code/modules/chat/helpers.py

from modules.config.constants import *
import chainlit as cl
from langchain_core.prompts import PromptTemplate


def get_sources(res, answer):
    source_elements = []
    source_dict = {}  # Dictionary to store URL elements

    for idx, source in enumerate(res["source_documents"]):
        source_metadata = source.metadata
        url = source_metadata["source"]
        score = source_metadata.get("score", "N/A")
        page = source_metadata.get("page", 1)

        lecture_tldr = source_metadata.get("tldr", "N/A")
        lecture_recording = source_metadata.get("lecture_recording", "N/A")
        suggested_readings = source_metadata.get("suggested_readings", "N/A")
        date = source_metadata.get("date", "N/A")

        source_type = source_metadata.get("source_type", "N/A")

        url_name = f"{url}_{page}"
        if url_name not in source_dict:
            source_dict[url_name] = {
                "text": source.page_content,
                "url": url,
                "score": score,
                "page": page,
                "lecture_tldr": lecture_tldr,
                "lecture_recording": lecture_recording,
                "suggested_readings": suggested_readings,
                "date": date,
                "source_type": source_type,
            }
        else:
            source_dict[url_name]["text"] += f"\n\n{source.page_content}"

    # First, display the answer
    full_answer = "**Answer:**\n"
    full_answer += answer

    # Then, display the sources
    full_answer += "\n\n**Sources:**\n"
    for idx, (url_name, source_data) in enumerate(source_dict.items()):
        full_answer += f"\nSource {idx + 1} (Score: {source_data['score']}): {source_data['url']}\n"

        name = f"Source {idx + 1} Text\n"
        full_answer += name
        source_elements.append(
            cl.Text(name=name, content=source_data["text"], display="side")
        )

        # Add a PDF element if the source is a PDF file
        if source_data["url"].lower().endswith(".pdf"):
            name = f"Source {idx + 1} PDF\n"
            full_answer += name
            pdf_url = f"{source_data['url']}#page={source_data['page']+1}"
            source_elements.append(cl.Pdf(name=name, url=pdf_url, display="side"))

    full_answer += "\n**Metadata:**\n"
    for idx, (url_name, source_data) in enumerate(source_dict.items()):
        full_answer += f"\nSource {idx + 1} Metadata:\n"
        source_elements.append(
            cl.Text(
                name=f"Source {idx + 1} Metadata",
                content=f"Source: {source_data['url']}\n"
                f"Page: {source_data['page']}\n"
                f"Type: {source_data['source_type']}\n"
                f"Date: {source_data['date']}\n"
                f"TL;DR: {source_data['lecture_tldr']}\n"
                f"Lecture Recording: {source_data['lecture_recording']}\n"
                f"Suggested Readings: {source_data['suggested_readings']}\n",
                display="side",
            )
        )

    return full_answer, source_elements


def get_prompt(config):
    if config["llm_params"]["use_history"]:
        if config["llm_params"]["llm_loader"] == "local_llm":
            custom_prompt_template = tinyllama_prompt_template_with_history
        elif config["llm_params"]["llm_loader"] == "openai":
            custom_prompt_template = openai_prompt_template_with_history
        # else:
        #     custom_prompt_template = tinyllama_prompt_template_with_history # default
        prompt = PromptTemplate(
            template=custom_prompt_template,
            input_variables=["context", "chat_history", "question"],
        )
    else:
        if config["llm_params"]["llm_loader"] == "local_llm":
            custom_prompt_template = tinyllama_prompt_template
        elif config["llm_params"]["llm_loader"] == "openai":
            custom_prompt_template = openai_prompt_template
        # else:
        #     custom_prompt_template = tinyllama_prompt_template
        prompt = PromptTemplate(
            template=custom_prompt_template,
            input_variables=["context", "question"],
        )
    return prompt