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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)
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