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| # Created by Leandro Carneiro at 19/01/2024 | |
| # Description: | |
| # ------------------------------------------------ | |
| #from langchain.embeddings import OpenAIEmbeddings | |
| from langchain_openai import OpenAIEmbeddings | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| from langchain_together.embeddings import TogetherEmbeddings | |
| from langchain_community.vectorstores import Chroma | |
| from langchain_community.document_loaders import DirectoryLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.prompts import PromptTemplate | |
| from langchain_openai import ChatOpenAI | |
| from langchain_together import Together | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chains import ConversationalRetrievalChain | |
| import os | |
| import csv | |
| import time | |
| def read_csv_to_dict(filename): | |
| data_dict = {} | |
| with open(filename, mode='r', encoding='utf-8') as file: | |
| csv_reader = csv.reader(file) | |
| for row in csv_reader: | |
| key, value = row[0].split(';') | |
| data_dict[key] = value | |
| return data_dict | |
| def generate_embeddings_and_vectorstore(path, model): | |
| try: | |
| loader = DirectoryLoader(path=path, glob="**/*.txt") | |
| corpus = loader.load() | |
| print(f' Total de documentos antes do text_split = {len(corpus)}') | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=4000, chunk_overlap=400) | |
| docs = text_splitter.split_documents(corpus) | |
| num_total_characters = sum([len(x.page_content) for x in docs]) | |
| print(f" Total de chunks depois do text_split = {len(docs)}") | |
| print(f" Média de caracteres por chunk = {num_total_characters / len(docs):,.0f}") | |
| dict_filename_url = read_csv_to_dict('./local_base/filename_url.csv') | |
| for doc in docs: | |
| filename = os.path.basename(doc.metadata["source"]) | |
| doc.metadata["link"] = dict_filename_url.get(filename) | |
| #print('docs') | |
| #print(docs) | |
| if model == 'openai': | |
| fc_embeddings = OpenAIEmbeddings(openai_api_key=os.environ['OPENAI_KEY']) | |
| vectorstore = Chroma.from_documents(docs, fc_embeddings) | |
| else: | |
| #fc_embeddings = HuggingFaceEmbeddings(model_name = 'intfloat/multilingual-e5-large-instruct') | |
| #vectorstore = Chroma.from_documents(docs, fc_embeddings) | |
| fc_embeddings = TogetherEmbeddings(model = 'togethercomputer/m2-bert-80M-8k-retrieval', together_api_key = os.environ['TOGETHER_KEY']) | |
| for doc in docs: | |
| vectorstore = Chroma.from_documents(documents=[doc], embedding=fc_embeddings) | |
| time.sleep(1.1) | |
| print('total de docs no vectorstore=',len(vectorstore.get()['documents'])) | |
| return vectorstore | |
| except Exception as e: | |
| print(str(e)) | |
| return str(e) | |
| class Rag: | |
| def __init__(self, vectorstore, min_words, max_words, model): | |
| self.text = None | |
| self.vectorstore = vectorstore | |
| self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, output_key="answer") | |
| if model == 'openai': | |
| prompt_template = """Your task is to create news for a newspaper based on pieces of text delimited by <> and a question delimited by <>. | |
| Do not use only your knowledge to make the news. Make the news based on the question, but using the pieces of text. | |
| If the pieces of text don't enough information about the question to create the news, just say that you need more sources of information, nothing more. | |
| The news should have a title. | |
| The news should be written in a formal language. | |
| The news should have between {min_words} and {max_words} words and it should be in Portuguese language. | |
| The news should be about the following context: <{context}> | |
| Question: <{question}> | |
| Answer here:""" | |
| self.prompt = PromptTemplate(template=prompt_template, | |
| input_variables=["context", "question"], | |
| partial_variables={"min_words": min_words, "max_words": max_words}) | |
| self.qa = ConversationalRetrievalChain.from_llm( | |
| llm=ChatOpenAI(model_name="gpt-3.5-turbo-0125", #0125 #1106 | |
| temperature=0, | |
| openai_api_key=os.environ['OPENAI_KEY'], | |
| max_tokens=int(int(max_words) + (int(max_words) / 2))), #número máximo de tokens para a resposta | |
| memory=self.memory, | |
| # retriever=vectorstore.as_retriever(search_type='similarity_score_threshold', | |
| # search_kwargs={'k':4, 'score_threshold':0.8}), #search_kwargs={'k': 3} | |
| retriever=vectorstore.as_retriever(), | |
| combine_docs_chain_kwargs={"prompt": self.prompt}, | |
| chain_type="stuff",#map_reduce, refine, map_rerank | |
| return_source_documents=True, | |
| ) | |
| else: | |
| prompt_template = """Your task is to create news for a newspaper based on pieces of text delimited by <> and a question delimited by <>. | |
| The news should be written in Portuguese language. | |
| Do not use only your knowledge to make the news. Make the news based on the question, but using the pieces of text. | |
| If the pieces of text don't enough information about the question to create the news, just say that you need more sources of information, nothing more. | |
| The news should have a title. | |
| The news should be written in a formal language. | |
| The news should have between {min_words} and {max_words} words. | |
| The source should not be shown in the news. | |
| The total of words should no be shown in the news. | |
| The news should be written in Portuguese language. | |
| Answer the title and the news, nothing more. | |
| The news should be about the following context: <{context}> | |
| Question: <{question}> | |
| Answer here: """ | |
| self.prompt = PromptTemplate(template=prompt_template, | |
| input_variables=["context", "question"], | |
| partial_variables={"min_words": min_words, "max_words": max_words}) | |
| self.qa = ConversationalRetrievalChain.from_llm( | |
| llm=Together(model="mistralai/Mixtral-8x7B-Instruct-v0.1", #0125 #1106 | |
| temperature=0, | |
| together_api_key=os.environ['TOGETHER_KEY'], | |
| max_tokens=int(int(max_words) + (int(max_words) / 2))), #número máximo de tokens para a resposta | |
| memory=self.memory, | |
| # retriever=vectorstore.as_retriever(search_type='similarity_score_threshold', | |
| # search_kwargs={'k':4, 'score_threshold':0.8}), #search_kwargs={'k': 3} | |
| retriever=vectorstore.as_retriever(), | |
| combine_docs_chain_kwargs={"prompt": self.prompt}, | |
| chain_type="stuff",#map_reduce, refine, map_rerank | |
| return_source_documents=True, | |
| ) | |
| # from langchain_together import Together | |
| # self.qa = ConversationalRetrievalChain.from_llm( | |
| # llm=Together(model="mistralai/Mixtral-8x7B-Instruct-v0.1", # 0125 #1106 | |
| # temperature=0, | |
| # #top_k=20, | |
| # together_api_key=os.environ['TOGETHER_KEY'], | |
| # max_tokens=int(int(max_words) + (int(max_words) / 2))), | |
| # # número máximo de tokens para a resposta | |
| # memory=self.memory, | |
| # # retriever=vectorstore.as_retriever(search_type='similarity_score_threshold', | |
| # # search_kwargs={'k':4, 'score_threshold':0.8}), #search_kwargs={'k': 3} | |
| # retriever=vectorstore.as_retriever(), | |
| # combine_docs_chain_kwargs={"prompt": self.prompt}, | |
| # chain_type="stuff", # map_reduce, refine, map_rerank | |
| # return_source_documents=True, | |
| # ) | |
| def generate_text(self, subject): | |
| try: | |
| query = f"Elabore uma nova notícia sobre {subject}." | |
| result_text = self.qa.invoke({"question": query}) | |
| print('##### result', result_text) | |
| list_result_sources = [] | |
| str_result_sources = '' | |
| for doc in result_text["source_documents"]: | |
| list_result_sources.append(doc.metadata['link']) | |
| result_sources = list(set(list_result_sources)) | |
| for i in range(len(result_sources)): | |
| str_result_sources += f'{i + 1}) {result_sources[i]}' + '\n' | |
| self.vectorstore.delete_collection() | |
| return (result_text["answer"], str_result_sources) | |
| except Exception as e: | |
| self.vectorstore.delete_collection() | |
| return str(e) | |