| from langchain.chains import ConversationalRetrievalChain | |
| from langchain_openai import ChatOpenAI | |
| from langchain.prompts import PromptTemplate | |
| from langchain_community.vectorstores import Pinecone | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| import os | |
| openai_api_key = os.environ.get("OPENAI_API_KEY") | |
| model_name = os.environ.get('MODEL_NAME', 'all-mpnet-base-v2') | |
| pinecone_index = os.environ.get("PINECONE_INDEX") | |
| class Conversation_RAG: | |
| def __init__(self, model_name="gpt-3.5-turbo-instruct"): | |
| self.model_name = model_name | |
| def get_vectordb(self, pc): | |
| index = pc.Index(pinecone_index) | |
| embeddings = HuggingFaceEmbeddings(model_name=f"model/{model_name}") | |
| vectordb = Pinecone(index, embeddings, "text") | |
| return vectordb | |
| def create_model(self, max_new_tokens=512, temperature=0.8): | |
| 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 + """ | |
| **Document Context Input**:\n | |
| {context}\n | |
| **Client Case Input**: {question}\n | |
| """ | |
| 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 | |
| ) | |
| return qa |