| from langchain.vectorstores import FAISS | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.embeddings import OpenAIEmbeddings | |
| from langchain.prompts import PromptTemplate | |
| from langfuse.callback import CallbackHandler | |
| import os | |
| openai_api_key = os.environ.get("OPENAI_API_KEY") | |
| langfuse_public_key = os.environ.get("LANGFUSE_PUBLIC_KEY") | |
| langfuse_secret_key = os.environ.get("LANGFUSE_SECRET_KEY") | |
| class Conversation_RAG: | |
| def __init__(self, model_name="gpt-3.5-turbo"): | |
| self.model_name = model_name | |
| def create_vectordb(self): | |
| vectordb = FAISS.load_local("./db/faiss_index", OpenAIEmbeddings()) | |
| return vectordb | |
| def create_model(self, max_new_tokens=512, temperature=0.1): | |
| llm = ChatOpenAI( | |
| openai_api_key=openai_api_key, | |
| model_name=self.model_name, | |
| temperature=temperature, | |
| max_tokens=max_new_tokens, | |
| ) | |
| return llm | |
| def create_conversation(self, model, vectordb, k_context=5, instruction="Use the following pieces of context to answer the question at the end by. Generate the answer based on the given context only. If you do not find any information related to the question in the given context, just say that you don't know, don't try to make up an answer. Keep your answer expressive."): | |
| template = instruction + """ | |
| context:\n | |
| {context}\n | |
| data: {question}\n | |
| """ | |
| handler = CallbackHandler(langfuse_public_key, langfuse_secret_key) | |
| QCA_PROMPT = PromptTemplate(input_variables=["instruction", "context", "question"], template=template) | |
| qa = ConversationalRetrievalChain.from_llm( | |
| llm=model, | |
| chain_type='stuff', | |
| retriever=vectordb.as_retriever(search_kwargs={"k": k_context}), | |
| combine_docs_chain_kwargs={"prompt": QCA_PROMPT}, | |
| get_chat_history=lambda h: h, | |
| verbose=True, | |
| callbacks=[handler] | |
| ) | |
| return qa |