Merge pull request #130 from mistralai/doc/v0.0.77

Update docs to v0.0.77

docs/capabilities/agents.md

@@ -199,6 +199,8 @@ Here is an example of how you can create this agent with using the La Plateforme
 <details>
     <summary><b>Use case 3: Python agent workflow</b></summary>
 
+<img src="/img/agent_demo1.png" alt="drawing" width="600"/>
+
 You can use the Python agent we created in use case 2 in an assistant coding workflow. For example, here is a very simple Python agent workflow with the following steps:
 
 1. User Query:
@@ -230,6 +232,8 @@ Check out this [example notebook](https://github.com/mistralai/cookbook/blob/mai
 <details>
     <summary><b>Use case 4: Data analytical multi-agent workflow</b></summary>
 
+<img src="/img/agent_demo2.png" alt="drawing" width="600"/>
+
 You can also leverage multiple agents in a workflow. Here is an example: 
 
 1. Data Analysis Planning:

docs/deployment/cloud/overview.mdx

@@ -10,5 +10,5 @@ In particular, Mistral's optimized commercial models are available on:
 - [Azure AI](../azure)
 - [AWS Bedrock](../aws)
 - [Google Cloud Vertex AI Model Garden](../vertex)
-- Snowflake Cortex
+- [Snowflake Cortex](../sfcortex)
 

docs/deployment/cloud/sfcortex.mdx

@@ -0,0 +1,83 @@
+---
+id: sfcortex
+title: Snowflake Cortex
+sidebar_position: 3.24
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+## Introduction
+
+Mistral AI's open and commercial models can be leveraged from the Snowflake Cortex platform
+as fully managed endpoints. Mistral models on Snowflake Cortex are serverless services so
+you don't have to manage any infrastructure.
+
+As of today, the following models are available:
+
+- Mistral Large
+- Mistral 7B
+
+For more details, visit the [models](../../../getting-started/models) page.
+
+## Getting started
+
+The following sections outline the steps to query the latest version of Mistral Large 
+on the Snowflake Cortex platform.
+
+### Getting access to the model
+
+The following items are required:
+
+- The associated Snowflake account must be in a compatible region (see the
+  [region list](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions#availability)
+  in the Snowflake documentation).
+- The principal that is calling the model must have the `CORTEX_USER` database role.
+
+### Querying the model (chat completion)
+
+The model can be called either directly in SQL or in Python using Snowpark ML.
+It is exposed via the
+[`COMPLETE` _LLM function_](https://docs.snowflake.com/en/sql-reference/functions/complete-snowflake-cortex).
+
+<Tabs>
+    <TabItem value="sql" label="SQL">
+    ```SQL
+    SELECT SNOWFLAKE.CORTEX.COMPLETE('mistral-large2', 'Who is the best French painter? Answer in one short sentence.');
+    ```
+    </TabItem>
+    <TabItem value="Python" label="Python">
+        Execute this code either from a hosted Snowflake notebook or from your local machine.
+
+        For local execution you need to:
+            - Create a virtual environment with the following package:
+                - `snowflake-ml-python` (tested with version `1.6.1`)
+            - Ensure that you have a [configuration file](https://docs.snowflake.com/en/user-guide/snowsql-config)
+              with the proper credentials on your system. The example below assumes that you have a named connection
+              called `mistral` that is configured appropriately.
+
+        ```python
+        from snowflake.snowpark import Session
+        from snowflake.ml.utils import connection_params
+        from snowflake.cortex import Complete
+
+        # Start session (local execution only)
+        params = connection_params.SnowflakeLoginOptions(connection_name="mistral")
+        session = Session.builder.configs(params).create()
+
+        # Query the model
+        prompt = "Who is the best French painter? Answer in one short sentence."
+        completion = Complete(model="mistral-large2", prompt=prompt)
+        print(completion)
+        ```
+    </TabItem>
+</Tabs>
+
+## Going further
+
+For more information and examples, you can check the Snowflake documentation for:
+
+- [LLM functions](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions)
+- The API of the `COMPLETE` function in 
+  [SQL](https://docs.snowflake.com/en/sql-reference/functions/complete-snowflake-cortex)
+  and [Python](https://docs.snowflake.com/en/developer-guide/snowpark-ml/reference/latest/api/model/snowflake.cortex.Complete).

package.json

@@ -15,23 +15,24 @@
     "typecheck": "tsc"
   },
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-    "@docusaurus/preset-classic": "3.2.1",
-    "@easyops-cn/docusaurus-search-local": "^0.38.1",
-    "@mdx-js/react": "^3.0.0",
+    "@docusaurus/core": "3.5.2",
+    "@docusaurus/preset-classic": "3.5.2",
+    "@docusaurus/theme-common": "3.5.2",
+    "@easyops-cn/docusaurus-search-local": "^0.44.5",
+    "@mdx-js/react": "^3.0.1",
     "clsx": "^1.2.1",
-    "docusaurus-lunr-search": "^3.3.1",
+    "docusaurus-lunr-search": "^3.4.0",
     "lunr": "^2.3.9",
     "prism-react-renderer": "^2.3.1",
-    "react": "^18.2.0",
-    "react-dom": "^18.2.0",
-    "redocusaurus": "^2.0.2"
+    "react": "^18.3.1",
+    "react-dom": "^18.3.1",
+    "redocusaurus": "^2.1.1"
   },
   "devDependencies": {
     "@docusaurus/module-type-aliases": "3.2.1",
-    "@docusaurus/tsconfig": "^3.2.1",
-    "@types/react": "^18.2.42",
-    "typescript": "^5.3.2"
+    "@docusaurus/tsconfig": "^3.5.2",
+    "@types/react": "^18.3.4",
+    "typescript": "^5.5.4"
   },
   "browserslist": {
     "production": [
@@ -47,5 +48,6 @@
   },
   "engines": {
     "node": ">=18.0"
-  }
+  },
+  "packageManager": "[email protected]+sha256.56a9e76b51796ca7f73b85e44cf83712862091f4d498c0ce4d5b7ecdc6ba18f7"
 }