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mistral-core-workflow-b Execute Mistral AI embeddings, function calling, and RAG pipelines.
Use when implementing semantic search, RAG applications,
tool-augmented LLM interactions, or code embeddings.
Trigger with phrases like "mistral embeddings", "mistral function calling",
"mistral tools", "mistral RAG", "mistral semantic search".
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langchain-deploy-integration Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe correctly — with timeouts sized for chain length, cold-start mitigation, SSE anti-buffering headers, and Secret Manager over .env. Use when prepping a first production deploy, debugging a stream that hangs behind a proxy, or diagnosing p99 latency spikes. Trigger with "langchain deploy", "langchain cloud run", "langchain vercel python", "langchain langserve", or "langchain docker".
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2 fichiers name mistral-core-workflow-b description Execute Mistral AI embeddings, function calling, and RAG pipelines.
Use when implementing semantic search, RAG applications,
tool-augmented LLM interactions, or code embeddings.
Trigger with phrases like "mistral embeddings", "mistral function calling",
"mistral tools", "mistral RAG", "mistral semantic search".
allowed-tools Read, Write, Edit, Bash(npm:*), Grep version 1.12.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","mistral","llm","embeddings","workflow"] compatibility Designed for Claude Code
Mistral AI Core Workflow B: Embeddings & Function Calling
Overview
Secondary workflows for Mistral AI: text/code embeddings with mistral-embed (1024 dimensions), function calling (tool use) with any chat model, and RAG pipeline combining both. Mistral supports auto, any, and none tool choice modes.
Prerequisites
Completed mistral-install-auth setup
MISTRAL_API_KEY environment variable setFamiliarity with mistral-core-workflow-a
Instructions
Step 1: Generate Text Embeddings
import { Mistral } from '@mistralai/mistralai' ;
const client = new Mistral ({ apiKey : process.env .MISTRAL_API_KEY });
const response = await client.embeddings .create ({
model : 'mistral-embed' ,
inputs : ['Machine learning is fascinating.' ],
});
const vector = response.data [0 ].embedding ;
console .log (`Dimensions: ${vector.length} ` );
console .log (`Tokens used: ${response.usage.totalTokens} ` );
Step 2: Batch Embeddings with Rate Awareness
async function batchEmbed
texts : string [],
batchSize = 64 ,
Promise
number
const
allEmbeddings
number
for
let
0
length
const
slice
const
await
embeddings
create
model
'mistral-embed'
inputs
push
data
map
d =>
embedding
return
const
'doc1...'
'doc2...'
const
await
batchEmbed
Step 3: Semantic Search with Cosine Similarity function cosineSimilarity (a : number [], b : number []number {
let dot = 0 , normA = 0 , normB = 0 ;
for (let i = 0 ; i < a.length ; i++) {
dot += a[i] * b[i];
normA += a[i] * a[i];
normB += b[i] * b[i];
}
return dot / (Math .sqrt (normA) * Math .sqrt (normB));
}
class SemanticSearch {
private documents : Array <{ text : string ; embedding : number [] }> = [];
private client : Mistral ;
constructor (this .client = new Mistral ({ apiKey : process.env .MISTRAL_API_KEY });
}
async index (texts : string []): Promise <void > {
const response = await this .client .embeddings .create ({
model : 'mistral-embed' ,
inputs : texts,
});
this .documents = texts.map ((text, i ) => ({
text,
embedding : response.data [i].embedding ,
}));
}
async search (query : string , topK = 5 ): Promise <Array <{ text : string ; score : number }>> {
const qEmbed = await this .client .embeddings .create ({
model : 'mistral-embed' ,
inputs : [query],
});
const qVec = qEmbed.data [0 ].embedding ;
return this .documents
.map (doc =>text : doc.text , score : cosineSimilarity (qVec, doc.embedding ) }))
.sort ((a, b ) => b.score - a.score )
.slice (0 , topK);
}
}
Step 4: Function Calling (Tool Use)
const tools = [
{
type : 'function' as const ,
function : {
name : 'get_weather' ,
description : 'Get current weather for a city' ,
parameters : {
type : 'object' ,
properties : {
city : { type : 'string' , description : 'City name (e.g., "Paris")' },
units : { type : 'string' , enum : ['celsius' , 'fahrenheit' ], default : 'celsius' },
},
required : ['city' ],
},
},
},
{
type : 'function' as const ,
function : {
name : 'search_database' ,
description : 'Search product database by query' ,
parameters : {
type : 'object' ,
properties : {
query : { type : 'string' },
limit : { type : 'integer' , default : 10 },
},
required : ['query' ],
},
},
},
];
const response = await client.chat .complete ({
model : 'mistral-large-latest' ,
messages : [{ role : 'user' , content : "What's the weather in Paris?" }],
tools,
toolChoice : 'auto' ,
});
Step 5: Tool Execution Loop
const toolRegistry : Record <string , (args : any Promise <any >> = {
get_weather : async ({ city, units }) => ({ city, temp : 22 , units : units ?? 'celsius' }),
search_database : async ({ query, limit }) => ({ results : [], total : 0 }),
};
async function chatWithTools (userMessage : string Promise <string > {
const messages : any [] = [{ role : 'user' , content : userMessage }];
while (true ) {
const response = await client.chat .complete ({
model : 'mistral-large-latest' ,
messages,
tools,
toolChoice : 'auto' ,
});
const choice = response.choices ?.[0 ];
if (!choice) throw new Error ('No response from model' );
if (choice.message .toolCalls ?.length ) {
messages.push (choice.message );
for (const call of choice.message .toolCalls ) {
const fn = toolRegistry[call.function .name ];
if (!fn) throw new Error (`Unknown tool: ${call.function .name} ` );
const args = JSON .parse (call.function .arguments );
const result = await fn (args);
messages.push ({
role : 'tool' ,
name : call.function .name ,
content : JSON .stringify (result),
toolCallId : call.id ,
});
}
continue ;
}
return choice.message .content ?? '' ;
}
}
Step 6: RAG Pipeline (Retrieval-Augmented Generation) async function ragChat (
query : string ,
searcher : SemanticSearch ,
topK = 3 ,
): Promise <{ answer : string ; sources : string [] }> {
const results = await searcher.search (query, topK);
const context = results.map ((r, i ) => `[${i + 1 } ] ${r.text} ` ).join ('\n\n' );
const response = await client.chat .complete ({
model : 'mistral-small-latest' ,
messages : [
{
role : 'system' ,
content : `Answer based ONLY on the provided context. Cite sources as [1], [2], etc. If the context doesn't contain the answer, say "I don't have enough information."` ,
},
{
role : 'user' ,
content : `Context:\n${context} \n\nQuestion: ${query} ` ,
},
],
temperature : 0.1 ,
});
return {
answer : response.choices ?.[0 ]?.message ?.content ?? '' ,
sources : results.map (r =>text ),
};
}
Output
Text embeddings with mistral-embed (1024 dimensions)
Semantic search with cosine similarity ranking
Function calling with tool execution loop
RAG pipeline 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 tool definitions Infinite tool loop Model keeps calling tools Add max iteration count (e.g., 10) RAG hallucination Insufficient context Add more documents, increase topK 400 Bad RequestMissing toolCallId Each tool result must include the matching toolCallId
Resources
Next Steps For SDK patterns, see mistral-sdk-patterns. For agents, see mistral-webhooks-events.
