Description

In Hypothetical Document Embeddings (HyDE), the approach differs from traditional techniques. Instead of generating alternative questions based on the user’s original query, HyDE prompts the model to produce a direct answer to the query. This generated response is then used as the basis for similarity search.

Using the search results relevant content snippets matched to the model’s response we can accurately pull related documents from a trusted knowledge base.

For instance, consider a lengthy tutorial video where a user wants to find only the specific segments discussing a certain topic. In such a case, the HyDE method proves to be particularly effective.

HyDE (Hypothetical Document Embeddings) is a specialized technique used within Retrieval Augmented Generation (RAG) systems. Instead of searching directly using the user's original query which might be too vague or worded differently than the source content HyDE takes a two-step approach:

1. Generate a hypothetical answer or document to the user's query using a large language model (LLM).

2. Convert that generated response into an embedding, and use it to search a vector database for the most relevant documents.

This process sometimes called “HyDE query translation” is particularly effective when the user’s question doesn’t closely match the way information is phrased in the underlying documents. It helps bridge that semantic gap and improves the quality of retrieved passages.

Even though it's a niche method, it can significantly boost retrieval quality in edge cases where traditional keyword or embedding searches fall short.

Example

Imagine an app designed for rural artisans seeking support:

User: “Kagaz ka kaam kam chal raha hai, koi madad milegi kya?”

🔍 HyDE Retrieval:

1. The AI interprets the concern and generates a possible answer:
   “Government subsidies and skill development programs for traditional paper craft workers.”

2. That generated response is converted into an embedding.

3. The app then searches a vector database of government circulars, NGO support docs, or policy PDFs for similar content.

4. Results might include documents like:
   “Scheme for Preservation of Traditional Art Forms” or “MSME Handicrafts Grant Program.”

🧱 Regular Retrieval:
Query → Vector → Search
✅ Effective when the query uses phrases similar to those found in the documents.

🔮 HyDE Retrieval:

Query → Generate Answer → Vectorize → Search
✅ More powerful when the original query is unclear, informal, or uses local terms.

Code walkthrough

Pipeline Overview

step 1 : Install Required Packages

step 2 : Setup Your API Key

step 3: Create the LLM + Basic QA Chain

step 4: Add HyDE – Hypothetical Answer Generation

step 5: Add Structured Output with Pydantic

🛠 Step 1: Install Required Packages

Install all the dependencies:

bashCopyEditpip install openai faiss-cpu gript langchain langchain-openai python-dotenv

🔑 Step 2: Setup Your API Key

Create a .env file in your project directory and add:

envCopyEditOPENAI_API_KEY=your-openai-api-key

Load it in your script:

pythonCopyEditimport os
from dotenv import load_dotenv

load_dotenv()
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")

🧠 Step 3: Create the LLM + Basic QA Chain

pythonCopyEditfrom langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

# Define the system prompt
system_prompt = """You are an expert in LangChain, LangGraph, LangServe, and LangSmith..."""

# Create the chat prompt
prompt = ChatPromptTemplate.from_messages([
    ("system", system_prompt),
    ("human", "{question}")
])

# Initialize LLM
llm = ChatOpenAI(model="gpt-4o", temperature=0)

# Build the basic QA chain
qa_chain = prompt | llm | StrOutputParser()

# Test the chain
response = qa_chain.invoke({
    "question": "how to use multi-modal models in a chain and turn chain into a rest api"
})
print("\n[QA Chain Response]:", response)

💡 Step 4: Add HyDE – Hypothetical Answer Generation

pythonCopyEditfrom langchain_core.runnables import RunnablePassthrough

# Wrap the QA chain to generate a hypothetical document
hyde_chain = RunnablePassthrough.assign(hypothetical_document=qa_chain)

# Invoke HyDE
hyde_output = hyde_chain.invoke({
    "question": "how to use multi-modal models in a chain and turn chain into a rest api"
})
print("\n[HYDE Chain Output]:", hyde_output)

📦 Step 5: Add Structured Output with Pydantic

pythonCopyEditfrom langchain_core.output_parsers.openai_tools import PydanticToolsParser
from langchain_core.pydantic_v1 import BaseModel, Field

# Define a structured output schema
class Query(BaseModel):
    answer: str = Field(..., description="Tutorial-style answer.")

# Bind the LLM to the output schema
llm_with_tools = llm.bind_tools([Query])

# Create structured response chain
structured_chain = prompt | llm_with_tools | PydanticToolsParser(tools=[Query])

# Invoke the structured output
structured_response = structured_chain.invoke({
    "question": "how to use multi-modal models in a chain and turn chain into a rest api"
})
print("\n[Structured Output]:", structured_response)

Conclusion

HyDE (Hypothetical Document Embeddings) is more than just an AI trick it's a shift in how machines understand and retrieve information. By generating a possible answer before searching, HyDE bridges the gap between human thought and machine logic.

Whether it’s helping a farmer find the right irrigation scheme, guiding a student to the best learning resource, or matching vague queries to specific government documents HyDE empowers AI to think more like us.

As we build tools for the next generation of users, especially in diverse and underserved communities, HyDE can play a crucial role in making AI systems more intuitive, accessible, and impactful.

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