diff --git a/ai/ai-starter-kit/helm-chart/ai-starter-kit/Chart.yaml b/ai/ai-starter-kit/helm-chart/ai-starter-kit/Chart.yaml
index dc2c0674..5f6ad71b 100644
--- a/ai/ai-starter-kit/helm-chart/ai-starter-kit/Chart.yaml
+++ b/ai/ai-starter-kit/helm-chart/ai-starter-kit/Chart.yaml
@@ -29,8 +29,8 @@ dependencies:
condition: ray-cluster.enabled
version: "1.3.0"
repository: "https://ray-project.github.io/kuberay-helm"
- - condition: ray-cluster.enabled
- name: ray-cluster
+ - name: ray-cluster
+ condition: ray-cluster.enabled
version: "1.3.0"
repository: "https://ray-project.github.io/kuberay-helm"
- name: jupyterhub
@@ -39,3 +39,7 @@ dependencies:
- name: mlflow
version: "0.12.0"
repository: "https://community-charts.github.io/helm-charts"
+ - name: ollama
+ condition: ollama.enabled
+ version: "1.27.0"
+ repository: "https://helm.otwld.com"
diff --git a/ai/ai-starter-kit/helm-chart/ai-starter-kit/files/chat_bot.ipynb b/ai/ai-starter-kit/helm-chart/ai-starter-kit/files/chat_bot.ipynb
new file mode 100644
index 00000000..0834cf6c
--- /dev/null
+++ b/ai/ai-starter-kit/helm-chart/ai-starter-kit/files/chat_bot.ipynb
@@ -0,0 +1,312 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9e3dd59-b4d9-4de5-a6aa-a72d1480ac77",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from ollama import Client\n",
+ "\n",
+ "client = Client(\n",
+ " host='http://ai-starter-kit-ollama:11434',\n",
+ " headers={'x-some-header': 'some-value'}\n",
+ ")\n",
+ "\n",
+ "def get_response(prompt):\n",
+ " response = client.chat(model='gemma3', messages=[\n",
+ " {\n",
+ " 'role': 'user',\n",
+ " 'content': prompt,\n",
+ " },\n",
+ " ])\n",
+ " return response.message.content"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "dd1513d4-18c5-46d7-8260-f90be004d315",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.7.5/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.3.min.js\", \"https://cdn.holoviz.org/panel/1.7.5/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));",
+ "application/vnd.holoviews_load.v0+json": ""
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n let retries = 0;\n const open = () => {\n if (comm.active) {\n comm.open();\n } else if (retries > 3) {\n console.warn('Comm target never activated')\n } else {\n retries += 1\n setTimeout(open, 500)\n }\n }\n if (comm.active) {\n comm.open();\n } else {\n setTimeout(open, 500)\n }\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n })\n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n",
+ "application/vnd.holoviews_load.v0+json": ""
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.holoviews_exec.v0+json": "",
+ "text/html": [
+ "\n",
+ ""
+ ]
+ },
+ "metadata": {
+ "application/vnd.holoviews_exec.v0+json": {
+ "id": "b6fd14e0-f8d2-46e7-9c4d-722893d04d7e"
+ }
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {},
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.holoviews_exec.v0+json": "",
+ "text/html": [
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "Column\n",
+ " [0] TextInput(placeholder='Enter text here…')\n",
+ " [1] Row\n",
+ " [0] Button(name='Chat!')\n",
+ " [2] ParamFunction(function, _pane=Column, defer_load=False, height=300, loading_indicator=True, sizing_mode='fixed', width=300)"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {
+ "application/vnd.holoviews_exec.v0+json": {
+ "id": "2854d6b0-689d-4dc0-8861-1834489708e9"
+ }
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import panel as pn # GUI\n",
+ "pn.extension()\n",
+ "\n",
+ "panels = [] # collect display \n",
+ "context = [ ] # accumulate messages\n",
+ "\n",
+ "\n",
+ "def collect_messages(_):\n",
+ " prompt = inp.value_input\n",
+ " inp.value = ''\n",
+ " if (not prompt):\n",
+ " return pn.Column(*panels)\n",
+ "\n",
+ " response = get_response(prompt)\n",
+ " context.append({'role':'user', 'content':f\"{prompt}\"})\n",
+ " context.append({'role':'assistant', 'content':f\"{response}\"})\n",
+ " panels.append(\n",
+ " pn.Row('User:', pn.pane.Markdown(prompt, width=600)))\n",
+ " panels.append(\n",
+ " pn.Row('Assistant:', pn.pane.Markdown(response, width=600)))\n",
+ " \n",
+ " return pn.Column(*panels)\n",
+ "\n",
+ "\n",
+ "inp = pn.widgets.TextInput(value=\"Hi\", placeholder='Enter text here…')\n",
+ "button_conversation = pn.widgets.Button(name=\"Chat!\")\n",
+ "interactive_conversation = pn.bind(collect_messages, button_conversation)\n",
+ "dashboard = pn.Column(\n",
+ " inp,\n",
+ " pn.Row(button_conversation),\n",
+ " pn.panel(interactive_conversation, loading_indicator=True, height=300, width=300),\n",
+ ")\n",
+ "\n",
+ "dashboard"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ai/ai-starter-kit/helm-chart/ai-starter-kit/files/requirements.txt b/ai/ai-starter-kit/helm-chart/ai-starter-kit/files/requirements.txt
index 1acc536e..cfc1bd12 100644
--- a/ai/ai-starter-kit/helm-chart/ai-starter-kit/files/requirements.txt
+++ b/ai/ai-starter-kit/helm-chart/ai-starter-kit/files/requirements.txt
@@ -5,3 +5,5 @@ huggingface_hub
numpy
ipywidgets
mlflow
+ollama
+panel
diff --git a/ai/ai-starter-kit/helm-chart/ai-starter-kit/values-gke.yaml b/ai/ai-starter-kit/helm-chart/ai-starter-kit/values-gke.yaml
index 0906deda..84015be5 100644
--- a/ai/ai-starter-kit/helm-chart/ai-starter-kit/values-gke.yaml
+++ b/ai/ai-starter-kit/helm-chart/ai-starter-kit/values-gke.yaml
@@ -81,6 +81,9 @@ jupyterhub:
- name: welcome-ipynb
configMap:
name: "ai-starter-kit-welcome-ipynb"
+ - name: ray-ipynb
+ configMap:
+ name: "ai-starter-kit-ray-ipynb"
- name: chat-bot-ipynb
configMap:
name: "ai-starter-kit-chat-bot-ipynb"
@@ -91,6 +94,9 @@ jupyterhub:
- name: download-models-py
mountPath: /tmp/download_models.py
subPath: download_models.py
+ - name: ray-ipynb
+ mountPath: /tmp/ray.ipynb
+ subPath: ray.ipynb
- name: chat-bot-ipynb
mountPath: /tmp/chat_bot.ipynb
subPath: chat_bot.ipynb
@@ -102,8 +108,8 @@ jupyterhub:
secretKeyRef:
name: ai-starter-kit-hf-token-secret
key: token
- RAY_ADDRESS: "ai-starter-kit-kuberay-head-svc:6379"
- MLFLOW_TRACKING_URI: "http://ai-starter-kit-mlflow-tracking"
+ RAY_ADDRESS: "ai-starter-kit-kuberay-head-svc:10001"
+ MLFLOW_TRACKING_URI: "http://ai-starter-kit-mlflow:5000"
cloudMetadata:
# Without this disabled, the GKE Autopilot Warden will raise an error about container with escalated privilieges
blockWithIptables: false
@@ -138,12 +144,12 @@ ray-cluster:
serviceType: ClusterIP
resources:
requests:
- cpu: "1"
- memory: "2G"
+ cpu: "4"
+ memory: "4G"
ephemeral-storage: 10Gi
limits:
- cpu: "4"
- memory: "8G"
+ cpu: "8"
+ memory: "6G"
ephemeral-storage: 10Gi
volumes:
- name: ray-pvc-storage
@@ -157,12 +163,12 @@ ray-cluster:
worker:
resources:
requests:
- cpu: "1"
- memory: "2G"
+ cpu: "4"
+ memory: "4G"
ephemeral-storage: 10Gi
limits:
- cpu: "4"
- memory: "8G"
+ cpu: "8"
+ memory: "6G"
ephemeral-storage: 10Gi
volumes:
- name: ray-pvc-storage
diff --git a/ai/ai-starter-kit/helm-chart/ai-starter-kit/values-minikube.yaml b/ai/ai-starter-kit/helm-chart/ai-starter-kit/values-minikube.yaml
index e4841453..fcbb7152 100644
--- a/ai/ai-starter-kit/helm-chart/ai-starter-kit/values-minikube.yaml
+++ b/ai/ai-starter-kit/helm-chart/ai-starter-kit/values-minikube.yaml
@@ -38,6 +38,7 @@ jupyterhub:
python /tmp/download_models.py
+ # populate workspace with initial files
if [ ! -f /home/jovyan/welcome.ipynb ]; then
cp /tmp/welcome.ipynb /home/jovyan/welcome.ipynb
fi
@@ -46,10 +47,12 @@ jupyterhub:
mountPath: /tmp/requirements.txt
subPath: requirements.txt
readOnly: true
+ # This 'home' volume is created by the helm chart's 'homeMountPath' option.
+ # We mount it to initContainer too, so all downloads and installations are persisted in this mounted home folder.
- name: home
mountPath: /home/jovyan
subPath: jupyterhub_workspace
- - name: "download-models-py"
+ - name: "download-models-py"
mountPath: /tmp/download_models.py
subPath: download_models.py
readOnly: true
@@ -78,6 +81,12 @@ jupyterhub:
- name: welcome-ipynb
configMap:
name: "ai-starter-kit-welcome-ipynb"
+ - name: ray-ipynb
+ configMap:
+ name: "ai-starter-kit-ray-ipynb"
+ - name: chat-bot-ipynb
+ configMap:
+ name: "ai-starter-kit-chat-bot-ipynb"
extraVolumeMounts:
- name: requirements-txt
mountPath: /tmp/requirements.txt
@@ -85,16 +94,22 @@ jupyterhub:
- name: download-models-py
mountPath: /tmp/download_models.py
subPath: download_models.py
+ - name: ray-ipynb
+ mountPath: /tmp/ray.ipynb
+ subPath: ray.ipynb
+ - name: chat-bot-ipynb
+ mountPath: /tmp/chat_bot.ipynb
+ subPath: chat_bot.ipynb
# This environment variables list have its own format: https://z2jh.jupyter.org/en/latest/resources/reference.html#singleuser-extraenv
extraEnv:
HF_TOKEN:
name: HF_TOKEN
valueFrom:
secretKeyRef:
- name: ai-starter-kit-hf-token-secret
+ name: ai-starter-kit-hf-token-secret
key: token
- RAY_ADDRESS: "ai-starter-kit-kuberay-head-svc:6379"
- MLFLOW_TRACKING_URI: "http://ai-starter-kit-mlflow-tracking"
+ RAY_ADDRESS: "ai-starter-kit-kuberay-head-svc:10001"
+ MLFLOW_TRACKING_URI: "http://ai-starter-kit-mlflow:5000"
hub:
db:
type: sqlite-pvc
diff --git a/ai/ai-starter-kit/helm-chart/ai-starter-kit/values.yaml b/ai/ai-starter-kit/helm-chart/ai-starter-kit/values.yaml
index a83ed2df..122884fd 100644
--- a/ai/ai-starter-kit/helm-chart/ai-starter-kit/values.yaml
+++ b/ai/ai-starter-kit/helm-chart/ai-starter-kit/values.yaml
@@ -81,6 +81,12 @@ jupyterhub:
- name: welcome-ipynb
configMap:
name: "ai-starter-kit-welcome-ipynb"
+ - name: ray-ipynb
+ configMap:
+ name: "ai-starter-kit-ray-ipynb"
+ - name: chat-bot-ipynb
+ configMap:
+ name: "ai-starter-kit-chat-bot-ipynb"
extraVolumeMounts:
- name: requirements-txt
mountPath: /tmp/requirements.txt
@@ -88,6 +94,12 @@ jupyterhub:
- name: download-models-py
mountPath: /tmp/download_models.py
subPath: download_models.py
+ - name: ray-ipynb
+ mountPath: /tmp/ray.ipynb
+ subPath: ray.ipynb
+ - name: chat-bot-ipynb
+ mountPath: /tmp/chat_bot.ipynb
+ subPath: chat_bot.ipynb
# This environment variables list have its own format: https://z2jh.jupyter.org/en/latest/resources/reference.html#singleuser-extraenv
extraEnv:
HF_TOKEN:
@@ -96,8 +108,8 @@ jupyterhub:
secretKeyRef:
name: ai-starter-kit-hf-token-secret
key: token
- RAY_ADDRESS: "ai-starter-kit-kuberay-head-svc:6379"
- MLFLOW_TRACKING_URI: "http://ai-starter-kit-mlflow-tracking"
+ RAY_ADDRESS: "ai-starter-kit-kuberay-head-svc:10001"
+ MLFLOW_TRACKING_URI: "http://ai-starter-kit-mlflow:5000"
hub:
db:
type: sqlite-pvc
@@ -162,6 +174,18 @@ localPersistence:
# This path must match the destination path inside the minikube node.
hostPath: "/tmp/models-cache"
+ollama:
+ enabled: true
+ ollama:
+ models:
+ pull:
+ - gemma3
+ persistentVolume:
+ enabled: true
+ existingClaim: "ai-starter-kit-models-cache-pvc"
+ subPath: "ollama"
+
+
ramalama:
enabled: true
command: ["sh", "-c" , "trap 'exit 0' TERM; while true; do sleep 60 & wait; done"]
diff --git a/ai/ai-starter-kit/notebooks/multi-agent.ipynb b/ai/ai-starter-kit/notebooks/multi-agent.ipynb
new file mode 100644
index 00000000..ea2f3caa
--- /dev/null
+++ b/ai/ai-starter-kit/notebooks/multi-agent.ipynb
@@ -0,0 +1,621 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "079fadd2-200e-4d37-8ae2-be2792e3a24e",
+ "metadata": {},
+ "source": [
+ "### Cell 1 - Initialize Ray endpoints and verify dashboard\n",
+ "\n",
+ "Installs requests, derives the Ray head host from RAY_ADDRESS, builds Dashboard/Serve/MLflow URLs, reads an Hugging Face token, and prints the endpoints plus the Jobs API version for a quick health check."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "79db57cd-fb72-4b10-b0fb-5e9cd5c007b6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip -q install requests==2.* --disable-pip-version-check\n",
+ "\n",
+ "import os, textwrap, base64, time, json, requests\n",
+ "from string import Template\n",
+ "\n",
+ "raw_addr = os.getenv(\"RAY_ADDRESS\", \"ray://ai-starter-kit-kuberay-head-svc:10001\")\n",
+ "if raw_addr.startswith(\"ray://\"):\n",
+ " HEAD_HOST = raw_addr.split(\"://\", 1)[1].split(\":\", 1)[0]\n",
+ "else:\n",
+ " HEAD_HOST = raw_addr.split(\":\", 1)[0] or \"ai-starter-kit-kuberay-head-svc\"\n",
+ "\n",
+ "DASH_URL = f\"http://{HEAD_HOST}:8265\"\n",
+ "SERVE_PORT = int(os.getenv(\"SERVE_PORT\", \"8000\"))\n",
+ "SERVE_ROUTE = \"/v1\"\n",
+ "\n",
+ "HF_TOKEN_PATH = \"/etc/secrets/huggingface/token\"\n",
+ "HF_TOKEN = \"\"\n",
+ "if os.path.exists(HF_TOKEN_PATH):\n",
+ " try:\n",
+ " HF_TOKEN = open(HF_TOKEN_PATH).read().strip()\n",
+ " except Exception:\n",
+ " HF_TOKEN = \"\"\n",
+ "\n",
+ "print(\"Head host:\", HEAD_HOST)\n",
+ "print(\"Jobs API :\", f\"{DASH_URL}/api/jobs/\")\n",
+ "print(\"Serve URL:\", f\"http://{HEAD_HOST}:{SERVE_PORT}{SERVE_ROUTE}\")\n",
+ "print(\"MLflow :\", os.getenv(\"MLFLOW_TRACKING_URI\", \"http://ai-starter-kit-mlflow:5000\"))\n",
+ "\n",
+ "print(\"Jobs API version:\", requests.get(f\"{DASH_URL}/api/version\", timeout=10).json())\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe862173-fd9a-41ae-a27b-63875f788024",
+ "metadata": {},
+ "source": [
+ "### Cell 2 - Deploy a minimal Ray Serve smoke test and verify readiness\n",
+ "\n",
+ "Submits a tiny FastAPI app to Ray Serve (one /healthz endpoint under /smoke) as a Ray Job, installing FastAPI on the fly. It polls the Jobs API for status and hits :8000/smoke/healthz up to 60 seconds, printing when the service responds 200 (i.e., smoke test passes)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "34da3e26-6276-48b7-b3ac-c90359df6547",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os, base64, textwrap, time, requests\n",
+ "\n",
+ "DASH_URL = \"http://ai-starter-kit-kuberay-head-svc:8265\"\n",
+ "\n",
+ "print(\"Jobs API:\", requests.get(f\"{DASH_URL}/api/version\", timeout=10).json())\n",
+ "\n",
+ "serve_py = textwrap.dedent(\"\"\"\n",
+ " from fastapi import FastAPI\n",
+ " from ray import serve\n",
+ " serve.start(detached=True, http_options={\"host\":\"0.0.0.0\",\"port\":8000})\n",
+ " app = FastAPI()\n",
+ "\n",
+ " @serve.deployment(name=\"smoke\", num_replicas=1)\n",
+ " @serve.ingress(app)\n",
+ " class Smoke:\n",
+ " @app.get(\"/healthz\")\n",
+ " async def health(self): return {\"ok\": True}\n",
+ "\n",
+ " serve.run(Smoke.bind(), route_prefix=\"/smoke\")\n",
+ " print(\"READY: smoke\", flush=True)\n",
+ "\"\"\").strip()\n",
+ "\n",
+ "b64 = base64.b64encode(serve_py.encode()).decode()\n",
+ "entry = f'python -c \"import base64; exec(base64.b64decode(\\'{b64}\\'))\"'\n",
+ "submit = requests.post(f\"{DASH_URL}/api/jobs/\", json={\"entrypoint\": entry, \"runtime_env\": {\"pip\": [\"fastapi>=0.110\"]}}, timeout=60).json()\n",
+ "job_id = submit[\"job_id\"]\n",
+ "print(\"Job:\", job_id)\n",
+ "\n",
+ "svc = \"http://ai-starter-kit-kuberay-head-svc:8000/smoke/healthz\"\n",
+ "for i in range(60):\n",
+ " s = requests.get(f\"{DASH_URL}/api/jobs/{job_id}\", timeout=10).json()[\"status\"]\n",
+ " try:\n",
+ " r = requests.get(svc, timeout=2)\n",
+ " print(f\"tick {i:02d}: job={s}, health={r.status_code}\")\n",
+ " if r.status_code == 200:\n",
+ " print(\"Smoke OK\")\n",
+ " break\n",
+ " except Exception as e:\n",
+ " print(f\"tick {i:02d}: job={s}, health=ERR {e}\")\n",
+ " time.sleep(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8111d705-595e-4e65-8479-bdc76191fa31",
+ "metadata": {},
+ "source": [
+ "### Cell 3 - Deploy model on Ray Serve with llama-cpp\n",
+ "\n",
+ "Packages and submits a Ray Job that spins up a Ray Serve app exposing /v1/healthz and /v1/chat/completions. It downloads the preferred GGUF from Hugging Face, initializes llama-cpp-python, logs to MLflow, and prints the deployed health/chat URLs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bbea1539-e9ab-460a-9cfc-20a42807f616",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os, base64, textwrap, requests\n",
+ "\n",
+ "HEAD = os.environ.get(\"RAY_HEAD_SVC\", \"ai-starter-kit-kuberay-head-svc\")\n",
+ "DASH_URL = f\"http://{HEAD}:8265\"\n",
+ "SERVE_PORT = 8000\n",
+ "SERVE_ROUTE = \"/v1\"\n",
+ "\n",
+ "runtime_env = {\n",
+ " \"pip\": [\n",
+ " \"fastapi==0.110.0\",\n",
+ " \"uvicorn==0.23.2\",\n",
+ " \"huggingface_hub==0.25.2\",\n",
+ " \"llama-cpp-python==0.3.16\", \n",
+ " \"hf_transfer==0.1.6\",\n",
+ " \"mlflow==2.14.3\", \n",
+ " ],\n",
+ " \"env_vars\": {\n",
+ " \"HF_HUB_ENABLE_HF_TRANSFER\": \"1\",\n",
+ " \"HUGGINGFACE_HUB_TOKEN\": os.environ.get(\"HUGGINGFACE_HUB_TOKEN\", \"\"),\n",
+ " \"SERVE_PORT\": str(SERVE_PORT),\n",
+ "\n",
+ " \"MODEL_REPO\": \"Qwen/Qwen2.5-1.5B-Instruct-GGUF\",\n",
+ " \"GGUF_PREF_ORDER\": \"q4_k_m,q4_0,q3_k_m,q2_k\",\n",
+ "\n",
+ " \"LLM_CONTEXT\": os.environ.get(\"LLM_CONTEXT\", \"1024\"),\n",
+ " \"LLM_MAX_TOKENS\": os.environ.get(\"LLM_MAX_TOKENS\", \"256\"),\n",
+ " \"SERVER_MAX_NEW_TOKENS\": os.environ.get(\"SERVER_MAX_NEW_TOKENS\", \"512\"),\n",
+ "\n",
+ " \"LLM_THREADS\": os.environ.get(\"LLM_THREADS\", \"6\"),\n",
+ " \"OMP_NUM_THREADS\": os.environ.get(\"OMP_NUM_THREADS\", \"6\"),\n",
+ " \"GPU_LAYERS\": \"0\", \n",
+ " \n",
+ " \"PIP_PREFER_BINARY\": \"1\",\n",
+ " \"CMAKE_ARGS\": \"-DGGML_OPENMP=OFF -DLLAMA_NATIVE=OFF\",\n",
+ "\n",
+ " \"HF_HOME\": \"/tmp/hf-cache\",\n",
+ " \"TRANSFORMERS_CACHE\": \"/tmp/hf-cache\",\n",
+ "\n",
+ " \"MLFLOW_TRACKING_URI\": os.environ.get(\"MLFLOW_TRACKING_URI\", \"\"),\n",
+ " \"MLFLOW_EXPERIMENT_NAME\": os.environ.get(\"MLFLOW_EXPERIMENT_NAME\", \"ray-llama-cpp\"),\n",
+ " },\n",
+ "}\n",
+ "\n",
+ "serve_py = textwrap.dedent(f\"\"\"\n",
+ "import os, time, multiprocessing, uuid\n",
+ "from typing import List, Dict, Any\n",
+ "from fastapi import FastAPI, Request\n",
+ "from fastapi.responses import JSONResponse\n",
+ "from huggingface_hub import HfApi, hf_hub_download\n",
+ "from ray import serve\n",
+ "from llama_cpp import Llama\n",
+ "\n",
+ "USE_MLFLOW = False\n",
+ "try:\n",
+ " import mlflow\n",
+ " if os.getenv(\"MLFLOW_TRACKING_URI\"):\n",
+ " mlflow.set_tracking_uri(os.getenv(\"MLFLOW_TRACKING_URI\"))\n",
+ " mlflow.set_experiment(os.getenv(\"MLFLOW_EXPERIMENT_NAME\",\"ray-llama-cpp\"))\n",
+ " USE_MLFLOW = True\n",
+ "except Exception as _e:\n",
+ " USE_MLFLOW = False\n",
+ "\n",
+ "SERVE_PORT = int(os.getenv(\"SERVE_PORT\", \"{SERVE_PORT}\"))\n",
+ "SERVE_ROUTE = \"{SERVE_ROUTE}\"\n",
+ "MODEL_REPO = os.getenv(\"MODEL_REPO\", \"Qwen/Qwen2.5-1.5B-Instruct-GGUF\")\n",
+ "GGUF_PREFS = [s.strip() for s in os.getenv(\"GGUF_PREF_ORDER\",\"q4_k_m,q4_0,q3_k_m,q2_k\").split(\",\") if s.strip()]\n",
+ "CTX_LEN = int(os.getenv(\"LLM_CONTEXT\", \"2048\"))\n",
+ "MAX_TOKENS = int(os.getenv(\"LLM_MAX_TOKENS\", \"256\"))\n",
+ "HF_TOKEN = os.getenv(\"HUGGINGFACE_HUB_TOKEN\") or None\n",
+ "\n",
+ "serve.start(detached=True, http_options={{\"host\":\"0.0.0.0\", \"port\":SERVE_PORT}})\n",
+ "app = FastAPI()\n",
+ "\n",
+ "def pick_one_file(repo_id: str, prefs):\n",
+ " api = HfApi()\n",
+ " files = api.list_repo_files(repo_id=repo_id, repo_type=\"model\", token=HF_TOKEN)\n",
+ " ggufs = [f for f in files if f.lower().endswith(\".gguf\")]\n",
+ " if not ggufs:\n",
+ " raise RuntimeError(f\"No .gguf files visible in {{repo_id}}\")\n",
+ " for pref in prefs:\n",
+ " for f in ggufs:\n",
+ " if pref.lower() in f.lower():\n",
+ " return f\n",
+ " return ggufs[0]\n",
+ "\n",
+ "def pick_chat_format(repo: str, fname: str) -> str:\n",
+ " return \"qwen\"\n",
+ "\n",
+ "@serve.deployment(name=\"qwen\", num_replicas=1, ray_actor_options={{\"num_cpus\": 6}})\n",
+ "@serve.ingress(app)\n",
+ "class OpenAICompatLlama:\n",
+ " def __init__(self, repo_id: str = MODEL_REPO):\n",
+ " target = pick_one_file(repo_id, GGUF_PREFS)\n",
+ " print(f\"[env] model repo: {{repo_id}} file: {{target}}\", flush=True)\n",
+ " local_dir = \"/tmp/hf-gguf\"; os.makedirs(local_dir, exist_ok=True)\n",
+ "\n",
+ " gguf_path = hf_hub_download(\n",
+ " repo_id=repo_id, filename=target, token=HF_TOKEN,\n",
+ " local_dir=local_dir, local_dir_use_symlinks=False,\n",
+ " force_download=False, resume_download=True\n",
+ " )\n",
+ " print(f\"[download] done: {{gguf_path}}\", flush=True)\n",
+ "\n",
+ " n_threads = int(os.getenv(\"LLM_THREADS\", max(2, (multiprocessing.cpu_count() or 4)//2)))\n",
+ " print(f\"[load] llama-cpp-python | ctx={{CTX_LEN}} threads={{n_threads}} gpu_layers={{int(os.getenv('GPU_LAYERS','0'))}}\", flush=True)\n",
+ "\n",
+ " self.model_file = os.path.basename(gguf_path)\n",
+ " self.model_repo = repo_id\n",
+ " chat_format = pick_chat_format(self.model_repo, self.model_file)\n",
+ " print(f\"[load] chat_format={{chat_format}}\", flush=True)\n",
+ "\n",
+ " self.llm = Llama(\n",
+ " model_path=gguf_path,\n",
+ " n_ctx=CTX_LEN,\n",
+ " n_threads=n_threads,\n",
+ " n_batch=256, \n",
+ " n_gpu_layers=int(os.getenv(\"GPU_LAYERS\",\"0\")),\n",
+ " chat_format=chat_format,\n",
+ " verbose=False\n",
+ " )\n",
+ " print(\"[ready] model loaded\", flush=True)\n",
+ "\n",
+ " @app.get(\"/healthz\")\n",
+ " async def health(self):\n",
+ " return {{\"status\":\"ok\"}}\n",
+ "\n",
+ " @app.post(\"/chat/completions\")\n",
+ " async def chat_completions(self, request: Request):\n",
+ " t0 = time.time()\n",
+ " body = await request.json()\n",
+ "\n",
+ " messages = body.get(\"messages\", [])\n",
+ " temperature = float(body.get(\"temperature\", 0.2))\n",
+ " req_max = body.get(\"max_tokens\", None)\n",
+ " stop_words = (body.get(\"stop\", []) or []) + [\"<|im_end|>\", \"\"]\n",
+ "\n",
+ " SERVER_MAX = int(os.getenv(\"SERVER_MAX_NEW_TOKENS\", \"512\"))\n",
+ " max_tokens = int(req_max if isinstance(req_max, int) else MAX_TOKENS)\n",
+ " max_tokens = max(32, min(max_tokens, CTX_LEN - 128, SERVER_MAX))\n",
+ "\n",
+ " rid = \"chatcmpl-\" + uuid.uuid4().hex[:24]\n",
+ " created = int(time.time())\n",
+ " model_name = f\"{{self.model_repo}}/{{self.model_file}}\"\n",
+ "\n",
+ " try:\n",
+ " result = self.llm.create_chat_completion(\n",
+ " messages=messages,\n",
+ " temperature=temperature,\n",
+ " max_tokens=max_tokens,\n",
+ " top_k=50,\n",
+ " top_p=0.9,\n",
+ " repeat_penalty=1.1,\n",
+ " stop=stop_words,\n",
+ " )\n",
+ " out_text = (result[\"choices\"][0][\"message\"][\"content\"] or \"\").strip()\n",
+ " usage_raw = result.get(\"usage\") or {{}}\n",
+ " p_tokens = int(usage_raw.get(\"prompt_tokens\") or 0)\n",
+ " c_tokens = int(usage_raw.get(\"completion_tokens\") or 0)\n",
+ " err = None\n",
+ " except Exception as e:\n",
+ " out_text = \"\"\n",
+ " p_tokens = c_tokens = 0\n",
+ " err = str(e)\n",
+ "\n",
+ " if USE_MLFLOW:\n",
+ " try:\n",
+ " dur_ms = int((time.time()-t0) * 1000)\n",
+ " with mlflow.start_run(run_name=\"chat\"):\n",
+ " mlflow.set_tags({{\n",
+ " \"model_repo\": self.model_repo,\n",
+ " \"model_file\": self.model_file,\n",
+ " \"framework\": \"llama-cpp-python\",\n",
+ " }})\n",
+ " mlflow.log_params({{\n",
+ " \"temperature\": temperature,\n",
+ " \"max_tokens\": max_tokens,\n",
+ " \"ctx\": CTX_LEN,\n",
+ " }})\n",
+ " if not (p_tokens and c_tokens):\n",
+ " p_tokens = p_tokens or max(1, len(\" \".join(m.get(\"content\",\"\") for m in messages).split()))\n",
+ " c_tokens = c_tokens or max(0, len(out_text.split()))\n",
+ " mlflow.log_metrics({{\n",
+ " \"duration_ms\": dur_ms,\n",
+ " \"prompt_tokens_approx\": p_tokens,\n",
+ " \"completion_tokens_approx\": c_tokens,\n",
+ " \"total_tokens_approx\": p_tokens + c_tokens,\n",
+ " }})\n",
+ " except Exception:\n",
+ " pass\n",
+ "\n",
+ " if err:\n",
+ " return JSONResponse(status_code=500, content={{\"error\": err, \"type\":\"generation_error\"}})\n",
+ "\n",
+ " usage = {{\n",
+ " \"prompt_tokens\": p_tokens,\n",
+ " \"completion_tokens\": c_tokens,\n",
+ " \"total_tokens\": p_tokens + c_tokens,\n",
+ " }}\n",
+ " return {{\n",
+ " \"id\": rid,\n",
+ " \"object\": \"chat.completion\",\n",
+ " \"created\": created,\n",
+ " \"model\": model_name,\n",
+ " \"choices\": [\n",
+ " {{\n",
+ " \"index\": 0,\n",
+ " \"message\": {{\"role\":\"assistant\",\"content\": out_text}},\n",
+ " \"finish_reason\": \"stop\"\n",
+ " }}\n",
+ " ],\n",
+ " \"usage\": usage\n",
+ " }}\n",
+ "\n",
+ "serve.run(OpenAICompatLlama.bind(), route_prefix=SERVE_ROUTE)\n",
+ "print(\"READY\", flush=True)\n",
+ "\"\"\").strip()\n",
+ "\n",
+ "payload = base64.b64encode(serve_py.encode()).decode()\n",
+ "entrypoint = 'python -c \"import base64,sys;exec(base64.b64decode(\\'{}\\').decode())\"'.format(payload)\n",
+ "\n",
+ "job = requests.post(\n",
+ " f\"{DASH_URL}/api/jobs/\",\n",
+ " json={\n",
+ " \"entrypoint\": entrypoint,\n",
+ " \"runtime_env\": runtime_env,\n",
+ " \"metadata\": {\"job_name\": \"serve-qwen2_5-llama_cpp-openai\"},\n",
+ " },\n",
+ " timeout=45\n",
+ ").json()\n",
+ "\n",
+ "print(\"Job:\", job.get(\"job_id\"))\n",
+ "print(\"Health:\", f\"http://{HEAD}:{SERVE_PORT}{SERVE_ROUTE}/healthz\")\n",
+ "print(\"Chat: \", f\"http://{HEAD}:{SERVE_PORT}{SERVE_ROUTE}/chat/completions\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a411c015-c802-4ca1-81bb-3f4790d9626a",
+ "metadata": {},
+ "source": [
+ "### Cell 4 - Basic client + latency test\n",
+ "\n",
+ "Calls /v1/healthz and then sends an OpenAI-style chat request to /v1/chat/completions with a short prompt. Prints latency and token usage, returning the assistant text."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3be634e2-a82f-42c9-8e31-57e6868a86ee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os, time, requests, json\n",
+ "\n",
+ "HEAD = os.environ.get(\"RAY_HEAD_SVC\", \"ai-starter-kit-kuberay-head-svc\")\n",
+ "SERVE_PORT = 8000\n",
+ "BASE_URL = f\"http://{HEAD}:{SERVE_PORT}/v1\"\n",
+ "\n",
+ "def health():\n",
+ " r = requests.get(f\"{BASE_URL}/healthz\", timeout=10)\n",
+ " print(\"Health:\", r.status_code, r.json())\n",
+ "\n",
+ "def chat(prompt, temperature=0.4, max_tokens=220, stop=None):\n",
+ " body = {\n",
+ " \"model\": \"qwen2.5-1.5b-instruct-gguf\",\n",
+ " \"temperature\": float(temperature),\n",
+ " \"max_tokens\": int(max_tokens),\n",
+ " \"messages\": [\n",
+ " {\"role\": \"system\", \"content\": \"You are Qwen2.5 Instruct running on a tiny CPU host. Be concise, complete sentences.\"},\n",
+ " {\"role\": \"user\", \"content\": prompt},\n",
+ " ],\n",
+ " }\n",
+ " if stop:\n",
+ " body[\"stop\"] = stop\n",
+ "\n",
+ " t0 = time.time()\n",
+ " r = requests.post(f\"{BASE_URL}/chat/completions\", json=body, timeout=300)\n",
+ " dt = time.time() - t0\n",
+ " r.raise_for_status()\n",
+ " out = r.json()[\"choices\"][0][\"message\"][\"content\"]\n",
+ " usage = r.json().get(\"usage\", {})\n",
+ " print(f\"\\nLatency: {dt:.2f}s | usage: {usage}\")\n",
+ " print(\"\\n---\\n\", out)\n",
+ " return out\n",
+ "\n",
+ "health()\n",
+ "_ = chat(\"Say 'test ok' then give me one short fun fact about llamas.\", stop=[\"<|im_end|>\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "553d2756-8949-43e3-8342-71387688e0fa",
+ "metadata": {},
+ "source": [
+ "### Cell 5 - Multi-agent (Autogen) pipeline\n",
+ "\n",
+ "Installs Autogen, configures OpenAIWrapper to hit Ray Serve /v1 endpoint, warms up the model, then runs a simple three-agent workflow (Researcher -> Writer -> Critic) to produce and refine a short report."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0f6713f3-8b60-40b2-ad3c-ebf6db4f66e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip -q install pyautogen~=0.2.35 \"flaml[automl]\" --disable-pip-version-check\n",
+ "\n",
+ "import os, sys\n",
+ "\n",
+ "for p in [\n",
+ " \"/tmp/models-cache/lib/python3.11/site-packages\", \n",
+ " os.path.expanduser(\"~/.local/lib/python3.11/site-packages\"), \n",
+ "]:\n",
+ " if os.path.isdir(p) and p not in sys.path:\n",
+ " sys.path.insert(0, p)\n",
+ "\n",
+ "import os, autogen\n",
+ "from autogen import AssistantAgent, UserProxyAgent\n",
+ "\n",
+ "HEAD = os.environ.get(\"RAY_HEAD_SVC\", \"ai-starter-kit-kuberay-head-svc\")\n",
+ "SERVE_PORT = 8000\n",
+ "BASE_URL = f\"http://{HEAD}:{SERVE_PORT}/v1\" \n",
+ "\n",
+ "config_list = [\n",
+ " {\n",
+ " \"model\": \"qwen2.5-1.5b-instruct-gguf\", \n",
+ " \"base_url\": BASE_URL, \n",
+ " \"api_key\": \"local\", \n",
+ " \"price\": [0.0, 0.0],\n",
+ " }\n",
+ "]\n",
+ "\n",
+ "llm = autogen.OpenAIWrapper(config_list=config_list)\n",
+ "try:\n",
+ " r = llm.create(messages=[{\"role\":\"user\",\"content\":\"Say 'test ok'.\"}], temperature=0.2, max_tokens=16)\n",
+ " print(\"Warmup:\", r.choices[0].message.content)\n",
+ "except Exception as e:\n",
+ " print(\"Warmup failed:\", e)\n",
+ "\n",
+ "user_proxy = UserProxyAgent(\n",
+ " name=\"UserProxy\",\n",
+ " system_message=\"You are the human admin. Initiate the task.\",\n",
+ " code_execution_config=False,\n",
+ " human_input_mode=\"NEVER\",\n",
+ ")\n",
+ "\n",
+ "researcher = AssistantAgent(\n",
+ " name=\"Researcher\",\n",
+ " system_message=(\n",
+ " \"You are a researcher. Gather concise, verified facts on the topic. \"\n",
+ " \"Return several bullet points with inline source domains (e.g., nature.com, ibm.com). \"\n",
+ " \"Keep under 100 words total. No made-up sources. \"\n",
+ " \"Do not include any special end token.\"\n",
+ " ),\n",
+ " llm_config={\"config_list\": config_list, \"temperature\": 0.35, \"max_tokens\": 140, \"timeout\": 300},\n",
+ ")\n",
+ "\n",
+ "writer = AssistantAgent(\n",
+ " name=\"Writer\",\n",
+ " system_message=(\n",
+ " \"You are a writer. Using the Researcher’s notes, produce a clear word report under 160 words. \"\n",
+ " \"Avoid speculation. Keep it structured and readable. \"\n",
+ " \"Do not include any special end token.\"\n",
+ " ),\n",
+ " llm_config={\"config_list\": config_list, \"temperature\": 0.55, \"max_tokens\": 220, \"timeout\": 180},\n",
+ ")\n",
+ "\n",
+ "critic = AssistantAgent(\n",
+ " name=\"Critic\",\n",
+ " system_message=(\n",
+ " \"You are a critic. Review the Writer’s report for accuracy, clarity, and flow.\"\n",
+ " \"Present the tightened final text and keep it under 140 words. On a new last line output exactly: <|END|>\"\n",
+ " ),\n",
+ " llm_config={\"config_list\": config_list, \"temperature\": 0.45, \"max_tokens\": 160, \"timeout\": 300},\n",
+ ")\n",
+ "\n",
+ "def run_sequential(task):\n",
+ " research_response = researcher.generate_reply(messages=[{\"content\": task, \"role\": \"user\"}])\n",
+ " research_notes = research_response if isinstance(research_response, str) else research_response.get(\"content\", \"[no output]\")\n",
+ " print(\"\\nResearch Notes:\\n\", research_notes)\n",
+ "\n",
+ " writer_prompt = f\"Using these research notes, write the report:\\n{research_notes}\"\n",
+ " writer_response = writer.generate_reply(messages=[{\"content\": writer_prompt, \"role\": \"user\"}])\n",
+ " report = writer_response if isinstance(writer_response, str) else writer_response.get(\"content\", \"[no output]\")\n",
+ " print(\"\\nDraft Report:\\n\", report)\n",
+ "\n",
+ " critic_prompt = f\"Review this report:\\n{report}\"\n",
+ " critic_response = critic.generate_reply(messages=[{\"content\": critic_prompt, \"role\": \"user\"}])\n",
+ " final_text = critic_response if isinstance(critic_response, str) else critic_response.get(\"content\", \"[no output]\")\n",
+ " print(\"\\nFinal Review:\\n\", final_text)\n",
+ " return final_text\n",
+ "\n",
+ "task = \"Research the latest advancements in quantum computing as of 2025. Gather key facts, then write a short report (200–300 words). Have the Critic review and finalize.\"\n",
+ "final_output = run_sequential(task)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0af596cf-5ba6-42df-a030-61d7a20d6f7b",
+ "metadata": {},
+ "source": [
+ "### Cell 6 - MLFlow: connect to tracking server and list recent chat runs\n",
+ "\n",
+ "Installs MLflow, sets the tracking URI and experiment, then queries and prints the latest runs with key params/metrics (temperature, max_tokens, duration) to verify Serve logging."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "03a1b042-04df-4cd0-9099-4cc763ecfe9d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip -q install mlflow==2.14.3 --disable-pip-version-check\n",
+ "\n",
+ "import os, mlflow\n",
+ "from datetime import datetime\n",
+ "\n",
+ "tracking_uri = os.getenv(\"MLFLOW_TRACKING_URI\", \"http://ai-starter-kit-mlflow:5000\")\n",
+ "mlflow.set_tracking_uri(tracking_uri)\n",
+ "print(f\"MLflow Tracking URI: {tracking_uri}\")\n",
+ "\n",
+ "exp_name = os.getenv(\"MLFLOW_EXPERIMENT_NAME\", \"ray-llama-cpp\")\n",
+ "exp = mlflow.set_experiment(exp_name)\n",
+ "print(f\"Experiment: {exp.name} (ID: {exp.experiment_id})\")\n",
+ "print(\"-\" * 60)\n",
+ "\n",
+ "client = mlflow.tracking.MlflowClient()\n",
+ "runs = client.search_runs(\n",
+ " exp.experiment_id, \n",
+ " order_by=[\"attributes.start_time DESC\"], \n",
+ " max_results=10\n",
+ ")\n",
+ "\n",
+ "if not runs:\n",
+ " print(\"No runs found. Run cells 4 or 5 first to generate inference requests.\")\n",
+ "else:\n",
+ " print(f\"\\nFound {len(runs)} recent runs:\")\n",
+ " print(\"-\" * 60)\n",
+ " \n",
+ " for i, run in enumerate(runs, 1):\n",
+ " start_time = datetime.fromtimestamp(run.info.start_time/1000).strftime('%Y-%m-%d %H:%M:%S')\n",
+ " duration = run.data.metrics.get('duration_ms', 'N/A')\n",
+ " temp = run.data.params.get('temperature', 'N/A')\n",
+ " max_tokens = run.data.params.get('max_tokens', 'N/A')\n",
+ " total_tokens = run.data.metrics.get('total_tokens_approx', 'N/A')\n",
+ " \n",
+ " print(f\"\\nRun {i}:\")\n",
+ " print(f\" ID: {run.info.run_id[:12]}...\")\n",
+ " print(f\" Time: {start_time}\")\n",
+ " print(f\" Status: {run.info.status}\")\n",
+ " print(f\" Temperature: {temp}\")\n",
+ " print(f\" Max Tokens: {max_tokens}\")\n",
+ " print(f\" Duration: {duration} ms\")\n",
+ " print(f\" Total Tokens: {total_tokens}\")\n",
+ " \n",
+ " print(\"\\n\" + \"=\" * 60)\n",
+ " print(\"SUMMARY:\")\n",
+ " successful = sum(1 for r in runs if r.info.status == 'FINISHED')\n",
+ " durations = [r.data.metrics.get('duration_ms', 0) for r in runs if r.data.metrics.get('duration_ms')]\n",
+ " avg_duration = sum(durations) / len(durations) if durations else 0\n",
+ " \n",
+ " print(f\" Total Runs: {len(runs)}\")\n",
+ " print(f\" Successful: {successful}\")\n",
+ " print(f\" Failed: {len(runs) - successful}\")\n",
+ " print(f\" Avg Duration: {avg_duration:.1f} ms\" if avg_duration else \" Avg Duration: N/A\")\n",
+ "\n",
+ "print(\"\\n\" + \"=\" * 60)\n",
+ "print(\"MLflow verification complete\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ai/ai-starter-kit/notebooks/test_ollama.py b/ai/ai-starter-kit/notebooks/test_ollama.py
new file mode 100644
index 00000000..58cf22e0
--- /dev/null
+++ b/ai/ai-starter-kit/notebooks/test_ollama.py
@@ -0,0 +1,11 @@
+from ollama import Client
+client = Client(
+ host='http://ai-starter-kit-ollama:11434',
+ headers={'x-some-header': 'some-value'}
+)
+response = client.chat(model='gemma3', messages=[
+ {
+ 'role': 'user',
+ 'content': 'Why is the sky blue?',
+ },
+])