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from langchain.chains.summarize import load_summarize_chain
from langchain import PromptTemplate, LLMChain
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.text_splitter import TokenTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.docstore.document import Document
from langchain.tools import BaseTool, StructuredTool, Tool, tool
from langchain.callbacks.stdout import StdOutCallbackHandler
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.callbacks.manager import BaseCallbackManager
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.input import print_text
from langchain.schema import AgentAction, AgentFinish, LLMResult
from pydantic import BaseModel, Field
import requests
from bs4 import BeautifulSoup
from threading import Thread, Condition
from collections import deque
from .base_model import BaseLLMModel, CallbackToIterator, ChuanhuCallbackHandler
from ..config import default_chuanhu_assistant_model
from ..presets import SUMMARIZE_PROMPT
import logging
class WebBrowsingInput(BaseModel):
url: str = Field(description="URL of a webpage")
class WebAskingInput(BaseModel):
url: str = Field(description="URL of a webpage")
question: str = Field(description="Question that you want to know the answer to, based on the webpage's content.")
class ChuanhuAgent_Client(BaseLLMModel):
def __init__(self, model_name, openai_api_key, user_name="") -> None:
super().__init__(model_name=model_name, user=user_name)
self.text_splitter = TokenTextSplitter(chunk_size=500, chunk_overlap=30)
self.api_key = openai_api_key
self.llm = ChatOpenAI(openai_api_key=openai_api_key, temperature=0, model_name=default_chuanhu_assistant_model)
self.cheap_llm = ChatOpenAI(openai_api_key=openai_api_key, temperature=0, model_name="gpt-3.5-turbo")
PROMPT = PromptTemplate(template=SUMMARIZE_PROMPT, input_variables=["text"])
self.summarize_chain = load_summarize_chain(self.cheap_llm, chain_type="map_reduce", return_intermediate_steps=True, map_prompt=PROMPT, combine_prompt=PROMPT)
if "Pro" in self.model_name:
self.tools = load_tools(["google-search-results-json", "llm-math", "arxiv", "wikipedia", "wolfram-alpha"], llm=self.llm)
else:
self.tools = load_tools(["ddg-search", "llm-math", "arxiv", "wikipedia"], llm=self.llm)
self.tools.append(
Tool.from_function(
func=self.summary_url,
name="Summary Webpage",
description="useful when you need to know the overall content of a webpage.",
args_schema=WebBrowsingInput
)
)
self.tools.append(
StructuredTool.from_function(
func=self.ask_url,
name="Ask Webpage",
description="useful when you need to ask detailed questions about a webpage.",
args_schema=WebAskingInput
)
)
def summary(self, text):
texts = Document(page_content=text)
texts = self.text_splitter.split_documents([texts])
return self.summarize_chain({"input_documents": texts}, return_only_outputs=True)["output_text"]
def fetch_url_content(self, url):
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
# 提取所有的文本
text = ''.join(s.getText() for s in soup.find_all('p'))
logging.info(f"Extracted text from {url}")
return text
def summary_url(self, url):
text = self.fetch_url_content(url)
text_summary = self.summary(text)
url_content = "webpage content summary:\n" + text_summary
return url_content
def ask_url(self, url, question):
text = self.fetch_url_content(url)
texts = Document(page_content=text)
texts = self.text_splitter.split_documents([texts])
# use embedding
embeddings = OpenAIEmbeddings(openai_api_key=self.api_key)
# create vectorstore
db = FAISS.from_documents(texts, embeddings)
retriever = db.as_retriever()
qa = RetrievalQA.from_chain_type(llm=self.cheap_llm, chain_type="stuff", retriever=retriever)
return qa.run(f"{question} Reply in 中文")
def get_answer_at_once(self):
question = self.history[-1]["content"]
# llm=ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")
agent = initialize_agent(self.tools, self.llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
reply = agent.run(input=f"{question} Reply in 简体中文")
return reply, -1
def get_answer_stream_iter(self):
question = self.history[-1]["content"]
it = CallbackToIterator()
manager = BaseCallbackManager(handlers=[ChuanhuCallbackHandler(it.callback)])
def thread_func():
agent = initialize_agent(self.tools, self.llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True, callback_manager=manager)
reply = agent.run(input=f"{question} Reply in 简体中文")
it.callback(reply)
it.finish()
t = Thread(target=thread_func)
t.start()
partial_text = ""
for value in it:
partial_text += value
yield partial_text
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