GPT-Slackbot w/ Python / Bolt + Socket mode enabled

app.py

# inspired by: https://github.com/hollaugo/chatgpt-slack-app/blob/main/app.py
import json
import pymongo
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import AzureOpenAI
import os
import requests
from slack_bolt import App
from slack_bolt.adapter.socket_mode import SocketModeHandler
from dotenv import load_dotenv
from langchain.vectorstores import MongoDBAtlasVectorSearch
from langchain.chains import RetrievalQAWithSourcesChain

load_dotenv()  # take environment variables from .env
# Initializes your app with your bot token and socket mode handler
app = App(token=os.environ.get("SLACK_BOT_TOKEN"))
MONGO_URI = os.environ.get("MONGODB_URI")
MONGODB_DATABASE = ""
MONGODB_COLLECTION = ""
 # Connect to the MongoDB server
client = pymongo.MongoClient(MONGO_URI)
# Get the collection
collection = client[MONGODB_DATABASE][MONGODB_COLLECTION]

os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_KEY"] = os.environ.get("AZURE_OPENAI_TOKEN")
os.environ["OPENAI_API_BASE"] = "https://.openai.azure.com/"
os.environ["OPENAI_API_VERSION"] = "2023-05-15"

azureEmbeddings = OpenAIEmbeddings(
            deployment="",
            model="text-embedding-ada-002",
            openai_api_base="https://.openai.azure.com/",
            openai_api_key=os.environ.get("AZURE_OPENAI_TOKEN"),
            openai_api_type="azure",
            chunk_size=1
            )

llm = AzureOpenAI(
    model_name="gpt-4",
    engine="gpt-4",
    openai_api_base="https://.openai.azure.com/",
    openai_api_key=os.environ.get("AZURE_OPENAI_TOKEN"),
    temperature=0.7
)

vectorstore = MongoDBAtlasVectorSearch(collection, azureEmbeddings)
mdb_retriever = vectorstore.as_retriever()

#Langchain implementation
qa = RetrievalQAWithSourcesChain.from_chain_type(llm=llm, chain_type="stuff", retriever=mdb_retriever, verbose=True)


#Message handler for Slack
@app.action({
    "action_id": "thumbs_up"
})
def handleMessageFeedbackThumbsUp(ack, body, say, respond):
    ack()
    print(str(body['message']['text']).replace(":+1: button :-1: button",""))
    blocks = [
        {
            "type": "section",
            "text": {
                "type": "mrkdwn",
                "text": "Thank you for your feedback! \n\n You rated the below response (:+1:) \n\n"+(str(body['message']['text']).replace(":+1: button :-1: button",""))
            }
        }
    ]
    url = body['response_url']
    headers = {"Content-type": "application/json"}  
    payload = {  
        "replace_original": "true",  
        "blocks": blocks,
        "text":"Thank you for your feedback! \n\n You rated the below response (:+1:) \n\n"+(str(body['message']['text']).replace(":+1: button :-1: button",""))
    }  
    
    response = requests.post(url, headers=headers, data=json.dumps(payload))  
    
    print(response.status_code)  
    print(response.text)  


@app.action({
    "action_id": "thumbs_down"
})
def handleMessageFeedbackThumbsDown(ack, client, body, say, respond):
    ack()
    print(str(body['message']['text']).replace(":+1: button :-1: button",""))
    blocks = [
        {
            "type": "section",
            "text": {
                "type": "mrkdwn",
                "text": "Thank you for your feedback! \n\n You rated the below response (:-1:) \n\n"+(str(body['message']['text']).replace(":+1: button :-1: button",""))
            }
        }
    ]
    url = body['response_url']
    headers = {"Content-type": "application/json"}  
    payload = {  
        "replace_original": "true",  
        "blocks": blocks,
        "text":"Thank you for your feedback! \n\n You rated the below response (:-1:) \n\n"+(str(body['message']['text']).replace(":+1: button :-1: button",""))
    }  
    
    response = requests.post(url, headers=headers, data=json.dumps(payload))  
    
    print(response.status_code)  
    print(response.text)  

@app.event("app_mention")
def handleAppMentionEvent(body, say, logger):
    event = body["event"]
    thread_ts = event.get("thread_ts", None) or event["ts"] 
    templated_Q = """<USERINPUT>
    """
    QUESTION =str(body['event']['text']).replace("<@U05GTJQCARG>","") #replace the mention of your specific BOT before sending to LLM
    QUESTION=str(QUESTION).strip()
    templated_Q = templated_Q.replace("<USERINPUT>",QUESTION)
    output = qa({"question":templated_Q})
    
    blocks = [
        {
            "type": "section",
            "text": {
                "type": "mrkdwn",
                "text": output['answer']
            }
        },
        {
            "type": "divider"
        },
        {
            "type": "section",
            "text": {
                "type": "mrkdwn",
                "text": output['sources'].replace(",","\n").replace("("," (").strip() #change the commas for \n
            }
        },
        {
            "type": "divider"
        },
        {
            "type": "actions",
            "elements": [
                {
                    "type": "button",
                    "text": {
                        "type": "plain_text",
                        "emoji": True,
                        "text": ":+1:"
                    },
                    "value": ":+1:",
                    "action_id": "thumbs_up"
                },
                {
                    "type": "button",
                    "text": {
                        "type": "plain_text",
                        "emoji": True,
                        "text": ":-1:"
                    },
                    "value": ":-1:",
                    "action_id": "thumbs_down"
                }
            ]
        }
    ]
    say(blocks=blocks,thread_ts=thread_ts)



# Start your app
if __name__ == "__main__":
    SocketModeHandler(app, os.environ["SLACK_APP_TOKEN"]).start()