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| import openai | |
| from openai import OpenAI | |
| class Answering_Agent: | |
| def __init__(self, openai_api_key) -> None: | |
| self.openai_client = openai | |
| openai.api_key = openai_api_key | |
| def get_document_content(self, doc_id): | |
| # Placeholder for retrieving document content | |
| return "Document content for ID " + doc_id | |
| def is_relevant(self, query, context_texts, history_str): | |
| # Simple relevance check based on keyword presence | |
| keywords = query.lower().split() | |
| context = (context_texts + " " + history_str).lower() | |
| return any(keyword in context for keyword in keywords) | |
| def generate_response(self, query, docs, conv_history, k=5, mode="chatty"): | |
| # Concatenate the contents of the top k relevant documents | |
| context_texts = "\n\n".join([f"Context {idx + 1}: {result[2]}" for idx, result in enumerate(docs)]) | |
| history_str = "\n".join([f"{turn['role']}: {turn['content']}" for turn in conv_history]) if conv_history else "" | |
| print("context_texts: " + context_texts) | |
| # Check relevance of the context and history to the query | |
| relevant = self.is_relevant(query, context_texts, history_str) | |
| # If not relevant, return a predefined message | |
| if not relevant: | |
| return "No relevant documents found in the documents. Please ask a relevant question to the book on Machine Learning." | |
| # Formulate the prompt, incorporating conversation history if present | |
| conversation_history = f'Conversation:\n{history_str}\n' if conv_history else '' | |
| prompt = f"Based on the following documents{' and conversation history' if conv_history else ''}, answer the query:\nDocuments:\n{context_texts}\n{conversation_history}Query: {query}\nAnswer:" | |
| if mode == "chatty": | |
| prompt += " Please provide a detailed and comprehensive response that includes background information, relevant examples, and any important distinctions or perspectives related to the topic. Where possible, include step-by-step explanations or descriptions to ensure clarity and depth in your answer." | |
| # Configure max_tokens and temperature based on the specified mode | |
| # a longer response | |
| max_tokens = 3500 if mode == "chatty" else 1000 | |
| temperature = 0.9 if mode == "chatty" else 0.5 | |
| # generate the response | |
| client = OpenAI(api_key=openai.api_key) | |
| message = {"role": "user", "content": prompt} | |
| response = client.chat.completions.create( | |
| model="gpt-3.5-turbo", | |
| messages=[message], | |
| max_tokens=max_tokens, | |
| temperature=temperature, | |
| stop=["\n", "Query:"] | |
| ) | |
| return response.choices[0].message.content | |