This code implements three query transformation techniques to enhance the retrieval process in Retrieval-Augmented Generation (RAG) systems:
- Query Rewriting
- Step-back Prompting
- Sub-query Decomposition
Each technique aims to improve the relevance and comprehensiveness of retrieved information by modifying or expanding the original query.
Motivation
RAG systems often face challenges in retrieving the most relevant information, especially when dealing with complex or ambiguous queries. These query transformation techniques address this issue by reformulating queries to better match relevant documents or to retrieve more comprehensive information.
Key Components
- Query Rewriting: Reformulates queries to be more specific and detailed.
- Step-back Prompting: Generates broader queries for better context retrieval.
- Sub-query Decomposition: Breaks down complex queries into simpler sub-queries.
Method Details
1. Query Rewriting
- Purpose: To make queries more specific and detailed, improving the likelihood of retrieving relevant information.
- Implementation:
- Uses a GPT-4 model with a custom prompt template.
- Takes the original query and reformulates it to be more specific and detailed.
2. Step-back Prompting
- Purpose: To generate broader, more general queries that can help retrieve relevant background information.
- Implementation:
- Uses a GPT-4 model with a custom prompt template.
- Takes the original query and generates a more general “step-back” query.
3. Sub-query Decomposition
- Purpose: To break down complex queries into simpler sub-queries for more comprehensive information retrieval.
- Implementation:
- Uses a GPT-4 model with a custom prompt template.
- Decomposes the original query into 2-4 simpler sub-queries.
Benefits of these Approaches
- Improved Relevance: Query rewriting helps in retrieving more specific and relevant information.
- Better Context: Step-back prompting allows for retrieval of broader context and background information.
- Comprehensive Results: Sub-query decomposition enables retrieval of information that covers different aspects of a complex query.
- Flexibility: Each technique can be used independently or in combination, depending on the specific use case.
Implementation Details
- All techniques use OpenAI’s GPT-4 model for query transformation.
- Custom prompt templates are used to guide the model in generating appropriate transformations.
- The code provides separate functions for each transformation technique, allowing for easy integration into existing RAG systems.
Example Use Case
The code demonstrates each technique using the example query: “What are the impacts of climate change on the environment?”
- Query Rewriting expands this to include specific aspects like temperature changes and biodiversity.
- Step-back Prompting generalizes it to “What are the general effects of climate change?”
- Sub-query Decomposition breaks it down into questions about biodiversity, oceans, weather patterns, and terrestrial environments.
These query transformation techniques offer powerful ways to enhance the retrieval capabilities of RAG systems. By reformulating queries in various ways, they can significantly improve the relevance, context, and comprehensiveness of retrieved information. These methods are particularly valuable in domains where queries can be complex or multifaceted, such as scientific research, legal analysis, or comprehensive fact-finding tasks.
Import libraries and set environment variables
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
import os
from dotenv import load_dotenv
# Load environment variables from a .env file
load_dotenv()
# Set the OpenAI API key environment variable
os.environ["OPENAI_API_KEY"] = os.getenv('OPENAI_API_KEY')
1 – Query Rewriting: Reformulating queries to improve retrieval.
re_write_llm = ChatOpenAI(temperature=0, model_name="gpt-4o", max_tokens=4000)
# Create a prompt template for query rewriting
query_rewrite_template = """You are an AI assistant tasked with reformulating user queries to improve retrieval in a RAG system.
Given the original query, rewrite it to be more specific, detailed, and likely to retrieve relevant information.
Original query: {original_query}
Rewritten query:"""
query_rewrite_prompt = PromptTemplate(
input_variables=["original_query"],
template=query_rewrite_template
)
# Create an LLMChain for query rewriting
query_rewriter = query_rewrite_prompt | re_write_llm
def rewrite_query(original_query):
"""
Rewrite the original query to improve retrieval.
Args:
original_query (str): The original user query
Returns:
str: The rewritten query
"""
response = query_rewriter.invoke(original_query)
return response.content
Demonstrate on a use case
# example query over the understanding climate change dataset
original_query = "What are the impacts of climate change on the environment?"
rewritten_query = rewrite_query(original_query)
print("Original query:", original_query)
print("\nRewritten query:", rewritten_query)
Original query: What are the impacts of climate change on the environment? Rewritten query: What are the specific effects of climate change on various ecosystems, including changes in temperature, precipitation patterns, sea levels, and biodiversity?
2 – Step-back Prompting: Generating broader queries for better context retrieval.
step_back_llm = ChatOpenAI(temperature=0, model_name="gpt-4o", max_tokens=4000)
# Create a prompt template for step-back prompting
step_back_template = """You are an AI assistant tasked with generating broader, more general queries to improve context retrieval in a RAG system.
Given the original query, generate a step-back query that is more general and can help retrieve relevant background information.
Original query: {original_query}
Step-back query:"""
step_back_prompt = PromptTemplate(
input_variables=["original_query"],
template=step_back_template
)
# Create an LLMChain for step-back prompting
step_back_chain = step_back_prompt | step_back_llm
def generate_step_back_query(original_query):
"""
Generate a step-back query to retrieve broader context.
Args:
original_query (str): The original user query
Returns:
str: The step-back query
"""
response = step_back_chain.invoke(original_query)
return response.content
Demonstrate on a use case
# example query over the understanding climate change dataset
original_query = "What are the impacts of climate change on the environment?"
step_back_query = generate_step_back_query(original_query)
print("Original query:", original_query)
print("\nStep-back query:", step_back_query)
Original query: What are the impacts of climate change on the environment? Step-back query: What are the general effects of climate changes
3- Sub-query Decomposition: Breaking complex queries into simpler sub-queries.
sub_query_llm = ChatOpenAI(temperature=0, model_name="gpt-4o", max_tokens=4000)
# Create a prompt template for sub-query decomposition
subquery_decomposition_template = """You are an AI assistant tasked with breaking down complex queries into simpler sub-queries for a RAG system.
Given the original query, decompose it into 2-4 simpler sub-queries that, when answered together, would provide a comprehensive response to the original query.
Original query: {original_query}
example: What are the impacts of climate change on the environment?
Sub-queries:
1. What are the impacts of climate change on biodiversity?
2. How does climate change affect the oceans?
3. What are the effects of climate change on agriculture?
4. What are the impacts of climate change on human health?"""
subquery_decomposition_prompt = PromptTemplate(
input_variables=["original_query"],
template=subquery_decomposition_template
)
# Create an LLMChain for sub-query decomposition
subquery_decomposer_chain = subquery_decomposition_prompt | sub_query_llm
def decompose_query(original_query: str):
"""
Decompose the original query into simpler sub-queries.
Args:
original_query (str): The original complex query
Returns:
List[str]: A list of simpler sub-queries
"""
response = subquery_decomposer_chain.invoke(original_query).content
sub_queries = [q.strip() for q in response.split('\n') if q.strip() and not q.strip().startswith('Sub-queries:')]
return sub_queries
Demonstrate on a use case
# example query over the understanding climate change dataset
original_query = "What are the impacts of climate change on the environment?"
sub_queries = decompose_query(original_query)
print("\nSub-queries:")
for i, sub_query in enumerate(sub_queries, 1):
print(sub_query)
Sub-queries: Original query: What are the impacts of climate change on the environment? 1. How does climate change affect biodiversity and ecosystems? 2. What are the impacts of climate change on oceanic conditions and marine life? 3. How does climate change influence weather patterns and extreme weather events? 4. What are the effects of climate change on terrestrial environments, such as forests and deserts?
Propositions Chunking
Dr. Md. Harun Ar Rashid, MPH, MD, PhD, is a highly respected medical specialist celebrated for his exceptional clinical expertise and unwavering commitment to patient care. With advanced qualifications including MPH, MD, and PhD, he integrates cutting-edge research with a compassionate approach to medicine, ensuring that every patient receives personalized and effective treatment. His extensive training and hands-on experience enable him to diagnose complex conditions accurately and develop innovative treatment strategies tailored to individual needs. In addition to his clinical practice, Dr. Harun Ar Rashid is dedicated to medical education and research, writing and inventory creative thinking, innovative idea, critical care managementing make in his community to outreach, often participating in initiatives that promote health awareness and advance medical knowledge. His career is a testament to the high standards represented by his credentials, and he continues to contribute significantly to his field, driving improvements in both patient outcomes and healthcare practices.
