PackagePublished package

@pasar6987/sf-core-ai-einstein

Name: Semantic model package @pasar6987/sf-core-ai-einstein
Creator: pasar6987

Machine learning models, AI insights, discovery predictions, natural language processing, recommendation engines, and generative AI capabilities powered by Salesforce Einstein.

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 · 48 datasets / 25 measures in the latest review · Updated Mar 11, 2026

Release path

1.0.1Published Mar 4, 2026

Publisher

@pasar6987Published Mar 4, 2026

Structure snapshot

48 datasets25 measures · 612 dimensions

Semantic Graph

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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-core-ai-einstein

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-core-ai-einstein")
prompt = rawctx.to_prompt(
    "@pasar6987/sf-core-ai-einstein",
    datasets=["AIInsightAction", "AIInsightFeedback"],
    max_tokens=2000,
)

print(model.datasets)        # ["AIInsightAction", "AIInsightFeedback", "AIInsightReason"]
print(model.measures)        # [Measure(name="AIInsightAction.Confidence", ...), Measure(name="AIInsightFeedback.Rank", ...), Measure(name="AIInsightReason.Contribution", ...)]
print(model.dimensions)      # [Dimension(name="ActionId", ...), Dimension(name="ActionName", ...), Dimension(name="AiRecordInsightId", ...)]
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-core-ai-einstein

Machine learning models, AI insights, discovery predictions, natural language processing, recommendation engines, and generative AI capabilities powered by Salesforce Einstein.

Overview

	Count
Objects (Datasets)	48
Dimensions	612
Measures	25
Relationships	24

Objects

AIInsightAction — Represents an Einstein prediction insight action.
AIInsightFeedback — Represents an Einstein prediction insight feedback.
AIInsightReason — Represents an Einstein prediction insight reason.
AIInsightValue — Represents an Einstein prediction insight value.
AIMetric — AI Live Model Metrics - AIMetric
AIRecordInsight — Represents an Einstein prediction insight.
AIResearchPromptResult — Represents the research result generated by Einstein from a prompt template.
AiExternalModelReference — AI External Model References - AiExternalModelReference
AiGenActionItem — Represents business actions suggested by generative AI. AI-generated action items are sent to either agents for automatic execution or human users for review, depending on org preference and if there are any errors in the process.
AiJobRun — Represents an execution instance of an AI job. This object tracks the overall status and manages the lifecycle of the job from initiation to completion.
AiJobRunItem — Stores an individual item associated with a parent AiJobRun, including the inputs and resulting response.
AiModelLanguage — An object that stores language related information that is generated for each AI model.
DiscoveryAIModel — Discovery AI Models - DiscoveryAIModel
DiscoveryDeployedModel — Discovery Deployed Models - DiscoveryDeployedModel
DiscoveryDplyMdlCstmzField — Discovery Deployed Model Customizable Fields - DiscoveryDplyMdlCstmzField
DiscoveryFieldMap — Discovery Field Maps - DiscoveryFieldMap
DiscoveryFilter — Discovery Filters - DiscoveryFilter
DiscoveryFilterValue — Discovery Filter Values - DiscoveryFilterValue
DiscoveryGoal — Discovery Goals - DiscoveryGoal
DiscoveryModelField — Discovery Model Fields - DiscoveryModelField
DiscoveryRecipient — Discovery Recipients - DiscoveryRecipient
DiscoveryRefreshConfig — Discovery Refresh Configs - DiscoveryRefreshConfig
DiscoveryRefreshJob — Discovery Refresh Jobs - DiscoveryRefreshJob
DiscoveryRefreshTask — Discovery Refresh Tasks - DiscoveryRefreshTask
DiscoveryScoringJob — Discovery Scoring Jobs - DiscoveryScoringJob
DiscoveryScoringJobItem — Discovery Scoring Job Items - DiscoveryScoringJobItem
DiscvDplyMdlCstmzFieldTxt — Discovery Deployed Model Customizable Field Text - DiscvDplyMdlCstmzFieldTxt
EinsteinAnswerFeedback — Einstein Answer Feedback - EinsteinAnswerFeedback
GenAIConversationSummary — Represents a generated summary of a voice or video call.
GenAiPlannerFunctionDef — Represents a relationship between the agent planner service and agent actions.
GenAiPluginDefinition — Represents an agent topic, which is a category of actions related to a particular job to be done by AI agents.
MLFilterValue — ML Filter Values - MLFilterValue
MLModel — Represents an AI model that can be used in Einstein Prediction Builder, Einstein Recommendation Builder, and other Einstein features.
MLModelFactor — Represents a field value that has a positive or negative effect on the model’s score.
MLModelFactorComponent — Represents information about the related MLModelFactor. For example, this object can represent a field value or a field range such as “Title = CEO” or “Annual Revenue >10000000”.
MLModelMetric — Represents a metric or statistic about the related model, such as accuracy, precision, or RSquared. Use a model’s metrics to learn about its performance and to compare it with other models.
MLRecommendationDefinition — For internal use only.
MlFeatureValueMetric — ML Feature Value Metrics - MlFeatureValueMetric
MlIntentUtteranceSuggestion — Represents a customer input, used for training purposes in the feedback loop process of a conversation. Admins can add these inputs to the intent training model.
ModelFactor — ModelFactors - ModelFactor
NLPhrase — CQ Phrases - NLPhrase
NLQueryFragment — CQ Fragments - NLQueryFragment
Recommendation — Represents the recommendations surfaced as offers and actions for Einstein Next Best Action.
RecommendationFeedback — Recommendation Feedback - RecommendationFeedback
RecommendationResponse — Represents the user responses to a presented offer or recommendation for Einstein Next Best Action. This object is available in API version 51.0 and later.
RecordRecommendation — Record Recommendations - RecordRecommendation
ReplyAIRecommendation — Reply AI Recommendations - ReplyAIRecommendation
TopInsight — For internal use only.

Install

rawctx snapshot-download @pasar6987/sf-core-ai-einstein

Structure review

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

Models1

datasets48

measures25

dimensions612

relationships24

AI context1

models/sf-core-ai-einstein.osi.yamlAI context included

48 datasets25 measures612 dimensions24 relationships