SaschaHeyer

import pickle

def unpickle_file(file_path):
    """Load a pickled file."""
    try:
        with open(file_path, 'rb') as file:
            data = pickle.load(file)
        return data
    except (pickle.UnpicklingError, FileNotFoundError, EOFError) as e:
        print(f"Error unpickling file: {e}")

SaschaHeyer

import vertexai
from vertexai.generative_models import (
    GenerativeModel,
    Tool,
    FunctionDeclaration,
)
from vertexai.preview import reasoning_engines
from google.cloud import bigquery, firestore
from langchain_google_firestore import FirestoreChatMessageHistory
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

SaschaHeyer

from google.cloud import storage
import cv2
import tempfile

# Initialize Cloud Storage client
storage_client = storage.Client()
bucket_name = "your-bucket-name"
blob_name = "path/to/image.jpg"

# Download to a temporary file

SaschaHeyer

import vertexai
from vertexai.preview import reasoning_engines
from vertexai.preview import rag
from vertexai.preview.generative_models import Tool

from langchain.agents.format_scratchpad.tools import format_to_tool_messages
from langchain.memory import ChatMessageHistory
from langchain_core import prompts
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

SaschaHeyer

import re
import time
import streamlit as st
import pandas as pd
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import vertexai
from vertexai.generative_models import GenerativeModel
import vertexai.preview.generative_models as generative_models

SaschaHeyer

gcloud config set project sascha-playground-doit export PROJECT_ID=$(gcloud config get project) export REGION=us-central1 export CLUSTER_NAME=vllm export HF_TOKEN=XXX

gcloud container clusters create-auto ${CLUSTER_NAME}
--project=${PROJECT_ID}
--region=${REGION}
--release-channel=rapid \

SaschaHeyer

Question: how can I deploy a LLM to Google cloud? The model size is around 80GB.

Answer: Deploying an 80GB LLM to Google Cloud is a great goal! It's a bit like moving a large piece of furniture - you need the right tools and plan to make it smooth. Here's a breakdown of how to do it:

1. Choose Your Deployment Platform:

Vertex AI: This is Google Cloud's managed machine learning platform. It's designed for deploying and managing models, including large ones. Vertex AI offers:

Model Serving: You can deploy your LLM as a model endpoint, allowing you to send requests for predictions.

SaschaHeyer

Question: How can I create text embeddings with google cloud?

Answer: You can create text embeddings with Google Cloud using the Text Embeddings API on Vertex AI. This API allows you to generate numerical representations of text, which can be used for various tasks like:

Semantic Search: Finding documents similar in meaning to a given query. Recommendation Systems: Suggesting relevant content based on user preferences. Clustering: Grouping similar documents together. Question Answering: Finding answers to questions based on a knowledge base.

SaschaHeyer

import vertexai
from vertexai.language_models import TextGenerationModel
from google.cloud import translate_v2 as translate

vertexai.init(project="sascha-playground-doit", location="us-central1")
parameters = {
    "candidate_count": 1,
    "max_output_tokens": 1024,
    "temperature": 0.9,
    "top_p": 1

SaschaHeyer

response = index_endpoint.match(
    deployed_index_id="similar_text_deployed",
    queries=[embedding[0]],
    num_neighbors=5
)