This Eval evaluates whether a retrieved chunk contains an answer to the query. It's extremely useful for evaluating retrieval systems.
RAG Eval Template
You are comparing a reference text to a question and trying to determine if the reference textcontains information relevant to answering the question. Here is the data: [BEGIN DATA]************ [Question]:{query}************ [Reference text]:{reference} [END DATA]Compare the Question above to the Reference text. You must determine whether the Reference textcontains information that can answer the Question. Please focus on whether the very specificquestion can be answered by the information in the Reference text.Your response must be single word, either "relevant"or"unrelated",and should not contain any text or characters aside from that word."unrelated" means that the reference text does not contain an answer to the Question."relevant" means the reference text contains an answer to the Question.
We are continually iterating our templates, view the most up-to-date template on GitHub. Last updated on 10/12/2023
Benchmark Results
GPT-4 Result
GPT-3.5 Results
Claude V2 Results
GPT 4 Turbo
How To Run the Eval
from phoenix.evals import ( RAG_RELEVANCY_PROMPT_RAILS_MAP, RAG_RELEVANCY_PROMPT_TEMPLATE, OpenAIModel, download_benchmark_dataset, llm_classify,)model =OpenAIModel( model_name="gpt-4", temperature=0.0,)#The rails is used to hold the output to specific values based on the template#It will remove text such as ",,," or "..."#Will ensure the binary value expected from the template is returnedrails =list(RAG_RELEVANCY_PROMPT_RAILS_MAP.values())relevance_classifications =llm_classify( dataframe=df, template=RAG_RELEVANCY_PROMPT_TEMPLATE, model=model, rails=rails, provide_explanation=True, #optional to generate explanations for the value produced by the eval LLM)
The above runs the RAG relevancy LLM template against the dataframe df.
