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| # qa_system.py | |
| from langchain.vectorstores import Pinecone | |
| from langchain_openai import OpenAIEmbeddings, ChatOpenAI | |
| import pinecone | |
| from langchain_pinecone import PineconeVectorStore | |
| from langchain.chains import create_retrieval_chain | |
| from langchain.chains.combine_documents import create_stuff_documents_chain | |
| from langchain_core.prompts import ChatPromptTemplate | |
| class PineconeQA: | |
| def __init__(self, pinecone_api_key, openai_api_key, index_name): | |
| # Initialize Pinecone | |
| self.pc = pinecone.Pinecone(api_key=pinecone_api_key) | |
| self.index = self.pc.Index(index_name) | |
| # Initialize embeddings | |
| self.embeddings = OpenAIEmbeddings( | |
| openai_api_key=openai_api_key | |
| ) | |
| # Create retriever | |
| self.retriever = PineconeVectorStore( | |
| index=self.index, | |
| embedding=self.embeddings | |
| ) | |
| # Initialize LLM | |
| self.llm = ChatOpenAI( | |
| openai_api_key=openai_api_key, | |
| model="gpt-4o", | |
| temperature=0.2 | |
| ) | |
| # Create the RAG chain | |
| self._create_rag_chain() | |
| def _create_rag_chain(self): | |
| # Define system prompt | |
| # system_prompt = ( | |
| # "You are an assistant for question-answering tasks. " | |
| # "Use the following pieces of retrieved context to answer " | |
| # "the question. If you don't know the answer, say that you " | |
| # "don't know. Use three sentences maximum and keep the " | |
| # "answer concise." | |
| # "\n\n" | |
| # "{context}" | |
| # ) | |
| system_prompt = ( | |
| "You are an expert assistant for biomedical question-answering tasks. " | |
| "You will be provided with context retrieved from medical literature." | |
| "The medical literature is all from PubMed Open Access Articles. " | |
| "Use this context to answer the question as accurately as possible. " | |
| "The response might not be added precisly, so try to derive the answers from it as much as possible." | |
| "If the context does not contain the required information, explain why. " | |
| "Provide a concise and accurate answer " | |
| "\n\n" | |
| "Context:\n{context}\n" | |
| ) | |
| # Create chat prompt template | |
| prompt = ChatPromptTemplate.from_messages([ | |
| ("system", system_prompt), | |
| ("human", "{input}"), | |
| ]) | |
| # Create question-answer chain | |
| question_answer_chain = create_stuff_documents_chain( | |
| self.llm, | |
| prompt | |
| ) | |
| # Create the RAG chain | |
| self.rag_chain = create_retrieval_chain( | |
| self.retriever.as_retriever(search_type="mmr"), | |
| question_answer_chain | |
| ) | |
| def merge_relevant_chunks(self, retrieved_docs, question, max_tokens=1500): | |
| """ | |
| Merge document chunks based on their semantic relevance to the question. | |
| """ | |
| merged_context = "" | |
| current_tokens = 0 | |
| for doc in retrieved_docs: | |
| tokens = doc.page_content.split() | |
| if current_tokens + len(tokens) <= max_tokens: | |
| merged_context += doc.page_content + "\n" | |
| current_tokens += len(tokens) | |
| else: | |
| break | |
| return merged_context | |
| def ask(self, question): | |
| """ | |
| Ask a question and get response with sources | |
| """ | |
| # Initialize conversation history if it doesn't exist | |
| if not hasattr(self, "conversation_history"): | |
| self.conversation_history = [] | |
| try: | |
| system_prompt = ( | |
| "You are an expert assistant for biomedical question-answering tasks. " | |
| "You will be provided with context retrieved from medical literature, specifically PubMed Open Access Articles. " | |
| "Use the provided context to directly answer the question in the most accurate and concise manner possible. " | |
| "If the context does not provide sufficient information, state that the specific details are not available in the context." | |
| "Do not include statements about limitations of the context in your response. " | |
| "Your answer should sound authoritative and professional, tailored for a medical audience." | |
| "\n\n" | |
| "Context:\n{context}\n" | |
| ) | |
| # Create chat prompt template | |
| prompt = ChatPromptTemplate.from_messages([ | |
| ("system", system_prompt), | |
| ("human", "{input}"), | |
| ]) | |
| # Create question-answer chain | |
| question_answer_chain = create_stuff_documents_chain( | |
| self.llm, | |
| prompt | |
| ) | |
| results = create_retrieval_chain( | |
| self.retriever.as_retriever(seach_type="mmr"), | |
| question_answer_chain | |
| ).invoke({"input": question}) | |
| return { | |
| "answer": results["answer"], | |
| "context": results["context"] | |
| } | |
| except Exception as e: | |
| return { | |
| "error": str(e) | |
| } |