You are comparing a reference text to a question and trying to determine if the reference text
contains 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 text
contains information that can answer the Question. Please focus on whether the very specific
question can be answered by the information in the Reference text.
Your response must be single word, either "relevant" or "irrelevant",
and should not contain any text or characters aside from that word.
"irrelevant" 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
Scikit GPT-4
GPT-3.5 Results
Claude V2 Results
How To Run the Eval
from phoenix.experimental.evals import (
RAG_RELEVANCY_PROMPT_RAILS_MAP,
RAG_RELEVANCY_PROMPT_TEMPLATE_STR,
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 returned
rails = list(RAG_RELEVANCY_PROMPT_RAILS_MAP.values())
relevance_classifications = llm_classify(
dataframe=df,
template=RAG_RELEVANCY_PROMPT_TEMPLATE_STR,
model=model,
rails=rails,
)
The above runs the RAG relevancy LLM template against the dataframe df.
