PackagePublished package

@pasar6987/sf-service-livechat

Name: Semantic model package @pasar6987/sf-service-livechat
Creator: pasar6987

Real-time chat infrastructure for agent-to-customer conversations with deployment, routing, and transcript management.

OSISchema 0.1.1Domain crm Source developer.salesforce.com/docs/atlas.en-us.object_reference.meta/object_reference

Published package · Latest published 1.0.1 Mar 4, 2026 · 15 datasets / 34 measures in the latest review · Updated Mar 11, 2026

Release path

1.0.1Published Mar 4, 2026

Publisher

@pasar6987Published Mar 4, 2026

Structure snapshot

15 datasets34 measures · 261 dimensions

Semantic Graph

Relationship counts appear after the graph loads.

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Package relationships will appear when the summary is ready.

Reference context

Secondary package facts stay compact

Updated: Mar 11, 2026
Visibility: Public hub listing
License: MIT
Created: Mar 4, 2026

Reference facts

Secondary package facts after the usage path is clear

Licensing, categorization, ownership, and linked metadata stay below the runnable path so the page reads in the right order.

Format

OSI

Upstream

developer.salesforce.com/docs/atlas.en-us.object_reference.meta/object_reference

Repository

Not linked

Domain

crm

Source

developer.salesforce.com/docs/atlas.en-us.object_reference.meta/object_reference

Owner

@pasar6987

Organization

Independent

License

MIT

Visibility

Public hub listing

Publisher

@pasar6987Published Mar 4, 2026

Latest published version

1.0.1Published Mar 4, 2026

Use this package in code and AI

After structure review, move straight into typed reads with load() or compact LLM context with to_prompt().

Python SDK

Python example for @pasar6987/sf-service-livechat

This example uses the current package ref and, when preview data is available, fills in real dataset names from the published summary.

import rawctx

model = rawctx.load("@pasar6987/sf-service-livechat")
prompt = rawctx.to_prompt(
    "@pasar6987/sf-service-livechat",
    datasets=["LiveAgentSession", "LiveChatBlockingRule"],
    max_tokens=2000,
)

print(model.datasets)        # ["LiveAgentSession", "LiveChatBlockingRule", "LiveChatButton"]
print(model.measures)        # [Measure(name="LiveAgentSession.ChatReqAssigned", ...), Measure(name="LiveAgentSession.ChatReqDeclined", ...), Measure(name="LiveAgentSession.ChatReqEngaged", ...)]
print(model.dimensions)      # [Dimension(name="AgentId", ...), Dimension(name="LastReferencedDate", ...), Dimension(name="LastViewedDate", ...)]
print(model.relationships)   # [Relationship(name='...', ...)]
print(prompt)

See Python docs

README

Package narrative and examples

Use documentation after the package clears provenance, ownership, and runnable-path checks.

@pasar6987/sf-service-livechat

Real-time chat infrastructure for agent-to-customer conversations with deployment, routing, and transcript management.

Overview

	Count
Objects (Datasets)	15
Dimensions	261
Measures	34
Relationships	10

Objects

LiveAgentSession — This object is automatically created for each Chat session and stores information about the session.
LiveChatBlockingRule — Represents a rule for blocking chat visitors’ IP addresses from starting new chats with agents.
LiveChatButton — Represents a button that allows visitors to request chats with Chat users.
LiveChatButtonDeployment — Associates an automated chat invitation with a specific deployment.
LiveChatButtonSkill — Represents all the skills available to a LiveChatButton except the one currently assigned. To retrieve the skill currently assigned, query LiveChatButton.
LiveChatDeployment — Represents the general settings for deploying Live Agent on a website.
LiveChatObjectAccessConfig — Represents the action you can perform on a specified object by the Chat API.
LiveChatObjectAccessDefinition — Represents the parent record for one or more LiveChatObjectAccessConfig objects.
LiveChatSensitiveDataRule — Represents a rule for masking or deleting data of a specified pattern. Written as a regular expression (regex).
LiveChatTranscript — This object is automatically created for each Live Agent chat session and stores information about the session.
LiveChatTranscriptSkill — Represents a join between LiveChatTranscript and Skill.
LiveChatUserConfig — Represents a setting that controls the console settings for Chat users.
LiveChatUserConfigProfile — Represents a join between LiveChatUserConfig and Profile.
LiveChatUserConfigUser — Represents a join between Live Chat User Config and User.
LiveChatVisitor — Represents a website visitor who has started or tried to start a chat session.

Install

rawctx snapshot-download @pasar6987/sf-service-livechat

Structure review

Inspect package structure after the usage path is clear

Use the structural review when you need the package footprint, field counts, and model paths before a deeper explorer pass.

Models1

datasets15

measures34

dimensions261

relationships10

AI context1

models/sf-service-livechat.osi.yamlAI context included

15 datasets34 measures261 dimensions10 relationships