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