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#####################################################
### DOCUMENT PROCESSOR [PROMPTS]
#####################################################
# Jonathan Wang
# ABOUT:
# This project creates an app to chat with PDFs.
# This is the prompts sent to the LLM.
#####################################################
## TODOS:
# Use the row names instead of .at indesx locators
# This is kinda dumb because we read the same .csv file over again
# Should we structure this abstraction differently?
#####################################################
## IMPORTS:
import pandas as pd
from llama_index.core import PromptTemplate
#####################################################
## CODE:
# https://github.com/run-llama/llama_index/blob/main/llama-index-core/llama_index/core/prompts/default_prompts.py
QA_PROMPT = """Context information is below.\n
---------------------
{context_str}
---------------------
Given the context information, answer the query.
You must adhere to the following rules:
- Use the context information, not prior knowledge.
- End the answer with any brief quote(s) from the context that are the most essential in answering the question.
- If the context is not helpful in answering the question, do not include a quote.
Query: {query_str}
Answer: """
# https://github.com/run-llama/llama_index/blob/main/llama-index-core/llama_index/core/prompts/default_prompts.py
REFINE_PROMPT = """The original query is as follows: {query_str}
We have provided an existing answer: {existing_answer}
We have the opportunity to refine the existing answer (only if needed) with some more context below.
---------------------
{context_msg}
---------------------
Given the new context, refine the original answer to better answer the query.
You must adhere to the following rules:
- If the context isn't useful, return the original answer.
- End the answer with any brief quote(s) from the original answer or new context that are the most essential in answering the question.
- If the new context is not helpful in answering the question, leave the original answer unchanged.
Refined Answer: """
def get_qa_prompt(
# prompt_file_path: str
) -> PromptTemplate:
"""Given a path to the prompts, get prompt for Question-Answering"""
# prompts = pd.read_csv(prompt_file_path)
# https://github.com/run-llama/llama_index/blob/main/llama-index-core/llama_index/core/prompts/default_prompts.py
custom_qa_prompt = PromptTemplate(
QA_PROMPT
)
return (custom_qa_prompt)
def get_refine_prompt(
# prompt_file_path: str
) -> PromptTemplate:
"""Given a path to the prompts, get prompt to Refine answer after new info"""
# prompts = pd.read_csv(prompt_file_path)
# https://github.com/run-llama/llama_index/blob/main/llama-index-core/llama_index/core/prompts/default_prompts.py
custom_refine_prompt = PromptTemplate(
REFINE_PROMPT
)
return (custom_refine_prompt)
# def get_reqdoc_prompt(
# prompt_file_path: str
# ) -> PromptTemplate:
# """Given a path to the prompts, get prompt to identify requested info from document."""
# prompts = pd.read_csv(prompt_file_path)
# # https://github.com/run-llama/llama_index/blob/main/llama-index-core/llama_index/core/prompts/default_prompts.py
# reqdoc_prompt = PromptTemplate(
# prompts.at[2, 'Prompt']
# )
# return (reqdoc_prompt) |