HyDE: Enhancing Information Retrieval with Hypothetical Document Embeddings

What is Information Retrieval (IR)?

Information retrieval involves finding and extracting relevant information from large collections of data to satisfy a user's query. Think of it like a digital librarian who knows exactly where to find the right book or section in a vast library. In the digital world, IR systems scan through massive amounts of text (like web pages, documents, or databases) to find the most relevant pieces of information. Techniques range from simple keyword matching to sophisticated algorithms that understand the semantics of queries and documents. In traditional settings, IR systems like search engines index vast amounts of data and use various ranking algorithms to present the most relevant results to users. However, in the context of RAG systems, IR serves a dual role: it not only retrieves relevant information but also augments the generation of new content.

What is Retrieval-Augmented Generation (RAG) in the context of information retrieval?

Retrieval-Augmented Generation, or RAG, is an advanced system combining the best parts of IR with the capabilities of generating new text. Think of it as a supercharged version of IR. Instead of just finding existing information, RAG can also create new, informative content based on the data it retrieves. It improves the quality and relevance of large language model (LLM) outputs by providing them with a custom knowledge base. RAG systems combine retrieval-based methods and generative models for various applications, such as chatbots, virtual assistants, and automated content creation tools.

What is HyDE (Hypothetical Document Embeddings)?

HyDE, introduced in a paper by Gao et al., is a novel approach to dense retrieval, which is the method of retrieving documents using semantic embedding similarities. When searching for information in a knowledge base, retrieval algorithms typically rely on examples of what good matches look like (relevance labels). However, in zero-shot retrieval scenarios, no such examples are available. HyDE addresses this by using two main tools: a "language model" and a "contrastive encoder." When you ask a question, HyDE uses a language model (like InstructGPT) to create a hypothetical document relevant to the question. This document is then processed by the contrastive encoder (like Contriever), which turns it into an embedding vector that captures the core meaning of the document. This vector is used to search through a collection of real documents, finding those that are similar to the hypothetical one. Despite not having examples, HyDE can still perform very well and often finds accurate matches for a wide range of questions and languages. It is used for various tasks like web searches, answering questions, and fact-checking in multiple languages.

Why Should You Use HyDE?

When searching using your own words (the query), the IR system looks for documents that match those exact words or similar words, sometimes missing the broader context. HyDE helps by first generating a hypothetical document based on your query, capturing the main ideas and context better than the query alone. The IR system then searches for real documents with similar "fingerprints," improving the chances of finding the best matches.

For instance, if you ask, "What are the long-term effects of climate change on polar bear populations?" a direct search might find documents containing the exact words "long-term effects," "climate change," "polar bear," and "populations," but miss the overall context. HyDE, however, would generate a detailed hypothetical document, such as "The long-term effects of climate change on polar bears include habitat loss due to melting ice, reduced access to prey, and increased stress levels. These factors can lead to declining populations over time." This approach captures the broader context of your query, finds documents based on ideas rather than just words, filters out irrelevant details due to vector embedding, and works well for complex or nuanced queries.

From a technical perspective, HyDE excels in scenarios where no relevance labels are available, making it useful for new or evolving domains with insufficient labeled data. Its adaptability across tasks is another strength, performing robustly in various applications such as web search, question answering, and fact verification. Despite being an unsupervised method, HyDE's performance is comparable to fine-tuned models, avoiding the need for training new models from scratch. This efficiency translates to faster deployment and lower operational costs.

HyDE Implementation

HyDE combines two main components: a generative instruction-following language model and a contrastive encoder. Given a query, the language model generates a hypothetical document that captures the essence of the query. The contrastive encoder then transforms this document into an embedding vector, which can be used to search the corpus for similar real documents based on vector similarity.

HyDE with LlamaIndex

LlamaIndex is a powerful tool for indexing and retrieving documents. By integrating HyDE with LlamaIndex, you can leverage its indexing capabilities to manage and search large document collections efficiently. This integration combines hypothetical document generation and advanced indexing, providing a robust solution for information retrieval.

To utilize HyDE with LlamaIndex, follow these steps:

1. Document Generation: Use HyDE to generate hypothetical documents from queries. This involves creating relevant, possibly fictional, documents that capture the essence of the query, enhancing search results' accuracy and relevance.
2. Indexing: Employ LlamaIndex to index these hypothetical documents alongside real documents. This step ensures both types of documents are efficiently organized and easily retrievable.
3. Search and Retrieval: Conduct searches using LlamaIndex's efficient retrieval algorithms, augmented by HyDE's enhanced document relevance embeddings. This combination improves the accuracy and relevance of search results.

Here's a code snippet demonstrating how to generate a hypothetical document embedding with LlamaIndex:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.indices.query.query_transform import HyDEQueryTransform
from llama_index.core.query_engine import TransformQueryEngine

# Loading the documents
documents = SimpleDirectoryReader("load_some_data_here").load_data()

# Creating a vector store index with the loaded documents
index = VectorStoreIndex.from_documents(documents)

# Setting up the index as the query engine
query_engine = index.as_query_engine()

query_str = "Can you provide me with the history of the world cup?"

# Creating a HyDE query engine from the initial query engine
hyde = HyDEQueryTransform(include_original=True)
hyde_query_engine = TransformQueryEngine(query_engine, hyde)

# Creating HyDE and retrieving from the vector store
response = hyde_query_engine.query(query_str)

print(response)

The generated response used for semantic similarity search:

"The FIFA World Cup is an international association football competition among the senior men's national teams of the members of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The tournament has been held every four years since the inaugural tournament in 1930, with the exception of 1942 and 1946 due to the Second World War. The first World Cup was held in Uruguay in 1930, and Uruguay became the first nation to win the World Cup. The competition starts with the qualification phase, which takes place over the preceding three years to determine which teams qualify for the tournament phase. In the tournament phase, 32 teams compete for the title at venues within the host nation(s) over the course of about a month. The host nation(s) automatically qualify for the group stage of the tournament. The competition is scheduled to expand to 48 teams, starting with the 2026 tournament. As of the 2022 FIFA World Cup, 22 final tournaments have been held since the event's inception in 1930, and a total of 80 national teams have competed. The trophy has been won by eight national teams. Brazil, with five wins, are the only team to have played in every tournament. The other World Cup winners are Germany and Italy, with four titles each; Argentina, with three titles; France and inaugural winner Uruguay, each with two titles; and England and Spain, with one title each."

LlamaIndex simplifies the entire process, from setting up the vector store to generating HyDE, embedding, and retrieval, making it a valuable tool for efficient and effective document indexing and search capabilities. For further details and demos, refer to the LlamaIndex documentation and examples.

HyDE with LangChain

LangChain is a framework designed to simplify the deployment of large language models. Integrating HyDE with LangChain allows for seamless deployment and management of the models required for HyDE, making the process straightforward and efficient.

To integrate HyDE with LangChain, follow these steps:

1. Model Deployment: Deploy the language and embedding models required for HyDE using LangChain's infrastructure. This involves setting up the necessary models to generate hypothetical documents and their embeddings.
2. Query Handling: Implement a pipeline in LangChain to handle queries. This includes generating hypothetical documents based on queries, creating embeddings for these documents, and retrieving relevant information.
3. Optimization and Scaling: Use LangChain's features to optimize and scale the retrieval process, ensuring robust performance even under high load.

Here's a code snippet to implement HyDE in LangChain:

from langchain.chains import HypotheticalDocumentEmbedder, LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI, OpenAIEmbeddings

# Initialize language model and embedding model
base_embeddings = OpenAIEmbeddings()
llm = OpenAI()

# Initialize the hypothetical document embedder class
# "web_search" parameter sets the prompt template to be used for generation
embeddings = HypotheticalDocumentEmbedder.from_llm(llm, base_embeddings, "web_search")

# Create HyDE
result = embeddings.embed_query("Can you provide me with the history of the world cup?")

The code returns a list containing embeddings of the generated hypothetical answer, which will be used for semantic similarity search. The answer can be viewed by calling the function embeddings.invoke("Can you provide me with the history of the world cup?"). This outputs the generated answer:

"The FIFA World Cup, often referred to as the 'World Cup', is an international association football tournament contested by the men's national teams of the members of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The championship has been awarded every four years since the inaugural tournament in 1930, with the exception of 1942 and 1946 when it was not held due to World War II. The tournament consists of two stages: the qualification phase, which takes place over the preceding three years, and the final phase, which is held during a period of about one month. The World Cup has become one of the most widely viewed and followed sporting events in the world, with an estimated 715.1 million people watching the 2018 edition. The tournament has a rich and fascinating history, with its origins dating back to the 1920s when FIFA President Jules Rimet proposed the idea of a global football tournament. The first World Cup was held in Uruguay in 1930, with only 13 teams participating. Since then, the tournament has grown in both size and popularity, with 32 teams currently competing in the final phase. Over the years, the World Cup has witnessed iconic moments and unforgettable matches."

LangChain simplifies integrating HyDE, from model deployment to query handling and optimization, making it an essential tool for deploying and managing large language models efficiently.

Is It Better to Implement HyDE for Your Own Use Case or Use API from Frameworks?

Deciding whether to implement HyDE independently or use an existing API from frameworks like LlamaIndex or LangChain depends on various factors:

1. Control and Customization: Implementing HyDE independently offers full control and extensive customization to meet specific needs, ideal for unique requirements or deep integration into existing systems.
2. Development Time and Complexity: Using APIs from frameworks provides a ready-to-use solution that reduces development time and complexity. Frameworks handle much of the heavy lifting, making it easier to get started and maintain the system.
3. Maintenance and Scalability: Implementing independently requires ongoing maintenance and scaling efforts. Frameworks typically handle scalability and maintenance, offering built-in optimizations and support, freeing up resources for other aspects of your project.
4. Technical Expertise and Resources: Implementing independently requires significant technical expertise and resources. Using framework APIs lowers the barrier to entry, making advanced retrieval capabilities accessible to teams with limited resources or expertise.

Frameworks like LangChain and LlamaIndex use large language models for generating hypothetical documents, employing pre-defined prompts that might not always be customizable. This can be inconvenient if the generated documents aren't ideal for your use case. Creating custom prompts clarifies specific task details to the language model, enhancing the quality of generated answers.