1 Apr 2025
Mastraエージェント開発で役立ちそうなYAML出来たので共有します（実験用）
✂︎--------------
# 事前準備
access mastraDocs
# ディレクトリ構成
src/
mastra/
workflows/
index.ts
agents/
index.ts
name: "Deep Research Agent ワークフロー"
description: "ユーザークエリから深い調査を行うワークフロー"
steps:
- id: "userQuery"
inputSchema:
query: string
outputSchema:
analyzedQuery: string
execute: async ({ context }) => {
# ユーザーからの質問受け取り処理
return { analyzedQuery: context.query };
}
- id: "activateAgent"
inputSchema:
analyzedQuery: string
outputSchema:
activatedAgent: object
execute: async ({ context }) => {
# Deep Research Agentの起動処理
return { activatedAgent: { id: "deep-research", status: "active" } };
}
- id: "planning"
inputSchema:
analyzedQuery: string
activatedAgent: object
outputSchema:
researchPlan: object
execute: async ({ context }) => {
# 調査計画の立案
return { researchPlan: { steps: [], priorities: [] } };
}
- id: "initialQueryCreation"
inputSchema:
researchPlan: object
outputSchema:
searchQueries: string[]
execute: async ({ context }) => {
# 初期検索クエリの生成
return { searchQueries: [] };
}
- id: "webSearch"
inputSchema:
searchQueries: string[]
outputSchema:
searchResults: object[]
execute: async ({ context }) => {
# Web検索の実行
return { searchResults: [] };
}
- id: "searchResultAnalysis"
inputSchema:
searchResults: object[]
outputSchema:
analyzedResults: object
execute: async ({ context }) => {
# 検索結果の分析
return { analyzedResults: {} };
}
- id: "infoOrganization"
inputSchema:
analyzedResults: object
outputSchema:
organizedInfo: object
execute: async ({ context }) => {
# 情報整理
return { organizedInfo: {} };
}
- id: "gapResolutionTool"
inputSchema:
organizedInfo: object
outputSchema:
gapAnalysis: object
execute: async ({ context }) => {
# 情報ギャップの分析と解決ツール実行
return { gapAnalysis: { hasGaps: false } };
}
- id: "infoSufficiencyCheck"
inputSchema:
gapAnalysis: object
outputSchema:
isSufficient: boolean
execute: async ({ context }) => {
# 情報の十分性チェック
return { isSufficient: context.gapAnalysis.hasGaps ? false : true };
}
- id: "additionalQueryTool"
inputSchema:
gapAnalysis: object
outputSchema:
additionalQueries: string[]
execute: async ({ context }) => {
# 追加クエリの生成
return { additionalQueries: [] };
}
- id: "additionalWebSearch"
inputSchema:
additionalQueries: string[]
outputSchema:
additionalResults: object[]
execute: async ({ context }) => {
# 追加Web検索の実行
return { additionalResults: [] };
}
- id: "resultAnalysisOrganization"
inputSchema:
additionalResults: object[]
organizedInfo: object
outputSchema:
updatedInfo: object
iterationCount: number
execute: async ({ context }) => {
# 結果の分析と整理、繰り返し回数の追跡
return {
updatedInfo: {},
iterationCount: 1
};
}
- id: "maxIterationCheck"
inputSchema:
iterationCount: number
outputSchema:
canContinue: boolean
execute: async ({ context }) => {
# 最大繰り返し回数のチェック
const MAX_ITERATIONS = 3;
return { canContinue: context.iterationCount < MAX_ITERATIONS };
}
- id: "documentIntegration"
inputSchema:
organizedInfo: object
outputSchema:
integratedDocument: object
execute: async ({ context }) => {
# 文書の統合
return { integratedDocument: {} };
}
- id: "finalDocumentCreation"
inputSchema:
integratedDocument: object
outputSchema:
finalDocument: object
execute: async ({ context }) => {
# 最終文書の生成
return { finalDocument: {} };
}
- id: "userResponse"
inputSchema:
finalDocument: object
outputSchema:
response: string
execute: async ({ context }) => {
# ユーザーへの応答作成
return { response: "" };
}
workflow:
step: "userQuery"
then: "activateAgent"
then: "planning"
then: "initialQueryCreation"
then: "webSearch"
then: "searchResultAnalysis"
then: "infoOrganization"
then: "gapResolutionTool"
then: "infoSufficiencyCheck"
after: "infoSufficiencyCheck"
step: "additionalQueryTool"
when: "context.isSufficient === false"
then: "additionalWebSearch"
then: "resultAnalysisOrganization"
then: "maxIterationCheck"
after: "maxIterationCheck"
step: "gapResolutionTool"
when: "context.canContinue === true"
after: "infoSufficiencyCheck"
step: "documentIntegration"
when: "context.isSufficient === true"
then: "finalDocumentCreation"
then: "userResponse"
after: "maxIterationCheck"
step: "documentIntegration"
when: "context.canContinue === false"
----------------
6
112
7,532
23 Oct 2023
New in #AICopilot: 'Add Documents as Context to Chat'. Choose the documents for AI reference, balancing detail & breadth. Tailor the AI's perspective to your needs 📚🤖. Dive in: solveintelligence.com/contac… #LegalTech #AI #DocumentIntegration #Innovation
1
2
6
1,719
26 Apr 2023
This thread is saved to your Notion database.
Tags: [Chatplatform, Bot, Chatgpt, Documentintegration, Chathub]
191