LangChain 文档问答

此示例提供了一个界面，用于向 PDF 文档提问。

设置

pip install -r requirements.txt

运行

python main.py

当可以由您提问时，将出现一个提示符：

Query: How many locations does WeWork have?

源码

main.py

from langchain.document_loaders import OnlinePDFLoader
from langchain.vectorstores import Chroma
from langchain.embeddings import GPT4AllEmbeddings
from langchain import PromptTemplate
from langchain.llms import Ollama
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains import RetrievalQA
import sys
import os

class SuppressStdout:
    def __enter__(self):
        self._original_stdout = sys.stdout
        self._original_stderr = sys.stderr
        sys.stdout = open(os.devnull, 'w')
        sys.stderr = open(os.devnull, 'w')

    def __exit__(self, exc_type, exc_val, exc_tb):
        sys.stdout.close()
        sys.stdout = self._original_stdout
        sys.stderr = self._original_stderr

# load the pdf and split it into chunks
loader = OnlinePDFLoader("https://d18rn0p25nwr6d.cloudfront.net/CIK-0001813756/975b3e9b-268e-4798-a9e4-2a9a7c92dc10.pdf")
data = loader.load()

from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
all_splits = text_splitter.split_documents(data)

with SuppressStdout():
    vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())

while True:
    query = input("\nQuery: ")
    if query == "exit":
        break
    if query.strip() == "":
        continue

    # Prompt
    template = """Use the following pieces of context to answer the question at the end.
    If you don't know the answer, just say that you don't know, don't try to make up an answer.
    Use three sentences maximum and keep the answer as concise as possible.
    {context}
    Question: {question}
    Helpful Answer:"""
    QA_CHAIN_PROMPT = PromptTemplate(
        input_variables=["context", "question"],
        template=template,
    )

    llm = Ollama(model="llama2:13b", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
    qa_chain = RetrievalQA.from_chain_type(
        llm,
        retriever=vectorstore.as_retriever(),
        chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},
    )

    result = qa_chain({"query": query})

仓库地址

langchain-python-rag-document