feat(ifr): added reference page

Author: tgenaitay

ai-data/managed-inference/reference-content/function-calling-support.mdx

@@ -0,0 +1,49 @@
+---
+meta:
+  title: Support for function calling in Scaleway Managed Inference
+  description: Function calling allows models to connect to external tools. 
+content:
+  h1: Support for function calling in Scaleway Managed Inference
+  paragraph: Function calling allows models to connect to external tools. 
+tags:
+categories:
+  - ai-data
+---
+
+## What is function calling?
+
+Function calling allows a language model (LLM) to interact with external tools or APIs, executing specific tasks based on user requests. The LLM identifies the appropriate function, extracts needed parameters, and returns the results as structured data, typically in JSON format. While errors can occur, custom parsers or tools like LlamaIndex and LangChain can help ensure valid results.
+
+## How to implement function calling in Scaleway Managed Inference?
+
+[This tutorial](/tutorials/building-ai-application-function-calling/) will guide you through the steps of creating a simple flight schedule assistant that can understand natural language queries about flights and return structured information. 
+
+## What are models with function calling capabilities?
+
+The following models in Scaleway's Managed Inference library can call tools as per the OpenAI method:
+
+* meta/llama-3.1-8b-instruct
+* meta/llama-3.1-70b-instruct
+* mistral/mistral-7b-instruct-v0.3
+* mistral/mistral-nemo-instruct-2407
+
+## Understanding function calling
+
+Function calling consists of three main components:
+- **Tool definitions**: JSON schemas that describe available functions and their parameters
+- **Tool selection**: Automatic or manual selection of appropriate functions based on user queries
+- **Tool execution**: Processing function calls and handling their responses
+
+The workflow typically follows these steps:
+1. Define available tools using JSON schema
+2. Send system and user query along with tool definitions
+3. Process model's function selection
+4. Execute selected functions
+5. Return results to model for final response
+
+## Further resources
+
+For more information about function calling and advanced implementations, refer to these resources:
+
+- [OpenAI Function Calling Guide](https://platform.openai.com/docs/guides/function-calling)
+- [JSON Schema Specification](https://json-schema.org/specification)

tutorials/building-ai-application-function-calling/index.mdx

@@ -24,7 +24,7 @@ This tutorial will guide you through creating a simple flight schedule assistant
 - A Scaleway account logged into the [console](https://console.scaleway.com)
 - Python 3.7 or higher
 - An API key from Scaleway [Identity and Access Management](https://www.scaleway.com/en/docs/identity-and-access-management/iam/)
-- Access to Scaleway [Generative APIs](/ai-data/generative-apis/quickstart/)
+- Access to Scaleway [Generative APIs](/ai-data/generative-apis/quickstart/) or to Scaleway [Managed Inference](/ai-data/managed-inference/quickstart/)
 - The `openai` Python library installed
 
 ## Understanding function calling
@@ -106,9 +106,17 @@ import json
 from flight_schedule import get_flight_schedule
 
 # Initialize the OpenAI client with Scaleway configuration
+
+MODEL="meta/llama-3.1-70b-instruct:fp8" 
+# use the right name according to your Managed Inference deployment or Generative APIs model
+
+API_KEY = os.environ.get("SCALEWAY_API_KEY")
+BASE_URL = os.environ.get("SCALEWAY_INFERENCE_ENDPOINT_URL")
+# use https://api.scaleway.ai/v1 for Scaleway Generative APIs
+
 client = OpenAI(
-    base_url="https://api.scaleway.com/v1",
-    api_key=os.environ.get("SCALEWAY_API_KEY")
+    base_url=BASE_URL,
+    api_key=API_KEY
 )
 
 # Define the tool specification
@@ -155,7 +163,7 @@ def process_query(user_query: str) -> str:
 
     # Get the model's response
     response = client.chat.completions.create(
-        model="llama-3.1-70b-instruct",  # Use appropriate model name
+        model=MODEL,
         messages=messages,
         tools=tools,
         tool_choice="auto"
@@ -184,7 +192,7 @@ def process_query(user_query: str) -> str:
 
             # Get final response
             final_response = client.chat.completions.create(
-                model="llama-3.1-70b-instruct",
+                model=MODEL,
                 messages=messages
             )