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| from langchain.prompts.prompt import PromptTemplate | |
| from langchain.llms import OpenAI | |
| from langchain.vectorstores import Pinecone | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.chains.question_answering import load_qa_chain | |
| import pinecone | |
| import os | |
| PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY") | |
| PINECONE_API_ENV = os.environ.get("PINECONE_API_ENV") | |
| OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY") | |
| PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX_NAME") | |
| _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. | |
| You can assume the question about the Internal Revenue Manuals. | |
| Chat History: | |
| {chat_history} | |
| Follow Up Input: {question} | |
| Standalone question:""" | |
| CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template) | |
| template = """You are an AI assistant for answering questions about the Internal Revenue Manuals. You are given the following extracted parts of a long document and a question. Provide a conversational answer. | |
| If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer. | |
| If the question is not about the war in Internal Revenue Manuals, politely inform them that you are tuned to only answer questions about the Internal Revenue Manuals | |
| Question: {question} | |
| ========= | |
| {context} | |
| ========= | |
| Answer in Markdown:""" | |
| QA_PROMPT = PromptTemplate(template=template, input_variables=["question", "context"]) | |
| def get_chain(vector): | |
| llm = OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY) | |
| chain = load_qa_chain(llm) | |
| return chain | |
| if __name__ == "__main__": | |
| embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY) | |
| pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_API_ENV) | |
| vectorstore = Pinecone.from_existing_index(PINECONE_INDEX_NAME, embeddings) | |
| qa_chain = get_chain(vectorstore) | |
| chat_history = [] | |
| print("Chat with your docs!") | |
| while True: | |
| print("Human:") | |
| question = input() | |
| docs = vectorstore.similarity_search(question, k=2) | |
| result = qa_chain.run( | |
| input_documents=docs, question=question, chat_history=chat_history | |
| ) | |
| chat_history.append(result) | |
| print("AI:") | |
| print(result) | |