@memberjunction/aiengine

Server-side AI Engine for MemberJunction. Wraps AIEngineBase and adds server-only capabilities including LLM execution, embedding generation, vector-based semantic search for agents and actions, and conversation attachment management. This package is the main orchestration layer for AI operations on the server.

Architecture

``mermaid
graph TD
AIB["AIEngineBase
Metadata Cache"]
style AIB fill:#2d6a9f,stroke:#1a4971,color:#fff

AIE["AIEngine
Server-Side Singleton"]
style AIE fill:#2d8659,stroke:#1a5c3a,color:#fff

subgraph "Server Capabilities"
LLM["LLM Execution
ChatCompletion, Classify, Summarize"]
style LLM fill:#7c5295,stroke:#563a6b,color:#fff

EMB["Embedding Services
Agent & Action Embeddings"]
style EMB fill:#7c5295,stroke:#563a6b,color:#fff

VS["Vector Search
Semantic Agent/Action/Note Matching"]
style VS fill:#b8762f,stroke:#8a5722,color:#fff

ATT["Attachment Service
Conversation Media Management"]
style ATT fill:#b8762f,stroke:#8a5722,color:#fff
end

AIB --> AIE
AIE --> LLM
AIE --> EMB
AIE --> VS
AIE --> ATT

subgraph "Result Types"
AMR["AgentMatchResult"]
style AMR fill:#7c5295,stroke:#563a6b,color:#fff

ACMR["ActionMatchResult"]
style ACMR fill:#7c5295,stroke:#563a6b,color:#fff

NMR["NoteMatchResult"]
style NMR fill:#7c5295,stroke:#563a6b,color:#fff

EMR["ExampleMatchResult"]
style EMR fill:#7c5295,stroke:#563a6b,color:#fff
end

VS --> AMR
VS --> ACMR
VS --> NMR
VS --> EMR
`

Installation

`bash
npm install @memberjunction/aiengine
`

Note: This package is server-side only. For metadata access on the client, use @memberjunction/ai-engine-base directly.

Key Exports

$3

The main server-side engine. Uses composition (not inheritance) to delegate metadata operations to AIEngineBase.Instance while adding server-specific features.

`typescript
import { AIEngine } from '@memberjunction/aiengine';

// Initialize
await AIEngine.Instance.Config(false, contextUser);

// All AIEngineBase properties are delegated:
const models = AIEngine.Instance.Models;
const agents = AIEngine.Instance.Agents;
`

#### LLM Execution

`typescript
// Direct chat completion
const result = await AIEngine.Instance.ChatCompletion({
model: 'gpt-4',
messages: [{ role: 'user', content: 'Explain quantum computing' }]
});

// Summarize text
const summary = await AIEngine.Instance.SummarizeText({
model: 'gpt-4',
text: longDocument
});

// Classify text
const classification = await AIEngine.Instance.ClassifyText({
model: 'gpt-4',
text: inputText,
categories: ['positive', 'negative', 'neutral']
});
`

#### Semantic Search

Find agents, actions, notes, and examples using vector similarity:

`typescript
// Find agents matching a user query
const agentMatches: AgentMatchResult[] = await AIEngine.Instance.FindSimilarAgents(
'Help me analyze sales data',
5, // topK
contextUser
);

// Find relevant actions
const actionMatches: ActionMatchResult[] = await AIEngine.Instance.FindSimilarActions(
'Send an email notification',
5,
contextUser
);

// Find relevant notes for an agent
const noteMatches: NoteMatchResult[] = await AIEngine.Instance.FindSimilarNotes(
agentId,
'Customer wants a refund',
10,
contextUser
);

// Find relevant examples for an agent
const exampleMatches: ExampleMatchResult[] = await AIEngine.Instance.FindSimilarExamples(
agentId,
'How do I reset my password?',
5,
contextUser
);
`

$3

| Class | Purpose |
|---|---|
| AgentEmbeddingService | Generates and manages embeddings for AI agents, enabling semantic agent discovery |
| ActionEmbeddingService | Generates and manages embeddings for actions, enabling semantic action matching |

$3

| Type | Fields | Description |
|---|---|---|
| AgentMatchResult | agent, score, metadata | Agent found via semantic similarity |
| ActionMatchResult | action, score, metadata | Action found via semantic similarity |
| NoteMatchResult | note, score, metadata | Agent note found via semantic similarity |
| ExampleMatchResult | example, score, metadata | Agent example found via semantic similarity |

$3

Manages media attachments (images, audio, video, files) in agent conversations:

`typescript
import { ConversationAttachmentService } from '@memberjunction/aiengine';

const service = new ConversationAttachmentService();

// Process uploaded attachments for a conversation
await service.ProcessAttachments(conversationId, attachments, contextUser);
`

Usage Pattern

`typescript
import { AIEngine } from '@memberjunction/aiengine';

// 1. Initialize at server startup
await AIEngine.Instance.Config(false, contextUser);

// 2. Access metadata (delegated to AIEngineBase)
const model = AIEngine.Instance.Models.find(m => m.Name === 'GPT-4');
const agent = AIEngine.Instance.GetAgentByName('Sales Assistant');

// 3. Use server-side capabilities
const similar = await AIEngine.Instance.FindSimilarAgents(userQuery, 5, contextUser);
`

Dependencies

- @memberjunction/ai-engine-base -- Base metadata cache (AIEngineBase)
- @memberjunction/ai -- Core AI abstractions (BaseLLM, BaseEmbeddings)
- @memberjunction/ai-core-plus -- Extended entity classes
- @memberjunction/ai-vectors-memory -- In-memory vector service for semantic search
- @memberjunction/core -- MJ framework core
- @memberjunction/core-entities -- Generated entity classes
- @memberjunction/actions-base -- Action framework integration
- @memberjunction/storage` -- File storage integration for attachments