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Learning Haystack

"Unusual" features

Haystack first comes across as a document retrieval system. However, it has other features that may not be immediately obvious.

1. Fine-tuning on our own data (and this one).
2. Redirect search to different nodes, based on type (tutorial) - distinguish between keyword and sentence searches.
3. Agents (plugins).
4. Multimodal (image) search.

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"""A simple logger to share logging configuration across the project.

- Info and debug messages go to stdout.
- Error and warning messages go to stderr.

Note that use a module-level logger is not the best practice. It's enough for hobbyist projects. Use the logging module
configuration file for more complex projects (https://docs.python.org/3/howto/logging.html#configuring-logging).

Usage:

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Learning Go

Good sources, organized in a logical sequence (simpler to more complex)

1. The Go Programming Language and Environment: Written by the creators of Go, it explains the principles and design decisions that went into creating Go and its environment. Read this first to understand the motivation behind language features, tools, and environment.
2. A Tour of Go: Learn the important language features in bite-sized explanations. Part of the official documentation.
3. How to Write Go Code: Write a simple Go program to learn the environment and tools. Part of the official documentation. The program is simple. The goal is to teach the mechanics of getting a Go program to run.
4. Learn Go in ten minutes: Now that we are familiar with the language we can use this page as a quick reminder of the Go syntax and features

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How to compare versions of a LaTeX project

Instructions tested on Mac OS

Tool: https://ctan.org/pkg/latexdiff

Using it with multi-file projects: https://tex.stackexchange.com/a/167894

Mac OS installation: https://tex.stackexchange.com/a/548553

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Go to https://www.codecogs.com/latex/eqneditor.php

Edit the equation as needed

Go to the bottom of the page and choose "URL Encoded"

Add URL to markdown page as an external image:

![equation](https://latex.codecogs.com/svg.latex?dx_i%20%3D%20x_i%20-%20%5Cbar%7Bx%7D)

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# Based on https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.08-Aggregation-and-Grouping.ipynb#scrollTo=uwsvvB3-s0yO

import numpy as np
import pandas as pd

rng = np.random.RandomState(0)
df = pd.DataFrame({'key': ['A', 'B', 'C', 'A', 'B', 'C'],
                   'data1': range(6),
                   'data2': rng.randint(0, 10, 6)},
                  columns=['key', 'data1', 'data2'])

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\begin{figure}
    \centering
    \begin{subfigure}[t]{0.45\columnwidth}
        \centering
        \includegraphics[width=\linewidth]{figures/figures_other/MnistExamples.png} 
        \caption{Generic} \label{fig:timing1}
    \end{subfigure}
    \hfill
    \begin{subfigure}[t]{0.45\columnwidth}
        \centering

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Instructions to create a Google Cloud account and set up an environment for machine learning on it.

NOTE: the command line examples assume a Linux environment.

Setting up the cloud environment

Google Cloud environment

At this time (November 2018) Google gives a U$300 credit when opening an account. It may ask for a credit card number when creating the account, but it won't be billed until the credits are used up.

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# Always start with a clean environment to avoid subtle bugs
rm(list = ls())

# To get repeatable results with random numbers (easier to debug multiple runs)
set.seed(123)

# Create a circle data set, change labels to -1/1, convert to data.frame
library(mlbench)
circle <- mlbench.circle(500, 2)
labels <- sign(as.numeric(circle$classes) - 1.5)

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