You might want to consider the start of this tutorial.

In [1]:

%load_ext autoreload
%autoreload 2

In [2]:

from tf.fabric import Fabric
from tf.extra.bhsa import Bhsa

In [3]:

VERSION = '2017'
DATABASE = '~/github/etcbc'
BHSA = f'bhsa/tf/{VERSION}'
PARA = f'parallels/tf/{VERSION}'
TF = Fabric(locations=[DATABASE], modules=[BHSA, PARA], silent=True )

In [4]:

api = TF.load('', silent=True)
api.makeAvailableIn(globals())
B = Bhsa(api, 'search', version=VERSION)

Documentation: BHSA Feature docs BHSA API Text-Fabric API 5.0.4 Search Reference

This notebook online: NBViewer GitHub

Rough edges¶

It might be helpful to peek under the hood, especially when exploring searches that go slow.

If you went through the previous parts of the tutorial you have encountered cases where things come to a grinding halt.

Yet we can get a hunch of what is going on, even in those cases. For that, we use the lower-level search api S of Text-Fabric, and not the wrappers that the corpus specific B api provides.

The main difference is, that S.search() returns a generator of the results, whereas B.search() returns a list of the results. In fact, B.search() calls the generator function delivered by S.search() as often as needed.

For some queries, the fetching of results is quite costly, so costly that we do not want to fetch all results up-front. Rather we want to fetch a few, to see how it goes. In these cases, directly using S.search() is preferred over B.search().

In [5]:

query = '''
book
  chapter
    verse
      phrase det=und
        word lex=>LHJM/
'''

Study¶

First we call S.study(query).

The syntax will be checked, features loaded, the search space will be set up, narrowed down, and the fetching of results will be prepared, but not yet executed.

In order to make the query a bit more interesting, we lift the constraint that the results must be in Genesis 1-2.

In [6]:

S.study(query)

   |     0.00s Feature overview: 109 for nodes; 8 for edges; 1 configs; 7 computed
  0.00s Checking search template ...
  0.19s Setting up search space for 5 objects ...
  1.23s Constraining search space with 4 relations ...
  1.24s Setting up retrieval plan ...
  1.26s Ready to deliver results from 2735 nodes
Iterate over S.fetch() to get the results
See S.showPlan() to interpret the results

Before we rush to the results, lets have a look at the plan.

In [7]:

S.showPlan()

  5.16s The results are connected to the original search template as follows:
 0     
 1 R0  book
 2 R1    chapter
 3 R2      verse
 4 R3        phrase det=und
 5 R4          word lex=>LHJM/
 6

Here you see already what your results will look like. Each result r is a tuple of nodes:

(R0, R1, R2, R3, R4)

that instantiate the objects in your template.

In case you are curious, you can get details about the search space as well:

In [8]:

S.showPlan(details=True)

Search with 5 objects and 4 relations
Results are instantiations of the following objects:
node  0-book                              (    29   choices)
node  1-chapter                           (   329   choices)
node  2-verse                             (   754   choices)
node  3-phrase                            (   805   choices)
node  4-word                              (   818   choices)
Instantiations are computed along the following relations:
node                      0-book          (    29   choices)
edge  0-book          [[  1-chapter       (    15.0 choices)
edge  1-chapter       [[  2-verse         (     2.4 choices)
edge  2-verse         [[  3-phrase        (     1.0 choices)
edge  3-phrase        [[  4-word          (     1.0 choices)
  7.44s The results are connected to the original search template as follows:
 0     
 1 R0  book
 2 R1    chapter
 3 R2      verse
 4 R3        phrase det=und
 5 R4          word lex=>LHJM/
 6

The part about the nodes shows you how many possible instantiations for each object in your template has been found. These are not results yet, because only combinations of instantiations that satisfy all constraints are results.

The constraints come from the relations between the objects that you specified. In this case, there is only an implicit relation: embedding [[. Later on we'll examine all basic relations.

The part about the edges shows you the constraints, and in what order they will be computed when stitching results together. In this case the order is exactly the order by which the relations appear in the template, but that will not always be the case. Text-Fabric spends some time and ingenuity to find out an optimal stitch plan. Fetching results is like selecting a node, stitching it to another node with an edge, and so on, until a full stitch of nodes intersects with all the node sets from which they must be chosen (the yarns).

Fetching results may take time.

For some queries, it can take a large amount of time to walk through all results. Even worse, it may happen that it takes a large amount of time before getting the first result. During stitching, many stitchings will be tried and fail before they can be completed.

This has to do with search strategies on the one hand, and the very likely possibility to encounter pathological search patterns, which have billions of results, mostly unintended. For example, a simple query that asks for 5 words in the Hebrew Bible without further constraints, will have 425,000 to the power of 5 results. That is 10-e28 (a one with 28 zeros), roughly the number of molecules in a few hundred liters of air. That may not sound much, but it is 10,000 times the amount of bytes that can be currently stored on the whole Internet.

Text-Fabric search is not yet done with finding optimal search strategies, and I hope to refine its arsenal of methods in the future, depending on what you report.

Counting results¶

It is always a good idea to get a feel for the amount of results, before you dive into them head-on.

In [9]:

S.count(progress=1, limit=5)

  0.00s Counting results per 1 up to 5 ...
   |     0.01s 1
   |     0.01s 2
   |     0.01s 3
   |     0.01s 4
   |     0.01s 5
  0.02s Done: 5 results

We asked for 5 results in total, with a progress message for every one. That was a bit conservative.

In [10]:

S.count(progress=100, limit=500)

  0.00s Counting results per 100 up to 500 ...
   |     0.01s 100
   |     0.03s 200
   |     0.05s 300
   |     0.08s 400
   |     0.12s 500
  0.12s Done: 500 results

Still pretty quick, now we want to count all results.

In [11]:

S.count(progress=200, limit=-1)

  0.00s Counting results per 200 up to  the end of the results ...
   |     0.02s 200
   |     0.07s 400
   |     0.11s 600
   |     0.13s 800
  0.14s Done: 818 results

Fetching results¶

It is time to see something of those results.

In [12]:

S.fetch(limit=10)

Out[12]:

((426585, 426624, 1414190, 651505, 4),
 (426585, 426624, 1414191, 651515, 26),
 (426585, 426624, 1414192, 651520, 34),
 (426585, 426624, 1414193, 651528, 42),
 (426585, 426624, 1414193, 651534, 50),
 (426585, 426624, 1414194, 651538, 60),
 (426585, 426624, 1414195, 651554, 81),
 (426585, 426624, 1414196, 651564, 97),
 (426585, 426624, 1414197, 651578, 127),
 (426585, 426624, 1414198, 651590, 142))

Not very informative. Just a quick observation: look at the last column. These are the result nodes for the word part in the query, indicated as R7 by showPlan() before. And indeed, they are all below 425,000, the number of words in the Hebrew Bible.

Nevertheless, we want to glean a bit more information off them.

In [13]:

for r in S.fetch(limit=10):
    print(S.glean(r))

  Genesis 1:1 phrase[אֱלֹהִ֑ים ] אֱלֹהִ֑ים 
  Genesis 1:2 phrase[ר֣וּחַ אֱלֹהִ֔ים ] אֱלֹהִ֔ים 
  Genesis 1:3 phrase[אֱלֹהִ֖ים ] אֱלֹהִ֖ים 
  Genesis 1:4 phrase[אֱלֹהִ֛ים ] אֱלֹהִ֛ים 
  Genesis 1:4 phrase[אֱלֹהִ֔ים ] אֱלֹהִ֔ים 
  Genesis 1:5 phrase[אֱלֹהִ֤ים׀ ] אֱלֹהִ֤ים׀ 
  Genesis 1:6 phrase[אֱלֹהִ֔ים ] אֱלֹהִ֔ים 
  Genesis 1:7 phrase[אֱלֹהִים֮ ] אֱלֹהִים֮ 
  Genesis 1:8 phrase[אֱלֹהִ֛ים ] אֱלֹהִ֛ים 
  Genesis 1:9 phrase[אֱלֹהִ֗ים ] אֱלֹהִ֗ים

Caution¶

It is not possible to do len(S.fetch()).

Because fetch() is a generator, not a list. It will deliver a result every time it is being asked and for as long as there are results, but it does not know in advance how many there will be.

Fetching a result can be costly, because due to the constraints, a lot of possibilities

may have to be tried and rejected before a the next result is found.

That is why you often see results coming in at varying speeds when counting them.

We can also use B.table() to make a list of results. This function is part of the Bhsa API, not of the generic Text-Fabric machinery, as opposed to S.glean().

So, you can use S.glean() for every Text-Fabric corpus, but the output is still not very nice. B.table() gives much nicer output, but works only for the BHSA corpus.

We put hyperlinks to SHEBANQ under column 3.

In [14]:

B.table(S.fetch(limit=10), linked=3)

n	book	chapter	verse	phrase	word
1	Genesis	Genesis 1	Genesis 1:1	אֱלֹהִ֑ים	אֱלֹהִ֑ים
2	Genesis	Genesis 1	Genesis 1:2	ר֣וּחַ אֱלֹהִ֔ים	אֱלֹהִ֔ים
3	Genesis	Genesis 1	Genesis 1:3	אֱלֹהִ֖ים	אֱלֹהִ֖ים
4	Genesis	Genesis 1	Genesis 1:4	אֱלֹהִ֛ים	אֱלֹהִ֛ים
5	Genesis	Genesis 1	Genesis 1:4	אֱלֹהִ֔ים	אֱלֹהִ֔ים
6	Genesis	Genesis 1	Genesis 1:5	אֱלֹהִ֤ים׀	אֱלֹהִ֤ים׀
7	Genesis	Genesis 1	Genesis 1:6	אֱלֹהִ֔ים	אֱלֹהִ֔ים
8	Genesis	Genesis 1	Genesis 1:7	אֱלֹהִים֮	אֱלֹהִים֮
9	Genesis	Genesis 1	Genesis 1:8	אֱלֹהִ֛ים	אֱלֹהִ֛ים
10	Genesis	Genesis 1	Genesis 1:9	אֱלֹהִ֗ים	אֱלֹהִ֗ים

Slow queries¶

The search template above has some pretty tight constraints on one of its objects, so the amount of data to deal with is pretty limited.

If the constraints are weak, search may become slow.

For example:

In [15]:

query = '''
# test
# verse book=Genesis chapter=2 verse=25
verse
  clause
                                 
    p1:phrase
        w1:word
        w3:word
        w1 < w3

    p2:phrase
        w2:word
        w1 < w2 
        w3 > w2
    
    p1 < p2   
'''

A couple of remarks you may have encountered before.

some objects have got a name
there are additional relations specified between named objects
< means: comes before, and >: comes after in the canonical order for nodes, which for words means: comes textually before/after, but for other nodes the meaning is explained here
later on we describe those relations in more detail

Note on order

Look at the words w1 and w3 below phrase p1. Although in the template w1 comes before w3, this is not translated in a search constraint of the same nature.

Order between objects in a template is never significant, only embedding is.

Because order is not significant, you have to specify order yourself, using relations.

It turns out that this is better than the other way around. In MQL order is significant, and it is very difficult to search for w1 and w2 in any order. Especially if your are looking for more than 2 complex objects with lots of feature conditions, your search template would explode if you had to spell out all possible permutations. See the example of Reinoud Oosting below.

Note on gaps

Look at the phrases p1 and p2. We do not specify an order here, only that they are different. In order to prevent duplicated searches with p1 and p2 interchanged, we even stipulate that p1 < p2. There are many spatial relationships possible between different objects. In many cases, neither the one comes before the other, nor vice versa. They can overlap, one can occur in a gap of the other, they can be completely disjoint and interleaved, etc.

In [16]:

S.study(query)

   |     0.00s Feature overview: 109 for nodes; 8 for edges; 1 configs; 7 computed
  0.00s Checking search template ...
  0.00s Setting up search space for 7 objects ...
  0.45s Constraining search space with 10 relations ...
  0.48s Setting up retrieval plan ...
  0.53s Ready to deliver results from 1897440 nodes
Iterate over S.fetch() to get the results
See S.showPlan() to interpret the results

That was quick! Well, Text-Fabric knows that narrowing down the search space in this case would take ages, without resulting in a significantly shrunken space. So it skips doing so for most constraints.

Let us see the plan, with details.

In [17]:

S.showPlan(details=True)

Search with 7 objects and 10 relations
Results are instantiations of the following objects:
node  0-verse                             ( 23213   choices)
node  1-clause                            ( 88101   choices)
node  2-phrase                            (253187   choices)
node  3-word                              (426584   choices)
node  4-word                              (426584   choices)
node  5-phrase                            (253187   choices)
node  6-word                              (426584   choices)
Instantiations are computed along the following relations:
node                      0-verse         ( 23213   choices)
edge  0-verse         [[  1-clause        (     3.9 choices)
edge  1-clause        [[  5-phrase        (     2.6 choices)
edge  5-phrase        [[  6-word          (     1.0 choices)
edge  1-clause        [[  2-phrase        (     2.9 choices)
edge  2-phrase        <   5-phrase        (126593.5 choices)
edge  2-phrase        [[  4-word          (     1.1 choices)
edge  4-word          >   6-word          (213292.0 choices)
edge  2-phrase        [[  3-word          (     2.2 choices)
edge  3-word          <   6-word          (213292.0 choices)
edge  3-word          <   4-word          (213292.0 choices)
  2.55s The results are connected to the original search template as follows:
 0     
 1     # test
 2     # verse book=Genesis chapter=2 verse=25
 3 R0  verse
 4 R1    clause
 5                                      
 6 R2      p1:phrase
 7 R3          w1:word
 8 R4          w3:word
 9             w1 < w3
10     
11 R5      p2:phrase
12 R6          w2:word
13             w1 < w2 
14             w3 > w2
15         
16         p1 < p2   
17

As you see, we have a hefty search space here. Let us play with the count() function.

In [18]:

S.count(progress=10, limit=100)

  0.00s Counting results per 10 up to 100 ...
   |     0.08s 10
   |     0.09s 20
   |     0.09s 30
   |     0.11s 40
   |     0.11s 50
   |     0.12s 60
   |     0.13s 70
   |     0.13s 80
   |     0.13s 90
   |     0.14s 100
  0.14s Done: 100 results

We can be bolder than this!

In [19]:

S.count(progress=100, limit=1000)

  0.00s Counting results per 100 up to 1000 ...
   |     0.11s 100
   |     0.14s 200
   |     0.15s 300
   |     0.30s 400
   |     0.35s 500
   |     0.35s 600
   |     0.41s 700
   |     0.52s 800
   |     0.54s 900
   |     0.67s 1000
  0.68s Done: 1000 results

OK, not too bad, but note that it takes a big fraction of a second to get just 100 results.

Now let us go for all of them by the thousand.

In [20]:

S.count(progress=1000, limit=-1)

  0.00s Counting results per 1000 up to  the end of the results ...
   |     0.63s 1000
   |     0.98s 2000
   |     1.35s 3000
   |     1.70s 4000
   |     2.14s 5000
   |     3.08s 6000
   |     4.81s 7000
  5.57s Done: 7618 results

See? This is substantial work.

In [21]:

B.table(S.fetch(limit=10))

n	verse	clause	phrase	word	word	phrase	word
1	Genesis 2:25	וַיִּֽהְי֤וּ שְׁנֵיהֶם֙ עֲרוּמִּ֔ים הָֽאָדָ֖ם וְאִשְׁתֹּ֑ו	שְׁנֵיהֶם֙ הָֽאָדָ֖ם וְאִשְׁתֹּ֑ו	שְׁנֵיהֶם֙	הָֽ	עֲרוּמִּ֔ים	עֲרוּמִּ֔ים
2	Genesis 2:25	וַיִּֽהְי֤וּ שְׁנֵיהֶם֙ עֲרוּמִּ֔ים הָֽאָדָ֖ם וְאִשְׁתֹּ֑ו	שְׁנֵיהֶם֙ הָֽאָדָ֖ם וְאִשְׁתֹּ֑ו	שְׁנֵיהֶם֙	אָדָ֖ם	עֲרוּמִּ֔ים	עֲרוּמִּ֔ים
3	Genesis 2:25	וַיִּֽהְי֤וּ שְׁנֵיהֶם֙ עֲרוּמִּ֔ים הָֽאָדָ֖ם וְאִשְׁתֹּ֑ו	שְׁנֵיהֶם֙ הָֽאָדָ֖ם וְאִשְׁתֹּ֑ו	שְׁנֵיהֶם֙	וְ	עֲרוּמִּ֔ים	עֲרוּמִּ֔ים
4	Genesis 2:25	וַיִּֽהְי֤וּ שְׁנֵיהֶם֙ עֲרוּמִּ֔ים הָֽאָדָ֖ם וְאִשְׁתֹּ֑ו	שְׁנֵיהֶם֙ הָֽאָדָ֖ם וְאִשְׁתֹּ֑ו	שְׁנֵיהֶם֙	אִשְׁתֹּ֑ו	עֲרוּמִּ֔ים	עֲרוּמִּ֔ים
5	Genesis 4:4	וְהֶ֨בֶל הֵבִ֥יא גַם־ה֛וּא מִבְּכֹרֹ֥ות צֹאנֹ֖ו וּמֵֽחֶלְבֵהֶ֑ן	הֶ֨בֶל גַם־ה֛וּא	הֶ֨בֶל	גַם־	הֵבִ֥יא	הֵבִ֥יא
6	Genesis 4:4	וְהֶ֨בֶל הֵבִ֥יא גַם־ה֛וּא מִבְּכֹרֹ֥ות צֹאנֹ֖ו וּמֵֽחֶלְבֵהֶ֑ן	הֶ֨בֶל גַם־ה֛וּא	הֶ֨בֶל	ה֛וּא	הֵבִ֥יא	הֵבִ֥יא
7	Genesis 10:21	גַּם־ה֑וּא אֲבִי֙ כָּל־בְּנֵי־עֵ֔בֶר אֲחִ֖י יֶ֥פֶת הַגָּדֹֽול׃	גַּם־ה֑וּא אֲחִ֖י יֶ֥פֶת הַגָּדֹֽול׃	גַּם־	אֲחִ֖י	אֲבִי֙ כָּל־בְּנֵי־עֵ֔בֶר	עֵ֔בֶר
8	Genesis 10:21	גַּם־ה֑וּא אֲבִי֙ כָּל־בְּנֵי־עֵ֔בֶר אֲחִ֖י יֶ֥פֶת הַגָּדֹֽול׃	גַּם־ה֑וּא אֲחִ֖י יֶ֥פֶת הַגָּדֹֽול׃	ה֑וּא	אֲחִ֖י	אֲבִי֙ כָּל־בְּנֵי־עֵ֔בֶר	עֵ֔בֶר
9	Genesis 10:21	גַּם־ה֑וּא אֲבִי֙ כָּל־בְּנֵי־עֵ֔בֶר אֲחִ֖י יֶ֥פֶת הַגָּדֹֽול׃	גַּם־ה֑וּא אֲחִ֖י יֶ֥פֶת הַגָּדֹֽול׃	גַּם־	יֶ֥פֶת	אֲבִי֙ כָּל־בְּנֵי־עֵ֔בֶר	עֵ֔בֶר
10	Genesis 10:21	גַּם־ה֑וּא אֲבִי֙ כָּל־בְּנֵי־עֵ֔בֶר אֲחִ֖י יֶ֥פֶת הַגָּדֹֽול׃	גַּם־ה֑וּא אֲחִ֖י יֶ֥פֶת הַגָּדֹֽול׃	ה֑וּא	יֶ֥פֶת	אֲבִי֙ כָּל־בְּנֵי־עֵ֔בֶר	עֵ֔בֶר

Hand-coding¶

As a check, here is some code that looks for basically the same phenomenon: a phrase within the gap of another phrase. It does not use search, and it gets a bit more focused results, in half the time compared to the search with the template.

Hint

If you are comfortable with programming, and what you look for is fairly generic, you may be better off without search, provided you can translate your insight in the data into an effective procedure within Text-Fabric. But wait till we are completely done with this example!

In [22]:

indent(reset=True)
info('Getting gapped phrases')
results = []
for v in F.otype.s('verse'):
    for c in L.d(v, otype='clause'):
        ps = L.d(c, otype='phrase')
        first = {}
        last = {}
        slots = {}
        # make index of phrase boundaries
        for p in ps:
            words = L.d(p, otype='word')
            first[p] = words[0]
            last[p] = words[-1]
            slots[p] = set(words)
        for p1 in ps:
            for p2 in ps:
                if p2 < p1: continue
                if len(slots[p1] & slots[p2]) != 0: continue
                if first[p1] < first[p2] and last[p2] < last[p1]:
                    results.append((v, c, p1, p2, first[p1], first[p2], last[p2], last[p1]))
info('{} results'.format(len(results)))

  0.00s Getting gapped phrases
  3.04s 368 results

Pretty printing¶

We can use the pretty printing of B.table() and B.show() here as well, even though we have not used search!

Not that you can show the node numbers. In this case it helps to see where the gaps are.

In [23]:

B.table(results, withNodes=True, end=10)
B.show(results, start=1, end=1)

n	verse	clause	phrase	phrase	word	word	word	word
1	Genesis 2:25 1414245	וַיִּֽהְי֤וּ שְׁנֵיהֶם֙ עֲרוּמִּ֔ים הָֽאָדָ֖ם וְאִשְׁתֹּ֑ו 427767	שְׁנֵיהֶם֙ הָֽאָדָ֖ם וְאִשְׁתֹּ֑ו 652147	עֲרוּמִּ֔ים 652148	שְׁנֵיהֶם֙ 1159	עֲרוּמִּ֔ים 1160	עֲרוּמִּ֔ים 1160	אִשְׁתֹּ֑ו 1164
2	Genesis 4:4 1414273	וְהֶ֨בֶל הֵבִ֥יא גַם־ה֛וּא מִבְּכֹרֹ֥ות צֹאנֹ֖ו וּמֵֽחֶלְבֵהֶ֑ן 427889	הֶ֨בֶל גַם־ה֛וּא 652504	הֵבִ֥יא 652505	הֶ֨בֶל 1720	הֵבִ֥יא 1721	הֵבִ֥יא 1721	ה֛וּא 1723
3	Genesis 10:21 1414445	גַּם־ה֑וּא אֲבִי֙ כָּל־בְּנֵי־עֵ֔בֶר אֲחִ֖י יֶ֥פֶת הַגָּדֹֽול׃ 428386	גַּם־ה֑וּא אֲחִ֖י יֶ֥פֶת הַגָּדֹֽול׃ 654102	אֲבִי֙ כָּל־בְּנֵי־עֵ֔בֶר 654103	גַּם־ 4819	אֲבִי֙ 4821	עֵ֔בֶר 4824	גָּדֹֽול׃ 4828
4	Genesis 12:17 1414505	וַיְנַגַּ֨ע יְהוָ֧ה׀ אֶת־פַּרְעֹ֛ה נְגָעִ֥ים גְּדֹלִ֖ים וְאֶת־בֵּיתֹ֑ו עַל־דְּבַ֥ר שָׂרַ֖י אֵ֥שֶׁת אַבְרָֽם׃ 428569	אֶת־פַּרְעֹ֛ה וְאֶת־בֵּיתֹ֑ו 654678	נְגָעִ֥ים גְּדֹלִ֖ים 654679	אֶת־ 5803	נְגָעִ֥ים 5805	גְּדֹלִ֖ים 5806	בֵּיתֹ֑ו 5809
5	Genesis 13:1 1414509	וַיַּעַל֩ אַבְרָ֨ם מִמִּצְרַ֜יִם ה֠וּא וְאִשְׁתֹּ֧ו וְכָל־הַנֶּֽגְבָּה׃ 428585	אַבְרָ֨ם ה֠וּא וְאִשְׁתֹּ֧ו וְכָל־ 654725	מִמִּצְרַ֜יִם 654726	אַבְרָ֨ם 5868	מִ 5869	מִּצְרַ֜יִם 5870	כָל־ 5875
6	Genesis 14:16 1414542	וְגַם֩ אֶת־לֹ֨וט אָחִ֤יו וּרְכֻשֹׁו֙ הֵשִׁ֔יב וְגַ֥ם אֶת־הַנָּשִׁ֖ים וְאֶת־הָעָֽם׃ 428692	גַם֩ אֶת־לֹ֨וט אָחִ֤יו וּרְכֻשֹׁו֙ וְגַ֥ם אֶת־הַנָּשִׁ֖ים וְאֶת־הָעָֽם׃ 655061	הֵשִׁ֔יב 655062	גַם֩ 6515	הֵשִׁ֔יב 6521	הֵשִׁ֔יב 6521	עָֽם׃ 6530
7	Genesis 17:7 1414594	לִהְיֹ֤ות לְךָ֙ לֵֽאלֹהִ֔ים וּֽלְזַרְעֲךָ֖ אַחֲרֶֽיךָ׃ 428886	לְךָ֙ וּֽלְזַרְעֲךָ֖ אַחֲרֶֽיךָ׃ 655642	לֵֽאלֹהִ֔ים 655643	לְךָ֙ 7431	לֵֽ 7432	אלֹהִ֔ים 7433	אַחֲרֶֽיךָ׃ 7437
8	Genesis 19:4 1414651	וְאַנְשֵׁ֨י הָעִ֜יר אַנְשֵׁ֤י סְדֹם֙ נָסַ֣בּוּ עַל־הַבַּ֔יִת מִנַּ֖עַר וְעַד־זָקֵ֑ן כָּל־הָעָ֖ם מִקָּצֶֽה׃ 429128	אַנְשֵׁ֨י הָעִ֜יר אַנְשֵׁ֤י סְדֹם֙ מִנַּ֖עַר וְעַד־זָקֵ֑ן כָּל־הָעָ֖ם מִקָּצֶֽה׃ 656353	נָסַ֣בּוּ 656354	אַנְשֵׁ֨י 8502	נָסַ֣בּוּ 8507	נָסַ֣בּוּ 8507	קָּצֶֽה׃ 8520
9	Genesis 19:4 1414651	וְאַנְשֵׁ֨י הָעִ֜יר אַנְשֵׁ֤י סְדֹם֙ נָסַ֣בּוּ עַל־הַבַּ֔יִת מִנַּ֖עַר וְעַד־זָקֵ֑ן כָּל־הָעָ֖ם מִקָּצֶֽה׃ 429128	אַנְשֵׁ֨י הָעִ֜יר אַנְשֵׁ֤י סְדֹם֙ מִנַּ֖עַר וְעַד־זָקֵ֑ן כָּל־הָעָ֖ם מִקָּצֶֽה׃ 656353	עַל־הַבַּ֔יִת 656355	אַנְשֵׁ֨י 8502	עַל־ 8508	בַּ֔יִת 8510	קָּצֶֽה׃ 8520
10	Genesis 22:3 1414740	וַיִּקַּ֞ח אֶת־שְׁנֵ֤י נְעָרָיו֙ אִתֹּ֔ו וְאֵ֖ת יִצְחָ֣ק בְּנֹ֑ו 429497	אֶת־שְׁנֵ֤י נְעָרָיו֙ וְאֵ֖ת יִצְחָ֣ק בְּנֹ֑ו 657505	אִתֹּ֔ו 657506	אֶת־ 10284	אִתֹּ֔ו 10287	אִתֹּ֔ו 10287	בְּנֹ֑ו 10291