Create app.py

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
+from glob import glob
+import shutil
+import gradio as gr
+
+git.Clone("https://github.com/TheMITTech/shakespeare", "shakespeare")
+files = glob("./shakespeare/**/*.html")
+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
+
+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)
+
+from langchain.embeddings.openai import OpenAIEmbeddings
+
+embeddings = OpenAIEmbeddings()
+from chromadb.segment.impl.vector.local_hnsw import HnswParams
+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()
+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."
+    ),
+]
+
+agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
+
+
+def make_inference(query):
+    return(agent.run(query))
+
+if __name__ == "__main__":
+    gr.Interface(
+        make_inference,
+        [
+            gr.inputs.Textbox(lines=2, label="Query"),
+        ],
+        gr.outputs.Textbox(label="Response"),
+        title="Shakespeare",
+        description="Shakespeare is a tool that allows you to ask questions about Shakespeare.",
+    ).launch()