add ollama meetup demo

demos/20240321_ollama_meetup/README.md

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+# Ollama meetup demo (5mn)
+
+- Make sure that Docker is installed and running on your laptop with Kubernetes enabled.
+
+- Install ollama, start it and pull the mistral model: 
+
+ ```shell
+ ollama pull mistral
+ ```
+
+ By default, the server is reachable on port 11434.
+
+- Install the Python requirements in a virtualenv and activate it.
+
+- To prove that you are not scamming people, start some random deployment in a custom namespace:
+
+ ```shell
+ kubectl create ns demo
+ kubectl apply -f https://k8s.io/examples/controllers/nginx-deployment.yaml -n demo
+ ```
+
+- Profit:
+
+ ```shell
+ python run.py
+ ```
+
+There is a lot to improve, namely:
+- handling empty outputs gracefully
+- asking explicitly for the namespace when it is not provided by the user
+- just shelling out kubectl instead of installing the K8S Python client
+
+...but that's the beauty of the game: giving ideas to the audience for building cool stuff !
+

demos/20240321_ollama_meetup/myfuncs.py

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+from kubernetes import client, config
+
+def list_pods(namespace):
+ # Load kubeconfig
+ config.load_kube_config()
+ # Create API client instance
+ api_instance = client.CoreV1Api()
+ # Call API to list all pods in the given namespace
+ response = api_instance.list_namespaced_pod(namespace)
+ out = {"pods": []}
+ for pod in response.items:
+ out["pods"].append({"name": pod.metadata.name, "status": pod.status.phase})
+ return out
+
+

demos/20240321_ollama_meetup/requirements.txt

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+openai==1.14.2
+kubernetes==29.0.0
+mistralai==0.1.6

demos/20240321_ollama_meetup/run.py

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+import functools
+import json
+import os
+
+from mistralai.client import MistralClient
+from openai import OpenAI
+
+from myfuncs import list_pods
+
+api_key = os.environ.get("MISTRAL_API_KEY")
+
+online_model = "mistral-small-latest"
+offline_model = "mistral" # ollama naming convention
+
+tools = [
+ {
+ "type": "function",
+ "function": {
+ "name": "list_pods",
+ "description": "Get the list of all Kubernetes pods and their status in a given namespace",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "namespace": {
+ "type": "string",
+ "description": "The name of the namespace to look into",
+ },
+ },
+ "required": ["namespace"],
+ },
+ }
+ },
+]
+
+callables = {"list_pods": list_pods}
+
+user_input = input("😺 Hello! How can I help you?\n")
+
+# Retrieve user input then generate function inputs with mistral-small
+messages = []
+messages.append({"role": "system", "content": "Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous."})
+messages.append({"role": "user", "content": f"Execute the following task on your K8S cluster: {user_input}"})
+online_client = MistralClient(api_key=api_key)
+resp_tool= online_client.chat(model=online_model, messages=messages, tools=tools, tool_choice="any")
+print(f"⏳ Using online model {online_model} to generate function inputs, un instant svp...")
+tool_call = resp_tool.choices[0].message.tool_calls[0]
+function_name = tool_call.function.name
+function_params = json.loads(tool_call.function.arguments)
+print(f"😎 Switching to offline execution and calling ollama's {offline_model}. C'est parti!\n\n")
+
+# Run the function
+partial_func = functools.partial(callables[function_name], **function_params)
+out = partial_func()
+
+# Format the function output with ollama-mistral (7b)
+local_client = OpenAI(base_url = "https://127.0.0.1:11434/v1", api_key='ollama')
+response = local_client.chat.completions.create(stream=True, model=offline_model, messages=[
+ {"role": "system", "content": "You are a master of Kubernetes who likes friendly French-related jokes."},
+ {"role": "user", "content": f"""
+ Here is a list of K8S pods in a JSON format:\n {out}
+ Transform it into a bullet-point-like list in idiomatic English and add a few French-related jokes.
+ """}])
+
+for chunk in response:
+ if chunk.choices[0].delta.content is not None:
+ print(chunk.choices[0].delta.content, end="") 
+