RAG (evaluate intermediate steps) | LangSmith Evaluations - Part 16

Summary

In this 16th part of the Langs withth evaluation series, Lance discusses improving the evaluation process for the RAG (Retrieval-Augmented Generation) pipeline. He addresses the limitation of previous approaches that required pipelines to return both the answer and the relevant documents, which is often unrealistic. Lance introduces a technique to extract intermediate components, like documents, directly from the trace, simplifying the evaluation process. He demonstrates how to use this method for both hallucination grading and document relevance grading, showing how it streamlines evaluations without needing to alter the pipeline structure.

Takeaways

• 📝 Lance introduces a new technique for evaluating the performance of a RAG (Retrieval-Augmented Generation) model without needing to output additional documents or information.
• 🔍 The traditional evaluation methods for RAG models, such as hallucination grading and retrieval grading, were critiqued for their unrealistic requirement of outputting documents alongside the answer.
• 🛠 Lance demonstrates a trick where evaluation code can access components from the trace, specifically the retrieved documents, without altering the RAG pipeline to include these in its output.
• 🔑 The script explains how to use the traceable decorator to make functions retrievable by their names, which is key to accessing intermediate steps like retrieved documents.
• 📚 Lance shows how to isolate child runs and extract specific functions, such as 'get answer' and 'retrieve docs', from the overall run of the RAG pipeline.
• 📑 The script details the process of extracting documents from the trace and using them directly in the evaluation process, streamlining the evaluation of document relevance.
• 📝 Lance discusses the use of a binary scoring system for evaluating the relevance of retrieved documents, using a grading prompt to determine a score of one or zero.
• 🔄 The script outlines the process of defining functions for hallucination grading, similar to document relevance grading, but focusing on the generation output from the RAG pipeline.
• 📈 Lance emphasizes the simplicity and convenience of the new evaluation method, which avoids the need for a pipeline that outputs intermediate documents for evaluation.
• 🔧 The script provides an example of setting up a data model for grading, using a grading prompt, and invoking a retrieval grader with the question and document text.
• 📊 The final part of the script shows the practical application of the evaluation method, with Lance running the evaluation on a test dataset and confirming the logic and scores.

Q & A

What is the main topic of the video script?

The main topic of the video script is discussing different approaches to evaluate the performance of a 'RAG' (Retrieval-Augmented Generation) pipeline, focusing on a method to simplify the evaluation process by accessing intermediate components from the trace.

What are the different evaluation methods mentioned in the script?

The script mentions evaluating the answer relative to a reference, hallucination grading of the answer, and retrieval grading of relevant documents.

What does the speaker find 'ugly' about the hallucination grading and retrieval grading approaches?

The speaker finds it unrealistic that most RAG pipelines would return both the answer and the retrieved documents, as it is not common to enforce this requirement for evaluations.

What trick does the speaker introduce to simplify the evaluation process?

The speaker introduces a trick to access the trace and retrieve certain components, such as the documents, directly from the trace itself during the evaluation process.

How does the speaker plan to use the trace to improve the evaluation process?

The speaker plans to use the trace to isolate child runs and retrieve the output of specific functions, like 'get answer' and 'retrieve docs', to perform evaluations without needing to output these components from the pipeline.

What is the purpose of the 'get answer' function in the script?

The 'get answer' function is used to retrieve the answer from the RAG pipeline, and it is also used to access other functions within it, such as 'retrieve docs'.

What is the role of the 'retriever run outputs' in the evaluation?

The 'retriever run outputs' are used to extract the documents needed for the evaluation, which simplifies the process by not requiring these documents to be output from the pipeline separately.

How does the speaker define the grading model for document relevance?

The speaker defines a binary score for the relevance of retrieved documents using a data model, with a grading prompt that assigns a score of one for relevant documents and zero otherwise.

What is the grading prompt used in the script for evaluating document relevance?

The grading prompt used is a simple instruction to pick one for relevant documents and zero for others.

How does the speaker ensure the grading process is realistic and not overly complicated?

The speaker ensures the grading process is realistic by accessing intermediate objects directly from the trace, avoiding the need to define a pipeline or chain that outputs them separately.

What is the final outcome of the evaluation process as described in the script?

The final outcome is a simplified and more realistic evaluation process that can evaluate both answer hallucinations and document relevance in a single step, using the ability to fish out intermediate documents from the trace.