Parallel Query Retrieval (Fan Out) in AI

As AI and search systems grow smarter, they need to handle more information faster and more accurately. One smart method that helps with this is called Parallel Query Retrieval, also known as Fan Out. It sounds technical, but don’t worry we’ll break it down in a way that’s easy to understand.

Before understand the Parallel Query Retrieval (Fan Out) you need to understand What is RAG?.

What is Parallel Query Retrieval (Fan Out)?

In Parallel Query Retrieval, the user's query is given to a large language model (LLM), which generates multiple versions of the query. These varied queries help retrieve more relevant and diverse information from the data source. And we run these queries paralleley, so it’s called Fan out.

What is Fan Out

In the context of Retrieval-Augmented Generation (RAG) in AI, fan-out refers to the number of documents or passages retrieved from a knowledge source in response to a user query. These retrieved documents are then provided to a language model to help generate a more accurate and grounded response. A higher fan-out increases the chances of including relevant information but can also introduce noise and slow down processing. On the other hand, a lower fan-out is faster and more efficient but may miss important content. Thus, fan-out plays a critical role in balancing response quality and system performance in RAG-based systems.

Code for Parallel Query Generation

This is a basic code for multiple query generation using Gemini Key and OpenAI SDK in Python.

from dotenv import load_dotenv
from openai import OpenAI
import os
load_dotenv()

client = OpenAI(
      api_key=os.getenv("API_KEY"),
    base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
)

system_prompt = """
You are an AI Assistant who is specialized in convert user query to 3 diffrent querys
"""

result = client.chat.completions.create(
    model="gemini-2.0-flash",
    messages=[
        { "role": "system", "content": system_prompt },
        { "role": "user", "content": "Why we need Oxigen?" }
    ]
)

print(result.choices[0].message.content)

" Input -
 Why we need Oxigen?
Output - 
 1.  What is the role of oxygen in human survival and bodily functions?
 2.  How do living organisms, including plants and animals, utilize oxygen for respiration
 and energy production?
 3.  What are the consequences of oxygen deprivation or deficiency in the human body? "

Use of Parallel Query Retrieval (Fan Out)

When we get multiple queries, we search for information in Our Database and retrive the chunks on the basic of these queries and get some data, then we filter that data and remove duplicates. arenge tham on basic of ocurence.

Here is a simple flow diagram of Parallel Query Retrieval. In this diagram, we have a user query we give to LLM, and it generates 3 queries, and with the help of their queries, we search for information in the Database. than filter the information in the pass to LLM and also pass the real user query for the same context, and LLM returns the output on the basis of the information.

Summary

Parallel Query Retrieval (Fan Out) is a powerful way for AI systems to quickly search multiple places at the same time. It helps the AI find better and faster answers by casting a wide net and then filtering the results smartly. As AI becomes more advanced, methods like Fan Out make it even more helpful, accurate, and responsive.

Links of RAG-related Blogs

1. What is RAG

2. Chain of Thought

3. Step-Back Prompting

4. Reciprocal Rank Fusion

5. Hypothetical Document Embeddings