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| from modules.config.constants import * | |
| import chainlit as cl | |
| from langchain_core.prompts import PromptTemplate | |
| def get_sources(res, answer, view_sources=False): | |
| source_elements = [] | |
| source_dict = {} # Dictionary to store URL elements | |
| for idx, source in enumerate(res["source_documents"]): | |
| source_metadata = source.metadata | |
| url = source_metadata.get("source", "N/A") | |
| 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 | |
| if view_sources: | |
| # 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, source_dict | |
| 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 | |