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‎01_basic/03_chain_skill_call.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 53,
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"metadata": {},
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"outputs": [
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{
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"ename": "SyntaxError",
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"evalue": "f-string: unmatched '(' (2166899585.py, line 16)",
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"output_type": "error",
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"traceback": [
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"\u001b[1;36m Cell \u001b[1;32mIn[3], line 16\u001b[1;36m\u001b[0m\n\u001b[1;33m print(f\"Using deploy {os.getenv(\"OPENAI_API_BASE\")} with model {os.getenv(\"AZURE_GPT4_MODEL\")}\")\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m f-string: unmatched '('\n"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Using deploy https://alkopenai2.openai.azure.com/ with model gpt42\n"
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]
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}
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],
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"source": [
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"# https://devblogs.microsoft.com/semantic-kernel/now-in-beta-explore-the-enhanced-python-sdk-for-semantic-kernel/\n",
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"from semantic_kernel import Kernel\n",
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"from semantic_kernel.connectors.ai.open_ai import (\n",
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" AzureChatCompletion,\n",
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" AzureTextCompletion,\n",
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")\n",
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"\n",
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"model = os.getenv(\"AZURE_GPT4_MODEL\", \"gpt4\")\n",
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"model = os.getenv(\"AZURE_GPT4_MODEL\", \"gpt4o\")\n",
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"endpoint = os.getenv(\"OPENAI_API_BASE\")\n",
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"kernel = Kernel(log=logger)\n",
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"kernel.add_service(\n",
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" AzureChatCompletion(\n",
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" model,\n",
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" endpoint = endpoint,\n",
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" api_key = os.getenv(\"OPENAI_API_KEY\")\n",
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" ),\n",
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"chat_completion = AzureChatCompletion(\n",
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" service_id=\"openai_chat\",\n",
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" deployment_name=model,\n",
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" endpoint = endpoint,\n",
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" api_key = os.getenv(\"OPENAI_API_KEY\")\n",
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")\n",
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"kernel.add_service(chat_completion)\n",
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"\n",
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"print(f\"Using deploy {endpoint} with model {model}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Arrr, the capital o' France be Paris, matey!\n",
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"-------------------------\n"
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]
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}
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],
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"source": [
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"question = \"What is the capital of France?\"\n",
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"prompt = f\"You will answer in a pirate language to the user question. question {question}\"\n",
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"prompt_function = kernel.add_function(function_name=\"pirate_question\", plugin_name=\"pirate_matey\", prompt=prompt)\n",
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"result = await kernel.invoke(prompt_function)\n",
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"print(result)\n",
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"print(\"-------------------------\")\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 57,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Yarr, the capital be Paris, ye landlubber!\n",
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"-------------------------\n"
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]
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}
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],
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"source": [
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"#https://github.com/microsoft/semantic-kernel/blob/6be43bc7ff0b0304f443b3d00f6316599a3e8707/python/README.md?plain=1#L90\n",
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"from semantic_kernel.prompt_template import PromptTemplateConfig\n",
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"\n",
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"question = \"What is the capital of France?\"\n",
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"\n",
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"req_settings = kernel.get_prompt_execution_settings_from_service_id(\"openai_chat\")\n",
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"req_settings.max_tokens = 2000\n",
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"req_settings.temperature = 1.0\n",
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"req_settings.top_p = 0.8\n",
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"\n",
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"prompt_function = kernel.add_function(\n",
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" function_name=\"pirate_question_parameter\", \n",
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" plugin_name=\"pirate_matey_parameter\",\n",
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" prompt= \"You will answer in a pirate language to the user question. question {{$question}}\",\n",
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" execution_settings=req_settings)\n",
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"result = await kernel.invoke(prompt_function, question=question)\n",
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"print(result)\n",
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"print(\"-------------------------\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"nction(function_name=\"pirate_question\", plugin_name=\"pirate_matey\", prompt=prompt)\n",
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"result = await kernel.invoke(prompt_function)\n",
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"print(result)\n",
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"print(\"-------------------------\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from plugins.AudioVideoPlugin.AudioVideo import AudioVideo\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Now we need to import the plugin\n",
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"from plugins.AudioVideoPlugin.AudioVideo import AudioVideo\n",
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"\n",
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"# Now you can import the plugin importing skill directly from the function you declared\n",
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"# in the plugin directory. The import_skill does not need the path, it only need an\n",
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"# instance of the skill and the name of the skill\n",
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"extractaudio_plugin = kernel.add_plugin(AudioVideo(), \"AudioVideoPlugin\")\n",
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"\n",
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"plugin = AudioVideo()\n",
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"extractaudio_plugin = kernel.add_plugin(plugin, \"AudioVideoPlugin\")\n",
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"\n",
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"plugins_directory = \"./plugins\"\n",
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"\n",
@@ -115,37 +195,16 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"KernelFunctionFromMethod(metadata=KernelFunctionMetadata(name='ExtractAudio', plugin_name='AudioVideoPlugin', description='extract audio in wav format from an mp4 file', parameters=[KernelParameterMetadata(name='videofile', description='Full path to the mp4 file', default_value=None, type_='str', is_required=True, type_object=<class 'str'>)], is_prompt=False, is_asynchronous=False, return_parameter=KernelParameterMetadata(name='return', description='output audio file path', default_value=None, type_='str', is_required=True, type_object=None)), method=<bound method AudioVideo.extract_audio of <plugins.AudioVideoPlugin.AudioVideo.AudioVideo object at 0x00000212EC39C250>>, stream_method=None)"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"extractaudio_plugin[\"ExtractAudio\"] #This is how you can call the plug"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"KernelFunctionFromMethod(metadata=KernelFunctionMetadata(name='ExtractAudio', plugin_name='AudioVideoPlugin', description='extract audio in wav format from an mp4 file', parameters=[KernelParameterMetadata(name='videofile', description='Full path to the mp4 file', default_value=None, type_='str', is_required=True, type_object=<class 'str'>)], is_prompt=False, is_asynchronous=False, return_parameter=KernelParameterMetadata(name='return', description='output audio file path', default_value=None, type_='str', is_required=True, type_object=None)), method=<bound method AudioVideo.extract_audio of <plugins.AudioVideoPlugin.AudioVideo.AudioVideo object at 0x00000212EC39C250>>, stream_method=None)\n",
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"KernelFunctionFromMethod(metadata=KernelFunctionMetadata(name='TranscriptTimeline', plugin_name='AudioVideoPlugin', description='Transcript audio from a wav file to a timeline', parameters=[KernelParameterMetadata(name='audiofile', description='Full path to the wav file', default_value=None, type_='str', is_required=True, type_object=<class 'str'>)], is_prompt=False, is_asynchronous=False, return_parameter=KernelParameterMetadata(name='return', description='', default_value=None, type_='str', is_required=True, type_object=None)), method=<bound method AudioVideo.transcript_timeline of <plugins.AudioVideoPlugin.AudioVideo.AudioVideo object at 0x00000212EC39C250>>, stream_method=None)\n",
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"KernelFunctionFromPrompt(metadata=KernelFunctionMetadata(name='VideoTimelineCreator', plugin_name='PublishingPlugin', description='Given a video transcript it can summarize and generate a timeline', parameters=[KernelParameterMetadata(name='input', description='', default_value='', type_='', is_required=True, type_object=None)], is_prompt=True, is_asynchronous=True, return_parameter=KernelParameterMetadata(name='return', description='The completion result', default_value=None, type_='FunctionResult', is_required=True, type_object=None)), prompt_template=KernelPromptTemplate(prompt_template_config=PromptTemplateConfig(name='VideoTimelineCreator', description='Given a video transcript it can summarize and generate a timeline', template='I will give you a transcript of a video. The transcript contains phrases prefixed by the timestamp where the phrase starts. I want you to identify between three and ten main sections of the video. You must never identify more than ten sections.\\nFor each section you will create a brief title prefixed with the start timestamp of the section obtained analyzing all the text belonging to that section.\\n\\nEXAMPLE ANSWER - Maximum of ten sections\\n00:00 - Title of section 1\\n00:33 - Title of section 2\\n01:23 - Title of section 3\\n\\n[DATA]\\n{{$input}}', template_format='semantic-kernel', input_variables=[InputVariable(name='input', description='', default='', is_required=True, json_schema='')], execution_settings={})), prompt_execution_settings={})\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"from pprint import pprint\n",
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"# want to print all the keys of extractaudio_plugin that is a dictionary\n",
@@ -157,20 +216,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"# you can verify if cuda is available.\n",
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"import torch\n",
@@ -179,25 +227,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Extracting auio file from video S:\\OneDrive\\Youtube\\AI\\SemanticChain\\MontaggiCompleti\\010-CsharpIntro.mp4\n",
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"S:\\OneDrive\\Youtube\\AI\\SemanticChain\\MontaggiCompleti\\010-CsharpIntro.wav\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import time\n",
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"\n",
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"result = await kernel.invoke(\n",
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" extractaudio_plugin[\"ExtractAudio\"],\n",
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" #videofile =\"S:\\\\OneDrive\\\\Youtube\\\\AI\\\\SemanticChain\\\\MontaggiCompleti\\\\250-NlpPrecisionRecallRerank.mp4\"\n",
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" videofile =\"S:\\\\OneDrive\\\\Youtube\\\\AI\\\\SemanticChain\\\\MontaggiCompleti\\\\010-CsharpIntro.mp4\"\n",
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" videofile =\"S:\\\\OneDrive\\\\Youtube\\\\AI\\\\Various\\\\Montaggi\\\\200-Word2Vec.mp4\"\n",
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")\n",
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"\n",
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"print (result)\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Extracting transcript from audio file S:\\OneDrive\\Youtube\\AI\\SemanticChain\\MontaggiCompleti\\010-CsharpIntro.wav\n",
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"Using device: cuda:0 to run whisper with model large-v3\n",
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"Detected language: English\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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" 18%|█▊ | 8598/46811 [08:58<47:41, 13.36frames/s]"
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]
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}
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],
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"outputs": [],
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"source": [
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"\n",
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"# now invoke the plugin to transcript\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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"version": "3.10.11"
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}
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},
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"nbformat": 4,

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