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from modules.config.constants import *
import chainlit as cl
from langchain_core.prompts import PromptTemplate
from langchain_core.prompts import ChatPromptTemplate
def get_sources(res, answer, view_sources=False):
source_elements = []
source_dict = {} # Dictionary to store URL elements
for idx, source in enumerate(res["context"]):
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
# check if the answer has sources
if len(source_dict) == 0:
full_answer += "\n\n**No sources found.**"
return full_answer, source_elements, source_dict
else:
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, prompt_type):
llm_params = config["llm_params"]
llm_loader = llm_params["llm_loader"]
use_history = llm_params["use_history"]
if prompt_type == "qa":
if llm_loader == "openai":
return (
OPENAI_PROMPT_WITH_HISTORY if use_history else OPENAI_PROMPT_NO_HISTORY
)
elif (
llm_loader == "local_llm"
and llm_params.get("local_llm_params") == "tiny-llama"
):
return (
TINYLLAMA_PROMPT_TEMPLATE_WITH_HISTORY
if use_history
else TINYLLAMA_PROMPT_TEMPLATE_NO_HISTORY
)
elif prompt_type == "rephrase":
prompt = ChatPromptTemplate.from_messages(
[
("system", OPENAI_REPHRASE_PROMPT),
("human", "{question}, {chat_history}"),
]
)
return OPENAI_REPHRASE_PROMPT
return None
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