multi token patterns

I might be working on a tutorial on this project, so I figured I'd double-check explicitly: are multi-token phrases supported? My impression is that they're not, and that's totally fine, but I just wanted to make sure.
This example:
import spacy
from spacy import displacy
import concise_concepts

data = {
    "fruit": ["apple", "pear", "orange"],
    "vegetable": ["broccoli", "spinach", "tomato"],
    "meat": ["beef", "pork", "fish", "lamb"],
    "utensil": ["large oven", "warm stove", "big knife"]
}

text = """
    Heat the oil in a large pan and add the Onion, celery and carrots.
    Then, cook over a medium–low heat for 10 minutes, or until softened.
    Add the courgette, garlic, red peppers and oregano and cook for 2–3 minutes.
    Later, add some oranges and chickens. """

nlp = spacy.load("en_core_web_lg", disable=["ner"])

# ent_score for entity condifence scoring
nlp.add_pipe("concise_concepts", config={"data": data, "ent_score": True})
doc = nlp(text)

options = {"colors": {"fruit": "darkorange", "vegetable": "limegreen", "meat": "salmon", "utensil": "gray"},
           "ents": ["fruit", "vegetable", "meat", "utensil"]}

ents = doc.ents
for ent in ents:
    new_label = f"{ent.label_} ({float(ent._.ent_score):.0%})"
    options["colors"][new_label] = options["colors"].get(ent.label_.lower(), None)
    options["ents"].append(new_label)
    ent.label_ = new_label
doc.ents = ents

displacy.render(doc, style="ent", options=options)

Yields this error:
word ´large oven´ from key ´utensil´ not present in vector model
word ´warm stove´ from key ´utensil´ not present in vector model
word ´big knife´ from key ´utensil´ not present in vector model
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
Input In [4], in <cell line: 21>()
     18 nlp = spacy.load("en_core_web_lg", disable=["ner"])
     20 # ent_score for entity condifence scoring
---> 21 nlp.add_pipe("concise_concepts", config={"data": data, "ent_score": True})
     22 doc = nlp(text)
     24 options = {"colors": {"fruit": "darkorange", "vegetable": "limegreen", "meat": "salmon", "utensil": "gray"},
     25            "ents": ["fruit", "vegetable", "meat", "utensil"]}

File ~/Development/prodigy-demos/venv/lib/python3.8/site-packages/spacy/language.py:795, in Language.add_pipe(self, factory_name, name, before, after, first, last, source, config, raw_config, validate)
    787     if not self.has_factory(factory_name):
    788         err = Errors.E002.format(
    789             name=factory_name,
    790             opts=", ".join(self.factory_names),
   (...)
    793             lang_code=self.lang,
    794         )
--> 795     pipe_component = self.create_pipe(
    796         factory_name,
    797         name=name,
    798         config=config,
    799         raw_config=raw_config,
    800         validate=validate,
    801     )
    802 pipe_index = self._get_pipe_index(before, after, first, last)
    803 self._pipe_meta[name] = self.get_factory_meta(factory_name)

File ~/Development/prodigy-demos/venv/lib/python3.8/site-packages/spacy/language.py:674, in Language.create_pipe(self, factory_name, name, config, raw_config, validate)
    671 cfg = {factory_name: config}
    672 # We're calling the internal _fill here to avoid constructing the
    673 # registered functions twice
--> 674 resolved = registry.resolve(cfg, validate=validate)
    675 filled = registry.fill({"cfg": cfg[factory_name]}, validate=validate)["cfg"]
    676 filled = Config(filled)

File ~/Development/prodigy-demos/venv/lib/python3.8/site-packages/thinc/config.py:747, in registry.resolve(cls, config, schema, overrides, validate)
    738 @classmethod
    739 def resolve(
    740     cls,
   (...)
    745     validate: bool = True,
    746 ) -> Dict[str, Any]:
--> 747     resolved, _ = cls._make(
    748         config, schema=schema, overrides=overrides, validate=validate, resolve=True
    749     )
    750     return resolved

File ~/Development/prodigy-demos/venv/lib/python3.8/site-packages/thinc/config.py:796, in registry._make(cls, config, schema, overrides, resolve, validate)
    794 if not is_interpolated:
    795     config = Config(orig_config).interpolate()
--> 796 filled, _, resolved = cls._fill(
    797     config, schema, validate=validate, overrides=overrides, resolve=resolve
    798 )
    799 filled = Config(filled, section_order=section_order)
    800 # Check that overrides didn't include invalid properties not in config

File ~/Development/prodigy-demos/venv/lib/python3.8/site-packages/thinc/config.py:868, in registry._fill(cls, config, schema, validate, resolve, parent, overrides)
    865     getter = cls.get(reg_name, func_name)
    866     # We don't want to try/except this and raise our own error
    867     # here, because we want the traceback if the function fails.
--> 868     getter_result = getter(*args, **kwargs)
    869 else:
    870     # We're not resolving and calling the function, so replace
    871     # the getter_result with a Promise class
    872     getter_result = Promise(
    873         registry=reg_name, name=func_name, args=args, kwargs=kwargs
    874     )

File ~/Development/prodigy-demos/venv/lib/python3.8/site-packages/concise_concepts/__init__.py:47, in make_concise_concepts(nlp, name, data, topn, model_path, word_delimiter, ent_score, exclude_pos, exclude_dep, include_compound_words, case_sensitive)
      9 @Language.factory(
     10     "concise_concepts",
     11     default_config={
   (...)
     45     case_sensitive: bool,
     46 ):
---> 47     return Conceptualizer(
     48         nlp=nlp,
     49         name=name,
     50         data=data,
     51         topn=topn,
     52         model_path=model_path,
     53         word_delimiter=word_delimiter,
     54         ent_score=ent_score,
     55         exclude_pos=exclude_pos,
     56         exclude_dep=exclude_dep,
     57         include_compound_words=include_compound_words,
     58         case_sensitive=case_sensitive,
     59     )

File ~/Development/prodigy-demos/venv/lib/python3.8/site-packages/concise_concepts/conceptualizer/Conceptualizer.py:95, in Conceptualizer.__init__(self, nlp, name, data, topn, model_path, word_delimiter, ent_score, exclude_pos, exclude_dep, include_compound_words, case_sensitive)
     93 else:
     94     self.match_key = "LEMMA"
---> 95 self.run()
     96 self.data_upper = {k.upper(): v for k, v in data.items()}

File ~/Development/prodigy-demos/venv/lib/python3.8/site-packages/concise_concepts/conceptualizer/Conceptualizer.py:101, in Conceptualizer.run(self)
     99 self.determine_topn()
    100 self.set_gensim_model()
--> 101 self.verify_data()
    102 self.expand_concepts()
    103 self.verify_data(verbose=False)

File ~/Development/prodigy-demos/venv/lib/python3.8/site-packages/concise_concepts/conceptualizer/Conceptualizer.py:193, in Conceptualizer.verify_data(self, verbose)
    188                 logger.warning(
    189                     f"word ´{word}´ from key ´{key}´ not present in vector"
    190                     " model"
    191                 )
    192     verified_data[key] = verified_values
--> 193     assert len(
    194         verified_values
    195     ), f"None of the entries for key {key} are present in the vector model"
    196 self.data = deepcopy(verified_data)
    197 self.original_data = deepcopy(self.data)

AssertionError: None of the entries for key utensil are present in the vector model

@koaning I closed this for now. Will review the solution after your blogpost.

It will be a two-part thing, the first part will be on YouTube. The thing about the solution though is that it is already implemented in another library 😉

That library being? 😅 or are you talking about the doc.noun_chunks part?

https://twitter.com/explosion_ai/status/1579840012174204928

Cool. I´ll do some testing and look into a way to integrate this.


A way to extract differentially expressed proteins

Hello,
I'm doing some work that requires me to find up and down-regulated proteins. Is there a way to extract the proteins selected from either of the volcano plots and export them into a text file or an excel sheet?
Thank you

Hi,
The data folder should have a file called “protein_summarized_data.tsv”. This is the full results file for the protein analysis. The statistical results for the comparisons should be at the end of the columns. You will need the “logFC” columns to get the up and down list of proteins. Positive values are proteins up-regulated in the group listed first in the contrast_matrix file. Negative values will be the proteins down-regulated in the first group of the contrast_matrix file.
Best,
Stephanie
From: BadassBagle<EMAIL_ADDRESS>Sent: Wednesday, June 17, 2020 12:57 PM
To: ByrumLab/ProteoViz<EMAIL_ADDRESS>Cc: Subscribed<EMAIL_ADDRESS>Subject: [ByrumLab/ProteoViz] A way to extract differentially expressed proteins (#2)
Hello,
I'm doing some work that requires me to find up and down-regulated proteins. Is there a way to extract the proteins selected from either of the volcano plots and export them into a text file or an excel sheet?
Thank you
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Hi,
Respected Stephanie,
I am new to proteomics analysis. Could you please help me in doing differential expression analysis. Below are the attached "proteinGroups.txt" file and "evidence.txt" file. I don't know how to create an annotation file with these files.
proteinGroups.txt
evidence.txt


Add reader mode to all pages

Currently only works for the about page so it seems

It's a browser decission apperently, can't mark up any better then we already do..


The MyFW_MNIST.ipynb in Lesson 4 requires the file: /main/data/mnist.pkl.gz, but it is not present in the directory.

https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/04-OwnFramework/lab/MyFW_MNIST.ipynb
The file mentioned in the following code in this notebook: /main/data/mnist.pkl.gz does not exist in this project.
!rm *.pkl
!wget https://raw.githubusercontent.com/microsoft/AI-For-Beginners/main/data/mnist.pkl.gz
!gzip -d mnist.pkl.gz

i faced the same issue and i downloaded the file from another place
https://github.com/mnielsen/neural-networks-and-deep-learning/blob/master/data/mnist.pkl.gz


Procedure for reviews / merges

Hey all - we should come up with some guidelines for how to get commits merged. Now that BinderHub has grown, we need to make sure that decisions are made w/ community input and without jumping too far ahead of everybody.
I propose something like the following (as a starting point for discussion):

PRs that touch a non-trivial amount of code require 2 approvals. If there are 2 approvals then anybody can merge.
PRs that touch only documentation require 1 approval for merge.
PRs that touch a small amount of code (think bugfixes) and are critical require 1 approval to merge.
If on a PR anything other than points 1-3 is requested, that will supercede the rules above (e.g. requesting review from a specific person)

What do you guys think about this? If we agree on edits etc, I can change the top-level comment.
@willingc @yuvipanda @minrk

Are there general guidelines written down somewhere that rest of the Jupyter project follow?

I prefer a much simpler set of general guidelines that I shared with @fperez and @ellisonbg:

We will share all relevant data received with the entire team.
We will be sure that each member has the opportunity to provide input to any decision that affects the team.
I will not take sole credit for something the team did together.
The entire team should be cc’d on all emails.
We will refrain from self merging PRs unless open for more than 24 hours or is a disaster recovery situation.
We will make an effort to submit detailed, actionable issues. Side discussions should be recapped.

The general guidelines of the Jupyter project have been to encourage reviews, discourage self merges unless necessary (i.e. recovery from an outage, has been open over 48 hours without comment/review), and respect the input of all team members.
cc: @minrk

+1 to what @willingc said.

I think it would be great to have this written down somewhere! Wikimedia had https://www.mediawiki.org/wiki/Gerrit/%2B2 and https://www.mediawiki.org/wiki/Gerrit/Code_review (and other documents), and it was very easy to see when you were transgressing social norms (or were about to).

Those sounds great to me as well - agree with what yuvi said as well...as somebody that has only recently started intersecting with the jupyter dev world, I'm still getting a feel for how the group operates

Strong +1 to @willingc's short but to the point list. I think it's an excellent outline of practical things to follow for good team dynamics.
And I agree we should elevate them to project-wide status!

+1
The other aspect that is implicit in the above points is this: Who is the
team?
There is benefit in talking and establishing a clear answer to that
question - even if it is relatively obvious. We have found it was very
helpful in JupyterLab to make it explicit, but also informal and open. To
help with that we have this "Team" section in our README:
https://github.com/jupyterlab/jupyterlab#team
That lists the folks who self-elect to be listed as maintainers and
self-describe what they are working on. It has helped us in talking to
others and helped the team better understand who the "we" is at any given
time for communication purposes.
On Mon, Oct 9, 2017 at 11:38 AM, Fernando Perez<EMAIL_ADDRESS>wrote:

Strong +1 to @willingc https://github.com/willingc's short but to the
point list. I think it's an excellent outline of practical things to follow
for good team dynamics.
And I agree we should elevate them to project-wide status!
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Excellent points @willingc! I agree that the looser guidelines are a good fit, especially for a team as small as ours. It is a good idea to add that to the guides around here.

@minrk @willingc @fperez @ellisonbg do you think we should just make these guidelines 'official' for JupyterHub + BinderHub related repos first, and then bring it up for community discussion? Or since there's enough BDFL support JDI for the whole project?

I have mixed feelings about applying specific mechanics to the whole
Jupyter org - even though the principles are general. We have enough
semi-independent teams working, and I think it is healthy for the given
teams to "own" the mechanics/details of their development process, while
following the overall spirit. At the same time, our teams/repos are very
dynamic and it is helpful to have org-wide standards that also help new
contributors. I found that same tension when I submitted this PR about
label/milestone to the governance repo:
https://github.com/jupyter/governance/pull/29
I still need to return to that and see if we can extract principle and
ideas that apply to the entire org, while still allowing different teams
flexibility on the details.
On Wed, Oct 11, 2017 at 8:50 AM, Yuvi Panda<EMAIL_ADDRESS>wrote:

@minrk https://github.com/minrk @willingc https://github.com/willingc
@fperez https://github.com/fperez @ellisonbg
https://github.com/ellisonbg do you think we should just make these
guidelines 'official' for JupyterHub + BinderHub related repos first, and
then bring it up for community discussion? Or since there's enough BDFL
support JDI for the whole project?
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My two cents: made good experiences for small-ish projects by requiring that someone else merge your PR, never be in a hurry with a decision (no automatic 48h rule or such), and discuss everything in issues (in person doesn't count until someone writes it up in an issue). It does sometimes feel like this slows you down, but if you know about that constraint it makes you plan accordingly which has the nice benefit of making things less stressful. Empirically the case of "in a hurry + didn't regret rushed decision" seems to be incredibly rare.

Does someone want to document this (if it's not already)?

We do have a section on this in the team compass now: https://jupyterhub-team-compass.readthedocs.io/en/latest/team/member-guide.html#when-should-i-merge-a-pull-request
it is more "guidelines" than "strict rules" but I think it does follow the spirit of what @willingc and @betatim suggested above. If folks think it is useful I can try to add in a few extra suggestions that cover more of what @willingc suggested above. I liked the extra bits about making sure conversations happen in public issues, discussions are re-capped, people are included in any non-github discussions, etc

@choldgraf I think the "Default to open discussions." would cover my suggestions. Thanks @manics for raising the issue and for all the triaging of old stuff.


NCBI GEO

There are lots of relevant datasets. Many of them appear to be good old fashioned microarrays. This can be processed semiautomatically, and so we could try to download "lots" of datasets. This will need some further investigation
https://www.ncbi.nlm.nih.gov/gds?term=coronavirus&cmd=correctspelling

I'm going to sign up for this one, assuming no other takers or higher priority data. Along the way I can also generate biclusters. I also have a biclustering collaborator at UPenn who is willing to donate time to help with dataset creation and applying methods. More on Monday!

Hi Marcin, we need this data for another project as well. Please contact me about this. We did this about 10 years ago for 10K datasets in GEO and things went reasonably well with some Bioconductor scripts, although I woundn't be who I am if I could still find those scripts today....

@pnrobinson @realmarcin -
Since there will likely be several GEO datasets we download, I'm wondering if we want to try and create (or use @pnrobinson existing scripts)  a primary script that handles the bulk of the analyses that will be the same for these resources. Then, add specific transformation scripts as needed for each source. Seems more reproducible and robust in the long run. Thoughts? If you agree, I'm happy to help set-up a quick call so we formulate a plan for moving forward.
Linking other relevant issues: #11, #12, #19

@pnrobinson @realmarcin -
Since there will likely be several GEO datasets we download, I'm wondering if we want to try and create (or use @pnrobinson existing scripts) a primary script that handles the bulk of the analyses that will be the same for these resources. Then, add specific transformation scripts as needed for each source. Seems more reproducible and robust in the long run. Thoughts? If you agree, I'm happy to help set-up a quick call so we formulate a plan for moving forward.
Linking other relevant issues: #11, #12, #19

@pnrobinson ,  @realmarcin - did you guys by chance talk about this today? Just curious on your thoughts.

@pnrobinson discussed this ticket a bit with @realmarcin today, and we had some questions. possibly could discuss on on the n2v call (Thursday 12 pm your time), if you are going to be around


Doesn't present a link, only javascript backed button, when offering to open in desktop / vscode. I want to open in vscode-insiders (so could alter the link if allowed)

Can't launch repo from github web editor to vscode-insiders desktop.
Recreation steps: goto a repo, press full stop / period (.), then try to use debug tab, see continue on button, use it to open in local vscode-insiders.
It only offers to open in vscode, and if that fails there is a try again button (which could be backed by a hyperlink) that attempts to open it again (a vscode: link).

If that button was a hyperlink instead of a javascript monstrosity, then the user could copy the link into their clipboard and alter it (to begin vscode-insider: or whatever) before pasting again into the address bar.
Alternatively you could offer that as an option from the try again dialog, or the command palette, or as a development environment extension (one of the options is to install a new one which effectively searches extensions for @tag:continueOn )

Or maybe you could attempt the reinvention of the visual studio version selector dialog, which gave users the choice of installed visual studio editions whenever a suitable link was invoked. You could include both visual studio code and visual studio full fat editions.
It was full of complaints, probably because it didn't offer the user an easy way to reenable the dialog after choosing don't show again, although my memory of it is foggy. Maybe there was a key combo when loading things to get it to show, but who remembers all of those... I'd suggest instead showing a brief dialog with the key combo shown clearly so a user could cancel the ongoing opening version (or at least rerun the open command and be ready the next time).

Another alternative is to try https://insiders.vscode.dev.
Reassigning to Joyce.

Another alternative is to try https://insiders.vscode.dev. Reassigning to Joyce.

If MS/github could add the insiders subdomain to github.dev that might be a good solution

If you are using the Insiders version of GitHub.dev, we will try to open VS Code Insiders on the desktop, and vice versa for vscode.dev and VS Code Stable on desktop. You can make sure you are using Insiders here:


updateRecommendation API.

@msvinaykumar have you tested if the recommendation changes when old result data is added after a recommendation is produced

@msvinaykumar have you tested if the recommendation changes when old result data is added after a recommendation is produced

Yes

@msvinaykumar there seems to be a build failure, can you PTAL
Error: ERROR] Failed to execute goal org.apache.maven.plugins:maven-compiler-plugin:3.8.0:compile (default-compile) on project autotune: Compilation failure
Error: ERROR] /home/autotune/src/autotune/src/main/java/com/autotune/database/dao/ExperimentDAOImpl.java:[28,8] com.autotune.database.dao.ExperimentDAOImpl is not abstract and does not override abstract method isDataExistsInResults(java.lang.String,java.sql.Timestamp) in com.autotune.database.dao.ExperimentDAO

@msvinaykumar there seems to be a build failure, can you PTAL
Error: ERROR] Failed to execute goal org.apache.maven.plugins:maven-compiler-plugin:3.8.0:compile (default-compile) on project autotune: Compilation failure
Error: ERROR] /home/autotune/src/autotune/src/main/java/com/autotune/database/dao/ExperimentDAOImpl.java:[28,8] com.autotune.database.dao.ExperimentDAOImpl is not abstract and does not override abstract method isDataExistsInResults(java.lang.String,java.sql.Timestamp) in com.autotune.database.dao.ExperimentDAO

fixed

build_crc PR job is expected to fail until #836 gets merged


[Bug]: VectorStoreIndex.from_documents changes break examples

Bug Description
VectorStoreIndex class method from_documents chunks things differently in current version as compared to prior versions leading to max token issues. This code worked in 0.8.69 but fails at 0.9.0.post1 (or earlier).
from llama_index import VectorStoreIndex, ServiceContext, set_global_service_context
from llama_index.readers.web import SimpleWebPageReader
from llama_index.llms import OpenAI

llm = OpenAI(temperature=0.0)
service_context = ServiceContext.from_defaults(llm=llm)
set_global_service_context(service_context)

documents = SimpleWebPageReader(
    html_to_text=True, metadata_fn=lambda url: dict(url=url)
).load_data(["http://paulgraham.com/worked.html"])
index = VectorStoreIndex.from_documents(documents, show_progress=True)

It fails on the from_documents step with the included exception traceback.
Version
0.9.0.post1
Steps to Reproduce
Run the included code.
Relevant Logs/Tracbacks
---------------------------------------------------------------------------
BadRequestError                           Traceback (most recent call last)
[/Volumes/dev_new/trulens/trulens_eval/examples/experimental/appui_example.ipynb](https://file+.vscode-resource.vscode-cdn.net/Volumes/dev_new/trulens/trulens_eval/examples/experimental/appui_example.ipynb) Cell 2 line <cell line: 12>()
      [7](vscode-notebook-cell:/Volumes/dev_new/trulens/trulens_eval/examples/experimental/appui_example.ipynb#X22sZmlsZQ%3D%3D?line=6) set_global_service_context(service_context)
      [9](vscode-notebook-cell:/Volumes/dev_new/trulens/trulens_eval/examples/experimental/appui_example.ipynb#X22sZmlsZQ%3D%3D?line=8) documents = SimpleWebPageReader(
     [10](vscode-notebook-cell:/Volumes/dev_new/trulens/trulens_eval/examples/experimental/appui_example.ipynb#X22sZmlsZQ%3D%3D?line=9)     html_to_text=True, metadata_fn=lambda url: dict(url=url)
     [11](vscode-notebook-cell:/Volumes/dev_new/trulens/trulens_eval/examples/experimental/appui_example.ipynb#X22sZmlsZQ%3D%3D?line=10) ).load_data(["http://paulgraham.com/worked.html"])
---> [12](vscode-notebook-cell:/Volumes/dev_new/trulens/trulens_eval/examples/experimental/appui_example.ipynb#X22sZmlsZQ%3D%3D?line=11) index = VectorStoreIndex.from_documents(documents, show_progress=True)
     [14](vscode-notebook-cell:/Volumes/dev_new/trulens/trulens_eval/examples/experimental/appui_example.ipynb#X22sZmlsZQ%3D%3D?line=13) #query_engine = index.as_query_engine()

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:106](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:106), in BaseIndex.from_documents(cls, documents, storage_context, service_context, show_progress, **kwargs)
     [97](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:97)     docstore.set_document_hash(doc.get_doc_id(), doc.hash)
     [99](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:99) nodes = run_transformations(
    [100](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:100)     documents,  # type: ignore
    [101](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:101)     service_context.transformations,
    [102](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:102)     show_progress=show_progress,
    [103](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:103)     **kwargs,
    [104](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:104) )
--> [106](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:106) return cls(
    [107](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:107)     nodes=nodes,
    [108](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:108)     storage_context=storage_context,
    [109](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:109)     service_context=service_context,
    [110](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:110)     show_progress=show_progress,
    [111](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:111)     **kwargs,
    [112](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:112) )

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:49](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:49), in VectorStoreIndex.__init__(self, nodes, index_struct, service_context, storage_context, use_async, store_nodes_override, show_progress, **kwargs)
     [47](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:47) self._use_async = use_async
     [48](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:48) self._store_nodes_override = store_nodes_override
---> [49](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:49) super().__init__(
     [50](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:50)     nodes=nodes,
     [51](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:51)     index_struct=index_struct,
     [52](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:52)     service_context=service_context,
     [53](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:53)     storage_context=storage_context,
     [54](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:54)     show_progress=show_progress,
     [55](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:55)     **kwargs,
     [56](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:56) )

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:71](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:71), in BaseIndex.__init__(self, nodes, index_struct, storage_context, service_context, show_progress, **kwargs)
     [69](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:69) if index_struct is None:
     [70](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:70)     assert nodes is not None
---> [71](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:71)     index_struct = self.build_index_from_nodes(nodes)
     [72](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:72) self._index_struct = index_struct
     [73](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/base.py:73) self._storage_context.index_store.add_index_struct(self._index_struct)

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:254](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:254), in VectorStoreIndex.build_index_from_nodes(self, nodes, **insert_kwargs)
    [243](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:243) def build_index_from_nodes(
    [244](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:244)     self,
    [245](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:245)     nodes: Sequence[BaseNode],
    [246](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:246)     **insert_kwargs: Any,
    [247](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:247) ) -> IndexDict:
    [248](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:248)     """Build the index from nodes.
    [249](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:249)
    [250](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:250)     NOTE: Overrides BaseIndex.build_index_from_nodes.
    [251](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:251)         VectorStoreIndex only stores nodes in document store
    [252](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:252)         if vector store does not store text
    [253](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:253)     """
--> [254](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:254)     return self._build_index_from_nodes(nodes, **insert_kwargs)

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:235](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:235), in VectorStoreIndex._build_index_from_nodes(self, nodes, **insert_kwargs)
    [233](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:233)     run_async_tasks(tasks)
    [234](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:234) else:
--> [235](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:235)     self._add_nodes_to_index(
    [236](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:236)         index_struct,
    [237](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:237)         nodes,
    [238](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:238)         show_progress=self._show_progress,
    [239](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:239)         **insert_kwargs,
    [240](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:240)     )
    [241](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:241) return index_struct

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:188](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:188), in VectorStoreIndex._add_nodes_to_index(self, index_struct, nodes, show_progress, **insert_kwargs)
    [185](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:185) if not nodes:
    [186](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:186)     return
--> [188](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:188) nodes = self._get_node_with_embedding(nodes, show_progress)
    [189](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:189) new_ids = self._vector_store.add(nodes, **insert_kwargs)
    [191](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:191) if not self._vector_store.stores_text or self._store_nodes_override:
    [192](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:192)     # NOTE: if the vector store doesn't store text,
    [193](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:193)     # we need to add the nodes to the index struct and document store

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:100](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:100), in VectorStoreIndex._get_node_with_embedding(self, nodes, show_progress)
     [89](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:89) def _get_node_with_embedding(
     [90](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:90)     self,
     [91](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:91)     nodes: Sequence[BaseNode],
     [92](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:92)     show_progress: bool = False,
     [93](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:93) ) -> List[BaseNode]:
     [94](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:94)     """Get tuples of id, node, and embedding.
     [95](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:95)
     [96](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:96)     Allows us to store these nodes in a vector store.
     [97](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:97)     Embeddings are called in batches.
     [98](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:98)
     [99](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:99)     """
--> [100](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:100)     id_to_embed_map = embed_nodes(
    [101](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:101)         nodes, self._service_context.embed_model, show_progress=show_progress
    [102](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:102)     )
    [104](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:104)     results = []
    [105](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/vector_store/base.py:105)     for node in nodes:

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/utils.py:137](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/utils.py:137), in embed_nodes(nodes, embed_model, show_progress)
    [134](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/utils.py:134)     else:
    [135](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/utils.py:135)         id_to_embed_map[node.node_id] = node.embedding
--> [137](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/utils.py:137) new_embeddings = embed_model.get_text_embedding_batch(
    [138](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/utils.py:138)     texts_to_embed, show_progress=show_progress
    [139](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/utils.py:139) )
    [141](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/utils.py:141) for new_id, text_embedding in zip(ids_to_embed, new_embeddings):
    [142](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/indices/utils.py:142)     id_to_embed_map[new_id] = text_embedding

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:255](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:255), in BaseEmbedding.get_text_embedding_batch(self, texts, show_progress, **kwargs)
    [249](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:249) if idx == len(texts) - 1 or len(cur_batch) == self.embed_batch_size:
    [250](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:250)     # flush
    [251](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:251)     with self.callback_manager.event(
    [252](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:252)         CBEventType.EMBEDDING,
    [253](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:253)         payload={EventPayload.SERIALIZED: self.to_dict()},
    [254](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:254)     ) as event:
--> [255](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:255)         embeddings = self._get_text_embeddings(cur_batch)
    [256](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:256)         result_embeddings.extend(embeddings)
    [257](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:257)         event.on_end(
    [258](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:258)             payload={
    [259](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:259)                 EventPayload.CHUNKS: cur_batch,
    [260](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:260)                 EventPayload.EMBEDDINGS: embeddings,
    [261](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:261)             },
    [262](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/base.py:262)         )

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:349](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:349), in OpenAIEmbedding._get_text_embeddings(self, texts)
    [342](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:342) def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
    [343](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:343)     """Get text embeddings.
    [344](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:344)
    [345](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:345)     By default, this is a wrapper around _get_text_embedding.
    [346](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:346)     Can be overridden for batch queries.
    [347](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:347)
    [348](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:348)     """
--> [349](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:349)     return get_embeddings(
    [350](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:350)         self._client,
    [351](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:351)         texts,
    [352](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:352)         engine=self._text_engine,
    [353](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:353)         **self.additional_kwargs,
    [354](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:354)     )

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:289](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:289), in BaseRetrying.wraps.<locals>.wrapped_f(*args, **kw)
    [287](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:287) @functools.wraps(f)
    [288](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:288) def wrapped_f(*args: t.Any, **kw: t.Any) -> t.Any:
--> [289](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:289)     return self(f, *args, **kw)

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:379](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:379), in Retrying.__call__(self, fn, *args, **kwargs)
    [377](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:377) retry_state = RetryCallState(retry_object=self, fn=fn, args=args, kwargs=kwargs)
    [378](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:378) while True:
--> [379](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:379)     do = self.iter(retry_state=retry_state)
    [380](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:380)     if isinstance(do, DoAttempt):
    [381](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:381)         try:

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:325](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:325), in BaseRetrying.iter(self, retry_state)
    [323](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:323)     retry_exc = self.retry_error_cls(fut)
    [324](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:324)     if self.reraise:
--> [325](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:325)         raise retry_exc.reraise()
    [326](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:326)     raise retry_exc from fut.exception()
    [328](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:328) if self.wait:

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:158](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:158), in RetryError.reraise(self)
    [156](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:156) def reraise(self) -> t.NoReturn:
    [157](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:157)     if self.last_attempt.failed:
--> [158](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:158)         raise self.last_attempt.result()
    [159](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:159)     raise self

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/concurrent/futures/_base.py:437](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/concurrent/futures/_base.py:437), in Future.result(self, timeout)
    [435](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/concurrent/futures/_base.py:435)     raise CancelledError()
    [436](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/concurrent/futures/_base.py:436) elif self._state == FINISHED:
--> [437](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/concurrent/futures/_base.py:437)     return self.__get_result()
    [439](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/concurrent/futures/_base.py:439) self._condition.wait(timeout)
    [441](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/concurrent/futures/_base.py:441) if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/concurrent/futures/_base.py:389](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/concurrent/futures/_base.py:389), in Future.__get_result(self)
    [387](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/concurrent/futures/_base.py:387) if self._exception:
    [388](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/concurrent/futures/_base.py:388)     try:
--> [389](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/concurrent/futures/_base.py:389)         raise self._exception
    [390](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/concurrent/futures/_base.py:390)     finally:
    [391](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/concurrent/futures/_base.py:391)         # Break a reference cycle with the exception in self._exception
    [392](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/concurrent/futures/_base.py:392)         self = None

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:382](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:382), in Retrying.__call__(self, fn, *args, **kwargs)
    [380](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:380) if isinstance(do, DoAttempt):
    [381](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:381)     try:
--> [382](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:382)         result = fn(*args, **kwargs)
    [383](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:383)     except BaseException:  # noqa: B902
    [384](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/tenacity/__init__.py:384)         retry_state.set_exception(sys.exc_info())  # type: ignore[arg-type]

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:161](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:161), in get_embeddings(client, list_of_text, engine, **kwargs)
    [157](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:157) assert len(list_of_text) <= 2048, "The batch size should not be larger than 2048."
    [159](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:159) list_of_text = [text.replace("\n", " ") for text in list_of_text]
--> [161](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:161) data = client.embeddings.create(input=list_of_text, model=engine, **kwargs).data
    [162](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/llama_index/embeddings/openai.py:162) return [d.embedding for d in data]

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:105](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:105), in Embeddings.create(self, input, model, encoding_format, user, extra_headers, extra_query, extra_body, timeout)
     [99](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:99)         embedding.embedding = np.frombuffer(  # type: ignore[no-untyped-call]
    [100](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:100)             base64.b64decode(data), dtype="float32"
    [101](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:101)         ).tolist()
    [103](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:103)     return obj
--> [105](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:105) return self._post(
    [106](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:106)     "[/embeddings](https://file+.vscode-resource.vscode-cdn.net/embeddings)",
    [107](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:107)     body=maybe_transform(params, embedding_create_params.EmbeddingCreateParams),
    [108](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:108)     options=make_request_options(
    [109](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:109)         extra_headers=extra_headers,
    [110](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:110)         extra_query=extra_query,
    [111](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:111)         extra_body=extra_body,
    [112](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:112)         timeout=timeout,
    [113](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:113)         post_parser=parser,
    [114](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:114)     ),
    [115](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:115)     cast_to=CreateEmbeddingResponse,
    [116](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/resources/embeddings.py:116) )

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:1055](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:1055), in SyncAPIClient.post(self, path, cast_to, body, options, files, stream, stream_cls)
   [1041](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:1041) def post(
   [1042](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:1042)     self,
   [1043](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:1043)     path: str,
   (...)
   [1050](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:1050)     stream_cls: type[_StreamT] | None = None,
   [1051](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:1051) ) -> ResponseT | _StreamT:
   [1052](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:1052)     opts = FinalRequestOptions.construct(
   [1053](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:1053)         method="post", url=path, json_data=body, files=to_httpx_files(files), **options
   [1054](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:1054)     )
-> [1055](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:1055)     return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:834](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:834), in SyncAPIClient.request(self, cast_to, options, remaining_retries, stream, stream_cls)
    [825](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:825) def request(
    [826](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:826)     self,
    [827](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:827)     cast_to: Type[ResponseT],
   (...)
    [832](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:832)     stream_cls: type[_StreamT] | None = None,
    [833](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:833) ) -> ResponseT | _StreamT:
--> [834](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:834)     return self._request(
    [835](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:835)         cast_to=cast_to,
    [836](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:836)         options=options,
    [837](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:837)         stream=stream,
    [838](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:838)         stream_cls=stream_cls,
    [839](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:839)         remaining_retries=remaining_retries,
    [840](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:840)     )

File [/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:877](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:877), in SyncAPIClient._request(self, cast_to, options, remaining_retries, stream, stream_cls)
    [874](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:874)     # If the response is streamed then we need to explicitly read the response
    [875](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:875)     # to completion before attempting to access the response text.
    [876](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:876)     err.response.read()
--> [877](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:877)     raise self._make_status_error_from_response(err.response) from None
    [878](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:878) except httpx.TimeoutException as err:
    [879](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/py38_trulens/lib/python3.8/site-packages/openai/_base_client.py:879)     if retries > 0:

BadRequestError: Error code: 400 - {'error': {'message': "This model's maximum context length is 8192 tokens, however you requested 16976 tokens (16976 in your prompt; 0 for the completion). Please reduce your prompt; or completion length.", 'type': 'invalid_request_error', 'param': None, 'code': None}}

@piotrm0 This is a small bug with the global service context. Should have it patched in a few minutes

Thanks for identifying this!


[Solution] Fix more code/compiler warnings

https://dev.azure.com/Stratisplatformuk/StratisBitcoinFullNode/_workitems/edit/4070

I have synced main and test net and were still able to stake.


Implement using the official Elasticsearch library.

Create a new implementation using the official Elasticsearch library in a separate package, without impacting those who use the Olivere implementation.
Refine the abstraction of Elasticsearch Search functionality even more, enabling the use of this library only.

Acho que vle adicionar testes pelo menos para os JSONs de queries, fora isso, me parece ok.


Ignore pycache in make_versioneer

This PR fixes #112 by ignoring the __pycache__ folder when python setup.py make_versioneer is invoked.

This one's been annoying me, but I didn't have plans to try to fix it before getting a release out. Thanks for tracking it down.


PROD SUPPORT : Change Respondent  to Accused , Its on the page while accused advocate is joining the case

Describe the bug

Hi @ashishbeehyv, @Susmitabe , @Ramu-kandimalla , @rajeshcherukumalli
THIS BUG IS RE-PRODUCED IN UAT

Hi @ashishbeehyv, @Ramu-kandimalla , @rajeshcherukumalli
This Issue is Resolved in UAT

**TO CHECK IN PROD
Please clear local stoarage of your browser**

Please make this change. Thanks!


Kani primitive to query max allocation size

Due to the pointer model of CBMC [0], not all of the bits of a pointer are available for addressing (a certain number of the top bits are used for the "object-id"). CBMC exposes this to a user as __CPROVER_max_malloc_size. It would be useful for Kani to expose a similar constant for proof harnesses. This is useful when allocating large symbolic buffers (e.g., Vec::with_capacity(kani::any());
[0] http://cprover.diffblue.com/memory-primitives.html

Right now the workaround is for the user to calculate (based on the object-bits) the max allocation size. This is 1 << (pointer-width - object-bits - 1)


benchtop: fix units

#66  👈
master

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make sure to warn when we have self encrypted ssl certs on route

Try to warn if we have a self signed cert when creating app
When creating github-app directly ask for github enteprise or not

/retest

Codecov Report

Merging #372 (afb5c5a) into main (311f8ff) will decrease coverage by 0.33%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##             main     #372      +/-   ##
==========================================
- Coverage   71.08%   70.74%   -0.34%
==========================================
  Files          50       50
  Lines        2545     2577      +32
==========================================
+ Hits         1809     1823      +14
- Misses        558      576      +18
  Partials      178      178

Impacted Files
Coverage Δ

pkg/consoleui/interface.go
0.00% <0.00%> (ø)

pkg/kubeinteraction/labels.go
0.00% <0.00%> (ø)

pkg/cmd/pipelineascode/pipelineascode.go
50.00% <0.00%> (ø)

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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Field type inference

Field type could be inferred in case an initial value is provided.
class Inference
  let field = "field" // syntax error: expected : after field

  fun method() =>
    let variable = "variable" // type inferred

I'm a big fan of type inference, too. Writing:
let var: Map[Edge[String, U64], Float] = Map[Edge[String, U64], Float].create()

is inferior to:
let var = Map[Edge[String, U64], Float].create()

In some cases, we do need to specify types though (U32 or U64 or U128 or ULong or USize, F32 or ...).

The reason we didn't put in field type inference to start with is that, since fields are accessible from outside their class, field inference affects package API and hence has potential problems with recursive and unstable definitions. In the simplest case 2 fields can be defined that each take the type of the other. Local variable type inference does not have this problem since the variables can only be accessed locally and are inferred in the order in which they are defined.
I was going to give you a detailed explanation of exactly why we can't do this, complete with a few nasty example cases. However, when I came to look at it in detail I realised that all of the problems appear to be solvable. I need to investigate further, but I think that it should be perfectly feasible, and I would certainly welcome it.

@andymcn
Is the approach you're thinking of Hindley-Milner ?
I know crystal uses it, and it works really well when messing around with it.

@ryanai3, no that's way more complicated than we need. Pony has enough places where types have to be specified and doesn't have real method polymorphism, so inferring types is pretty easy. We already do it for local variables simply by saying the variable has the type of the initialiser expression. We can extend that to fields.
Full type inference violates one of the basic principles of Pony, it should be obvious what's going on. If the compiler uses a complex algorithm to determine a type then the programmer has to mentally do the same thing and they might get different answers. That leads to bugs.

I like this.
Related to @ryanai3 's point about specifying types for literals sometime, ie:
let i = 3 // Wait, what type is i?

One of the things we have discussed intermittently is whether or not there should be a default literal type, and if so, what it should be. That is, if a type for a literal can't be inferred locally, should we just pick a type for it?

@sylvanc
so there's certainly cases where it doesn't matter - if a method expects a union type and you pass in a literal, you shouldn't need to specify the type (if it accepts (U32 | U64) no need to do take(U64(3))
I'm not so sure we should have default literal types. We could do something like USize if not negative, and the Signed equivalent if negative, but it seems like that might be confusing, and it's implicit behavior.

opened a request for RFC for this: https://github.com/ponylang/rfcs/issues/55
closing this issue.


100793 - Pipeline and initial minor solution

AB#100793
Initial solution
Ready to copy projects

Equinor.ProCoSys.Auth
Equinor.ProCoSys.IPO.MainApi.Tests

@TordJoranger gave approval via Teams meeting. I'll merge


Only with the English language.

The add-on works only with the English language. Please expand the number of supported languages 🙏🏻

love the idea! I'll make an issue


[stable/pgadmin] README incorrect example

Within the README file there was an incorrect example where env.username
was given rather then env.email.
Pointed out in issue: https://github.com/helm/charts/issues/20789
Signed-off-by: Rowan Ruseler<EMAIL_ADDRESS>

/ok-to-test

/retest

/lgtm


⚠ Processors has degraded performance

In b4550de, Processors ($STATUS_URL) experienced degraded performance:

HTTP code: 200
Response time: 42 ms

Resolved: Processors performance has improved in cc8be36.


fixed typos, examples, added descriptions for samples

Removed a couple of code snippets in the docs that still had "tableau.addIn"
Removed the Known Issues link that was broken (Known issues moved to the CC site).

I changed it to PORT and reworked the sentence.
Ran spell check over the docs files.
Changed the text that appears in the browser tab (now Extensions API Home)


fix(IAM Access Groups): Add new member trusted profile as ag member

PR summary

Add trusted profile as new member to Access group
PR Checklist
Please make sure that your PR fulfills the following requirements:

[x] The commit message follows the Angular Commit Message Guidelines.
[ ] Tests for the changes have been added (for bug fixes / features)
[ ] Docs have been added / updated (for bug fixes / features)

Current vs new behavior

Does this PR introduce a breaking change?

[ ] Yes
[x] No

Other information

:tada: This PR is included in version 0.22.7 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


Installation issue preparing datadir

Encountered error installing on Linux Mint:
(venv) me@computer:~/coinswaps/basicswap$ CURRENT_XMR_HEIGHT=$(curl https://localmonero.co/blocks/api/get_stats | jq .height)
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   169    0   169    0     0    243      0 --:--:-- --:--:-- --:--:--   243
(venv) me@computer:~/coinswaps/basicswap$ basicswap-prepare --datadir=$SWAP_DATADIR --withcoins=monero --xmrrestoreheight=$CURRENT_XMR_HEIGHT
Traceback (most recent call last):
  File "/home/me/coinswaps/venv/bin/basicswap-prepare", line 33, in <module>
    sys.exit(load_entry_point('basicswap==0.12.7', 'console_scripts', 'basicswap-prepare')())
  File "/home/me/coinswaps/venv/bin/basicswap-prepare", line 25, in importlib_load_entry_point
    return next(matches).load()
  File "/usr/lib/python3.10/importlib/metadata/__init__.py", line 171, in load
    module = import_module(match.group('module'))
  File "/usr/lib/python3.10/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 883, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/home/me/coinswaps/venv/lib/python3.10/site-packages/bin/basicswap_prepare.py", line 33, in <module>
    from basicswap.basicswap import BasicSwap
  File "/home/me/coinswaps/venv/lib/python3.10/site-packages/basicswap/basicswap.py", line 69, in <module>
    from .messages_pb2 import (
  File "/home/me/coinswaps/venv/lib/python3.10/site-packages/basicswap/messages_pb2.py", line 34, in <module>
    _descriptor.EnumValueDescriptor(
  File "/home/me/coinswaps/venv/lib/python3.10/site-packages/google/protobuf/descriptor.py", line 789, in __new__
    _message.Message._CheckCalledFromGeneratedFile()
TypeError: Descriptors cannot be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
 1. Downgrade the protobuf package to 3.20.x or lower.
 2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).

More information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates
(venv) me@computer:~/coinswaps/basicswap$

Delete coinswaps folder
Use installer: (temporary link) https://github.com/nahuhh/basicswap-bash/releases/tag/beta
Will move to here after confirming mac support:
https://github.com/nahuhh/basicswap-bash/releases/latest

@link-leo Did this solve your problem or do you need more help?

The linked installer works around the issue my installing the correct protobuf version within the venv

@link-leo the protobuf issues should be remedied on latest versions. Please reopen if youre still having issues


send error if obtaining of types-registry package failed

fixes #14398
NOTE: types installer does not immediately send the error to the parent process since it will be prone to races - event can be sent before parent process started listening to events. Instead types installer sends error after it for the first time receives request from the parent process

Can you please port this to release-2.2

@mhegazy sure


Server Side Queries

Could someone please post a fully working server side query example? Or point me to a working example other than the one on the site, I'm really struggling to work out which JS relates in the demo/js/index.js and please not just a mock but actually calling via $http.
Would really appreciate any feedback!!
Would love to use this grid as it looks the cleanest out of the ones I've looked ....

@joebordes trNgGrid implementation in MarvelJs really helped. In that App you have a conclusion table in which you compared it with angular smart table. Do you have a demo on angular smart table too? If yes please show that as I am having some problem in implementing server side pagination with smart table. lorenzofox hasnt given the full demo of server side pagination filtering and sorting on the demo page. And to me, trNgGrid is really the second choice.

Hi,
Glad it helped, that is what I shared it for :-)
As I explain on the conclusion page, I couldn't get smarttable to do what I needed (note that this is about 5 months old, so it may very well have changed). I haven't looked back since, just kept on going with trngrid.
The best I can suggest is that you look at the commits of the smart table testing which are towards the end of august, I think I got the server side working (although I don't really remember).
Let us know what you decide.


[CDSADAPTERS-2187]: Added integration dependency for cds import for events

JIRA: https://jira.tools.sap/browse/CDSADAPTERS-2187
Github issue: https://github.com/cap-js/ord/issues/81

I did a small example with
srv/external/myRemote.cds
service myRemote {
  event Foo { key ID: UUID };
}

package.json contains
{
  "cds": {
    "requires": {
      "myRemote": {
        "kind": "odata",
        "model": "srv/external/myRemote.cds",
        "credentials": {
          "url": "http://www.foo.com"
        }
      }
  }
}

in custom code during bootstrapping:
  const ext = await cds.connect.to('myRemote')

The problem is that the remote service definitions are not part of
  app.get("/open-resource-discovery/v1/documents/1", async (req, res) => {
      /* ... */
      const csn = await cds.load(cds.env.folders.srv);  // <-- doesn't include myRemote

Note: cds.model would include it, shouldn't this be used, also for performance reasons? In mtx scenarios, it should be the customer's model.
Since csn doesn't include the remote definitions (incl. events), integrationDependencies is empty.

I don't see my comments addressed.

@SebastianWolf-SAP for David's comment, currently we're assuming that the services will be given in package.json only and we'll set up a call for further discussion to address that. For now trying to get the other comments here resolved,.


expired auth when opening macbook after a while

I closed my macbook around 1am and opened it around 9am. Lockbook's icon bounced in my tray and it showed the following error:

Unexpected("LbError {\n    kind: Unexpected(\n        \"ExpiredAuth\",\n    ),\n    backtrace: Some(\n        Backtrace [\n            { fn: \"std::backtrace::Backtrace::create\" },\n            { fn: \"<lockbook_core::model::errors::LbError as core::convert::From<lockbook_core::service::api_service::ApiError<lockbook_shared::api::GetFileIdsError>>>::from\" },\n            { fn: \"lockbook_core::service::sync_service::SyncContext<Base,Local,Client,Docs>::prune\" },\n            { fn: \"lockbook_core::service::sync_service::SyncContext<Base,Local,Client,Docs>::sync\" },\n            { fn: \"lockbook_core::CoreLib<Client,Docs>::sync\" },\n            { fn: \"lockbook_core_external_interface::FfiCore::sync\" },\n            { fn: \"_sync_all\" },\n            { fn: \"$s17SwiftLockbookCore0C3ApiV7syncAllAA9FfiResultOyAA5EmptyVAA04SyncF5ErrorOGyF\", file: \"./Users/travis/Code/lockbook/clients/apple/SwiftLockbookCore/Sources/SwiftLockbookCore/Apis/CoreApi.swift\", line: 48 },\n            { fn: \"$s17SwiftLockbookCore0C3ApiVAA0bD0A2aDP7syncAllAA9FfiResultOyAA5EmptyVAA04SyncF5ErrorOGyFTW\", file: \"./Users/travis/Code/lockbook/clients/apple/SwiftLockbookCore/<compiler-generated>\" },\n            { fn: \"$s8Lockbook11SyncServiceC4syncyyFyycfU_\", file: \"./Users/travis/Code/lockbook/clients/apple/Shared/Stateful Logic/SyncService.swift\", line: 109 },\n            { fn: \"$sIeg_IeyB_TR\", file: \"./Users/travis/Code/lockbook/clients/apple/<compiler-generated>\" },\n            { fn: \"__dispatch_call_block_and_release\" },\n            { fn: \"__dispatch_client_callout\" },\n            { fn: \"__dispatch_root_queue_drain\" },\n            { fn: \"__dispatch_worker_thread2\" },\n            { fn: \"__pthread_wqthread\" },\n        ],\n    ),\n}")

Presumably, lockbook was trying to sync when I closed the macbook, and I closed it between it signing a request and sending it, then it sent it when I opened my macbook in the morning.

It has been a long time since I've experienced this, certainly there's no popup anymore and #2939 will retry network requests 3 times. Probably this is a thing of the past


Azure Spatial Anchors: Describe how to use an external GPS tracker on HoloLens

Provide the instructions for interfacing with external GPS devices directly in the article.

@pabusse : Thanks for your contribution! The author(s) have been notified to review your proposed change.


[Do not review] HttpURLConnectionHttpClient updates

API change check
APIView has identified API level changes in this PR and created following API reviews.
azure-core-test


Failure while migrating from CentOS 8

I tried running the migrate2rocky tool but arrived at the following error:
Getting system package names for CentOS Linux 8........Error: No Matches found

Can't get package that provides /etc/yum.repos.d/local-repository-0.repo.

This is right after running the CentOS 8 (8.3.2011) installation and rebooting. I also tried with: CentOS 8.4.2105 and AlmaLinux 8.4 (all originally from the same ISO file) and got the same results.
More details:
[root@g-vm06 ~]# curl https://raw.githubusercontent.com/rocky-linux/rocky-tools/main/migrate2rocky/migrate2rocky.sh -o migrate2rocky.sh
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 37406  100 37406    0     0   176k      0 --:--:-- --:--:-- --:--:--  175k
[root@g-vm06 ~]# chmod +x migrate2rocky.sh
[root@g-vm06 ~]# ./migrate2rocky.sh -r

Removing dnf cache
Preparing to migrate CentOS Linux 8 to Rocky Linux 8.

Determining repository names for CentOS Linux 8.....

Found the following repositories which map from CentOS Linux 8 to Rocky Linux 8:
CentOS Linux 8                                Rocky Linux 8
appstream                                     appstream
local-centos8-x86_64--install-centos8-x86_64  baseos
extras                                        extras

Getting system package names for CentOS Linux 8........Error: No Matches found

Can't get package that provides /etc/yum.repos.d/local-repository-0.repo.

An error occurred while we were attempting to convert your system to Rocky Linux. Your system may be unstable. Script will now exit to prevent possible damage.

 A log of this installation can be found at /var/log/migrate2rocky.log

I think you should update to centos 8.5 first and then run the migration script.
from Rocky Docs:

Did you modify the repos to point to a local repo, perhaps with a different name other than baseos?
Where did you get the install isos from?  This doesn't look to be a stock CentOS 8 install.

I'm facing the same issue. Did you ever resolve this? And if so, how?

I'm facing the same issue.

Me too.
Some time ago an older version of this script did not have this problem (I think).

Same issue here, anybody fixed?

If you're trying to migrate from CentOS Stream 8 please see #215 as it's a known issue and there is a workaround until it can be fixed.
For migrations from CentOS Linux 8 (Not Stream) please post your migrate2rocky.log file.

你只要使用原版的yum.repos.d就不会出这个问题了，恶心了我很久这问题，你可以虚拟机中先从8.2升级到了8.5.2111，然后把里面的yum.repos.d拷贝出来，替换报有问题的机器上的yum.repos.d，然后执行sed -i 's/mirrorlist/#mirrorlist/g' /etc/yum.repos.d/CentOS-*
sed -i 's|#baseurl=http://mirror.centos.org|baseurl=http://vault.centos.org|g' /etc/yum.repos.d/CentOS-*，然后yum update -y --allowerasing，然后再运行migrate2rocky.sh就没问题了


noVNCClient class definitions

Summary
Adds some definitions for the noVNCClient class. This class essentially surrounds the RFB object of the noVNC library, taking care of remoting, what happens if someone connects, and what happens if someone disconnects. It also allows users to change their ability to interact with the object and see if the object is currently connected.

I'm not sure why theres 40 commits here, hold on.


Added coderabbit icon

Description

Closes #2530
Contribution Guidelines

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[x] I have read the Code Of Conduct and promise to abide by these rules

@PKief, please review this icon


Add Python data structure and algorithms repos and some cleanup

🤔 This is a

[ ] Feature
[ ] Bug
[X] Documentation Update
[ ] Translation

✍ Description

Write a short description
Add Python data structure and algorithms repos and some cleanup

Added to documentation

[X] readme file
[ ] No documentation needed

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@depimomo, please go through the contribution md file to read the structure of contributing to this project.


Making the certificate manager ordering optional, as well as adding Edge Certificate ordering to IKS

Since we need to order an Edge Certificate for IKS as well as Code Engine, I moved certcreate.py and delete_certs.py to the common folder.
iks.py now orders an edge certificate and can delete it with the delete option.
iks.py now also asks the user if they want to create a new secret before ordering a TLS certificate in the certificate manager.

I added more to the terraform implementation. The ingress creation was also not working before and I attempted to fix it.

I think the variable stuff in root_terraform is almost there; we might just need default values for all of the variables that don't currently have them though, cause if we try to make a ce module for instance, the workspace will error out and say that cluster_id, ingress, and cert_name don't have assigned values.

Looks good. Maybe wait until we merge the testing branch into master before we merge this one since I think this has the latest updates to iks.py.

Been changing a bunch of the stuff in the terraform. Creating a secret in iks after ordering a certificate in the certificate manager was not working and I tried to fix it. Not 100% sure it's working because I got rate-limited in both CIS instances


uiSchema: "ui:title" does not work for all widget/field types

Description
Basically the basic behavior of customizing generated labels seems to not work well.
"ui:title" in uiSchema does not work consistently for all types (namely boolean, custom components, titles of array and object)
Am I missing something?
Example:
https://jsfiddle.net/f2y3fq7L/34/
Expected behavior
uiSchema { prop { "ui:title": "Some Custom Title" } } should change the label text for all default and custom components, and for object and array
Actual behavior
uiSchema { prop { "ui:title": "Some Custom Title" } } does not show custom title for custom components, and for default components, and for title of object and array
Version
^0.48.2

I've just come across the same issue. It looks like this was implemented in #587 but only for SchemaField and none of the others.
I just worked on a PR that I will reference here for ObjectField, but I'm an absolute beginner on this lib and not sure what I'm doing, I hope someone with more experience can check the PR and take this over for the remaining fields?

cc @stepek and @n1k0

Close dby #619.


Can admin join call secretly in ongoing call?

Admin can not join ongoing call secretly, my requirement is when two user is on call then admin can join call, and admin is invisible for those two users.

@OXReactDev As @pr-1 said this feature can be handled in your business logic.