main.py

from langchain_ollama import OllamaLLM
import similarity
from langchain.chains.question_answering import load_qa_chain
from langchain import PromptTemplate
# Initialize an instance of the Ollama model
llm = OllamaLLM(model="llama3.2")
# query_text = "ما فضل صلاة العصر؟"

# print(f'Query : {query_text}')
# similar_docs = similarity.get_similar_docs(query_text)
# # print(f'similar_docs : {similar_docs}')

# qna_template = '\n'.join([
#     "Answer the following question using the context provided.",
#     'please provide answer within context with details If exist.'
#     "If the answer is not included in the context, say ",
#     "No answer available",
#     "### Context:",
#     "{context}",
#     """,
#     "### Question:",
#     "{question}",
#     """,
#     "### Answer:",
# ])
# qna_prompt = PromptTemplate(
#     template = qna_template,
#     input_variables=['context', 'question'],
#     verbose=True
# )

# stuff_chain = load_qa_chain(llm, chain_type="stuff", prompt=qna_prompt)

# final_answer = stuff_chain({
#     "input_documents": similar_docs,
#     "question": query_text
# }, return_only_outputs=True)

# print(final_answer)

def ask_llms(query_text):
    similar_docs = similarity.get_similar_docs(query_text)
    # print(f'similar_docs : {similar_docs}')

    qna_template = '\n'.join([
        "Answer the following question using the context provided.",
        "If the answer is not included in the context, say ",
        "No answer available",
        "### Context:",
        "{context}",
        """,
        "### Question:",
        "{question}",
        """,
        "### Answer:",
    ])
    qna_prompt = PromptTemplate(
        template = qna_template,
        input_variables=['context', 'question'],
        verbose=True
    )

    stuff_chain = load_qa_chain(llm, chain_type="stuff", prompt=qna_prompt)

    final_answer = stuff_chain.invoke({
    "input_documents": similar_docs,
    "question": query_text
    })

    return final_answer['output_text']