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# @memberjunction/ai-mistral

MemberJunction AI provider for Mistral AI. Provides both LLM and embedding capabilities, implementing `BaseLLM` and `BaseEmbeddings` from `@memberjunction/ai`.

## Architecture

```mermaid
graph TD
    A["MistralLLM<br/>(Provider)"] -->|extends| B["BaseLLM<br/>(@memberjunction/ai)"]
    C["MistralEmbedding<br/>(Provider)"] -->|extends| D["BaseEmbeddings<br/>(@memberjunction/ai)"]
    A -->|wraps| E["Mistral Client<br/>(@mistralai/mistralai)"]
    C -->|wraps| E
    A -->|provides| F["Chat Completions<br/>+ Streaming"]
    C -->|provides| G["Text Embeddings"]
    B -->|registered via| H["@RegisterClass"]
    D -->|registered via| H

    style A fill:#7c5295,stroke:#563a6b,color:#fff
    style C fill:#7c5295,stroke:#563a6b,color:#fff
    style B fill:#2d6a9f,stroke:#1a4971,color:#fff
    style D fill:#2d6a9f,stroke:#1a4971,color:#fff
    style E fill:#2d8659,stroke:#1a5c3a,color:#fff
    style F fill:#b8762f,stroke:#8a5722,color:#fff
    style G fill:#b8762f,stroke:#8a5722,color:#fff
    style H fill:#b8762f,stroke:#8a5722,color:#fff
```

## Features

- **Chat Completions**: Conversational AI with Mistral Large, Medium, Small, and Codestral models
- **Streaming**: Real-time response streaming support
- **Text Embeddings**: Vector embeddings via Mistral's embedding models
- **Thinking/Reasoning**: Extraction of thinking content from reasoning models
- **JSON Mode**: Response format control for structured outputs
- **Multimodal Support**: Handling of image content in messages

## Installation

```bash
npm install @memberjunction/ai-mistral
```

## Usage

### Chat Completion

```typescript
import { MistralLLM } from "@memberjunction/ai-mistral";

const llm = new MistralLLM("your-mistral-api-key");

const result = await llm.ChatCompletion({
    model: "mistral-large-latest",
    messages: [
        { role: "user", content: "Explain transformers in machine learning." },
    ],
    temperature: 0.7,
});

if (result.success) {
    console.log(result.data.choices[0].message.content);
}
```

### Streaming

```typescript
const result = await llm.ChatCompletion({
    model: "mistral-small-latest",
    messages: [{ role: "user", content: "Write a poem." }],
    streaming: true,
    streamingCallbacks: {
        OnContent: (content) => process.stdout.write(content),
        OnComplete: () => console.log("\nDone!"),
    },
});
```

### Embeddings

```typescript
import { MistralEmbedding } from "@memberjunction/ai-mistral";

const embedder = new MistralEmbedding("your-mistral-api-key");

const result = await embedder.EmbedText({
    text: "Sample text for embedding",
    model: "mistral-embed",
});

console.log(`Dimensions: ${result.vector.length}`);
```

## Supported Parameters

| Parameter | Supported | Notes |
|-----------|-----------|-------|
| temperature | Yes | Controls randomness |
| maxOutputTokens | Yes | Maximum response length |
| topP | Yes | Nucleus sampling |
| seed | Yes | Deterministic outputs |
| stopSequences | Yes | Custom stop sequences |
| assistantPrefill | Yes | Pre-seed the start of the assistant's response via Mistral's `prefix` flag ([guide](../../Prompts/PREFILL_AND_STOP_SEQUENCES.md)) |
| responseFormat | Yes | JSON mode support |
| streaming | Yes | Real-time streaming |

## Class Registration

- `MistralLLM` -- Registered via `@RegisterClass(BaseLLM, 'MistralLLM')`
- `MistralEmbedding` -- Registered via `@RegisterClass(BaseEmbeddings, 'MistralEmbedding')`

## Dependencies

- `@memberjunction/ai` - Core AI abstractions
- `@memberjunction/global` - Class registration
- `@mistralai/mistralai` - Official Mistral AI SDK