🤖 Building RAG chatbot with NeMo Guardrails !💡

Discover how the power of NeMo Guardrails ensures safer and more controlled conversations chatbot !

🛠️ Tools Overview:

1. NeMo Guardrails 🛡️
2. LangChain 🔗
3. Gradio 🎛️
4. Chroma DB 🗃️

⚔️ NeMo Guardrails

Checkout more on why NeMo Guardrails ?

Here are some key components a config folder should be containing:

• Config: Houses essential details such as models and high-level flow instructions.
• Rails: Define dialogue flows using colang, including welcoming messages.
• Actions: Specify actions for different dialogue scenarios, such as blocking certain words.

Explore more on configuring Nemo Guardrails here.

🔗 LangChain with NeMo Guardrails

LangChain gives developers a framework to construct LLM‑powered apps easily.

Integrate LangChain with Nemo Guardrails effortlessly:

# Initialization
llm = ChatOpenAI(openai_api_key=openai_api_key, model_name="gpt-3.5-turbo-16k")

# Loading configs
config = RailsConfig.from_path("config")
app = LLMRails(config=config, llm=llm)

# Generate response
response = await app.generate_async(messages="Here's our message")

🛢 Vector Database with NeMo Guardrails

NeMo Guardrails support knowledge base only in markdown format.

Is there any other way ? Yes, using vector database.

Lets use Chroma DB to:

1. Create a Vector Database: Ingest documents of any format to build a robust knowledge base.
2. Perform Similarity Search: Connect to Croma DB and retrieve relevant chunks for user messages.

Now you can generate response with message and relevant_chunks

relevant_chunks = vector_search(message)
response = await app.generate_async(messages=[{"role": "context", "content": {"relevant_chunks": relevant_chunks}}, {"role": "user", "content": message}])

🚀 Demo

AWS Guard Chatbot

Note: It has only minimal guards added from NeMo for demo,
So its possible to prompt injection 

Without Guardrails

With Guardrails

💻 Explore the Source Code

Curious to explore further? Head over to the GitHub repository for the complete source code!