HyDE - Overview of Hypothetical Document Embeddings

Disclaimer:?the opinions I share are solely my own and do not reflect those of my employer.

In?Natural Language Processing (NLP), document embeddings convert text into numerical vectors essential for understanding meaning and retrieving information. Traditional methods like bag-of-words and TF-IDF often miss subtle word relationships. Single-vector representations also fail to capture diverse query aspects. Many dense retrieval systems require large, expensive labeled datasets.?

Hypothetical Document Embeddings (HyDE)?offer a solution by combining generative language models with contrastive encoders. With a Large Language Model (LLM), HyDE creates hypothetical answers to queries, which are transformed into embedding vectors. This improves retrieval accuracy by emphasizing answer similarity. The following section will discuss HyDE's significant role and potential enhancements.

What role do document embeddings play in LLMs?

Before exploring HyDE, it's crucial to understand Document embeddings. These serve as numerical representations of text documents, capturing their semantic meanings in a high-dimensional vector space. Traditional methods like bag-of-words or TF-IDF struggle to detect subtle relationships. Still, document embeddings enhance this process by providing dense representations communicating meanings, relationships, and key features.

Document embeddings assist Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems in the following ways:

• Semantic Understanding: Document embeddings allow LLMs to understand documents' content and context, going beyond simple keyword matching.
• Information Retrieval: By converting documents into vector embeddings, RAG systems can perform similarity searches to retrieve relevant documents based on the semantic similarity between the query and the document embeddings.
• Contextual Responses: In RAG pipelines, document embeddings enable LLMs to generate contextually informed responses by grounding the LLM's output in the retrieved documents.

Limitations of Document embeddings:

• Semantic Capture: Traditional text representations, such as bag-of-words or TF-IDF, may fail to capture nuanced semantic relationships and the contextual interdependencies within document structures.
• Contextual Understanding: Single-vector representations cannot capture different aspects of the queries and documents in relevance matching.
• Data Dependency: Dense retrieval systems require extensive labeled datasets, often in pairs of query and document, which is labor and cost-intensive.

Why is HyDE?

Hypothetical Document Embeddings (HyDE) are necessary to overcome these limitations and enhance document retrieval accuracy.

HyDE uses an LLM to generate a hypothetical answer to a query, which is then converted into an embedding vector. This approach shifts the focus from question/query similarity to answer-to-answer embedding similarity, improving retrieval accuracy.

HyDE usage explained.

Imagine you're trying to find information about "how animals adapt to the desert." Instead of typing that into a search engine, HyDE does something sneaky.

1. The AI Guesses: First, an innovative computer program tries to answer the question without looking at any documents. It "hallucinates" or makes an educated guess about the answer.
2. Creating a "Fake" Answer: The AI writes a "fake" or hypothetical document that answers your question. It might say, "Desert animals adapt by storing water, hunting at night, and having sandy fur."
3. Finding Real Answers: The AI then uses this?"fake" answer?to search for?similar real documents. It's like using your own guess to find the best information!
4. Smarter Searching: By comparing answers to answers, HyDE can find more relevant results than if you just searched for the question itself.

HyDE: A History

Hyde isn't that old. The "Precise Zero-Shot Dense Retrieval without Relevance Labels" research paper came out in December 2022.

But, HyDE quickly became a popular tool in AI programs because it helps them find the right information faster.

Why is HyDE Helpful?

• Saves Time: HyDE helps you find the important documents faster, so you don't have to read through a bunch of useless information.
• Smarter Results: Sometimes, the way we ask questions isn't the best way to find answers. HyDE helps bridge that gap by focusing on what the answer looks like.
• Works in Many Languages: HyDE can be used in different languages, which makes it useful for many people.

How HyDE enhances document retrieval accuracy?

• Capturing Relevance Patterns: Generating hypothetical documents captures relevance patterns, even if the details are inaccurate.
• Bridging the Query-Document Gap: The hypothetical document is an intermediary between the query and the document space, capturing intent more effectively than direct query encoding.
• Improving RAG Pipelines: HyDE optimizes document queries and handles vague questions in RAG pipelines.

Key steps for Hypothetical Document Embeddings (HyDE):

1. Query Input: A user submits a query.
2. Hypothetical Document Generation: A Large Language Model (LLM) generates a hypothetical answer, creating a "fake" document. The LLM is instructed to structure this document to answer the query.
3. Document Encoding: An unsupervised, contrastive encoder converts the hypothetical document into an embedding vector, simplifying the text and preserving essential meaning. The model distinguishes between similar and dissimilar data points, generating an embedding space where similar documents are close together.
4. Similarity Search: The vector embedding searches pre-encoded document embeddings for similarities.
5. Result Retrieval: The most similar documents are returned as results.

Step-by-Step Implementation in LangChain for HyDE

• Set Up Your Environment

Ensure you have LangChain and the necessary libraries installed. If you haven’t done so, install them using pip:

pip install langchain openai        

• Import Required Libraries

Import the modules from LangChain to interact with the LLM, perform document encoding, and handle the embeddings.

from langchain.llms import OpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.document import Document
from langchain.chains import SimilaritySearch        

• Query Input

Capture the user’s query. This can be a string input from the user.

user_query = "What are the benefits of Hypothetical Document Embeddings?"        

• Hypothetical Document Generation

Use an LLM to generate a document based on the user query, utilizing OpenAI’s technology to provide answers.

llm = OpenAI(model="text-davinci-003")  # Use an appropriate model for your needs

# Generate a hypothetical document
generated_document = llm(f"Create a structured document that answers the following query: {user_query}")
hypothetical_doc = Document(content=generated_document)        

• Document Encoding

Use a contrastive encoder to convert the document into an embedding vector, such as OpenAI’s model.

embedding_model = OpenAIEmbeddings(model="text-embedding-ada-002")  # Ensure you're using a suitable model

# Encode the hypothetical document
hypothetical_embedding = embedding_model.embed(hypothetical_doc)        

• Similarity Search

Prepare a set of pre-encoded document embeddings that allow us to search for similar documents.

# Pre-encoded document embeddings (mock example; replace with your actual data)
encoded_documents = [embedding_model.embed(Document(content="Existing document content 1")),
                     embedding_model.embed(Document(content="Existing document content 2")),
                     embedding_model.embed(Document(content="Existing document content 3"))]

# Perform similarity search
similarity_search = SimilaritySearch(embedding_model, encoded_documents)
results = similarity_search.search(hypothetical_embedding)        

• Result Retrieval

Finally, retrieve and display the most similar documents based on the embedding comparison.

# Display the results of the similarity search
for i, result in enumerate(results):
    print(f"Result {i + 1}: {result.content}")        

Complete Example

Here’s the entire implementation compiled into a single Python script:

from langchain.llms import OpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.document import Document
from langchain.chains import SimilaritySearch

# User's query input
user_query = "What are the benefits of Hypothetical Document Embeddings?"

# 1. Initialize the LLM
llm = OpenAI(model="text-davinci-003")

# 2. Generate Hypothetical Document
generated_document = llm(f"Create a structured document that answers the following query: {user_query}")
hypothetical_doc = Document(content=generated_document)

# 3. Initialize the embedding model
embedding_model = OpenAIEmbeddings(model="text-embedding-ada-002")

# 4. Encode the hypothetical document
hypothetical_embedding = embedding_model.embed(hypothetical_doc)

# 5. Pre-encoded document embeddings (mock examples)
encoded_documents = [
    embedding_model.embed(Document(content="Existing document content 1")),
    embedding_model.embed(Document(content="Existing document content 2")),
    embedding_model.embed(Document(content="Existing document content 3"))
]

# 6. Perform similarity search
similarity_search = SimilaritySearch(embedding_model, encoded_documents)
results = similarity_search.search(hypothetical_embedding)

# 7. Display the results of the similarity search
for i, result in enumerate(results):
    print(f"Result {i + 1}: {result.content}")        

HyDE is not Perfect

HyDE might make mistakes if the AI knows nothing about the topic. Also, having?the AI generate a "fake" document for every search can sometimes be expensive.

In conclusion, HyDE improves RAG within LLM.

HyDE can improve Retrieval Augmented Generation (RAG) systems. RAG systems use an LLM and a database to provide context-aware answers. HyDE improves basic RAG systems by:

• Creating a hypothetical document related to the query.
• Providing additional context to vague questions with a LLM.
• Finding documents that are answers rather than questions.

The Future of HyDE

Even though it's still new, HyDE is changing how AI helps us find information. As AI becomes even more brilliant, HyDE will likely become even more helpful.

So, next time you search for something online, remember that AI is working hard behind the scenes to understand what you need and find the best answers!