> ## Documentation Index
> Fetch the complete documentation index at: https://gr-docs.aporia.com/llms.txt
> Use this file to discover all available pages before exploring further.

# RAG Chatbot: Embedchain + Chainlit

> Learn how to build a streaming RAG chatbot with Embedchain, OpenAI, Chainlit for chat UI, and Aporia Guardrails.

## Setup

Install required libraries:

```bash theme={null}
pip3 install chainlit embedchain --upgrade
```

Import libraries:

```python theme={null}
import chainlit as cl
from embedchain import App
import uuid
```

## Build a RAG chatbot

When Chainlit starts, initialize a new Embedchain app using GPT-3.5 and streaming enabled.

This is where you can add documents to be used as knowledge for your RAG chatbot. For more information, see the [Embedchain docs](https://docs.embedchain.ai/components/data-sources/overview).

```python theme={null}
@cl.on_chat_start
async def chat_startup():
  app = App.from_config(config={
    "app": {
      "config": {
        "name": "my-chatbot",
        "id": str(uuid.uuid4()),
        "collect_metrics": False
      }
    },
    "llm": {
      "config": {
        "model": "gpt-3.5-turbo-0125",
        "stream": True,
        "temperature": 0.0,
      }
    }
  })

  # Add documents to be used as knowledge base for the chatbot
  app.add("my_knowledge.pdf", data_type='pdf_file')

  cl.user_session.set("app", app)
```

When a user writes a message in the chat UI, call the Embedchain RAG app:

```python theme={null}
@cl.on_message
async def on_new_message(message: cl.Message):
  app = cl.user_session.get("app")
  msg = cl.Message(content="")
  for chunk in await cl.make_async(app.chat)(message.content):
    await msg.stream_token(chunk)
  
  await msg.send()
```

To run the application, run:

```bash theme={null}
chainlit run <your script>.py
```

## Integrate Aporia Guardrails

Next, to integrate Aporia Guardrails, get your Aporia API Key and base URL per the [OpenAI proxy](/fundamentals/integration/) documentation.

You can then add it like this to the Embedchain app from the configuration:

```python theme={null}
app = App.from_config(config={
  "llm": {
    "config": {
      "base_url": "https://gr-prd.aporia.com/<PROJECT_ID>",
      "model_kwargs": {
        "default_headers": { "X-APORIA-API-KEY": "<YOUR_APORIA_API_KEY>" }
      },
      # ...
    }
  },
  # ...
})
```

### AGT Test

You can now test the integration using the [AGT Test](/policies/agt-test). Try this prompt:

```
X5O!P%@AP[4\PZX54(P^)7CC)7}$AGT-STANDARD-GUARDRAILS-TEST-MSG!$H+H*
```

# Conclusion

That's it. You have successfully created an LLM application using Embedchain, Chainlit, and Aporia.