app.py

import torch
import time
import pinecone
import pickle
import os
import numpy as np
import hashlib
import gradio as gr
from transformers.generation.stopping_criteria import StoppingCriteria, StoppingCriteriaList
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from torch import nn
from sentence_transformers.cross_encoder import CrossEncoder
from peft import PeftModel
from sentence_transformers import SentenceTransformer
from bs4 import BeautifulSoup
import requests

headers = {
  "User-Agent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_5) AppleWebKit 537.36 (KHTML, like Gecko) Chrome",
  "Accept":"text/html,application/xhtml+xml,application/xml; q=0.9,image/webp,*/*;q=0.8",
  'Cookie':'CONSENT=YES+cb.20210418-17-p0.it+FX+917; '
}

def google_search(text):
    print(f"Google search on: {text}")
    try:
        site = requests.get(f'https://www.google.com/search?hl=en&q={text}', headers=headers)
        main = BeautifulSoup(site.text, features="html.parser").select_one('#main').select('.VwiC3b.lyLwlc.yDYNvb.W8l4ac')
        res =  '\n\n'.join([m.get_text() for m in main])
    except Exception as ex:
        print(f"Error: {ex}")
        res = ""

    print(f"The result of the google search is: {res}")

    return res

PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY")

sentencetransformer_model = SentenceTransformer('sentence-transformers/multi-qa-mpnet-base-cos-v1')
pinecone.init(api_key=PINECONE_API_KEY, environment="gcp-starter")


CACHE_DIR = "./.cache"
INDEX_NAME = "k8s-semantic-search"

if not os.path.exists(CACHE_DIR):
    os.makedirs(CACHE_DIR)


def cached(func):
    def wrapper(*args, **kwargs):
        SEP = "$|$"
        cache_token = (
            f"{func.__name__}{SEP}"
            f"{SEP.join(str(arg) for arg in args)}{SEP}"
            f"{SEP.join( str(key) + SEP * 2 + str(val) for key, val in kwargs.items())}"
        )

        hex_hash = hashlib.sha256(cache_token.encode()).hexdigest()
        cache_filename: str = os.path.join(CACHE_DIR, f"{hex_hash}")

        if os.path.exists(cache_filename):
            with open(cache_filename, "rb") as cache_file:
                return pickle.load(cache_file)

        result = func(*args, **kwargs)
        with open(cache_filename, "wb") as cache_file:
            pickle.dump(result, cache_file)

        return result

    return wrapper


@cached
def create_embedding(text: str):
    embed_text = sentencetransformer_model.encode(text)
    
    return embed_text.tolist()

index = pinecone.Index(INDEX_NAME)


def query_from_pinecone(query, top_k=3):
    embedding = create_embedding(query)
    if not embedding:
        return None

    return index.query(vector=embedding, top_k=top_k, include_metadata=True).get("matches")  # gets the metadata (text)


cross_encoder = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-12-v2")


def get_results_from_pinecone(query, top_k=3, re_rank=True, verbose=True):
    results_from_pinecone = query_from_pinecone(query, top_k=top_k)
    if not results_from_pinecone:
        return []

    if verbose:
        print("Query:", query)

    final_results = []

    if re_rank:
        if verbose:
            print("Document ID (Hash)\t\tRetrieval Score\tCE Score\tText")

        sentence_combinations = [
            [query, result_from_pinecone["metadata"]["text"]] for result_from_pinecone in results_from_pinecone
        ]

        # Compute the similarity scores for these combinations
        similarity_scores = cross_encoder.predict(sentence_combinations, activation_fct=nn.Sigmoid())

        # Sort the scores in decreasing order
        sim_scores_argsort = reversed(np.argsort(similarity_scores))

        # Print the scores
        for idx in sim_scores_argsort:
            result_from_pinecone = results_from_pinecone[idx]
            final_results.append(result_from_pinecone)
            if verbose:
                print(
                    f"{result_from_pinecone['id']}\t{result_from_pinecone['score']:.2f}\t{similarity_scores[idx]:.2f}\t{result_from_pinecone['metadata']['text'][:50]}"
                )
        return final_results

    if verbose:
        print("Document ID (Hash)\t\tRetrieval Score\tText")
    for result_from_pinecone in results_from_pinecone:
        final_results.append(result_from_pinecone)
        if verbose:
            print(
                f"{result_from_pinecone['id']}\t{result_from_pinecone['score']:.2f}\t{result_from_pinecone['metadata']['text'][:50]}"
            )

    return final_results


def semantic_search(prompt):
    final_results = get_results_from_pinecone(prompt, top_k=9, re_rank=True, verbose=True)
    if not final_results:
        return ""

    return "\n\n".join(res["metadata"]["text"].strip() for res in final_results[:3])


base_model_id = "mistralai/Mistral-7B-Instruct-v0.1"
lora_model_id = "ComponentSoft/mistral-kubectl-instruct"

tokenizer = AutoTokenizer.from_pretrained(
    lora_model_id,
    padding_side="left",
    add_eos_token=False,
    add_bos_token=True,
)
tokenizer.pad_token = tokenizer.eos_token

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
)

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    quantization_config=bnb_config,
    use_cache=True,
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(base_model, lora_model_id)
model.eval()


def create_stop_criterion(*args):
    term_tokens = [torch.tensor(tokenizer.encode(term, add_special_tokens=False)).to("cuda") for term in args]

    class CustomStopCriterion(StoppingCriteria):
        def __call__(self, input_ids: torch.LongTensor, score: torch.FloatTensor, **kwargs):
            return any(torch.equal(e, input_ids[0][-len(e) :]) for e in term_tokens)

    return CustomStopCriterion()


eval_stop_criterion = create_stop_criterion("</s>", "#End")
category_stop_criterion = create_stop_criterion("</s>", "\n")

start_template = "### Answer:"
command_template = "# Command:"
end_template = "#End"


def text_to_text_generation(verbose, prompt):
    prompt = prompt.strip()

    is_kubectl_prompt = (
        f"You are a helpful assistant who classifies prompts into three categories. [INST] Respond with 0 if it pertains to a 'kubectl' operation. This is an instruction that can be answered with a 'kubectl' action. Look for keywords like 'get', 'list', 'create', 'show', 'view', and other command-like words. This category is an instruction instead of a question. Respond with 1 only if the prompt is a question, and is about a definition related to Kubernetes, or non-action inquiries. Respond with 2 every other scenario, for example if the question is a general question, not related to Kubernetes or 'kubectl'.\n"
        f"So for instance the following:\n"
        f'text: "List all pods in Kubernetes"\n'
        f"Would get a response:\n"
        f"response (0/1/2): 0 [/INST] \n"
        f'text: "{prompt}"'
        f"response (0/1/2): "
    )

    model_input = tokenizer(is_kubectl_prompt, return_tensors="pt").to("cuda")
    with torch.no_grad():
        response = tokenizer.decode(
            model.generate(
                **model_input,
                max_new_tokens=8,
                pad_token_id=tokenizer.eos_token_id,
                repetition_penalty=1.15,
                stopping_criteria=StoppingCriteriaList([category_stop_criterion]),
            )[0],
            skip_special_tokens=True,
        )
    response = response[len(is_kubectl_prompt) :]

    print(f'{" Query Start ":-^40}')
    print("Classified as: " + response)

    response_num = 0 if "0" in response else (1 if "1" in response else 2)

    def generate(response_num, prompt, retriever, verbose):
        match response_num:
            case 0:
                prompt = f"[INST] {prompt}\n Lets think step by step. [/INST] {start_template}"

            case 1:
                if retriever == "semantic_search":
                    retrieved_results = semantic_search(prompt)
                    prompt = f"You are a helpful kubernetes professional. [INST] Use the following documentation, if it is relevant to answer the question below. [/INST]\nDocumentation: {retrieved_results} </s>\n<s> [INST] Answer the following question: {prompt} [/INST]\nAnswer: "
                elif retriever == "google_search":
                    retrieved_results = google_search(prompt)
                    prompt = f"You are a helpful kubernetes professional. [INST] Use the following documentation, if it is relevant to answer the question below. [/INST]\nDocumentation: {retrieved_results} </s>\n<s> [INST] Answer the following question: {prompt} [/INST]\nAnswer: "
                else:
                    prompt = f"[INST] Answer the following question: {prompt} [/INST]\nAnswer: "

            case _:
                prompt = f"[INST] {prompt} [/INST]"

        print("Query:")
        print(prompt)

        # Generate output
        model_input = tokenizer(prompt, return_tensors="pt").to("cuda")
        with torch.no_grad():
            response = tokenizer.decode(
                model.generate(
                    **model_input,
                    max_new_tokens=256,
                    pad_token_id=tokenizer.eos_token_id,
                    repetition_penalty=1.15,
                    stopping_criteria=StoppingCriteriaList([eval_stop_criterion]),
                )[0],
                skip_special_tokens=True,
            )

        decoded_prompt = tokenizer.decode(tokenizer(prompt).input_ids, skip_special_tokens=True)

        start = (
            response.index(start_template) + len(start_template) if start_template in response else len(decoded_prompt)
        )
        start = response.index(command_template) + len(command_template) if command_template in response else start
        end = response.index(end_template) if end_template in response else len(response)

        return response if verbose else response[start:end].strip()

    true_response = generate(response_num, prompt, False, verbose)
    true_response_semantic_search = generate(response_num, prompt, "semantic_search", verbose)
    true_response_google_search = generate(response_num, prompt, "google_search", verbose)


    print("Returned: " + true_response)
    print(f'{" QUERY END ":-^40}')

    match response_num:
        case 0:
            mode = "Kubectl"
        case 1:
            mode = "Kubernetes"
        case _:
            mode = "Normal"

    return (
        f"*Mode*: {mode}",
        f"# Answer\n\n {true_response}",
        f"# Answer with RAG\n\n {true_response_semantic_search}",
        f"# Answer with Google search\n\n {true_response_google_search}"
    )


iface = gr.Interface(
    fn=text_to_text_generation,
    inputs=[
        gr.components.Checkbox(label="Verbose"),
        gr.components.Text(placeholder="prompt here ...", label="Prompt"),
    ],
    outputs=[
        gr.components.Markdown(label="Mode"),
        gr.components.Markdown(label="Answer Without Retriever"),
        gr.components.Markdown(label="Answer With Retriever"),
        gr.components.Markdown(label="Answer With Google search"),
    ],
    allow_flagging="never",
)

iface.launch()