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import random
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain.retrievers import EnsembleRetriever
from ai_config import n_of_questions, openai_api_key
from prompt_instructions import get_interview_prompt_sarah, get_interview_prompt_aaron, get_report_prompt
n_of_questions = n_of_questions()
def setup_knowledge_retrieval(llm, language='english', voice='Sarah'):
embedding_model = OpenAIEmbeddings(openai_api_key=openai_api_key)
documents_faiss_index = FAISS.load_local("knowledge/faiss_index_all_documents", embedding_model,
allow_dangerous_deserialization=True)
documents_retriever = documents_faiss_index.as_retriever()
combined_retriever = EnsembleRetriever(
retrievers=[documents_retriever]
)
if voice == 'Sarah':
interview_prompt = ChatPromptTemplate.from_messages([
("system", get_interview_prompt_sarah(language, n_of_questions)),
("human", "{input}")
])
else:
interview_prompt = ChatPromptTemplate.from_messages([
("system", get_interview_prompt_aaron(language, n_of_questions)),
("human", "{input}")
])
report_prompt = ChatPromptTemplate.from_messages([
("system", get_report_prompt(language)),
("human", "Please provide a concise clinical report based on the interview.")
])
interview_chain = create_stuff_documents_chain(llm, interview_prompt)
report_chain = create_stuff_documents_chain(llm, report_prompt)
interview_retrieval_chain = create_retrieval_chain(combined_retriever, interview_chain)
report_retrieval_chain = create_retrieval_chain(combined_retriever, report_chain)
return interview_retrieval_chain, report_retrieval_chain, combined_retriever
def get_next_response(interview_chain, message, history, question_count):
combined_history = "\n".join(history)
# Check if the interview should end
if question_count >= n_of_questions:
return "Thank you for your responses. I will now prepare a report."
# Generate the next question
result = interview_chain.invoke({
"input": f"Based on the patient's last response: '{message}', and considering the full interview history, ask a specific, detailed question that hasn't been asked before and is relevant to the patient's situation.",
"history": combined_history,
"question_number": question_count + 1 # Increment question number here
})
next_question = result.get("answer", "Could you provide more details on that?")
# Update history with the new question and response
history.append(f"Q{question_count + 1}: {next_question}")
history.append(f"A{question_count + 1}: {message}")
return next_question
def generate_report(report_chain, history, language):
combined_history = "\n".join(history)
result = report_chain.invoke({
"input": "Please provide a clinical report based on the interview.",
"history": combined_history,
"language": language
})
return result.get("answer", "Unable to generate report due to insufficient information.")
def get_initial_question(interview_chain):
result = interview_chain.invoke({
"input": "What should be the first question in a clinical psychology interview?",
"history": "",
"question_number": 1
})
return result.get("answer", "Could you tell me a little bit about yourself and what brings you here today?")
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