### Vectorstores
from langchain_community.vectorstores import Chroma
import openai

def retrieve_relevant_context(vectordb, input_text, num_documents=5):
    # Use the vector store to retrieve the most relevant documents
    results = vectordb.similarity_search(query=input_text,k=num_documents) # Without MMR
    # Extract the page content of the retrieved documents
    context = "\n".join([result.page_content for result in results])
    return context

def generate_response_with_context(input_text, context):
    prompt = (
        f"You're a GenAI powered assistant to users who're using communication platforms such as Whatsapp, Instagram, Facebook Messenger, etc. to interact with other users. "
        f"You need to generate responses to the incoming texts on these platforms using the Words, their Meanings, and the Usages contained in the context: {context}\n\n"
        f"Input text: {input_text}\nResponse:"
    )

    response = openai.completions.create(
        model="gpt-3.5-turbo-instruct",
        prompt=prompt,
        max_tokens=150,
        n=1,  # Generate one completion per prompt
        stop=None,
        temperature=0.5,
        frequency_penalty=0.5,
        presence_penalty=0.5,
        best_of=1,
    )

    return response.choices[0].text.strip()