Hypothetical Document Embeddings-HyDE

🤓 Explanation

In HyDE - Hypothetical Document Embeddings, when a user asks a question, we use an LLM like GPT-4.1 to provide an initial answer. We then use that answer to search through the documents and produce the final answer. Essentially, the LLM first answers the question, and that answer is used to generate the final response.

In the standard technique, when a user asks a question, the question is directly searched in the documents to provide relevant answers to the user.

📖 Step by step Explanation of the diagram

🔍 Step 1: User Poses a Query

The process starts when the user asks a question .
Reference: The icon of a person as a"User Query".

🧠 Step 2: LLM Generates Hypothetical Documents

The query is sent to a Large Language Model (LLM) like GPT-4.1, which creates hypothetical documents that could contain the answers. These documents are generated by the LLM.
Reference: The arrow from the LLM icon to the "Hypothetical Documents" box.

🧭 Step 3: Embeddings and Similarity Search

These hypothetical documents are turned into vector embeddings. Then, a similarity search is done in the knowledge base to find real documents that have similar meanings.
Reference: This is shown in the right section of the diagram, where you can see the vector space with the user query location and nearby "nodes".

🌐 Step 4: Locate Relevant Nodes

The user's query position in vector space is found (🔲 in the upper right), and nearby nodes (small ovals) that are similar in context are chosen.
Reference: Labels like “User query is here” and “These are more nodes related to user queries”.

🧩 Step 5: Gather All Related Nodes

All relevant nodes are gathered to create the final document set. These are the real documents from the database that are most similar in meaning to the hypothetical ones.
Reference: “we use all those nodes” leading to a box saying “Final documents that consist of all those nodes”.

🤝 Step 6: Merge with User Query

The user query is combined with these real documents that were found.
Reference: Box at the bottom showing the combination of "User Query" + OpenAI icon.

💬 Step 7: Generate the Final Response

This combination is sent to the LLM again , which uses it to create a final, accurate, and well-supported answer to the user's original question.
Reference: The final arrow pointing to “Final Response”.

🧑‍💻 Implementation in Code :

💻 Code example

I have previously posted a blog on “ Understanding RAG: The Smart Foundation of Advanced AI “ , In which I have explain step by step how to implement RAG with a simple RAG project “PDF Chatbot” , on the top of it , I have built this Hypothetical Document Embeddings-HyDE technique .

Here is the code for Hypothetical Document Embeddings-HyDE you can add this code on the top of “PDF Chatbot” code for implementing it .

If there is some issue you can comment or you can check my code at github : github.com/Kamraanmulani

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from langchain.schema import HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI

model = ChatOpenAI(
    model="gpt-3.5-turbo",
    temperature=0.2,
    api_key="YOUR_OPENAI_API_KEY",
)

# HyDE - function to generate hypothetical documents based on the query asked by the user
def generate_hypothetical_document(query):
    hyde_prompt = f"""Based on the question: "{query}" about Node.js, write a detailed, 
    technical passage that would contain the answer to this question. 

    This passage should be written as if it came directly from documentation and should include:
    - Technical explanations
    - Any relevant code examples
    - Necessary context for understanding the concept

    Write only the hypothetical document, not an introduction or explanation.
    """

    messages = [
        SystemMessage(content="You are an expert in Node.js who generates hypothetical documentation that might answer technical questions."),
        HumanMessage(content=hyde_prompt)
    ]

    response = model.invoke(messages)
    return response.content

✅ Code Execution Result

❔ Why & How ?

🤔 Why is HyDE (Hypothetical Document Embeddings) used?

HyDE is used to make search results better by creating a possible answer document for a user question before looking in the vector database.

It is useful when:

1. The user's original question is too short or unclear.

2. You want to help the search process with more detailed information.

3. You want to show what a real answer might look like, making the results more relevant.

⚙️ How does HyDE work?

Step-by-step (simple):

1. A user asks a question (e.g., "What is an event loop in Node.js?").

2. Instead of searching right away, HyDE creates a detailed hypothetical document that might have the answer, using a language model like GPT.

3. This generated document is then embedded and used as a new query to search in the vector store (e.g., Qdrant, Pinecone).

4. The results found are more relevant because they are based on a full, meaningful context, not just the short original question.

✅ Benefits:

• Works well even if the original questions are not detailed.

• Makes search results more accurate by creating documents with more context.

• Uses the advantages of language generation and vector search together.

• No need for fine-tuning or complex model integration.

• Simple to set up with LangChain and OpenAI.

🌍 Real life applications

🧠 HyDE (Hypothetical Document Embeddings) is useful in real life situations where short or unclear questions need more context to get accurate answers. It is particularly helpful for finding information, searching internal documents, or educational tools.

🌐 1. Developer Documentation Search

Use Case: A developer asks, “How does the event loop handle async in Node.js?”

The original question is short and might not include important terms or context.

Different documents in the knowledge base might:

• Use different wording (eg : "asynchronous event handling")

• Have the answer spread across various sections

• Need more context to find the right match

🧠 How HyDE Helps:
HyDE creates a hypothetical answer with technical details, code examples, and important terms like “callbacks”, “event queue”.
This more detailed passage is then used for vector search, increasing the chances of finding documents that truly answer the question.

🎓 2. Internal Company Knowledge Base

Use Case: An employee asks, “What's our refund policy for international customers?”

The question might be:

• Too unclear

• Missing specific policy terms (like “customs” or “cross border fees”)

📌 Why HyDE Works Here:
HyDE transforms the vague question into a hypothetical document that imitates how the policy might be described internally, using a formal tone, key terms, and policy structure.
This helps find internal documents with similar content, even if they don't match the original wording.

📑 Summary

HyDE (Hypothetical Document Embeddings) improves search accuracy by using large language models (LLMs) like GPT-4.1 to create detailed hypothetical documents from user queries. These documents are turned into vector embeddings for similarity searches in knowledge bases, leading to more relevant answers.

HyDE is especially useful for clarifying short or unclear questions and works well for developer documentation and internal company knowledge bases, enhancing search results without needing complex model integration.