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+docs/chroma/chroma.sqlite3 filter=lfs diff=lfs merge=lfs -text

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requirements.txt

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+streamlit
+streamlit-chat
+langchain
+chromadb
+pysqlite3-binary

st.py

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+import streamlit as st
+import os
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import RetrievalQA, LLMChain
+from langchain import PromptTemplate
+from langchain.memory import ConversationBufferMemory
+from langchain.agents import initialize_agent, Tool, AgentExecutor
+from langchain.text_splitter import CharacterTextSplitter
+import chromadb
+
+
+# API
+openai_api_key = st.secrets["OPENAI_API_KEY"]
+
+
+# Define the path to your document files
+file1 = "./DIVISION OF ASSETS AFTER DIVORCE.txt"
+file2 = "./INHERITANCE.txt"
+
+
+# Function to initialize the OpenAI embeddings and model
+def openai_setting():
+    embedding = OpenAIEmbeddings()
+    model_name = "gpt-3.5-turbo"
+    llm = ChatOpenAI(model_name=model_name, temperature=0)
+    return embedding, llm
+
+
+# Function to split the law content
+def law_content_splitter(path, splitter="CIVIL CODE"):
+    with open(path) as f:
+        law_content = f.read()
+    law_content_by_article = law_content.split(splitter)[1:]
+    text_splitter = CharacterTextSplitter()
+    return text_splitter.create_documents(law_content_by_article)
+
+
+# Splitting the content of law documents
+divorce_splitted = law_content_splitter(file1)
+inheritance_splitted = law_content_splitter(file2)
+
+# Initializing embedding and language model
+embedding, llm = openai_setting()
+
+# Define the prompts
+divorce_prompt = """As a specialized bot in divorce law, you should offer accurate insights on Italian divorce regulations.
+You should always cite the article numbers you reference. 
+Ensure you provide detailed and exact data. 
+If a query doesn't pertain to the legal documents, you should remind the user that it falls outside your expertise.
+You should be adept at discussing the various Italian divorce categories, including fault-based divorce, mutual-consent divorce, and divorce due to infidelity.
+You should guide users through the prerequisites and procedures of each divorce type, detailing the essential paperwork, expected duration, and potential legal repercussions. 
+You should capably address queries regarding asset allocation, child custody, spousal support, and other financial concerns related to divorce, all while staying true to Italian legislation.
+{context}
+
+Question: {question}"""
+DIVORCE_BOT_PROMPT = PromptTemplate(
+    template=divorce_prompt, input_variables=["context", "question"]
+)
+
+
+# define inheritance prompt
+inheritance_prompt = """As a specialist in Italian inheritance law, you should deliver detailed and accurate insights about inheritance regulations in Italy.
+You should always cite the article numbers you reference. 
+When responding to user queries, you should always base your answers on the provided context.
+Always cite the specific article numbers you mention and refrain from speculating.
+Maintain precision in all your responses.
+If a user's question doesn't align with the legal documents, you should point out that it's beyond your domain of expertise.
+You should elucidate Italian inheritance law comprehensively, touching on topics such as testamentary inheritance, intestate inheritance, and other pertinent subjects.
+Make sure to elaborate on the obligations and rights of inheritors, the methodology of estate distribution, asset assessment, and settling debts, all while adhering to Italian law specifics.
+You should adeptly tackle questions about various will forms like holographic or notarial wills, ensuring you clarify their legitimacy within Italian jurisdiction. 
+Offer advice on creating a will, naming heirs, and managing potential conflicts.
+You should provide detailed information on tax nuances associated with inheritance in Italy, inclusive of exemptions, tax rates, and mandatory disclosures.
+
+{context}
+
+Question: {question}"""
+INHERITANCE_BOT_PROMPT = PromptTemplate(
+    template=inheritance_prompt, input_variables=["context", "question"]
+)
+
+
+# Setup for Chroma databases and RetrievalQA
+
+chroma_directory = "./docs/chroma/"
+
+
+inheritance_db = Chroma.from_documents(
+    documents=inheritance_splitted,
+    embedding=embedding,
+    persist_directory=chroma_directory,
+)
+
+inheritance = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=inheritance_db.as_retriever(),
+    chain_type_kwargs={"prompt": INHERITANCE_BOT_PROMPT},
+)
+
+
+divorce_db = Chroma.from_documents(
+    documents=divorce_splitted, embedding=embedding, persist_directory=chroma_directory
+)
+
+
+divorce = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=divorce_db.as_retriever(),
+    chain_type_kwargs={"prompt": DIVORCE_BOT_PROMPT},
+)
+
+
+# Define the tools for the chatbot
+tools = [
+    Tool(
+        name="Divorce Italian law QA System",
+        func=divorce.run,
+        description="useful for when you need to answer questions about divorce laws in Italy.Give also the number of article you use for it.",
+    ),
+    Tool(
+        name="Inheritance Italian law QA System",
+        func=inheritance.run,
+        description="useful for when you need to answer questions about inheritance laws in Italy.Give also the number of article you use for it.",
+    ),
+]
+
+# Initialize conversation memory
+memory = ConversationBufferMemory(
+    memory_key="chat_history", input_key="input", output_key="output"
+)
+
+
+# initialize ReAct agent
+react = initialize_agent(tools, llm, agent="zero-shot-react-description")
+
+agent = AgentExecutor.from_agent_and_tools(
+    tools=tools, agent=react.agent, memory=memory, verbose=False
+)
+
+
+# Define the chatbot function
+# question = "I'm getting divorced,what's happen at my children"
+
+
+def questions(question):
+    return agent.run(question)
+
+
+def chatbot1(question):
+    try:
+        return questions(question)
+
+    except:
+        return "I'm sorry, I'm having trouble understanding your question. Could you please rephrase it or provide more context"
+
+
+def is_greeting(input_str):
+    """Check if the input is a greeting."""
+    greetings = [
+        "hello",
+        "hi",
+        "hey",
+        "greetings",
+        "good morning",
+        "good afternoon",
+        "good evening",
+        "hi there",
+        "hello there",
+        "hey there",
+        "howdy",
+        "sup",
+        "what's up",
+        "how's it going",
+        "how are you",
+        "good day",
+        "salutations",
+        "hiya",
+        "yo",
+        "hola",
+        "bonjour",
+        "g'day",
+        "how do you do",
+        "what’s new",
+        "what’s up",
+        "how’s everything",
+        "how are things",
+        "how’s life",
+        "how’s your day",
+        "how’s your day going",
+        "good to see you",
+        "nice to see you",
+        "great to see you",
+        "lovely to see you",
+        "how have you been",
+        "what’s going on",
+        "what’s happening",
+        "what’s new",
+        "long time no see",
+        # Italian greetings
+        "ciao",
+        "salve",
+        "buongiorno",
+        "buona sera",
+        "buonasera",
+        "buon pomeriggio",
+        "buonpomeriggio",
+        "come stai",
+        "comestai",
+        "come va",
+        "comeva",
+        "come sta",
+        "comesta",
+        "piacere di conoscerti",
+        "piacere",
+        "benvenuto",
+        "ben trovato",
+    ]
+    return any(greet in input_str.lower() for greet in greetings)
+
+
+def chatbot(input_str):
+    # Check for greetings first
+    if is_greeting(input_str):
+        return "Hello! Ask me your question about Italian Divorce or Inheritance Law?"
+
+    # Existing chatbot logic
+    response = chatbot1(input_str)
+    if response == "N/A":
+        return "I'm sorry, I'm having trouble understanding your question. Could you please rephrase it or provide more context"
+    else:
+        return response
+
+
+# Streamlit Chat UI
+st.set_page_config(
+    page_title="Italian Law Chatbot",
+    page_icon="⚖️",
+    layout="centered",
+    initial_sidebar_state="auto",
+)
+
+st.title("Italian Law Chatbot 🏛️")
+st.info(
+    "Check out the full tutorial to build this app in our [📝 blog post](https://blog.streamlit.io/build-a-chatbot-with-custom-data-sources-powered-by-llamaindex/) — "
+    "[GitHub Repository](https://sattari.org)",
+    icon="ℹ️",
+)
+
+
+st.success(
+    "Check out [Prompt Examples List](https://blog.streamlit.io/build-a-chatbot-with-custom-data-sources-powered-by-llamaindex/) to learn how to interact with this ChatBot 🤗 ",
+    icon="✅",
+)
+
+# Initialize session state for conversation history
+if "messages" not in st.session_state:
+    st.session_state.messages = [
+        {
+            "role": "assistant",
+            "content": "Hello! I'm here to help you with Italian Divorce or Inheritance Law. How can I assist you today?",
+        }
+    ]
+
+# Display previous messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Handle new user input
+if user_input := st.chat_input(
+    "Ask a question about Italian Divorce or Inheritance Law:"
+):
+    st.session_state.messages.append({"role": "user", "content": user_input})
+    with st.chat_message("user"):
+        st.markdown(user_input)
+
+    # Generate and display chatbot response
+    with st.chat_message("assistant"):
+        response_placeholder = st.empty()
+        response = chatbot(user_input)  # Your existing chatbot function
+        response_placeholder.markdown(response)
+
+    # Append the response to the conversation history
+    st.session_state.messages.append({"role": "assistant", "content": response})