README.md

Query Decomposition RAG

Example Features

This example deploys a recursive query decomposition example for chat Q&A.

Query decomposition can perform RAG when the agent needs to access information from several different documents (also referred to as chunks) or to perform some computation on the answers. This example uses a custom LangChain agent that recursively breaks down the questions into subquestions. The agent then attempts to answer the subquestions.

The agent has access to two tools:

• search: to perform standard RAG on a subquestion.
• math: to pose a math question to the LLM.

The agent continues to break down the question into subquestions until it has the answers that it needs to form the final answer.

Model	Embedding	Framework	Vector Database	File Types
meta/llama3-70b-instruct	nvidia/nv-embedqa-e5-v5	LangChain	Milvus	TXT, PDF, MD

Prerequisites

Complete the common prerequisites.

Build and Start the Containers

1. Export your NVIDIA API key as an environment variable:

   export NVIDIA_API_KEY="nvapi-<...>"
   

2. Start the containers:

   cd RAG/examples/advanced_rag/query_decomposition_rag/
   docker compose up -d --build

   Example Output

    ✔ Network nvidia-rag           Created
    ✔ Container rag-playground     Started
    ✔ Container milvus-minio       Started
    ✔ Container chain-server       Started
    ✔ Container milvus-etcd        Started
    ✔ Container milvus-standalone  Started
   

3. Confirm the containers are running:

   docker ps --format "table {{.ID}}\t{{.Names}}\t{{.Status}}"

   Example Output

   CONTAINER ID   NAMES               STATUS
   39a8524829da   rag-playground      Up 2 minutes
   bfbd0193dbd2   chain-server        Up 2 minutes
   ec02ff3cc58b   milvus-standalone   Up 3 minutes
   6969cf5b4342   milvus-minio        Up 3 minutes (healthy)
   57a068d62fbb   milvus-etcd         Up 3 minutes (healthy)
   

4. Open a web browser and access http://localhost:8090 to use the RAG Playground.

   Refer to Using the Sample Web Application for information about uploading documents and using the web interface.

Next Steps

• Vector Database Customizations
• Stop the containers by running docker compose down.
• Use the RAG Application: Query Decomposition Agent Helm chart to deploy this example in Kubernetes.