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jinchat-server
Unverified 提交 7497b261 作者: shrimp 提交者： GitHub
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完善知识库路径问题，完善api接口 (#245) 

* Fix 知识库无法上载,NLTK_DATA_PATH路径错误 (#236)

* Update chatglm_llm.py (#242)

* 完善知识库路径问题，完善api接口

统一webui、API接口知识库路径，后续路径如下：
知识库路经就是：/项目代码文件夹/vector_store/'知识库名字'
文件存放路经：/项目代码文件夹/content/'知识库名字'

修复通过api接口创建知识库的BUG，完善API接口功能。

* Update model_config.py

---------

Co-authored-by: Bob Chang <bob-chang@outlook.com>
Co-authored-by: imClumsyPanda <littlepanda0716@gmail.com>
上级 fc7197fe
正在显示 包含 62 行增加 和 95 行删除
api.py
......@@ -13,11 +13,10 @@ from fastapi import Body, FastAPI, File, Form, Query, UploadFile, WebSocket
from fastapi.openapi.utils import get_openapi
from pydantic import BaseModel
from typing_extensions import Annotated
from starlette.responses import RedirectResponse
from chains.local_doc_qa import LocalDocQA
from configs.model_config import (API_UPLOAD_ROOT_PATH, EMBEDDING_DEVICE,
EMBEDDING_MODEL, LLM_MODEL, NLTK_DATA_PATH,
VECTOR_SEARCH_TOP_K, LLM_HISTORY_LEN)
from configs.model_config import (VS_ROOT_PATH, EMBEDDING_DEVICE, EMBEDDING_MODEL, LLM_MODEL, UPLOAD_ROOT_PATH,
NLTK_DATA_PATH, VECTOR_SEARCH_TOP_K, LLM_HISTORY_LEN)
nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
......@@ -76,37 +75,47 @@ class ChatMessage(BaseModel):
def get_folder_path(local_doc_id: str):
return os.path.join(API_UPLOAD_ROOT_PATH, local_doc_id)
return os.path.join(UPLOAD_ROOT_PATH, local_doc_id)
def get_vs_path(local_doc_id: str):
return os.path.join(API_UPLOAD_ROOT_PATH, local_doc_id, "vector_store")
return os.path.join(VS_ROOT_PATH, local_doc_id)
def get_file_path(local_doc_id: str, doc_name: str):
return os.path.join(API_UPLOAD_ROOT_PATH, local_doc_id, doc_name)
return os.path.join(UPLOAD_ROOT_PATH, local_doc_id, doc_name)
async def upload_file(
files: Annotated[
List[UploadFile], File(description="Multiple files as UploadFile")
],
knowledge_base_id: str = Form(..., description="Knowledge Base Name", example="kb1"),
files: Annotated[
List[UploadFile], File(description="Multiple files as UploadFile")
],
knowledge_base_id: str = Form(..., description="Knowledge Base Name", example="kb1"),
):
saved_path = get_folder_path(knowledge_base_id)
if not os.path.exists(saved_path):
os.makedirs(saved_path)
filelist = []
for file in files:
file_content = ''
file_path = os.path.join(saved_path, file.filename)
with open(file_path, "wb") as f:
f.write(file.file.read())
local_doc_qa.init_knowledge_vector_store(saved_path, get_vs_path(knowledge_base_id))
return BaseResponse()
file_content = file.file.read()
if os.path.exists(file_path) and os.path.getsize(file_path) == len(file_content):
continue
with open(file_path, "ab+") as f:
f.write(file_content)
filelist.append(file_path)
if filelist:
vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, get_vs_path(knowledge_base_id))
if len(loaded_files):
file_status = f"已上传 {'、'.join([os.path.split(i)[-1] for i in loaded_files])} 至知识库，并已加载知识库，请开始提问"
return BaseResponse(code=200, msg=file_status)
file_status = "文件未成功加载，请重新上传文件"
return BaseResponse(code=500, msg=file_status)
async def list_docs(
knowledge_base_id: Optional[str] = Query(description="Knowledge Base Name", example="kb1")
knowledge_base_id: Optional[str] = Query(description="Knowledge Base Name", example="kb1")
):
if knowledge_base_id:
local_doc_folder = get_folder_path(knowledge_base_id)
......@@ -119,25 +128,27 @@ async def list_docs(
]
return ListDocsResponse(data=all_doc_names)
else:
if not os.path.exists(API_UPLOAD_ROOT_PATH):
if not os.path.exists(UPLOAD_ROOT_PATH):
all_doc_ids = []
else:
all_doc_ids = [
folder
for folder in os.listdir(API_UPLOAD_ROOT_PATH)
if os.path.isdir(os.path.join(API_UPLOAD_ROOT_PATH, folder))
for folder in os.listdir(UPLOAD_ROOT_PATH)
if os.path.isdir(os.path.join(UPLOAD_ROOT_PATH, folder))
]
return ListDocsResponse(data=all_doc_ids)
async def delete_docs(
knowledge_base_id: str = Form(..., description="Knowledge Base Name", example="kb1"),
doc_name: Optional[str] = Form(
None, description="doc name", example="doc_name_1.pdf"
),
knowledge_base_id: str = Form(...,
description="Knowledge Base Name(注意此方法仅删除上传的文件并不会删除知识库(FAISS)内数据)",
example="kb1"),
doc_name: Optional[str] = Form(
None, description="doc name", example="doc_name_1.pdf"
),
):
if not os.path.exists(os.path.join(API_UPLOAD_ROOT_PATH, knowledge_base_id)):
if not os.path.exists(os.path.join(UPLOAD_ROOT_PATH, knowledge_base_id)):
return {"code": 1, "msg": f"Knowledge base {knowledge_base_id} not found"}
if doc_name:
doc_path = get_file_path(knowledge_base_id, doc_name)
......@@ -159,25 +170,25 @@ async def delete_docs(
async def chat(
knowledge_base_id: str = Body(..., description="Knowledge Base Name", example="kb1"),
question: str = Body(..., description="Question", example="工伤保险是什么？"),
history: List[List[str]] = Body(
[],
description="History of previous questions and answers",
example=[
[
"工伤保险是什么？",
"工伤保险是指用人单位按照国家规定，为本单位的职工和用人单位的其他人员，缴纳工伤保险费，由保险机构按照国家规定的标准，给予工伤保险待遇的社会保险制度。",
]
],
),
knowledge_base_id: str = Body(..., description="Knowledge Base Name", example="kb1"),
question: str = Body(..., description="Question", example="工伤保险是什么？"),
history: List[List[str]] = Body(
[],
description="History of previous questions and answers",
example=[
[
"工伤保险是什么？",
"工伤保险是指用人单位按照国家规定，为本单位的职工和用人单位的其他人员，缴纳工伤保险费，由保险机构按照国家规定的标准，给予工伤保险待遇的社会保险制度。",
]
],
),
):
vs_path = os.path.join(API_UPLOAD_ROOT_PATH, knowledge_base_id, "vector_store")
vs_path = os.path.join(VS_ROOT_PATH, knowledge_base_id)
if not os.path.exists(vs_path):
raise ValueError(f"Knowledge base {knowledge_base_id} not found")
for resp, history in local_doc_qa.get_knowledge_based_answer(
query=question, vs_path=vs_path, chat_history=history, streaming=True
query=question, vs_path=vs_path, chat_history=history, streaming=True
):
pass
source_documents = [
......@@ -196,7 +207,7 @@ async def chat(
async def stream_chat(websocket: WebSocket, knowledge_base_id: str):
await websocket.accept()
vs_path = os.path.join(API_UPLOAD_ROOT_PATH, knowledge_base_id, "vector_store")
vs_path = os.path.join(VS_ROOT_PATH, knowledge_base_id)
if not os.path.exists(vs_path):
await websocket.send_json({"error": f"Knowledge base {knowledge_base_id} not found"})
......@@ -211,7 +222,7 @@ async def stream_chat(websocket: WebSocket, knowledge_base_id: str):
last_print_len = 0
for resp, history in local_doc_qa.get_knowledge_based_answer(
query=question, vs_path=vs_path, chat_history=history, streaming=True
query=question, vs_path=vs_path, chat_history=history, streaming=True
):
await websocket.send_text(resp["result"][last_print_len:])
last_print_len = len(resp["result"])
......@@ -236,40 +247,8 @@ async def stream_chat(websocket: WebSocket, knowledge_base_id: str):
turn += 1
def gen_docs():
global app
with tempfile.NamedTemporaryFile("w", encoding="utf-8", suffix=".json") as f:
json.dump(
get_openapi(
title=app.title,
version=app.version,
openapi_version=app.openapi_version,
description=app.description,
routes=app.routes,
),
f,
ensure_ascii=False,
)
f.flush()
# test whether widdershins is available
try:
subprocess.run(
[
"widdershins",
f.name,
"-o",
os.path.join(
os.path.dirname(os.path.abspath(__file__)),
"docs",
"API.md",
),
],
check=True,
)
except Exception:
raise RuntimeError(
"Failed to generate docs. Please install widdershins first."
)
async def document():
return RedirectResponse(url="/docs")
def main():
......@@ -278,7 +257,6 @@ def main():
parser = argparse.ArgumentParser()
parser.add_argument("--host", type=str, default="0.0.0.0")
parser.add_argument("--port", type=int, default=7861)
parser.add_argument("--gen-docs", action="store_true")
args = parser.parse_args()
app = FastAPI()
......@@ -287,10 +265,7 @@ def main():
app.post("/chat-docs/upload", response_model=BaseResponse)(upload_file)
app.get("/chat-docs/list", response_model=ListDocsResponse)(list_docs)
app.delete("/chat-docs/delete", response_model=BaseResponse)(delete_docs)
if args.gen_docs:
gen_docs()
return
app.get("/", response_model=BaseResponse)(document)
local_doc_qa = LocalDocQA()
local_doc_qa.init_cfg(
......
configs/model_config.py
......@@ -28,7 +28,6 @@ llm_model_dict = {
LLM_MODEL = "chatglm-6b"
# LLM lora path，默认为空，如果有请直接指定文件夹路径
# 推荐使用 chatglm-6b-belle-zh-lora
LLM_LORA_PATH = ""
USE_LORA = True if LLM_LORA_PATH else False
......@@ -45,8 +44,6 @@ VS_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "vector_
UPLOAD_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "content")
API_UPLOAD_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "api_content")
# 基于上下文的prompt模版，请务必保留"{question}"和"{context}"
PROMPT_TEMPLATE = """已知信息：
{context}
......@@ -62,4 +59,4 @@ LLM_HISTORY_LEN = 3
# return top-k text chunk from vector store
VECTOR_SEARCH_TOP_K = 5
NLTK_DATA_PATH = os.path.join(os.path.dirname(__file__), "nltk_data")
\ No newline at end of file
NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
models/chatglm_llm.py
......@@ -144,12 +144,12 @@ class ChatGLM(LLM):
config=model_config, **kwargs)
if LLM_LORA_PATH and use_lora:
from peft import PeftModel
model_auto = PeftModel.from_pretrained(model, LLM_LORA_PATH)
model = PeftModel.from_pretrained(model, LLM_LORA_PATH)
# 可传入device_map自定义每张卡的部署情况
if device_map is None:
device_map = auto_configure_device_map(num_gpus)
self.model = dispatch_model(model_auto.half(), device_map=device_map)
self.model = dispatch_model(model.half(), device_map=device_map)
else:
self.model = self.model.float().to(llm_device)
......
webui.py
......@@ -48,12 +48,6 @@ def get_answer(query, vs_path, history, mode,
yield history, ""
def update_status(history, status):
history = history + [[None, status]]
print(status)
return history
def init_model():
try:
local_doc_qa.init_cfg()
......@@ -92,10 +86,12 @@ def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, us
def get_vector_store(vs_id, files, history):
vs_path = os.path.join(VS_ROOT_PATH, vs_id)
filelist = []
if not os.path.exists(os.path.join(UPLOAD_ROOT_PATH, vs_id)):
os.makedirs(os.path.join(UPLOAD_ROOT_PATH, vs_id))
for file in files:
filename = os.path.split(file.name)[-1]
shutil.move(file.name, os.path.join(UPLOAD_ROOT_PATH, filename))
filelist.append(os.path.join(UPLOAD_ROOT_PATH, filename))
shutil.move(file.name, os.path.join(UPLOAD_ROOT_PATH, vs_id, filename))
filelist.append(os.path.join(UPLOAD_ROOT_PATH, vs_id, filename))
if local_doc_qa.llm and local_doc_qa.embeddings:
vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, vs_path)
if len(loaded_files):
......
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