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Langchain-Chatchat
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修复测试文件 (#1467) 

Co-authored-by: zR <zRzRzRzRzRzRzR>
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zR 2023-09-13 17:12:05 +08:00 committed by GitHub
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commit 769d75d784
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5 changed files with 40 additions and 85 deletions
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6
README.md
View File

@ -144,7 +144,7 @@ docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/ch
---
### 知识库Text Splitter类型支持
### Text Splitter 个性化支持
本项目支持调用 [Langchain](https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.text_splitter) 的Text Splitter分词器以及基于此改进的自定义分词器，已支持的Text Splitter类型如下：
@ -242,10 +242,8 @@ embedding_model_dict = {
```python
text_splitter_dict = {
"ChineseRecursiveTextSplitter": {
"source": "huggingface", ## 选择tiktoken则使用openai的方法
"source": "huggingface", ## 选择tiktoken则使用openai的方法,不填写则默认为字符长度切割方法。
"tokenizer_name_or_path": "", ## 空格不填则默认使用大模型的分词器。
"chunk_size": 250, # 知识库中单段文本长度
"overlap_size": 50, # 知识库中相邻文本重合长度
}
}
```

6
configs/model_config.py.example
View File

@ -123,11 +123,11 @@ LLM_DEVICE = "auto"
text_splitter_dict = {
"ChineseRecursiveTextSplitter": {
"source": "huggingface", ## 选择tiktoken则使用openai的方法
"source": "huggingface",
"tokenizer_name_or_path": "gpt2",
},
"SpacyTextSplitter": {
"source": "huggingface",
"source": "",
"tokenizer_name_or_path": "",
},
"RecursiveCharacterTextSplitter": {
@ -149,8 +149,6 @@ text_splitter_dict = {
# TEXT_SPLITTER 名称
TEXT_SPLITTER_NAME = "SpacyTextSplitter"
# 知识库中单段文本长度(不适用MarkdownHeaderTextSplitter)
CHUNK_SIZE = 250

8
docs/splitter.md
View File

@ -7,12 +7,10 @@ from .my_splitter import MySplitter
```
2. 修改```config/model_config.py```文件，将您的分词器名字添加到```text_splitter_dict```中，如下所示：
```python
MySplitter": {
MySplitter: {
"source": "huggingface", ## 选择tiktoken则使用openai的方法
"tokenizer_name_or_path": "your tokenizer",
"chunk_size": 250,
"overlap_size": 50,
},
"tokenizer_name_or_path": "your tokenizer", #如果选择huggingface则使用huggingface的方法，部分tokenizer需要从Huggingface下载
}
TEXT_SPLITTER_NAME = "MySplitter"
```
完成上述步骤后，就能使用自己的分词器了。

20
server/knowledge_base/utils.py
View File

@ -199,7 +199,7 @@ def make_text_splitter(
text_splitter_module = importlib.import_module('langchain.text_splitter')
TextSplitter = getattr(text_splitter_module, splitter_name)
if text_splitter_dict[splitter_name]["source"] == "tiktoken":
if text_splitter_dict[splitter_name]["source"] == "tiktoken": ## 从tiktoken加载
try:
text_splitter = TextSplitter.from_tiktoken_encoder(
encoding_name=text_splitter_dict[splitter_name]["tokenizer_name_or_path"],
@ -213,7 +213,7 @@ def make_text_splitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap
)
elif text_splitter_dict[splitter_name]["source"] == "huggingface":
elif text_splitter_dict[splitter_name]["source"] == "huggingface": ## 从huggingface加载
if text_splitter_dict[splitter_name]["tokenizer_name_or_path"] == "":
text_splitter_dict[splitter_name]["tokenizer_name_or_path"] = \
llm_model_dict[LLM_MODEL]["local_model_path"]
@ -221,8 +221,8 @@ def make_text_splitter(
if text_splitter_dict[splitter_name]["tokenizer_name_or_path"] == "gpt2":
from transformers import GPT2TokenizerFast
from langchain.text_splitter import CharacterTextSplitter
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2") ## 这里选择你用的tokenizer
else:
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
else: ## 字符长度加载
tokenizer = AutoTokenizer.from_pretrained(
text_splitter_dict[splitter_name]["tokenizer_name_or_path"],
trust_remote_code=True)
@ -231,6 +231,18 @@ def make_text_splitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap
)
else:
try:
text_splitter = TextSplitter(
pipeline="zh_core_web_sm",
chunk_size=chunk_size,
chunk_overlap=chunk_overlap
)
except:
text_splitter = TextSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap
)
except Exception as e:
print(e)
text_splitter_module = importlib.import_module('langchain.text_splitter')

85
tests/custom_splitter/test_different_splitter.py
View File

@ -6,87 +6,36 @@ import sys
sys.path.append("../..")
from configs.model_config import (
CHUNK_SIZE,
OVERLAP_SIZE,
text_splitter_dict, llm_model_dict, LLM_MODEL, TEXT_SPLITTER_NAME
OVERLAP_SIZE
)
import langchain.document_loaders
import importlib
from server.knowledge_base.utils import make_text_splitter
def test_different_splitter(
splitter_name,
chunk_size: int = CHUNK_SIZE,
chunk_overlap: int = OVERLAP_SIZE,
):
if splitter_name == "MarkdownHeaderTextSplitter": # MarkdownHeaderTextSplitter特殊判定
headers_to_split_on = text_splitter_dict[splitter_name]['headers_to_split_on']
text_splitter = langchain.text_splitter.MarkdownHeaderTextSplitter(
headers_to_split_on=headers_to_split_on)
else:
try: ## 优先使用用户自定义的text_splitter
text_splitter_module = importlib.import_module('text_splitter')
TextSplitter = getattr(text_splitter_module, splitter_name)
except: ## 否则使用langchain的text_splitter
text_splitter_module = importlib.import_module('langchain.text_splitter')
TextSplitter = getattr(text_splitter_module, splitter_name)
if text_splitter_dict[splitter_name]["source"] == "tiktoken":
try:
text_splitter = TextSplitter.from_tiktoken_encoder(
encoding_name=text_splitter_dict[splitter_name]["tokenizer_name_or_path"],
pipeline="zh_core_web_sm",
chunk_size=chunk_size,
chunk_overlap=chunk_overlap
)
except:
text_splitter = TextSplitter.from_tiktoken_encoder(
encoding_name=text_splitter_dict[splitter_name]["tokenizer_name_or_path"],
chunk_size=chunk_size,
chunk_overlap=chunk_overlap
)
elif text_splitter_dict[splitter_name]["source"] == "huggingface":
if text_splitter_dict[splitter_name]["tokenizer_name_or_path"] == "":
text_splitter_dict[splitter_name]["tokenizer_name_or_path"] = \
llm_model_dict[LLM_MODEL]["local_model_path"]
if text_splitter_dict[splitter_name]["tokenizer_name_or_path"] == "gpt2":
from transformers import GPT2TokenizerFast
from langchain.text_splitter import CharacterTextSplitter
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2") ## 这里选择你用的tokenizer
else:
tokenizer = AutoTokenizer.from_pretrained(
text_splitter_dict[splitter_name]["tokenizer_name_or_path"],
trust_remote_code=True)
text_splitter = TextSplitter.from_huggingface_tokenizer(
tokenizer=tokenizer,
chunk_size=chunk_size,
chunk_overlap=chunk_overlap
)
return text_splitter
if __name__ == "__main__":
def text(splitter_name):
from langchain import document_loaders
# 使用DocumentLoader读取文件
filepath = "../../knowledge_base/samples/content/test.txt"
loader = document_loaders.UnstructuredFileLoader(filepath, autodetect_encoding=True)
docs = loader.load()
text_splitter = test_different_splitter(TEXT_SPLITTER_NAME, CHUNK_SIZE, OVERLAP_SIZE)
# 使用text_splitter进行分词
if TEXT_SPLITTER_NAME == "MarkdownHeaderTextSplitter":
text_splitter = make_text_splitter(splitter_name, CHUNK_SIZE, OVERLAP_SIZE)
if splitter_name == "MarkdownHeaderTextSplitter":
split_docs = text_splitter.split_text(docs[0].page_content)
for doc in docs:
# 如果文档有元数据
if doc.metadata:
doc.metadata["source"] = os.path.basename(filepath)
else:
split_docs = text_splitter.split_documents(docs)
return docs
# 打印分词结果
for doc in split_docs:
print(doc)
import pytest
@pytest.mark.parametrize("splitter_name", ["ChineseRecursiveTextSplitter", "SpacyTextSplitter", "RecursiveCharacterTextSplitter","MarkdownHeaderTextSplitter"])
def test_different_splitter(splitter_name):
try:
docs = text(splitter_name)
assert docs is not None
except Exception as e:
pytest.fail(f"test_different_splitter failed with {splitter_name}, error: {str(e)}")