Introduction to Query Transformations

When working with large language models (LLMs) and vast amounts of data, getting the right information can be challenging. This is where query transformations come into play. In LlamaIndex, these transformations allow us to modify and enhance user queries, leading to more accurate and relevant results.

Types of Query Transformations

Let's explore some of the advanced query transformations available in LlamaIndex:

1. HyDE (Hypothetical Document Embeddings)

HyDE is a powerful technique that generates a hypothetical answer to the user's query and then uses this hypothetical document for retrieval. This approach can significantly improve the relevance of search results.

Here's how you can implement HyDE in LlamaIndex:

from llama_index.indices.query.query_transform.base import HyDEQueryTransform

hyde_transform = HyDEQueryTransform(include_original=True)
query_engine = index.as_query_engine(query_transform=hyde_transform)

2. Keyword Expansion

This transformation expands the original query with relevant keywords, broadening the search scope. It's particularly useful when users provide short or ambiguous queries.

from llama_index.indices.query.query_transform.base import KeywordExpandRetrieverQueryTransform

expand_transform = KeywordExpandRetrieverQueryTransform(llm=llm)
query_engine = index.as_query_engine(query_transform=expand_transform)

3. Multi-Step Query Decomposition

For complex queries, breaking them down into smaller, more manageable sub-queries can yield better results. LlamaIndex offers a multi-step query decomposition transformation:

from llama_index.indices.query.query_transform.base import StepDecomposeQueryTransform

step_decompose = StepDecomposeQueryTransform(llm=llm, verbose=True)
query_engine = index.as_query_engine(query_transform=step_decompose)

Combining Transformations

One of the most powerful aspects of LlamaIndex is the ability to combine multiple query transformations. This allows you to create a custom pipeline that suits your specific needs:

from llama_index.indices.query.query_transform.base import ComposableQueryTransform

combined_transform = ComposableQueryTransform([
    KeywordExpandRetrieverQueryTransform(llm=llm),
    HyDEQueryTransform(include_original=True)
])

query_engine = index.as_query_engine(query_transform=combined_transform)

Custom Query Transformations

While LlamaIndex provides several built-in transformations, you can also create your own custom transformations. Here's a simple example of a custom transformation that adds a prefix to every query:

from llama_index.indices.query.query_transform.base import BaseQueryTransform

class PrefixQueryTransform(BaseQueryTransform):
    def __init__(self, prefix: str):
        self.prefix = prefix

    def _run(self, query_str: str) -> str:
        return f"{self.prefix} {query_str}"

prefix_transform = PrefixQueryTransform("In the context of AI and machine learning,")
query_engine = index.as_query_engine(query_transform=prefix_transform)

Best Practices for Query Transformations

1. Understand Your Data: Different transformations work better for different types of data and queries. Experiment to find the best fit for your use case.

2. Monitor Performance: Keep track of how transformations affect your query results. Use metrics like relevance scores and user feedback to gauge effectiveness.

3. Balance Complexity: While combining transformations can be powerful, too many transformations can slow down query processing. Find the right balance for your application.

4. Stay Updated: LlamaIndex is continuously evolving. Keep an eye on new transformations and features that could enhance your query processing pipeline.

By leveraging these advanced query transformations, you can significantly improve the accuracy and relevance of your LLM-powered applications. Whether you're building a chatbot, a question-answering system, or any other AI-driven tool, mastering these techniques will give you a competitive edge in the world of natural language processing and information retrieval.