如何修复 InvalidRequestError：模型 `text-davinci-003` 已被弃用，在此处了解更多信息：https://platform.openai.com/docs/deprecations

Question

我正在使用 openai 和 langchain 制作 PDF 聊天机器人，我面临着这个 InvalidRequestError：模型

text-davinci-003

已被弃用，请在此处了解更多信息：https://platform.openai.com/docs/deprecations错误，我不知道如何修复它，我对这一切仍然很陌生.

这是我下面的代码

from PyPDF2 import PdfReader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS
from langchain.chains import RetrievalQA
import os
os.environ["OPENAI_API_KEY"] = "API KEY"
pdfs_folder = 'PDFs/'
pdf_files = [file for file in os.listdir(pdfs_folder) if file.endswith('.pdf')]
raw_text = ''
# Iterate through each PDF file
for pdf_file in pdf_files:
    # Construct the path to the PDF file
    pdf_path = os.path.join(pdfs_folder, pdf_file)
    # Read the PDF file
    with open(pdf_path, 'rb') as file:
        reader = PdfReader(file)
        
        # Extract raw text from pages
        for i, page in enumerate(reader.pages):
            text = page.extract_text()
            if text:
                raw_text += text
text_splitter = CharacterTextSplitter(        
    separator = "\n",
    chunk_size = 500,
    chunk_overlap  = 200,
    length_function = len,
)
chunks = text_splitter.split_text(raw_text)
embeddings = OpenAIEmbeddings()
docsearch = FAISS.from_texts(chunks, embeddings)
chain = load_qa_chain(OpenAI(), chain_type="stuff")
query = "What are the core values of JM Finance?"
docs = docsearch.similarity_search(query)
chain.run(input_documents=docs, question=query)

我为此使用笔记本 我在这一行中遇到错误：chain.run(input_documents=docs, Question=query)

如果有人可以帮助或教我如何更改模型，我将非常感激

Answer 1

我用更正的库稍微修改了你的代码并删除了未使用的库

from PyPDF2 import PdfReader
# from langchain.embeddings.openai import OpenAIEmbeddings
from langchain_openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
# from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS
from langchain_community.vectorstores import FAISS
from langchain.chains import RetrievalQA

pdfs_folder = 'PDFs/'
pdf_files = [file for file in os.listdir(pdfs_folder) if file.endswith('.pdf')]
raw_text = ''
# Iterate through each PDF file
for pdf_file in pdf_files:
    # Construct the path to the PDF file
    pdf_path = os.path.join(pdfs_folder, pdf_file)
    # Read the PDF file
    with open(pdf_path, 'rb') as file:
        reader = PdfReader(file)
        # Extract raw text from pages
        for i, page in enumerate(reader.pages):
            text = page.extract_text()
            if text:
                raw_text += text
text_splitter = CharacterTextSplitter(
    separator = "\n",
    chunk_size = 500,
    chunk_overlap  = 200,
    length_function = len,
)

chunks = text_splitter.split_text(raw_text)
embeddings = OpenAIEmbeddings()
docsearch = FAISS.from_texts(chunks, embeddings)

from langchain_openai import OpenAI
from langchain.chains.question_answering import load_qa_chain
chain = load_qa_chain(OpenAI(), chain_type="stuff")
query = "What are the core values of JM Finance?"
docs = docsearch.similarity_search(query)
print(chain.run(input_documents=docs, question=query))