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app.py

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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
+import os
+
+import subprocess
+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()

requirements.txt

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+langchain
+openai
+tiktoken
+chromadb
+beautifulsoup4 
+lxml
+bs4
+google-search-results