prompt_template_utils.py

"""
This file implements prompt template for llama based models.
Modify the prompt template based on the model you select.
This seems to have significant impact on the output of the LLM.
"""

from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
from langchain.memory.chat_message_histories import RedisChatMessageHistory

# message_history = RedisChatMessageHistory(
#     url="redis://localhost:6379/1", ttl=600, session_id="my-session"
# )

# this is specific to Llama-2.

system_prompt = """You are a helpful assistant, you will use the provided context to answer user questions.
Read the given context before answering questions and think step by step. If you can not answer a user question based on
the provided context, inform the user. Do not use any other information for answering user. Provide a detailed answer to the question."""

# system_prompt = """You are a helpful assistant, and you will use the context and documents provided in the training to answer users' questions. Please read the context provided carefully before responding to questions and follow a step-by-step thought process. If you cannot answer a user's question based on the provided context, please inform the user. Do not use any other information to answer the user. Provide a detailed response based on the content of locally trained documents."""

# system_prompt = """It's a useful assistant that will use the context and documents provided in the training to answer users' questions.
# Read the context provided before answering the questions and think step by step. If you can't answer, just say "I don't know" and don't try to work out an answer to respond to the user."""

def get_prompt_template(system_prompt=system_prompt, promptTemplate_type=None, history=False):
    if promptTemplate_type == "llama":
        B_INST, E_INST = "[INST]", "[/INST]"
        B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
        SYSTEM_PROMPT = B_SYS + system_prompt + E_SYS
        if history:
            instruction = """
            Context: {history} \n {context}
            User: {question}"""

            prompt_template = B_INST + SYSTEM_PROMPT + instruction + E_INST
            prompt = PromptTemplate(input_variables=["history", "context", "question"], template=prompt_template)
        else:
            instruction = """
            Context: {context}
            User: {question}"""

            prompt_template = B_INST + SYSTEM_PROMPT + instruction + E_INST
            prompt = PromptTemplate(input_variables=["context", "question"], template=prompt_template)
    elif promptTemplate_type == "mistral":
        B_INST, E_INST = "<s>[INST] ", " [/INST]"
        if history:
            prompt_template = (
                B_INST
                + system_prompt
                + """

            Context: {history} \n {context}
            User: {question}"""
                + E_INST
            )
            prompt = PromptTemplate(input_variables=["history", "context", "question"], template=prompt_template)
        else:
            prompt_template = (
                B_INST
                + system_prompt
                + """

            Context: {context}
            User: {question}"""
                + E_INST
            )
            prompt = PromptTemplate(input_variables=["context", "question"], template=prompt_template)
    else:
        # change this based on the model you have selected.
        if history:
            prompt_template = (
                system_prompt
                + """

            Context: {history} \n {context}
            User: {question}
            Answer:"""
            )
            prompt = PromptTemplate(input_variables=["history", "context", "question"], template=prompt_template)
        else:
            prompt_template = (
                system_prompt
                + """

            Context: {context}
            User: {question}
            Answer:"""
            )
            prompt = PromptTemplate(input_variables=["context", "question"], template=prompt_template)

    memory = ConversationBufferMemory(input_key="question", memory_key="history")

    return (
        prompt,
        memory,
    )