自修正查询引擎 - 评估与重试

!pip install llama-index

# Uncomment to add your OpenAI API key
# import os
# os.environ['OPENAI_API_KEY'] = "INSERT OPENAI KEY"

# Uncomment for debug level logging
# import logging
# import sys

# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from llama_index.core import VectorStoreIndex
from llama_index.core import SimpleDirectoryReader

# Needed for running async functions in Jupyter Notebook
import nest_asyncio

nest_asyncio.apply()

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(documents)
query = "What did the author do growing up?"

base_query_engine = index.as_query_engine()
response = base_query_engine.query(query)
print(response)

The author worked on writing and programming outside of school before college. They wrote short stories and tried writing programs on an IBM 1401 computer using an early version of Fortran. They later got a microcomputer and started programming on it, writing simple games and a word processor. They also mentioned their interest in philosophy and AI.

from llama_index.core.query_engine import RetryQueryEngine
from llama_index.core.evaluation import RelevancyEvaluator

query_response_evaluator = RelevancyEvaluator()
retry_query_engine = RetryQueryEngine(
    base_query_engine, query_response_evaluator
)
retry_response = retry_query_engine.query(query)
print(retry_response)

The author worked on writing and programming outside of school before college. They wrote short stories and tried writing programs on an IBM 1401 computer using an early version of Fortran. They later got a microcomputer, a TRS-80, and started programming more extensively, including writing simple games and a word processor.

from llama_index.core.query_engine import RetrySourceQueryEngine

retry_source_query_engine = RetrySourceQueryEngine(
    base_query_engine, query_response_evaluator
)
retry_source_response = retry_source_query_engine.query(query)
print(retry_source_response)

The author worked on writing and programming outside of school before college. They wrote short stories and tried writing programs on an IBM 1401 computer using an early version of Fortran. They later got a microcomputer and started programming on it, writing simple games and a word processor. They also mentioned their interest in philosophy and AI.

from llama_index.core.evaluation import GuidelineEvaluator
from llama_index.core.evaluation.guideline import DEFAULT_GUIDELINES
from llama_index.core import Response
from llama_index.core.indices.query.query_transform.feedback_transform import (
    FeedbackQueryTransformation,
)
from llama_index.core.query_engine import RetryGuidelineQueryEngine

# Guideline eval
guideline_eval = GuidelineEvaluator(
    guidelines=DEFAULT_GUIDELINES
    + "\nThe response should not be overly long.\n"
    "The response should try to summarize where possible.\n"
)  # just for example

typed_response = (
    response if isinstance(response, Response) else response.get_response()
)
eval = guideline_eval.evaluate_response(query, typed_response)
print(f"Guideline eval evaluation result: {eval.feedback}")

feedback_query_transform = FeedbackQueryTransformation(resynthesize_query=True)
transformed_query = feedback_query_transform.run(query, {"evaluation": eval})
print(f"Transformed query: {transformed_query.query_str}")

Guideline eval evaluation result: The response partially answers the query but lacks specific statistics or numbers. It provides some details about the author's activities growing up, such as writing short stories and programming on different computers, but it could be more concise and focused. Additionally, the response does not mention any statistics or numbers to support the author's experiences.
Transformed query: Here is a previous bad answer.
The author worked on writing and programming outside of school before college. They wrote short stories and tried writing programs on an IBM 1401 computer using an early version of Fortran. They later got a microcomputer and started programming on it, writing simple games and a word processor. They also mentioned their interest in philosophy and AI.
Here is some feedback from the evaluator about the response given.
The response partially answers the query but lacks specific statistics or numbers. It provides some details about the author's activities growing up, such as writing short stories and programming on different computers, but it could be more concise and focused. Additionally, the response does not mention any statistics or numbers to support the author's experiences.
Now answer the question.
What were the author's activities and interests during their childhood and adolescence?

retry_guideline_query_engine = RetryGuidelineQueryEngine(
    base_query_engine, guideline_eval, resynthesize_query=True
)
retry_guideline_response = retry_guideline_query_engine.query(query)
print(retry_guideline_response)

During their childhood and adolescence, the author worked on writing short stories and programming. They mentioned that their short stories were not very good, lacking plot but focusing on characters with strong feelings. In terms of programming, they tried writing programs on the IBM 1401 computer in 9th grade using an early version of Fortran. However, they mentioned being puzzled by the 1401 and not being able to do much with it due to the limited input options. They also mentioned getting a microcomputer, a TRS-80, and starting to write simple games, a program to predict rocket heights, and a word processor.