# Mistral AI Core Workflow B - Implementation Details

## Text Embeddings

```typescript
async function getEmbedding(text: string): Promise<number[]> {
  const response = await client.embeddings.create({ model: 'mistral-embed', inputs: [text] });
  return response.data[0].embedding; // 1024 dimensions
}

async function getBatchEmbeddings(texts: string[]): Promise<number[][]> {
  const response = await client.embeddings.create({ model: 'mistral-embed', inputs: texts });
  return response.data.map(d => d.embedding);
}
```

## Semantic Search

```typescript
class SemanticSearch {
  async indexDocuments(docs) {
    const response = await this.client.embeddings.create({ model: 'mistral-embed', inputs: docs.map(d => d.text) });
    this.documents = docs.map((doc, i) => ({ ...doc, embedding: response.data[i].embedding }));
  }

  async search(query: string, topK = 5) {
    const queryEmbedding = await this.getEmbedding(query);
    return this.documents
      .map(doc => ({ ...doc, score: cosineSimilarity(queryEmbedding, doc.embedding!) }))
      .sort((a, b) => b.score - a.score)
      .slice(0, topK);
  }
}
```

## Function Calling

```typescript
const tools = [
  { type: 'function', function: { name: 'get_weather', description: 'Get weather for a location', parameters: { type: 'object', properties: { location: { type: 'string' } }, required: ['location'] } } },
  { type: 'function', function: { name: 'search_web', description: 'Search the web', parameters: { type: 'object', properties: { query: { type: 'string' } }, required: ['query'] } } },
];

async function chatWithTools(userMessage: string): Promise<string> {
  const messages = [{ role: 'user', content: userMessage }];
  while (true) {
    const response = await client.chat.complete({ model: 'mistral-large-latest', messages, tools, toolChoice: 'auto' });
    const assistantMessage = response.choices?.[0]?.message;
    messages.push(assistantMessage);
    if (!assistantMessage.toolCalls?.length) return assistantMessage.content ?? '';
    for (const toolCall of assistantMessage.toolCalls) {
      const result = await toolFunctionstoolCall.function.name);
      messages.push({ role: 'tool', name: toolCall.function.name, content: result, toolCallId: toolCall.id });
    }
  }
}
```

## RAG (Retrieval-Augmented Generation)

```typescript
class RAGChat {
  async chat(userQuery: string): Promise<string> {
    const relevantDocs = await this.search.search(userQuery, 3);
    const context = relevantDocs.map(doc => `[Source ${doc.id}]: ${doc.text}`).join('\n\n');
    const response = await this.client.chat.complete({
      model: 'mistral-small-latest',
      messages: [
        { role: 'system', content: `Answer based on context. If not in context, say so.\n\nContext:\n${context}` },
        { role: 'user', content: userQuery },
      ],
    });
    return response.choices?.[0]?.message?.content ?? '';
  }
}
```

## Python Embeddings

```python
def get_embeddings(texts: list[str]) -> list[list[float]]:
    response = client.embeddings.create(model="mistral-embed", inputs=texts)
    return [d.embedding for d in response.data]
```

## Python Function Calling

```python
response = client.chat.complete(
    model="mistral-large-latest",
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
    tools=tools,
    tool_choice="auto"
)
```

---
*[Tons of Skills](https://tonsofskills.com) by [Intent Solutions](https://intentsolutions.io) | [jeremylongshore.com](https://jeremylongshore.com)*
