├── .env ├── LICENSE ├── README.md ├── app ├── routers │ └── chat.py └── tool │ ├── llm.py │ ├── pdf.py │ └── store.py ├── init_pdf.py ├── main.py ├── requirements.txt └── test └── demo.pdf /.env: -------------------------------------------------------------------------------- 1 | OPENAI_API_KEY=aaaaabbbbbccccc 2 | OPENAI_API_BASE=http://xxxxx/v1 3 | OPENAI_PROXY=http://localhost:1080 -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 2, June 1991 3 | 4 | Copyright (C) 1989, 1991 Free Software Foundation, Inc., 5 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 6 | Everyone is permitted to copy and distribute verbatim copies 7 | of this license document, but changing it is not allowed. 8 | 9 | Preamble 10 | 11 | The licenses for most software are designed to take away your 12 | freedom to share and change it. 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If this is what you want to do, use the GNU Lesser General 339 | Public License instead of this License. 340 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PDF 本地知识库 2 | 3 | ## 项目说明 4 | 基于 FastAPI 快速开发框架，整合了 LangChain、OpenAI、FAISS等技术链，实现了基于PDF文档构建问答知识库 5 | ### 6 | 7 | 8 | ## 如何使用 9 | 10 | ### 环境配置 11 | * 使用 python3 版本，测试通过版本为3.10.x 12 | 13 | ### OpenAI 14 | 修改项目下的 `.env` 文件，项目中使用到 api-key 和 api-base 代理，如果不需要使用代理，可以注释tool目录下 `llm.py` 和 `store.py` 的相应配置 15 | 16 | 17 | ### 创建虚拟环境 18 | ```python 19 | #当前目录创建虚拟环境 20 | python3 -m venv .venv 21 | 22 | #激活虚拟环境 23 | source venv/bin/activate 24 | 25 | #安装依赖 26 | pip3 install -r requirements.txt 27 | 28 | ``` 29 | 30 | ### 初始化PDF文件 31 | 修改 `init_pdf.py` 脚本的 `pdf_path` 路径，然后执行此脚本，成功后默认会生成 `faiss` 目录为向量数据库文件 32 | ```python 33 | python init_pdf.py 34 | ``` 35 | 36 | ### 项目启动 37 | ```python 38 | #其中python为虚拟环境bin目录下的 39 | python -m uvicorn main:app --reload 40 | ``` 41 | 42 | ### 接口调用 43 | 详情可查看 `chat.py` 文件 44 | ```javascript 45 | # 调用问答接口 46 | http://127.0.0.1:8000/chat/question 47 | ``` 48 | -------------------------------------------------------------------------------- /app/routers/chat.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, Body, Request 2 | 3 | from app.tool.llm import LlmEngine 4 | from app.tool.store import FaissEngine 5 | 6 | router = APIRouter( 7 | prefix="/chat" 8 | ) 9 | 10 | 11 | @router.post("/question") 12 | async def question( 13 | text: str = Body(embed=True) 14 | ): 15 | faiss = FaissEngine() 16 | vector_store = faiss.load_vector_store() 17 | 18 | llm = LlmEngine() 19 | chain = llm.get_qa_chain(vector_store) 20 | 21 | response = chain({"query": text}) 22 | # reply = "回复:" 23 | return {'success': True, "code": 0, "reply": response} 24 | 25 | 26 | @router.post("/question_history") 27 | async def question_history( 28 | request: Request, 29 | text: str = Body(embed=True) 30 | ): 31 | faiss = FaissEngine() 32 | vector_store = faiss.load_vector_store() 33 | 34 | llm = LlmEngine() 35 | chain = llm.get_history_chain(vector_store) 36 | 37 | # 使用session缓存对话 38 | chat_history = request["session"].get("chat_history") 39 | if chat_history is None: 40 | chat_history = [] 41 | 42 | # Convert chat history to list of tuples 43 | chat_history_tuples = [] 44 | for message in chat_history: 45 | chat_history_tuples.append((message[0], message[1])) 46 | 47 | response = chain({"question": text, "chat_history": chat_history_tuples}) 48 | 49 | # 保存聊天记录 50 | chat_history.append((text, response["answer"])) 51 | # 处理chat_history的长度，避免token过长 52 | chat_history = chat_history[0: 10] 53 | 54 | # 重新保存到session 55 | request["session"]["chat_history"] = chat_history 56 | 57 | # reply = "回复:" 58 | return {'success': True, "code": 0, "reply": response} 59 | -------------------------------------------------------------------------------- /app/tool/llm.py: -------------------------------------------------------------------------------- 1 | from langchain.chains import RetrievalQA, ConversationalRetrievalChain 2 | from langchain.chat_models import ChatOpenAI 3 | from langchain.prompts import PromptTemplate 4 | from langchain.text_splitter import RecursiveCharacterTextSplitter 5 | from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler 6 | import os 7 | 8 | 9 | class LlmEngine: 10 | 11 | def __init__( 12 | self, 13 | model_name: str = "gpt-3.5-turbo-16k", 14 | streaming: bool = False 15 | ): 16 | self.model_name = model_name 17 | self.streaming = streaming 18 | self.openai = ChatOpenAI( 19 | model_name=self.model_name, 20 | streaming=self.streaming, 21 | callbacks=[StreamingStdOutCallbackHandler()], 22 | openai_api_key=os.getenv("OPENAI_API_KEY"), openai_api_base=os.getenv("OPENAI_API_BASE")) 23 | 24 | # 拆分文本 25 | def get_text_chunks( 26 | self, 27 | text, 28 | chunk_size: int = 768, 29 | chunk_overlap: int = 200 30 | ): 31 | text_splitter = RecursiveCharacterTextSplitter( 32 | chunk_size=chunk_size, 33 | # chunk_size=768, 34 | chunk_overlap=chunk_overlap, 35 | length_function=len 36 | ) 37 | return text_splitter.split_text(text) 38 | 39 | # 获取检索型问答链 40 | def get_qa_chain( 41 | self, 42 | vector_store 43 | ): 44 | prompt_template = """基于以下已知内容，简洁和专业的来回答用户的问题。 45 | 如果无法从中得到答案，清说"根据已知内容无法回答该问题" 46 | 答案请使用中文。 47 | 已知内容: 48 | {context} 49 | 问题: 50 | {question}""" 51 | 52 | prompt = PromptTemplate(template=prompt_template, 53 | input_variables=["context", "question"]) 54 | 55 | return RetrievalQA.from_llm(llm=self.openai, 56 | retriever=vector_store.as_retriever(), 57 | prompt=prompt) 58 | 59 | # 获取对话式问答链 60 | def get_history_chain( 61 | self, 62 | vector_store 63 | ): 64 | prompt_template = """基于以下已知内容，简洁和专业的来回答用户的问题。 65 | 如果无法从中得到答案，清说"根据已知内容无法回答该问题" 66 | 答案请使用中文。 67 | 已知内容: 68 | {context} 69 | 问题: 70 | {question}""" 71 | 72 | prompt = PromptTemplate(template=prompt_template, 73 | input_variables=["context", "question"]) 74 | 75 | return ConversationalRetrievalChain.from_llm(llm=self.openai, 76 | retriever=vector_store.as_retriever(), 77 | combine_docs_chain_kwargs={'prompt': prompt}) 78 | -------------------------------------------------------------------------------- /app/tool/pdf.py: -------------------------------------------------------------------------------- 1 | from PyPDF2 import PdfReader 2 | 3 | 4 | class PdfEngine: 5 | 6 | def __init__(self, pdf): 7 | self.pdf = pdf 8 | 9 | # 获取pdf文件内容 10 | def get_pdf_text(self): 11 | text = "" 12 | pdf_reader = PdfReader(self.pdf) 13 | for page in pdf_reader.pages: 14 | text += page.extract_text() 15 | 16 | return text 17 | -------------------------------------------------------------------------------- /app/tool/store.py: -------------------------------------------------------------------------------- 1 | from langchain.vectorstores import FAISS 2 | from langchain.embeddings import OpenAIEmbeddings 3 | import os 4 | 5 | 6 | class FaissEngine: 7 | 8 | def __init__( 9 | self, 10 | path: str = "faiss" 11 | ): 12 | self.path = path 13 | self.llm = OpenAIEmbeddings( 14 | openai_api_key=os.getenv("OPENAI_API_KEY"), 15 | openai_api_base=os.getenv("OPENAI_API_BASE") 16 | ) 17 | 18 | # 保存 19 | def save_vector_store( 20 | self, 21 | text_chunks, 22 | path: str = "faiss" 23 | ): 24 | db = FAISS.from_texts(text_chunks, self.llm) 25 | db.save_local(path) 26 | 27 | # 加载 28 | def load_vector_store( 29 | self, 30 | path: str = "faiss" 31 | ): 32 | return FAISS.load_local(path, self.llm) 33 | -------------------------------------------------------------------------------- /init_pdf.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | from dotenv import load_dotenv, find_dotenv 4 | 5 | # 防止相对路径导入出错 6 | sys.path.append(os.path.join(os.path.dirname(__file__))) 7 | 8 | load_dotenv(find_dotenv('.env')) 9 | 10 | from app.tool.pdf import PdfEngine 11 | from app.tool.llm import LlmEngine 12 | from app.tool.store import FaissEngine 13 | 14 | # 将pdf切分块，嵌入和向量存储 15 | if __name__ == '__main__': 16 | pdf_path = './test/demo.pdf' 17 | 18 | pfdEngine = PdfEngine(pdf_path) 19 | raw_text = pfdEngine.get_pdf_text() 20 | 21 | llmEngine = LlmEngine() 22 | text_chunks = llmEngine.get_text_chunks(raw_text) 23 | 24 | faissEngine = FaissEngine() 25 | faissEngine.save_vector_store(text_chunks) 26 | 27 | print(pdf_path + ' is ok') 28 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | from fastapi import FastAPI, Request 2 | # session中间件 3 | from starlette.middleware.sessions import SessionMiddleware 4 | from app.routers import chat 5 | import sys 6 | import os 7 | 8 | from dotenv import load_dotenv, find_dotenv 9 | load_dotenv(find_dotenv()) 10 | 11 | # 防止相对路径导入出错 12 | sys.path.append(os.path.join(os.path.dirname(__file__))) 13 | 14 | app = FastAPI() 15 | # 配置session，使用cookie方式保存 16 | app.add_middleware(SessionMiddleware, secret_key='langchain_pdf_112233', max_age=86400, session_cookie='fast_api_sid') 17 | 18 | 19 | @app.middleware("http") 20 | async def some_middleware(request: Request, call_next): 21 | response = await call_next(request) 22 | session = request.cookies.get('session') 23 | if session: 24 | response.set_cookie(key='session', value=request.cookies.get('session'), httponly=True) 25 | return response 26 | 27 | 28 | # 将其余单独模块进行整合 29 | app.include_router(chat.router) 30 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | fastapi==0.104.1 2 | starlette==0.32.0 3 | itsdangerous==2.1.2 4 | langchain==0.0.340 5 | PyPDF2==3.0.1 6 | python-dotenv==1.0.0 7 | openai==1.3.5 8 | tiktoken==0.4.0 9 | faiss-cpu==1.7.4 10 | uvicorn==0.24.0 -------------------------------------------------------------------------------- /test/demo.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lrbmike/langchain_pdf/82f3f6841fbfcc51967311e69f6713d619f6c726/test/demo.pdf --------------------------------------------------------------------------------