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@@ -107,7 +107,7 @@ mode: "wide"
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</Tabs>
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|
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<Info>
|
||||
يوفر CrewAI تكاملات SDK أصلية لـ OpenAI و Anthropic و Google (Gemini API) و Azure و AWS Bedrock -- لا حاجة لتثبيت إضافي بخلاف الملحقات الخاصة بالمزود (مثل `uv add "crewai[openai]"`).
|
||||
يوفر CrewAI تكاملات SDK أصلية لـ OpenAI و Anthropic و Google (Gemini API) و Azure و AWS Bedrock و Snowflake Cortex -- لا حاجة لتثبيت إضافي بخلاف الملحقات الخاصة بالمزود (مثل `uv add "crewai[openai]"`).
|
||||
|
||||
جميع المزودين الآخرين مدعومون بواسطة **LiteLLM**. إذا كنت تخطط لاستخدام أي منهم، أضفه كتبعية لمشروعك:
|
||||
```bash
|
||||
@@ -291,6 +291,55 @@ mode: "wide"
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Snowflake Cortex">
|
||||
يوفر CrewAI تكاملًا أصليًا مع Snowflake Cortex REST API عبر endpoint Chat Completions المتوافق مع OpenAI. تستخدم نماذج `snowflake/...` هذا المسار بدون fallback إلى LiteLLM. يدعم Snowflake Cortex في CrewAI حاليًا Chat Completions فقط، لذلك استخدم وضع `api` الافتراضي ولا تضبط `api="responses"`.
|
||||
|
||||
```toml Code
|
||||
# Required
|
||||
SNOWFLAKE_PAT=<your-programmatic-access-token>
|
||||
SNOWFLAKE_ACCOUNT_URL=https://<account-identifier>.snowflakecomputing.com
|
||||
|
||||
# Alternative account configuration
|
||||
SNOWFLAKE_ACCOUNT=<account-identifier>
|
||||
```
|
||||
|
||||
**الاستخدام الأساسي:**
|
||||
```python Code
|
||||
from crewai import LLM
|
||||
|
||||
llm = LLM(
|
||||
model="snowflake/openai-gpt-4.1",
|
||||
temperature=0.7,
|
||||
max_completion_tokens=1024,
|
||||
)
|
||||
```
|
||||
|
||||
**نماذج Claude على Cortex:**
|
||||
```python Code
|
||||
from crewai import LLM
|
||||
|
||||
llm = LLM(
|
||||
model="snowflake/claude-sonnet-4-5",
|
||||
max_completion_tokens=1024,
|
||||
stream=True,
|
||||
)
|
||||
```
|
||||
|
||||
**متغيرات البيئة المدعومة:**
|
||||
- `SNOWFLAKE_PAT` أو `SNOWFLAKE_TOKEN` أو `SNOWFLAKE_JWT`: الرمز المستخدم كاعتماد Bearer
|
||||
- `SNOWFLAKE_ACCOUNT_URL`: عنوان URL الكامل لحساب Snowflake
|
||||
- `SNOWFLAKE_ACCOUNT` أو `SNOWFLAKE_ACCOUNT_ID` أو `SNOWFLAKE_ACCOUNT_IDENTIFIER`: معرف الحساب المستخدم لبناء URL
|
||||
|
||||
تستخدم طلبات Snowflake REST الدور الافتراضي للمستخدم. تأكد من أن هذا الدور لديه `SNOWFLAKE.CORTEX_USER` أو `SNOWFLAKE.CORTEX_REST_API_USER`. لا يتطلب endpoint Cortex REST Chat Completions معاملات database أو schema أو warehouse أو role صريح.
|
||||
|
||||
**الميزات:**
|
||||
- اختيار provider أصلي باستخدام `model="snowflake/<model-name>"`
|
||||
- Chat Completions مع streaming وبدونه فقط؛ `api="responses"` غير مدعوم
|
||||
- تتبع استخدام الرموز
|
||||
- استدعاء الدوال لنماذج OpenAI و Claude المستضافة في Snowflake
|
||||
- إزالة assistant prefill النهائي غير الصالح تلقائيًا لنماذج Claude في Snowflake
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Anthropic">
|
||||
يوفر CrewAI تكاملًا أصليًا مع Anthropic من خلال Anthropic Python SDK.
|
||||
|
||||
|
||||
123
docs/ar/enterprise/integrations/databricks.mdx
Normal file
123
docs/ar/enterprise/integrations/databricks.mdx
Normal file
@@ -0,0 +1,123 @@
|
||||
---
|
||||
title: تكامل Databricks
|
||||
description: "اربط وكلاء CrewAI بـ Databricks Genie وSQL وUnity Catalog Functions وVector Search عبر خوادم MCP المُدارة من Databricks."
|
||||
icon: "layer-group"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## نظرة عامة
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
## القدرات الرئيسية
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Genie" icon="comments">
|
||||
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.
|
||||
</Card>
|
||||
<Card title="Databricks SQL" icon="database">
|
||||
نفّذ SQL خاضعًا للحوكمة على مستودعات Databricks لديك للاستعلام عن البيانات وتحويلها وإنشاء خطوط أنابيب البيانات مباشرةً من وكلائك.
|
||||
</Card>
|
||||
<Card title="Unity Catalog Functions" icon="function">
|
||||
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.
|
||||
</Card>
|
||||
<Card title="Vector Search" icon="magnifying-glass">
|
||||
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.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
تعمل جميع الخوادم خلف Unity AI Gateway وتطبّق ضوابط الوصول في Unity Catalog، بحيث لا يرى وكلاؤك سوى البيانات والأدوات المصرَّح لهم باستخدامها.
|
||||
|
||||
## المتطلبات المسبقة
|
||||
|
||||
قبل استخدام تكامل Databricks، تأكّد من توفّر ما يلي:
|
||||
|
||||
- حساب [CrewAI AMP](https://app.crewai.com) باشتراك نشط
|
||||
- مساحة عمل Databricks تحتوي على القدرات التي تريد كشفها (Genie Spaces، مستودعات SQL، دوال Unity Catalog، أو فهارس Vector Search)
|
||||
- [امتيازات Unity Catalog](https://docs.databricks.com/aws/en/data-governance/unity-catalog) المناسبة على الكائنات الأساسية
|
||||
- اسم مضيف مساحة عمل Databricks الخاص بك (مثال: `your-workspace.cloud.databricks.com`)
|
||||
|
||||
## خوادم MCP المُدارة من Databricks
|
||||
|
||||
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:
|
||||
|
||||
| الخادم | الوظيفة | نمط عنوان MCP |
|
||||
|--------|---------|---------------|
|
||||
| **Genie** | أسئلة وأجوبة بلغة طبيعية على Genie Space | `https://<workspace-hostname>/api/2.0/mcp/genie/{genie_space_id}` |
|
||||
| **Databricks SQL** | تنفيذ SQL على مستودعاتك | `https://<workspace-hostname>/api/2.0/mcp/sql` |
|
||||
| **Unity Catalog Functions** | تشغيل دوال UC المسجّلة | `https://<workspace-hostname>/api/2.0/mcp/functions/{catalog}/{schema}` |
|
||||
| **Vector Search** | الاستعلام من فهرس Vector Search | `https://<workspace-hostname>/api/2.0/mcp/vector-search/{catalog}/{schema}` |
|
||||
|
||||
<Note>
|
||||
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).
|
||||
</Note>
|
||||
|
||||
## ربط Databricks في CrewAI AMP
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/databricks-configure.png" alt="تهيئة خادم MCP مُدار من Databricks في CrewAI AMP" />
|
||||
</Frame>
|
||||
|
||||
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:
|
||||
|
||||
<Steps>
|
||||
<Step title="افتح Tools & Integrations">
|
||||
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.
|
||||
</Step>
|
||||
|
||||
<Step title="هيّئ خادمًا">
|
||||
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.
|
||||
</Step>
|
||||
|
||||
<Step title="اختر طريقة المصادقة">
|
||||
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.
|
||||
- **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>
|
||||
|
||||
<Tip>
|
||||
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.
|
||||
</Tip>
|
||||
|
||||
## استخدام أدوات Databricks في فرقك
|
||||
|
||||
بمجرد الاتصال، تظهر الأدوات التي يكشفها كل خادم MCP جنبًا إلى جنب مع الاتصالات المدمجة في صفحة **Tools & Integrations**. يمكنك:
|
||||
|
||||
- **إسناد الأدوات إلى الوكلاء** في فرقك تمامًا مثل أي أداة أخرى في CrewAI.
|
||||
- **إدارة الرؤية** للتحكّم في أعضاء الفريق الذين يمكنهم استخدام كل اتصال.
|
||||
- **تعديل أو إزالة** أي اتصال في أي وقت من قائمة Connections.
|
||||
|
||||
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.
|
||||
|
||||
<Warning>
|
||||
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.
|
||||
</Warning>
|
||||
|
||||
## مزيد من المعلومات
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="خوادم MCP المُدارة من Databricks" icon="layer-group" href="https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp">
|
||||
وثائق Databricks الرسمية لخوادم MCP المُدارة Genie وSQL وUnity Catalog Functions وVector Search.
|
||||
</Card>
|
||||
<Card title="خوادم MCP المخصصة في CrewAI" icon="plug" href="/ar/enterprise/guides/custom-mcp-server">
|
||||
تعرّف على كيفية اتصال CrewAI بأي خادم MCP، وهو الأساس الذي يُبنى عليه تكامل Databricks.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
<Card title="بحاجة إلى مساعدة؟" icon="headset" href="mailto:support@crewai.com">
|
||||
تواصل مع فريق الدعم للحصول على المساعدة في تهيئة تكامل Databricks أو في حل المشكلات.
|
||||
</Card>
|
||||
@@ -460,6 +460,7 @@
|
||||
"en/enterprise/integrations/asana",
|
||||
"en/enterprise/integrations/box",
|
||||
"en/enterprise/integrations/clickup",
|
||||
"en/enterprise/integrations/databricks",
|
||||
"en/enterprise/integrations/github",
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||||
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@@ -978,6 +979,7 @@
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"en/enterprise/integrations/asana",
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||||
"en/enterprise/integrations/databricks",
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"en/enterprise/integrations/asana",
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||||
"en/enterprise/integrations/databricks",
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||||
"en/enterprise/integrations/github",
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"en/enterprise/integrations/google_calendar",
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@@ -1947,6 +1950,7 @@
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"en/enterprise/integrations/asana",
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"en/enterprise/integrations/box",
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"en/enterprise/integrations/clickup",
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"en/enterprise/integrations/databricks",
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"en/enterprise/integrations/github",
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"en/enterprise/integrations/gmail",
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"en/enterprise/integrations/google_calendar",
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@@ -2431,6 +2435,7 @@
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"en/enterprise/integrations/asana",
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||||
"en/enterprise/integrations/box",
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||||
"en/enterprise/integrations/clickup",
|
||||
"en/enterprise/integrations/databricks",
|
||||
"en/enterprise/integrations/github",
|
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"en/enterprise/integrations/gmail",
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"en/enterprise/integrations/google_calendar",
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@@ -2925,6 +2930,7 @@
|
||||
"en/enterprise/integrations/asana",
|
||||
"en/enterprise/integrations/box",
|
||||
"en/enterprise/integrations/clickup",
|
||||
"en/enterprise/integrations/databricks",
|
||||
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|
||||
"en/enterprise/integrations/gmail",
|
||||
"en/enterprise/integrations/google_calendar",
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@@ -3419,6 +3425,7 @@
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||||
"en/enterprise/integrations/asana",
|
||||
"en/enterprise/integrations/box",
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"en/enterprise/integrations/clickup",
|
||||
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|
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"en/enterprise/integrations/databricks",
|
||||
"en/enterprise/integrations/github",
|
||||
"en/enterprise/integrations/gmail",
|
||||
"en/enterprise/integrations/google_calendar",
|
||||
@@ -4407,6 +4415,7 @@
|
||||
"en/enterprise/integrations/asana",
|
||||
"en/enterprise/integrations/box",
|
||||
"en/enterprise/integrations/clickup",
|
||||
"en/enterprise/integrations/databricks",
|
||||
"en/enterprise/integrations/github",
|
||||
"en/enterprise/integrations/gmail",
|
||||
"en/enterprise/integrations/google_calendar",
|
||||
@@ -4890,6 +4899,7 @@
|
||||
"en/enterprise/integrations/asana",
|
||||
"en/enterprise/integrations/box",
|
||||
"en/enterprise/integrations/clickup",
|
||||
"en/enterprise/integrations/databricks",
|
||||
"en/enterprise/integrations/github",
|
||||
"en/enterprise/integrations/gmail",
|
||||
"en/enterprise/integrations/google_calendar",
|
||||
@@ -5373,6 +5383,7 @@
|
||||
"en/enterprise/integrations/asana",
|
||||
"en/enterprise/integrations/box",
|
||||
"en/enterprise/integrations/clickup",
|
||||
"en/enterprise/integrations/databricks",
|
||||
"en/enterprise/integrations/github",
|
||||
"en/enterprise/integrations/gmail",
|
||||
"en/enterprise/integrations/google_calendar",
|
||||
@@ -5856,6 +5867,7 @@
|
||||
"en/enterprise/integrations/asana",
|
||||
"en/enterprise/integrations/box",
|
||||
"en/enterprise/integrations/clickup",
|
||||
"en/enterprise/integrations/databricks",
|
||||
"en/enterprise/integrations/github",
|
||||
"en/enterprise/integrations/gmail",
|
||||
"en/enterprise/integrations/google_calendar",
|
||||
@@ -6341,6 +6353,7 @@
|
||||
"en/enterprise/integrations/asana",
|
||||
"en/enterprise/integrations/box",
|
||||
"en/enterprise/integrations/clickup",
|
||||
"en/enterprise/integrations/databricks",
|
||||
"en/enterprise/integrations/github",
|
||||
"en/enterprise/integrations/gmail",
|
||||
"en/enterprise/integrations/google_calendar",
|
||||
@@ -6824,6 +6837,7 @@
|
||||
"en/enterprise/integrations/asana",
|
||||
"en/enterprise/integrations/box",
|
||||
"en/enterprise/integrations/clickup",
|
||||
"en/enterprise/integrations/databricks",
|
||||
"en/enterprise/integrations/github",
|
||||
"en/enterprise/integrations/gmail",
|
||||
"en/enterprise/integrations/google_calendar",
|
||||
@@ -7310,6 +7324,7 @@
|
||||
"en/enterprise/integrations/asana",
|
||||
"en/enterprise/integrations/box",
|
||||
"en/enterprise/integrations/clickup",
|
||||
"en/enterprise/integrations/databricks",
|
||||
"en/enterprise/integrations/github",
|
||||
"en/enterprise/integrations/gmail",
|
||||
"en/enterprise/integrations/google_calendar",
|
||||
@@ -7835,6 +7850,7 @@
|
||||
"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",
|
||||
@@ -8330,6 +8346,7 @@
|
||||
"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",
|
||||
@@ -8792,6 +8809,7 @@
|
||||
"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",
|
||||
@@ -9254,6 +9272,7 @@
|
||||
"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",
|
||||
@@ -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",
|
||||
|
||||
@@ -107,7 +107,7 @@ There are different places in CrewAI code where you can specify the model to use
|
||||
</Tabs>
|
||||
|
||||
<Info>
|
||||
CrewAI provides native SDK integrations for OpenAI, Anthropic, Google (Gemini API), Azure, and AWS Bedrock — no extra install needed beyond the provider-specific extras (e.g. `uv add "crewai[openai]"`).
|
||||
CrewAI provides native SDK integrations for OpenAI, Anthropic, Google (Gemini API), Azure, AWS Bedrock, and Snowflake Cortex — no extra install needed beyond the provider-specific extras (e.g. `uv add "crewai[openai]"`).
|
||||
|
||||
All other providers are powered by **LiteLLM**. If you plan to use any of them, add it as a dependency to your project:
|
||||
```bash
|
||||
@@ -291,6 +291,55 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Snowflake Cortex">
|
||||
CrewAI provides native integration with the Snowflake Cortex REST API through its OpenAI-compatible Chat Completions endpoint. This avoids LiteLLM fallback for `snowflake/...` models. Snowflake Cortex currently supports Chat Completions only in CrewAI, so use the default `api` mode and do not set `api="responses"`.
|
||||
|
||||
```toml Code
|
||||
# Required
|
||||
SNOWFLAKE_PAT=<your-programmatic-access-token>
|
||||
SNOWFLAKE_ACCOUNT_URL=https://<account-identifier>.snowflakecomputing.com
|
||||
|
||||
# Alternative account configuration
|
||||
SNOWFLAKE_ACCOUNT=<account-identifier>
|
||||
```
|
||||
|
||||
**Basic Usage:**
|
||||
```python Code
|
||||
from crewai import LLM
|
||||
|
||||
llm = LLM(
|
||||
model="snowflake/openai-gpt-4.1",
|
||||
temperature=0.7,
|
||||
max_completion_tokens=1024,
|
||||
)
|
||||
```
|
||||
|
||||
**Claude Models on Cortex:**
|
||||
```python Code
|
||||
from crewai import LLM
|
||||
|
||||
llm = LLM(
|
||||
model="snowflake/claude-sonnet-4-5",
|
||||
max_completion_tokens=1024,
|
||||
stream=True,
|
||||
)
|
||||
```
|
||||
|
||||
**Supported Environment Variables:**
|
||||
- `SNOWFLAKE_PAT`, `SNOWFLAKE_TOKEN`, or `SNOWFLAKE_JWT`: token used as the Bearer credential
|
||||
- `SNOWFLAKE_ACCOUNT_URL`: full Snowflake account URL
|
||||
- `SNOWFLAKE_ACCOUNT`, `SNOWFLAKE_ACCOUNT_ID`, or `SNOWFLAKE_ACCOUNT_IDENTIFIER`: account identifier used to build the account URL
|
||||
|
||||
Snowflake REST requests use the user's default Snowflake role. Make sure that role has `SNOWFLAKE.CORTEX_USER` or `SNOWFLAKE.CORTEX_REST_API_USER`. Database, schema, warehouse, and explicit role parameters are not required by the Cortex REST Chat Completions endpoint.
|
||||
|
||||
**Features:**
|
||||
- Native provider selection with `model="snowflake/<model-name>"`
|
||||
- Streaming and non-streaming Chat Completions only; `api="responses"` is not supported
|
||||
- Token usage tracking
|
||||
- Function calling for Snowflake-hosted OpenAI and Claude models
|
||||
- Automatic removal of invalid trailing assistant prefill for Snowflake Claude models
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Anthropic">
|
||||
CrewAI provides native integration with Anthropic through the Anthropic Python SDK.
|
||||
|
||||
|
||||
@@ -187,7 +187,7 @@ flowchart TD
|
||||
- **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
|
||||
- **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
|
||||
- Trained guidance is applied at prompt time; it does not modify your Python/YAML agent configuration.
|
||||
- Agents automatically load trained suggestions from a file named `trained_agents_data.pkl` located in the current working directory. If you trained to a different filename, either rename it to `trained_agents_data.pkl` before running, or adjust the loader in code.
|
||||
- Agents automatically load trained suggestions from a file named `trained_agents_data.pkl` located in the current working directory. If you trained to a different filename, pass that path with `Crew(trained_agents_file="my_custom_trained.pkl")`, set `CREWAI_TRAINED_AGENTS_FILE`, or use `crewai run -f my_custom_trained.pkl`.
|
||||
- You can change the output filename when calling `crewai train` with `-f/--filename`. Absolute paths are supported if you want to save outside the CWD.
|
||||
|
||||
It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
|
||||
|
||||
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 |
@@ -106,7 +106,7 @@ CrewAI 코드 내에는 사용할 모델을 지정할 수 있는 여러 위치
|
||||
</Tabs>
|
||||
|
||||
<Info>
|
||||
CrewAI는 OpenAI, Anthropic, Google (Gemini API), Azure, AWS Bedrock에 대해 네이티브 SDK 통합을 제공합니다 — 제공자별 extras(예: `uv add "crewai[openai]"`) 외에 추가 설치가 필요하지 않습니다.
|
||||
CrewAI는 OpenAI, Anthropic, Google (Gemini API), Azure, AWS Bedrock, Snowflake Cortex에 대해 네이티브 SDK 통합을 제공합니다 — 제공자별 extras(예: `uv add "crewai[openai]"`) 외에 추가 설치가 필요하지 않습니다.
|
||||
|
||||
그 외 모든 제공자는 **LiteLLM**을 통해 지원됩니다. 이를 사용하려면 프로젝트에 의존성으로 추가하세요:
|
||||
```bash
|
||||
@@ -230,6 +230,55 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Snowflake Cortex">
|
||||
CrewAI는 OpenAI 호환 Chat Completions 엔드포인트를 통해 Snowflake Cortex REST API와 네이티브로 통합됩니다. `snowflake/...` 모델은 LiteLLM fallback 없이 사용됩니다. CrewAI에서 Snowflake Cortex는 현재 Chat Completions만 지원하므로 기본 `api` 모드를 사용하고 `api="responses"`를 설정하지 마세요.
|
||||
|
||||
```toml Code
|
||||
# Required
|
||||
SNOWFLAKE_PAT=<your-programmatic-access-token>
|
||||
SNOWFLAKE_ACCOUNT_URL=https://<account-identifier>.snowflakecomputing.com
|
||||
|
||||
# Alternative account configuration
|
||||
SNOWFLAKE_ACCOUNT=<account-identifier>
|
||||
```
|
||||
|
||||
**기본 사용법:**
|
||||
```python Code
|
||||
from crewai import LLM
|
||||
|
||||
llm = LLM(
|
||||
model="snowflake/openai-gpt-4.1",
|
||||
temperature=0.7,
|
||||
max_completion_tokens=1024,
|
||||
)
|
||||
```
|
||||
|
||||
**Cortex의 Claude 모델:**
|
||||
```python Code
|
||||
from crewai import LLM
|
||||
|
||||
llm = LLM(
|
||||
model="snowflake/claude-sonnet-4-5",
|
||||
max_completion_tokens=1024,
|
||||
stream=True,
|
||||
)
|
||||
```
|
||||
|
||||
**지원 환경 변수:**
|
||||
- `SNOWFLAKE_PAT`, `SNOWFLAKE_TOKEN`, 또는 `SNOWFLAKE_JWT`: Bearer 자격 증명으로 사용할 토큰
|
||||
- `SNOWFLAKE_ACCOUNT_URL`: 전체 Snowflake 계정 URL
|
||||
- `SNOWFLAKE_ACCOUNT`, `SNOWFLAKE_ACCOUNT_ID`, 또는 `SNOWFLAKE_ACCOUNT_IDENTIFIER`: 계정 URL을 만들 계정 식별자
|
||||
|
||||
Snowflake REST 요청은 사용자의 기본 Snowflake role을 사용합니다. 해당 role에 `SNOWFLAKE.CORTEX_USER` 또는 `SNOWFLAKE.CORTEX_REST_API_USER`가 있는지 확인하세요. Cortex REST Chat Completions 엔드포인트에는 database, schema, warehouse, 명시적 role 파라미터가 필요하지 않습니다.
|
||||
|
||||
**기능:**
|
||||
- `model="snowflake/<model-name>"`을 통한 네이티브 provider 선택
|
||||
- Streaming 및 non-streaming Chat Completions만 지원; `api="responses"`는 지원되지 않음
|
||||
- 토큰 사용량 추적
|
||||
- Snowflake 호스팅 OpenAI 및 Claude 모델의 함수 호출
|
||||
- Snowflake Claude 모델에서 유효하지 않은 마지막 assistant prefill 자동 제거
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Anthropic">
|
||||
```toml Code
|
||||
# Required
|
||||
|
||||
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>
|
||||
@@ -106,7 +106,7 @@ Existem diferentes locais no código do CrewAI onde você pode especificar o mod
|
||||
</Tabs>
|
||||
|
||||
<Info>
|
||||
O CrewAI oferece integrações nativas via SDK para OpenAI, Anthropic, Google (Gemini API), Azure e AWS Bedrock — sem necessidade de instalação extra além dos extras específicos do provedor (ex.: `uv add "crewai[openai]"`).
|
||||
O CrewAI oferece integrações nativas via SDK para OpenAI, Anthropic, Google (Gemini API), Azure, AWS Bedrock e Snowflake Cortex — sem necessidade de instalação extra além dos extras específicos do provedor (ex.: `uv add "crewai[openai]"`).
|
||||
|
||||
Todos os outros provedores são alimentados pelo **LiteLLM**. Se você planeja usar algum deles, adicione-o como dependência ao seu projeto:
|
||||
```bash
|
||||
@@ -230,6 +230,55 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Snowflake Cortex">
|
||||
O CrewAI oferece integração nativa com a API REST do Snowflake Cortex pelo endpoint Chat Completions compatível com OpenAI. Isso evita fallback para LiteLLM em modelos `snowflake/...`. Atualmente, o Snowflake Cortex no CrewAI oferece suporte apenas a Chat Completions, então use o modo `api` padrão e não defina `api="responses"`.
|
||||
|
||||
```toml Code
|
||||
# Obrigatório
|
||||
SNOWFLAKE_PAT=<your-programmatic-access-token>
|
||||
SNOWFLAKE_ACCOUNT_URL=https://<account-identifier>.snowflakecomputing.com
|
||||
|
||||
# Configuração alternativa da conta
|
||||
SNOWFLAKE_ACCOUNT=<account-identifier>
|
||||
```
|
||||
|
||||
**Uso básico:**
|
||||
```python Code
|
||||
from crewai import LLM
|
||||
|
||||
llm = LLM(
|
||||
model="snowflake/openai-gpt-4.1",
|
||||
temperature=0.7,
|
||||
max_completion_tokens=1024,
|
||||
)
|
||||
```
|
||||
|
||||
**Modelos Claude no Cortex:**
|
||||
```python Code
|
||||
from crewai import LLM
|
||||
|
||||
llm = LLM(
|
||||
model="snowflake/claude-sonnet-4-5",
|
||||
max_completion_tokens=1024,
|
||||
stream=True,
|
||||
)
|
||||
```
|
||||
|
||||
**Variáveis de ambiente suportadas:**
|
||||
- `SNOWFLAKE_PAT`, `SNOWFLAKE_TOKEN` ou `SNOWFLAKE_JWT`: token usado como credencial Bearer
|
||||
- `SNOWFLAKE_ACCOUNT_URL`: URL completa da conta Snowflake
|
||||
- `SNOWFLAKE_ACCOUNT`, `SNOWFLAKE_ACCOUNT_ID` ou `SNOWFLAKE_ACCOUNT_IDENTIFIER`: identificador da conta usado para montar a URL
|
||||
|
||||
As requisições REST do Snowflake usam a role padrão do usuário. Garanta que essa role tenha `SNOWFLAKE.CORTEX_USER` ou `SNOWFLAKE.CORTEX_REST_API_USER`. Parâmetros de banco de dados, schema, warehouse e role explícita não são exigidos pelo endpoint Cortex REST Chat Completions.
|
||||
|
||||
**Recursos:**
|
||||
- Seleção nativa com `model="snowflake/<model-name>"`
|
||||
- Chat Completions com e sem streaming apenas; `api="responses"` não é compatível
|
||||
- Rastreamento de uso de tokens
|
||||
- Chamadas de função para modelos OpenAI e Claude hospedados no Snowflake
|
||||
- Remoção automática de prefill final de assistant inválido para modelos Claude no Snowflake
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Anthropic">
|
||||
```toml Code
|
||||
# Obrigatório
|
||||
|
||||
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>
|
||||
@@ -15,7 +15,7 @@ dependencies = [
|
||||
"appdirs~=1.4.4",
|
||||
"cryptography>=42.0",
|
||||
"httpx~=0.28.1",
|
||||
"pyjwt>=2.9.0,<3",
|
||||
"pyjwt>=2.13.0,<3",
|
||||
"rich>=13.7.1",
|
||||
"tomli~=2.0.2",
|
||||
"tomli-w~=1.1.0",
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
|
||||
authors = [{ name = "Your Name", email = "you@example.com" }]
|
||||
requires-python = ">=3.10,<3.14"
|
||||
dependencies = [
|
||||
"crewai[tools]==1.14.5a2"
|
||||
"crewai[tools]==1.14.6"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
|
||||
authors = [{ name = "Your Name", email = "you@example.com" }]
|
||||
requires-python = ">=3.10,<3.14"
|
||||
dependencies = [
|
||||
"crewai[tools]==1.14.5a2"
|
||||
"crewai[tools]==1.14.6"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10,<3.14"
|
||||
dependencies = [
|
||||
"crewai[tools]==1.14.5a2"
|
||||
"crewai[tools]==1.14.6"
|
||||
]
|
||||
|
||||
[tool.crewai]
|
||||
|
||||
131
lib/cli/tests/test_click_compatibility.py
Normal file
131
lib/cli/tests/test_click_compatibility.py
Normal file
@@ -0,0 +1,131 @@
|
||||
"""Tests for click dependency compatibility.
|
||||
|
||||
Regression tests for https://github.com/crewAIInc/crewAI/issues/6002
|
||||
The click dependency was previously pinned to ~=8.1.7 (i.e. >=8.1.7,<8.2.0)
|
||||
which prevented users from upgrading to click 8.2+ as required by their
|
||||
security policies. The constraint has been widened to >=8.1.7,<9 to allow
|
||||
newer click 8.x releases while still guarding against a future major version
|
||||
break.
|
||||
"""
|
||||
|
||||
from importlib.metadata import requires
|
||||
from pathlib import Path
|
||||
|
||||
import click
|
||||
import pytest
|
||||
from click.testing import CliRunner
|
||||
from packaging.requirements import Requirement
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Verify the runtime click version satisfies the declared constraint
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _get_click_requirement_from_pyproject(package_dir: str) -> Requirement:
|
||||
"""Parse the click requirement directly from a pyproject.toml file."""
|
||||
import tomli
|
||||
|
||||
pyproject_path = Path(__file__).resolve().parents[3] / package_dir / "pyproject.toml"
|
||||
with open(pyproject_path, "rb") as f:
|
||||
data = tomli.load(f)
|
||||
deps = data["project"]["dependencies"]
|
||||
for dep in deps:
|
||||
req = Requirement(dep)
|
||||
if req.name == "click":
|
||||
return req
|
||||
raise ValueError(f"click not found in {pyproject_path}")
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"package_dir",
|
||||
[
|
||||
"lib/crewai",
|
||||
"lib/cli",
|
||||
"lib/devtools",
|
||||
],
|
||||
)
|
||||
def test_click_constraint_allows_8_3_3(package_dir: str):
|
||||
"""The declared click constraint must accept click 8.3.3 (issue #6002)."""
|
||||
req = _get_click_requirement_from_pyproject(package_dir)
|
||||
# packaging's Requirement.specifier supports `__contains__` for version checks
|
||||
assert "8.3.3" in req.specifier, (
|
||||
f"{package_dir}: click constraint {req.specifier} does not allow 8.3.3"
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"package_dir",
|
||||
[
|
||||
"lib/crewai",
|
||||
"lib/cli",
|
||||
"lib/devtools",
|
||||
],
|
||||
)
|
||||
def test_click_constraint_allows_8_1_7(package_dir: str):
|
||||
"""The declared click constraint must still accept the original minimum (8.1.7)."""
|
||||
req = _get_click_requirement_from_pyproject(package_dir)
|
||||
assert "8.1.7" in req.specifier, (
|
||||
f"{package_dir}: click constraint {req.specifier} does not allow 8.1.7"
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"package_dir",
|
||||
[
|
||||
"lib/crewai",
|
||||
"lib/cli",
|
||||
"lib/devtools",
|
||||
],
|
||||
)
|
||||
def test_click_constraint_rejects_next_major(package_dir: str):
|
||||
"""The declared click constraint must reject click 9.0.0."""
|
||||
req = _get_click_requirement_from_pyproject(package_dir)
|
||||
assert "9.0.0" not in req.specifier, (
|
||||
f"{package_dir}: click constraint {req.specifier} should not allow 9.0.0"
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Verify the installed click version works with the CLI
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def test_click_version_is_compatible():
|
||||
"""The installed click version must be within the 8.x range."""
|
||||
major = int(click.__version__.split(".")[0])
|
||||
assert major == 8, f"Expected click 8.x, got {click.__version__}"
|
||||
|
||||
|
||||
def test_cli_runner_works_with_installed_click():
|
||||
"""Smoke-test: CliRunner from the installed click can invoke a trivial command."""
|
||||
|
||||
@click.command()
|
||||
@click.option("--name", default="world")
|
||||
def hello(name: str) -> None:
|
||||
click.echo(f"Hello {name}!")
|
||||
|
||||
runner = CliRunner()
|
||||
result = runner.invoke(hello, ["--name", "crewai"])
|
||||
assert result.exit_code == 0
|
||||
assert "Hello crewai!" in result.output
|
||||
|
||||
|
||||
def test_cli_group_works_with_installed_click():
|
||||
"""Smoke-test: click.group, click.option, click.argument all work."""
|
||||
|
||||
@click.group()
|
||||
def grp() -> None:
|
||||
pass
|
||||
|
||||
@grp.command()
|
||||
@click.argument("task")
|
||||
@click.option("--verbose", is_flag=True)
|
||||
def run(task: str, verbose: bool) -> None:
|
||||
if verbose:
|
||||
click.echo(f"Running {task} (verbose)")
|
||||
else:
|
||||
click.echo(f"Running {task}")
|
||||
|
||||
runner = CliRunner()
|
||||
result = runner.invoke(grp, ["run", "test-task", "--verbose"])
|
||||
assert result.exit_code == 0
|
||||
assert "Running test-task (verbose)" in result.output
|
||||
@@ -13,7 +13,7 @@ dependencies = [
|
||||
"httpx~=0.28.1",
|
||||
"packaging>=23.0",
|
||||
"portalocker~=2.7.0",
|
||||
"pyjwt>=2.9.0,<3",
|
||||
"pyjwt>=2.13.0,<3",
|
||||
"pydantic>=2.11.9,<2.13",
|
||||
"rich>=13.7.1",
|
||||
"opentelemetry-api~=1.34.0",
|
||||
|
||||
@@ -27,7 +27,7 @@ dependencies = [
|
||||
"openpyxl~=3.1.5",
|
||||
# Authentication and Security
|
||||
"python-dotenv>=1.2.2,<2",
|
||||
"pyjwt>=2.9.0,<3",
|
||||
"pyjwt>=2.13.0,<3",
|
||||
# Configuration and Utils
|
||||
"click>=8.1.7,<9",
|
||||
"appdirs~=1.4.4",
|
||||
|
||||
@@ -1219,9 +1219,17 @@ class Agent(BaseAgent):
|
||||
|
||||
def _use_trained_data(self, task_prompt: str) -> str:
|
||||
"""Use trained data for the agent task prompt to improve output."""
|
||||
trained_file = os.getenv(
|
||||
CREWAI_TRAINED_AGENTS_FILE_ENV, TRAINED_AGENTS_DATA_FILE
|
||||
crew_trained_agents_file = (
|
||||
getattr(self.crew, "trained_agents_file", None)
|
||||
if self.crew and not isinstance(self.crew, str)
|
||||
else None
|
||||
)
|
||||
trained_file = (
|
||||
os.fspath(crew_trained_agents_file)
|
||||
if crew_trained_agents_file
|
||||
else os.getenv(CREWAI_TRAINED_AGENTS_FILE_ENV, TRAINED_AGENTS_DATA_FILE)
|
||||
)
|
||||
|
||||
if data := CrewTrainingHandler(trained_file).load():
|
||||
if trained_data_output := data.get(self.role):
|
||||
task_prompt += (
|
||||
|
||||
@@ -179,6 +179,7 @@ class Crew(FlowTrackable, BaseModel):
|
||||
max_rpm: Maximum number of requests per minute for the crew execution to
|
||||
be respected.
|
||||
prompt_file: Path to the prompt json file to be used for the crew.
|
||||
trained_agents_file: Path to trained agent suggestions loaded during inference.
|
||||
id: A unique identifier for the crew instance.
|
||||
task_callback: Callback to be executed after each task for every agents
|
||||
execution.
|
||||
@@ -303,6 +304,13 @@ class Crew(FlowTrackable, BaseModel):
|
||||
default=None,
|
||||
description="Path to the prompt json file to be used for the crew.",
|
||||
)
|
||||
trained_agents_file: str | Path | None = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Path to a trained-agents pickle produced by train(). "
|
||||
"When set, agents load suggestions from this file during inference."
|
||||
),
|
||||
)
|
||||
output_log_file: bool | str | None = Field(
|
||||
default=None,
|
||||
description="Path to the log file to be saved",
|
||||
|
||||
320
lib/crewai/src/crewai/flow/dsl.py
Normal file
320
lib/crewai/src/crewai/flow/dsl.py
Normal file
@@ -0,0 +1,320 @@
|
||||
"""Flow authoring DSL: the ``@start`` / ``@listen`` / ``@router`` decorators
|
||||
plus the ``or_`` / ``and_`` condition combinators.
|
||||
|
||||
These decorators wrap user methods into the typed wrappers defined in
|
||||
``flow_wrappers`` and record their trigger conditions. The structural model
|
||||
those conditions feed is built in ``flow_definition``; execution happens in
|
||||
``runtime``.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Callable
|
||||
from typing import Any, ParamSpec, TypeVar
|
||||
|
||||
from crewai.flow.constants import AND_CONDITION, OR_CONDITION
|
||||
from crewai.flow.flow_definition import (
|
||||
_extract_all_methods,
|
||||
is_flow_condition_dict,
|
||||
is_flow_method_callable,
|
||||
is_flow_method_name,
|
||||
)
|
||||
from crewai.flow.flow_wrappers import (
|
||||
FlowCondition,
|
||||
FlowConditions,
|
||||
ListenMethod,
|
||||
RouterMethod,
|
||||
StartMethod,
|
||||
)
|
||||
|
||||
|
||||
P = ParamSpec("P")
|
||||
R = TypeVar("R")
|
||||
|
||||
|
||||
def start(
|
||||
condition: str | FlowCondition | Callable[..., Any] | None = None,
|
||||
) -> Callable[[Callable[P, R]], StartMethod[P, R]]:
|
||||
"""Marks a method as a flow's starting point.
|
||||
|
||||
This decorator designates a method as an entry point for the flow execution.
|
||||
It can optionally specify conditions that trigger the start based on other
|
||||
method executions.
|
||||
|
||||
Args:
|
||||
condition: Defines when the start method should execute. Can be:
|
||||
- str: Name of a method that triggers this start
|
||||
- FlowCondition: Result from or_() or and_(), including nested conditions
|
||||
- Callable[..., Any]: A method reference that triggers this start
|
||||
Default is None, meaning unconditional start.
|
||||
|
||||
Returns:
|
||||
A decorator function that wraps the method as a flow start point and preserves its signature.
|
||||
|
||||
Raises:
|
||||
ValueError: If the condition format is invalid.
|
||||
|
||||
Examples:
|
||||
>>> @start() # Unconditional start
|
||||
>>> def begin_flow(self):
|
||||
... pass
|
||||
|
||||
>>> @start("method_name") # Start after specific method
|
||||
>>> def conditional_start(self):
|
||||
... pass
|
||||
|
||||
>>> @start(and_("method1", "method2")) # Start after multiple methods
|
||||
>>> def complex_start(self):
|
||||
... pass
|
||||
"""
|
||||
|
||||
def decorator(func: Callable[P, R]) -> StartMethod[P, R]:
|
||||
"""Decorator that wraps a function as a start method.
|
||||
|
||||
Args:
|
||||
func: The function to wrap as a start method.
|
||||
|
||||
Returns:
|
||||
A StartMethod wrapper around the function.
|
||||
"""
|
||||
wrapper = StartMethod(func)
|
||||
|
||||
if condition is not None:
|
||||
if is_flow_method_name(condition):
|
||||
wrapper.__trigger_methods__ = [condition]
|
||||
wrapper.__condition_type__ = OR_CONDITION
|
||||
elif is_flow_condition_dict(condition):
|
||||
if "conditions" in condition:
|
||||
wrapper.__trigger_condition__ = condition
|
||||
wrapper.__trigger_methods__ = _extract_all_methods(condition)
|
||||
wrapper.__condition_type__ = condition["type"]
|
||||
elif "methods" in condition:
|
||||
wrapper.__trigger_methods__ = condition["methods"]
|
||||
wrapper.__condition_type__ = condition["type"]
|
||||
else:
|
||||
raise ValueError(
|
||||
"Condition dict must contain 'conditions' or 'methods'"
|
||||
)
|
||||
elif is_flow_method_callable(condition):
|
||||
wrapper.__trigger_methods__ = [condition.__name__]
|
||||
wrapper.__condition_type__ = OR_CONDITION
|
||||
else:
|
||||
raise ValueError(
|
||||
"Condition must be a method, string, or a result of or_() or and_()"
|
||||
)
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
def listen(
|
||||
condition: str | FlowCondition | Callable[..., Any],
|
||||
) -> Callable[[Callable[P, R]], ListenMethod[P, R]]:
|
||||
"""Creates a listener that executes when specified conditions are met.
|
||||
|
||||
This decorator sets up a method to execute in response to other method
|
||||
executions in the flow. It supports both simple and complex triggering
|
||||
conditions.
|
||||
|
||||
Args:
|
||||
condition: Specifies when the listener should execute.
|
||||
|
||||
Returns:
|
||||
A decorator function that wraps the method as a flow listener and preserves its signature.
|
||||
|
||||
Raises:
|
||||
ValueError: If the condition format is invalid.
|
||||
|
||||
Examples:
|
||||
>>> @listen("process_data")
|
||||
>>> def handle_processed_data(self):
|
||||
... pass
|
||||
|
||||
>>> @listen("method_name")
|
||||
>>> def handle_completion(self):
|
||||
... pass
|
||||
"""
|
||||
|
||||
def decorator(func: Callable[P, R]) -> ListenMethod[P, R]:
|
||||
"""Decorator that wraps a function as a listener method.
|
||||
|
||||
Args:
|
||||
func: The function to wrap as a listener method.
|
||||
|
||||
Returns:
|
||||
A ListenMethod wrapper around the function.
|
||||
"""
|
||||
wrapper = ListenMethod(func)
|
||||
|
||||
if is_flow_method_name(condition):
|
||||
wrapper.__trigger_methods__ = [condition]
|
||||
wrapper.__condition_type__ = OR_CONDITION
|
||||
elif is_flow_condition_dict(condition):
|
||||
if "conditions" in condition:
|
||||
wrapper.__trigger_condition__ = condition
|
||||
wrapper.__trigger_methods__ = _extract_all_methods(condition)
|
||||
wrapper.__condition_type__ = condition["type"]
|
||||
elif "methods" in condition:
|
||||
wrapper.__trigger_methods__ = condition["methods"]
|
||||
wrapper.__condition_type__ = condition["type"]
|
||||
else:
|
||||
raise ValueError(
|
||||
"Condition dict must contain 'conditions' or 'methods'"
|
||||
)
|
||||
elif is_flow_method_callable(condition):
|
||||
wrapper.__trigger_methods__ = [condition.__name__]
|
||||
wrapper.__condition_type__ = OR_CONDITION
|
||||
else:
|
||||
raise ValueError(
|
||||
"Condition must be a method, string, or a result of or_() or and_()"
|
||||
)
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
def router(
|
||||
condition: str | FlowCondition | Callable[..., Any],
|
||||
) -> Callable[[Callable[P, R]], RouterMethod[P, R]]:
|
||||
"""Creates a routing method that directs flow execution based on conditions.
|
||||
|
||||
This decorator marks a method as a router, which can dynamically determine
|
||||
the next steps in the flow based on its return value. Routers are triggered
|
||||
by specified conditions and can return constants that determine which path
|
||||
the flow should take.
|
||||
|
||||
Args:
|
||||
condition: Specifies when the router should execute. Can be:
|
||||
- str: Name of a method that triggers this router
|
||||
- FlowCondition: Result from or_() or and_(), including nested conditions
|
||||
- Callable[..., Any]: A method reference that triggers this router
|
||||
|
||||
Returns:
|
||||
A decorator function that wraps the method as a router and preserves its signature.
|
||||
|
||||
Raises:
|
||||
ValueError: If the condition format is invalid.
|
||||
|
||||
Examples:
|
||||
>>> @router("check_status")
|
||||
>>> def route_based_on_status(self):
|
||||
... if self.state.status == "success":
|
||||
... return "SUCCESS"
|
||||
... return "FAILURE"
|
||||
|
||||
>>> @router(and_("validate", "process"))
|
||||
>>> def complex_routing(self):
|
||||
... if all([self.state.valid, self.state.processed]):
|
||||
... return "CONTINUE"
|
||||
... return "STOP"
|
||||
"""
|
||||
|
||||
def decorator(func: Callable[P, R]) -> RouterMethod[P, R]:
|
||||
"""Decorator that wraps a function as a router method.
|
||||
|
||||
Args:
|
||||
func: The function to wrap as a router method.
|
||||
|
||||
Returns:
|
||||
A RouterMethod wrapper around the function.
|
||||
"""
|
||||
wrapper = RouterMethod(func)
|
||||
|
||||
if is_flow_method_name(condition):
|
||||
wrapper.__trigger_methods__ = [condition]
|
||||
wrapper.__condition_type__ = OR_CONDITION
|
||||
elif is_flow_condition_dict(condition):
|
||||
if "conditions" in condition:
|
||||
wrapper.__trigger_condition__ = condition
|
||||
wrapper.__trigger_methods__ = _extract_all_methods(condition)
|
||||
wrapper.__condition_type__ = condition["type"]
|
||||
elif "methods" in condition:
|
||||
wrapper.__trigger_methods__ = condition["methods"]
|
||||
wrapper.__condition_type__ = condition["type"]
|
||||
else:
|
||||
raise ValueError(
|
||||
"Condition dict must contain 'conditions' or 'methods'"
|
||||
)
|
||||
elif is_flow_method_callable(condition):
|
||||
wrapper.__trigger_methods__ = [condition.__name__]
|
||||
wrapper.__condition_type__ = OR_CONDITION
|
||||
else:
|
||||
raise ValueError(
|
||||
"Condition must be a method, string, or a result of or_() or and_()"
|
||||
)
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
def or_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition:
|
||||
"""Combines multiple conditions with OR logic for flow control.
|
||||
|
||||
Creates a condition that is satisfied when any of the specified conditions
|
||||
are met. This is used with @start, @listen, or @router decorators to create
|
||||
complex triggering conditions.
|
||||
|
||||
Args:
|
||||
conditions: Variable number of conditions that can be method names, existing condition dictionaries, or method references.
|
||||
|
||||
Returns:
|
||||
A condition dictionary with format {"type": "OR", "conditions": list_of_conditions} where each condition can be a string (method name) or a nested dict
|
||||
|
||||
Raises:
|
||||
ValueError: If condition format is invalid.
|
||||
|
||||
Examples:
|
||||
>>> @listen(or_("success", "timeout"))
|
||||
>>> def handle_completion(self):
|
||||
... pass
|
||||
|
||||
>>> @listen(or_(and_("step1", "step2"), "step3"))
|
||||
>>> def handle_nested(self):
|
||||
... pass
|
||||
"""
|
||||
processed_conditions: FlowConditions = []
|
||||
for condition in conditions:
|
||||
if is_flow_condition_dict(condition) or is_flow_method_name(condition):
|
||||
processed_conditions.append(condition)
|
||||
elif is_flow_method_callable(condition):
|
||||
processed_conditions.append(condition.__name__)
|
||||
else:
|
||||
raise ValueError("Invalid condition in or_()")
|
||||
return {"type": OR_CONDITION, "conditions": processed_conditions}
|
||||
|
||||
|
||||
def and_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition:
|
||||
"""Combines multiple conditions with AND logic for flow control.
|
||||
|
||||
Creates a condition that is satisfied only when all specified conditions
|
||||
are met. This is used with @start, @listen, or @router decorators to create
|
||||
complex triggering conditions.
|
||||
|
||||
Args:
|
||||
*conditions: Variable number of conditions that can be method names, existing condition dictionaries, or method references.
|
||||
|
||||
Returns:
|
||||
A condition dictionary with format {"type": "AND", "conditions": list_of_conditions}
|
||||
where each condition can be a string (method name) or a nested dict
|
||||
|
||||
Raises:
|
||||
ValueError: If any condition is invalid.
|
||||
|
||||
Examples:
|
||||
>>> @listen(and_("validated", "processed"))
|
||||
>>> def handle_complete_data(self):
|
||||
... pass
|
||||
|
||||
>>> @listen(and_(or_("step1", "step2"), "step3"))
|
||||
>>> def handle_nested(self):
|
||||
... pass
|
||||
"""
|
||||
processed_conditions: FlowConditions = []
|
||||
for condition in conditions:
|
||||
if is_flow_condition_dict(condition) or is_flow_method_name(condition):
|
||||
processed_conditions.append(condition)
|
||||
elif is_flow_method_callable(condition):
|
||||
processed_conditions.append(condition.__name__)
|
||||
else:
|
||||
raise ValueError("Invalid condition in and_()")
|
||||
return {"type": AND_CONDITION, "conditions": processed_conditions}
|
||||
File diff suppressed because it is too large
Load Diff
1036
lib/crewai/src/crewai/flow/flow_definition.py
Normal file
1036
lib/crewai/src/crewai/flow/flow_definition.py
Normal file
File diff suppressed because it is too large
Load Diff
3320
lib/crewai/src/crewai/flow/runtime.py
Normal file
3320
lib/crewai/src/crewai/flow/runtime.py
Normal file
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -1,32 +1,84 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Iterator
|
||||
from functools import cache
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Any, Literal
|
||||
from typing import TYPE_CHECKING, Any, Literal, NamedTuple, cast
|
||||
from urllib.parse import urlparse
|
||||
|
||||
|
||||
try:
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.document_converter import DocumentConverter
|
||||
from docling.exceptions import ConversionError
|
||||
from docling_core.transforms.chunker.hierarchical_chunker import HierarchicalChunker
|
||||
from docling_core.types.doc.document import DoclingDocument
|
||||
|
||||
DOCLING_AVAILABLE = True
|
||||
except ImportError:
|
||||
DOCLING_AVAILABLE = False
|
||||
if TYPE_CHECKING:
|
||||
from docling.document_converter import DocumentConverter
|
||||
from docling_core.types.doc.document import DoclingDocument
|
||||
|
||||
from pydantic import Field
|
||||
from pydantic import Field, model_validator
|
||||
from typing_extensions import Self
|
||||
|
||||
from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
|
||||
from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
|
||||
from crewai.utilities.logger import Logger
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from docling.document_converter import DocumentConverter
|
||||
from docling_core.types.doc.document import DoclingDocument
|
||||
|
||||
|
||||
_DOCLING_IMPORT_ERROR = (
|
||||
"The docling package is required to use CrewDoclingSource. "
|
||||
"Please install it using: uv add docling"
|
||||
)
|
||||
|
||||
|
||||
class _DoclingModules(NamedTuple):
|
||||
"""Lazily-imported docling symbols used by ``CrewDoclingSource``."""
|
||||
|
||||
input_format: Any
|
||||
document_converter: Any
|
||||
conversion_error: type[BaseException]
|
||||
hierarchical_chunker: Any
|
||||
|
||||
|
||||
@cache
|
||||
def _import_docling() -> _DoclingModules:
|
||||
"""Import docling submodules lazily and cache the result.
|
||||
|
||||
Raises:
|
||||
ImportError: If the docling package is not installed.
|
||||
"""
|
||||
try:
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.document_converter import DocumentConverter
|
||||
from docling.exceptions import ConversionError
|
||||
from docling_core.transforms.chunker.hierarchical_chunker import (
|
||||
HierarchicalChunker,
|
||||
)
|
||||
except ImportError as e:
|
||||
raise ImportError(_DOCLING_IMPORT_ERROR) from e
|
||||
return _DoclingModules(
|
||||
input_format=InputFormat,
|
||||
document_converter=DocumentConverter,
|
||||
conversion_error=ConversionError,
|
||||
hierarchical_chunker=HierarchicalChunker,
|
||||
)
|
||||
|
||||
|
||||
def _build_default_document_converter() -> DocumentConverter:
|
||||
"""Construct the default ``DocumentConverter`` with crewAI's allowed formats."""
|
||||
docling = _import_docling()
|
||||
input_format = docling.input_format
|
||||
return cast(
|
||||
"DocumentConverter",
|
||||
docling.document_converter(
|
||||
allowed_formats=[
|
||||
input_format.MD,
|
||||
input_format.ASCIIDOC,
|
||||
input_format.PDF,
|
||||
input_format.DOCX,
|
||||
input_format.HTML,
|
||||
input_format.IMAGE,
|
||||
input_format.XLSX,
|
||||
input_format.PPTX,
|
||||
]
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
class CrewDoclingSource(BaseKnowledgeSource):
|
||||
"""Default Source class for converting documents to markdown or json.
|
||||
|
||||
@@ -34,13 +86,11 @@ class CrewDoclingSource(BaseKnowledgeSource):
|
||||
any additional dependencies and follows the docling package as the source of truth.
|
||||
"""
|
||||
|
||||
def __init__(self, *args: Any, **kwargs: Any) -> None:
|
||||
if not DOCLING_AVAILABLE:
|
||||
raise ImportError(
|
||||
"The docling package is required to use CrewDoclingSource. "
|
||||
"Please install it using: uv add docling"
|
||||
)
|
||||
super().__init__(*args, **kwargs)
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def _ensure_docling_available(cls, data: Any) -> Any:
|
||||
_import_docling()
|
||||
return data
|
||||
|
||||
_logger: Logger = Logger(verbose=True)
|
||||
|
||||
@@ -49,23 +99,11 @@ class CrewDoclingSource(BaseKnowledgeSource):
|
||||
file_paths: list[Path | str] = Field(default_factory=list)
|
||||
chunks: list[str] = Field(default_factory=list)
|
||||
safe_file_paths: list[Path | str] = Field(default_factory=list)
|
||||
content: list[DoclingDocument] = Field(default_factory=list)
|
||||
document_converter: DocumentConverter = Field(
|
||||
default_factory=lambda: DocumentConverter(
|
||||
allowed_formats=[
|
||||
InputFormat.MD,
|
||||
InputFormat.ASCIIDOC,
|
||||
InputFormat.PDF,
|
||||
InputFormat.DOCX,
|
||||
InputFormat.HTML,
|
||||
InputFormat.IMAGE,
|
||||
InputFormat.XLSX,
|
||||
InputFormat.PPTX,
|
||||
]
|
||||
)
|
||||
)
|
||||
content: list[Any] = Field(default_factory=list)
|
||||
document_converter: Any = Field(default_factory=_build_default_document_converter)
|
||||
|
||||
def model_post_init(self, _: Any) -> None:
|
||||
@model_validator(mode="after")
|
||||
def _load_sources(self) -> Self:
|
||||
if self.file_path:
|
||||
self._logger.log(
|
||||
"warning",
|
||||
@@ -75,11 +113,13 @@ class CrewDoclingSource(BaseKnowledgeSource):
|
||||
self.file_paths = self.file_path
|
||||
self.safe_file_paths = self.validate_content()
|
||||
self.content = self._load_content()
|
||||
return self
|
||||
|
||||
def _load_content(self) -> list[DoclingDocument]:
|
||||
conversion_error = _import_docling().conversion_error
|
||||
try:
|
||||
return self._convert_source_to_docling_documents()
|
||||
except ConversionError as e:
|
||||
except conversion_error as e:
|
||||
self._logger.log(
|
||||
"error",
|
||||
f"Error loading content: {e}. Supported formats: {self.document_converter.allowed_formats}",
|
||||
@@ -112,7 +152,7 @@ class CrewDoclingSource(BaseKnowledgeSource):
|
||||
return [result.document for result in conv_results_iter]
|
||||
|
||||
def _chunk_doc(self, doc: DoclingDocument) -> Iterator[str]:
|
||||
chunker = HierarchicalChunker()
|
||||
chunker = _import_docling().hierarchical_chunker()
|
||||
for chunk in chunker.chunk(doc):
|
||||
yield chunk.text
|
||||
|
||||
|
||||
@@ -288,6 +288,7 @@ SUPPORTED_NATIVE_PROVIDERS: Final[list[str]] = [
|
||||
"hosted_vllm",
|
||||
"cerebras",
|
||||
"dashscope",
|
||||
"snowflake",
|
||||
]
|
||||
|
||||
|
||||
@@ -376,6 +377,7 @@ class LLM(BaseLLM):
|
||||
"hosted_vllm": "hosted_vllm",
|
||||
"cerebras": "cerebras",
|
||||
"dashscope": "dashscope",
|
||||
"snowflake": "snowflake",
|
||||
}
|
||||
|
||||
canonical_provider = provider_mapping.get(prefix.lower())
|
||||
@@ -494,6 +496,9 @@ class LLM(BaseLLM):
|
||||
# OpenRouter uses org/model format but accepts anything
|
||||
return True
|
||||
|
||||
if provider == "snowflake":
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
@classmethod
|
||||
@@ -592,6 +597,11 @@ class LLM(BaseLLM):
|
||||
|
||||
return BedrockCompletion
|
||||
|
||||
if provider == "snowflake":
|
||||
from crewai.llms.providers.snowflake.completion import SnowflakeCompletion
|
||||
|
||||
return SnowflakeCompletion
|
||||
|
||||
openai_compatible_providers = {
|
||||
"openrouter",
|
||||
"deepseek",
|
||||
|
||||
358
lib/crewai/src/crewai/llms/providers/snowflake/completion.py
Normal file
358
lib/crewai/src/crewai/llms/providers/snowflake/completion.py
Normal file
@@ -0,0 +1,358 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import ast
|
||||
import os
|
||||
from typing import Any, Literal
|
||||
|
||||
from pydantic import model_validator
|
||||
|
||||
from crewai.llms.providers.openai.completion import OpenAICompletion
|
||||
from crewai.utilities.types import LLMMessage
|
||||
|
||||
|
||||
SNOWFLAKE_CORTEX_PATH = "/api/v2/cortex/v1"
|
||||
SNOWFLAKE_TOKEN_ENV_VARS = (
|
||||
"SNOWFLAKE_PAT",
|
||||
"SNOWFLAKE_TOKEN",
|
||||
"SNOWFLAKE_JWT",
|
||||
)
|
||||
|
||||
|
||||
def _normalize_snowflake_base_url(value: str) -> str:
|
||||
"""Return a Snowflake Cortex REST OpenAI-compatible base URL."""
|
||||
base_url = value.strip().rstrip("/")
|
||||
if not base_url:
|
||||
raise ValueError("Snowflake account URL cannot be empty")
|
||||
|
||||
if "://" not in base_url:
|
||||
base_url = f"https://{base_url}"
|
||||
|
||||
if base_url.endswith(SNOWFLAKE_CORTEX_PATH):
|
||||
return base_url
|
||||
|
||||
if "/api/v2/cortex" in base_url:
|
||||
raise ValueError(
|
||||
"Snowflake base URL must be the account URL or Cortex API root "
|
||||
f"ending in {SNOWFLAKE_CORTEX_PATH}; do not include endpoint paths."
|
||||
)
|
||||
|
||||
return f"{base_url}{SNOWFLAKE_CORTEX_PATH}"
|
||||
|
||||
|
||||
def _base_url_from_account_identifier(account_identifier: str) -> str:
|
||||
account = account_identifier.strip()
|
||||
if not account:
|
||||
raise ValueError("Snowflake account identifier cannot be empty")
|
||||
return _normalize_snowflake_base_url(f"{account}.snowflakecomputing.com")
|
||||
|
||||
|
||||
class SnowflakeCompletion(OpenAICompletion):
|
||||
"""Snowflake Cortex REST API native completion implementation.
|
||||
|
||||
Snowflake exposes an OpenAI-compatible Chat Completions endpoint at
|
||||
``/api/v2/cortex/v1/chat/completions``. This provider reuses CrewAI's
|
||||
native OpenAI transport while applying Snowflake-specific authentication,
|
||||
endpoint normalization, and Claude-family message constraints.
|
||||
"""
|
||||
|
||||
provider: str = "snowflake"
|
||||
api: Literal["completions"] = "completions"
|
||||
account_url: str | None = None
|
||||
account_identifier: str | None = None
|
||||
database: str | None = None
|
||||
schema_name: str | None = None
|
||||
warehouse: str | None = None
|
||||
role: str | None = None
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def _normalize_snowflake_fields(cls, data: Any) -> Any:
|
||||
if not isinstance(data, dict):
|
||||
return data
|
||||
|
||||
data["provider"] = "snowflake"
|
||||
api = data.get("api")
|
||||
if api and api != "completions":
|
||||
raise ValueError(
|
||||
"Snowflake Cortex native provider supports only the Chat Completions API"
|
||||
)
|
||||
data["api"] = "completions"
|
||||
|
||||
data["api_key"] = cls._resolve_token(data.get("api_key"))
|
||||
resolved_base_url = cls._resolve_base_url(data)
|
||||
data["base_url"] = resolved_base_url
|
||||
data["account_url"] = resolved_base_url
|
||||
|
||||
return data
|
||||
|
||||
@staticmethod
|
||||
def _resolve_token(api_key: str | None) -> str:
|
||||
token = api_key
|
||||
if not token:
|
||||
for env_var in SNOWFLAKE_TOKEN_ENV_VARS:
|
||||
token = os.getenv(env_var)
|
||||
if token:
|
||||
break
|
||||
|
||||
if not token:
|
||||
raise ValueError(
|
||||
"Snowflake token is required. Set SNOWFLAKE_PAT, SNOWFLAKE_TOKEN, "
|
||||
"or SNOWFLAKE_JWT, or pass api_key."
|
||||
)
|
||||
|
||||
if token.startswith("pat/"):
|
||||
token = token.removeprefix("pat/")
|
||||
|
||||
return token
|
||||
|
||||
@classmethod
|
||||
def _resolve_base_url(cls, data: dict[str, Any]) -> str:
|
||||
explicit_base_url = data.get("base_url") or data.get("api_base")
|
||||
if explicit_base_url:
|
||||
return _normalize_snowflake_base_url(explicit_base_url)
|
||||
|
||||
account_url = data.get("account_url") or os.getenv("SNOWFLAKE_ACCOUNT_URL")
|
||||
if account_url:
|
||||
return _normalize_snowflake_base_url(account_url)
|
||||
|
||||
account_identifier = (
|
||||
data.get("account_identifier")
|
||||
or data.get("account")
|
||||
or data.get("snowflake_account")
|
||||
or os.getenv("SNOWFLAKE_ACCOUNT")
|
||||
or os.getenv("SNOWFLAKE_ACCOUNT_ID")
|
||||
or os.getenv("SNOWFLAKE_ACCOUNT_IDENTIFIER")
|
||||
)
|
||||
if account_identifier:
|
||||
return _base_url_from_account_identifier(account_identifier)
|
||||
|
||||
raise ValueError(
|
||||
"Snowflake account URL is required. Set SNOWFLAKE_ACCOUNT_URL or "
|
||||
"SNOWFLAKE_ACCOUNT, or pass account_url/base_url/account_identifier."
|
||||
)
|
||||
|
||||
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
|
||||
)
|
||||
return self._ensure_claude_conversation_ends_with_user(formatted_messages)
|
||||
return formatted_messages
|
||||
|
||||
def _is_claude_model(self) -> bool:
|
||||
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],
|
||||
) -> list[LLMMessage]:
|
||||
"""Drop dangling Claude tool-use turns before sending to Snowflake.
|
||||
|
||||
Snowflake-hosted Claude models reject histories where an assistant tool
|
||||
use is not accompanied by matching tool results. CrewAI may retry or
|
||||
summarize after an interrupted tool cycle, leaving an assistant
|
||||
``tool_calls`` message in history without every corresponding
|
||||
``role='tool'`` result. OpenAI-family models tolerate that more often,
|
||||
but Claude through Snowflake returns:
|
||||
"Each 'toolUse' block must be accompanied with a matching 'toolResult' block."
|
||||
"""
|
||||
sanitized: list[LLMMessage] = []
|
||||
index = 0
|
||||
|
||||
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:
|
||||
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]
|
||||
)
|
||||
)
|
||||
lookahead += 1
|
||||
|
||||
if expected_ids.issubset(tool_result_ids):
|
||||
summary = SnowflakeCompletion._summarize_tool_results(
|
||||
messages[index + 1 : lookahead], 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],
|
||||
) -> list[LLMMessage]:
|
||||
if not messages:
|
||||
return [{"role": "user", "content": "Hello"}]
|
||||
|
||||
if messages[-1].get("role") == "assistant" and not messages[-1].get(
|
||||
"tool_calls"
|
||||
):
|
||||
messages = messages[:-1]
|
||||
|
||||
if not messages:
|
||||
return [{"role": "user", "content": "Hello"}]
|
||||
|
||||
if messages[-1].get("role") == "user":
|
||||
return messages
|
||||
|
||||
return [
|
||||
*messages,
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Please continue and provide your final answer.",
|
||||
},
|
||||
]
|
||||
|
||||
def _prepare_completion_params(
|
||||
self, messages: list[LLMMessage], tools: list[dict[str, Any]] | None = None
|
||||
) -> dict[str, Any]:
|
||||
params = super()._prepare_completion_params(messages=messages, tools=tools)
|
||||
if self._is_claude_model() and "max_tokens" in params:
|
||||
params["max_completion_tokens"] = params.pop("max_tokens")
|
||||
return params
|
||||
|
||||
def supports_function_calling(self) -> bool:
|
||||
model = self.model.lower()
|
||||
return model.startswith(("openai-", "claude-", "anthropic."))
|
||||
|
||||
def supports_multimodal(self) -> bool:
|
||||
model = self.model.lower()
|
||||
return model.startswith(("openai-", "claude-", "anthropic."))
|
||||
@@ -1067,6 +1067,62 @@ def test_agent_use_trained_data_honors_env_var(crew_training_handler, monkeypatc
|
||||
)
|
||||
|
||||
|
||||
@patch("crewai.agent.core.CrewTrainingHandler")
|
||||
def test_agent_use_trained_data_prefers_crew_trained_agents_file(
|
||||
crew_training_handler, monkeypatch
|
||||
):
|
||||
monkeypatch.setenv("CREWAI_TRAINED_AGENTS_FILE", "env_trained.pkl")
|
||||
agent = Agent(
|
||||
role="researcher",
|
||||
goal="test goal",
|
||||
backstory="test backstory",
|
||||
)
|
||||
task = Task(
|
||||
description="Research the topic",
|
||||
expected_output="A short report",
|
||||
agent=agent,
|
||||
)
|
||||
crew = Crew(agents=[agent], tasks=[task], trained_agents_file="crew_trained.pkl")
|
||||
agent.crew = crew
|
||||
crew_training_handler.return_value.load.return_value = {}
|
||||
|
||||
agent._use_trained_data(task_prompt="What is 1 + 1?")
|
||||
|
||||
crew_training_handler.assert_has_calls(
|
||||
[mock.call("crew_trained.pkl"), mock.call().load()]
|
||||
)
|
||||
|
||||
|
||||
@patch("crewai.agent.core.CrewTrainingHandler")
|
||||
def test_agent_use_trained_data_accepts_crew_trained_agents_file_path(
|
||||
crew_training_handler, tmp_path
|
||||
):
|
||||
agent = Agent(
|
||||
role="researcher",
|
||||
goal="test goal",
|
||||
backstory="test backstory",
|
||||
)
|
||||
task = Task(
|
||||
description="Research the topic",
|
||||
expected_output="A short report",
|
||||
agent=agent,
|
||||
)
|
||||
trained_agents_file = tmp_path / "crew_trained.pkl"
|
||||
crew = Crew(
|
||||
agents=[agent],
|
||||
tasks=[task],
|
||||
trained_agents_file=trained_agents_file,
|
||||
)
|
||||
agent.crew = crew
|
||||
crew_training_handler.return_value.load.return_value = {}
|
||||
|
||||
agent._use_trained_data(task_prompt="What is 1 + 1?")
|
||||
|
||||
crew_training_handler.assert_has_calls(
|
||||
[mock.call(str(trained_agents_file)), mock.call().load()]
|
||||
)
|
||||
|
||||
|
||||
def test_agent_use_trained_data_skips_load_when_file_missing(tmp_path, monkeypatch):
|
||||
monkeypatch.setenv(
|
||||
"CREWAI_TRAINED_AGENTS_FILE", str(tmp_path / "does_not_exist.pkl")
|
||||
|
||||
427
lib/crewai/tests/llms/snowflake/test_snowflake.py
Normal file
427
lib/crewai/tests/llms/snowflake/test_snowflake.py
Normal file
@@ -0,0 +1,427 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from types import SimpleNamespace
|
||||
from unittest.mock import Mock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
from crewai.llm import LLM
|
||||
from crewai.llms.providers.snowflake.completion import (
|
||||
SNOWFLAKE_CORTEX_PATH,
|
||||
SnowflakeCompletion,
|
||||
_normalize_snowflake_base_url,
|
||||
)
|
||||
|
||||
|
||||
def _snowflake_env(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
monkeypatch.setenv("SNOWFLAKE_PAT", "test-pat")
|
||||
monkeypatch.setenv("SNOWFLAKE_ACCOUNT_URL", "https://org-account.snowflakecomputing.com")
|
||||
monkeypatch.delenv("SNOWFLAKE_TOKEN", raising=False)
|
||||
monkeypatch.delenv("SNOWFLAKE_JWT", raising=False)
|
||||
monkeypatch.delenv("SNOWFLAKE_ACCOUNT", raising=False)
|
||||
monkeypatch.delenv("SNOWFLAKE_ACCOUNT_ID", raising=False)
|
||||
monkeypatch.delenv("SNOWFLAKE_ACCOUNT_IDENTIFIER", raising=False)
|
||||
|
||||
|
||||
class TestSnowflakeConfig:
|
||||
def test_normalizes_account_url_to_cortex_base_url(self):
|
||||
assert (
|
||||
_normalize_snowflake_base_url("https://org-account.snowflakecomputing.com")
|
||||
== f"https://org-account.snowflakecomputing.com{SNOWFLAKE_CORTEX_PATH}"
|
||||
)
|
||||
|
||||
def test_preserves_existing_cortex_base_url(self):
|
||||
base_url = f"https://org-account.snowflakecomputing.com{SNOWFLAKE_CORTEX_PATH}"
|
||||
assert _normalize_snowflake_base_url(base_url) == base_url
|
||||
|
||||
def test_rejects_endpoint_path_in_base_url(self):
|
||||
with pytest.raises(ValueError, match="do not include endpoint paths"):
|
||||
_normalize_snowflake_base_url(
|
||||
"https://org-account.snowflakecomputing.com"
|
||||
f"{SNOWFLAKE_CORTEX_PATH}/chat/completions"
|
||||
)
|
||||
|
||||
def test_empty_api_key_falls_back_to_env_token(
|
||||
self, monkeypatch: pytest.MonkeyPatch
|
||||
):
|
||||
_snowflake_env(monkeypatch)
|
||||
|
||||
llm = SnowflakeCompletion(model="openai-gpt-4.1", api_key="")
|
||||
|
||||
assert llm.api_key == "test-pat"
|
||||
|
||||
def test_uses_env_token_and_account_url(self, monkeypatch: pytest.MonkeyPatch):
|
||||
_snowflake_env(monkeypatch)
|
||||
|
||||
llm = SnowflakeCompletion(model="openai-gpt-4.1")
|
||||
|
||||
assert llm.api_key == "test-pat"
|
||||
assert llm.base_url == (
|
||||
f"https://org-account.snowflakecomputing.com{SNOWFLAKE_CORTEX_PATH}"
|
||||
)
|
||||
assert llm.account_url == llm.base_url
|
||||
|
||||
def test_strips_litellm_pat_prefix_for_compatibility(
|
||||
self, monkeypatch: pytest.MonkeyPatch
|
||||
):
|
||||
monkeypatch.setenv("SNOWFLAKE_PAT", "pat/test-pat")
|
||||
monkeypatch.setenv("SNOWFLAKE_ACCOUNT", "org-account")
|
||||
|
||||
llm = SnowflakeCompletion(model="openai-gpt-4.1")
|
||||
|
||||
assert llm.api_key == "test-pat"
|
||||
|
||||
def test_missing_token_raises_clear_error(self, monkeypatch: pytest.MonkeyPatch):
|
||||
monkeypatch.delenv("SNOWFLAKE_PAT", raising=False)
|
||||
monkeypatch.delenv("SNOWFLAKE_TOKEN", raising=False)
|
||||
monkeypatch.delenv("SNOWFLAKE_JWT", raising=False)
|
||||
monkeypatch.setenv("SNOWFLAKE_ACCOUNT_URL", "https://org-account.snowflakecomputing.com")
|
||||
|
||||
with pytest.raises(ValueError, match="Snowflake token is required"):
|
||||
SnowflakeCompletion(model="openai-gpt-4.1")
|
||||
|
||||
def test_missing_account_raises_clear_error(self, monkeypatch: pytest.MonkeyPatch):
|
||||
monkeypatch.setenv("SNOWFLAKE_PAT", "test-pat")
|
||||
monkeypatch.delenv("SNOWFLAKE_ACCOUNT_URL", raising=False)
|
||||
monkeypatch.delenv("SNOWFLAKE_ACCOUNT", raising=False)
|
||||
monkeypatch.delenv("SNOWFLAKE_ACCOUNT_ID", raising=False)
|
||||
monkeypatch.delenv("SNOWFLAKE_ACCOUNT_IDENTIFIER", raising=False)
|
||||
|
||||
with pytest.raises(ValueError, match="Snowflake account URL is required"):
|
||||
SnowflakeCompletion(model="openai-gpt-4.1")
|
||||
|
||||
def test_responses_api_is_rejected(self, monkeypatch: pytest.MonkeyPatch):
|
||||
_snowflake_env(monkeypatch)
|
||||
|
||||
with pytest.raises(ValueError, match="supports only the Chat Completions API"):
|
||||
SnowflakeCompletion(model="openai-gpt-4.1", api="responses")
|
||||
|
||||
|
||||
class TestSnowflakeFactory:
|
||||
def test_llm_creates_native_snowflake_provider(
|
||||
self, monkeypatch: pytest.MonkeyPatch
|
||||
):
|
||||
_snowflake_env(monkeypatch)
|
||||
|
||||
llm = LLM(model="snowflake/openai-gpt-4.1")
|
||||
|
||||
assert isinstance(llm, SnowflakeCompletion)
|
||||
assert llm.provider == "snowflake"
|
||||
assert llm.model == "openai-gpt-4.1"
|
||||
assert llm.is_litellm is False
|
||||
|
||||
def test_explicit_provider_creates_native_snowflake_provider(
|
||||
self, monkeypatch: pytest.MonkeyPatch
|
||||
):
|
||||
_snowflake_env(monkeypatch)
|
||||
|
||||
llm = LLM(model="claude-sonnet-4-5", provider="snowflake")
|
||||
|
||||
assert isinstance(llm, SnowflakeCompletion)
|
||||
assert llm.model == "claude-sonnet-4-5"
|
||||
|
||||
|
||||
class TestSnowflakeRequests:
|
||||
def test_prepare_completion_params_uses_snowflake_model_name(
|
||||
self, monkeypatch: pytest.MonkeyPatch
|
||||
):
|
||||
_snowflake_env(monkeypatch)
|
||||
llm = SnowflakeCompletion(
|
||||
model="openai-gpt-4.1",
|
||||
temperature=0.2,
|
||||
max_completion_tokens=128,
|
||||
)
|
||||
|
||||
params = llm._prepare_completion_params(
|
||||
[{"role": "user", "content": "Hello"}]
|
||||
)
|
||||
|
||||
assert params["model"] == "openai-gpt-4.1"
|
||||
assert params["temperature"] == 0.2
|
||||
assert params["max_completion_tokens"] == 128
|
||||
assert params["messages"] == [{"role": "user", "content": "Hello"}]
|
||||
|
||||
def test_claude_model_removes_trailing_assistant_prefill(
|
||||
self, monkeypatch: pytest.MonkeyPatch
|
||||
):
|
||||
_snowflake_env(monkeypatch)
|
||||
llm = SnowflakeCompletion(model="claude-sonnet-4-5")
|
||||
|
||||
messages = llm._format_messages(
|
||||
[
|
||||
{"role": "user", "content": "Write a summary."},
|
||||
{"role": "assistant", "content": "Here is"},
|
||||
]
|
||||
)
|
||||
|
||||
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
|
||||
):
|
||||
_snowflake_env(monkeypatch)
|
||||
llm = SnowflakeCompletion(model="claude-sonnet-4-5")
|
||||
|
||||
messages = llm._format_messages(
|
||||
[
|
||||
{"role": "user", "content": "Use the tool."},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": None,
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": "call_1",
|
||||
"type": "function",
|
||||
"function": {"name": "lookup", "arguments": "{}"},
|
||||
}
|
||||
],
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
assert messages == [{"role": "user", "content": "Use the tool."}]
|
||||
|
||||
def test_claude_model_preserves_complete_tool_call_result_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": None,
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": "call_1",
|
||||
"type": "function",
|
||||
"function": {"name": "lookup", "arguments": "{}"},
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": "call_1",
|
||||
"content": "result",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
assert messages[-2] == {"role": "user", "content": "Use the tool."}
|
||||
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
|
||||
):
|
||||
_snowflake_env(monkeypatch)
|
||||
llm = SnowflakeCompletion(model="claude-sonnet-4-5")
|
||||
|
||||
messages = llm._format_messages(
|
||||
[
|
||||
{"role": "user", "content": "Use the tool."},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": None,
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": "call_1",
|
||||
"type": "function",
|
||||
"function": {"name": "lookup", "arguments": "{}"},
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": "call_1",
|
||||
"content": "valid result",
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": "unrelated_call",
|
||||
"content": "unrelated result",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
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 all(
|
||||
not (
|
||||
message.get("role") == "assistant"
|
||||
and isinstance(message.get("content"), list)
|
||||
)
|
||||
for message in messages
|
||||
)
|
||||
|
||||
def test_claude_model_maps_max_tokens_to_max_completion_tokens(
|
||||
self, monkeypatch: pytest.MonkeyPatch
|
||||
):
|
||||
_snowflake_env(monkeypatch)
|
||||
llm = SnowflakeCompletion(model="claude-sonnet-4-5", max_tokens=256)
|
||||
|
||||
params = llm._prepare_completion_params(
|
||||
[{"role": "user", "content": "Hello"}]
|
||||
)
|
||||
|
||||
assert "max_tokens" not in params
|
||||
assert params["max_completion_tokens"] == 256
|
||||
|
||||
def test_streaming_params_include_usage(self, monkeypatch: pytest.MonkeyPatch):
|
||||
_snowflake_env(monkeypatch)
|
||||
llm = SnowflakeCompletion(model="openai-gpt-4.1", stream=True)
|
||||
|
||||
params = llm._prepare_completion_params(
|
||||
[{"role": "user", "content": "Hello"}]
|
||||
)
|
||||
|
||||
assert params["stream"] is True
|
||||
assert params["stream_options"] == {"include_usage": True}
|
||||
|
||||
def test_non_streaming_call_uses_native_openai_client(
|
||||
self, monkeypatch: pytest.MonkeyPatch
|
||||
):
|
||||
_snowflake_env(monkeypatch)
|
||||
llm = SnowflakeCompletion(model="openai-gpt-4.1")
|
||||
fake_response = SimpleNamespace(
|
||||
usage=SimpleNamespace(
|
||||
prompt_tokens=3,
|
||||
completion_tokens=2,
|
||||
total_tokens=5,
|
||||
prompt_tokens_details=None,
|
||||
completion_tokens_details=None,
|
||||
),
|
||||
choices=[
|
||||
SimpleNamespace(
|
||||
message=SimpleNamespace(content="Snowflake response", tool_calls=None)
|
||||
)
|
||||
],
|
||||
)
|
||||
create = Mock(return_value=fake_response)
|
||||
fake_client = SimpleNamespace(
|
||||
chat=SimpleNamespace(completions=SimpleNamespace(create=create))
|
||||
)
|
||||
|
||||
with patch.object(llm, "_get_sync_client", return_value=fake_client):
|
||||
response = llm.call([{"role": "user", "content": "Hello"}])
|
||||
|
||||
assert response == "Snowflake response"
|
||||
create.assert_called_once()
|
||||
assert create.call_args.kwargs["model"] == "openai-gpt-4.1"
|
||||
assert create.call_args.kwargs["messages"] == [
|
||||
{"role": "user", "content": "Hello"}
|
||||
]
|
||||
@@ -569,13 +569,13 @@ class TestFlowResumeWithFeedback:
|
||||
|
||||
flow = TestFlow.from_pending("async-direct-test", persistence)
|
||||
|
||||
with patch("crewai.flow.flow.crewai_event_bus.emit"):
|
||||
with patch("crewai.flow.runtime.crewai_event_bus.emit"):
|
||||
result = await flow.resume_async("async feedback")
|
||||
|
||||
assert flow.last_human_feedback is not None
|
||||
assert flow.last_human_feedback.feedback == "async feedback"
|
||||
|
||||
@patch("crewai.flow.flow.crewai_event_bus.emit")
|
||||
@patch("crewai.flow.runtime.crewai_event_bus.emit")
|
||||
def test_resume_basic(self, mock_emit: MagicMock) -> None:
|
||||
"""Test basic resume functionality."""
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
@@ -615,7 +615,7 @@ class TestFlowResumeWithFeedback:
|
||||
|
||||
assert persistence.load_pending_feedback("resume-test-123") is None
|
||||
|
||||
@patch("crewai.flow.flow.crewai_event_bus.emit")
|
||||
@patch("crewai.flow.runtime.crewai_event_bus.emit")
|
||||
def test_resume_routing(self, mock_emit: MagicMock) -> None:
|
||||
"""Test resume with routing."""
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
@@ -697,7 +697,7 @@ class TestAsyncHumanFeedbackIntegration:
|
||||
assert hasattr(method, "__human_feedback_config__")
|
||||
assert method.__human_feedback_config__.provider is not None
|
||||
|
||||
@patch("crewai.flow.flow.crewai_event_bus.emit")
|
||||
@patch("crewai.flow.runtime.crewai_event_bus.emit")
|
||||
def test_async_provider_pauses_flow(self, mock_emit: MagicMock) -> None:
|
||||
"""Test that async provider pauses flow execution."""
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
@@ -743,7 +743,7 @@ class TestAsyncHumanFeedbackIntegration:
|
||||
persisted = persistence.load_pending_feedback(flow_id)
|
||||
assert persisted is not None
|
||||
|
||||
@patch("crewai.flow.flow.crewai_event_bus.emit")
|
||||
@patch("crewai.flow.runtime.crewai_event_bus.emit")
|
||||
def test_full_async_flow_cycle(self, mock_emit: MagicMock) -> None:
|
||||
"""Test complete async flow: start -> pause -> resume."""
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
@@ -804,7 +804,7 @@ class TestAsyncHumanFeedbackIntegration:
|
||||
class TestAutoPersistence:
|
||||
"""Tests for automatic persistence when no persistence is provided."""
|
||||
|
||||
@patch("crewai.flow.flow.crewai_event_bus.emit")
|
||||
@patch("crewai.flow.runtime.crewai_event_bus.emit")
|
||||
def test_auto_persistence_when_none_provided(self, mock_emit: MagicMock) -> None:
|
||||
"""Test that persistence is auto-created when HumanFeedbackPending is raised."""
|
||||
|
||||
@@ -925,7 +925,7 @@ class TestCollapseToOutcomeJsonParsing:
|
||||
class TestLLMObjectPreservedInContext:
|
||||
"""Tests that BaseLLM objects have their model string preserved in PendingFeedbackContext."""
|
||||
|
||||
@patch("crewai.flow.flow.crewai_event_bus.emit")
|
||||
@patch("crewai.flow.runtime.crewai_event_bus.emit")
|
||||
def test_basellm_object_model_string_survives_roundtrip(self, mock_emit: MagicMock) -> None:
|
||||
"""Test that when llm is a BaseLLM object, its model string is stored in context
|
||||
so that outcome collapsing works after async pause/resume.
|
||||
@@ -1125,7 +1125,7 @@ class TestAsyncHumanFeedbackEdgeCases:
|
||||
|
||||
flow = TestFlow.from_pending("default-test", persistence)
|
||||
|
||||
with patch("crewai.flow.flow.crewai_event_bus.emit"):
|
||||
with patch("crewai.flow.runtime.crewai_event_bus.emit"):
|
||||
result = flow.resume("")
|
||||
|
||||
assert flow.last_human_feedback.outcome == "approved"
|
||||
@@ -1159,7 +1159,7 @@ class TestAsyncHumanFeedbackEdgeCases:
|
||||
|
||||
flow = TestFlow.from_pending("no-feedback-test", persistence)
|
||||
|
||||
with patch("crewai.flow.flow.crewai_event_bus.emit"):
|
||||
with patch("crewai.flow.runtime.crewai_event_bus.emit"):
|
||||
result = flow.resume()
|
||||
|
||||
assert flow.last_human_feedback.outcome == "approved"
|
||||
@@ -1213,7 +1213,7 @@ class TestLiveLLMPreservationOnResume:
|
||||
assert hasattr(method, "_hf_llm")
|
||||
assert method._hf_llm == "gpt-4o-mini"
|
||||
|
||||
@patch("crewai.flow.flow.crewai_event_bus.emit")
|
||||
@patch("crewai.flow.runtime.crewai_event_bus.emit")
|
||||
def test_resume_async_uses_live_basellm_over_serialized_string(
|
||||
self, mock_emit: MagicMock
|
||||
) -> None:
|
||||
@@ -1286,7 +1286,7 @@ class TestLiveLLMPreservationOnResume:
|
||||
# And verify it's a BaseLLM instance, not a string
|
||||
assert isinstance(captured_llm[0], BaseLLM)
|
||||
|
||||
@patch("crewai.flow.flow.crewai_event_bus.emit")
|
||||
@patch("crewai.flow.runtime.crewai_event_bus.emit")
|
||||
def test_resume_async_falls_back_to_serialized_string_when_no_hf_llm(
|
||||
self, mock_emit: MagicMock
|
||||
) -> None:
|
||||
@@ -1344,7 +1344,7 @@ class TestLiveLLMPreservationOnResume:
|
||||
assert isinstance(captured_llm[0], BaseLLMClass)
|
||||
assert captured_llm[0].model == "gpt-4o-mini"
|
||||
|
||||
@patch("crewai.flow.flow.crewai_event_bus.emit")
|
||||
@patch("crewai.flow.runtime.crewai_event_bus.emit")
|
||||
def test_resume_async_uses_string_from_context_when_hf_llm_is_string(
|
||||
self, mock_emit: MagicMock
|
||||
) -> None:
|
||||
|
||||
@@ -3010,6 +3010,23 @@ def test__setup_for_training(researcher, writer):
|
||||
assert agent.allow_delegation is False
|
||||
|
||||
|
||||
def test_crew_trained_agents_file_is_preserved_on_copy(researcher):
|
||||
task = Task(
|
||||
description="Come up with a list of 5 interesting ideas to explore for an article",
|
||||
expected_output="5 bullet points with a paragraph for each idea.",
|
||||
agent=researcher,
|
||||
)
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
trained_agents_file="custom_trained_agents.pkl",
|
||||
)
|
||||
|
||||
cloned_crew = crew.copy()
|
||||
|
||||
assert cloned_crew.trained_agents_file == "custom_trained_agents.pkl"
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_replay_feature(researcher, writer):
|
||||
list_ideas = Task(
|
||||
|
||||
@@ -161,6 +161,87 @@ def test_flow_with_or_condition():
|
||||
)
|
||||
|
||||
|
||||
def test_or_listener_fires_once_across_parallel_starts():
|
||||
"""Parallel ``@start`` paths feeding ``or_`` must not double-fire the listener."""
|
||||
fire_count = 0
|
||||
|
||||
class ParallelOrFlow(Flow):
|
||||
@start()
|
||||
async def fast_start(self):
|
||||
return "fast"
|
||||
|
||||
@start()
|
||||
async def slow_start(self):
|
||||
await asyncio.sleep(0.2)
|
||||
return "slow"
|
||||
|
||||
@listen(or_(fast_start, slow_start))
|
||||
def handler(self):
|
||||
nonlocal fire_count
|
||||
fire_count += 1
|
||||
|
||||
asyncio.run(ParallelOrFlow().kickoff_async())
|
||||
|
||||
assert fire_count == 1
|
||||
|
||||
|
||||
def test_or_listener_re_arms_across_router_loop():
|
||||
"""Regression for #5972: multi-source ``or_`` re-fires on each router emission."""
|
||||
fire_count = 0
|
||||
|
||||
class CyclicOrFlow(Flow):
|
||||
iteration = 0
|
||||
|
||||
@start()
|
||||
def kick(self):
|
||||
return "kick"
|
||||
|
||||
@router(kick)
|
||||
def initial_router(self):
|
||||
return "SignalA"
|
||||
|
||||
@listen(or_("SignalA", "SignalB"))
|
||||
def handler(self):
|
||||
nonlocal fire_count
|
||||
fire_count += 1
|
||||
|
||||
@router(handler)
|
||||
def loop_router(self):
|
||||
self.iteration += 1
|
||||
return "stop" if self.iteration >= 3 else "SignalB"
|
||||
|
||||
CyclicOrFlow().kickoff()
|
||||
|
||||
assert fire_count == 3
|
||||
|
||||
|
||||
def test_or_listener_does_not_double_fire_across_chained_routers():
|
||||
"""Chained routers within one dispatch wave must not re-fire the same ``or_`` listener."""
|
||||
fire_count = 0
|
||||
|
||||
class ChainedRouterOrFlow(Flow):
|
||||
@start()
|
||||
def kick(self):
|
||||
return "kick"
|
||||
|
||||
@router(kick)
|
||||
def router_a(self):
|
||||
return "SignalA"
|
||||
|
||||
@router("SignalA")
|
||||
def router_b(self):
|
||||
return "SignalB"
|
||||
|
||||
@listen(or_("SignalA", "SignalB"))
|
||||
def handler(self):
|
||||
nonlocal fire_count
|
||||
fire_count += 1
|
||||
|
||||
ChainedRouterOrFlow().kickoff()
|
||||
|
||||
assert fire_count == 1
|
||||
|
||||
|
||||
def test_flow_with_router():
|
||||
"""Test a flow that uses a router method to determine the next step."""
|
||||
execution_order = []
|
||||
|
||||
@@ -918,7 +918,7 @@ def _update_all_versions(
|
||||
"[yellow]Warning:[/yellow] No __version__ attributes found to update"
|
||||
)
|
||||
|
||||
templates_dir = lib_dir / "crewai" / "src" / "crewai" / "cli" / "templates"
|
||||
templates_dir = lib_dir / "cli" / "src" / "crewai_cli" / "templates"
|
||||
if templates_dir.exists():
|
||||
if dry_run:
|
||||
for tpl in templates_dir.rglob("pyproject.toml"):
|
||||
|
||||
20
uv.lock
generated
20
uv.lock
generated
@@ -13,7 +13,7 @@ resolution-markers = [
|
||||
]
|
||||
|
||||
[options]
|
||||
exclude-newer = "2026-05-29T06:38:49.259217Z"
|
||||
exclude-newer = "2026-05-30T15:40:20.821639605Z"
|
||||
exclude-newer-span = "P3D"
|
||||
|
||||
[manifest]
|
||||
@@ -1389,7 +1389,7 @@ requires-dist = [
|
||||
{ name = "boto3", marker = "extra == 'aws'", specifier = "~=1.42.79" },
|
||||
{ name = "boto3", marker = "extra == 'bedrock'", specifier = "~=1.42.79" },
|
||||
{ name = "chromadb", specifier = "~=1.1.0" },
|
||||
{ name = "click", specifier = "~=8.1.7" },
|
||||
{ name = "click", specifier = ">=8.1.7,<9" },
|
||||
{ name = "crewai-cli", editable = "lib/cli" },
|
||||
{ name = "crewai-core", editable = "lib/crewai-core" },
|
||||
{ name = "crewai-files", marker = "extra == 'file-processing'", editable = "lib/crewai-files" },
|
||||
@@ -1419,7 +1419,7 @@ requires-dist = [
|
||||
{ name = "portalocker", specifier = "~=2.7.0" },
|
||||
{ name = "pydantic", specifier = ">=2.11.9,<2.13" },
|
||||
{ name = "pydantic-settings", specifier = "~=2.10.1" },
|
||||
{ name = "pyjwt", specifier = ">=2.9.0,<3" },
|
||||
{ name = "pyjwt", specifier = ">=2.13.0,<3" },
|
||||
{ name = "python-dotenv", specifier = ">=1.2.2,<2" },
|
||||
{ name = "pyyaml", specifier = "~=6.0" },
|
||||
{ name = "qdrant-client", extras = ["fastembed"], marker = "extra == 'qdrant'", specifier = "~=1.14.3" },
|
||||
@@ -1459,14 +1459,14 @@ dependencies = [
|
||||
requires-dist = [
|
||||
{ name = "appdirs", specifier = "~=1.4.4" },
|
||||
{ name = "certifi" },
|
||||
{ name = "click", specifier = "~=8.1.7" },
|
||||
{ name = "click", specifier = ">=8.1.7,<9" },
|
||||
{ name = "crewai-core", editable = "lib/crewai-core" },
|
||||
{ name = "cryptography", specifier = ">=42.0" },
|
||||
{ name = "httpx", specifier = "~=0.28.1" },
|
||||
{ name = "packaging", specifier = ">=23.0" },
|
||||
{ name = "pydantic", specifier = ">=2.11.9,<2.13" },
|
||||
{ name = "pydantic-settings", specifier = "~=2.10.1" },
|
||||
{ name = "pyjwt", specifier = ">=2.9.0,<3" },
|
||||
{ name = "pyjwt", specifier = ">=2.13.0,<3" },
|
||||
{ name = "python-dotenv", specifier = ">=1.2.2,<2" },
|
||||
{ name = "rich", specifier = ">=13.7.1" },
|
||||
{ name = "textual", specifier = ">=7.5.0" },
|
||||
@@ -1504,7 +1504,7 @@ requires-dist = [
|
||||
{ name = "packaging", specifier = ">=23.0" },
|
||||
{ name = "portalocker", specifier = "~=2.7.0" },
|
||||
{ name = "pydantic", specifier = ">=2.11.9,<2.13" },
|
||||
{ name = "pyjwt", specifier = ">=2.9.0,<3" },
|
||||
{ name = "pyjwt", specifier = ">=2.13.0,<3" },
|
||||
{ name = "rich", specifier = ">=13.7.1" },
|
||||
{ name = "tomli", specifier = "~=2.0.2" },
|
||||
]
|
||||
@@ -1523,7 +1523,7 @@ dependencies = [
|
||||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "click", specifier = "~=8.1.7" },
|
||||
{ name = "click", specifier = ">=8.1.7,<9" },
|
||||
{ name = "openai", specifier = ">=1.83.0,<3" },
|
||||
{ name = "pygithub", specifier = "~=1.59.1" },
|
||||
{ name = "python-dotenv", specifier = ">=1.2.2,<2" },
|
||||
@@ -6902,14 +6902,14 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "pyjwt"
|
||||
version = "2.12.1"
|
||||
version = "2.13.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.11'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c2/27/a3b6e5bf6ff856d2509292e95c8f57f0df7017cf5394921fc4e4ef40308a/pyjwt-2.12.1.tar.gz", hash = "sha256:c74a7a2adf861c04d002db713dd85f84beb242228e671280bf709d765b03672b", size = 102564, upload-time = "2026-03-13T19:27:37.25Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/3b/81/58d0ac84e1ef3a3843791d6954d94c0b33d526c75eeb1efbce9d0a4c4077/pyjwt-2.13.0.tar.gz", hash = "sha256:41571c89ca91598c79e8ef18a2d07367d4810fbbd6f637794879baf1b7703423", size = 107515, upload-time = "2026-05-21T19:54:36.618Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/e5/7a/8dd906bd22e79e47397a61742927f6747fe93242ef86645ee9092e610244/pyjwt-2.12.1-py3-none-any.whl", hash = "sha256:28ca37c070cad8ba8cd9790cd940535d40274d22f80ab87f3ac6a713e6e8454c", size = 29726, upload-time = "2026-03-13T19:27:35.677Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/a3/5e/ecf12fdb62546d64385c158514e9b2b671f7832108ef2ecd2020ce0af2d1/pyjwt-2.13.0-py3-none-any.whl", hash = "sha256:66adcc2aff09b3f1bbd95fc1e1577df8ac8723c978552fd43304c8a290ac5728", size = 31274, upload-time = "2026-05-21T19:54:35.362Z" },
|
||||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
|
||||
Reference in New Issue
Block a user