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from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
# from dotenv import load_dotenv
import os
import subprocess
# Load environment variables from .env file
# load_dotenv()
# # Access environment variables
# OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
# SERPAPI_API_KEY = os.getenv("SERPAPI_API_KEY")
subprocess.run(["git", "clone", "https://github.com/TheMITTech/shakespeare"], check=True)
from glob import glob
files = glob("./shakespeare/**/*.html")
import shutil
import os
os.mkdir('./data')
destination_folder = './data/'
for html_file in files:
shutil.move(html_file, destination_folder + html_file.split("/")[-1])
from langchain.document_loaders import BSHTMLLoader, DirectoryLoader
from bs4 import BeautifulSoup
bshtml_dir_loader = DirectoryLoader('./data/', loader_cls=BSHTMLLoader)
data = bshtml_dir_loader.load()
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
chunk_size = 1000,
chunk_overlap = 20,
length_function = len,
)
documents = text_splitter.split_documents(data)
embeddings = OpenAIEmbeddings()
from langchain.vectorstores import Chroma
persist_directory = "vector_db"
vectordb = Chroma.from_documents(documents=documents, embedding=embeddings, persist_directory=persist_directory)
vectordb.persist()
vectordb = None
vectordb = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(temperature=0, model="gpt-4")
doc_retriever = vectordb.as_retriever()
chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
from langchain.chains import RetrievalQA
shakespeare_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=doc_retriever)
from langchain.utilities import SerpAPIWrapper
search = SerpAPIWrapper()
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.tools import BaseTool
from langchain.llms import OpenAI
from langchain import LLMMathChain, SerpAPIWrapper
tools = [
Tool(
name = "Shakespeare QA System",
func=shakespeare_qa.run,
description="useful for when you need to answer questions about Shakespeare's works. Input should be a fully formed question."
),
Tool(
name = "SERP API Search",
func=search.run,
description="useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question."
),
]
from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
memory = ConversationBufferMemory(memory_key="chat_history")
readonlymemory = ReadOnlySharedMemory(memory=memory)
from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
suffix = """Begin!"
{chat_history}
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "chat_history", "agent_scratchpad"]
)
from langchain import OpenAI, LLMChain, PromptTemplate
llm_chain = LLMChain(llm=llm, prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)
def make_inference(query):
return(agent_chain.run(input=query))
if __name__ == "__main__":
# make a gradio interface
import gradio as gr
gr.Interface(
make_inference,
[
gr.inputs.Textbox(lines=2, label="Query"),
],
gr.outputs.Textbox(label="Response"),
title="🗣️QuestionMyDoc-OpenAI📄",
description="🗣️QuestionMyDoc-OpenAI📄 is a tool that allows you to ask questions about a document. In this case - Shakespears.",
).launch() |