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docs/ar/enterprise/integrations/databricks.mdx
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---
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title: تكامل Databricks
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description: "اربط وكلاء CrewAI بـ Databricks Genie وSQL وUnity Catalog Functions وVector Search عبر خوادم MCP المُدارة من Databricks."
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icon: "layer-group"
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mode: "wide"
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---
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## نظرة عامة
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اربط وكلاء CrewAI مباشرةً بمساحة عمل Databricks الخاصة بك عبر [خوادم MCP المُدارة من Databricks](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp). يتيح تكامل Databricks لوكلائك طرح أسئلة بلغة طبيعية باستخدام **Genie**، وتنفيذ **SQL** خاضع للحوكمة، واستدعاء **Unity Catalog Functions**، واسترجاع المستندات باستخدام **Vector Search** — كل ذلك دون كتابة أو استضافة أي كود موصِّل، مع تطبيق أذونات Unity Catalog في كل استدعاء.
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|
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في الخلفية، يُعدّ تكامل Databricks غلافًا مُدارًا حول دعم [خوادم MCP المخصصة](/ar/enterprise/guides/custom-mcp-server) في CrewAI. تكشف Databricks عن كل قدرة كنقطة نهاية [Model Context Protocol](https://modelcontextprotocol.io/) خاصة بها، ويتصل بها CrewAI بأمان نيابةً عنك. ولأن كل خادم يُضاف بشكل منفصل، يمكنك تفعيل القدرات التي تحتاجها فرقك (crews) بالضبط.
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## القدرات الرئيسية
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<CardGroup cols={2}>
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<Card title="Genie" icon="comments">
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اطرح أسئلة بلغة طبيعية واحصل على إجابات مستندة إلى بياناتك باستخدام [Genie](https://docs.databricks.com/aws/en/genie/)، الذي يستعلم من Genie Spaces وUnity Catalog ويوفّر روابط تعود إلى واجهة Databricks.
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</Card>
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<Card title="Databricks SQL" icon="database">
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نفّذ SQL خاضعًا للحوكمة على مستودعات Databricks لديك للاستعلام عن البيانات وتحويلها وإنشاء خطوط أنابيب البيانات مباشرةً من وكلائك.
|
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</Card>
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<Card title="Unity Catalog Functions" icon="function">
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استدعِ [دوال Unity Catalog](https://docs.databricks.com/aws/en/udf/unity-catalog) لتنفيذ SQL مُعرّف مسبقًا ومنطق أعمال مخصّص كأدوات قابلة لإعادة الاستخدام وخاضعة للحوكمة.
|
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</Card>
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<Card title="Vector Search" icon="magnifying-glass">
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استرجع المستندات ذات الصلة لسير عمل RAG والمعرفة من فهارس [Mosaic AI Vector Search](https://docs.databricks.com/aws/en/generative-ai/vector-search) باستخدام التشابه الدلالي.
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</Card>
|
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</CardGroup>
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|
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تعمل جميع الخوادم خلف Unity AI Gateway وتطبّق ضوابط الوصول في Unity Catalog، بحيث لا يرى وكلاؤك سوى البيانات والأدوات المصرَّح لهم باستخدامها.
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## المتطلبات المسبقة
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قبل استخدام تكامل Databricks، تأكّد من توفّر ما يلي:
|
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|
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- حساب [CrewAI AMP](https://app.crewai.com) باشتراك نشط
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- مساحة عمل Databricks تحتوي على القدرات التي تريد كشفها (Genie Spaces، مستودعات SQL، دوال Unity Catalog، أو فهارس Vector Search)
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- [امتيازات Unity Catalog](https://docs.databricks.com/aws/en/data-governance/unity-catalog) المناسبة على الكائنات الأساسية
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- اسم مضيف مساحة عمل Databricks الخاص بك (مثال: `your-workspace.cloud.databricks.com`)
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|
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## خوادم MCP المُدارة من Databricks
|
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تنشر Databricks خادم MCP مُدارًا منفصلًا لكل قدرة. يكشف CrewAI عنها كاتصالات فردية، يُهيَّأ كل منها باستخدام مضيف مساحة العمل ومعرّفات Unity Catalog ذات الصلة. تتبع نقاط النهاية الأنماط التالية:
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|
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| الخادم | الوظيفة | نمط عنوان MCP |
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|--------|---------|---------------|
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| **Genie** | أسئلة وأجوبة بلغة طبيعية على Genie Space | `https://<workspace-hostname>/api/2.0/mcp/genie/{genie_space_id}` |
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| **Databricks SQL** | تنفيذ SQL على مستودعاتك | `https://<workspace-hostname>/api/2.0/mcp/sql` |
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| **Unity Catalog Functions** | تشغيل دوال UC المسجّلة | `https://<workspace-hostname>/api/2.0/mcp/functions/{catalog}/{schema}` |
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| **Vector Search** | الاستعلام من فهرس Vector Search | `https://<workspace-hostname>/api/2.0/mcp/vector-search/{catalog}/{schema}` |
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|
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<Note>
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لا حاجة لإنشاء عناوين URL هذه يدويًا — يُنشئ CrewAI كل نقطة نهاية من مضيف مساحة العمل والمعرّفات (Genie Space ID، أو catalog/schema) التي تقدّمها عند تهيئة الاتصال. للاطّلاع على المواصفات الكاملة وأحدث تفاصيل نقاط النهاية، راجع [وثائق MCP المُدارة من Databricks](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp).
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</Note>
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|
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## ربط Databricks في CrewAI AMP
|
||||
|
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<Frame>
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<img src="/images/enterprise/databricks-configure.png" alt="تهيئة خادم MCP مُدار من Databricks في CrewAI AMP" />
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</Frame>
|
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|
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تظهر كل قدرة من قدرات Databricks — **Databricks Genie** و**Databricks SQL** و**Databricks Unity Catalog Functions** و**Databricks Vector Search** — كخادم MCP خاص بها ضمن مجموعة Databricks في صفحة **Tools & Integrations**. هيّئ ما تحتاجه:
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<Steps>
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<Step title="افتح Tools & Integrations">
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انتقل إلى **Tools & Integrations** في الشريط الجانبي الأيسر في CrewAI AMP وحدِّد مجموعة **Databricks** في قائمة Connections. سترى خوادم Genie وSQL وUnity Catalog Functions وVector Search مُدرجة أسفلها.
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</Step>
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|
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<Step title="هيّئ خادمًا">
|
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انقر على **Configure** بجوار القدرة التي تريد تفعيلها وقدّم تفاصيل الاتصال الخاصة بها:
|
||||
|
||||
- **Workspace Host** — اسم مضيف مساحة عمل Databricks الخاص بك (مثال: `my-workspace.cloud.databricks.com`).
|
||||
- **Genie** — **Genie Space ID** المراد الاستعلام عنه.
|
||||
- **Unity Catalog Functions** — الـ **catalog** والـ **schema** اللذان يحتويان على دوالك.
|
||||
- **Vector Search** — الـ **catalog** والـ **schema** اللذان يحتويان على الفهرس.
|
||||
- **Databricks SQL** — لا توجد معرّفات إضافية؛ تُنفَّذ الاستعلامات على مستودعات SQL في مساحة عملك.
|
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</Step>
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<Step title="اختر طريقة المصادقة">
|
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اختر كيف يصادق CrewAI على Databricks. يُوصى باستخدام **OAuth**.
|
||||
|
||||
- **Use OAuth** — اتصل بأمان باستخدام OAuth 2.0. يصادق كل مستخدم على حدة، وتُصدر Databricks رموزًا (tokens) محدّدة النطاق للقدرة (`genie` أو `sql` أو `unity-catalog` أو `vector-search`). يتولّى CrewAI تدفّق التفويض ويُجدّد الرموز تلقائيًا.
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- **Use personal access token** — صادِق باستخدام [رمز وصول شخصي من Databricks](https://docs.databricks.com/aws/en/dev-tools/auth/pat). استخدم هوية بأقل الامتيازات للحدّ من التعرّض.
|
||||
</Step>
|
||||
|
||||
<Step title="صادِق">
|
||||
أكمل المصادقة. بمجرد الاتصال، تصبح أدوات الخادم متاحة لفرقك. كرّر العملية لأي قدرات Databricks أخرى تريد تفعيلها.
|
||||
</Step>
|
||||
</Steps>
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<Tip>
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لأن كل قدرة هي اتصال منفصل، يمكنك المزج والمطابقة — على سبيل المثال، فعّل Genie وVector Search لفريق بحث، مع حجز SQL وUnity Catalog Functions لفريق هندسة البيانات. تتيح لك إعدادات الرؤية (Visibility) التحكّم في أعضاء الفريق الذين يمكنهم استخدام كل منها.
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</Tip>
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## استخدام أدوات Databricks في فرقك
|
||||
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بمجرد الاتصال، تظهر الأدوات التي يكشفها كل خادم MCP جنبًا إلى جنب مع الاتصالات المدمجة في صفحة **Tools & Integrations**. يمكنك:
|
||||
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||||
- **إسناد الأدوات إلى الوكلاء** في فرقك تمامًا مثل أي أداة أخرى في CrewAI.
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||||
- **إدارة الرؤية** للتحكّم في أعضاء الفريق الذين يمكنهم استخدام كل اتصال.
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- **تعديل أو إزالة** أي اتصال في أي وقت من قائمة Connections.
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||||
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||||
يمكن لوكلائك الآن طلب إجابات مستندة من Genie، وتنفيذ SQL على مستودعاتك، واستدعاء دوال Unity Catalog، والبحث في فهارس Vector Search — مع تدفّق النتائج تلقائيًا إلى استدلالهم.
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||||
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||||
<Warning>
|
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تطبّق Databricks الحوكمة عبر Unity Catalog وUnity AI Gateway: لا يمكن للمستخدم اكتشاف الأدوات واستدعاؤها إلا تلك المصرَّح بها لهوية مساحة عمله. إذا فشل استدعاء أداة، فتأكّد من أن المستخدم المتصل (أو هوية الرمز) يمتلك امتيازات Unity Catalog المطلوبة على Genie Space أو المستودع أو الدالة أو الفهرس. تُنفَّذ بعض استعلامات Genie وSQL بشكل غير متزامن وقد تستغرق لحظة لإرجاع النتائج.
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</Warning>
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## مزيد من المعلومات
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<CardGroup cols={2}>
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<Card title="خوادم MCP المُدارة من Databricks" icon="layer-group" href="https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp">
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وثائق Databricks الرسمية لخوادم MCP المُدارة Genie وSQL وUnity Catalog Functions وVector Search.
|
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</Card>
|
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<Card title="خوادم MCP المخصصة في CrewAI" icon="plug" href="/ar/enterprise/guides/custom-mcp-server">
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||||
تعرّف على كيفية اتصال CrewAI بأي خادم MCP، وهو الأساس الذي يُبنى عليه تكامل Databricks.
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</Card>
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</CardGroup>
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||||
|
||||
<Card title="بحاجة إلى مساعدة؟" icon="headset" href="mailto:support@crewai.com">
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||||
تواصل مع فريق الدعم للحصول على المساعدة في تهيئة تكامل Databricks أو في حل المشكلات.
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</Card>
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|
||||
"pt-BR/enterprise/integrations/databricks",
|
||||
"pt-BR/enterprise/integrations/github",
|
||||
"pt-BR/enterprise/integrations/gmail",
|
||||
"pt-BR/enterprise/integrations/google_calendar",
|
||||
@@ -9715,6 +9734,7 @@
|
||||
"pt-BR/enterprise/integrations/asana",
|
||||
"pt-BR/enterprise/integrations/box",
|
||||
"pt-BR/enterprise/integrations/clickup",
|
||||
"pt-BR/enterprise/integrations/databricks",
|
||||
"pt-BR/enterprise/integrations/github",
|
||||
"pt-BR/enterprise/integrations/gmail",
|
||||
"pt-BR/enterprise/integrations/google_calendar",
|
||||
@@ -10186,6 +10206,7 @@
|
||||
"pt-BR/enterprise/integrations/asana",
|
||||
"pt-BR/enterprise/integrations/box",
|
||||
"pt-BR/enterprise/integrations/clickup",
|
||||
"pt-BR/enterprise/integrations/databricks",
|
||||
"pt-BR/enterprise/integrations/github",
|
||||
"pt-BR/enterprise/integrations/gmail",
|
||||
"pt-BR/enterprise/integrations/google_calendar",
|
||||
@@ -10657,6 +10678,7 @@
|
||||
"pt-BR/enterprise/integrations/asana",
|
||||
"pt-BR/enterprise/integrations/box",
|
||||
"pt-BR/enterprise/integrations/clickup",
|
||||
"pt-BR/enterprise/integrations/databricks",
|
||||
"pt-BR/enterprise/integrations/github",
|
||||
"pt-BR/enterprise/integrations/gmail",
|
||||
"pt-BR/enterprise/integrations/google_calendar",
|
||||
@@ -11128,6 +11150,7 @@
|
||||
"pt-BR/enterprise/integrations/asana",
|
||||
"pt-BR/enterprise/integrations/box",
|
||||
"pt-BR/enterprise/integrations/clickup",
|
||||
"pt-BR/enterprise/integrations/databricks",
|
||||
"pt-BR/enterprise/integrations/github",
|
||||
"pt-BR/enterprise/integrations/gmail",
|
||||
"pt-BR/enterprise/integrations/google_calendar",
|
||||
@@ -11599,6 +11622,7 @@
|
||||
"pt-BR/enterprise/integrations/asana",
|
||||
"pt-BR/enterprise/integrations/box",
|
||||
"pt-BR/enterprise/integrations/clickup",
|
||||
"pt-BR/enterprise/integrations/databricks",
|
||||
"pt-BR/enterprise/integrations/github",
|
||||
"pt-BR/enterprise/integrations/gmail",
|
||||
"pt-BR/enterprise/integrations/google_calendar",
|
||||
@@ -12060,6 +12084,7 @@
|
||||
"pt-BR/enterprise/integrations/asana",
|
||||
"pt-BR/enterprise/integrations/box",
|
||||
"pt-BR/enterprise/integrations/clickup",
|
||||
"pt-BR/enterprise/integrations/databricks",
|
||||
"pt-BR/enterprise/integrations/github",
|
||||
"pt-BR/enterprise/integrations/gmail",
|
||||
"pt-BR/enterprise/integrations/google_calendar",
|
||||
@@ -12521,6 +12546,7 @@
|
||||
"pt-BR/enterprise/integrations/asana",
|
||||
"pt-BR/enterprise/integrations/box",
|
||||
"pt-BR/enterprise/integrations/clickup",
|
||||
"pt-BR/enterprise/integrations/databricks",
|
||||
"pt-BR/enterprise/integrations/github",
|
||||
"pt-BR/enterprise/integrations/gmail",
|
||||
"pt-BR/enterprise/integrations/google_calendar",
|
||||
@@ -12982,6 +13008,7 @@
|
||||
"pt-BR/enterprise/integrations/asana",
|
||||
"pt-BR/enterprise/integrations/box",
|
||||
"pt-BR/enterprise/integrations/clickup",
|
||||
"pt-BR/enterprise/integrations/databricks",
|
||||
"pt-BR/enterprise/integrations/github",
|
||||
"pt-BR/enterprise/integrations/gmail",
|
||||
"pt-BR/enterprise/integrations/google_calendar",
|
||||
@@ -13442,6 +13469,7 @@
|
||||
"pt-BR/enterprise/integrations/asana",
|
||||
"pt-BR/enterprise/integrations/box",
|
||||
"pt-BR/enterprise/integrations/clickup",
|
||||
"pt-BR/enterprise/integrations/databricks",
|
||||
"pt-BR/enterprise/integrations/github",
|
||||
"pt-BR/enterprise/integrations/gmail",
|
||||
"pt-BR/enterprise/integrations/google_calendar",
|
||||
@@ -13902,6 +13930,7 @@
|
||||
"pt-BR/enterprise/integrations/asana",
|
||||
"pt-BR/enterprise/integrations/box",
|
||||
"pt-BR/enterprise/integrations/clickup",
|
||||
"pt-BR/enterprise/integrations/databricks",
|
||||
"pt-BR/enterprise/integrations/github",
|
||||
"pt-BR/enterprise/integrations/gmail",
|
||||
"pt-BR/enterprise/integrations/google_calendar",
|
||||
@@ -14363,6 +14392,7 @@
|
||||
"pt-BR/enterprise/integrations/asana",
|
||||
"pt-BR/enterprise/integrations/box",
|
||||
"pt-BR/enterprise/integrations/clickup",
|
||||
"pt-BR/enterprise/integrations/databricks",
|
||||
"pt-BR/enterprise/integrations/github",
|
||||
"pt-BR/enterprise/integrations/gmail",
|
||||
"pt-BR/enterprise/integrations/google_calendar",
|
||||
@@ -14900,6 +14930,7 @@
|
||||
"ko/enterprise/integrations/asana",
|
||||
"ko/enterprise/integrations/box",
|
||||
"ko/enterprise/integrations/clickup",
|
||||
"ko/enterprise/integrations/databricks",
|
||||
"ko/enterprise/integrations/github",
|
||||
"ko/enterprise/integrations/gmail",
|
||||
"ko/enterprise/integrations/google_calendar",
|
||||
@@ -15407,6 +15438,7 @@
|
||||
"ko/enterprise/integrations/asana",
|
||||
"ko/enterprise/integrations/box",
|
||||
"ko/enterprise/integrations/clickup",
|
||||
"ko/enterprise/integrations/databricks",
|
||||
"ko/enterprise/integrations/github",
|
||||
"ko/enterprise/integrations/gmail",
|
||||
"ko/enterprise/integrations/google_calendar",
|
||||
@@ -15881,6 +15913,7 @@
|
||||
"ko/enterprise/integrations/asana",
|
||||
"ko/enterprise/integrations/box",
|
||||
"ko/enterprise/integrations/clickup",
|
||||
"ko/enterprise/integrations/databricks",
|
||||
"ko/enterprise/integrations/github",
|
||||
"ko/enterprise/integrations/gmail",
|
||||
"ko/enterprise/integrations/google_calendar",
|
||||
@@ -16355,6 +16388,7 @@
|
||||
"ko/enterprise/integrations/asana",
|
||||
"ko/enterprise/integrations/box",
|
||||
"ko/enterprise/integrations/clickup",
|
||||
"ko/enterprise/integrations/databricks",
|
||||
"ko/enterprise/integrations/github",
|
||||
"ko/enterprise/integrations/gmail",
|
||||
"ko/enterprise/integrations/google_calendar",
|
||||
@@ -16829,6 +16863,7 @@
|
||||
"ko/enterprise/integrations/asana",
|
||||
"ko/enterprise/integrations/box",
|
||||
"ko/enterprise/integrations/clickup",
|
||||
"ko/enterprise/integrations/databricks",
|
||||
"ko/enterprise/integrations/github",
|
||||
"ko/enterprise/integrations/gmail",
|
||||
"ko/enterprise/integrations/google_calendar",
|
||||
@@ -17313,6 +17348,7 @@
|
||||
"ko/enterprise/integrations/asana",
|
||||
"ko/enterprise/integrations/box",
|
||||
"ko/enterprise/integrations/clickup",
|
||||
"ko/enterprise/integrations/databricks",
|
||||
"ko/enterprise/integrations/github",
|
||||
"ko/enterprise/integrations/gmail",
|
||||
"ko/enterprise/integrations/google_calendar",
|
||||
@@ -17797,6 +17833,7 @@
|
||||
"ko/enterprise/integrations/asana",
|
||||
"ko/enterprise/integrations/box",
|
||||
"ko/enterprise/integrations/clickup",
|
||||
"ko/enterprise/integrations/databricks",
|
||||
"ko/enterprise/integrations/github",
|
||||
"ko/enterprise/integrations/gmail",
|
||||
"ko/enterprise/integrations/google_calendar",
|
||||
@@ -18281,6 +18318,7 @@
|
||||
"ko/enterprise/integrations/asana",
|
||||
"ko/enterprise/integrations/box",
|
||||
"ko/enterprise/integrations/clickup",
|
||||
"ko/enterprise/integrations/databricks",
|
||||
"ko/enterprise/integrations/github",
|
||||
"ko/enterprise/integrations/gmail",
|
||||
"ko/enterprise/integrations/google_calendar",
|
||||
@@ -18765,6 +18803,7 @@
|
||||
"ko/enterprise/integrations/asana",
|
||||
"ko/enterprise/integrations/box",
|
||||
"ko/enterprise/integrations/clickup",
|
||||
"ko/enterprise/integrations/databricks",
|
||||
"ko/enterprise/integrations/github",
|
||||
"ko/enterprise/integrations/gmail",
|
||||
"ko/enterprise/integrations/google_calendar",
|
||||
@@ -19239,6 +19278,7 @@
|
||||
"ko/enterprise/integrations/asana",
|
||||
"ko/enterprise/integrations/box",
|
||||
"ko/enterprise/integrations/clickup",
|
||||
"ko/enterprise/integrations/databricks",
|
||||
"ko/enterprise/integrations/github",
|
||||
"ko/enterprise/integrations/gmail",
|
||||
"ko/enterprise/integrations/google_calendar",
|
||||
@@ -19713,6 +19753,7 @@
|
||||
"ko/enterprise/integrations/asana",
|
||||
"ko/enterprise/integrations/box",
|
||||
"ko/enterprise/integrations/clickup",
|
||||
"ko/enterprise/integrations/databricks",
|
||||
"ko/enterprise/integrations/github",
|
||||
"ko/enterprise/integrations/gmail",
|
||||
"ko/enterprise/integrations/google_calendar",
|
||||
@@ -20187,6 +20228,7 @@
|
||||
"ko/enterprise/integrations/asana",
|
||||
"ko/enterprise/integrations/box",
|
||||
"ko/enterprise/integrations/clickup",
|
||||
"ko/enterprise/integrations/databricks",
|
||||
"ko/enterprise/integrations/github",
|
||||
"ko/enterprise/integrations/gmail",
|
||||
"ko/enterprise/integrations/google_calendar",
|
||||
@@ -20660,6 +20702,7 @@
|
||||
"ko/enterprise/integrations/asana",
|
||||
"ko/enterprise/integrations/box",
|
||||
"ko/enterprise/integrations/clickup",
|
||||
"ko/enterprise/integrations/databricks",
|
||||
"ko/enterprise/integrations/github",
|
||||
"ko/enterprise/integrations/gmail",
|
||||
"ko/enterprise/integrations/google_calendar",
|
||||
@@ -21133,6 +21176,7 @@
|
||||
"ko/enterprise/integrations/asana",
|
||||
"ko/enterprise/integrations/box",
|
||||
"ko/enterprise/integrations/clickup",
|
||||
"ko/enterprise/integrations/databricks",
|
||||
"ko/enterprise/integrations/github",
|
||||
"ko/enterprise/integrations/gmail",
|
||||
"ko/enterprise/integrations/google_calendar",
|
||||
@@ -21607,6 +21651,7 @@
|
||||
"ko/enterprise/integrations/asana",
|
||||
"ko/enterprise/integrations/box",
|
||||
"ko/enterprise/integrations/clickup",
|
||||
"ko/enterprise/integrations/databricks",
|
||||
"ko/enterprise/integrations/github",
|
||||
"ko/enterprise/integrations/gmail",
|
||||
"ko/enterprise/integrations/google_calendar",
|
||||
@@ -22144,6 +22189,7 @@
|
||||
"ar/enterprise/integrations/asana",
|
||||
"ar/enterprise/integrations/box",
|
||||
"ar/enterprise/integrations/clickup",
|
||||
"ar/enterprise/integrations/databricks",
|
||||
"ar/enterprise/integrations/github",
|
||||
"ar/enterprise/integrations/gmail",
|
||||
"ar/enterprise/integrations/google_calendar",
|
||||
@@ -22651,6 +22697,7 @@
|
||||
"ar/enterprise/integrations/asana",
|
||||
"ar/enterprise/integrations/box",
|
||||
"ar/enterprise/integrations/clickup",
|
||||
"ar/enterprise/integrations/databricks",
|
||||
"ar/enterprise/integrations/github",
|
||||
"ar/enterprise/integrations/gmail",
|
||||
"ar/enterprise/integrations/google_calendar",
|
||||
@@ -23125,6 +23172,7 @@
|
||||
"ar/enterprise/integrations/asana",
|
||||
"ar/enterprise/integrations/box",
|
||||
"ar/enterprise/integrations/clickup",
|
||||
"ar/enterprise/integrations/databricks",
|
||||
"ar/enterprise/integrations/github",
|
||||
"ar/enterprise/integrations/gmail",
|
||||
"ar/enterprise/integrations/google_calendar",
|
||||
@@ -23599,6 +23647,7 @@
|
||||
"ar/enterprise/integrations/asana",
|
||||
"ar/enterprise/integrations/box",
|
||||
"ar/enterprise/integrations/clickup",
|
||||
"ar/enterprise/integrations/databricks",
|
||||
"ar/enterprise/integrations/github",
|
||||
"ar/enterprise/integrations/gmail",
|
||||
"ar/enterprise/integrations/google_calendar",
|
||||
@@ -24073,6 +24122,7 @@
|
||||
"ar/enterprise/integrations/asana",
|
||||
"ar/enterprise/integrations/box",
|
||||
"ar/enterprise/integrations/clickup",
|
||||
"ar/enterprise/integrations/databricks",
|
||||
"ar/enterprise/integrations/github",
|
||||
"ar/enterprise/integrations/gmail",
|
||||
"ar/enterprise/integrations/google_calendar",
|
||||
@@ -24557,6 +24607,7 @@
|
||||
"ar/enterprise/integrations/asana",
|
||||
"ar/enterprise/integrations/box",
|
||||
"ar/enterprise/integrations/clickup",
|
||||
"ar/enterprise/integrations/databricks",
|
||||
"ar/enterprise/integrations/github",
|
||||
"ar/enterprise/integrations/gmail",
|
||||
"ar/enterprise/integrations/google_calendar",
|
||||
@@ -25041,6 +25092,7 @@
|
||||
"ar/enterprise/integrations/asana",
|
||||
"ar/enterprise/integrations/box",
|
||||
"ar/enterprise/integrations/clickup",
|
||||
"ar/enterprise/integrations/databricks",
|
||||
"ar/enterprise/integrations/github",
|
||||
"ar/enterprise/integrations/gmail",
|
||||
"ar/enterprise/integrations/google_calendar",
|
||||
@@ -25525,6 +25577,7 @@
|
||||
"ar/enterprise/integrations/asana",
|
||||
"ar/enterprise/integrations/box",
|
||||
"ar/enterprise/integrations/clickup",
|
||||
"ar/enterprise/integrations/databricks",
|
||||
"ar/enterprise/integrations/github",
|
||||
"ar/enterprise/integrations/gmail",
|
||||
"ar/enterprise/integrations/google_calendar",
|
||||
@@ -26009,6 +26062,7 @@
|
||||
"ar/enterprise/integrations/asana",
|
||||
"ar/enterprise/integrations/box",
|
||||
"ar/enterprise/integrations/clickup",
|
||||
"ar/enterprise/integrations/databricks",
|
||||
"ar/enterprise/integrations/github",
|
||||
"ar/enterprise/integrations/gmail",
|
||||
"ar/enterprise/integrations/google_calendar",
|
||||
@@ -26483,6 +26537,7 @@
|
||||
"ar/enterprise/integrations/asana",
|
||||
"ar/enterprise/integrations/box",
|
||||
"ar/enterprise/integrations/clickup",
|
||||
"ar/enterprise/integrations/databricks",
|
||||
"ar/enterprise/integrations/github",
|
||||
"ar/enterprise/integrations/gmail",
|
||||
"ar/enterprise/integrations/google_calendar",
|
||||
@@ -26957,6 +27012,7 @@
|
||||
"ar/enterprise/integrations/asana",
|
||||
"ar/enterprise/integrations/box",
|
||||
"ar/enterprise/integrations/clickup",
|
||||
"ar/enterprise/integrations/databricks",
|
||||
"ar/enterprise/integrations/github",
|
||||
"ar/enterprise/integrations/gmail",
|
||||
"ar/enterprise/integrations/google_calendar",
|
||||
@@ -27431,6 +27487,7 @@
|
||||
"ar/enterprise/integrations/asana",
|
||||
"ar/enterprise/integrations/box",
|
||||
"ar/enterprise/integrations/clickup",
|
||||
"ar/enterprise/integrations/databricks",
|
||||
"ar/enterprise/integrations/github",
|
||||
"ar/enterprise/integrations/gmail",
|
||||
"ar/enterprise/integrations/google_calendar",
|
||||
@@ -27904,6 +27961,7 @@
|
||||
"ar/enterprise/integrations/asana",
|
||||
"ar/enterprise/integrations/box",
|
||||
"ar/enterprise/integrations/clickup",
|
||||
"ar/enterprise/integrations/databricks",
|
||||
"ar/enterprise/integrations/github",
|
||||
"ar/enterprise/integrations/gmail",
|
||||
"ar/enterprise/integrations/google_calendar",
|
||||
@@ -28377,6 +28435,7 @@
|
||||
"ar/enterprise/integrations/asana",
|
||||
"ar/enterprise/integrations/box",
|
||||
"ar/enterprise/integrations/clickup",
|
||||
"ar/enterprise/integrations/databricks",
|
||||
"ar/enterprise/integrations/github",
|
||||
"ar/enterprise/integrations/gmail",
|
||||
"ar/enterprise/integrations/google_calendar",
|
||||
@@ -28851,6 +28910,7 @@
|
||||
"ar/enterprise/integrations/asana",
|
||||
"ar/enterprise/integrations/box",
|
||||
"ar/enterprise/integrations/clickup",
|
||||
"ar/enterprise/integrations/databricks",
|
||||
"ar/enterprise/integrations/github",
|
||||
"ar/enterprise/integrations/gmail",
|
||||
"ar/enterprise/integrations/google_calendar",
|
||||
|
||||
123
docs/en/enterprise/integrations/databricks.mdx
Normal file
123
docs/en/enterprise/integrations/databricks.mdx
Normal file
@@ -0,0 +1,123 @@
|
||||
---
|
||||
title: Databricks Integration
|
||||
description: "Connect CrewAI agents to Databricks Genie, SQL, Unity Catalog Functions, and Vector Search through Databricks managed MCP servers."
|
||||
icon: "layer-group"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
Connect your CrewAI agents directly to your Databricks workspace through [Databricks managed MCP servers](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp). The Databricks integration lets your agents ask natural-language questions with **Genie**, run governed **SQL**, call **Unity Catalog Functions**, and retrieve documents with **Vector Search** — all without writing or hosting any connector code, and with Unity Catalog permissions enforced on every call.
|
||||
|
||||
Under the hood, the Databricks integration is a managed wrapper around CrewAI's [Custom MCP Server](/en/enterprise/guides/custom-mcp-server) support. Databricks exposes each capability as its own [Model Context Protocol](https://modelcontextprotocol.io/) endpoint, and CrewAI connects to them securely on your behalf. Because each server is added separately, you can enable exactly the capabilities your crews need.
|
||||
|
||||
## Key Capabilities
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Genie" icon="comments">
|
||||
Ask questions in plain language and get grounded answers from your data with [Genie](https://docs.databricks.com/aws/en/genie/), which queries Genie Spaces and Unity Catalog and links back to the Databricks UI.
|
||||
</Card>
|
||||
<Card title="Databricks SQL" icon="database">
|
||||
Run governed SQL against your Databricks warehouses to query, transform, and author data pipelines directly from your agents.
|
||||
</Card>
|
||||
<Card title="Unity Catalog Functions" icon="function">
|
||||
Invoke [Unity Catalog functions](https://docs.databricks.com/aws/en/udf/unity-catalog) to run predefined SQL and custom business logic as governed, reusable tools.
|
||||
</Card>
|
||||
<Card title="Vector Search" icon="magnifying-glass">
|
||||
Retrieve relevant documents for RAG and knowledge workflows from [Mosaic AI Vector Search](https://docs.databricks.com/aws/en/generative-ai/vector-search) indexes using semantic similarity.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
Every server runs behind the Unity AI Gateway and enforces Unity Catalog access controls, so your agents only ever see the data and tools they're permitted to use.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
Before using the Databricks integration, ensure you have:
|
||||
|
||||
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
|
||||
- A Databricks workspace with the capabilities you want to expose (Genie Spaces, SQL warehouses, Unity Catalog functions, or Vector Search indexes)
|
||||
- Appropriate [Unity Catalog privileges](https://docs.databricks.com/aws/en/data-governance/unity-catalog) on the underlying objects
|
||||
- Your Databricks workspace hostname (e.g. `your-workspace.cloud.databricks.com`)
|
||||
|
||||
## Databricks Managed MCP Servers
|
||||
|
||||
Databricks publishes a separate managed MCP server for each capability. CrewAI exposes these as individual connections, each configured with your workspace host and the relevant Unity Catalog identifiers. The endpoints follow these patterns:
|
||||
|
||||
| Server | What it does | MCP URL pattern |
|
||||
|--------|--------------|-----------------|
|
||||
| **Genie** | Natural-language Q&A over a Genie Space | `https://<workspace-hostname>/api/2.0/mcp/genie/{genie_space_id}` |
|
||||
| **Databricks SQL** | Execute SQL against your warehouses | `https://<workspace-hostname>/api/2.0/mcp/sql` |
|
||||
| **Unity Catalog Functions** | Run registered UC functions | `https://<workspace-hostname>/api/2.0/mcp/functions/{catalog}/{schema}` |
|
||||
| **Vector Search** | Query a Vector Search index | `https://<workspace-hostname>/api/2.0/mcp/vector-search/{catalog}/{schema}` |
|
||||
|
||||
<Note>
|
||||
You don't construct these URLs by hand — CrewAI builds each endpoint from the workspace host and identifiers (Genie Space ID, or catalog/schema) you provide when configuring the connection. For the full specification and the latest endpoint details, see the [Databricks managed MCP documentation](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp).
|
||||
</Note>
|
||||
|
||||
## Connecting Databricks in CrewAI AMP
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/databricks-configure.png" alt="Configure a Databricks managed MCP server in CrewAI AMP" />
|
||||
</Frame>
|
||||
|
||||
Each Databricks capability — **Databricks Genie**, **Databricks SQL**, **Databricks Unity Catalog Functions**, and **Databricks Vector Search** — appears as its own MCP server under the Databricks group on the **Tools & Integrations** page. Configure the ones you need:
|
||||
|
||||
<Steps>
|
||||
<Step title="Open Tools & Integrations">
|
||||
Navigate to **Tools & Integrations** in the left sidebar of CrewAI AMP and locate the **Databricks** group in the Connections list. You'll see the Genie, SQL, Unity Catalog Functions, and Vector Search servers listed beneath it.
|
||||
</Step>
|
||||
|
||||
<Step title="Configure a server">
|
||||
Click **Configure** next to the capability you want to enable and provide its connection details:
|
||||
|
||||
- **Workspace Host** — your Databricks workspace hostname (e.g. `my-workspace.cloud.databricks.com`).
|
||||
- **Genie** — the **Genie Space ID** to query.
|
||||
- **Unity Catalog Functions** — the **catalog** and **schema** that contain your functions.
|
||||
- **Vector Search** — the **catalog** and **schema** that contain your index.
|
||||
- **Databricks SQL** — no additional identifiers; queries run against your workspace's SQL warehouses.
|
||||
</Step>
|
||||
|
||||
<Step title="Choose an authentication method">
|
||||
Select how CrewAI authenticates to Databricks. **OAuth** is recommended.
|
||||
|
||||
- **Use OAuth** — Connect securely using OAuth 2.0. Each user authenticates individually, and Databricks issues tokens scoped to the capability (`genie`, `sql`, `unity-catalog`, or `vector-search`). CrewAI handles the authorization flow and refreshes tokens automatically.
|
||||
- **Use personal access token** — Authenticate with a [Databricks personal access token](https://docs.databricks.com/aws/en/dev-tools/auth/pat). Use a least-privileged identity to limit exposure.
|
||||
</Step>
|
||||
|
||||
<Step title="Authenticate">
|
||||
Complete authentication. Once connected, the server's tools become available to your crews. Repeat for any other Databricks capabilities you want to enable.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
<Tip>
|
||||
Because each capability is a separate connection, you can mix and match — for example, enable Genie and Vector Search for a research crew while reserving SQL and Unity Catalog Functions for a data-engineering crew. Visibility settings let you control which team members can use each one.
|
||||
</Tip>
|
||||
|
||||
## Using Databricks Tools in Your Crews
|
||||
|
||||
Once connected, the tools each MCP server exposes appear alongside built-in connections on the **Tools & Integrations** page. You can:
|
||||
|
||||
- **Assign tools to agents** in your crews just like any other CrewAI tool.
|
||||
- **Manage visibility** to control which team members can use each connection.
|
||||
- **Edit or remove** any connection at any time from the Connections list.
|
||||
|
||||
Your agents can now ask Genie for grounded answers, run SQL against your warehouses, call Unity Catalog functions, and search Vector Search indexes — with results flowing back into their reasoning automatically.
|
||||
|
||||
<Warning>
|
||||
Databricks enforces governance through Unity Catalog and the Unity AI Gateway: a user can only discover and invoke tools their workspace identity is permitted to use. If a tool call fails, confirm the connecting user (or token identity) has the required Unity Catalog privileges on the Genie Space, warehouse, function, or index. Some Genie and SQL queries run asynchronously and may take a moment to return results.
|
||||
</Warning>
|
||||
|
||||
## Learn More
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Databricks Managed MCP Servers" icon="layer-group" href="https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp">
|
||||
Official Databricks documentation for the managed Genie, SQL, Unity Catalog Functions, and Vector Search MCP servers.
|
||||
</Card>
|
||||
<Card title="Custom MCP Servers in CrewAI" icon="plug" href="/en/enterprise/guides/custom-mcp-server">
|
||||
Learn how CrewAI connects to any MCP server, the foundation the Databricks integration builds on.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
|
||||
Contact our support team for assistance with the Databricks integration or troubleshooting.
|
||||
</Card>
|
||||
BIN
docs/images/enterprise/databricks-configure.png
Normal file
BIN
docs/images/enterprise/databricks-configure.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 5.0 MiB |
123
docs/ko/enterprise/integrations/databricks.mdx
Normal file
123
docs/ko/enterprise/integrations/databricks.mdx
Normal file
@@ -0,0 +1,123 @@
|
||||
---
|
||||
title: Databricks 연동
|
||||
description: "Databricks 관리형 MCP 서버를 통해 CrewAI 에이전트를 Databricks Genie, SQL, Unity Catalog Functions, Vector Search에 연결하세요."
|
||||
icon: "layer-group"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## 개요
|
||||
|
||||
[Databricks 관리형 MCP 서버](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp)를 통해 CrewAI 에이전트를 Databricks 워크스페이스에 직접 연결하세요. Databricks 연동을 사용하면 에이전트가 **Genie**로 자연어 질문을 하고, 거버넌스가 적용된 **SQL**을 실행하며, **Unity Catalog Functions**를 호출하고, **Vector Search**로 문서를 검색할 수 있습니다. 커넥터 코드를 작성하거나 호스팅할 필요가 없으며, 모든 호출에 Unity Catalog 권한이 적용됩니다.
|
||||
|
||||
내부적으로 Databricks 연동은 CrewAI의 [커스텀 MCP 서버](/ko/enterprise/guides/custom-mcp-server) 지원을 감싼 관리형 래퍼입니다. Databricks는 각 기능을 개별 [Model Context Protocol](https://modelcontextprotocol.io/) 엔드포인트로 노출하며, CrewAI가 사용자를 대신해 안전하게 연결합니다. 각 서버를 개별적으로 추가하므로 크루에 필요한 기능만 정확히 활성화할 수 있습니다.
|
||||
|
||||
## 주요 기능
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Genie" icon="comments">
|
||||
[Genie](https://docs.databricks.com/aws/en/genie/)로 자연어 질문을 하고 데이터 기반의 근거 있는 답변을 받으세요. Genie는 Genie Spaces와 Unity Catalog를 조회하고 Databricks UI로 연결되는 링크를 제공합니다.
|
||||
</Card>
|
||||
<Card title="Databricks SQL" icon="database">
|
||||
에이전트에서 직접 Databricks 웨어하우스에 거버넌스가 적용된 SQL을 실행하여 데이터를 조회, 변환하고 데이터 파이프라인을 작성하세요.
|
||||
</Card>
|
||||
<Card title="Unity Catalog Functions" icon="function">
|
||||
[Unity Catalog 함수](https://docs.databricks.com/aws/en/udf/unity-catalog)를 호출하여 사전 정의된 SQL과 맞춤형 비즈니스 로직을 거버넌스가 적용된 재사용 가능한 도구로 실행하세요.
|
||||
</Card>
|
||||
<Card title="Vector Search" icon="magnifying-glass">
|
||||
[Mosaic AI Vector Search](https://docs.databricks.com/aws/en/generative-ai/vector-search) 인덱스에서 의미 유사도를 사용해 RAG 및 지식 워크플로우에 필요한 관련 문서를 검색하세요.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
모든 서버는 Unity AI Gateway 뒤에서 실행되며 Unity Catalog 접근 제어를 적용하므로, 에이전트는 허용된 데이터와 도구만 볼 수 있습니다.
|
||||
|
||||
## 사전 준비사항
|
||||
|
||||
Databricks 연동을 사용하기 전에 다음을 준비해야 합니다:
|
||||
|
||||
- 활성 구독이 있는 [CrewAI AMP](https://app.crewai.com) 계정
|
||||
- 노출하려는 기능이 있는 Databricks 워크스페이스(Genie Spaces, SQL 웨어하우스, Unity Catalog 함수 또는 Vector Search 인덱스)
|
||||
- 기본 객체에 대한 적절한 [Unity Catalog 권한](https://docs.databricks.com/aws/en/data-governance/unity-catalog)
|
||||
- Databricks 워크스페이스 호스트명(예: `your-workspace.cloud.databricks.com`)
|
||||
|
||||
## Databricks 관리형 MCP 서버
|
||||
|
||||
Databricks는 각 기능마다 별도의 관리형 MCP 서버를 게시합니다. CrewAI는 이를 개별 연결로 노출하며, 각 연결은 워크스페이스 호스트와 관련 Unity Catalog 식별자로 구성됩니다. 엔드포인트는 다음 패턴을 따릅니다:
|
||||
|
||||
| 서버 | 기능 | MCP URL 패턴 |
|
||||
|------|------|--------------|
|
||||
| **Genie** | Genie Space에 대한 자연어 Q&A | `https://<workspace-hostname>/api/2.0/mcp/genie/{genie_space_id}` |
|
||||
| **Databricks SQL** | 웨어하우스에 SQL 실행 | `https://<workspace-hostname>/api/2.0/mcp/sql` |
|
||||
| **Unity Catalog Functions** | 등록된 UC 함수 실행 | `https://<workspace-hostname>/api/2.0/mcp/functions/{catalog}/{schema}` |
|
||||
| **Vector Search** | Vector Search 인덱스 조회 | `https://<workspace-hostname>/api/2.0/mcp/vector-search/{catalog}/{schema}` |
|
||||
|
||||
<Note>
|
||||
이러한 URL을 직접 만들 필요는 없습니다. CrewAI는 연결을 구성할 때 입력한 워크스페이스 호스트와 식별자(Genie Space ID 또는 catalog/schema)로 각 엔드포인트를 생성합니다. 전체 사양과 최신 엔드포인트 세부 정보는 [Databricks 관리형 MCP 문서](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp)를 참고하세요.
|
||||
</Note>
|
||||
|
||||
## CrewAI AMP에서 Databricks 연결하기
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/databricks-configure.png" alt="CrewAI AMP에서 Databricks 관리형 MCP 서버 구성" />
|
||||
</Frame>
|
||||
|
||||
각 Databricks 기능(**Databricks Genie**, **Databricks SQL**, **Databricks Unity Catalog Functions**, **Databricks Vector Search**)은 **Tools & Integrations** 페이지의 Databricks 그룹 아래에 별도의 MCP 서버로 표시됩니다. 필요한 것을 구성하세요:
|
||||
|
||||
<Steps>
|
||||
<Step title="Tools & Integrations 열기">
|
||||
CrewAI AMP 왼쪽 사이드바에서 **Tools & Integrations**로 이동하여 Connections 목록에서 **Databricks** 그룹을 찾습니다. 그 아래에 Genie, SQL, Unity Catalog Functions, Vector Search 서버가 나열됩니다.
|
||||
</Step>
|
||||
|
||||
<Step title="서버 구성하기">
|
||||
활성화하려는 기능 옆의 **Configure**를 클릭하고 연결 세부 정보를 입력합니다:
|
||||
|
||||
- **Workspace Host** — Databricks 워크스페이스 호스트명(예: `my-workspace.cloud.databricks.com`).
|
||||
- **Genie** — 조회할 **Genie Space ID**.
|
||||
- **Unity Catalog Functions** — 함수가 포함된 **catalog**와 **schema**.
|
||||
- **Vector Search** — 인덱스가 포함된 **catalog**와 **schema**.
|
||||
- **Databricks SQL** — 추가 식별자가 필요 없으며, 쿼리는 워크스페이스의 SQL 웨어하우스에서 실행됩니다.
|
||||
</Step>
|
||||
|
||||
<Step title="인증 방법 선택하기">
|
||||
CrewAI가 Databricks에 인증하는 방법을 선택합니다. **OAuth**를 권장합니다.
|
||||
|
||||
- **Use OAuth** — OAuth 2.0으로 안전하게 연결합니다. 각 사용자가 개별적으로 인증하며, Databricks는 해당 기능(`genie`, `sql`, `unity-catalog` 또는 `vector-search`)으로 범위가 지정된 토큰을 발급합니다. CrewAI가 인증 흐름을 처리하고 토큰을 자동으로 갱신합니다.
|
||||
- **Use personal access token** — [Databricks 개인 액세스 토큰](https://docs.databricks.com/aws/en/dev-tools/auth/pat)으로 인증합니다. 노출을 제한하려면 최소 권한 ID를 사용하세요.
|
||||
</Step>
|
||||
|
||||
<Step title="인증하기">
|
||||
인증을 완료합니다. 연결되면 해당 서버의 도구를 크루에서 사용할 수 있습니다. 활성화하려는 다른 Databricks 기능에 대해서도 반복합니다.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
<Tip>
|
||||
각 기능이 별도의 연결이므로 자유롭게 조합할 수 있습니다. 예를 들어 리서치 크루에는 Genie와 Vector Search를 활성화하고, 데이터 엔지니어링 크루에는 SQL과 Unity Catalog Functions를 사용하도록 할 수 있습니다. 가시성(Visibility) 설정으로 각 기능을 사용할 수 있는 팀원을 제어할 수 있습니다.
|
||||
</Tip>
|
||||
|
||||
## 크루에서 Databricks 도구 사용하기
|
||||
|
||||
연결되면 각 MCP 서버가 노출하는 도구가 **Tools & Integrations** 페이지의 기본 제공 연결과 함께 표시됩니다. 다음을 할 수 있습니다:
|
||||
|
||||
- 다른 CrewAI 도구와 마찬가지로 크루의 에이전트에 **도구 할당**.
|
||||
- 각 연결을 사용할 수 있는 팀원을 제어하는 **가시성 관리**.
|
||||
- Connections 목록에서 언제든지 연결 **편집 또는 제거**.
|
||||
|
||||
이제 에이전트는 Genie에 근거 있는 답변을 요청하고, 웨어하우스에 SQL을 실행하며, Unity Catalog 함수를 호출하고, Vector Search 인덱스를 검색할 수 있으며, 그 결과가 자동으로 추론에 반영됩니다.
|
||||
|
||||
<Warning>
|
||||
Databricks는 Unity Catalog와 Unity AI Gateway를 통해 거버넌스를 적용합니다. 사용자는 워크스페이스 ID에 허용된 도구만 검색하고 호출할 수 있습니다. 도구 호출이 실패하면 연결하는 사용자(또는 토큰 ID)가 Genie Space, 웨어하우스, 함수 또는 인덱스에 필요한 Unity Catalog 권한을 가지고 있는지 확인하세요. 일부 Genie 및 SQL 쿼리는 비동기로 실행되어 결과를 반환하는 데 시간이 걸릴 수 있습니다.
|
||||
</Warning>
|
||||
|
||||
## 더 알아보기
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Databricks 관리형 MCP 서버" icon="layer-group" href="https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp">
|
||||
관리형 Genie, SQL, Unity Catalog Functions, Vector Search MCP 서버에 대한 공식 Databricks 문서입니다.
|
||||
</Card>
|
||||
<Card title="CrewAI의 커스텀 MCP 서버" icon="plug" href="/ko/enterprise/guides/custom-mcp-server">
|
||||
Databricks 연동의 기반이 되는, CrewAI가 모든 MCP 서버에 연결하는 방법을 알아보세요.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
<Card title="도움이 필요하신가요?" icon="headset" href="mailto:support@crewai.com">
|
||||
Databricks 연동 구성 또는 문제 해결에 대한 지원이 필요하면 지원팀에 문의하세요.
|
||||
</Card>
|
||||
123
docs/pt-BR/enterprise/integrations/databricks.mdx
Normal file
123
docs/pt-BR/enterprise/integrations/databricks.mdx
Normal file
@@ -0,0 +1,123 @@
|
||||
---
|
||||
title: Integração com Databricks
|
||||
description: "Conecte agentes CrewAI ao Databricks Genie, SQL, Unity Catalog Functions e Vector Search por meio dos servidores MCP gerenciados do Databricks."
|
||||
icon: "layer-group"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Visão geral
|
||||
|
||||
Conecte seus agentes CrewAI diretamente ao seu workspace do Databricks por meio dos [servidores MCP gerenciados do Databricks](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp). A integração com o Databricks permite que seus agentes façam perguntas em linguagem natural com o **Genie**, executem **SQL** governado, chamem **Unity Catalog Functions** e recuperem documentos com o **Vector Search** — tudo sem escrever ou hospedar qualquer código de conector, e com as permissões do Unity Catalog aplicadas em cada chamada.
|
||||
|
||||
Nos bastidores, a integração com o Databricks é um wrapper gerenciado sobre o suporte a [Servidores MCP personalizados](/pt-BR/enterprise/guides/custom-mcp-server) do CrewAI. O Databricks expõe cada recurso como seu próprio endpoint do [Model Context Protocol](https://modelcontextprotocol.io/), e o CrewAI se conecta a eles com segurança em seu nome. Como cada servidor é adicionado separadamente, você pode habilitar exatamente os recursos de que suas crews precisam.
|
||||
|
||||
## Principais recursos
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Genie" icon="comments">
|
||||
Faça perguntas em linguagem natural e obtenha respostas fundamentadas em seus dados com o [Genie](https://docs.databricks.com/aws/en/genie/), que consulta Genie Spaces e o Unity Catalog e fornece links de volta para a interface do Databricks.
|
||||
</Card>
|
||||
<Card title="Databricks SQL" icon="database">
|
||||
Execute SQL governado nos seus warehouses do Databricks para consultar, transformar e criar pipelines de dados diretamente a partir dos seus agentes.
|
||||
</Card>
|
||||
<Card title="Unity Catalog Functions" icon="function">
|
||||
Invoque [funções do Unity Catalog](https://docs.databricks.com/aws/en/udf/unity-catalog) para executar SQL predefinido e lógica de negócio personalizada como ferramentas governadas e reutilizáveis.
|
||||
</Card>
|
||||
<Card title="Vector Search" icon="magnifying-glass">
|
||||
Recupere documentos relevantes para fluxos de RAG e de conhecimento a partir de índices do [Mosaic AI Vector Search](https://docs.databricks.com/aws/en/generative-ai/vector-search) usando similaridade semântica.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
Todos os servidores são executados por trás do Unity AI Gateway e aplicam os controles de acesso do Unity Catalog, de modo que seus agentes só enxergam os dados e as ferramentas que têm permissão para usar.
|
||||
|
||||
## Pré-requisitos
|
||||
|
||||
Antes de usar a integração com o Databricks, certifique-se de ter:
|
||||
|
||||
- Uma conta [CrewAI AMP](https://app.crewai.com) com assinatura ativa
|
||||
- Um workspace do Databricks com os recursos que você deseja expor (Genie Spaces, warehouses SQL, funções do Unity Catalog ou índices do Vector Search)
|
||||
- [Privilégios apropriados do Unity Catalog](https://docs.databricks.com/aws/en/data-governance/unity-catalog) nos objetos subjacentes
|
||||
- O hostname do seu workspace do Databricks (ex.: `your-workspace.cloud.databricks.com`)
|
||||
|
||||
## Servidores MCP gerenciados do Databricks
|
||||
|
||||
O Databricks publica um servidor MCP gerenciado separado para cada recurso. O CrewAI os expõe como conexões individuais, cada uma configurada com o host do seu workspace e os identificadores relevantes do Unity Catalog. Os endpoints seguem estes padrões:
|
||||
|
||||
| Servidor | O que faz | Padrão de URL MCP |
|
||||
|----------|-----------|-------------------|
|
||||
| **Genie** | Perguntas e respostas em linguagem natural sobre um Genie Space | `https://<workspace-hostname>/api/2.0/mcp/genie/{genie_space_id}` |
|
||||
| **Databricks SQL** | Executa SQL nos seus warehouses | `https://<workspace-hostname>/api/2.0/mcp/sql` |
|
||||
| **Unity Catalog Functions** | Executa funções UC registradas | `https://<workspace-hostname>/api/2.0/mcp/functions/{catalog}/{schema}` |
|
||||
| **Vector Search** | Consulta um índice do Vector Search | `https://<workspace-hostname>/api/2.0/mcp/vector-search/{catalog}/{schema}` |
|
||||
|
||||
<Note>
|
||||
Você não precisa construir essas URLs manualmente — o CrewAI cria cada endpoint a partir do host do workspace e dos identificadores (Genie Space ID, ou catalog/schema) que você fornece ao configurar a conexão. Para a especificação completa e os detalhes mais recentes dos endpoints, consulte a [documentação de MCP gerenciado do Databricks](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp).
|
||||
</Note>
|
||||
|
||||
## Conectando o Databricks no CrewAI AMP
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/databricks-configure.png" alt="Configurar um servidor MCP gerenciado do Databricks no CrewAI AMP" />
|
||||
</Frame>
|
||||
|
||||
Cada recurso do Databricks — **Databricks Genie**, **Databricks SQL**, **Databricks Unity Catalog Functions** e **Databricks Vector Search** — aparece como seu próprio servidor MCP no grupo Databricks da página **Tools & Integrations**. Configure os que você precisar:
|
||||
|
||||
<Steps>
|
||||
<Step title="Abra Tools & Integrations">
|
||||
Navegue até **Tools & Integrations** na barra lateral esquerda do CrewAI AMP e localize o grupo **Databricks** na lista de Connections. Você verá os servidores Genie, SQL, Unity Catalog Functions e Vector Search listados abaixo dele.
|
||||
</Step>
|
||||
|
||||
<Step title="Configure um servidor">
|
||||
Clique em **Configure** ao lado do recurso que deseja habilitar e forneça os detalhes da conexão:
|
||||
|
||||
- **Workspace Host** — o hostname do seu workspace do Databricks (ex.: `my-workspace.cloud.databricks.com`).
|
||||
- **Genie** — o **Genie Space ID** a ser consultado.
|
||||
- **Unity Catalog Functions** — o **catalog** e o **schema** que contêm suas funções.
|
||||
- **Vector Search** — o **catalog** e o **schema** que contêm seu índice.
|
||||
- **Databricks SQL** — sem identificadores adicionais; as consultas são executadas nos warehouses SQL do seu workspace.
|
||||
</Step>
|
||||
|
||||
<Step title="Escolha um método de autenticação">
|
||||
Selecione como o CrewAI se autentica no Databricks. **OAuth** é recomendado.
|
||||
|
||||
- **Use OAuth** — Conecte-se com segurança usando OAuth 2.0. Cada usuário se autentica individualmente, e o Databricks emite tokens com escopo para o recurso (`genie`, `sql`, `unity-catalog` ou `vector-search`). O CrewAI gerencia o fluxo de autorização e renova os tokens automaticamente.
|
||||
- **Use personal access token** — Autentique-se com um [token de acesso pessoal do Databricks](https://docs.databricks.com/aws/en/dev-tools/auth/pat). Use uma identidade com privilégios mínimos para limitar a exposição.
|
||||
</Step>
|
||||
|
||||
<Step title="Autentique">
|
||||
Conclua a autenticação. Uma vez conectado, as ferramentas do servidor ficam disponíveis para suas crews. Repita para qualquer outro recurso do Databricks que você queira habilitar.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
<Tip>
|
||||
Como cada recurso é uma conexão separada, você pode combiná-los livremente — por exemplo, habilitar Genie e Vector Search para uma crew de pesquisa e reservar SQL e Unity Catalog Functions para uma crew de engenharia de dados. As configurações de visibilidade permitem controlar quais membros da equipe podem usar cada um.
|
||||
</Tip>
|
||||
|
||||
## Usando as ferramentas do Databricks nas suas crews
|
||||
|
||||
Uma vez conectado, as ferramentas que cada servidor MCP expõe aparecem junto às conexões integradas na página **Tools & Integrations**. Você pode:
|
||||
|
||||
- **Atribuir ferramentas aos agentes** nas suas crews, como qualquer outra ferramenta do CrewAI.
|
||||
- **Gerenciar a visibilidade** para controlar quais membros da equipe podem usar cada conexão.
|
||||
- **Editar ou remover** qualquer conexão a qualquer momento na lista de Connections.
|
||||
|
||||
Seus agentes agora podem pedir respostas fundamentadas ao Genie, executar SQL nos seus warehouses, chamar funções do Unity Catalog e pesquisar índices do Vector Search — com os resultados retornando automaticamente ao raciocínio deles.
|
||||
|
||||
<Warning>
|
||||
O Databricks aplica governança por meio do Unity Catalog e do Unity AI Gateway: um usuário só pode descobrir e invocar ferramentas que a identidade do seu workspace tem permissão para usar. Se uma chamada de ferramenta falhar, confirme se o usuário (ou a identidade do token) que está conectando tem os privilégios necessários do Unity Catalog no Genie Space, warehouse, função ou índice. Algumas consultas do Genie e do SQL são executadas de forma assíncrona e podem levar um momento para retornar resultados.
|
||||
</Warning>
|
||||
|
||||
## Saiba mais
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Servidores MCP gerenciados do Databricks" icon="layer-group" href="https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp">
|
||||
Documentação oficial do Databricks para os servidores MCP gerenciados Genie, SQL, Unity Catalog Functions e Vector Search.
|
||||
</Card>
|
||||
<Card title="Servidores MCP personalizados no CrewAI" icon="plug" href="/pt-BR/enterprise/guides/custom-mcp-server">
|
||||
Saiba como o CrewAI se conecta a qualquer servidor MCP, a base sobre a qual a integração com o Databricks é construída.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
<Card title="Precisa de ajuda?" icon="headset" href="mailto:support@crewai.com">
|
||||
Entre em contato com nossa equipe de suporte para obter ajuda com a configuração da integração com o Databricks ou com a solução de problemas.
|
||||
</Card>
|
||||
@@ -1,6 +1,5 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import ast
|
||||
import os
|
||||
from typing import Any, Literal
|
||||
|
||||
@@ -134,9 +133,6 @@ class SnowflakeCompletion(OpenAICompletion):
|
||||
def _format_messages(self, messages: str | list[LLMMessage]) -> list[LLMMessage]:
|
||||
formatted_messages = super()._format_messages(messages)
|
||||
if self._is_claude_model():
|
||||
formatted_messages = self._normalize_stringified_tool_calls(
|
||||
formatted_messages
|
||||
)
|
||||
formatted_messages = self._remove_incomplete_claude_tool_uses(
|
||||
formatted_messages
|
||||
)
|
||||
@@ -147,41 +143,6 @@ class SnowflakeCompletion(OpenAICompletion):
|
||||
model = self.model.lower()
|
||||
return model.startswith(("claude-", "anthropic."))
|
||||
|
||||
@staticmethod
|
||||
def _normalize_stringified_tool_calls(
|
||||
messages: list[LLMMessage],
|
||||
) -> list[LLMMessage]:
|
||||
normalized_messages: list[LLMMessage] = []
|
||||
for message in messages:
|
||||
tool_calls = message.get("tool_calls")
|
||||
if not isinstance(tool_calls, list) or not tool_calls:
|
||||
normalized_messages.append(message)
|
||||
continue
|
||||
|
||||
normalized_tool_calls: list[Any] = []
|
||||
changed = False
|
||||
for tool_call in tool_calls:
|
||||
if isinstance(tool_call, str):
|
||||
try:
|
||||
parsed_tool_call = ast.literal_eval(tool_call)
|
||||
except (ValueError, SyntaxError):
|
||||
normalized_tool_calls.append(tool_call)
|
||||
continue
|
||||
if isinstance(parsed_tool_call, dict):
|
||||
normalized_tool_calls.append(parsed_tool_call)
|
||||
changed = True
|
||||
continue
|
||||
normalized_tool_calls.append(tool_call)
|
||||
|
||||
if changed:
|
||||
normalized_message = dict(message)
|
||||
normalized_message["tool_calls"] = normalized_tool_calls
|
||||
normalized_messages.append(normalized_message) # type: ignore[arg-type]
|
||||
else:
|
||||
normalized_messages.append(message)
|
||||
|
||||
return normalized_messages
|
||||
|
||||
@staticmethod
|
||||
def _remove_incomplete_claude_tool_uses(
|
||||
messages: list[LLMMessage],
|
||||
@@ -201,120 +162,45 @@ class SnowflakeCompletion(OpenAICompletion):
|
||||
|
||||
while index < len(messages):
|
||||
message = messages[index]
|
||||
expected_ids = SnowflakeCompletion._extract_claude_tool_use_ids(message)
|
||||
if message.get("role") != "assistant" or not expected_ids:
|
||||
tool_calls = message.get("tool_calls") or []
|
||||
if message.get("role") != "assistant" or not tool_calls:
|
||||
sanitized.append(message)
|
||||
index += 1
|
||||
continue
|
||||
|
||||
expected_ids = {
|
||||
tool_call.get("id")
|
||||
for tool_call in tool_calls
|
||||
if isinstance(tool_call, dict) and tool_call.get("id")
|
||||
}
|
||||
if not expected_ids:
|
||||
sanitized.append(message)
|
||||
index += 1
|
||||
continue
|
||||
|
||||
tool_result_ids: set[str] = set()
|
||||
lookahead = index + 1
|
||||
while lookahead < len(
|
||||
messages
|
||||
) and SnowflakeCompletion._is_tool_result_message(messages[lookahead]):
|
||||
tool_result_ids.update(
|
||||
SnowflakeCompletion._extract_claude_tool_result_ids(
|
||||
messages[lookahead]
|
||||
)
|
||||
)
|
||||
while (
|
||||
lookahead < len(messages) and messages[lookahead].get("role") == "tool"
|
||||
):
|
||||
tool_call_id = messages[lookahead].get("tool_call_id")
|
||||
if isinstance(tool_call_id, str):
|
||||
tool_result_ids.add(tool_call_id)
|
||||
lookahead += 1
|
||||
|
||||
if expected_ids.issubset(tool_result_ids):
|
||||
summary = SnowflakeCompletion._summarize_tool_results(
|
||||
messages[index + 1 : lookahead], expected_ids
|
||||
sanitized.append(message)
|
||||
sanitized.extend(
|
||||
tool_message
|
||||
for tool_message in messages[index + 1 : lookahead]
|
||||
if tool_message.get("role") == "tool"
|
||||
and tool_message.get("tool_call_id") in expected_ids
|
||||
)
|
||||
if summary:
|
||||
sanitized.append({"role": "user", "content": summary})
|
||||
|
||||
index = lookahead
|
||||
|
||||
return sanitized
|
||||
|
||||
@staticmethod
|
||||
def _summarize_tool_results(
|
||||
messages: list[LLMMessage], expected_ids: set[str]
|
||||
) -> str:
|
||||
summaries: list[str] = []
|
||||
for message in messages:
|
||||
result_ids = SnowflakeCompletion._extract_claude_tool_result_ids(message)
|
||||
if not result_ids & expected_ids:
|
||||
continue
|
||||
|
||||
name = message.get("name") or "tool"
|
||||
content = message.get("content")
|
||||
if isinstance(content, str):
|
||||
summaries.append(f"{name}: {content}")
|
||||
elif isinstance(content, list):
|
||||
extracted_text = SnowflakeCompletion._extract_tool_result_text(content)
|
||||
summaries.append(f"{name}: {extracted_text or content}")
|
||||
|
||||
if not summaries:
|
||||
return ""
|
||||
|
||||
return "Tool results from previous tool calls:\n" + "\n".join(
|
||||
f"- {summary}" for summary in summaries
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _extract_tool_result_text(content: list[Any]) -> str:
|
||||
texts: list[str] = []
|
||||
for item in content:
|
||||
if not isinstance(item, dict) or not isinstance(
|
||||
item.get("toolResult"), dict
|
||||
):
|
||||
continue
|
||||
result_content = item["toolResult"].get("content", [])
|
||||
texts.extend(
|
||||
str(inner["text"])
|
||||
for inner in result_content
|
||||
if isinstance(inner, dict) and "text" in inner
|
||||
)
|
||||
return " ".join(texts)
|
||||
|
||||
@staticmethod
|
||||
def _extract_claude_tool_use_ids(message: LLMMessage) -> set[str]:
|
||||
tool_calls = message.get("tool_calls") or []
|
||||
ids: set[str] = set()
|
||||
for tool_call in tool_calls:
|
||||
if not isinstance(tool_call, dict):
|
||||
continue
|
||||
tool_call_id = tool_call.get("id")
|
||||
if isinstance(tool_call_id, str):
|
||||
ids.add(tool_call_id)
|
||||
|
||||
content = message.get("content")
|
||||
if isinstance(content, list):
|
||||
for block in content:
|
||||
if isinstance(block, dict) and isinstance(block.get("toolUse"), dict):
|
||||
tool_use_id = block["toolUse"].get("toolUseId")
|
||||
if isinstance(tool_use_id, str):
|
||||
ids.add(tool_use_id)
|
||||
return ids
|
||||
|
||||
@staticmethod
|
||||
def _extract_claude_tool_result_ids(message: LLMMessage) -> set[str]:
|
||||
ids: set[str] = set()
|
||||
tool_call_id = message.get("tool_call_id")
|
||||
if isinstance(tool_call_id, str):
|
||||
ids.add(tool_call_id)
|
||||
|
||||
content = message.get("content")
|
||||
if isinstance(content, list):
|
||||
for block in content:
|
||||
if isinstance(block, dict) and isinstance(
|
||||
block.get("toolResult"), dict
|
||||
):
|
||||
tool_use_id = block["toolResult"].get("toolUseId")
|
||||
if isinstance(tool_use_id, str):
|
||||
ids.add(tool_use_id)
|
||||
return ids
|
||||
|
||||
@staticmethod
|
||||
def _is_tool_result_message(message: LLMMessage) -> bool:
|
||||
return message.get("role") == "tool" or bool(
|
||||
SnowflakeCompletion._extract_claude_tool_result_ids(message)
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _ensure_claude_conversation_ends_with_user(
|
||||
messages: list[LLMMessage],
|
||||
|
||||
@@ -156,44 +156,6 @@ class TestSnowflakeRequests:
|
||||
|
||||
assert messages == [{"role": "user", "content": "Write a summary."}]
|
||||
|
||||
def test_claude_model_normalizes_stringified_tool_calls_with_results(
|
||||
self, monkeypatch: pytest.MonkeyPatch
|
||||
):
|
||||
_snowflake_env(monkeypatch)
|
||||
llm = SnowflakeCompletion(model="claude-sonnet-4-5")
|
||||
|
||||
messages = llm._format_messages(
|
||||
[
|
||||
{"role": "user", "content": "Use the tools."},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": None,
|
||||
"tool_calls": [
|
||||
"{'id': 'toolu_1', 'type': 'function', 'function': {'name': \"'search_the_internet_with_serper'\", 'arguments': '\\\'{\"search_query\":\"CrewAI tools\"}\\\''}}",
|
||||
"{'id': 'toolu_2', 'type': 'function', 'function': {'name': \"'search_the_internet_with_serper'\", 'arguments': '\\\'{\"search_query\":\"CrewAI demos\"}\\\''}}",
|
||||
],
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": "toolu_1",
|
||||
"name": "search_the_internet_with_serper",
|
||||
"content": "result 1",
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": "toolu_2",
|
||||
"name": "search_the_internet_with_serper",
|
||||
"content": "result 2",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
assert messages[-2] == {"role": "user", "content": "Use the tools."}
|
||||
assert messages[-1]["role"] == "user"
|
||||
assert "result 1" in messages[-1]["content"]
|
||||
assert "result 2" in messages[-1]["content"]
|
||||
assert all("tool_calls" not in message for message in messages)
|
||||
|
||||
def test_claude_model_removes_dangling_tool_call_without_result(
|
||||
self, monkeypatch: pytest.MonkeyPatch
|
||||
):
|
||||
@@ -247,10 +209,14 @@ class TestSnowflakeRequests:
|
||||
]
|
||||
)
|
||||
|
||||
assert messages[-2] == {"role": "user", "content": "Use the tool."}
|
||||
assert messages[-3]["role"] == "assistant"
|
||||
assert messages[-3]["tool_calls"][0]["id"] == "call_1"
|
||||
assert messages[-2] == {
|
||||
"role": "tool",
|
||||
"tool_call_id": "call_1",
|
||||
"content": "result",
|
||||
}
|
||||
assert messages[-1]["role"] == "user"
|
||||
assert "result" in messages[-1]["content"]
|
||||
assert all("tool_calls" not in message for message in messages)
|
||||
|
||||
def test_claude_model_drops_unrelated_tool_results_from_preserved_pair(
|
||||
self, monkeypatch: pytest.MonkeyPatch
|
||||
@@ -285,88 +251,16 @@ class TestSnowflakeRequests:
|
||||
]
|
||||
)
|
||||
|
||||
assert messages[-2] == {"role": "user", "content": "Use the tool."}
|
||||
assert messages[-1]["role"] == "user"
|
||||
assert "valid result" in messages[-1]["content"]
|
||||
assert "unrelated result" not in messages[-1]["content"]
|
||||
assert all("tool_call_id" not in message for message in messages)
|
||||
|
||||
def test_claude_model_removes_dangling_tool_use_content_block(
|
||||
self, monkeypatch: pytest.MonkeyPatch
|
||||
):
|
||||
_snowflake_env(monkeypatch)
|
||||
llm = SnowflakeCompletion(model="claude-sonnet-4-5")
|
||||
|
||||
messages = llm._format_messages(
|
||||
[
|
||||
{"role": "user", "content": "Use the tool."},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": [
|
||||
{
|
||||
"toolUse": {
|
||||
"toolUseId": "tooluse_1",
|
||||
"name": "lookup",
|
||||
"input": {},
|
||||
}
|
||||
}
|
||||
],
|
||||
},
|
||||
{"role": "user", "content": "Continue."},
|
||||
]
|
||||
)
|
||||
|
||||
assert messages == [
|
||||
{"role": "user", "content": "Use the tool."},
|
||||
{"role": "user", "content": "Continue."},
|
||||
]
|
||||
|
||||
def test_claude_model_preserves_complete_tool_use_content_block_pair(
|
||||
self, monkeypatch: pytest.MonkeyPatch
|
||||
):
|
||||
_snowflake_env(monkeypatch)
|
||||
llm = SnowflakeCompletion(model="claude-sonnet-4-5")
|
||||
|
||||
messages = llm._format_messages(
|
||||
[
|
||||
{"role": "user", "content": "Use the tool."},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": [
|
||||
{
|
||||
"toolUse": {
|
||||
"toolUseId": "tooluse_1",
|
||||
"name": "lookup",
|
||||
"input": {},
|
||||
}
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"toolResult": {
|
||||
"toolUseId": "tooluse_1",
|
||||
"content": [{"text": "result"}],
|
||||
}
|
||||
}
|
||||
],
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
assert messages[-2] == {"role": "user", "content": "Use the tool."}
|
||||
assert messages[-1]["role"] == "user"
|
||||
assert "result" in messages[-1]["content"]
|
||||
assert "toolResult" not in messages[-1]["content"]
|
||||
assert messages[-3]["role"] == "assistant"
|
||||
assert messages[-2] == {
|
||||
"role": "tool",
|
||||
"tool_call_id": "call_1",
|
||||
"content": "valid result",
|
||||
}
|
||||
assert all(
|
||||
not (
|
||||
message.get("role") == "assistant"
|
||||
and isinstance(message.get("content"), list)
|
||||
)
|
||||
for message in messages
|
||||
message.get("tool_call_id") != "unrelated_call" for message in messages
|
||||
)
|
||||
assert messages[-1]["role"] == "user"
|
||||
|
||||
def test_claude_model_maps_max_tokens_to_max_completion_tokens(
|
||||
self, monkeypatch: pytest.MonkeyPatch
|
||||
|
||||
Reference in New Issue
Block a user