Add proper sorting of context documents based on similarity score

app.py

@@ -2,4 +2,5 @@
 
 if __name__ == "__main__":
  qa = QuestionAnswerer()
- qa.answer_question()
+ answer = qa.answer_question()
+ print(answer)

question_answerer.py

@@ -10,18 +10,16 @@
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 
 template = """
- Answer the question as truthfully as possible using the provided context, 
- and if the answer is not contained within the text below, say "I don't know."
+ Answer the question as truthfully as possible using the provided context, and assume 
+ that the question is about dbt, analytics engineering, or data. Prefer code snippets 
+ over prose where possible and relevant. 
+ If the answer is not contained within the text below, say "I don't know."\n
  \n
+ Context:\n
+ {context} \n
  \n
- Context:
- \n
- {context}
- \n
- \n
- Question:
- \n
- {question}
+ Question: \n
+ {question}
 """
 
 
@@ -37,4 +35,4 @@ def answer_question(self) -> str:
  question = input("Ask a question: ")
  context = self.db.rank_and_truncate_documents(question)
  answer = self.llm(prompt.format(question=question, context=context))
- print(answer)
+ return answer

vector_store.py

@@ -81,7 +81,8 @@ def rank_and_truncate_documents(self, question: str, max_tokens: int = 3000):
  chosen_sections = ""
  chosen_sections_len = 0
 
- for result in results:
+ for result in sorted(results, key=lambda x: x[1], reverse=True):
+ print(results)
  result_content = result[0].page_content.replace("\n", " ")
  if (
  chosen_sections_len