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app.py

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+import streamlit as st
+from langchain.chains import RetrievalQA
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.vectorstores import Pinecone
+from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings
+from langchain.llms import HuggingFaceEndpoint
+from langchain.prompts import PromptTemplate
+from pinecone import Pinecone
+from langchain_pinecone import PineconeVectorStore
+from streamlit_chat import message
+import re
+
+def main():
+    # Set your Hugging Face API token and Pinecone API key
+    huggingfacehub_api_token = huggingfacehub_api_token
+    pinecone_api_key = pinecone_api_key
+
+    # Initialize embeddings
+    embeddings = HuggingFaceInferenceAPIEmbeddings(
+        api_key=huggingfacehub_api_token, model_name="sentence-transformers/all-MiniLM-l6-v2"
+    )
+
+    # Initialize Pinecone
+    vectorstore = PineconeVectorStore(
+        index_name="chatbot-law",
+        embedding=embeddings, 
+        pinecone_api_key=pinecone_api_key
+    )
+
+    # Define the LLM
+    llm = HuggingFaceEndpoint(repo_id="togethercomputer/RedPajama-INCITE-Chat-3B-v1", huggingfacehub_api_token=huggingfacehub_api_token)
+
+    # Define the prompt template
+    prompt_template = """You are a Nigerian legal chatbot. Advise lawyers on questions regarding Nigerian law.
+    Use the following piece of context to answer the question.
+    If you don't know the answer, just say you don't know.
+    Keep the answer within six sentences and never ask users to seek advise from a professional lawyer.
+
+    Context: {context}
+    Question: {question}
+
+    Answer the question and provide additional helpful information, based on the pieces of information, if applicable.
+    """
+
+    prompt = PromptTemplate(
+        template=prompt_template,
+        input_variables=["context", "question"]
+    )
+
+    # Initialize memory
+    memory = ConversationBufferWindowMemory(k=5)
+
+    # Initialize the RetrievalQA chain with memory
+    qa = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=vectorstore.as_retriever(),
+        chain_type_kwargs={"prompt": prompt, "verbose": False},
+        memory=memory
+    )
+
+    # Function to generate response
+    def generate_response(user_input):
+        response = qa({"query": user_input})
+        # Remove any long dashes or unwanted characters from the response
+        cleaned_response = re.sub(r"^\s*[-–—]+\s*", "", response['result'])
+        cleaned_response = cleaned_response.replace("\n", " ")
+        return cleaned_response.strip()
+
+    # Set the title and default styling
+    st.title("Nigerian Lawyer Chatbot")
+
+    # Initialize session state for messages
+    if 'messages' not in st.session_state:
+        st.session_state.messages = []
+
+    # Display the chat
+    for i, msg in enumerate(st.session_state.messages):
+        if msg["is_user"]:
+            message(msg["content"], is_user=True, key=str(i), avatar_style="micah")
+        else:
+            message(msg["content"], is_user=False, key=str(i), avatar_style="bottts")
+
+    # Handle user input
+    user_input = st.chat_input("Ask a legal question:")
+
+    if user_input:
+        # Append user message and generate response
+        st.session_state.messages.append({"content": user_input, "is_user": True})
+        response = generate_response(user_input)
+        st.session_state.messages.append({"content": response, "is_user": False})
+        st.rerun()  # Refresh the app to display the new messages
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+langchain
+pinecone-client
+sentence-transformers
+langchain-pinecone
+langchain_community
+langchain_huggingface
+streamlit_chat