Basic RAG Example

Learn how to implement a basic Retrieval-Augmented Generation (RAG) system using Galileo and OpenAI

When implementing RAG systems, it’s crucial to properly handle document retrieval, context management, and response generation. This guide demonstrates a basic RAG implementation using Galileo’s observability features.

What you’ll need

• OpenAI API key
• Galileo API key
• Python environment with required packages
• Basic understanding of RAG concepts

Setup instructions

1. Set Up Your Environment

   Create a .env file with your API keys:

   .env

   # Your Galileo API key
   GALILEO_API_KEY="your-galileo-api-key"
   
   # Your Galileo project name
   GALILEO_PROJECT="your-galileo-project-name"
   
   # The name of the Log stream you want to use for logging
   GALILEO_LOG_STREAM="your-galileo-log-stream"
   
   # Provide the console url below if you are using a
   # custom deployment, and not using the free tier, or app.galileo.ai.
   # This will look something like “console.galileo.yourcompany.com”.
   # GALILEO_CONSOLE_URL="your-galileo-console-url"
   
   # OpenAI properties
   OPENAI_API_KEY="your-openai-api-key"
   
   # Optional. The base URL of your OpenAI deployment.
   # Leave this commented out if you are using the default OpenAI API.
   # OPENAI_BASE_URL="your-openai-base-url-here"
   
   # Optional. Your OpenAI organization.
   # OPENAI_ORGANIZATION="your-openai-organization-here"
   
2. Install Dependencies

   Install required dependencies:

   requirements.txt

   galileo[openai]
   python-dotenv
   rich
   questionary
   
3. Running and Monitoring

   Execute the application:

   python app.py
   

   Use Galileo to monitor:

   • Document retrieval performance
     • Chunk relevance
     • Chunk attribution utilization
     • Completeness
   • System performance metrics

Implementation guide

Let’s break down the implementation into manageable sections:

1. setting up the environment

First, we’ll set up our imports and initialize our environment:

app.py

import os
from dotenv import load_dotenv
from galileo import log
from galileo.openai import openai
from rich.console import Console
from rich.panel import Panel
from rich.markdown import Markdown
import questionary
import sys

load_dotenv()

# Initialize console for rich output
console = Console()

# Check if Galileo logging is enabled
logging_enabled = os.environ.get("GALILEO_API_KEY") is not None

# Initialize OpenAI client directly
client = openai.OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))

This section:

• Imports necessary libraries
• Loads environment variables
• Sets up rich console output
• Initializes the OpenAI client with Galileo integration

2. document retrieval system

The document retrieval function is decorated with Galileo’s logging:

app.py

@log(span_type="retriever")
def retrieve_documents(query: str):
    # TODO: Replace with actual RAG retrieval
    documents = [
        {
            "id": "doc1",
            "text": """
Galileo is an observability platform for LLM applications. It helps developers
monitor, debug, and improve their AI systems by tracking inputs, outputs,
and performance metrics.""",
            "metadata": {
                "source": "galileo_docs",
                "category": "product_overview"
            }
        },
        {
            "id": "doc2",
            "text": """
RAG (Retrieval-Augmented Generation) is a technique that enhances LLM responses
by retrieving relevant information from external knowledge sources before
generating an answer.""",
            "metadata": {
                "source": "ai_techniques",
                "category": "methodology"
            }
        },
        {
            "id": "doc3",
            "text": """
Common RAG challenges include hallucinations, retrieval quality issues,
and context window limitations. Proper evaluation metrics include relevance,
faithfulness, and answer correctness.""",
            "metadata": {
                "source": "ai_techniques",
                "category": "challenges"
            }
        },
        {
            "id": "doc4",
            "text": """
Vector databases like Pinecone, Weaviate, and Chroma are optimized for
storing embeddings and performing similarity searches, making them
ideal for RAG applications.""",
            "metadata": {
                "source": "tech_stack",
                "category": "databases"
            }
        },
        {
            "id": "doc5",
            "text": """
Prompt engineering is crucial for RAG systems. Well-crafted prompts
should instruct the model to use retrieved context, avoid making up
information, and cite sources when possible.""",
            "metadata": {
                "source": "best_practices",
                "category": "prompting"
            }
        }
    ]
    return documents

Key points:

• Uses @log decorator with the retriever span type
• Returns structured document objects
• Includes metadata for tracking sources
• Simulates a real document retrieval system

3. RAG pipeline implementation

The core RAG functionality:

app.py

def rag(query: str):
    documents = retrieve_documents(query)

    # Format documents for better readability in the prompt
    formatted_docs = ""
    for i, doc in enumerate(documents):
        formatted_docs += f"Document {i+1} (Source: {doc['metadata']['source']}):\n{doc['text']}\n\n"

    prompt = f"""
    Answer the following question based on the context provided.
    If the answer is not in the context, say you don't know.

    Question: {query}

    Context:
    {formatted_docs}
    """

    try:
        console.print("[bold blue]Generating answer...[/bold blue]")
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {
                    "role": "system", 
                    "content": """
You are a helpful assistant that answers questions based only on the 
provided context."""
                },
                {
                    "role": "user",
                    "content": prompt
                }
            ],
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"Error generating response: {str(e)}"

This section:

• Retrieves relevant documents
• Formats context for the LLM
• Constructs a clear prompt
• Handles API calls and errors
• Uses the gpt-4o model for responses

4. interactive interface

The main application interface:

app.py

def main():
    console.print(Panel.fit(
        "RAG Demo\nThis demo uses a simulated RAG system to answer your questions.",
        title="Galileo RAG Terminal Demo",
        border_style="blue"
    ))

    # Check environment setup
    if logging_enabled:
        console.print("[green]✅ Galileo logging is enabled[/green]")
    else:
        console.print("[yellow]⚠️ Galileo logging is disabled[/yellow]")

    api_key = os.environ.get("OPENAI_API_KEY")
    if api_key:
        console.print("[green]✅ OpenAI API Key is set[/green]")
    else:
        console.print("[red]❌ OpenAI API Key is missing[/red]")
        sys.exit(1)

    # Main interaction loop
    while True:
        query = questionary.text(
            "Enter your question about Galileo, RAG, or AI techniques:",
            validate=lambda text: len(text) > 0
        ).ask()

        if query.lower() in ['exit', 'quit', 'q']:
            break

        try:
            result = rag(query)
            console.print("\n[bold green]Answer:[/bold green]")
            console.print(Panel(Markdown(result), border_style="green"))

            continue_session = questionary.confirm(
                "Do you want to ask another question?",
                default=True
            ).ask()

            if not continue_session:
                break

        except Exception as e:
            console.print(f"[bold red]Error:[/bold red] {str(e)}")

if __name__ == "__main__":
    try:
        main()
    except KeyboardInterrupt:
        console.print("\n[bold]Exiting RAG Demo. Goodbye![/bold]")

This section provides:

• Environment validation
• Interactive question-answer loop
• Rich formatting for outputs
• Graceful error handling
• Clean exit handling

Key features

• Galileo Logging: Track document retrieval and LLM interactions
• Rich Console Interface: User-friendly terminal interface
• Error Handling: Graceful handling of API and runtime errors
• Context Management: Proper formatting of retrieved documents
• Interactive Experience: Easy-to-use question-answering interface

Next steps

• Implement real document retrieval using a vector database
• Add response streaming for better user experience
• Implement more sophisticated prompt engineering
• Add evaluation metrics for retrieval quality
• Integrate advanced Galileo logging features