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Update app.py
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app.py
CHANGED
@@ -6,9 +6,9 @@ from sentence_transformers import SentenceTransformer
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def generate_prompts(user_input):
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prompt_template = PromptTemplate
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input_variables=["Question"],
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template=f"
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)
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config = {'max_new_tokens': 2048, 'temperature': 0.7, 'context_length': 4096}
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llm = CTransformers(model="TheBloke/Mistral-7B-Instruct-v0.1-GGUF",
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@@ -17,11 +17,16 @@ def generate_prompts(user_input):
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hub_chain = LLMChain(prompt = prompt_template, llm = llm)
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input_data = {"Question": user_input}
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generated_prompts = hub_chain.run(input_data)
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questions_list = generated_prompts.split('\n')
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formatted_questions = "\n".join(f"Question: {question}" for i, question in enumerate(questions_list) if question.strip())
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questions_list = formatted_questions.split("Question:")[1:]
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return questions_list
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def answer_question(prompt):
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def generate_prompts(user_input):
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prompt_template = PromptTemplate(
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input_variables=["Question"],
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template= f"Your task is to formulate 5 unique queries for each given question. These queries must adhere to the criteria of relevance and diversity.write the questions in seperate lines.{user_input} "
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)
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config = {'max_new_tokens': 2048, 'temperature': 0.7, 'context_length': 4096}
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llm = CTransformers(model="TheBloke/Mistral-7B-Instruct-v0.1-GGUF",
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hub_chain = LLMChain(prompt = prompt_template, llm = llm)
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input_data = {"Question": user_input}
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# Here you would integrate your prompt template with your model
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# For demonstration, this is just a placeholder
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generated_prompts = hub_chain.run(input_data)
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questions_list = generated_prompts.split('\n')
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formatted_questions = "\n".join(f"Question: {question}" for i, question in enumerate(questions_list) if question.strip())
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questions_list = formatted_questions.split("Question:")[1:]
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return questions_list
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def answer_question(prompt):
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