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expertai-nlapi-2.5.0
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Python client for expert.ai Natural Language API
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | expertai-nlapi-2.5.0 |
| نام | expertai-nlapi |
| نسخه کتابخانه | 2.5.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Expert.ai S.p.A. |
| ایمیل نویسنده | api.support@expert.ai |
| آدرس صفحه اصلی | https://github.com/therealexpertai/nlapi-python |
| آدرس اینترنتی | https://pypi.org/project/expertai-nlapi/ |
| مجوز | Apache License 2.0 |
<img style="float: right;" src="https://docs.expert.ai/logo.png" width="150px">
# expert.ai Natural Language API client for Python
Python client for the expert.ai Natural Language API adds Natural Language understanding capabilities to your Python apps.
The client can use either the Cloud based [Natural Language API](https://docs.expert.ai/nlapi/latest/) or [Studio Local Deployment Agent](https://docs.expert.ai/studio/latest/lda-api/).
## APIs capabilites
Make reference to the [Natural Language API](https://docs.expert.ai/nlapi/latest/guide/) and [Studio Local Deployment Agent](https://docs.expert.ai/studio/latest/lda-api/guide/) documentation to know more about the APIs capabilities.
Here is a side-by-side comparison of the two APIs:
Capability | Natural Language API | Studio Local Deployment Agent
--- | --- | ---
Where does it run? | In the Cloud, shared by all users | On user's PC
Document analysis: | YES | YES
Document classification with ready-to-use taxonomies (IPTC Media topics, GeoTax, Emotional traits, Behavioral traits) | YES | NO
Custom document classification | NO | YES*
Information detection with ready-to-use detectors (Personally Identifiable Information (PII), Writeprint, Temporal information, ESG Sentiment, Hate speech) | YES | NO
Custom information extraction | NO | YES*
Document size limit? | YES (<= 10KB)| NO
Document number limit? | NO | See the [Service Level Agreement](https://policies.expert.ai/slda/sla/)
Characters limit? | YES (<= 10 million characters per month when using the free tier) | NO
\* Based on [Studio](https://docs.expert.ai/studio/latest/) project.
## Installation
To install the client library with `pip`:
```bash
pip install expertai-nlapi
```
To install using `conda`:
```bash
conda install -c conda-forge expertai-nlapi
```
## Usage
### Authorization
You need an expert.ai developer account to use the APIs and you can get one for free registering on the expert.ai [developer portal](https://developer.expert.ai).
Your developer account credentials must be specified as environment variables:
- Linux:
```bash
export EAI_USERNAME=YOUR_USERNAME
export EAI_PASSWORD=YOUR_PASSWORD
```
- Windows:
```bash
SET EAI_USERNAME=YOUR_USERNAME
SET EAI_PASSWORD=YOUR_PASSWORD
```
Replace `YOUR_USERNAME` with the email address you specyfied during registration and `YOUR_PASSWORD` with the actual account password.
You can also define credentials inside your code:
```python
import os
os.environ["EAI_USERNAME"] = 'YOUR_USERNAME'
os.environ["EAI_PASSWORD"] = 'YOUR_PASSWORD'
```
### Create the client
The next thing to do inside your code is importing the client section of the library and instantiating the client based on the API you want to use.
- To use Natural Language API:
```python
from expertai.nlapi.cloud.client import ExpertAiClient
client = ExpertAiClient()
```
- To use Studio Local Deployment Agent:
```python
from expertai.nlapi.edge.client import ExpertAiClient
client = ExpertAiClient()
```
### Access capabilities and self-documentation resources
In the `samples` directory of this repository you can find ready-to-run scripts showing how to access all API capabilities for English texts and API self-documentation resources.
Scripts are listed and described in the tables below.
#### Natural Language API
You can find these scripts under the `/samples/natural language api` folder.
Capability | Sample
--- | ---
Document analysis, `standard` context, **full analysis** | `document analysis/full.py`
Document analysis, `standard` context, sub-analysis: **Deep linguistic analysis** | `document analysis/deep-linguistic-analysis.py`
Document analysis, `standard` context, sub-analysis: **Keyphrase extraction** | `document analysis/keyphrase-extraction.py`
Document analysis, `standard` context, sub-analysis: **Named entity recognition** | `document analysis/named-entity-recognition.py`
Document analysis, `standard` context, sub-analysis: **Relation extraction** | `document analysis/relation-extraction.py`
Document analysis, `standard` context, sub-analysis: **Sentiment analysis** | `document analysis/sentiment-analysis.py`
Document analysis, self-documemtation esources: list of the available **contexts** | `document analysis/contexts.py`
Document classification with **`iptc`** taxonomy | `document classification/iptc.py`
Document classification with **`geotax`** taxonomy | `document classification/geotax.py`
Document classification with **`emotional-traits`** taxonomy | `document classification/emotional-traits.py`
Document classification with **`behavioral-traits`** taxonomy | `document classification/behavioral-traits.py`
Document classification, self-documentation resources: list of available taxonomies | `document classification/taxonomies.py`
Document classification, self-documentation resources: category tree of the `iptc` taxonomy | `document classification/category-tree-iptc.py`
Document classification, self-documentation resources: category tree of the `geotax` taxonomy | `document classification/category-tree-geotax.py`
Document classification, self-documentation resources: category tree of the `emotional-traits` taxonomy | `document classification/category-tree-emotional-traits.py`
Document classification, self-documentation resources: category tree of the `behavioral-traits` taxonomy | `document classification/category-tree-behavioral-traits.py`
Information detection with **`pii`** detector | `information detection/pii.py`
Information detection with **`writeprint`** detector | `information detection/writeprint.py`
Information detection with **`temporal-information`** detector | `information detection/temporal-information.py`
Information detection with **`esg-sentiment`** detector | `information detection/esg-sentiment.py`
Information detection with **`hate-speech`** detector | `information detection/hate-speech.py`
Information detection, self-documentation resources: list of available detectors | `information detection/detectors.py`
#### Studio Local Deployment Agent
You can find these scripts under the `/samples/studio lda` folder.
Capability | Sample
--- | ---
Document analysis, **full analysis** | `document analysis/full.py`
Document analysis, sub-analysis: **Deep linguistic analysis** | `document analysis/deep-linguistic-analysis.py`
Document analysis, sub-analysis: **Keyphrase extraction** | `document analysis/keyphrase-extraction.py`
Document analysis, sub-analysis: **Named entity recognition** | `document analysis/named-entity-recognition.py`
Document analysis, sub-analysis: **Relation extraction** | `document analysis/relation-extraction.py`
Document analysis, sub-analysis: **Sentiment analysis** | `document analysis/sentiment-analysis.py`
### Changing the language
Natural Language API is capable of processing texts of various languages.
You can use self-documentations resources like `contexts` and `taxonomies`—look for the corresponding ready-to-run scripts in the first of the two tables above—to determine if a functionality is available for a given language.
If it's available, specify the `language` parameter in your code, for example:
```python
text = "Michael Jordan was one of the best basketball players of all time. Scoring was Jordan's stand-out skill, but he still holds a defensive NBA record, with eight steals in a half."
language= 'en'
output = client.specific_resource_analysis(
body={"document": {"text": text}},
params={'language': language, 'resource': 'disambiguation'
})
```
See also the documentation pages about [contexts](https://docs.expert.ai/nlapi/latest/guide/contexts-and-kg/) and [taxonomies](https://docs.expert.ai/nlapi/latest/guide/taxonomies/).
Studio Local Deployment Agent supports the language of your Studio project.
## More examples of document analysis
### Start with deep linguistic analysis...
To perform the [deep linguistic analysis](https://docs.expert.ai/nlapi/latest/guide/linguistic-analysis/) of a text:
- Natural Language API:
```python
output = client.specific_resource_analysis(
body={"document": {"text": text}},
params={'language': language, 'resource': 'disambiguation'
})
```
- Studio Local Deployment Agent:
```python
output = client.deep_linguistic_analysis(text)
```
### ...then examine tokenization and lemmatization...
Lemmatization looks beyond word reduction, using a language's full vocabulary to apply a *morphological analysis* to words. The lemma of 'was' is 'be' and the lemma of 'mice' is 'mouse'. Further, the lemma of 'meeting' might be 'meet' or 'meeting' depending on its use in a sentence.
```python
print (f'{"TOKEN":{20}} {"LEMMA":{8}}')
for token in output.tokens:
print (f'{text[token.start:token.end]:{20}} {token.lemma:{8}}')
```
TOKEN LEMMA
Facebook Facebook Inc.
is is
looking at look at
buying buy
an an
American American
startup startup
for for
$6 million 6,000,000 dollar
based base
in in
Springfield, IL Springfield
. .
### ...Part of Speech (PoS) tagging...
Analysis determines the part-of-speech of tokens. PoS labels are from the [Universal Dependencies](https://universaldependencies.org/) framework.
```python
print (f'{"TOKEN":{18}} {"PoS":{4}}')
for token in output.tokens:
print (f'{text[token.start:token.end]:{18}} {token.pos:{4}} ' )
```
TOKEN PoS
Facebook PROPN
is AUX
looking at VERB
buying VERB
an DET
American ADJ
startup NOUN
for ADP
$6 million NOUN
based VERB
in ADP
Springfield, IL PROPN
. PUNCT
### ...and dependency parsing information
The analysis returns the dependency parsing information assigned to each token, using the Universal Dependencies framework as well.
```python
print (f'{"TOKEN":{18}} {"Dependency label":{8}}')
for token in output.tokens:
print (f'{text[token.start:token.end]:{18}} {token.dependency.label:{4}} ' )
```
TOKEN Dependency label
Facebook nsubj
is aux
looking at root
buying advcl
an det
American amod
startup obj
for case
$6 million obl
based acl
in case
Springfield, IL obl
. punct
### Named entity recognition
Going a step beyond linguistic analysis, *named entities* add another layer of context. Named entities are recognized by the `entities` sub-analysis.
- Natural Language API:
```python
output = client.specific_resource_analysis(
body={"document": {"text": text}},
params={'language': language, 'resource': 'entities'})
```
- Studio Local Depolyment Agent:
```python
output = client.named_entity_recognition(text)
```
Printing results:
```python
print (f'{"ENTITY":{40}} {"TYPE":{10}}')
for entity in output.entities:
print (f'{entity.lemma:{40}} {entity.type_:{10}}')
```
ENTITY TYPE
6,000,000 dollar MON
Springfield GEO
Facebook Inc. COM
In addition to the entity type, the API provides some metadata from Linked Open Data sources such as WikiData and GeoNames.
For example, you can get the open data connected with the entity `Springfield, IL`
```python
print(output.entities[1].lemma)
```
Springfield
```python
for entry in output.knowledge:
if (entry.syncon == document.entities[1].syncon):
for prop in entry.properties:
print (f'{prop.type_:{12}} {prop.value:{30}}')
```
Coordinate Lat:39.47.58N/39.799446;Long:89.39.18W/-89.654999
DBpediaId dbpedia.org/page/Springfield
GeoNamesId 4250542
WikiDataId Q28515
Springfield has been recognized as [Q28515](https://www.wikidata.org/wiki/Q28515) on Wikidata, that is the Q-id for Springfield, IL (i.e.not for Springfield in Vermont o in California)
### Key elements
**Key elements**, the result of keyphrase extraction, are obtained with the `relevants` sub-analysis and identified from the document as main sentences, main concepts (called "syncons"), main lemmas and relevant Knowledge Graph topics.
Let's focus on the main lemmas of the document; each lemma is provided with a relevance score.
- Natural Language API:
```python
document = client.specific_resource_analysis(
body={"document": {"text": text}},
params={'language': language, 'resource': 'relevants'})
```
- Studio Local Deployment Agent:
```python
document = client.keyphrase_extraction(text)
```
```python
print (f'{"LEMMA":{20}} {"SCORE":{5}} ')
for mainlemma in document.main_lemmas:
print (f'{mainlemma.value:{20}} {mainlemma.score:{5}}')
```
LEMMA SCORE
Facebook Inc. 43.5
startup 40.4
Springfield 15
### Sentiment analysis
**Sentiment** is obtained with the `sentiment` sub-analysis and expresses the **polarity**—positive or negative—and the intensity of the tone of the text is.
```
text='Today is a good day. I love to go to mountain.'
```
- Natural Language API:
```python
document = client.specific_resource_analysis(
body={"document": {"text": text}},
params={'language': language, 'resource': 'sentiment'})
```
- Studio Local Deployment Agent:
```python
document = client.sentiment(text)
```
Printing results:
```python
print("sentiment:", document.sentiment.overall)
```
### Relations
**Relations** are obtained with the `relations` sub-analysis that labels concepts expressed in the text with their semantic role with respect to the verb they are connected to.
```
text='John sent a letter to Mary.'
```
- Natural Language API:
```python
document = client.specific_resource_analysis(
body={"document": {"text": text}},
params={'language': language, 'resource': 'relations'})
```
- Studio Local Deployment Agent:
```python
document = client.relations(text)
```
Printing results:
```python
for rel in document.relations:
print("Verb:", rel.verb.lemma)
for r in rel.related:
print("Relation:", r.relation, "Lemma:", r.lemma )
```
## More examples of document classification
Let's see how to classify documents according to the [**IPTC Media Topics Taxonomy**](https://iptc.org/standards/media-topics/) provided by the Natural Language API; then we'll use the matplot lib to display categorization results as a bar chart.
```python
text = """Strategic acquisitions have been important to the growth of Facebook (FB).
Mark Zuckerberg founded the company in 2004, and since then it has acquired scores of companies,
ranging from tiny two-person start-ups to well-established businesses such as WhatsApp. For 2019,
Facebook reported 2.5 billion monthly active users (MAU) and $70.69 billion in revenue."""
```
```python
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
taxonomy='iptc'
document = client.classification(body={"document": {"text": text}}, params={'taxonomy': taxonomy,'language': language})
categories = []
scores = []
print (f'{"CATEGORY":{27}} {"IPTC ID":{10}} {"FREQUENCY":{8}}')
for category in document.categories:
categories.append(category.label)
scores.append(category.frequency)
print (f'{category.label:{27}} {category.id_:{10}}{category.frequency:{8}}')
```
CATEGORY ID FREQUENCY
Earnings 20000178 29.63
Social networking 20000769 21.95
```python
plt.bar(categories, scores, color='#17a2b8')
plt.xlabel("Categories")
plt.ylabel("Frequency")
plt.title("Media Topics Classification")
plt.show()
```
If your [Studio](https://docs.expert.ai/studio/latest) project performs document classification, to request it to Studio Local Deployment Agent use:
```python
document = client.classification(text)
```
Results structure is the same as for the Natural Language API.
## Information detection example
**Information detection** leverages deep linguistic analysis to extract particular types of information from the text.
For example, the Personal Identifiable Informtion (PII) detector of the Natural Language API extract personal information such as people names, dates, addresses, telephone numbers, etc. that could be considered "sensitive".
```
text='Longtime TFS News reporter Marcus Smith died unexpectedly Monday at the age of 60'
```
- Natural Language API:
```python
document = client.detection(
body={"document": {"text": text}},
params={'language': language,'detector':'pii'})
for extraction in document.extractions:
print("Template:", extraction.template)
for field in extraction.fields:
print("field: ", field.name," value: " , field.value)
for position in field.positions :
print("start: ", position.start, "end: " , position.end)
```
If your [Studio](https://docs.expert.ai/studio/latest) project performs information extraction, to request it to Studio Local Deployment Agent use:
```python
document = client.extraction(text)
```
Results structure is the same as for the Natural Language API.
## ...and if you made it this far...
Good job! You're an expert in the expert.ai community! :clap: :tada:
Check out other language SDKs available on our [Github page](https://github.com/therealexpertai).
## Contribute!
Clone this GitHub repository and run the following script:
```bash
$ cd nlapi-python
$ pip install -r requirements-dev.txt
```
> As good practice it's recommended to work in an isolated Python environment, creating a virtual environment with [virtualenv package](https://virtualenv.pypa.io/en/stable/installation.html) before building the package. You can create your environment with the command:
>
> ```bash
>$ virtualenv expertai
>$ source expertai/bin/activate
>```
نیازمندی
| مقدار | نام |
|---|---|
| - | requests |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl expertai-nlapi-2.5.0:
pip install expertai-nlapi-2.5.0.whl
نصب پکیج tar.gz expertai-nlapi-2.5.0:
pip install expertai-nlapi-2.5.0.tar.gz