LlamaIndexを完全に理解するチュートリアル その１：処理の概念や流れを理解する基礎編（v0.6.8対応） | DevelopersIO
なおRetrieverModeは、後述するas_query_engineでQueryEngineを作成する際に与えることができます。 Contextについて IndexとRetrieverは密接に関連しているものですが、それとは別に依存する処理クラスをContextとして与えます。 このContextは具体的には、Storage ContextとService Contextの２種類です。 冒頭のサンプルでは、デフォルトで動作しているためContextが見えないのですが、明示的に書くと以下のようになります。 from llama_index import StorageContext from llama_index.storage.docstore import SimpleDocumentStore from llama_index.storage.index_store import
RDF Triple Stores vs. Labeled Property Graphs: What's the Difference? - Graph Database & Analytics
Editor’s Note: This presentation was given by Jesús Barrasa at GraphConnect San Francisco in October 2016. Presentation Summary Resource Description Framework (RDF) triple stores and labeled property graphs both provide ways to explore and graphically depict connected data. But the two are very different – and each have different strengths in different use cases. In this presentation, Dr. Jesús Ba
Writing Clean Python With Namedtuples – dbader.org
Python comes with a specialized “namedtuple” container type that doesn’t seem to get the attention it deserves. It’s one of these amazing features in Python that’s hidden in plain sight. Namedtuples can be a great alternative to defining a class manually and they have some other interesting features that I want to introduce you to in this article. Now, what’s a namedtuple and what makes it so spec
What is the pythonic way to read CSV file data as rows of namedtuples?
What is the best way to take a data file that contains a header row and read this row into a named tuple so that the data rows can be accessed by header name? I was attempting something like this: import csv from collections import namedtuple with open('data_file.txt', mode="r") as infile: reader = csv.reader(infile) Data = namedtuple("Data", ", ".join(i for i in reader[0])) next(reader) for row i
Rob Pike's Rules of Programming (1989) | Hacker News
> Rule 1. You can't tell where a program is going to spend its time. Bottlenecks occur in surprising places, so don't try to second guess and put in a speed hack until you've proven that's where the bottleneck is.I wish people would follow this rule and just let stuff work. I recently encountered the most extreme version of this I've ever seen in my career: a design review where a guy proposed a R
「Goの父」ロブ・パイクの「プログラミング5カ条」、ネット上で話題に
「UNIXはただ死んだだけでなく、本当にひどい臭いを放ち始めている」「キャッシュはアーキテクチャではない。単なる最適化だ」などの語録を生んだ「Goの父」とも呼ばれるロブ・パイク氏の「プログラミング5カ条」について、ネット上で話題となっています users.ece.utexas.edu/~adnan/pike.html http://users.ece.utexas.edu/~adnan/pike.html Rob Pike's Rules of Programming (1989) | Hacker News https://news.ycombinator.com/item?id=24135189 パイク氏の「プログラミング5カ条」は以下。 ルール1：プログラムのどこで処理時間がかかるかはわからない。ボトルネックは意外な場所で発生するので、ボトルネックがどこにあるかを証明するまでは、臆測
非再帰型Segment TreeのPythonによる実装 - Qiita
はじめに タイトルのとおり、この記事ではPythonによる非再帰型Segment Treeの実装を紹介したいと思います。 前提知識を「ほぼ」1 必要としないようにSegment Treeの説明から入るので、もう知ってるという方は読み飛ばしてください。→非再帰型Segment Treeの実装 Segment Treeとは 通称セグ木、セグメント木などと呼ばれているデータ構造で、長さ $N$ の配列{$a_i$}$_{i=0}^{N-1}$に対して、モノイド $(S,•)$ に関する次の2つの操作がどちらも時間計算量が $O(logN)$ で行えます。 $update(i, x)$: $a_i$ を $x$ に変更する $query(l, r)$: 半開区間 $[l,r)$ に対して $a_l•a_{l+1}•$…$•a_{r-1}$ を返す モノイドとして、一般的な足し算を考えると、$q
Regular Expression Search with Suffix Arrays - Made of Bugs
But because the suffix array is sorted, we can find those entries efficiently by binary searching in the suffix array. The indexes then give us the location of the search string in the larger text. Towards Regular Expression Search 🔗︎ We begin with a large corpus of source code we want to do regular expression search over. During indexing, livegrep reads all of the source and flattens them toget
Tensor Developer Summit 2020
Tensor Developer Summit 2020 March 19–20, Berkeley Institute for Data Science, Berkeley In consideration of the health of our participants and the community, the Tensor Developer Summit has been postponed. A new event date will be announced later. Timeline 20 February: Registration has closed 24 February: Participant selection has been announced 19 March: The Tensor Developer Summit! About Tensor
A Look at Conflict-Free Replicated Data Types (CRDT)
You may have heard about CRDTs in the past few years if you are into distributed systems. In this post I will give a brief summary of what they are and what kind of guarantees they provide. In short, CRDTs are objects that can be updated without expensive synchronization/consensus and they are guaranteed to converge eventually if all concurrent updates are commutative (see below) and if all update
[1712.01208] The Case for Learned Index Structures
Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be
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https://web.stanford.edu/class/cs520/2020/abstracts/rathle.pdf
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