> For the complete documentation index, see [llms.txt](https://sds-bit-mesra.gitbook.io/llm-course/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://sds-bit-mesra.gitbook.io/llm-course/hands-on-development/1-first-rag-pipeline/building-rag-with-open-ai.md).

# Building RAG with Open AI

## Step 1: Verify Docker Installation

First, let’s verify that Docker is properly installed and open in your system. Open your terminal (Command Prompt on Windows) and run:

```
docker --version
```

You should see the Docker version information if it's installed correctly. If not, you might want to revist the [prerequisites](/llm-course/hands-on-development/prerequisites-must.md) and the [Docker Basics](/llm-course/hands-on-development/docker-basics.md) section.

## Step 2: Clone the [LLM App](https://github.com/pathwaycom/llm-app) Templates Repository

Next, clone the  llm-app repository from GitHub. This repository contains all the files you’ll need.

{% code overflow="wrap" %}

```
git clone https://github.com/pathwaycom/llm-app.git
```

{% endcode %}

If you get an error because you have previously cloned an older version of the [llm-app](https://github.com/pathwaycom/llm-app) repository, ensure you're in the correct repository directory and update it using:

```
git pull
```

This will update your local repository with the latest changes from the remote repository.

## Step 3: Navigate to the relevant project directory

Change to the directory where the example is located.

```
cd examples/pipelines/demo-question-answering
```

## Step 4: Create a .env File and put your Open API key

Create a file named .env and add your Open AI API key using the Bash command below. 

```
echo "OPENAI_API_KEY=<your_openai_api_key>" > .
```

Alternatively, you can open your preferred text editor (e.g., Notepad), create a new file and add just the following line:

```
OPENAI_API_KEY=<your_openai_api_key>
```

Then, save the file as environment (`.env`) in the demo-question-answering folder. It will do the same thing.

### Step 5: Build the Docker Image

Now, let’s build the Docker image. This step might take a few minutes depending on your machine. Ensure you have enough space (approximately 8 GB).

```
docker build -t rag .
```

The **`-t rag`** part is tagging the Docker image with the name ‘rag’. Whereas the  **`.`** at the end specifies the build context directory, which is the current directory. This tells Docker to look for the Dockerfile in the current directory and include any files/subdirectories in the build context.

### Step 6: Run the Docker Container

Run the Docker container, mounting (described below) the data folder, and exposing port 8000.

For Windows:

```
docker run -v "%cd%/data:/app/data" -p 8000:8000 rag
```

For Linux/Mac:

```
docker run -v "$(pwd)/data:/app/data" -p 8000:8000 --env-file .env rag
```

**Note:** You will see the logs for parsing & embedding documents in the Docker image logs. Give it a few minutes to finish up on embeddings. You will see 0 entries (x minibatch(es)) have been... message. If there are no more updates, this means the app is ready for use!&#x20;

**Handling Port Conflicts:** If `port 8000` is already in use and you see an error related to it, you can specify a different port. For example, if you want to use `port 8080` instead, modify the command as follows:

For Windows:

```
docker run -v "${PWD}/data:/app/data" -p 8000:8000 rag
```

For Linux/Mac:

```
docker run -v "$(pwd)/data:/app/data" -p 8080:8000 --env-file .env rag
```

This will map `port 8080` on your local machine to `port 8000` in the Docker container. Just remember to update the port in the next step as well.

Open up **another terminal window** and follow the next steps.

### Step 7: Check the List of Files

You will see the logs for parsing & embedding documents in the Docker image logs. Give it a few minutes to finish up on embeddings, you will see 0 entries (x minibatch(es)) have been… message. If there are no more updates, this means the app is ready for use!\
\
Now let’s see the files from which we’ll retrieve information for our LLMs.To test it, let's query to  get the list of available inputs and associated metadata using [the curl command](https://docs.google.com/document/d/1GfFb0FyVTIayWlugyCJIjvn0TP9tqYlGH5z7-1F5JwU/edit#heading=h.oqzorkc4n4k6):

{% code overflow="wrap" %}

```
curl -X 'POST'   'http://localhost:8000/v1/pw_list_documents'   -H 'accept: */*'   -H 'Content-Type: application/json'
```

{% endcode %}

This will return the list of files e.g. if you start with the [data folder](https://github.com/pathwaycom/llm-app/tree/main/examples/pipelines/demo-question-answering/data) provided in the demo, the answer will be as follows:

`[{"created_at": null, "modified_at": 1718810417, "owner": "root", "path":"data/IdeanomicsInc_20160330_10-K_EX-10.26_9512211_EX-10.26_Content License Agreement.pdf", "seen_at": 1718902304}]`

### Step 8: Last Step – Run the RAG Service

You can now run the RAG service. Start by asking a simple question. For example:

```python
curl -X 'POST' \
  'http://0.0.0.0:8000/v1/pw_ai_answer' \
  -H 'accept: */*' \
  -H 'Content-Type: application/json' \
  -d '{
  "prompt": "What is the start date of the contract?"
}'
```

It should return the following answer:

December 21, 2015

***

But how do you tweak this for your use-case? Let's see that by understanding the contents of the repo which just used.