* 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: shrimp <411161555@qq.com> Co-authored-by: Bob Chang <bob-chang@outlook.com>
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135
api.py
135
api.py
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@ -13,11 +13,10 @@ from fastapi import Body, FastAPI, File, Form, Query, UploadFile, WebSocket
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from fastapi.openapi.utils import get_openapi
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from pydantic import BaseModel
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from typing_extensions import Annotated
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from starlette.responses import RedirectResponse
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from chains.local_doc_qa import LocalDocQA
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from configs.model_config import (API_UPLOAD_ROOT_PATH, EMBEDDING_DEVICE,
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EMBEDDING_MODEL, LLM_MODEL, NLTK_DATA_PATH,
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VECTOR_SEARCH_TOP_K, LLM_HISTORY_LEN)
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from configs.model_config import (VS_ROOT_PATH, EMBEDDING_DEVICE, EMBEDDING_MODEL, LLM_MODEL, UPLOAD_ROOT_PATH,
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NLTK_DATA_PATH, VECTOR_SEARCH_TOP_K, LLM_HISTORY_LEN)
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nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
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@ -76,37 +75,47 @@ class ChatMessage(BaseModel):
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def get_folder_path(local_doc_id: str):
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return os.path.join(API_UPLOAD_ROOT_PATH, local_doc_id)
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return os.path.join(UPLOAD_ROOT_PATH, local_doc_id)
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def get_vs_path(local_doc_id: str):
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return os.path.join(API_UPLOAD_ROOT_PATH, local_doc_id, "vector_store")
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return os.path.join(VS_ROOT_PATH, local_doc_id)
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def get_file_path(local_doc_id: str, doc_name: str):
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return os.path.join(API_UPLOAD_ROOT_PATH, local_doc_id, doc_name)
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return os.path.join(UPLOAD_ROOT_PATH, local_doc_id, doc_name)
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async def upload_file(
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files: Annotated[
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List[UploadFile], File(description="Multiple files as UploadFile")
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],
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knowledge_base_id: str = Form(..., description="Knowledge Base Name", example="kb1"),
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files: Annotated[
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List[UploadFile], File(description="Multiple files as UploadFile")
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],
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knowledge_base_id: str = Form(..., description="Knowledge Base Name", example="kb1"),
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):
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saved_path = get_folder_path(knowledge_base_id)
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if not os.path.exists(saved_path):
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os.makedirs(saved_path)
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filelist = []
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for file in files:
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file_content = ''
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file_path = os.path.join(saved_path, file.filename)
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with open(file_path, "wb") as f:
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f.write(file.file.read())
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local_doc_qa.init_knowledge_vector_store(saved_path, get_vs_path(knowledge_base_id))
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return BaseResponse()
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file_content = file.file.read()
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if os.path.exists(file_path) and os.path.getsize(file_path) == len(file_content):
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continue
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with open(file_path, "ab+") as f:
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f.write(file_content)
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filelist.append(file_path)
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if filelist:
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vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, get_vs_path(knowledge_base_id))
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if len(loaded_files):
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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"
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return BaseResponse(code=200, msg=file_status)
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file_status = "文件未成功加载，请重新上传文件"
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return BaseResponse(code=500, msg=file_status)
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async def list_docs(
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knowledge_base_id: Optional[str] = Query(description="Knowledge Base Name", example="kb1")
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knowledge_base_id: Optional[str] = Query(description="Knowledge Base Name", example="kb1")
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):
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if knowledge_base_id:
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local_doc_folder = get_folder_path(knowledge_base_id)
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@ -119,25 +128,27 @@ async def list_docs(
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]
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return ListDocsResponse(data=all_doc_names)
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else:
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if not os.path.exists(API_UPLOAD_ROOT_PATH):
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if not os.path.exists(UPLOAD_ROOT_PATH):
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all_doc_ids = []
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else:
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all_doc_ids = [
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folder
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for folder in os.listdir(API_UPLOAD_ROOT_PATH)
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if os.path.isdir(os.path.join(API_UPLOAD_ROOT_PATH, folder))
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for folder in os.listdir(UPLOAD_ROOT_PATH)
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if os.path.isdir(os.path.join(UPLOAD_ROOT_PATH, folder))
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]
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return ListDocsResponse(data=all_doc_ids)
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async def delete_docs(
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knowledge_base_id: str = Form(..., description="Knowledge Base Name", example="kb1"),
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doc_name: Optional[str] = Form(
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None, description="doc name", example="doc_name_1.pdf"
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),
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knowledge_base_id: str = Form(...,
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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)",
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example="kb1"),
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doc_name: Optional[str] = Form(
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None, description="doc name", example="doc_name_1.pdf"
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),
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):
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if not os.path.exists(os.path.join(API_UPLOAD_ROOT_PATH, knowledge_base_id)):
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if not os.path.exists(os.path.join(UPLOAD_ROOT_PATH, knowledge_base_id)):
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return {"code": 1, "msg": f"Knowledge base {knowledge_base_id} not found"}
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if doc_name:
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doc_path = get_file_path(knowledge_base_id, doc_name)
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@ -159,25 +170,25 @@ async def delete_docs(
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async def chat(
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knowledge_base_id: str = Body(..., description="Knowledge Base Name", example="kb1"),
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question: str = Body(..., description="Question", example="工伤保险是什么？"),
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history: List[List[str]] = Body(
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[],
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description="History of previous questions and answers",
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example=[
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[
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"工伤保险是什么？",
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"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。",
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]
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],
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),
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knowledge_base_id: str = Body(..., description="Knowledge Base Name", example="kb1"),
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question: str = Body(..., description="Question", example="工伤保险是什么？"),
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history: List[List[str]] = Body(
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[],
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description="History of previous questions and answers",
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example=[
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[
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"工伤保险是什么？",
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"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。",
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]
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],
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),
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):
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vs_path = os.path.join(API_UPLOAD_ROOT_PATH, knowledge_base_id, "vector_store")
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vs_path = os.path.join(VS_ROOT_PATH, knowledge_base_id)
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if not os.path.exists(vs_path):
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raise ValueError(f"Knowledge base {knowledge_base_id} not found")
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for resp, history in local_doc_qa.get_knowledge_based_answer(
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query=question, vs_path=vs_path, chat_history=history, streaming=True
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query=question, vs_path=vs_path, chat_history=history, streaming=True
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):
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pass
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source_documents = [
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@ -196,7 +207,7 @@ async def chat(
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async def stream_chat(websocket: WebSocket, knowledge_base_id: str):
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await websocket.accept()
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vs_path = os.path.join(API_UPLOAD_ROOT_PATH, knowledge_base_id, "vector_store")
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vs_path = os.path.join(VS_ROOT_PATH, knowledge_base_id)
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if not os.path.exists(vs_path):
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await websocket.send_json({"error": f"Knowledge base {knowledge_base_id} not found"})
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@ -211,7 +222,7 @@ async def stream_chat(websocket: WebSocket, knowledge_base_id: str):
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last_print_len = 0
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for resp, history in local_doc_qa.get_knowledge_based_answer(
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query=question, vs_path=vs_path, chat_history=history, streaming=True
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query=question, vs_path=vs_path, chat_history=history, streaming=True
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):
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await websocket.send_text(resp["result"][last_print_len:])
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last_print_len = len(resp["result"])
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@ -236,40 +247,8 @@ async def stream_chat(websocket: WebSocket, knowledge_base_id: str):
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turn += 1
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def gen_docs():
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global app
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with tempfile.NamedTemporaryFile("w", encoding="utf-8", suffix=".json") as f:
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json.dump(
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get_openapi(
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title=app.title,
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version=app.version,
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openapi_version=app.openapi_version,
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description=app.description,
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routes=app.routes,
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),
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f,
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ensure_ascii=False,
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)
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f.flush()
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# test whether widdershins is available
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try:
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subprocess.run(
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[
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"widdershins",
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f.name,
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"-o",
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os.path.join(
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os.path.dirname(os.path.abspath(__file__)),
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"docs",
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"API.md",
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),
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],
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check=True,
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)
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except Exception:
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raise RuntimeError(
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"Failed to generate docs. Please install widdershins first."
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)
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async def document():
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return RedirectResponse(url="/docs")
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def main():
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@ -278,7 +257,6 @@ def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--host", type=str, default="0.0.0.0")
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parser.add_argument("--port", type=int, default=7861)
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parser.add_argument("--gen-docs", action="store_true")
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args = parser.parse_args()
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app = FastAPI()
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@ -287,10 +265,7 @@ def main():
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app.post("/chat-docs/upload", response_model=BaseResponse)(upload_file)
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app.get("/chat-docs/list", response_model=ListDocsResponse)(list_docs)
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app.delete("/chat-docs/delete", response_model=BaseResponse)(delete_docs)
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if args.gen_docs:
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gen_docs()
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return
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app.get("/", response_model=BaseResponse)(document)
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local_doc_qa = LocalDocQA()
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local_doc_qa.init_cfg(
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@ -28,7 +28,6 @@ llm_model_dict = {
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LLM_MODEL = "chatglm-6b"
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# LLM lora path，默认为空，如果有请直接指定文件夹路径
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# 推荐使用 chatglm-6b-belle-zh-lora
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LLM_LORA_PATH = ""
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USE_LORA = True if LLM_LORA_PATH else False
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@ -45,8 +44,6 @@ VS_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "vector_
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UPLOAD_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "content")
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API_UPLOAD_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "api_content")
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# 基于上下文的prompt模版，请务必保留"{question}"和"{context}"
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PROMPT_TEMPLATE = """已知信息：
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{context}
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# return top-k text chunk from vector store
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VECTOR_SEARCH_TOP_K = 5
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NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
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NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
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12
webui.py
12
webui.py
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@ -48,12 +48,6 @@ def get_answer(query, vs_path, history, mode,
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yield history, ""
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def update_status(history, status):
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history = history + [[None, status]]
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print(status)
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return history
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def init_model():
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try:
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local_doc_qa.init_cfg()
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@ -92,10 +86,12 @@ def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, us
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def get_vector_store(vs_id, files, history):
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vs_path = os.path.join(VS_ROOT_PATH, vs_id)
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filelist = []
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if not os.path.exists(os.path.join(UPLOAD_ROOT_PATH, vs_id)):
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os.makedirs(os.path.join(UPLOAD_ROOT_PATH, vs_id))
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for file in files:
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filename = os.path.split(file.name)[-1]
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shutil.move(file.name, os.path.join(UPLOAD_ROOT_PATH, filename))
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filelist.append(os.path.join(UPLOAD_ROOT_PATH, filename))
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shutil.move(file.name, os.path.join(UPLOAD_ROOT_PATH, vs_id, filename))
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filelist.append(os.path.join(UPLOAD_ROOT_PATH, vs_id, filename))
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if local_doc_qa.llm and local_doc_qa.embeddings:
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vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, vs_path)
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if len(loaded_files):
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