Confident 集成

pip install -qU  langchain langchain-openai langchain-community deepeval langchain-chroma

!deepeval login

from deepeval.metrics.answer_relevancy import AnswerRelevancy

# Here we want to make sure the answer is minimally relevant
answer_relevancy_metric = AnswerRelevancy(minimum_score=0.5)

from langchain_community.callbacks.confident_callback import DeepEvalCallbackHandler

deepeval_callback = DeepEvalCallbackHandler(
    implementation_name="langchainQuickstart", metrics=[answer_relevancy_metric]
)

from langchain_openai import OpenAI

llm = OpenAI(
    temperature=0,
    callbacks=[deepeval_callback],
    verbose=True,
    openai_api_key="<YOUR_API_KEY>",
)
output = llm.generate(
    [
        "What is the best evaluation tool out there? (no bias at all)",
    ]
)

LLMResult(generations=[[Generation(text='\n\nQ: What did the fish say when he hit the wall? \nA: Dam.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nThe Moon \n\nThe moon is high in the midnight sky,\nSparkling like a star above.\nThe night so peaceful, so serene,\nFilling up the air with love.\n\nEver changing and renewing,\nA never-ending light of grace.\nThe moon remains a constant view,\nA reminder of life’s gentle pace.\n\nThrough time and space it guides us on,\nA never-fading beacon of hope.\nThe moon shines down on us all,\nAs it continues to rise and elope.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nQ. What did one magnet say to the other magnet?\nA. "I find you very attractive!"', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text="\n\nThe world is charged with the grandeur of God.\nIt will flame out, like shining from shook foil;\nIt gathers to a greatness, like the ooze of oil\nCrushed. Why do men then now not reck his rod?\n\nGenerations have trod, have trod, have trod;\nAnd all is seared with trade; bleared, smeared with toil;\nAnd wears man's smudge and shares man's smell: the soil\nIs bare now, nor can foot feel, being shod.\n\nAnd for all this, nature is never spent;\nThere lives the dearest freshness deep down things;\nAnd though the last lights off the black West went\nOh, morning, at the brown brink eastward, springs —\n\nBecause the Holy Ghost over the bent\nWorld broods with warm breast and with ah! bright wings.\n\n~Gerard Manley Hopkins", generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nQ: What did one ocean say to the other ocean?\nA: Nothing, they just waved.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text="\n\nA poem for you\n\nOn a field of green\n\nThe sky so blue\n\nA gentle breeze, the sun above\n\nA beautiful world, for us to love\n\nLife is a journey, full of surprise\n\nFull of joy and full of surprise\n\nBe brave and take small steps\n\nThe future will be revealed with depth\n\nIn the morning, when dawn arrives\n\nA fresh start, no reason to hide\n\nSomewhere down the road, there's a heart that beats\n\nBelieve in yourself, you'll always succeed.", generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'completion_tokens': 504, 'total_tokens': 528, 'prompt_tokens': 24}, 'model_name': 'text-davinci-003'})

answer_relevancy_metric.is_successful()
# returns True/False

import requests
from langchain_classic.chains import RetrievalQA
from langchain_chroma import Chroma
from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

text_file_url = "https://raw.githubusercontent.com/hwchase17/chat-your-data/master/state_of_the_union.txt"

openai_api_key = "sk-XXX"

with open("state_of_the_union.txt", "w") as f:
    response = requests.get(text_file_url)
    f.write(response.text)

loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
docsearch = Chroma.from_documents(texts, embeddings)

qa = RetrievalQA.from_chain_type(
    llm=OpenAI(openai_api_key=openai_api_key),
    chain_type="stuff",
    retriever=docsearch.as_retriever(),
)

# Providing a new question-answering pipeline
query = "Who is the president?"
result = qa.run(query)

answer_relevancy_metric.measure(result, query)
answer_relevancy_metric.is_successful()