ixp
latest
false
- Introduction
- Setting up your account
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields
- Labels (predictions, confidence levels, label hierarchy, and label sentiment)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Annotated and unannotated messages
- Extraction Fields
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Access Control and Administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Creating or deleting a data source in the GUI
- Uploading a CSV file into a source
- Preparing data for .CSV upload
- Creating a dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amending dataset settings
- Deleting a message
- Deleting a dataset
- Exporting a dataset
- Using Exchange integrations
- Model training and maintenance
- Understanding labels, general fields, and metadata
- Label hierarchy and best practices
- Comparing analytics and automation use cases
- Turning your objectives into labels
- Overview of the model training process
- Generative Annotation
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Training chat and calls data
- Understanding data requirements
- Train
- Introduction to Refine
- Precision and recall explained
- Precision and Recall
- How validation works
- Understanding and improving model performance
- Reasons for label low average precision
- Training using Check label and Missed label
- Training using Teach label (Refine)
- Training using Search (Refine)
- Understanding and increasing coverage
- Improving Balance and using Rebalance
- When to stop training your model
- Using general fields
- Generative extraction
- Using analytics and monitoring
- Automations and Communications Mining™
- Selecting label confidence thresholds
- Creating a stream
- Updating or deleting a stream
- Developer
- Exchange Integration with Azure service user
- Exchange Integration with Azure Application Authentication
- Exchange Integration with Azure Application Authentication and Graph
- Fetching data for Tableau with Python
- Elasticsearch integration
- Self-hosted Exchange integration
- UiPath® Automation Framework
- UiPath® Marketplace activities
- UiPath® official activities
- How machines learn to understand words: a guide to embeddings in NLP
- Prompt-based learning with Transformers
- Efficient Transformers II: knowledge distillation & fine-tuning
- Efficient Transformers I: attention mechanisms
- Deep hierarchical unsupervised intent modelling: getting value without training data
- Fixing annotating bias with Communications Mining™
- Active learning: better ML models in less time
- It's all in the numbers - assessing model performance with metrics
- Why model validation is important
- Comparing Communications Mining™ and Google AutoML for conversational data intelligence
- Licensing
- FAQs and more
Important :
Communications Mining is now part of UiPath IXP. Check the Introduction in the Overview Guide for more details.
Communications Mining user guide
Last updated Aug 1, 2025
Create a stream
/api/v1/datasets/<project>/<dataset_name>/streams
/api/v1/datasets/<project>/<dataset_name>/streamsPermissions required: Streams admin, View labels
- Bash
curl -X PUT 'https://<my_api_endpoint>/api/v1/datasets/project1/collateral/streams' \ -H "Authorization: Bearer $REINFER_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "stream": { "comment_filter": { "user_properties": { "number:Spend": { "maximum": 100000, "minimum": 100 }, "number:Transactions": { "one_of": [ 1 ] }, "string:Country": { "one_of": [ "uk", "de" ] } } }, "description": "Used by ACME RPA to create tickets for disputes.", "model": { "label_thresholds": [ { "name": [ "Some Label" ], "threshold": 0.37 }, { "name": [ "Another Label" ], "threshold": 0.46 }, { "name": [ "Parent Label", "Child Label" ], "threshold": 0.41 } ], "version": 8 }, "name": "dispute", "title": "Collateral Disputes" } }'curl -X PUT 'https://<my_api_endpoint>/api/v1/datasets/project1/collateral/streams' \ -H "Authorization: Bearer $REINFER_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "stream": { "comment_filter": { "user_properties": { "number:Spend": { "maximum": 100000, "minimum": 100 }, "number:Transactions": { "one_of": [ 1 ] }, "string:Country": { "one_of": [ "uk", "de" ] } } }, "description": "Used by ACME RPA to create tickets for disputes.", "model": { "label_thresholds": [ { "name": [ "Some Label" ], "threshold": 0.37 }, { "name": [ "Another Label" ], "threshold": 0.46 }, { "name": [ "Parent Label", "Child Label" ], "threshold": 0.41 } ], "version": 8 }, "name": "dispute", "title": "Collateral Disputes" } }' - Node
const request = require("request"); request.put( { url: "https://<my_api_endpoint>/api/v1/datasets/project1/collateral/streams", headers: { Authorization: "Bearer " + process.env.REINFER_TOKEN, }, json: true, body: { stream: { comment_filter: { user_properties: { "number:Spend": { maximum: 100000, minimum: 100 }, "number:Transactions": { one_of: [1] }, "string:Country": { one_of: ["uk", "de"] }, }, }, description: "Used by ACME RPA to create tickets for disputes.", model: { label_thresholds: [ { name: ["Some Label"], threshold: 0.37 }, { name: ["Another Label"], threshold: 0.46 }, { name: ["Parent Label", "Child Label"], threshold: 0.41 }, ], version: 8, }, name: "dispute", title: "Collateral Disputes", }, }, }, function (error, response, json) { // digest response console.log(JSON.stringify(json, null, 2)); } );const request = require("request"); request.put( { url: "https://<my_api_endpoint>/api/v1/datasets/project1/collateral/streams", headers: { Authorization: "Bearer " + process.env.REINFER_TOKEN, }, json: true, body: { stream: { comment_filter: { user_properties: { "number:Spend": { maximum: 100000, minimum: 100 }, "number:Transactions": { one_of: [1] }, "string:Country": { one_of: ["uk", "de"] }, }, }, description: "Used by ACME RPA to create tickets for disputes.", model: { label_thresholds: [ { name: ["Some Label"], threshold: 0.37 }, { name: ["Another Label"], threshold: 0.46 }, { name: ["Parent Label", "Child Label"], threshold: 0.41 }, ], version: 8, }, name: "dispute", title: "Collateral Disputes", }, }, }, function (error, response, json) { // digest response console.log(JSON.stringify(json, null, 2)); } ); - Python
import json import os import requests response = requests.put( "https://<my_api_endpoint>/api/v1/datasets/project1/collateral/streams", headers={"Authorization": "Bearer " + os.environ["REINFER_TOKEN"]}, json={ "stream": { "name": "dispute", "title": "Collateral Disputes", "description": "Used by ACME RPA to create tickets for disputes.", "model": { "version": 8, "label_thresholds": [ {"name": ["Some Label"], "threshold": 0.37}, {"name": ["Another Label"], "threshold": 0.46}, { "name": ["Parent Label", "Child Label"], "threshold": 0.41, }, ], }, "comment_filter": { "user_properties": { "string:Country": {"one_of": ["uk", "de"]}, "number:Spend": {"minimum": 100, "maximum": 100000}, "number:Transactions": {"one_of": [1]}, } }, } }, ) print(json.dumps(response.json(), indent=2, sort_keys=True))import json import os import requests response = requests.put( "https://<my_api_endpoint>/api/v1/datasets/project1/collateral/streams", headers={"Authorization": "Bearer " + os.environ["REINFER_TOKEN"]}, json={ "stream": { "name": "dispute", "title": "Collateral Disputes", "description": "Used by ACME RPA to create tickets for disputes.", "model": { "version": 8, "label_thresholds": [ {"name": ["Some Label"], "threshold": 0.37}, {"name": ["Another Label"], "threshold": 0.46}, { "name": ["Parent Label", "Child Label"], "threshold": 0.41, }, ], }, "comment_filter": { "user_properties": { "string:Country": {"one_of": ["uk", "de"]}, "number:Spend": {"minimum": 100, "maximum": 100000}, "number:Transactions": {"one_of": [1]}, } }, } }, ) print(json.dumps(response.json(), indent=2, sort_keys=True)) - Response
{ "status": "ok", "stream": { "context": "0", "created_at": "2019-08-03T12:30:00.123456Z", "dataset_id": "abcdef0123456789", "description": "Used by ACME RPA to create tickets for disputes.", "id": "0123456789abcdef", "model": { "version": 8 }, "name": "dispute", "title": "Collateral Disputes", "updated_at": "2019-08-03T12:30:00.123456Z" } }{ "status": "ok", "stream": { "context": "0", "created_at": "2019-08-03T12:30:00.123456Z", "dataset_id": "abcdef0123456789", "description": "Used by ACME RPA to create tickets for disputes.", "id": "0123456789abcdef", "model": { "version": 8 }, "name": "dispute", "title": "Collateral Disputes", "updated_at": "2019-08-03T12:30:00.123456Z" } }
Streams enable persistent, stateful iteration through comments in a dataset, with predicted labels and general fields computed using a pinned model.
Once a stream is created, the and methods can be used to iterate through comments.
| NAME | TYPE | REQUIRED | DESCRIPTION |
|---|---|---|---|
name | string | yes | API name for the stream, used in URLs. Must be unique within a dataset and must match [A-Za-z0-9-_]{1,256}.
|
title | string | no | One-line human-readable title for the stream. |
description | string | no | A longer description of the stream. |
model | Model | no | If specified, comments fetched from this stream will contain predictions from a pinned model. |
comment_filter | CommentFilter | no | If specified, comments not matching the filter will not be returned. For details on how the comment filter will affect the results returned by the stream, check |
Where
Model has the following format:
| NAME | TYPE | REQUIRED | DESCRIPTION |
|---|---|---|---|
version | integer | yes | A model version that has been pinned via the Models page. |
label_thresholds | array<LabelThreshold> | no | If set, only values matching the given label_thresholds are returned. If not set, all labels and all prediction values will be returned.
|
Where
LabelThreshold has the following format:
| NAME | TYPE | REQUIRED | DESCRIPTION |
|---|---|---|---|
name | array<string> | yes | The name of the label to be returned, formatted as a list of hierarchical labels. For instance, the label "Some Label" will have the format ["Some Label"], and the label "Parent Label > Child Label" will have the format ["Parent Label", "Child Label"].
|
threshold | number | yes | The confidence threshold to use for the label (a number between 0.0 and 1.0). The label will only be returned for a comment if its prediction is above this threshold. |
Where
CommentFilter has the following format:
| NAME | TYPE | REQUIRED | DESCRIPTION |
|---|---|---|---|
user_properties | UserPropertyFilter | no | A filter that applies to the user properties of a comment. For more on user properties, check Comment Reference. |
The
UserPropertyFilter is a map of user property name to filter. String properties may be filtered to values in a set ({"one_of": ["val_1", "val_2"]}). Number properties may be filtered either to values in a set ({"one_of": [123, 456]}) or to a range ({"minimum": 123, "maximum": 456}).
