RagFlow部署

一、ragflow相关信息‍‍‍‍‍‍

git地址:https://github.com/infiniflow/ragflow

文档地址:‍https://ragflow.io/docs/dev/

二、部署

复制代码
git clone https://github.com/infiniflow/ragflow.gi
docker compose -f docker/docker-compose.yml up -d
在浏览器中对应的IP地址并登录RAGFlow 默认打开ragflow地址  http://localhost:80

附件代码

复制代码
import streamlit as st
from langchain_community.document_loaders import PDFPlumberLoader
from langchain_experimental.text_splitter import SemanticChunker
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.llms import Ollama
from langchain.prompts import PromptTemplate
from langchain.chains.llm import LLMChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains import RetrievalQA

# color palette
primary_color = "#1E90FF"
secondary_color = "#FF6347"
background_color = "#F5F5F5"
text_color = "#4561e9"

# Custom CSS
st.markdown(f"""
    <style>
    .stApp {{
        background-color: {background_color};
        color: {text_color};
    }}
    .stButton>button {{
        background-color: {primary_color};
        color: white;
        border-radius: 5px;
        border: none;
        padding: 10px 20px;
        font-size: 16px;
    }}
    .stTextInput>div>div>input {{
        border: 2px solid {primary_color};
        border-radius: 5px;
        padding: 10px;
        font-size: 16px;
    }}
    .stFileUploader>div>div>div>button {{
        background-color: {secondary_color};
        color: white;
        border-radius: 5px;
        border: none;
        padding: 10px 20px;
        font-size: 16px;
    }}
    </style>
""", unsafe_allow_html=True)

# Streamlit app title
st.title("Build a RAG System with DeepSeek R1 & Ollama")

# Load the PDF
uploaded_file = st.file_uploader("Upload a PDF file", type="pdf")

if uploaded_file is not None:
    # Save the uploaded file to a temporary location
    with open("temp.pdf", "wb") as f:
        f.write(uploaded_file.getvalue())

    # Load the PDF
    loader = PDFPlumberLoader("temp.pdf")
    docs = loader.load()

    # Split into chunks
    text_splitter = SemanticChunker(HuggingFaceEmbeddings())
    documents = text_splitter.split_documents(docs)

    # Instantiate the embedding model
    embedder = HuggingFaceEmbeddings()

    # Create the vector store and fill it with embeddings
    vector = FAISS.from_documents(documents, embedder)
    retriever = vector.as_retriever(search_type="similarity", search_kwargs={"k": 3})

    # Define llm
    llm = Ollama(model="deepseek-r1")

    # Define the prompt
    prompt = """
    1. Use the following pieces of context to answer the question at the end.
    2. If you don't know the answer, just say that "I don't know" but don't make up an answer on your own.\n
    3. Keep the answer crisp and limited to 3,4 sentences.

    Context: {context}

    Question: {question}

    Helpful Answer:"""

    QA_CHAIN_PROMPT = PromptTemplate.from_template(prompt)

    llm_chain = LLMChain(
        llm=llm,
        prompt=QA_CHAIN_PROMPT,
        callbacks=None,
        verbose=True)

    document_prompt = PromptTemplate(
        input_variables=["page_content", "source"],
        template="Context:\ncontent:{page_content}\nsource:{source}",
    )

    combine_documents_chain = StuffDocumentsChain(
        llm_chain=llm_chain,
        document_variable_name="context",
        document_prompt=document_prompt,
        callbacks=None)

    qa = RetrievalQA(
        combine_documents_chain=combine_documents_chain,
        verbose=True,
        retriever=retriever,
        return_source_documents=True)

    # User input
    user_input = st.text_input("Ask a question related to the PDF :")

    # Process user input
    if user_input:
        with st.spinner("Processing..."):
            response = qa(user_input)["result"]
            st.write("Response:")
            st.write(response)
else:
    st.write("Please upload a PDF file to proceed.")
相关推荐
databook14 小时前
Manim实现脉冲闪烁特效
后端·python·动效
程序设计实验室14 小时前
2025年了,在 Django 之外,Python Web 框架还能怎么选?
python
倔强青铜三16 小时前
苦练Python第46天:文件写入与上下文管理器
人工智能·python·面试
用户25191624271119 小时前
Python之语言特点
python
刘立军20 小时前
使用pyHugeGraph查询HugeGraph图数据
python·graphql
数据智能老司机1 天前
精通 Python 设计模式——创建型设计模式
python·设计模式·架构
数据智能老司机1 天前
精通 Python 设计模式——SOLID 原则
python·设计模式·架构
c8i1 天前
django中的FBV 和 CBV
python·django
c8i1 天前
python中的闭包和装饰器
python
这里有鱼汤1 天前
小白必看:QMT里的miniQMT入门教程
后端·python