TypeError: 'NoneType' object cannot be interpreted as an integer #37242

20040206dfhDFH · 2025-04-03T10:27:40Z

System Info

from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter

from langchain_chroma import Chroma
from langchain_ollama import OllamaEmbeddings

from langchain_ollama import ChatOllama

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

import os

from langchain.document_loaders import TextLoader,Docx2txtLoader

from langchain_community.document_loaders import TextLoader, Docx2txtLoader
from PyPDF2 import PdfReader

from langchain_core.runnables import RunnablePassthrough
from langchain_community.vectorstores import FAISS

from transformers import AutoModelForCausalLM, AutoTokenizer,BitsAndBytesConfig
import torch

os.environ["http_proxy"] = "http://127.0.0.1:11434"
os.environ["https_proxy"] = "http://127.0.0.1:11434"

#加载pdf文件并返回文本段
def load_single_pdf(file_path):
pdf_reader = PdfReader(file_path)
if not pdf_reader:
return None
ret = ''
for i,page in enumerate(pdf_reader.pages):
txt = page.extractText()
if txt:
ret += txt
return ret

#文档加载器
def data_loader(directory_path=r'E:\llm\deepseek\document_hc'):
all_docs = []
for root, dirs, files in os.walk(directory_path):
for file in files:
file_path = os.path.join(root, file)
if file.endswith('.txt'):
loader = TextLoader(file_path, encoding='utf-8')
elif file.endswith('.docx'):
loader = Docx2txtLoader(file_path)
elif file.endswith('.pdf'):
loader = load_single_pdf(file_path)
else:
continue
docs = loader.load()
all_docs.extend(docs)
return all_docs

将文本拆分为 docs文档

def split_text(txt,chunk_size=1000,overlap=100,directory_path =r'E:\llm\deepseek\document_hc'):
if not txt:
return None
splitter = RecursiveCharacterTextSplitter(chunk_size = chunk_size, chunk_overlap = overlap)
docs = splitter.split_documents(data_loader(directory_path))

# docs = splitter.split_text(txt)
return docs

def create_embeddings():
embeddings = OllamaEmbeddings(model="bge-m3:567m")
return embeddings

def load_llm_ollama(model_path_name = "deepseek-r1:7b"):
chat_model = OllamaEmbeddings(model=model_path_name)
return chat_model

使用Embeddings嵌入模型将文档保存到向量知识库存储

def create_vector_store(docs, embeddings, store_path):
# vectorstore = Chroma.from_documents(documents=split, embedding=local_embeddings)
# vector_store = FAISS.from_texts(docs,embeddings)

vector_store = FAISS.from_documents(docs, embeddings)
vector_store.save_local(store_path)
return vector_store

从文件加载向量知识库

def load_vector_store(store_path,embeddings):
if os.path.exists(store_path):
vector_store = FAISS.load_local(
store_path,
embeddings = embeddings,
allow_dangerous_deserialization=True
)
return vector_store
else:
return None

加载或者创建向量知识库

def load_or_create_vector_store(store_path,doc_file_path):
embeddings = create_embeddings()
vector_store = load_vector_store(store_path,embeddings)
if not vector_store:
docs = split_text(doc_file_path)
vector_store = create_vector_store(docs,embeddings,store_path)
return vector_store

从vector store查询上下文

def query_vector_store(vector_store, query, k=4, relevance_threshold=0.8):
similar_docs = vector_store.similarity_search_with_relevance_scores(query, k=k)
related_docs = list(filter(lambda x: x[1] > relevance_threshold, similar_docs))
context = [doc[0].page_content for doc in related_docs]
return context

Load llm(deepseek) and tokenizer

def load_llm(model_path,CUDA_Device):
quant_config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(model_path,
device_map=CUDA_Device,
torch_dtype=torch.float16,
quantization_config=quant_config)
model = model.eval()

tokenizer = AutoTokenizer.from_pretrained(model_path,
    use_fast=False)
tokenizer.pad_token = tokenizer.eos_token

return model, tokenizer

def ask(model, tokenizer, prompt, CUDA_Device,max_tokens=512 ):
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids('<|eot_id|>')
]
input_ids = tokenizer([prompt],
return_tensors='pt',
add_special_tokens=False).input_ids.to(CUDA_Device)
generated_input = {
'input_ids': input_ids,
'max_new_tokens': max_tokens,
'do_sample': True,
'top_p': 0.95,
'temperature': 0.9,
'repetition_penalty': 1.1,
'eos_token_id': terminators,
'bos_token_id': tokenizer.bos_token_id,
'pad_token_id': tokenizer.pad_token_id
}

generated_ids = model.generate(**generated_input)
ans = tokenizer.decode(generated_ids[0], skip_special_token=True)
return ans

def main():
# 初始化
doc_file_path = r'E:\llm\deepseek\document_hc'
# store_path = './Data/Aquila.faiss'
store_path = r'E:\llm\deepseek\Data_vecstore\Aquila.faiss'
Embedding_Model = 'bge-m3:567m'
LLM_Model = r'E:\llm\deepseek\DeepSeek-R1-Distill-Qwen-1.5B'
# LLM_Model_path = r'E:\llm\deepseek\DeepSeek-R1-Distill-Qwen-7B'
CUDA_Device = 'cuda:0'

vector_store = load_or_create_vector_store(store_path, doc_file_path)
model, tokenizer = load_llm(LLM_Model,CUDA_Device)

while True:
    qiz = input('请输入您的问题: ')
    if qiz == 'bye' or qiz == 'exit':
        print('Bye~')
        break


    # Query context from vector store based on question, and compose prompt
    context = query_vector_store(vector_store, qiz, 6, 0.75)
    if len(context) == 0:
        # No satisfying context is found inside vector store
        print('无法从保存的向量存储中找到限定的上下文,在没有上下文的情况下与LLM交谈')
        prompt = f'请回答以下问题: \n{qiz}\n'
    else:
        context = '\n'.join(context)
        prompt = f'基于以下上下文: \n{context}\n请回答以下问题: \n{qiz}\n'

    ans = ask(model, tokenizer, prompt,CUDA_Device)[len(prompt):]
    print(ans)

if name == 'main':
main()

Who can help?

No response

Information

The official example scripts
My own modified scripts

Tasks

An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
My own task or dataset (give details below)

Reproduction

from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter

from langchain_chroma import Chroma
from langchain_ollama import OllamaEmbeddings

from langchain_ollama import ChatOllama

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

import os

from langchain.document_loaders import TextLoader,Docx2txtLoader

from langchain_community.document_loaders import TextLoader, Docx2txtLoader
from PyPDF2 import PdfReader

from langchain_core.runnables import RunnablePassthrough
from langchain_community.vectorstores import FAISS

from transformers import AutoModelForCausalLM, AutoTokenizer,BitsAndBytesConfig
import torch

os.environ["http_proxy"] = "http://127.0.0.1:11434"
os.environ["https_proxy"] = "http://127.0.0.1:11434"

#加载pdf文件并返回文本段
def load_single_pdf(file_path):
pdf_reader = PdfReader(file_path)
if not pdf_reader:
return None
ret = ''
for i,page in enumerate(pdf_reader.pages):
txt = page.extractText()
if txt:
ret += txt
return ret

#文档加载器
def data_loader(directory_path=r'E:\llm\deepseek\document_hc'):
all_docs = []
for root, dirs, files in os.walk(directory_path):
for file in files:
file_path = os.path.join(root, file)
if file.endswith('.txt'):
loader = TextLoader(file_path, encoding='utf-8')
elif file.endswith('.docx'):
loader = Docx2txtLoader(file_path)
elif file.endswith('.pdf'):
loader = load_single_pdf(file_path)
else:
continue
docs = loader.load()
all_docs.extend(docs)
return all_docs

将文本拆分为 docs文档

def split_text(txt,chunk_size=1000,overlap=100,directory_path =r'E:\llm\deepseek\document_hc'):
if not txt:
return None
splitter = RecursiveCharacterTextSplitter(chunk_size = chunk_size, chunk_overlap = overlap)
docs = splitter.split_documents(data_loader(directory_path))

# docs = splitter.split_text(txt)
return docs

def create_embeddings():
embeddings = OllamaEmbeddings(model="bge-m3:567m")
return embeddings

def load_llm_ollama(model_path_name = "deepseek-r1:7b"):
chat_model = OllamaEmbeddings(model=model_path_name)
return chat_model

使用Embeddings嵌入模型将文档保存到向量知识库存储

def create_vector_store(docs, embeddings, store_path):
# vectorstore = Chroma.from_documents(documents=split, embedding=local_embeddings)
# vector_store = FAISS.from_texts(docs,embeddings)

vector_store = FAISS.from_documents(docs, embeddings)
vector_store.save_local(store_path)
return vector_store

从文件加载向量知识库

def load_vector_store(store_path,embeddings):
if os.path.exists(store_path):
vector_store = FAISS.load_local(
store_path,
embeddings = embeddings,
allow_dangerous_deserialization=True
)
return vector_store
else:
return None

加载或者创建向量知识库

def load_or_create_vector_store(store_path,doc_file_path):
embeddings = create_embeddings()
vector_store = load_vector_store(store_path,embeddings)
if not vector_store:
docs = split_text(doc_file_path)
vector_store = create_vector_store(docs,embeddings,store_path)
return vector_store

从vector store查询上下文

def query_vector_store(vector_store, query, k=4, relevance_threshold=0.8):
similar_docs = vector_store.similarity_search_with_relevance_scores(query, k=k)
related_docs = list(filter(lambda x: x[1] > relevance_threshold, similar_docs))
context = [doc[0].page_content for doc in related_docs]
return context

Load llm(deepseek) and tokenizer

def load_llm(model_path,CUDA_Device):
quant_config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(model_path,
device_map=CUDA_Device,
torch_dtype=torch.float16,
quantization_config=quant_config)
model = model.eval()

tokenizer = AutoTokenizer.from_pretrained(model_path,
    use_fast=False)
tokenizer.pad_token = tokenizer.eos_token

return model, tokenizer

def ask(model, tokenizer, prompt, CUDA_Device,max_tokens=512 ):
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids('<|eot_id|>')
]
input_ids = tokenizer([prompt],
return_tensors='pt',
add_special_tokens=False).input_ids.to(CUDA_Device)
generated_input = {
'input_ids': input_ids,
'max_new_tokens': max_tokens,
'do_sample': True,
'top_p': 0.95,
'temperature': 0.9,
'repetition_penalty': 1.1,
'eos_token_id': terminators,
'bos_token_id': tokenizer.bos_token_id,
'pad_token_id': tokenizer.pad_token_id
}

generated_ids = model.generate(**generated_input)
ans = tokenizer.decode(generated_ids[0], skip_special_token=True)
return ans

def main():
# 初始化
doc_file_path = r'E:\llm\deepseek\document_hc'
# store_path = './Data/Aquila.faiss'
store_path = r'E:\llm\deepseek\Data_vecstore\Aquila.faiss'
Embedding_Model = 'bge-m3:567m'
LLM_Model = r'E:\llm\deepseek\DeepSeek-R1-Distill-Qwen-1.5B'
# LLM_Model_path = r'E:\llm\deepseek\DeepSeek-R1-Distill-Qwen-7B'
CUDA_Device = 'cuda:0'

vector_store = load_or_create_vector_store(store_path, doc_file_path)
model, tokenizer = load_llm(LLM_Model,CUDA_Device)

while True:
    qiz = input('请输入您的问题: ')
    if qiz == 'bye' or qiz == 'exit':
        print('Bye~')
        break


    # Query context from vector store based on question, and compose prompt
    context = query_vector_store(vector_store, qiz, 6, 0.75)
    if len(context) == 0:
        # No satisfying context is found inside vector store
        print('无法从保存的向量存储中找到限定的上下文,在没有上下文的情况下与LLM交谈')
        prompt = f'请回答以下问题: \n{qiz}\n'
    else:
        context = '\n'.join(context)
        prompt = f'基于以下上下文: \n{context}\n请回答以下问题: \n{qiz}\n'

    ans = ask(model, tokenizer, prompt,CUDA_Device)[len(prompt):]
    print(ans)

if name == 'main':
main()

Expected behavior

Traceback (most recent call last):
File "E:\llm\deepseek\demo_test\langchain_0402.py", line 199, in
main()
File "E:\llm\deepseek\demo_test\langchain_0402.py", line 195, in main
ans = ask(model, tokenizer, prompt,CUDA_Device)[len(prompt):]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\llm\deepseek\demo_test\langchain_0402.py", line 159, in ask
generated_ids = model.generate(**generated_input)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Program Files\anaconda3\envs\LLM\Lib\site-packages\torch\utils_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "D:\Program Files\anaconda3\envs\LLM\Lib\site-packages\transformers\generation\utils.py", line 2004, in generate
self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=device)
File "D:\Program Files\anaconda3\envs\LLM\Lib\site-packages\transformers\generation\utils.py", line 1820, in _prepare_special_tokens
eos_token_tensor = _tensor_or_none(generation_config.eos_token_id, device=device)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Program Files\anaconda3\envs\LLM\Lib\site-packages\transformers\generation\utils.py", line 1817, in _tensor_or_none
return torch.tensor(token, device=device, dtype=torch.long)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: 'NoneType' object cannot be interpreted as an integer

The text was updated successfully, but these errors were encountered:

20040206dfhDFH added the bug label Apr 3, 2025

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TypeError: 'NoneType' object cannot be interpreted as an integer #37242

TypeError: 'NoneType' object cannot be interpreted as an integer #37242

20040206dfhDFH commented Apr 3, 2025

TypeError: 'NoneType' object cannot be interpreted as an integer #37242

TypeError: 'NoneType' object cannot be interpreted as an integer #37242

Comments

20040206dfhDFH commented Apr 3, 2025

System Info

from langchain.document_loaders import TextLoader,Docx2txtLoader

将文本拆分为 docs文档

使用Embeddings嵌入模型将文档保存到向量知识库存储

从文件加载向量知识库

加载或者创建向量知识库

从vector store查询上下文

Load llm(deepseek) and tokenizer

Who can help?

Information

Tasks

Reproduction

from langchain.document_loaders import TextLoader,Docx2txtLoader

将文本拆分为 docs文档

使用Embeddings嵌入模型将文档保存到向量知识库存储

从文件加载向量知识库

加载或者创建向量知识库

从vector store查询上下文

Load llm(deepseek) and tokenizer

Expected behavior