From b101e7195624eeccbd5f9e023c9a692a281d0293 Mon Sep 17 00:00:00 2001 From: babasahebpinjar Date: Mon, 19 Jun 2023 15:58:19 -0400 Subject: [PATCH] Adding files for slackbot --- docs/apps/slackbot/.env | 4 + docs/apps/slackbot/Dockerfile | 24 ++ docs/apps/slackbot/README.md | 54 +++ docs/apps/slackbot/app.py | 328 ++++++++++++++++++ ...Become-a-Professional-Artist-in-Canada.pdf | Bin 0 -> 11125573 bytes .../slackbot/files/Large-Language-Models.pdf | Bin 0 -> 3928680 bytes docs/apps/slackbot/files/Test file - Copy.pdf | Bin 0 -> 30624 bytes docs/apps/slackbot/files/Test file.pdf | Bin 0 -> 30624 bytes docs/apps/slackbot/requirements.txt | 11 + 9 files changed, 421 insertions(+) create mode 100644 docs/apps/slackbot/.env create mode 100644 docs/apps/slackbot/Dockerfile create mode 100644 docs/apps/slackbot/README.md create mode 100644 docs/apps/slackbot/app.py create mode 100644 docs/apps/slackbot/files/How-to-Become-a-Professional-Artist-in-Canada.pdf create mode 100644 docs/apps/slackbot/files/Large-Language-Models.pdf create mode 100644 docs/apps/slackbot/files/Test file - Copy.pdf create mode 100644 docs/apps/slackbot/files/Test file.pdf create mode 100644 docs/apps/slackbot/requirements.txt diff --git a/docs/apps/slackbot/.env b/docs/apps/slackbot/.env new file mode 100644 index 00000000..cd82a94e --- /dev/null +++ b/docs/apps/slackbot/.env @@ -0,0 +1,4 @@ +SLACK_SIGNING_SECRET= +SLACK_OAUTH_TOKEN= +VERIFICATION_TOKEN= +OPENAI_KEY= \ No newline at end of file diff --git a/docs/apps/slackbot/Dockerfile b/docs/apps/slackbot/Dockerfile new file mode 100644 index 00000000..77b62553 --- /dev/null +++ b/docs/apps/slackbot/Dockerfile @@ -0,0 +1,24 @@ +# Use the official Python base image +FROM python:3.9-slim-buster + +# Set the working directory in the container +WORKDIR /app + +# Copy the requirements.txt file +COPY requirements.txt . + + +RUN apt-get update && apt-get install -y build-essential \ + && pip install --upgrade pip setuptools \ + && pip install hnswlib +# Install the Python dependencies +RUN pip install --no-cache-dir -r requirements.txt + +# Copy the application code to the container +COPY . . + +# Expose the port that the Flask app will listen on +EXPOSE 3000 + +# Run the Flask app +CMD ["python", "app.py"] diff --git a/docs/apps/slackbot/README.md b/docs/apps/slackbot/README.md new file mode 100644 index 00000000..f5d8f912 --- /dev/null +++ b/docs/apps/slackbot/README.md @@ -0,0 +1,54 @@ +# llmlivebook + +# Chatbot with Flask and Slack + +This repository contains a chatbot implementation using Flask and Slack. The chatbot leverages the Langchain library for question-answering and text processing tasks. + +## Features + +- Receives events from Slack using SlackEventAdapter. +- Handles app mentions and responds with a message. +- Implements a conversational retrieval chain for question-answering. +- Integrates with OpenAI GPT-3.5 Turbo for language modeling. +- Utilizes Flask and FastAPI frameworks for building the web server. + +## Installation + +1. Clone the repository: + + ```bash + git clone + +2. Create new app in slack workspace + +3. Configuration + Before running the code, you need to configure the following environment variables: + + . All these tokens should be added in .env file + + SLACK_SIGNING_SECRET: Slack apps signing secret. + SLACK_BOT_TOKEN: Slack bot token for authentication. + VERIFICATION_TOKEN: Slack verification token. + OPENAI_API_KEY: OpenAI API key for language modeling. + + All these tokens should be added in .env file + +4. Add all the files on which you want to build the Vectore Db index. + Right now its working only with PDF + +5. Usage + 1. Run the docker image: + + docker build -it slackbot . + docker run -p 3000:3000 slackbot + + 2. Expose the server to the internet using a tool like ngrok. Not required in hosted on public IP + + 3. Set up the Slack app's Event Subscriptions and provide the ngrok URL as the Request URL. + + # Reference + + + 4. Start interacting with the chatbot by mentioning the app in a Slack channel. + + diff --git a/docs/apps/slackbot/app.py b/docs/apps/slackbot/app.py new file mode 100644 index 00000000..882ee83b --- /dev/null +++ b/docs/apps/slackbot/app.py @@ -0,0 +1,328 @@ +############################################## +# Importing necessary modules +############################################## + +from flask import Flask, Response +from slackeventsapi import SlackEventAdapter +import os +from threading import Thread +from slack import WebClient +from langchain.chains.question_answering import load_qa_chain +from langchain.embeddings.openai import OpenAIEmbeddings +from langchain.text_splitter import CharacterTextSplitter +from langchain.vectorstores import FAISS +from langchain.llms import OpenAI +from fastapi import FastAPI +from fastapi.responses import ORJSONResponse +from fastapi import FastAPI, File, UploadFile, HTTPException +from PyPDF2 import PdfReader +from os import environ +from dotenv import load_dotenv +load_dotenv() + + +# This `app` represents your existing Flask app +app = Flask(__name__) +greetings = ["hi", "hello", "hello there", "hey"] + + +##################################################################################################### +# Setting up environment variables and Slack configuration: +# The code retrieves various environment variables using os.environ.get() method. +# Environment variables include Slack signing secret, OAuth token, verification token, and OpenAI key. +##################################################################################################### + +SLACK_SIGNING_SECRET = environ.get("SLACK_SIGNING_SECRET") +SLACK_OAUTH_TOKEN = environ.get("SLACK_OAUTH_TOKEN") +VERIFICATION_TOKEN = environ.get("VERIFICATION_TOKEN") +OPENAI_KEY=environ.get("OPENAI_KEY") + + + +########################################################################### +# Instantiating Slack client and Flask app: +########################################################################### + +#instantiating slack client +slack_client = WebClient(SLACK_OAUTH_TOKEN) +os.environ['OPENAI_API_KEY'] = OPENAI_KEY + +# An example of one of your Flask app's routes +@app.route("/") +def event_hook(request): + json_dict = json.loads(request.body.decode("utf-8")) + if json_dict["token"] != VERIFICATION_TOKEN: + return {"status": 403} + + if "type" in json_dict: + if json_dict["type"] == "url_verification": + response_dict = {"challenge": json_dict["challenge"]} + return response_dict + return {"status": 500} + return + + +slack_events_adapter = SlackEventAdapter( + SLACK_SIGNING_SECRET, "/slack/events", app +) + +chat_history = [] +@slack_events_adapter.on("app_mention") +def handle_message(event_data): + global chat_history + chat_history = [] + def send_reply(value): + event_data = value + message = event_data["event"] + if message.get("subtype") is None: + command = message.get("text") + + print('-------',command) + channel_id = message["channel"] + + # C056RU8PEJ0 : llm live project + # C059L0UFLR4 : bottest + # allowed_channels = ["C056RU8PEJ0", "C059L0UFLR4"] # Add the desired channel IDs + # if channel_id not in allowed_channels: + # print("Not allowed to process") + # return + #if any(item in command.lower() for item in greetings): + #result = qa({"question": command, "chat_history": []}) + #message= (result["answer"]) + message = ("Helloooooooo <@%s>! :tada: I am good how are you" % message["user"] ) + + print(channel_id) + global chain + message = chain.run(command) + print(message) + slack_client.chat_postMessage(channel=channel_id, text=message) + command = '' + thread = Thread(target=send_reply, kwargs={"value": event_data}) + thread.start() + return Response(status=200) + + +############################################################################ + +# Get code from John to create index. Below code is for Experiment +# and not used anywhere + +############################################################################ + + +# import getpass +# import pinecone +# from tqdm.autonotebook import tqdm +# from langchain.text_splitter import CharacterTextSplitter +# from langchain.vectorstores import Pinecone, Chroma +# from langchain.embeddings.openai import OpenAIEmbeddings +# from langchain.chains import ConversationalRetrievalChain +# from langchain.chat_models import ChatOpenAI +# from langchain.document_loaders import DirectoryLoader +# from langchain.llms import OpenAI +# from langchain.vectorstores import Chroma +# from fastapi import FastAPI, File, UploadFile, HTTPException + +chatHistory = 0 +vectorstore = '' +qa = '' +index_created = 0 +# os.environ['OPENAI_API_KEY'] = OPENAI_KEY + + + +#@app.post('/createVectorStore', response_class=ORJSONResponse) +# @app.route("/createVectorStore") +# def createVectorStore(transcript: UploadFile = File(...)): +# if transcript.filename.endswith(".pdf"): +# raise HTTPException(status_code=400, detail="Only PDF files are allowed") + +# with open(transcript.filename, mode="wb") as new_file: +# new_file.write(transcript.file.read()) + +# new_file.close() +# doc_reader = PdfReader(transcript.filename) +# # read data from the file and put them into a variable called raw_text +# raw_text = '' +# for i, page in enumerate(doc_reader.pages): +# text = page.extract_text() +# if text: +# raw_text += text + +# # Splitting up the text into smaller chunks for indexing +# text_splitter = CharacterTextSplitter( +# separator = "\n", +# chunk_size = 1000, +# chunk_overlap = 200, #striding over the text +# length_function = len, +# ) +# texts = text_splitter.split_text(raw_text) + +# # Download embeddings from OpenAI +# embeddings = OpenAIEmbeddings() +# docsearch = FAISS.from_texts(texts, embeddings) + +# #extracted_list = [] +# chain = load_qa_chain(OpenAI(), +# chain_type="stuff") # we are going to stuff all the docs in at once + +# query = "hi" +# docs = docsearch.similarity_search(query) +# result = chain.run(input_documents=docs, question=query) + + +# return result + +# def getDocuments(folderName): + +# files = folderName +'/' +# pdf_loader = DirectoryLoader(files, glob="**/*.pdf") +# readme_loader = DirectoryLoader(files, glob="**/*.md") +# txt_loader = DirectoryLoader(files, glob="**/*.txt") +# #take all the loader +# loaders = [pdf_loader, readme_loader, txt_loader] + +# #lets create document +# documents = [] +# for loader in loaders: +# documents.extend(loader.load()) + +# print (f'You have {len(documents)} document(s) in your data') +# print (f'There are {len(documents[0].page_content)} characters in your document') + +# #Split the Text from the documents +# text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=40) #chunk overlap seems to work better +# documents = text_splitter.split_documents(documents) +# print(len(documents)) + +# return documents +# #Embeddings and storing it in Vectorestore + +# def getVectoreStore(documents): +# global vectorstore +# chromaVector = 1 +# pineconeVector = 0 +# loadExistingPineConeIndex = 0 + +# if chromaVector == 1: +# embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_KEY) +# vectorstore = Chroma.from_documents(documents, embeddings) +# index_created = 1 + +# # pinecone +# if pineconeVector == 1: + +# PINECONE_API_KEY = getpass.getpass('Pinecone API Key:') +# PINECONE_ENV = getpass.getpass('Pinecone Environment:') + +# # initialize pinecone +# pinecone.init( +# api_key=PINECONE_API_KEY, # find at app.pinecone.io +# environment=PINECONE_ENV # next to api key in console +# ) + +# index_name = "langchain-demo" +# vectorstore = Pinecone.from_documents(documents, embeddings, index_name=index_name) + + +# if loadExistingPineConeIndex == 1: + +# # if you already have an index, you can load it like this + +# # initialize pinecone +# pinecone.init( +# api_key=PINECONE_API_KEY, # find at app.pinecone.io +# environment=PINECONE_ENV # next to api key in console +# ) + +# index_name = "langchain-demo" +# vectorstore = Pinecone.from_existing_index(index_name, embeddings) + +# return vectorstore + + +# def getSimilarDocument(query): +# global vectorstore +# query = "Who are the authors of gpt4all paper ?" +# docs = vectorstore.similarity_search(query) + +# return docs + +# #print(docs[0].page_content) + + +# def createLangchainQA(vectorstore): +# global chatHistory,qa +# retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k":2}) +# llm = OpenAI(model_name="gpt-3.5-turbo",openai_api_key=OPENAI_KEY,temperature =0) +# qa = ConversationalRetrievalChain.from_llm(llm, retriever) + +# return qa + + +################################################################### +## Experiment Add John's code above to create Index +################################################################### + +import shutil +import atexit +def cleanup(): + print("Cleaning up before exit...") + if index_created == 1: + vectorstore = vectorstore.vectorstore + vectorstore.delete_collection() + if os.path.exists('.chroma'): + directory = ".chroma" + shutil.rmtree(directory) + # Add your code to be executed here + +atexit.register(cleanup) + + + +################################################################## +# LLM implementation with Chroma +################################################################## + +from langchain.document_loaders import UnstructuredPDFLoader +from langchain.indexes import VectorstoreIndexCreator +from langchain.chains import RetrievalQA + +loaders = '' +chain = '' +index = '' +def createIndex(pdf_folder_path): + files = os.listdir(pdf_folder_path) + + global OPENAI_KEY + # Print the list of files + print(files) + global loaders + global chain + global index + loaders = [UnstructuredPDFLoader(os.path.join(pdf_folder_path, fn)) for fn in os.listdir(pdf_folder_path)] + #loaders + documents = [] + for loader in loaders: + documents.extend(loader.load()) + + index = VectorstoreIndexCreator( + embedding=OpenAIEmbeddings(openai_api_key=OPENAI_KEY), + text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)).from_loaders(loaders) + + llm = OpenAI(model_name="gpt-3.5-turbo",openai_api_key=OPENAI_KEY)#, n=2, best_of=2) + chain = RetrievalQA.from_chain_type(llm=llm, + chain_type="stuff", + retriever=index.vectorstore.as_retriever(), + input_key="question") + + return chain + +# Start the server on port 3000 +if __name__ == "__main__": + # documents = getDocuments('files') + # vectorstore = getVectoreStore(documents) + # qa = createLangchainQA(vectorstore) + + chain = createIndex("files") + app.run(port=3000) \ No newline at end of file diff --git a/docs/apps/slackbot/files/How-to-Become-a-Professional-Artist-in-Canada.pdf b/docs/apps/slackbot/files/How-to-Become-a-Professional-Artist-in-Canada.pdf new file mode 100644 index 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