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mentor_chat.py
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mentor_chat.py
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from langchain_community.chat_models import ChatOllama
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain.prompts.prompt import PromptTemplate
llm = ChatOllama(model="llama2")
template_career_mentor="""
<s>[INST] <<SYS>>
The following is a structured conversation between a user from India and the AI Career-Mentor.
The AI Career-Mentor is knowledgeable, supportive, and provides detailed advice based on the user's input.
If the AI Career-Mentor knows the answer, it gives the answer directly without any information about itself.
If the AI Career-Mentor does not know the exact answer to a question, it truthfully says it does not know, otherwise, it provides helpful guidance.
Please be concise.
<</SYS>></s>
{# Capture the current conversation history and the latest user input #}
Current conversation:
{{ history }}
{# Check if there is an existing conversation history #}
{% if history %}
<s>[INST] Human: {{ input }} [/INST] Career-Mentor: </s>
{% else %}
<s>Human: {{ input }} [/INST] Career-Mentor: </s>
{% endif %}
"""
prompt_mentor = PromptTemplate(
input_variables = ["history", "input"],
template=template_career_mentor,
template_format = "jinja2"
)
# initialize the buffer memory
conversation_mentor= ConversationChain(
llm = llm,
memory = ConversationBufferMemory(),
prompt = prompt_mentor,
verbose = False
)
# Start the conversation
def predict_mentor(message: str, history: str):
response = conversation_mentor.predict(input=message)
return response