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from langchain.vectorstores import FAISS | |
from langchain.chains import ConversationalRetrievalChain | |
from langchain.llms import HuggingFaceHub | |
import gradio as gr | |
import os | |
from langchain.embeddings import HuggingFaceEmbeddings | |
from langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent | |
from langchain.document_loaders import PyPDFDirectoryLoader | |
from langchain.document_loaders.csv_loader import CSVLoader | |
from langchain.text_splitter import RecursiveCharacterTextSplitter | |
from langchain.memory import ConversationSummaryBufferMemory | |
import io | |
import contextlib | |
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L12-v2") | |
vector_store= FAISS.load_local("vector_db/", embeddings) | |
repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1" | |
llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0.1, "max_new_tokens": 4096}) | |
memory = ConversationSummaryBufferMemory( | |
llm=llm, | |
output_key='answer', | |
memory_key='chat_history', | |
max_token_limit=500, | |
return_messages=True) | |
retriever = vector_store.as_retriever( | |
search_type="similarity", | |
search_kwargs={"k": 10, "include_metadata": True}) | |
qa = ConversationalRetrievalChain.from_llm( | |
llm=llm, | |
memory=memory, | |
chain_type="stuff", | |
retriever=retriever, | |
return_source_documents=True, | |
get_chat_history=lambda h : h, | |
verbose=True) | |
agent=create_csv_agent(llm,['data/Gretel_Data.csv','data/RAN_Data _T.csv'],verbose=True) | |
def echo(message, history): | |
context_prompt = """Assume the role of Clara, a seasoned senior telecom network engineer with access to troubleshooting tickets data and various technical and product documentation. | |
Rely on information derived from these tickets to address queries. Feel free to seek clarification through relevant questions.""" | |
message= context_prompt + "User Query: "+ message + "If asked about Root cause analysis (rca), give only one possible awnser and then give the steps to resolve in the exact format which is :- The steps to resolve could be:" | |
result=qa({"question":message}) | |
bold_answer= "<b>" + result['answer'].split('Helpful Answer:')[-1] + "</b>" | |
return bold_answer + "<br></br>" +'1. ' + str(result["source_documents"][0]) +"<br>" + '2. ' + str(result["source_documents"][1]) + "<br>" + "3. " + str(result["source_documents"][2]) | |
def echo_agent(message, history): | |
message="There are 2 df's. If you find a KeyError check for the same in the other df." + "<br>" + message | |
try: | |
with io.StringIO() as buffer: | |
with contextlib.redirect_stdout(buffer): | |
result= agent.run(message) | |
verbose_output = buffer.getvalue() | |
verbose_output = verbose_output.replace("\x1b[36;1m\x1b[1;3m", "") | |
verbose_output = verbose_output.replace("[1m> ", "") | |
verbose_output = verbose_output.replace("[0m", "") | |
verbose_output = verbose_output.replace("[32;1m[1;3m", "") | |
result= "<b>" + verbose_output + "<br>" + result + "</b>" | |
return result | |
except Exception as e: | |
error_message = f"An error occurred: {e}"+str(e.with_traceback) + str(e.args) | |
return error_message | |
demo=gr.ChatInterface( | |
fn=echo, | |
chatbot=gr.Chatbot(height=300, label="Hi I am Clara!", show_label=True), | |
textbox=gr.Textbox(placeholder="Ask me a question", container=True, autofocus=True, scale=7), | |
title="Network Ticket Knowledge Management", | |
description="<span style='font-size: 16x;'>Welcome to Verizon Network Operations Center!! I am here to help the Verizon Field Operations team with technical queries & escalation. I am trained on 1000s of RAN, Backhaul, Core network & End user equipment trouble tickets. Ask me!!! ☺</span>", | |
theme=gr.themes.Soft(), | |
examples=["wifi connected but no internet showing", "internet stopped working after primary link down", "internet stopped working link not shifted to secondary after primary link down"], | |
cache_examples=False, | |
retry_btn=None, | |
undo_btn="Delete Previous", | |
clear_btn="Clear", | |
stop_btn="Stop", | |
) | |
demo1=gr.ChatInterface( | |
fn=echo_agent, | |
chatbot=gr.Chatbot(height=300, label="Hi I am Sam!", show_label=True), | |
textbox=gr.Textbox(placeholder="Ask me a question", container=True, autofocus=True, scale=7), | |
title="LLM Powered Agent", | |
description="<span style='font-size: 16x;'>Welcome to Verizon RAN Visualization & Analytics powered by GEN AI. I have access 100 of metrices generated by a RAN base station and can help in visualizing, correlating and generating insights, using power of Conversational AI ☺</span>", | |
theme=gr.themes.Soft(), | |
retry_btn=None, | |
undo_btn="Delete Previous", | |
clear_btn="Clear", | |
stop_btn="Stop", | |
) | |
demo2=gr.TabbedInterface([demo,demo1],["RAG","AGENT"], title='INCEDO', theme=gr.themes.Soft()) | |
demo2.launch(auth=("admin", "Sam&Clara")) | |