Document Reranking

Document reranking reorders retrieved documents based on query relevance, refining search results. Query expansion enhances search by adding related terms, improving recall and addressing ambiguity. Combining these techniques in RAG systems boosts retrieval accuracy and response quality.

Document reranking is the process of reordering retrieved documents based on their relevance to the user’s query. After an initial retrieval step, reranking refines the results by evaluating each document’s relevance more precisely, ensuring that the most pertinent documents are prioritized.

What Is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) is an advanced framework that combines the capabilities of Large Language Models (LLMs) with information retrieval systems. In RAG, when a user submits a query, the system retrieves relevant documents from a vast knowledge base and feeds this information into the LLM to generate informed and contextually accurate responses. This approach enhances the accuracy and relevance of AI-generated content by grounding it in factual data.

Understanding Query Expansion

What Is Query Expansion?

Definition

Query expansion is a technique used in information retrieval to enhance the effectiveness of search queries. It involves augmenting the original query with additional terms or phrases that are semantically related. The primary goal is to bridge the gap between the user’s intent and the language used in relevant documents, thereby improving the retrieval of pertinent information.

How It Works

In practice, query expansion can be achieved through various methods:

• Synonym Expansion: Incorporating synonyms of the query terms to cover different expressions of the same concept.
• Related Terms: Adding terms that are contextually related but not direct synonyms.
• LLM-Based Expansion: Using Large Language Models to generate expanded queries by predicting words or phrases that are relevant to the original query.

By expanding the query, the retrieval system can cast a wider net, capturing documents that might have been missed due to variations in terminology or phrasing.

Why Is Query Expansion Important in RAG Systems?

Improving Recall

Recall refers to the ability of the retrieval system to find all relevant documents. Query expansion enhances recall by:

• Retrieving documents that use different terms to describe the same concept.
• Capturing documents that cover related subtopics or broader aspects of the query.

Addressing Query Ambiguity

Users often submit short or ambiguous queries. Query expansion helps in:

• Clarifying the user’s intent by considering multiple interpretations.
• Providing a more comprehensive search by including various aspects of the topic.

Enhancing Document Matching

By including additional relevant terms, the system increases the likelihood of matching the query with documents that might use different vocabulary, thus improving the overall effectiveness of the retrieval process.

Methods of Query Expansion

1. Pseudo-Relevance Feedback (PRF)

What Is PRF?

Pseudo-Relevance Feedback is an automatic query expansion method where the system assumes that the top-ranked documents from an initial search are relevant. It extracts significant terms from these documents to refine the original query.

How PRF Works

• Initial Query Execution: The user’s original query is executed, and top documents are retrieved.
• Term Extraction: Key terms from these documents are identified based on frequency or significance.
• Query Refinement: The original query is expanded with these key terms.
• Second Retrieval: The expanded query is used to perform a new search, ideally retrieving more relevant documents.

Benefits and Drawbacks

• Benefits: Improves recall without requiring user intervention.
• Drawbacks: If the initial results contain irrelevant documents, the expansion may include misleading terms, reducing precision.

2. LLM-Based Query Expansion

Leveraging Large Language Models

With advancements in AI, LLMs like GPT-3 and GPT-4 can generate sophisticated query expansions by understanding context and semantics.

How LLM-Based Expansion Works

• Hypothetical Answer Generation: The LLM generates a hypothetical answer to the original query.
• Contextual Expansion: The answer provides additional context and related terms.
• Combined Query: The original query and the LLM’s output are combined to form an expanded query.

Example

Original Query:
“What were the most important factors that contributed to increases in revenue?”

LLM-Generated Answer:
“In the fiscal year, several key factors contributed to the significant increase in the company’s revenue, including successful marketing campaigns, product diversification, customer satisfaction initiatives, strategic pricing, and investments in technology.”

Expanded Query:
“Original Query: What were the most important factors that contributed to increases in revenue?
Hypothetical Answer: [LLM-Generated Answer]”

Advantages

• Deep Understanding: Captures nuanced relationships and concepts.
• Customization: Tailors the expansion to the specific domain or context.

Challenges

• Computational Resources: May require significant processing power.
• Over-Expansion: Risk of adding irrelevant or too many terms.

Implementing Query Expansion in RAG Systems

Step-by-Step Process

1. User Query Input: The system receives the user’s original query.
2. LLM-Based Expansion:
   • The system prompts the LLM to generate a hypothetical answer or related queries.
   • Example Prompt:
     “Provide a detailed answer or related queries for: [User’s Query]”
3. Combine Queries:
   • The original query and the expanded content are combined.
   • This ensures that the expanded query remains relevant to the user’s intent.
4. Use in Retrieval:
   • The expanded query is used to retrieve documents from the knowledge base.
   • This can be done using keyword search, semantic search, or a combination.

Benefits in RAG Systems

• Enhanced Retrieval: More relevant documents are retrieved, providing better context for the LLM.
• Improved User Experience: Users receive more accurate and informative responses.

Understanding Document Reranking

Why Reranking Is Necessary

• Initial Retrieval Limitations: Initial retrieval methods may rely on broad measures of similarity, which might not capture nuanced relevance.
• Overcoming Noise: Query expansion may introduce less relevant documents; reranking filters these out.
• Optimizing Context for LLMs: Providing the most relevant documents enhances the quality of the LLM’s generated responses.

Methods for Document Reranking

1. Cross-Encoder Models

Overview

Cross-encoders are neural network models that take a pair of inputs (the query and a document) and output a relevance score. Unlike bi-encoders, which encode query and document separately, cross-encoders process them jointly, allowing for richer interaction between the two.

How Cross-Encoders Work

• Input Pairing: Each document is paired with the query.
• Joint Encoding: The model encodes the pair together, capturing interactions.
• Scoring: Outputs a relevance score for each document.
• Ranking: Documents are sorted based on these scores.

Advantages

• High Precision: Provides more accurate relevance assessments.
• Contextual Understanding: Captures complex relationships between query and document.

Challenges

• Computationally Intensive: Requires significant processing power, especially for large document sets.

2. ColBERT (Late Interaction Models)

What Is ColBERT?

ColBERT (Contextualized Late Interaction over BERT) is a retrieval model designed to balance efficiency and effectiveness. It uses a late interaction mechanism that allows for detailed comparison between query and document tokens without heavy computational costs.

How ColBERT Works

• Token-Level Encoding: Separately encodes query and document tokens using BERT.
• Late Interaction: During scoring, compares query and document tokens using similarity measures.
• Efficiency: Enables pre-computation of document embeddings.

Advantages

• Efficient Scoring: Faster than full cross-encoders.
• Effective Retrieval: Maintains high retrieval quality.

Use Cases

• Suitable for large-scale retrieval where computational resources are limited.

3. FlashRank

Overview

FlashRank is a lightweight and fast reranking library that uses state-of-the-art cross-encoders. It’s designed to integrate easily into existing pipelines and improve reranking performance with minimal overhead.

Features

• Ease of Use: Simple API for quick integration.
• Speed: Optimized for rapid reranking.
• Accuracy: Employs effective models for high-quality reranking.

Example Usage

from flashrank import Ranker, RerankRequest

query = 'What were the most important factors that contributed to increases in revenue?'

ranker = Ranker(model_name="ms-marco-MiniLM-L-12-v2")
rerank_request = RerankRequest(query=query, passages=documents)
results = ranker.rerank(rerank_request)

Benefits

• Simplifies Reranking: Abstracts the complexities of model handling.
• Optimizes Performance: Balances speed and accuracy effectively.

Implementing Document Reranking in RAG Systems

Process

1. Initial Retrieval: Use the expanded query to retrieve a set of candidate documents.
2. Reranking: Apply a reranking model (e.g., Cross-Encoder, ColBERT) to assess the relevance of each document.
3. Selection: Select the top-ranked documents to use as context for the LLM.

Considerations

• Computational Resources: Reranking can be resource-intensive; balance is needed between performance and cost.
• Model Selection: Choose models that suit the application’s requirements in terms of accuracy and efficiency.
• Integration: Ensure that reranking fits seamlessly into the existing pipeline.

Combining Query Expansion and Document Reranking in RAG

Synergy Between Query Expansion and Reranking

Complementary Techniques

• Query Expansion broadens the search scope, retrieving more documents.
• Document Reranking refines these results, focusing on the most relevant ones.

Benefits of Combining

• Enhanced Recall and Precision: Together, they improve both the quantity and quality of retrieved documents.
• Robust Retrieval: Addresses the limitations of each method when used alone.
• Improved LLM Output: Provides better context, leading to more accurate and informative responses.

How They Work Together

1. User Query Input: The original query is received.
2. Query Expansion: The query is expanded using methods like LLM-based expansion, resulting in a more comprehensive search query.
3. Initial Retrieval: The expanded query is used to retrieve a broad set of documents.
4. Document Reranking: Reranking models evaluate and reorder the documents based on relevance to the original query.
5. Context Provision: The top-ranked documents are provided to the LLM as context.
6. Response Generation: The LLM generates a response informed by the most relevant documents.

Practical Implementation Steps

Example Workflow

• Query Expansion with LLM:

  def expand_query(query):
      prompt = f"Provide additional related queries for: '{query}'"
      expanded_queries = llm.generate(prompt)
      expanded_query = ' '.join([query] + expanded_queries)
      return expanded_query
  

• Initial Retrieval:

  documents = vector_db.retrieve_documents(expanded_query)
  

• Document Reranking:

  from sentence_transformers import CrossEncoder
  cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
  pairs = [[query, doc.text] for doc in documents]
  scores = cross_encoder.predict(pairs)
  ranked_docs = [doc for _, doc in sorted(zip(scores, documents), reverse=True)]
  

• Selecting Top Documents:

  top_documents = ranked_docs[:top_k]
  

• Generating Response with LLM:

  context = '\n'.join([doc.text for doc in top_documents])
  prompt = f"Answer the following question using the context provided:\n\nQuestion: {query}\n\nContext:\n{context}"
  response = llm.generate(prompt)
  

Monitoring and Optimization

• Performance Metrics: Regularly measure retrieval effectiveness using metrics like precision, recall, and relevance scores.
• Feedback Loops: Incorporate user feedback to improve query expansion and reranking strategies.
• Resource Management: Optimize computational resources, possibly by caching results or limiting the number of reranked documents.

Use Cases and Examples

Example 1: Enhancing AI Chatbots for Customer Support

Scenario

A company uses an AI chatbot to handle customer queries about their products and services. Customers often ask questions in various ways, using different terminologies or phrases.

Challenges

• Varying customer language and terminology.
• Need for accurate and prompt responses to maintain customer satisfaction.

Implementation

• Query Expansion: The chatbot expands customer queries to include synonyms and related terms.
  For example, if a customer asks, “How can I fix my gadget?”, the query is expanded to include terms like “repair device”, “troubleshoot appliance”, etc.
• Document Reranking: Retrieved help articles and FAQs are reranked to prioritize the most relevant solutions. Cross-encoders assess the relevance of each document to the customer’s specific issue.

Benefits

• Improved accuracy and relevance of responses.
• Enhanced customer satisfaction and reduced support resolution times.

Example 2: Optimizing AI-Powered Research Tools

Scenario

Researchers use an AI assistant to find relevant academic papers, data, and insights for their work.

Challenges

• Complex queries with specialized terminology.
• Large volumes of academic literature to sift through.

Implementation

• Query Expansion: The assistant uses LLMs to expand queries with related concepts and synonyms.
  A query like “quantum entanglement applications” is expanded to include “uses of quantum entanglement”, “quantum computing entanglement”, etc.
• Document Reranking: Academic papers are reranked based on relevance to the refined