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Langchain-Chatchat
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imClumsyPanda 2023-04-22 16:59:53 +08:00
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34
Dockerfile Normal file
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@ -0,0 +1,34 @@
FROM python:3.8
MAINTAINER "chatGLM"
COPY agent /chatGLM/agent
COPY chains /chatGLM/chains
COPY configs /chatGLM/configs
COPY content /chatGLM/content
COPY models /chatGLM/models
COPY nltk_data /chatGLM/content
COPY requirements.txt /chatGLM/
COPY cli_demo.py /chatGLM/
COPY webui.py /chatGLM/
WORKDIR /chatGLM
RUN pip install --user torch torchvision tensorboard cython -i https://pypi.tuna.tsinghua.edu.cn/simple
# RUN pip install --user 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
# RUN pip install --user 'git+https://github.com/facebookresearch/fvcore'
# install detectron2
# RUN git clone https://github.com/facebookresearch/detectron2
RUN pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple/ --trusted-host pypi.tuna.tsinghua.edu.cn
CMD ["python","-u", "webui.py"]

21
README.md
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@ -4,11 +4,11 @@
🌍 [_READ THIS IN ENGLISH_](README_en.md)
🤖️ 一种利用 [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) + [langchain](https://github.com/hwchase17/langchain) 实现的基于本地知识的 ChatGLM 应用。
🤖️ 一种利用 [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) + [langchain](https://github.com/hwchase17/langchain) 实现的基于本地知识的 ChatGLM 应用。增加 [clue-ai/ChatYuan](https://github.com/clue-ai/ChatYuan) 项目的模型 [ClueAI/ChatYuan-large-v2](https://huggingface.co/ClueAI/ChatYuan-large-v2) 的支持。
💡 受 [GanymedeNil](https://github.com/GanymedeNil) 的项目 [document.ai](https://github.com/GanymedeNil/document.ai) 和 [AlexZhangji](https://github.com/AlexZhangji) 创建的 [ChatGLM-6B Pull Request](https://github.com/THUDM/ChatGLM-6B/pull/216) 启发，建立了全部基于开源模型实现的本地知识问答应用。
✅ 本项目中 Embedding 选用的是 [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese/tree/main)，LLM 选用的是 [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B)。依托上述模型，本项目可实现全部使用**开源**模型**离线私有部署**。
✅ 本项目中 Embedding 默认选用的是 [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese/tree/main)，LLM 默认选用的是 [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B)。依托上述模型，本项目可实现全部使用**开源**模型**离线私有部署**。
⛓️ 本项目实现原理如下图所示，过程包括加载文件 -> 读取文本 -> 文本分割 -> 文本向量化 -> 问句向量化 -> 在文本向量中匹配出与问句向量最相似的`top k`个 -> 匹配出的文本作为上下文和问题一起添加到`prompt`中 -> 提交给`LLM`生成回答。
@ -22,9 +22,7 @@
参见 [变更日志](docs/CHANGELOG.md)。
## 使用方式
### 硬件需求
## 硬件需求
- ChatGLM-6B 模型硬件需求
@ -38,9 +36,19 @@
本项目中默认选用的 Embedding 模型 [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese/tree/main) 约占用显存 3GB，也可修改为在 CPU 中运行。
## Docker 部署
```commandline
$ docker build -t chatglm:v1.0 .
$ docker run -d --restart=always --name chatglm -p 7860:7860 -v /www/wwwroot/code/langchain-ChatGLM:/chatGLM chatglm
```
## 开发部署
### 软件需求
本项目已在 Python 3.8，CUDA 11.7 环境下完成测试。
本项目已在 Python 3.8 - 3.10，CUDA 11.7 环境下完成测试。已在 Windows、ARM 架构的 macOS、Linux 系统中完成测试。
### 从本地加载模型
@ -123,6 +131,7 @@ Web UI 可以实现如下功能：
- [x] THUDM/chatglm-6b
- [x] THUDM/chatglm-6b-int4
- [x] THUDM/chatglm-6b-int4-qe
- [x] ClueAI/ChatYuan-large-v2
- [ ] Web UI
- [x] 利用 gradio 实现 Web UI DEMO
- [x] 添加输出内容及错误提示

10
api.py
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@ -42,7 +42,7 @@ async def get_local_doc_qa():
@app.post("/file")
async def upload_file(UserFile: UploadFile=File(...)):
async def upload_file(UserFile: UploadFile=File(...),):
global vs_path
response = {
"msg": None,
@ -67,7 +67,7 @@ async def upload_file(UserFile: UploadFile=File(...)):
return response
@app.post("/qa")
async def get_answer(UserQuery: Query):
async def get_answer(query: str = ""):
response = {
"status": 0,
"message": "",
@ -76,7 +76,7 @@ async def get_answer(UserQuery: Query):
global vs_path
history = []
try:
resp, history = local_doc_qa.get_knowledge_based_answer(query=UserQuery.query,
resp, history = local_doc_qa.get_knowledge_based_answer(query=query,
vs_path=vs_path,
chat_history=history)
if REPLY_WITH_SOURCE:
@ -95,9 +95,9 @@ async def get_answer(UserQuery: Query):
if __name__ == "__main__":
uvicorn.run(
app='api:app',
app=app,
host='0.0.0.0',
port=8100,
reload = True,
reload=True,
)

10
chains/local_doc_qa.py
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@ -33,6 +33,7 @@ def load_file(filepath):
class LocalDocQA:
llm: object = None
embeddings: object = None
top_k: int = VECTOR_SEARCH_TOP_K
def init_cfg(self,
embedding_model: str = EMBEDDING_MODEL,
@ -49,9 +50,10 @@ class LocalDocQA:
use_ptuning_v2=use_ptuning_v2)
self.llm.history_len = llm_history_len
self.embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[embedding_model], )
self.embeddings.client = sentence_transformers.SentenceTransformer(self.embeddings.model_name,
device=embedding_device)
self.embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[embedding_model],
model_kwargs={'device': embedding_device})
# self.embeddings.client = sentence_transformers.SentenceTransformer(self.embeddings.model_name,
# device=embedding_device)
self.top_k = top_k
def init_knowledge_vector_store(self,
@ -133,7 +135,7 @@ class LocalDocQA:
)
knowledge_chain.return_source_documents = True
result = knowledge_chain({"query": query})
self.llm.history[-1][0] = query
return result, self.llm.history

1
configs/model_config.py
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@ -19,6 +19,7 @@ llm_model_dict = {
"chatglm-6b-int4-qe": "THUDM/chatglm-6b-int4-qe",
"chatglm-6b-int4": "THUDM/chatglm-6b-int4",
"chatglm-6b": "THUDM/chatglm-6b",
"chatyuan": "ClueAI/ChatYuan-large-v2",
}
# LLM model name

4
docs/FAQ.md
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@ -95,7 +95,7 @@ Q9: 下载完模型后，如何修改代码以执行本地模型？
A9: 模型下载完成后，请在 [configs/model_config.py](../configs/model_config.py) 文件中，对`embedding_model_dict`和`llm_model_dict`参数进行修改，如把`llm_model_dict`从
```json
```python
embedding_model_dict = {
"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
"ernie-base": "nghuyong/ernie-3.0-base-zh",
@ -105,7 +105,7 @@ embedding_model_dict = {
修改为
```json
```python
embedding_model_dict = {
"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
"ernie-base": "nghuyong/ernie-3.0-base-zh",

22
models/chatglm_llm.py
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@ -72,14 +72,27 @@ class ChatGLM(LLM):
response, _ = self.model.chat(
self.tokenizer,
prompt,
history=self.history[-self.history_len:] if self.history_len>0 else [],
history=self.history[-self.history_len:] if self.history_len > 0 else [],
max_length=self.max_token,
temperature=self.temperature,
)
torch_gc()
if stop is not None:
response = enforce_stop_tokens(response, stop)
self.history = self.history+[[None, response]]
self.history = self.history + [[None, response]]
return response
def chat(self,
prompt: str) -> str:
response, _ = self.model.chat(
self.tokenizer,
prompt,
history=self.history[-self.history_len:] if self.history_len > 0 else [],
max_length=self.max_token,
temperature=self.temperature,
)
torch_gc()
self.history = self.history + [[None, response]]
return response
def load_model(self,
@ -113,7 +126,7 @@ class ChatGLM(LLM):
AutoModel.from_pretrained(
model_name_or_path,
config=model_config,
trust_remote_code=True,
trust_remote_code=True,
**kwargs)
.half()
.cuda()
@ -146,7 +159,8 @@ class ChatGLM(LLM):
new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
self.model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
self.model.transformer.prefix_encoder.float()
except Exception:
except Exception as e:
print(e)
print("加载PrefixEncoder模型参数失败")
self.model = self.model.eval()

4
requirements.txt
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@ -1,4 +1,4 @@
langchain>=0.0.124
langchain>=0.0.146
transformers==4.27.1
unstructured[local-inference]
layoutparser[layoutmodels,tesseract]
@ -9,4 +9,4 @@ icetk
cpm_kernels
faiss-cpu
gradio>=3.25.0
detectron2@git+https://github.com/facebookresearch/detectron2.git@v0.6#egg=detectron2
#detectron2@git+https://github.com/facebookresearch/detectron2.git@v0.6#egg=detectron2

4
webui.py
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@ -17,10 +17,10 @@ LLM_HISTORY_LEN = 3
def get_vs_list():
if not os.path.exists(VS_ROOT_PATH):
return []
return ["新建知识库"] + os.listdir(VS_ROOT_PATH)
return os.listdir(VS_ROOT_PATH)
vs_list = get_vs_list()
vs_list = ["新建知识库"] + get_vs_list()
embedding_model_dict_list = list(embedding_model_dict.keys())