Mistral AI Core Workflow B: Embeddings & Function Calling

Table of Contents

• Overview
• Prerequisites
• Instructions
• Output
• Error Handling
• Examples
• Resources

Overview

Secondary workflows for Mistral AI: text embeddings for semantic search/RAG and function calling for tool-augmented interactions. Uses mistral-embed (1024 dimensions) for embeddings and mistral-large-latest for function calling.

Prerequisites

• Completed mistral-install-auth setup
• Familiarity with mistral-core-workflow-a
• Valid API credentials configured

Instructions

Step 1: Generate Text Embeddings

Use client.embeddings.create() with model mistral-embed and inputs array. Returns 1024-dimensional vectors per input text.

Step 2: Batch Embeddings

Pass multiple texts in the inputs array for efficient batch processing. Map response data array to extract embedding vectors.

Step 3: Build Semantic Search

Implement SemanticSearch class with indexDocuments() (embeds all docs) and search() (embeds query, ranks by cosine similarity, returns top-K results). Use cosine similarity: dot product divided by product of norms.

Step 4: Define Function Tools

Create tool definitions with JSON Schema parameters. Each tool has type function, name, description, and parameter schema with required fields.

Step 5: Implement Function Calling Loop

Send messages with tools and toolChoice: 'auto' to client.chat.complete(). Check for toolCalls in response. Execute matching tool function, add result as role: 'tool' message, and loop until model returns final text response.

Step 6: Build RAG Pipeline

Combine semantic search with chat completion. Retrieve relevant documents for user query, inject as context in system prompt, generate response with mistral-small-latest. Instruct model to answer from context only.

Output

• Text embeddings with mistral-embed (1024 dimensions)
• Semantic search with cosine similarity ranking
• Function calling with tool execution loop
• RAG chat combining retrieval and generation

Error Handling

Issue	Cause	Resolution
Empty embeddings	Invalid input text	Validate non-empty strings before API call
Tool not found	Unknown function name	Check tool registry matches definitions
RAG hallucination	Insufficient context	Add more documents, tune retrieval top-K
High latency	Large batch size	Split into smaller batches, add concurrency

Examples

Embeddings

const response = await client.embeddings.create({
  model: 'mistral-embed',
  inputs: ['Machine learning is fascinating.'],
});
console.log(`Dimensions: ${response.data[0].embedding.length}`); // 1024  # 1024: 1 KB

Function Calling

const response = await client.chat.complete({
  model: 'mistral-large-latest',
  messages: [{ role: 'user', content: 'Weather in Paris?' }],
  tools, toolChoice: 'auto',
});

See detailed implementation for advanced patterns.

Resources

• Mistral AI Embeddings
• Mistral AI Function Calling
• Mistral AI Agents