import os from langchain import FAISS from langchain.chains import ConversationalRetrievalChain from langchain.document_loaders import DirectoryLoader, UnstructuredHTMLLoader, TextLoader, CSVLoader from langchain.memory import ConversationSummaryBufferMemory from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate from langchain.text_splitter import RecursiveCharacterTextSplitter, Language from grader import Grader def search_index_from_docs(source_chunks, embeddings): # print("source chunks: " + str(len(source_chunks))) # print("embeddings: " + str(embeddings)) search_index = FAISS.from_documents(source_chunks, embeddings) return search_index def get_chat_history(inputs) -> str: res = [] for human, ai in inputs: res.append(f"Human:{human}\nAI:{ai}") return "\n".join(res) class GraderQA(): def __init__(self, grader, embeddings): self.grader = grader self.llm = self.grader.llm self.index_file = "vector_stores/canvas-discussions.faiss" self.pickle_file = "vector_stores/canvas-discussions.pkl" self.rubric_text = grader.rubric_text self.search_index = self.get_search_index(embeddings) self.chain = self.create_chain(embeddings) self.tokens = None self.question = None def get_search_index(self, embeddings): if os.path.isfile(self.pickle_file) and os.path.isfile(self.index_file) and os.path.getsize( self.pickle_file) > 0: # Load index from pickle file search_index = self.load_index(embeddings) else: search_index = self.create_index(embeddings) print("Created index") return search_index def load_index(self, embeddings): # Load index db = FAISS.load_local( folder_path="vector_stores/", index_name="canvas-discussions", embeddings=embeddings, ) print("Loaded index") return db def create_index(self, embeddings): source_chunks = self.create_chunk_documents() search_index = search_index_from_docs(source_chunks, embeddings) FAISS.save_local(search_index, folder_path="vector_stores/", index_name="canvas-discussions") return search_index def create_chunk_documents(self): sources = self.fetch_data_for_embeddings() splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) source_chunks = splitter.split_documents(sources) print("chunks: " + str(len(source_chunks))) print("sources: " + str(len(sources))) return source_chunks def fetch_data_for_embeddings(self): document_list = self.get_csv_files() print("document list: " + str(len(document_list))) return document_list def get_csv_files(self): loader = CSVLoader(file_path=self.grader.csv, source_column="student_name") document_list = loader.load() return document_list def create_chain(self, embeddings): if not self.search_index: self.search_index = self.load_index(embeddings) chain = ConversationalRetrievalChain.from_llm(self.llm, self.search_index.as_retriever(search_type='mmr', search_kwargs={'lambda_mult': 1, 'fetch_k': 50, 'k': 30}), return_source_documents=True, verbose=True, memory=ConversationSummaryBufferMemory(memory_key='chat_history', llm=self.llm, max_token_limit=40, return_messages=True, output_key='answer'), get_chat_history=get_chat_history, combine_docs_chain_kwargs={"prompt": self.create_prompt()}) return chain def create_prompt(self): system_template = f"""You are Canvas Discussions Grading + Feedback QA Bot. Have a conversation with a human, answering the following questions as best you can. You are a grading assistant who graded the canvas discussions to create the following grading results and feedback. Use the following pieces of the grading results and feedback to answer the users question. Use the following pieces of context to answer the users question. ---------------- {self.rubric_text} ---------------- {{context}}""" messages = [ SystemMessagePromptTemplate.from_template(system_template), HumanMessagePromptTemplate.from_template("{question}"), ] return ChatPromptTemplate.from_messages(messages) def get_tokens(self): total_tokens = 0 for doc in self.docs: chat_prompt = self.prompt.format(context=doc, question=self.question) num_tokens = self.llm.get_num_tokens(chat_prompt) total_tokens += num_tokens # summary = self.llm(summary_prompt) # print (f"Summary: {summary.strip()}") # print ("\n") return total_tokens def run_qa_chain(self, question): self.question = question self.get_tokens() answer = self.chain(question) return answer # system_template = """You are Canvas Discussions Grading + Feedback QA Bot. Have a conversation with a human, answering the following questions as best you can. # You are a grading assistant who graded the canvas discussions to create the following grading results and feedback. Use the following pieces of the grading results and feedback to answer the users question. # Use the following pieces of context to answer the users question. # ---------------- # {context}""" # # messages = [ # SystemMessagePromptTemplate.from_template(system_template), # HumanMessagePromptTemplate.from_template("{question}"), # ] # CHAT_PROMPT = ChatPromptTemplate.from_messages(messages) # # # def get_search_index(embeddings): # global vectorstore_index # if os.path.isfile(pickle_file) and os.path.isfile(index_file) and os.path.getsize(pickle_file) > 0: # # Load index from pickle file # search_index = load_index(embeddings) # else: # search_index = create_index(model) # print("Created index") # # vectorstore_index = search_index # return search_index # # # def create_index(embeddings): # source_chunks = create_chunk_documents() # search_index = search_index_from_docs(source_chunks, embeddings) # # search_index.persist() # FAISS.save_local(search_index, folder_path="vector_stores/", index_name="canvas-discussions") # # Save index to pickle file # # with open(pickle_file, "wb") as f: # # pickle.dump(search_index, f) # return search_index # # # def search_index_from_docs(source_chunks, embeddings): # # print("source chunks: " + str(len(source_chunks))) # # print("embeddings: " + str(embeddings)) # search_index = FAISS.from_documents(source_chunks, embeddings) # return search_index # # # def get_html_files(): # loader = DirectoryLoader('docs', glob="**/*.html", loader_cls=UnstructuredHTMLLoader, recursive=True) # document_list = loader.load() # for document in document_list: # document.metadata["name"] = document.metadata["source"].split("/")[-1].split(".")[0] # return document_list # # # def get_text_files(): # loader = DirectoryLoader('docs', glob="**/*.txt", loader_cls=TextLoader, recursive=True) # document_list = loader.load() # return document_list # # # def create_chunk_documents(): # sources = fetch_data_for_embeddings() # # splitter = RecursiveCharacterTextSplitter.from_language( # language=Language.HTML, chunk_size=500, chunk_overlap=0 # ) # # source_chunks = splitter.split_documents(sources) # # print("chunks: " + str(len(source_chunks))) # print("sources: " + str(len(sources))) # # return source_chunks # # # def create_chain(question, llm, embeddings): # db = load_index(embeddings) # # # Create chain # chain = ConversationalRetrievalChain.from_llm(llm, db.as_retriever(search_type='mmr', # search_kwargs={'lambda_mult': 1, 'fetch_k': 50, # 'k': 30}), # return_source_documents=True, # verbose=True, # memory=ConversationSummaryBufferMemory(memory_key='chat_history', # llm=llm, max_token_limit=40, # return_messages=True, # output_key='answer'), # get_chat_history=get_chat_history, # combine_docs_chain_kwargs={"prompt": CHAT_PROMPT}) # # result = chain({"question": question}) # # sources = [] # print(result) # # for document in result['source_documents']: # sources.append("\n" + str(document.metadata)) # print(sources) # # source = ',\n'.join(set(sources)) # return result['answer'] + '\nSOURCES: ' + source # # # def load_index(embeddings): # # Load index # db = FAISS.load_local( # folder_path="vector_stores/", # index_name="canvas-discussions", embeddings=embeddings, # ) # return db # # # def get_chat_history(inputs) -> str: # res = [] # for human, ai in inputs: # res.append(f"Human:{human}\nAI:{ai}") # return "\n".join(res)