you can test the `/api/chat` endpoint with the following curl request:\n\n```\ncurl --location 'localhost:8000/api/chat' \\\n--header 'Content-Type: application/json' \\\n--data '{ \"messages\": [{ \"role\": \"user\", \"content\": \"What standards for a letter exist?\" }] }'\n```\n\n## Learn More\n\nTo learn more about LlamaIndex, take a look at the following resources:\n\n- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex.\n- [Workflows Introduction](https://docs.llamaindex.ai/en/stable/understanding/workflows/) - learn about LlamaIndex workflows.\n\nYou can check out [the LlamaIndex GitHub repository](https://github.com/run-llama/llama_index) - your feedback and contributions are welcome!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgardner%2Fnais","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgardner%2Fnais","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgardner%2Fnais/lists"}