diff --git a/docs/apps/slackbot/.env b/docs/apps/slackbot/.env
new file mode 100644
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+++ 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
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--- /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 <repository_url>
+
+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
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diff --git a/docs/apps/slackbot/requirements.txt b/docs/apps/slackbot/requirements.txt
new file mode 100644
index 00000000..b4094ed8
--- /dev/null
+++ b/docs/apps/slackbot/requirements.txt
@@ -0,0 +1,11 @@
+Flask==2.3.2
+slackeventsapi==3.0.1
+slackclient==2.9.4
+langchain==0.0.205
+fastapi==0.97.0
+PyPDF2
+python-dotenv==0.19.1
+unstructured
+openai
+chromadb
+tiktoken
\ No newline at end of file