LANGCHAIN — Query Transformations

I’m not a great programmer; I’m just a good programmer with great habits. — Kent Beck.

Query transformations play a crucial role in optimizing retrieval and processing of user queries. In this tutorial, we’ll explore various query transformation techniques and their implementations using LangChain.

Multi Representation Indexing

One approach to improving retrieval is through multi-representation indexing. The following code demonstrates the use of the Multi Vector Retriever for creating a document representation suitable for retrieval.

from langchain.retrieval import MultiVectorRetriever

# Create a multi-vector retriever
retriever = MultiVectorRetriever()

# Index documents for retrieval
retriever.index_documents(documents)

Query Transformation

The next step involves transforming user questions to enhance retrieval. Here’s an example of query transformation using LangChain.

from langchain.query import QueryTransformer

# Initialize the query transformer
transformer = QueryTransformer()

# Transform a user question
transformed_query = transformer.transform_query(user_question)

Query Construction

Converting human questions into specific query syntax or language is essential for various applications. Although not covered in detail here, LangChain offers capabilities for query construction, which will be explored in subsequent posts.

Rewrite-Retrieve-Read

The following code snippet demonstrates the use of LangChain for rewrite-retrieve-read operations using an LLM.

from langchain.retrieval import RewriteRetriever

# Initialize the rewrite retriever
rewriter = RewriteRetriever()

# Rewrite user query and perform retrieval-augmented reading
rewritten_query = rewriter.rewrite_query(user_query)

Step Back Prompting

LangChain also supports the generation of “step back” questions using LLMs, as shown in the code snippet below.

from langchain.qa import StepBackPrompting

# Initialize the step back prompting module
prompter = StepBackPrompting()

# Generate a step back question
step_back_question = prompter.generate_step_back_question(original_question)

Follow Up Questions

Handling follow-up questions is crucial in conversational chains. Here’s an example of using LangChain for generating search terms from the entire conversation, including follow-up questions.

from langchain.conversation import FollowUpQuestionHandling

# Initialize follow-up question handler
handler = FollowUpQuestionHandling()

# Generate a search query from the conversation history
search_query = handler.generate_search_query(conversation_history)

Conclusion

In this tutorial, we’ve explored various query transformation techniques and their implementation using LangChain. From rewrite-retrieve-read to multi-query retrieval, LangChain offers versatile tools for optimizing query processing. The ability to leverage LLMs for query transformations opens up new possibilities for enhancing retrieval efficiency. As you explore these techniques, consider the prompts you can devise for unique query transformations using LangChain.