Initial commit from Create Llama

backend/README.md

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+This is a [LlamaIndex](https://www.llamaindex.ai/) project using [FastAPI](https://fastapi.tiangolo.com/) bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama).
+
+## Getting Started
+
+First, setup the environment with poetry:
+
+> **_Note:_** This step is not needed if you are using the dev-container.
+
+```
+poetry install
+poetry shell
+```
+
+Then check the parameters that have been pre-configured in the `.env` file in this directory. (E.g. you might need to configure an `OPENAI_API_KEY` if you're using OpenAI as model provider).
+
+If you are using any tools or data sources, you can update their config files in the `config` folder.
+
+Second, generate the embeddings of the documents in the `./data` directory (if this folder exists - otherwise, skip this step):
+
+```
+poetry run generate
+```
+
+Third, run the development server:
+
+```
+python main.py
+```
+
+The example provides two different API endpoints:
+
+1. `/api/chat` - a streaming chat endpoint
+2. `/api/chat/request` - a non-streaming chat endpoint
+
+You can test the streaming endpoint with the following curl request:
+
+```
+curl --location 'localhost:8000/api/chat' \
+--header 'Content-Type: application/json' \
+--data '{ "messages": [{ "role": "user", "content": "Hello" }] }'
+```
+
+And for the non-streaming endpoint run:
+
+```
+curl --location 'localhost:8000/api/chat/request' \
+--header 'Content-Type: application/json' \
+--data '{ "messages": [{ "role": "user", "content": "Hello" }] }'
+```
+
+You can start editing the API endpoints by modifying `app/api/routers/chat.py`. The endpoints auto-update as you save the file. You can delete the endpoint you're not using.
+
+Open [http://localhost:8000/docs](http://localhost:8000/docs) with your browser to see the Swagger UI of the API.
+
+The API allows CORS for all origins to simplify development. You can change this behavior by setting the `ENVIRONMENT` environment variable to `prod`:
+
+```
+ENVIRONMENT=prod python main.py
+```
+
+## Using Docker
+
+1. Build an image for the FastAPI app:
+
+```
+docker build -t <your_backend_image_name> .
+```
+
+2. Generate embeddings:
+
+Parse the data and generate the vector embeddings if the `./data` folder exists - otherwise, skip this step:
+
+```
+docker run \
+  --rm \
+  -v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
+  -v $(pwd)/config:/app/config \
+  -v $(pwd)/data:/app/data \ # Use your local folder to read the data
+  -v $(pwd)/storage:/app/storage \ # Use your file system to store the vector database
+  <your_backend_image_name> \
+  poetry run generate
+```
+
+3. Start the API:
+
+```
+docker run \
+  -v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
+  -v $(pwd)/config:/app/config \
+  -v $(pwd)/storage:/app/storage \ # Use your file system to store gea vector database
+  -p 8000:8000 \
+  <your_backend_image_name>
+```
+
+## Learn More
+
+To learn more about LlamaIndex, take a look at the following resources:
+
+- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex.
+
+You can check out [the LlamaIndex GitHub repository](https://github.com/run-llama/llama_index) - your feedback and contributions are welcome!