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Locality Sensitive Hashing (LSH) for Search with Shingling + MinHashing (Python)
2021-08-20 16:00:16 UTC
https://youtu.be/e_SBq3s20M8
e_SBq3s20M8
UCv83tO5cePwHMt1952IVVHw
e_SBq3s20M8-t1608.24
in more false negatives. So non candidate pairs where we should have candidate pairs. So it's just
1,608.24
1,622.8
Locality Sensitive Hashing (LSH) for Search with Shingling + MinHashing (Python)
2021-08-20 16:00:16 UTC
https://youtu.be/e_SBq3s20M8
e_SBq3s20M8
UCv83tO5cePwHMt1952IVVHw
e_SBq3s20M8-t1617.28
a case of balancing both of those. But that's everything for this video. I hope it's been useful.
1,617.28
1,638.8
Locality Sensitive Hashing (LSH) for Search with Shingling + MinHashing (Python)
2021-08-20 16:00:16 UTC
https://youtu.be/e_SBq3s20M8
e_SBq3s20M8
UCv83tO5cePwHMt1952IVVHw
e_SBq3s20M8-t1622.8
1,622.8
1,638.8
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t0.0
I welcome to this video on using the sentence transformers library to compare similarity
0
12.08
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t7.68
between different sentences. So this is going to be a pretty short video. I'm not going to go
7.68
18.4
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t12.08
really into depth. I'm just going to show you how to actually use the library. Now, if you do want
12.08
24.72
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t18.400000000000002
to go into a little more depth, I have another video that I'll be releasing just before this one.
18.4
33.28
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t24.72
And that will go into what is actually happening here, how we are calculating similarity or pulling
24.72
40.72
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t33.28
the how the BERT model that we'll be using is actually creating those embeddings and then how
33.28
46.08
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t40.72
we're actually calculating the similarity there. So if you're interested in that, go check it out.
40.72
53.52
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t46.96
Otherwise, if you just want to get a quick similarity score between two sentences,
46.96
60.96
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t53.52
this is probably the way to go. So we have these six sentences up here and this one,
53.52
67.84
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t61.68000000000001
three years later, the coffin was still full of jello. And this one, the person box was packed
61.68
76.24
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t67.84
with jelly many dozens of months later. They're saying the same thing, but the second one is
67.84
80.88
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t76.24000000000001
saying it in a way that most of us wouldn't normally say it. Instead of saying coffin,
76.24
85.6
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t80.88
we're saying person box instead of jello, we're saying jelly. I think that's kind of normal,
80.88
91.92
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t85.6
actually. And instead of years, we're saying dozens of months. So it's not really sharing
85.6
98.48
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t91.92
the same words, but we're going to see that we can actually find that these two sentences are
91.92
106.96
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t98.47999999999999
the most similar out of all of these. So we're taking those and we're going to be importing the
98.48
118.32
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t106.96
sentence transformers library. And we want to import the sentence transformer. And then from that,
106.96
126
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t119.11999999999999
we want to initialize a sentence transformer model. So we write sentence transformer.
119.12
132.72
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t127.83999999999999
And then in here, we're going to be using this model that I've already defined a model name,
127.84
142.08
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t132.72
which is a BERT base MLI mean tokens model. So initialize that. I need to rerun that.
132.72
148.88
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t143.44
So we have our model and I'll just show you really quickly. This model is coming from the
143.44
155.76
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t148.88
Hugging Face Transformers library behind sentence transformers. So this is the actual model we are
148.88
163.92
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t155.76
using. Now, first thing we do here is create our sentence vectors or sentence embeddings.
155.76
174.64
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t164.72
So we'll call this sentence Vets equals model and code. And all we need to do here is pass our
164.72
180
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t174.64
sentences. So we can pass a single sentence or a list of sentences. It's completely fine.
174.64
189.28
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t180.0
And then let's just have a quick look at what we have here. So you see that we have this big array.
180
199.6
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t190.08
And if we look at the shape, we see that we have a six by 768 array. So the six
190.08
209.36
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t200.16
refers to our six sentences here. And the 768 refers to the hidden state size within
200.16
215.12
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t209.36
the BERT model that we're using. So each one of these sentences is now being represented
209.36
223.12
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t215.12
by a dense vector containing 768 values. And that means that we already take those and
215.12
229.28
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t223.12
compare similarity between them. So to do that, we're going to be using the sklearn
223.12
240.56
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t229.28
implementation of cosine similarity, which we can import like this. So sklearn pairwise or metrics
229.28
250.8
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t244.96
pairwise. And we import cosine similarity.
244.96
260.4
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t250.8
And to calculate our cosine similarity, all we do is take that function. And inside here, we pass
250.8
266.88
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t261.2
our first sentence. So this three years later, the coffin is still full of jello. I want to pass
261.2
275.68
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t267.92
that sentence vector, which is just an index zero of our sentence Vets array.
267.92
283.92
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t275.68
And because we are extracting that single array value. So if we just have a look at this, you see
275.68
291.68
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t283.92
that we have a almost like a list of lists here. If we just extract this, we only get a list. So
283.92
296.16
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t291.68
what we want to do is actually keep that inside a list, otherwise we'll get dimension error.
291.68
304.32
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t297.6
And then we can also use the same method to extract the array. So we can extract the array
297.6
312.8
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t304.32
and then we do sentence Vets one onwards. So this will be the remaining sentences.
304.32
319.44
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t313.76
Okay, so let's take these or let's just bring them down here.
313.76
328.88
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t321.68
Calculate this. And we can see that our highest similarity by quite a bit is just 0.72.
321.68
338.08
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t328.88
Now, that means that between this sentence and this sentence, we have a similarity score of 0.72.
328.88
346.08
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t338.96
So clearly, it's working, it's scoring high similarity. And you can play around this and
338.96
353.44
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t346.08
and test multiple different words and sentences and just see how it works. But that's the easy
346.08
358.96
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t353.44
way of putting all this together. So I think it's really cool that we can do that so easily.
353.44
363.76
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t359.84
But I don't think there's really anything else to say about it. So
359.84
382.8
Sentence Similarity With Sentence-Transformers in Python
2021-05-05 15:00:09 UTC
https://youtu.be/Ey81KfQ3PQU
Ey81KfQ3PQU
UCv83tO5cePwHMt1952IVVHw
Ey81KfQ3PQU-t363.76
363.76
382.8
How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
UCv83tO5cePwHMt1952IVVHw
Vwq7Ucp9UCw-t0.0
Okay so in this video what we're going to do is actually index our data so at the moment we just
0
14.08
How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
UCv83tO5cePwHMt1952IVVHw
Vwq7Ucp9UCw-t5.92
have all of our paragraphs from Meditations by Marcus Aurelius and to do this we are going to
5.92
19.44
How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
UCv83tO5cePwHMt1952IVVHw
Vwq7Ucp9UCw-t14.08
be using the Elasticsearch document store. So of course if we're using Elasticsearch we first need
14.08
25.2
How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
UCv83tO5cePwHMt1952IVVHw
Vwq7Ucp9UCw-t19.44
to actually download and install it so I'm just going to take you through those steps now.
19.44
33.6
How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
UCv83tO5cePwHMt1952IVVHw
Vwq7Ucp9UCw-t25.2
And all we need to do is head on over to this website up here
25.2
43.84
How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
UCv83tO5cePwHMt1952IVVHw
Vwq7Ucp9UCw-t35.68
and it's elasticssearch.co and you can see the address just there. Now I'm going to follow the
35.68
49.28
How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
UCv83tO5cePwHMt1952IVVHw
Vwq7Ucp9UCw-t43.84
instructions for Windows but of course if you're on Linux or Mac just follow through it's very
43.84
59.76
How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
UCv83tO5cePwHMt1952IVVHw
Vwq7Ucp9UCw-t49.28
similar either way. So here we're going to install it on Windows using the MSI installer.
49.28
67.2
How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
UCv83tO5cePwHMt1952IVVHw
Vwq7Ucp9UCw-t61.120000000000005
So just scroll down here and we can see we can download the package from this link so download
61.12
75.76
How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
UCv83tO5cePwHMt1952IVVHw
Vwq7Ucp9UCw-t67.2
that and once you download it just open it and we'll see this window pop up. So once you see
67.2
82.8
How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
UCv83tO5cePwHMt1952IVVHw
Vwq7Ucp9UCw-t75.76
this window pop up we just go through with all of the default settings. So install as a service
75.76
89.2
How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
UCv83tO5cePwHMt1952IVVHw
Vwq7Ucp9UCw-t82.80000000000001
and continue through obviously if you do need to change anything change it but for me there's
82.8
97.28
How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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nothing here that I want to modify. Notice here we have the HTTP port and we're using 9200 we'll
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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be using that later. We just continue through here default settings and then we click install
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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and we just let that install. Okay so now that we've installed Elasticsearch we can go ahead
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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and actually check that it's running. So to do that we're going to import Python requests
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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and whenever we interact with Elasticsearch it's either going to be through Haystack or it will be
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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through the request library and we'll just interact with the Elasticsearch API. So to check the health
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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of our cluster so essentially check that it's actually up and running. What we need to do is
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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send a GET request to localhost and if you remember earlier we had it was port 9200. Of course if the
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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port on yours was different modify it this is just the default value and after this we need to reach
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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out to the cluster endpoint and we are checking the health and then we'll just format that as a
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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JSON. So what you should see here is we have our cluster which is Elasticsearch. It may have a different name
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
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if you modified it but by default it's Elasticsearch. The status is yellow which basically just means we
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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have one node up and running. You can have multiple nodes in Elasticsearch and for your cluster health
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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to be green it will expect your shards of indexes to have backup shards across different nodes and
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
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Vwq7Ucp9UCw-t201.92000000000002
obviously we can't do that if we only have one node but it's completely fine for us because we're
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
UCv83tO5cePwHMt1952IVVHw
Vwq7Ucp9UCw-t205.76000000000002
just in development. If you're in production yes you'd probably want it to have those backup shards.
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
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If none of that made any sense don't worry about it we really don't need to know any of that for what we're doing here.
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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Now what we can also do is we can check if we have any indices already.
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
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Now if I take a look at mine I will already have some indices set up which I've just set up prior
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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to recording this and to check that we go to localhost again
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
UCv83tO5cePwHMt1952IVVHw
Vwq7Ucp9UCw-t249.83999999999997
and this time we want to call the cat API which is what we would call whenever we want to see
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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data in a table human readable format rather than JSON and what we're checking here are the indices.
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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And we'll just add text on there so we can actually see that and this is quite messy so if we just
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
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print it instead look a bit cleaner. Okay so you can see I have these two indices you shouldn't I
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
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don't think have either of those no you won't have either of those so don't worry about that.
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
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Now what we are going to do is create a new index which will be called Aurelius and that is where we
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
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Vwq7Ucp9UCw-t296.55999999999995
will put our documents. Now to actually implement that we will be going through the Haystack library
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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which you can pip install farm Haystack and what we want to do is from Haystack dot document store
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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elastic search import
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
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elastic search document store. So this is our document store instance and of course
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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this is not aware of our elastic search instance we need to initialize that so
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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we'll store it in a variable called doc store
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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and all we write is elastic search document store.
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
Vwq7Ucp9UCw
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Now we need to initialize it with the parameters so it knows where to connect to our elastic search
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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instance. So to do that we write host and this is local host. Now if you have a username and
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How to Index Q&A Data With Haystack and Elasticsearch
2021-04-12 15:00:11 UTC
https://youtu.be/Vwq7Ucp9UCw
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password set which you don't by default you will need to enter them in here. I don't have any set so
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