Hypothetical Document Embeddings: For Retrieval Enhancement

Imagine asking a friend for a book recommendation, but instead of describing the book perfectly, you fumble with your words. A great friend might still guess correctly by focusing on the essence of what you need. Hypothetical Document Embeddings (HyDE) aims to do something similar for search engines: it helps them understand the heart of your query, even if your words aren’t perfect. Let’s break down how this works.

What Are Hypothetical Document Embeddings (HyDE)?

HyDE is a smart way to improve search results using AI. Instead of just searching with your question, it first imagines the perfect answer, then uses that to find real answers. Here’s how it works:

Step 1: Imagine the Perfect Answer

When you ask a question like “How do I fix a flat tire?”, the AI first makes up a helpful answer on its own, like a short guide that talks about using wrenches, tire levers, and patch kits. This is called a hypothetical document.

Step 2: Find Real Answers That Match

That made-up answer is turned into a list of numbers (called an embedding) that shows what it means. The AI then looks for real documents with similar meanings—even if they use different words (like “punctured tube” instead of “flat tire”).

So basically, HyDE helps the AI search smarter by first guessing what a great answer would look like, then using that to find the best real matches.

Why HyDE Works Better

• Handles “Brain Fog” Moments: If your query is messy (“stuff to stop bike wheel air loss”), the AI still generates a coherent hypothetical answer, making retrieval smarter.

• Ignores Keyword Traps: By focusing on the AI-generated ideal answer, HyDE skips over literal keyword matches and hunts for documents with the same meaning.

• Faster Than Reading Everything: Embeddings allow quick comparisons. HyDE doesn’t read every document—it just matches the “vibe” of the hypothetical answer.

Code for Hypothetical Document Embeddings

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

system_prompt = """
You are a helpful assistant who generate a detailed document with the help of user query
"""


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

response = client.chat.completions.create(
    model="gemini-2.0-flash",
    n=1,
    messages=[
        {"role": "system", "content": system_prompt},
        {
            "role": "user",
            "content": "What is Python?"
        }
    ]
)

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

Output of the code

'''Input -
What is Python?
Output-
Okay, here's a detailed document about Python, covering its key aspects, history, features,
 uses, and more.
**Python: A Comprehensive Overview**

**1. Introduction**

Python is a high-level, general-purpose programming language.  Its design philosophy emphasizes
code readability, and its syntax allows programmers to express concepts in fewer lines of code 
compared to languages like C++ or Java.  Python is dynamically typed and garbage-collected. 
It supports multiple programming paradigms, including 
structured (procedural), object-oriented, and functional programming.

**2. History and Development**

.
.
.
it's give a vary large document .............
.
.
.
**11. Conclusion**

Python is a versatile and powerful programming language that is widely used in various domains. Its readability, ease of learning, and extensive ecosystem make it an excellent choice for both beginners and experienced programmers. Whether you're interested in web development, data science, scripting, or automation, Python has the tools and libraries you need to succeed.
'''

Real-Life Example

Suppose you search for “why sky blue.” The AI might generate a hypothetical explanation about light scattering and Rayleigh scattering. Even if the actual article titles say “atmospheric light refraction,” their embeddings will align with the hypothetical answer, so you still get the right result.

Summary

Hypothetical Document Embeddings act like a translator between your messy queries and the pristine answers hiding in databases. By letting AI imagine what you really need, HyDE helps machines meet humans halfway. It’s not magic it’s just smart math!

note

To see the full code, go to GitHub

Links of RAG-related Blogs

1. What is RAG

2. Parallel Query Retrieval (Fan Out)

3. Chain of Thought

4. Reciprocal Rank Fusion

5. Step-Back Prompting