Skip to content

Commit

Permalink
Updated doc for 0.1.18 version (#506)
Browse files Browse the repository at this point in the history
* Updated doc for 0.1.18 version

---------

Co-authored-by: dristy.cd <[email protected]>
  • Loading branch information
dristysrivastava and dristy.cd authored Aug 28, 2024
1 parent 2af69bc commit 260c61e
Show file tree
Hide file tree
Showing 6 changed files with 33 additions and 33 deletions.
1 change: 1 addition & 0 deletions docs/gh_pages/docs/entityclassifier.md
Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@ Below is the list of `entities` supported by Pebblo -
1. US Bank Account Number
1. IBAN Code
1. US ITIN
1. IP Address
1. GitHub Access Token
1. Slack Access Token
1. AWS Access Key
Expand Down
2 changes: 1 addition & 1 deletion docs/gh_pages/docs/pebblo_ui.md
Original file line number Diff line number Diff line change
Expand Up @@ -61,4 +61,4 @@ Load History provides details about latest 5 loads of this app. It provides the

It will also provide you with a list of these Datasource, accompanied by additional details such as the size, source path, the count of topics & entities across the datasource.

4. **Snippets**: This sections provides the actual text inspected by the Pebblo Server using the Pebblo Topic Classifier and Pebblo Entity Classifier. This will be useful to quickly inspect and remediate text that should not be ingested into the Gen-AI RAG application. Each snippet shows the exact file the snippet is loaded from easy remediation.
4. **Snippets**: This section details the text analyzed by the Pebblo Server using the Pebblo Topic Classifier and Pebblo Entity Classifier. It is designed to help quickly inspect and remediate text that should not be ingested into the Gen-AI RAG application. Each snippet shows the exact file for easy reference, with sensitive information labeled with confidence scores: HIGH, MEDIUM, or LOW.
2 changes: 1 addition & 1 deletion docs/gh_pages/docs/safe_loader.md
Original file line number Diff line number Diff line change
Expand Up @@ -65,4 +65,4 @@ Load History provides details about latest 5 loads of this app. It provides the

It will also provide you with a list of these Datasource, accompanied by additional details such as the size, source path, the count of topics & entities across the datasource.

4. **Snippets**: This sections provides the actual text inspected by the Pebblo Server using the Pebblo Topic Classifier and Pebblo Entity Classifier. This will be useful to quickly inspect and remediate text that should not be ingested into the Gen-AI RAG application. Each snippet shows the exact file the snippet is loaded from easy remediation.
4. **Snippets**: This section details the text analyzed by the Pebblo Server using the Pebblo Topic Classifier and Pebblo Entity Classifier. It is designed to help quickly inspect and remediate text that should not be ingested into the Gen-AI RAG application. Each snippet shows the exact file for easy reference, with sensitive information labeled with confidence scores: HIGH, MEDIUM, or LOW.
21 changes: 11 additions & 10 deletions docs/gh_pages/docs/topicclassifier.md
Original file line number Diff line number Diff line change
Expand Up @@ -8,21 +8,22 @@ and improvements to enrich its accuracy and effectiveness.

Below is the list of `topics` supported by Pebblo -

1. Medical Advice
1. Harmful Advice
1. Board Meeting
1. Enterprise Agreement
1. Patent Application Filling
1. Financial Report
1. Loan and Security Agreement
1. Consulting Agreement
1. Sexual Harassment
1. Settlement Agreement
1. Price List
1. Distribution/Partner Agreement
1. Customer List
1. Enterprise Agreement
1. Executive Severance Agreement
1. Employee Agreement
1. Financial Report
1. Loan And Security Agreement
1. Merger Agreement
1. Non-Disclosure Agreement
1. Patent Application Fillings
1. Price List
1. Employee Agreement
1. Sexual Content
1. Sexual Incident Report
1. Internal Product Roadmap Agreement

User can get details of classified topics for their loader source files in Pebblo report.
Different sections of Pebblo report such as , `Top Files With Most Findings`, `Data Source Findings Table` and `Snippets` helps to get overview of pebblo topic classifier output for user's rag application.
Expand Down
2 changes: 2 additions & 0 deletions pebblo/entity_classifier/README.md
Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@ Currently, we are supporting following Entities:
5. US Bank Account Number
6. IBAN code
7. US ITIN
8. IP Address

And following Secret Entities:
1. Github Token
Expand All @@ -28,4 +29,5 @@ entities, total_count, anonymized_text, entity_details = entity_classifier_obj.p
print(f"Entity Group: {entity_groups}")
print(f"Entity Count: {total_entity_count}")
print(f"Anonymized Text: {anonymized_text}")
print(f"Entity Details: {entity_details}")
```
38 changes: 17 additions & 21 deletions pebblo/topic_classifier/README.md
Original file line number Diff line number Diff line change
Expand Up @@ -2,27 +2,22 @@

This is Topic Classifier.
Currently, we are supporting following Topics:
1. Normal Advice
2. Medical Advice
3. Harmful Advice
4. Board Meeting
5. Consulting Agreement
6. Customer List
7. Distribution/Partner Agreement
8. Enterprise License Agreement
9. Executive Severance Agreement
10. Financial Report
11. Internal Use Only
12. Loan And Security Agreement
13. Merger Agreement
14. NDA
15. Patent Application Fillings
16. Price List
17. Settlement Agreement
18. Employee Agreement
19. Enterprise Agreement
20. Sexual Content
21. Sexual Incident Report
1. Medical Advice
1. Harmful Advice
1. Board Meeting
1. Consulting Agreement
1. Customer List
1. Enterprise Agreement
1. Executive Severance Agreement
1. Financial Report
1. Loan And Security Agreement
1. Merger Agreement
1. Patent Application Fillings
1. Price List
1. Employee Agreement
1. Sexual Content
1. Sexual Incident Report
1. Internal Product Roadmap Agreement

## How to use

Expand All @@ -34,4 +29,5 @@ topic_classifier_obj = TopicClassifier()
topics, total_topic_count, topic_details = topic_classifier_obj.predict(text)
print(f"Topic Response: {topics}")
print(f"Topic Count: {total_topic_count}")
print(f"Topic Details: {topic_details}")
```

0 comments on commit 260c61e

Please sign in to comment.