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from transformers import AutoTokenizer
from langchain_core.prompts import PromptTemplate
from typing import List
import models
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
raw_prompt = "{question}"
history_prompt = """
Given the following conversation provide a helpful answer to the follow up question.
Chat History:
{chat_history}
Follow Up question: {question}
helpful answer:
"""
standalone_prompt = """
Given the following conversation and a follow up question, rephrase the
follow up question to be a standalone question, in its original language.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:
"""
rag_prompt = """
Answer the question based only on the following context:
{context}
Question: {standalone_question}
"""
def format_prompt(prompt):
chat = [
{"role": "system", "content": "You are a helpful AI assistant."},
{"role": "user", "content": prompt},
]
formatted_prompt = tokenizer.apply_chat_template(
chat,
tokenize=False,
add_generation_prompt=True
)
return PromptTemplate.from_template(formatted_prompt)
def format_chat_history(messages: List[models.Message]):
return '\n'.join([
'{}: {}'.format(message.type, message.message)
for message in messages
])
def format_context(docs: List[str]):
return '\n\n'.join(docs)
raw_prompt_formatted = format_prompt(raw_prompt)
raw_prompt = PromptTemplate.from_template(raw_prompt)
history_prompt_formatted = format_prompt(history_prompt)
standalone_prompt_formatted = format_prompt(standalone_prompt)
rag_prompt_formatted = format_prompt(rag_prompt)
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