app.py

from langchain.memory import ConversationBufferMemory

import pandas as pd
import gradio as gr

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS

import os


import pandas as pd
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.schema import Document
import os

from utils import make_html_source, make_pairs, get_llm, reset_textbox

from prompt import PROMPT_INTERPRATE_INTENTION, ANSWER_PROMPT


try:
    from dotenv import load_dotenv
    load_dotenv()
except Exception:
    pass


# Load your OpenAI API key
import os
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
assert OPENAI_API_KEY, "Please set your OpenAI API key"

embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)




new_vector_store = FAISS.load_local(
    "faiss_index", embeddings, allow_dangerous_deserialization=True
)

retriever = new_vector_store.as_retriever()



llm = get_llm()

memory = ConversationBufferMemory(
    return_messages=True, output_key="answer", input_key="question"
)

def make_qa_chain(

) :
    final_inputs = {
        "context": lambda x: x["context"],
        "question": lambda x: x["question"],
    }
        
        
        
    return final_inputs | ANSWER_PROMPT | llm


def load_documents_meeting(meeting_number):
    # Step 1: Load the CSV data
    csv_file_path = "../data/mfls.xlsx"  
    df = pd.read_excel(csv_file_path)
    
    df["meeting_number"]= df["Meeting"].apply(lambda x: x.split(" ")[0][:-2])
    df_meeting = df[df["meeting_number"] == meeting_number]
    def combine_title_and_content(row):
        return f"{row['Meeting']} {row['Issues']} {row['Content']}"

    df_meeting['combined'] = df_meeting.apply(combine_title_and_content, axis=1)

    # Step 3: Generate embeddings using OpenAI
    embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)

    # Generate embeddings for each document
    documents = [
        Document(
            page_content=row['combined'], 
            metadata={
                "Issues": row['Issues'], 
                "Title": row['Title'],
                "meeting_number": row["Meeting"].split(" ")[0][:-2],
                "Agencies": row["Agencies"],
                "project": row["Projects"],
            }
        ) for i,row in df_meeting.iterrows()]
    return documents


async def chat(
    query: str,
    history: list = [],
):
    """taking a query and a message history, use a pipeline (reformulation, retriever, answering) to yield a tuple of:
    (messages in gradio format, messages in langchain format, source documents)"""
    source_string = ""
    gradio_format = make_pairs([a.content for a in history]) + [(query, "")]
    qa_chain = make_qa_chain()
    
    # reset memory
    memory.clear()
    for message in history:
        memory.chat_memory.add_message(message)
    
    inputs = {"question": query}
    
    ## INTENT
    intent = await llm.abatch([PROMPT_INTERPRATE_INTENTION.format_prompt(query = query)])
    intent = intent[0].content
    print("intent", intent)
    
    ## RETRIEVER
    if intent.split(" ")[0] == "meeting":
        meeting_number = intent.split(" ")[-1]
        sources = load_documents_meeting(meeting_number)
    else :
        sources = new_vector_store.search(query, search_type="similarity", k=5)
    
    source_string = "\n\n".join([make_html_source(doc, i) for i, doc in enumerate(sources, 1)])

    ## RAG
    inputs_rag = {"question": query, "context": sources}
    
    result = qa_chain.astream_log(inputs_rag)

    reformulated_question_path_id = "/logs/ChatOpenAI/streamed_output_str/-"
    retriever_path_id =             "/logs/VectorStoreRetriever/final_output"
    final_answer_path_id =          "/streamed_output/-"
    
    async for op in result:
        op = op.ops[0]
        # print(op["path"])
        if op['path'] == reformulated_question_path_id: # reforulated question
            new_token = op['value'] # str
            
        elif op['path'] == retriever_path_id: # documents
            sources = op['value']['documents'] # List[Document]
            source_string = "\n\n".join([make_html_source(i, doc) for i, doc in enumerate(sources, 1)])

        elif op['path'] == final_answer_path_id: # final answer
            new_token = op['value'].content # str
            answer_yet = gradio_format[-1][1]
            gradio_format[-1] = (query, answer_yet + new_token  )          

        yield gradio_format, history, source_string

    memory.save_context(inputs, {"answer": gradio_format[-1][1]})
    yield gradio_format, memory.load_memory_variables({})["history"], source_string
    
    
### GRADIO UI
    
theme = gr.themes.Soft(
    primary_hue="sky",
    font=[gr.themes.GoogleFont("Poppins"), "ui-sans-serif", "system-ui", "sans-serif"],
)

demo_name = "UNEP Q&A"

with gr.Blocks(title=f"{demo_name}", theme=theme, css_paths=os.getcwd()+ "/style.css") as demo:
 
    gr.Markdown(f"<h1><center>{demo_name}</center></h1>")

    with gr.Row():
        with gr.Column(scale=2):
            chatbot = gr.Chatbot(
                value = [("","Hello ! How can I help you today ?")],
                elem_id="chatbot", 
                label=f"{demo_name} chatbot", 
                show_label=False
            )
            state = gr.State([])
 
            with gr.Row():
                ask = gr.Textbox(
                    show_label=False,
                    placeholder="Input your question then press enter",
                )
            
        with gr.Column(scale=1, variant="panel"):
            gr.Markdown("### Sources")
            sources_textbox = gr.HTML(show_label=False)
 
        ask.submit(
            fn=chat,
            inputs=[
                ask,
                state,
            ],
            outputs=[chatbot, state, sources_textbox],
        )

    ask.submit(reset_textbox, [], [ask])

demo.queue()
demo.launch(
    ssr_mode=False
)