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import re
from typing import List
import gradio as gr
import openai
import os
from dotenv import load_dotenv
import phoenix as px
import llama_index
from llama_index import OpenAIEmbedding, Prompt, ServiceContext, VectorStoreIndex, SimpleDirectoryReader
from llama_index.chat_engine.types import ChatMode
from llama_index.llms import ChatMessage, MessageRole, OpenAI
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.text_splitter import SentenceSplitter
from llama_index.extractors import TitleExtractor
from llama_index.ingestion import IngestionPipeline
from chat_template import CHAT_TEXT_QA_PROMPT
from chatbot import Chatbot, ChatbotVersion
from custom_io import UnstructuredReader, default_file_metadata_func
from qdrant import client as qdrantClient
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")
class AwesumCareChatbot(Chatbot):
DENIED_ANSWER_PROMPT = ""
SYSTEM_PROMPT = ""
CHAT_EXAMPLES = [
"什麼是安心三寶？",
"點樣立平安紙？"
]
def _load_doucments(self):
dir_reader = SimpleDirectoryReader('./awesumcare_data', file_extractor={
".pdf": UnstructuredReader(),
".docx": UnstructuredReader(),
".pptx": UnstructuredReader(),
},
recursive=True,
exclude=["*.png", "*.pptx"],
file_metadata=default_file_metadata_func)
self.documents = dir_reader.load_data()
super()._load_doucments()
def _setup_service_context(self):
self.service_context = ServiceContext.from_defaults(
chunk_size=self.chunk_size,
llm=self.llm,
embed_model=self.embed_model
)
super()._setup_service_context()
def _setup_vector_store(self):
self.vector_store = QdrantVectorStore(
client=qdrantClient, collection_name=self.vdb_collection_name)
super()._setup_vector_store()
def _setup_index(self):
if self.vdb_collection_name in [col.name for col in qdrantClient.get_collections().collections] and qdrantClient.get_collection(self.vdb_collection_name).vectors_count > 0:
self.index = VectorStoreIndex.from_vector_store(
self.vector_store, service_context=self.service_context)
print("set up index from vector store")
return
pipeline = IngestionPipeline(
transformations=[
SentenceSplitter(),
OpenAIEmbedding(),
],
vector_store=self.vector_store,
)
pipeline.run(documents=self.documents)
self.index = VectorStoreIndex.from_vector_store(
self.vector_store, service_context=self.service_context)
super()._setup_index()
# def _setup_index(self):
# self.index = VectorStoreIndex.from_documents(
# self.documents,
# service_context=self.service_context
# )
# super()._setup_index()
def _setup_chat_engine(self):
# testing #
from llama_index.agent import OpenAIAgent
from llama_index.tools.query_engine import QueryEngineTool
query_engine = self.index.as_query_engine(
text_qa_template=CHAT_TEXT_QA_PROMPT)
query_engine_tool = QueryEngineTool.from_defaults(
query_engine=query_engine)
self.chat_engine = OpenAIAgent.from_tools(
tools=[query_engine_tool],
llm=self.service_context.llm,
similarity_top_k=1,
verbose=True
)
print("set up agent as chat engine")
# testing #
# self.chat_engine = self.index.as_chat_engine(
# chat_mode=ChatMode.BEST,
# similarity_top_k=5,
# text_qa_template=CHAT_TEXT_QA_PROMPT)
super()._setup_chat_engine()
# gpt-3.5-turbo-1106, gpt-4-1106-preview
awesum_chatbot = AwesumCareChatbot(ChatbotVersion.CHATGPT_35.value,
chunk_size=2048,
vdb_collection_name="v2")
def vote(data: gr.LikeData):
if data.liked:
gr.Info("You up-voted this response: " + data.value)
else:
gr.Info("You down-voted this response: " + data.value)
chatbot = gr.Chatbot()
with gr.Blocks() as demo:
gr.Markdown("# Awesum Care demo")
with gr.Tab("With awesum care data prepared"):
gr.ChatInterface(
awesum_chatbot.stream_chat,
chatbot=chatbot,
examples=awesum_chatbot.CHAT_EXAMPLES,
)
chatbot.like(vote, None, None)
with gr.Tab("With Initial System Prompt (a.k.a. prompt wrapper)"):
gr.ChatInterface(
awesum_chatbot.predict_with_prompt_wrapper, examples=awesum_chatbot.CHAT_EXAMPLES)
with gr.Tab("Vanilla ChatGPT without modification"):
gr.ChatInterface(awesum_chatbot.predict_vanilla_chatgpt,
examples=awesum_chatbot.CHAT_EXAMPLES)
demo.queue()
demo.launch()
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