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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".
跳到安装 Skills Marketplace 发现并探索由社区构建的 Agent Skills
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill mistral-core-workflow-b命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
下载 Zip 下载中... 同仓库更多 Skills Implement user sign-up and sign-in flows with Clerk.
Use when building authentication UI, customizing sign-in experience,
or implementing OAuth social login.
Trigger with phrases like "clerk sign-in", "clerk sign-up",
"clerk login flow", "clerk OAuth", "clerk social login".
Implement session management and middleware with Clerk.
Use when managing user sessions, configuring route protection,
or implementing token refresh and custom JWT templates.
Trigger with phrases like "clerk session", "clerk middleware",
"clerk route protection", "clerk token", "clerk JWT".
Configure enterprise SSO, role-based access control, and organization management.
Use when implementing SSO integration, configuring role-based permissions,
or setting up organization-level controls.
Trigger with phrases like "clerk SSO", "clerk RBAC",
"clerk enterprise", "clerk roles", "clerk permissions", "clerk organizations".
jeremylongshore
jeremylongshore/claude-code-plugins-plus-skills
打开 GitHub 仓库 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, also compatible with Codex and OpenClaw
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.
