Query Expansion refers to the process of enhancing a user’s original query by adding additional terms or context before sending it to the retrieval mechanism. This augmentation helps in retrieving more relevant documents or pieces of information, which are then used to generate a more accurate and contextually appropriate response. If documents are searched with alternative queries and than reranked, RAG process gets much more price document results in the prompt context window.
How is Query Expansion Utilized in RAG Chatbots?
1. Basic Query Expansion
In basic query expansion, synonyms, related terms, or additional context words are added to the user’s original query. This can be achieved through various methods, such as:
- Thesaurus Lookup: Adding synonyms of key terms in the query.
- Contextual Analysis: Understanding the user’s intent and adding related terms to the query.
2. Hypothetical Document Embeddings (HyDE)
A more advanced form of query expansion is the use of Hypothetical Document Embeddings (HyDE). In this approach:
- A Language Model (LM) generates a hypothetical answer to the user’s query.
- This fabricated answer is appended to the original query.
- The combined string is then submitted to the retrieval mechanism.
This method enriches the query with semantic information and improves the chances of retrieving highly relevant documents.
Example:
- Original Query: “Who can get me a Salesforce login?”
- HyDE Query: “Who can get me a Salesforce login? To gain access to the Salesforce customer relationship manager (CRM), contact the Sales Operation department or the IT department.”
3. AI-Powered Query Expansion
In AI-powered query expansion, machine learning models are used to analyze and expand the query dynamically. These models can:
- Identify the most relevant terms and phrases from a vast corpus.
- Automatically generate expanded queries based on learned patterns and user intent.
4. LLM-Powered Query Expansion
Large Language Models are to trained on multiple NLP tasks and works very well for query expansion as well.
Example
Original query: What is query Expansion?
Expanded query:
- What does Query Expansion mean?
- Can you explain the concept of Query Expansion?
- What is the definition of Query Expansion?
- Could you describe what Query Expansion is?
- What does the term Query Expansion refer to?
Importance of Query Expansion in RAG Chatbots
Improved Retrieval Accuracy
By expanding the query, the retrieval mechanism can access a broader range of relevant documents, leading to more accurate and contextually appropriate responses.
Enhanced User Experience
Providing precise and relevant answers enhances the overall user experience, making the chatbot more effective and reliable.
Resource Optimization
While query expansion may increase the computational load, the trade-off often results in significantly better retrieval and generation outcomes, justifying the added complexity.