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jinchat-server
提交 a6184b01 作者: imClumsyPanda
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修改项目架构

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正在显示 包含 157 行增加 和 115 行删除
README.md
......@@ -16,6 +16,8 @@
🚩 本项目未涉及微调、训练过程，但可利用微调或训练对本项目效果进行优化。
[TOC]
## 更新信息
**[2023/04/07]**
......@@ -76,7 +78,7 @@ Web UI 可以实现如下功能：
3. 添加上传文件功能，通过下拉框选择已上传的文件，点击`loading`加载文件，过程中可随时更换加载的文件
4. 底部添加`use via API`可对接到自己系统
或执行 [knowledge_based_chatglm.py](knowledge_based_chatglm.py) 脚本体验**命令行交互**
或执行 [knowledge_based_chatglm.py](cli_demo.py) 脚本体验**命令行交互**
```commandline
python knowledge_based_chatglm.py
```
......
README_en.md
......@@ -68,7 +68,7 @@ pip install -r requirements.txt
```
Attention: With langchain.document_loaders.UnstructuredFileLoader used to connect with local knowledge file, you may need some other dependencies as mentioned in [langchain documentation](https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/unstructured_file.html)
### 2. Run [knowledge_based_chatglm.py](knowledge_based_chatglm.py) script
### 2. Run [knowledge_based_chatglm.py](cli_demo.py) script
```commandline
python knowledge_based_chatglm.py
```
......
knowledge_based_chatglm.py → chains/local_doc_qa.py
......@@ -3,122 +3,102 @@ from langchain.prompts import PromptTemplate
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.document_loaders import UnstructuredFileLoader
from chatglm_llm import ChatGLM
from models.chatglm_llm import ChatGLM
import sentence_transformers
import torch
import os
import readline
from configs.model_config import *
import datetime
# return top-k text chunk from vector store
VECTOR_SEARCH_TOP_K = 10
# Global Parameters
EMBEDDING_MODEL = "text2vec"
VECTOR_SEARCH_TOP_K = 6
LLM_MODEL = "chatglm-6b"
# LLM input history length
LLM_HISTORY_LEN = 3
DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
# Show reply with source text from input document
REPLY_WITH_SOURCE = True
embedding_model_dict = {
"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
"ernie-base": "nghuyong/ernie-3.0-base-zh",
"text2vec": "GanymedeNil/text2vec-large-chinese",
}
llm_model_dict = {
"chatglm-6b-int4-qe": "THUDM/chatglm-6b-int4-qe",
"chatglm-6b-int4": "THUDM/chatglm-6b-int4",
"chatglm-6b": "THUDM/chatglm-6b",
}
def init_cfg(LLM_MODEL, EMBEDDING_MODEL, LLM_HISTORY_LEN, V_SEARCH_TOP_K=6):
global chatglm, embeddings, VECTOR_SEARCH_TOP_K
VECTOR_SEARCH_TOP_K = V_SEARCH_TOP_K
chatglm = ChatGLM()
chatglm.load_model(model_name_or_path=llm_model_dict[LLM_MODEL])
chatglm.history_len = LLM_HISTORY_LEN
embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[EMBEDDING_MODEL],)
embeddings.client = sentence_transformers.SentenceTransformer(embeddings.model_name,
device=DEVICE)
def init_knowledge_vector_store(filepath:str):
if not os.path.exists(filepath):
print("路径不存在")
return None
elif os.path.isfile(filepath):
file = os.path.split(filepath)[-1]
try:
loader = UnstructuredFileLoader(filepath, mode="elements")
docs = loader.load()
print(f"{file} 已成功加载")
except:
print(f"{file} 未能成功加载")
class LocalDocQA:
llm: object = None
embeddings: object = None
def init_cfg(self,
embedding_model: str = EMBEDDING_MODEL,
embedding_device=EMBEDDING_DEVICE,
llm_history_len: int = LLM_HISTORY_LEN,
llm_model: str = LLM_MODEL,
llm_device=LLM_DEVICE
):
self.llm = ChatGLM()
self.llm.load_model(model_name_or_path=llm_model_dict[llm_model],
llm_device=llm_device)
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)
def init_knowledge_vector_store(self,
filepath: str):
if not os.path.exists(filepath):
print("路径不存在")
return None
elif os.path.isdir(filepath):
docs = []
for file in os.listdir(filepath):
fullfilepath = os.path.join(filepath, file)
elif os.path.isfile(filepath):
file = os.path.split(filepath)[-1]
try:
loader = UnstructuredFileLoader(fullfilepath, mode="elements")
docs += loader.load()
loader = UnstructuredFileLoader(filepath, mode="elements")
docs = loader.load()
print(f"{file} 已成功加载")
except:
print(f"{file} 未能成功加载")
vector_store = FAISS.from_documents(docs, embeddings)
return vector_store
def get_knowledge_based_answer(query, vector_store, chat_history=[]):
global chatglm, embeddings
prompt_template = """基于以下已知信息，简洁和专业的来回答用户的问题。
如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。
已知内容:
{context}
问题:
{question}"""
prompt = PromptTemplate(
template=prompt_template,
input_variables=["context", "question"]
)
chatglm.history = chat_history
knowledge_chain = RetrievalQA.from_llm(
llm=chatglm,
retriever=vector_store.as_retriever(search_kwargs={"k": VECTOR_SEARCH_TOP_K}),
prompt=prompt
)
knowledge_chain.combine_documents_chain.document_prompt = PromptTemplate(
return None
elif os.path.isdir(filepath):
docs = []
for file in os.listdir(filepath):
fullfilepath = os.path.join(filepath, file)
try:
loader = UnstructuredFileLoader(fullfilepath, mode="elements")
docs += loader.load()
print(f"{file} 已成功加载")
except:
print(f"{file} 未能成功加载")
vector_store = FAISS.from_documents(docs, self.embeddings)
vs_path = f"""./vector_store/{os.path.splitext(file)}_FAISS_{datetime.datetime.now().strftime("%Y%m%d_%H%M%S")}"""
vector_store.save_local(vs_path)
return vs_path
def get_knowledge_based_answer(self,
query,
vs_path,
chat_history=[],
top_k=VECTOR_SEARCH_TOP_K):
prompt_template = """基于以下已知信息，简洁和专业的来回答用户的问题。
如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。
已知内容:
{context}
问题:
{question}"""
prompt = PromptTemplate(
template=prompt_template,
input_variables=["context", "question"]
)
self.llm.history = chat_history
vector_store = FAISS.load_local(vs_path, self.embeddings)
knowledge_chain = RetrievalQA.from_llm(
llm=self.llm,
retriever=vector_store.as_retriever(search_kwargs={"k": top_k}),
prompt=prompt
)
knowledge_chain.combine_documents_chain.document_prompt = PromptTemplate(
input_variables=["page_content"], template="{page_content}"
)
knowledge_chain.return_source_documents = True
result = knowledge_chain({"query": query})
chatglm.history[-1][0] = query
return result, chatglm.history
knowledge_chain.return_source_documents = True
if __name__ == "__main__":
init_cfg(LLM_MODEL, EMBEDDING_MODEL, LLM_HISTORY_LEN)
vector_store = None
while not vector_store:
filepath = input("Input your local knowledge file path 请输入本地知识文件路径：")
vector_store = init_knowledge_vector_store(filepath)
history = []
while True:
query = input("Input your question 请输入问题：")
resp, history = get_knowledge_based_answer(query=query,
vector_store=vector_store,
chat_history=history)
if REPLY_WITH_SOURCE:
print(resp)
else:
print(resp["result"])
result = knowledge_chain({"query": query})
self.llm.history[-1][0] = query
return result, self.llm.history
cli_demo.py 0 → 100644
from configs.model_config import *
import datetime
from chains.local_doc_qa import LocalDocQA
# return top-k text chunk from vector store
VECTOR_SEARCH_TOP_K = 10
# LLM input history length
LLM_HISTORY_LEN = 3
# Show reply with source text from input document
REPLY_WITH_SOURCE = True
if __name__ == "__main__":
local_doc_qa = LocalDocQA()
local_doc_qa.init_cfg(llm_model=LLM_MODEL,
embedding_model=EMBEDDING_MODEL,
embedding_device=EMBEDDING_DEVICE,
llm_history_len=LLM_HISTORY_LEN)
vs_path = None
while not vs_path:
filepath = input("Input your local knowledge file path 请输入本地知识文件路径：")
vs_path = local_doc_qa.init_knowledge_vector_store(filepath)
history = []
while True:
query = input("Input your question 请输入问题：")
resp, history = local_doc_qa.get_knowledge_based_answer(query=query,
vs_path=vs_path,
chat_history=history)
if REPLY_WITH_SOURCE:
print(resp)
else:
print(resp["result"])
configs/model_config.py 0 → 100644
import torch.cuda
import torch.backends
embedding_model_dict = {
"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
"ernie-base": "nghuyong/ernie-3.0-base-zh",
"text2vec": "GanymedeNil/text2vec-large-chinese",
"local": "/Users/liuqian/Downloads/ChatGLM-6B/text2vec-large-chinese"
}
# Embedding model name
EMBEDDING_MODEL = "local"#"text2vec"
# Embedding running device
EMBEDDING_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
# supported LLM models
llm_model_dict = {
"chatglm-6b-int4-qe": "THUDM/chatglm-6b-int4-qe",
"chatglm-6b-int4": "THUDM/chatglm-6b-int4",
"chatglm-6b": "THUDM/chatglm-6b",
"local": "/Users/liuqian/Downloads/ChatGLM-6B/chatglm-6b"
}
# LLM model name
LLM_MODEL = "local"#"chatglm-6b"
# LLM running device
LLM_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
chatglm_llm.py → models/chatglm_llm.py
......@@ -3,8 +3,9 @@ from typing import Optional, List
from langchain.llms.utils import enforce_stop_tokens
from transformers import AutoTokenizer, AutoModel
import torch
from configs.model_config import LLM_DEVICE
DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
DEVICE = LLM_DEVICE
DEVICE_ID = "0" if torch.cuda.is_available() else None
CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE
......@@ -48,12 +49,14 @@ class ChatGLM(LLM):
self.history = self.history+[[None, response]]
return response
def load_model(self, model_name_or_path: str = "THUDM/chatglm-6b"):
def load_model(self,
model_name_or_path: str = "THUDM/chatglm-6b",
llm_device=LLM_DEVICE):
self.tokenizer = AutoTokenizer.from_pretrained(
model_name_or_path,
trust_remote_code=True
)
if torch.cuda.is_available():
if torch.cuda.is_available() and llm_device.lower().startswith("cuda"):
self.model = (
AutoModel.from_pretrained(
model_name_or_path,
......@@ -61,19 +64,12 @@ class ChatGLM(LLM):
.half()
.cuda()
)
elif torch.backends.mps.is_available():
self.model = (
AutoModel.from_pretrained(
model_name_or_path,
trust_remote_code=True)
.float()
.to('mps')
)
else:
self.model = (
AutoModel.from_pretrained(
model_name_or_path,
trust_remote_code=True)
.float()
.to(llm_device)
)
self.model = self.model.eval()
webui.py
import gradio as gr
import os
import shutil
import knowledge_based_chatglm as kb
import cli_demo as kb
def get_file_list():
......@@ -108,7 +108,7 @@ with gr.Blocks(css="""
value=file_list[0] if len(file_list) > 0 else None)
with gr.Tab("upload"):
file = gr.File(label="content file",
file_types=['.txt', '.md', '.docx']
file_types=['.txt', '.md', '.docx', '.pdf']
).style(height=100)
# 将上传的文件保存到content文件夹下,并更新下拉框
file.upload(upload_file,
......
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