app.py

# from typing import Any, Coroutine
import openai
import os
from langchain.vectorstores import Chroma
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.chat_models import AzureChatOpenAI
from langchain.document_loaders import DirectoryLoader
from langchain.chains import RetrievalQA  
from langchain.vectorstores import Pinecone
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.agents import Tool
# from langchain.agents import load_tools
from langchain.tools import BaseTool
from langchain.tools import DuckDuckGoSearchRun
from langchain.utilities import WikipediaAPIWrapper
from langchain.python import PythonREPL
from langchain.chains import LLMMathChain

import pinecone      
from pinecone.core.client.configuration import Configuration as OpenApiConfiguration
import gradio as gr
import time

import glob
from typing import List
from multiprocessing import Pool
from tqdm import tqdm

from langchain.document_loaders import (
    CSVLoader,
    EverNoteLoader,
    PyMuPDFLoader,
    TextLoader,
    UnstructuredEmailLoader,
    UnstructuredEPubLoader,
    UnstructuredHTMLLoader,
    UnstructuredMarkdownLoader,
    UnstructuredODTLoader,
    UnstructuredPowerPointLoader,
    UnstructuredWordDocumentLoader,
)
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.docstore.document import Document

# Custom document loaders
class MyElmLoader(UnstructuredEmailLoader):
    """Wrapper to fallback to text/plain when default does not work"""

    def load(self) -> List[Document]:
        """Wrapper adding fallback for elm without html"""
        try:
            try:
                doc = UnstructuredEmailLoader.load(self)
            except ValueError as e:
                if 'text/html content not found in email' in str(e):
                    # Try plain text
                    self.unstructured_kwargs["content_source"]="text/plain"
                    doc = UnstructuredEmailLoader.load(self)
                else:
                    raise
        except Exception as e:
            # Add file_path to exception message
            raise type(e)(f"{self.file_path}: {e}") from e

        return doc
    
LOADER_MAPPING = {
    ".csv": (CSVLoader, {}),
    # ".docx": (Docx2txtLoader, {}),
    ".doc": (UnstructuredWordDocumentLoader, {}),
    ".docx": (UnstructuredWordDocumentLoader, {}),
    ".enex": (EverNoteLoader, {}),
    ".eml": (MyElmLoader, {}),
    ".epub": (UnstructuredEPubLoader, {}),
    ".html": (UnstructuredHTMLLoader, {}),
    ".md": (UnstructuredMarkdownLoader, {}),
    ".odt": (UnstructuredODTLoader, {}),
    ".pdf": (PyMuPDFLoader, {}),
    ".ppt": (UnstructuredPowerPointLoader, {}),
    ".pptx": (UnstructuredPowerPointLoader, {}),
    ".txt": (TextLoader, {"encoding": "utf8"}),
    # Add more mappings for other file extensions and loaders as needed
}

source_directory = 'Upload Files'
file_path = ''
chunk_size = 500
chunk_overlap = 300

def load_single_document(file_path: str) -> List[Document]:
    ext = "." + file_path.rsplit(".", 1)[-1]
    if ext in LOADER_MAPPING:
        loader_class, loader_args = LOADER_MAPPING[ext]
        loader = loader_class(file_path, **loader_args)
        return loader.load()

    raise ValueError(f"Unsupported file extension '{ext}'")


def load_documents(source_dir: str, ignored_files: List[str] = []) -> List[Document]:
    """

    Loads all documents from the source documents directory, ignoring specified files

    """
    all_files = []
    for ext in LOADER_MAPPING:
        all_files.extend(
            glob.glob(os.path.join(source_dir, f"**/*{ext}"), recursive=True)
        )
    filtered_files = [file_path for file_path in all_files if file_path not in ignored_files]

    with Pool(processes=os.cpu_count()) as pool:
        results = []
        with tqdm(total=len(filtered_files), desc='Loading new documents', ncols=80) as pbar:
            for i, docs in enumerate(pool.imap_unordered(load_single_document, filtered_files)):
                results.extend(docs)
                pbar.update()

    return results

def process_documents(ignored_files: List[str] = []) -> List[Document]:
    """

    Load documents and split in chunks

    """
    print(f"Loading documents from {source_directory}")
    documents = load_documents(source_directory, ignored_files)
    if not documents:
        print("No new documents to load")
        exit(0)
    print(f"Loaded {len(documents)} new documents from {source_directory}")
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
    texts = text_splitter.split_documents(documents)
    print(f"Split into {len(texts)} chunks of text (max. {chunk_size} tokens each)")
    return texts

def process_documents_2(ignored_files: List[str] = []) -> List[Document]:
    """

    Load documents and split in chunks

    """
    print(f"Loading documents from {source_directory}")
    print("File Path to start processing:", file_path)
    documents = load_documents(file_path, ignored_files)
    if not documents:
        print("No new documents to load")
        exit(0)
    print(f"Loaded {len(documents)} new documents from {source_directory}")
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
    texts = text_splitter.split_documents(documents)
    print(f"Split into {len(texts)} chunks of text (max. {chunk_size} tokens each)")
    return texts

def UpdateDb():
    global vectordb_p
    # pinecone.Index(index_name).delete(delete_all=True, namespace='')
    # collection = vectordb_p.get()
    # split_docs = process_documents([metadata['source'] for metadata in collection['metadatas']])
    # split_docs = process_documents()
    split_docs = process_documents_2()
    tt = len(split_docs)
    print(split_docs[tt-1])
    print(f"Creating embeddings. May take some minutes...")
    vectordb_p = Pinecone.from_documents(split_docs, embeddings, index_name = "stla-baby")
    print("Pinecone Updated Done")
    print(index.describe_index_stats())


class DB_Search(BaseTool):
    name = "Vector Database Search"
    description = "This is the internal database to search information firstly. If information is found, it is trustful."
    def _run(self, query: str) -> str:
        response, source = QAQuery_p(query)
        # response = "test db_search feedback"
        return response

    def _arun(self, query: str):
        raise NotImplementedError("N/A")



Wikipedia = WikipediaAPIWrapper()
Netsearch = DuckDuckGoSearchRun()
Python_REPL = PythonREPL()

wikipedia_tool = Tool(
    name = "Wikipedia Search",
    func = Wikipedia.run,
    description = "Useful to search a topic, country or person when there is no availble information in vector database"
)

duckduckgo_tool = Tool(
    name = "Duckduckgo Internet Search",
    func = Netsearch.run,
    description = "Useful to search information in internet when it is not available in other tools"    
)

python_tool = Tool(
    name = "Python REPL",
    func = Python_REPL.run,
    description = "Useful when you need python to answer questions. You should input python code."    
)

# tools = [DB_Search(), wikipedia_tool, duckduckgo_tool, python_tool]


os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
os.environ["OPENAI_API_BASE"] = os.getenv("OPENAI_API_BASE")
os.environ["OPENAI_API_VERSION"] = "2023-05-15"
username = os.getenv("username")
password = os.getenv("password")
SysLock = os.getenv("SysLock") # 0=unlock 1=lock

chat = AzureChatOpenAI(
    deployment_name="Chattester",
    temperature=0,
)
llm = chat

llm_math = LLMMathChain.from_llm(llm)

math_tool = Tool(
    name ='Calculator',
    func = llm_math.run,
    description ='Useful for when you need to answer questions about math.'
)

tools = [DB_Search(), duckduckgo_tool, wikipedia_tool, python_tool, math_tool]

# tools = load_tools(["Vector Database Search","Wikipedia Search","Python REPL","llm-math"], llm=llm)

embeddings = OpenAIEmbeddings(deployment="model_embedding", chunk_size=15)


pinecone.init(      
	api_key = os.getenv("pinecone_api_key"),      
	environment='asia-southeast1-gcp-free',
    # openapi_config=openapi_config      
)
index_name = 'stla-baby'     
index = pinecone.Index(index_name)
# index.delete(delete_all=True, namespace='')
# print(pinecone.whoami())
# print(index.describe_index_stats())

PREFIX = """Answer the following questions as best you can with details. You must always check internal vector database first and try to answer the question based on the information in internal vector database only.

Only when there is no information available from vector database, you can search information by using another tools.

You have access to the following tools:



Vector Database Search: This is the internal database to search information firstly. If information is found, it is trustful.

Duckduckgo Internet Search: Useful to search information in internet when it is not available in other tools.

Wikipedia Search: Useful to search a topic, country or person when there is no availble information in vector database

Python REPL: Useful when you need python to answer questions. You should input python code.

Calculator: Useful for when you need to answer questions about math."""

FORMAT_INSTRUCTIONS = """Use the following format:



Question: the input question you must answer

Thought: you should always think about what to do

Action: the action to take, should be one of [Vector Database Search, Duckduckgo Internet Search, Python REPL, Calculator]

Action Input: the input to the action

Observation: the result of the action

... (this Thought/Action/Action Input/Observation can repeat N times)

Thought: I now know the final answer

Final Answer: the final answer to the original input question"""

SUFFIX = """Begin!

Question: {input}

Thought:{agent_scratchpad}"""

agent = initialize_agent(tools, llm, 
                         agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
                         verbose = True,
                         handle_parsing_errors = True,
                         max_iterations = int(os.getenv("max_iterations")),
                         early_stopping_method="generate",
                         agent_kwargs={
                            'prefix': PREFIX,
                            'format_instructions': FORMAT_INSTRUCTIONS,
                            'suffix': SUFFIX
                         }
                         )

print(agent.agent.llm_chain.prompt.template)

global vectordb
vectordb = Chroma(persist_directory='db', embedding_function=embeddings)
global vectordb_p
vectordb_p = Pinecone.from_existing_index(index_name, embeddings)

# loader = DirectoryLoader('./documents', glob='**/*.txt')
# documents = loader.load()
# text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=200)
# split_docs = text_splitter.split_documents(documents)
# print(split_docs)
# vectordb = Chroma.from_documents(split_docs, embeddings, persist_directory='db')



# question = "what is LCDV ?"
# rr = vectordb.similarity_search(query=question, k=4)
# vectordb.similarity_search(question)
# print(type(rr))
# print(rr)
def chathmi(message, history):
    # response = "I don't know"
    # print(message)
    response, source = QAQuery_p(message)
    time.sleep(0.3)
    print(history)
    yield response
    # yield history

def chathmi2(message, history):
    try:
        output = agent.run(message)
        time.sleep(0.3)
        print("History: ", history)
        response = output
        yield response
    except Exception as e:
        print("error:", e)

    # yield history
# chatbot = gr.Chatbot().style(color_map =("blue", "pink"))
# chatbot = gr.Chatbot(color_map =("blue", "pink"))

def func_upload_file(files, chat_history):
    file_path = files
    print(file_path)
    # UpdateDb()
    chat_history.append("Test File Upload")
    return chat_history

with gr.Blocks() as demo:
    main = gr.ChatInterface(
        chathmi2,
        title="STLA BABY - YOUR FRIENDLY GUIDE",
        description= "v0.3: Powered by MECH Core Team",
    )
    upload_button = gr.UploadButton("Upload File", file_count="multiple")
    upload_button.upload(func_upload_file, [upload_button, main.chatbot], main.chatbot)

# demo = gr.Interface(
#     chathmi,
#     ["text", "state"],
#     [chatbot, "state"],
#     allow_flagging="never",
# )

def CreatDb_P():
    global vectordb_p
    index_name = 'stla-baby'
    loader = DirectoryLoader('./documents', glob='**/*.txt')
    documents = loader.load()
    text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=200)
    split_docs = text_splitter.split_documents(documents)
    print(split_docs)
    pinecone.Index(index_name).delete(delete_all=True, namespace='')
    vectordb_p = Pinecone.from_documents(split_docs, embeddings, index_name = "stla-baby")
    print("Pinecone Updated Done")
    print(index.describe_index_stats())

def QAQuery_p(question: str):
    global vectordb_p
    # vectordb = Chroma(persist_directory='db', embedding_function=embeddings)
    retriever = vectordb_p.as_retriever()
    retriever.search_kwargs['k'] = int(os.getenv("search_kwargs_k"))
    # retriever.search_kwargs['fetch_k'] = 100

    qa = RetrievalQA.from_chain_type(llm=chat, chain_type="stuff", 
                                     retriever=retriever, return_source_documents = True,
                                     verbose = True)
    # qa = VectorDBQA.from_chain_type(llm=chat, chain_type="stuff", vectorstore=vectordb, return_source_documents=True)
    # res = qa.run(question)
    res = qa({"query": question})
    
    print("-" * 20)
    print("Question:", question)
    # print("Answer:", res)
    print("Answer:", res['result'])
    print("-" * 20)
    print("Source:", res['source_documents'])
    response = res['result']
    # response = res['source_documents']
    source = res['source_documents']
    return response, source

def CreatDb():
    global vectordb
    loader = DirectoryLoader('./documents', glob='**/*.txt')
    documents = loader.load()
    text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=200)
    split_docs = text_splitter.split_documents(documents)
    print(split_docs)
    vectordb = Chroma.from_documents(split_docs, embeddings, persist_directory='db')
    vectordb.persist()

def QAQuery(question: str):
    global vectordb
    # vectordb = Chroma(persist_directory='db', embedding_function=embeddings)
    retriever = vectordb.as_retriever()
    retriever.search_kwargs['k'] = 3
    # retriever.search_kwargs['fetch_k'] = 100

    qa = RetrievalQA.from_chain_type(llm=chat, chain_type="stuff", retriever=retriever, return_source_documents = True)
    # qa = VectorDBQA.from_chain_type(llm=chat, chain_type="stuff", vectorstore=vectordb, return_source_documents=True)
    # res = qa.run(question)
    res = qa({"query": question})
    
    print("-" * 20)
    print("Question:", question)
    # print("Answer:", res)
    print("Answer:", res['result'])
    print("-" * 20)
    print("Source:", res['source_documents'])
    response = res['result']
    return response

# Used to complete content
def completeText(Text): 
    deployment_id="Chattester"
    prompt = Text
    completion = openai.Completion.create(deployment_id=deployment_id,
                                        prompt=prompt, temperature=0)                              
    print(f"{prompt}{completion['choices'][0]['text']}.")

# Used to chat
def chatText(Text): 
    deployment_id="Chattester"
    conversation = [{"role": "system", "content": "You are a helpful assistant."}]
    user_input = Text
    conversation.append({"role": "user", "content": user_input})
    response = openai.ChatCompletion.create(messages=conversation,
        deployment_id="Chattester")
    print("\n" + response["choices"][0]["message"]["content"] + "\n")

if __name__ == '__main__':
    # chatText("what is AI?")
    # CreatDb()
    # QAQuery("what is COFOR ?")
    # CreatDb_P()
    # QAQuery_p("what is GST ?")
    if SysLock == "1":
        demo.queue().launch(auth=(username, password))
    else:
        demo.queue().launch()
    pass