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llama-stack-mirror/docs/notebooks/Llama_Stack_Showcase.ipynb
Xi Yan d145bb629d notebooks
2024-12-12 17:49:32 -08:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "c1e7571c",
"metadata": {
"id": "c1e7571c"
},
"source": [
"# Llama Stack Showcase\n",
"\n",
"\n",
"<img src=\"https://llama-stack.readthedocs.io/en/latest/_images/llama-stack.png\" alt=\"drawing\" width=\"500\"/>\n",
"\n",
"[Llama Stack](https://github.com/meta-llama/llama-stack) defines and standardizes the set of core building blocks needed to bring generative AI applications to market. These building blocks are presented in the form of interoperable APIs with a broad set of Service Providers providing their implementations.\n",
"\n",
"Read more about the project: https://llama-stack.readthedocs.io/en/latest/index.html\n",
"\n",
"In this guide, we will showcase how you can build LLM-powered agentic applications using Llama Stack.\n"
]
},
{
"cell_type": "markdown",
"id": "4CV1Q19BDMVw",
"metadata": {
"id": "4CV1Q19BDMVw"
},
"source": [
"## 1. Getting started with Llama Stack"
]
},
{
"cell_type": "markdown",
"id": "K4AvfUAJZOeS",
"metadata": {
"id": "K4AvfUAJZOeS"
},
"source": [
"### 1.1. Create TogetherAI account\n",
"\n",
"\n",
"In order to run inference for the llama models, you will need to use an inference provider. Llama stack supports a number of inference [providers](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote/inference).\n",
"\n",
"\n",
"In this showcase, we will use [together.ai](https://www.together.ai/) as the inference provider. So, you would first get an API key from Together if you dont have one already.\n",
"\n",
"Steps [here](https://docs.google.com/document/d/1Vg998IjRW_uujAPnHdQ9jQWvtmkZFt74FldW2MblxPY/edit?usp=sharing).\n",
"\n",
"You can also use Fireworks.ai or even Ollama if you would like to.\n",
"\n",
"\n",
"\n",
"> **Note:** Set the API Key in the Secrets of this notebook\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "oDUB7M_qe-Gs",
"metadata": {
"id": "oDUB7M_qe-Gs"
},
"source": [
"### 1.2. Install Llama Stack\n",
"\n",
"We will now start with installing the [llama-stack pypi package](https://pypi.org/project/llama-stack).\n",
"\n",
"In addition, we will install [bubblewrap](https://github.com/containers/bubblewrap), a low level light-weight container framework that runs in the user namespace. We will use it to execute code generated by Llama in one of the examples."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "J2kGed0R5PSf",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "J2kGed0R5PSf",
"outputId": "82206d68-57ad-407d-d789-8a112e2e61a9"
},
"outputs": [],
"source": [
"!apt-get install -y bubblewrap\n",
"!pip install -U llama-stack"
]
},
{
"cell_type": "markdown",
"id": "414301dc",
"metadata": {
"id": "414301dc"
},
"source": [
"### 1.3. Configure Llama Stack for Together\n",
"\n",
"\n",
"Llama Stack is architected as a collection of lego blocks which can be assembled as needed.\n",
"\n",
"\n",
"Typically, llama stack is available as a server with an endpoint that you can hit. We call this endpoint a [Distribution](https://llama-stack.readthedocs.io/en/latest/concepts/index.html#distributions). Partners like Together and Fireworks offer their own Llama Stack Distribution endpoints.\n",
"\n",
"In this showcase, we are going to use llama stack inline as a library. So, given a particular set of providers, we must first package up the right set of dependencies. We have a template to use Together as an inference provider and [faiss](https://ai.meta.com/tools/faiss/) for memory/RAG.\n",
"\n",
"We will run `llama stack build` to deploy all dependencies."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "HaepEZXCDgif",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "HaepEZXCDgif",
"outputId": "2f94a037-3990-47a8-bacb-cb194d76cc29"
},
"outputs": [],
"source": [
"# This will build all the dependencies you will need\n",
"!llama stack build --template together --image-type venv"
]
},
{
"cell_type": "markdown",
"id": "25b97dfe",
"metadata": {
"id": "25b97dfe"
},
"source": [
"### 1.4. Initialize Llama Stack\n",
"\n",
"Now that all dependencies have been installed, we can initialize llama stack. We will first set the `TOGETHER_API_KEY` environment variable\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "E1UFuJC570Tk",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"collapsed": true,
"id": "E1UFuJC570Tk",
"outputId": "5601fd2e-966d-4118-8ebf-c8da9a3125fc"
},
"outputs": [],
"source": [
"import os\n",
"from google.colab import userdata\n",
"\n",
"os.environ['TOGETHER_API_KEY'] = userdata.get('TOGETHER_API_KEY')\n",
"\n",
"from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n",
"client = LlamaStackAsLibraryClient(\"together\")\n",
"_ = client.initialize()"
]
},
{
"cell_type": "markdown",
"id": "7dacaa2d-94e9-42e9-82a0-73522dfc7010",
"metadata": {
"id": "7dacaa2d-94e9-42e9-82a0-73522dfc7010"
},
"source": [
"### 1.5. Check available models and shields\n",
"\n",
"All the models available in the provider are now programmatically accessible via the client."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ruO9jQna_t_S",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "ruO9jQna_t_S",
"outputId": "db0da8e1-8c5e-4c5e-f77f-4c2fdd5fab81"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Available models:\n",
"meta-llama/Llama-3.1-8B-Instruct (provider's alias: meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo) \n",
"meta-llama/Llama-3.1-70B-Instruct (provider's alias: meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo) \n",
"meta-llama/Llama-3.1-405B-Instruct-FP8 (provider's alias: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo) \n",
"meta-llama/Llama-3.2-3B-Instruct (provider's alias: meta-llama/Llama-3.2-3B-Instruct-Turbo) \n",
"meta-llama/Llama-3.2-11B-Vision-Instruct (provider's alias: meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo) \n",
"meta-llama/Llama-3.2-90B-Vision-Instruct (provider's alias: meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo) \n",
"meta-llama/Llama-Guard-3-8B (provider's alias: meta-llama/Meta-Llama-Guard-3-8B) \n",
"meta-llama/Llama-Guard-3-11B-Vision (provider's alias: meta-llama/Llama-Guard-3-11B-Vision-Turbo) \n",
"----\n",
"Available shields (safety models):\n",
"meta-llama/Llama-Guard-3-8B\n",
"----\n"
]
}
],
"source": [
"print(\"Available models:\")\n",
"for m in client.models.list():\n",
" print(f\"{m.identifier} (provider's alias: {m.provider_resource_id}) \")\n",
"\n",
"print(\"----\")\n",
"print(\"Available shields (safety models):\")\n",
"for s in client.shields.list():\n",
" print(s.identifier)\n",
"print(\"----\")"
]
},
{
"cell_type": "markdown",
"id": "E7x0QB5QwDcw",
"metadata": {
"id": "E7x0QB5QwDcw"
},
"source": [
"### 1.6. Pick the model\n",
"\n",
"We will use Llama3.2-3B-Instruct for our examples."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "LINBvv8lwTJh",
"metadata": {
"id": "LINBvv8lwTJh"
},
"outputs": [],
"source": [
"model_id = \"meta-llama/Llama-3.2-3B-Instruct\""
]
},
{
"cell_type": "markdown",
"id": "86366383",
"metadata": {
"id": "86366383"
},
"source": [
"### 1.7. Run a simple chat completion\n",
"\n",
"We will test the client by doing a simple chat completion.\n",
"We will use Llama3.1-8B-Instruct for our examples."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "77c29dba",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "77c29dba",
"outputId": "dab265a7-cf8f-4070-e5ed-81c2a6eb1f9a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Here is a two-sentence poem about llamas:\n",
"\n",
"With soft fur and gentle eyes, the llama wanders by,\n",
"A quiet companion, roaming the Andean sky.\n"
]
}
],
"source": [
"response = client.inference.chat_completion(\n",
" model_id=model_id,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a friendly assistant.\"},\n",
" {\"role\": \"user\", \"content\": \"Write a two-sentence poem about llama.\"}\n",
" ],\n",
")\n",
"\n",
"print(response.completion_message.content)"
]
},
{
"cell_type": "markdown",
"id": "8cf0d555",
"metadata": {
"id": "8cf0d555"
},
"source": [
"### 1.8. Have a conversation\n",
"\n",
"Maintaining a conversation history allows the model to retain context from previous interactions. Use a list to accumulate messages, enabling continuity throughout the chat session.\n",
"\n",
"Remember to type `quit` or `exit` after you are done chatting."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9496f75c",
"metadata": {
"id": "9496f75c"
},
"outputs": [],
"source": [
"from termcolor import cprint\n",
"\n",
"async def chat_loop():\n",
" conversation_history = []\n",
" while True:\n",
" user_input = input('User> ')\n",
" if user_input.lower() in ['exit', 'quit', 'bye']:\n",
" cprint('Ending conversation. Goodbye!', 'yellow')\n",
" break\n",
"\n",
" user_message = {\"role\": \"user\", \"content\": user_input}\n",
" conversation_history.append(user_message)\n",
"\n",
" response = client.inference.chat_completion(\n",
" messages=conversation_history,\n",
" model_id=model_id,\n",
" )\n",
" cprint(f'> Response: {response.completion_message.content}', 'cyan')\n",
"\n",
" assistant_message = {\n",
" \"role\": \"user\",\n",
" \"content\": response.completion_message.content,\n",
" }\n",
" conversation_history.append(assistant_message)\n",
"\n",
"await chat_loop()\n"
]
},
{
"cell_type": "markdown",
"id": "03fcf5e0",
"metadata": {
"id": "03fcf5e0"
},
"source": [
"### 1.9. Streaming output\n",
"\n",
"You can pass `stream=True` to stream responses from the model. You can then loop through the responses."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d119026e",
"metadata": {
"id": "d119026e"
},
"outputs": [],
"source": [
"from llama_stack_client.lib.inference.event_logger import EventLogger\n",
"\n",
"message = {\n",
" \"role\": \"user\",\n",
" \"content\": 'Write me a sonnet about llama'\n",
"}\n",
"cprint(f'User> {message[\"content\"]}', 'green')\n",
"\n",
"response = client.inference.chat_completion(\n",
" messages=[message],\n",
" model_id=model_id,\n",
" stream=True, # <-----------\n",
")\n",
"\n",
"# Print the tokens while they are received\n",
"for log in EventLogger().log(response):\n",
" log.print()"
]
},
{
"cell_type": "markdown",
"id": "LFC386wNQR-v",
"metadata": {
"id": "LFC386wNQR-v"
},
"source": [
"## 2. Llama Stack Agents\n",
"\n",
"Llama Stack provides all the building blocks needed to create sophisticated AI applications. This guide will walk you through how to use these components effectively.\n",
"\n",
"\n",
"\n",
"\n",
"<img src=\"https://github.com/meta-llama/llama-stack/blob/main/docs/resources/agentic-system.png?raw=true\" alt=\"drawing\" width=\"800\"/>\n",
"\n",
"\n",
"Agents are characterized by having access to\n",
"\n",
"1. Memory - for RAG\n",
"2. Tool calling - ability to call tools like search and code execution\n",
"3. Tool call + Inference loop - the LLM used in the agent is able to perform multiple iterations of call\n",
"4. Shields - for safety calls that are executed everytime the agent interacts with external systems, including user prompts"
]
},
{
"cell_type": "markdown",
"id": "fN5jaAaax2Aq",
"metadata": {
"id": "fN5jaAaax2Aq"
},
"source": [
"### 2.1. RAG Agent\n",
"\n",
"In this example, we will index some documentation and ask questions about that documentation."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "GvLWltzZCNkg",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 541,
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},
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"outputId": "6998e5f5-dfbc-4d1b-b674-b67e987b8b19"
},
"outputs": [],
"source": [
"from llama_stack_client.lib.agents.agent import Agent\n",
"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
"from llama_stack_client.types import Attachment\n",
"\n",
"urls = [\"chat.rst\", \"llama3.rst\", \"datasets.rst\", \"lora_finetune.rst\"]\n",
"attachments = [\n",
" Attachment(\n",
" content=f\"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}\",\n",
" mime_type=\"text/plain\",\n",
" )\n",
" for i, url in enumerate(urls)\n",
"]\n",
"\n",
"agent_config = AgentConfig(\n",
" model=model_id,\n",
" instructions=\"You are a helpful assistant\",\n",
" tools=[{\"type\": \"memory\"}], # enable Memory aka RAG\n",
" enable_session_persistence=False,\n",
")\n",
"\n",
"rag_agent = Agent(client, agent_config)\n",
"session_id = rag_agent.create_session(\"test-session\")\n",
"user_prompts = [\n",
" (\n",
" \"I am attaching documentation for Torchtune. Help me answer questions I will ask next.\",\n",
" attachments,\n",
" ),\n",
" (\n",
" \"What are the top 5 topics that were explained? Only list succinct bullet points.\",\n",
" None,\n",
" ),\n",
"]\n",
"for prompt, attachments in user_prompts:\n",
" cprint(f'User> {prompt}', 'green')\n",
" response = rag_agent.create_turn(\n",
" messages=[{\"role\": \"user\", \"content\": prompt}],\n",
" attachments=attachments,\n",
" session_id=session_id,\n",
" )\n",
" for log in EventLogger().log(response):\n",
" log.print()"
]
},
{
"cell_type": "markdown",
"id": "i2o0gDhrv2og",
"metadata": {
"id": "i2o0gDhrv2og"
},
"source": [
"### 2.2. Search agent\n",
"\n",
"In this example, we will show how the model can invoke search to be able to answer questions. We will first have to set the API key of the search tool.\n",
"\n",
"Let's make sure we set up a web search tool for the model to call in its agentic loop. In this tutorial, we will use [Tavily](https://tavily.com) as our search provider.\n",
"\n",
"See steps [here](https://docs.google.com/document/d/1Vg998IjRW_uujAPnHdQ9jQWvtmkZFt74FldW2MblxPY/edit?tab=t.0#heading=h.xx02wojfl2f9)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "HZPPv6nfytK7",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HZPPv6nfytK7",
"outputId": "690009f8-9a3f-42de-e0fd-995a57920664"
},
"outputs": [],
"source": [
"search_tool = {\n",
" \"type\": \"brave_search\",\n",
" \"engine\": \"tavily\",\n",
" \"api_key\": userdata.get(\"TAVILY_SEARCH_API_KEY\")\n",
"}\n",
"search_tool"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "WS8Gu5b0APHs",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WS8Gu5b0APHs",
"outputId": "c7ad1b34-b0df-4e82-dc46-6b8d8cc9a013"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"User> Hello\n",
"inference> Hello! How can I assist you today?\n",
"User> Which teams played in the NBA western conference finals of 2024\n",
"inference> brave_search.call(query=\"NBA Western Conference Finals 2024 teams\")\n",
"tool_execution> Tool:brave_search Args:{'query': 'NBA Western Conference Finals 2024 teams'}\n",
"tool_execution> Tool:brave_search Response:{\"query\": \"NBA Western Conference Finals 2024 teams\", \"top_k\": [{\"title\": \"2024 NBA Western Conference Finals - Basketball-Reference.com\", \"url\": \"https://www.basketball-reference.com/playoffs/2024-nba-western-conference-finals-mavericks-vs-timberwolves.html\", \"content\": \"2024 NBA Western Conference Finals Mavericks vs. Timberwolves League Champion: Boston Celtics. Finals MVP: Jaylen Brown (20.8 / 5.4 / 5.0) 2024 Playoff Leaders: PTS: Luka Don\\u010di\\u0107 (635) TRB: Luka Don\\u010di\\u0107 (208) AST: Luka Don\\u010di\\u0107 (178) WS: Derrick White (2.9) More playoffs info\", \"score\": 0.9982658, \"raw_content\": null}, {\"title\": \"2024 Playoffs: West Finals | Timberwolves (3) vs. Mavericks (5) - NBA.com\", \"url\": \"https://www.nba.com/playoffs/2024/west-final\", \"content\": \"The Dallas Mavericks and Minnesota Timberwolves have advanced to the 2024 Western Conference Finals during the NBA playoffs.\", \"score\": 0.9981969, \"raw_content\": null}, {\"title\": \"2024 NBA Playoff Bracket: Updated schedule, scores, standings\", \"url\": \"https://www.foxsports.com/stories/nba/nba-playoff-picture-bracket\", \"content\": \"OG Anunoby's impact, Doc Rivers' remedy and the Thunder's one weakness\\nNBA Champions by Year: Complete list of NBA Finals winners\\nCharges against Hornets forward Miles Bridges connected to domestic violence case dropped\\nShaq calls Orlando Magic jersey retirement 'his most impressive one'\\nFormer NBA player Bryn Forbes arrested on family violence charge\\nKnicks reportedly filing protest after refs admit mistake on foul call in loss to Rockets\\n2023-24 NBA Power Rankings: Cavs hold steady while Knicks, Clippers slip\\n2024 NBA All-Star Rosters: Starters, reserves, voting results\\n2024 NBA Buyout Market Tracker: Thaddeus Young to join Suns\\n2023-24 NBA odds: Mac McClung favored to win dunk contest\\n3 points: As of 2/9/2024\\n2024 NBA Playoffs Schedule & Key Dates\\n2023-24 NBA Power Rankings: Cavs hold steady while Knicks, Clippers slip\\n2024 NBA All-Star Rosters: Starters, reserves, voting results\\n2024 NBA Buyout Market Tracker: Thaddeus Young to join Suns\\n2023-24 NBA odds: Mac McClung favored to win dunk contest\\n3 points: OG Anunoby's impact, Doc Rivers' remedy and the Thunder's one weakness\\nNBA Champions by Year: Complete list of NBA Finals winners\\nCharges against Hornets forward Miles Bridges connected to domestic violence case dropped\\nShaq calls Orlando Magic jersey retirement 'his most impressive one'\\nFormer NBA player Bryn Forbes arrested on family violence charge Here's what the playoffs would look like if the season ended today*:\\nEastern Conference Seeding\\nEastern Conference Bracket\\nWestern Conference Seeding\\nWestern Conference Bracket\\nCheck out our NBA standings for up-to-the-minute updates.\\n* 2024 NBA playoff picture, bracket, standings\\nThe 2024 NBA Playoffs are still a ways off, but it's never too early to take a look at the playoff picture.\\n\", \"score\": 0.8785229, \"raw_content\": null}, {\"title\": \"Predicting NBA playoff teams and NBA Finals 2025 winner: Where do ...\", \"url\": \"https://sportsnaut.com/predicting-nba-playoff-teams-and-nba-finals-winner/\", \"content\": \"Check out the latest NBA playoff predictions for the 2023-24 season as the offseason is officially underway with teams chasing the NBA Finals! With the 2024-25 season underway, now is the perfect time to look into the future and take a spin at predicting the NBA playoff teams for next spring. Without further ado, it\\u2019s now time to start predicting the NBA playoff teams for later this season. NBA Finals prediction: New York Knicks over Oklahoma City Thunder (4-2) Mentioned in this article: Boston Celtics Brooklyn Nets Dallas Mavericks Denver Nuggets Golden State Warriors Indiana Pacers Los Angeles Clippers Los Angeles Lakers Miami Heat Milwaukee Bucks Minnesota Timberwolves New Orleans Pelicans Philadelphia 76ers Phoenix Suns Toronto Raptors Utah Jazz Washington Wizards\", \"score\": 0.83169055, \"raw_content\": null}, {\"title\": \"2024 NBA Western Conference playoff bracket - Basketnews.com\", \"url\": \"https://basketnews.com/news-204687-2024-nba-western-conference-playoff-bracket.html\", \"content\": \"The conference semifinalswill begin on May 6-7 but can move up to May 4-5 if series from the prior round end early. Conference finals schedule. The conference finals will begin May 21-22 but can move up to May 19-20 if the series from the prior round ends early. NBA Finals schedule. The 2024 NBA Finals will begin June 6, with ABC as the\", \"score\": 0.6171666, \"raw_content\": null}]}\n",
"shield_call> No Violation\n",
"inference> The teams that played in the NBA Western Conference Finals of 2024 were the Dallas Mavericks and the Minnesota Timberwolves.\n"
]
}
],
"source": [
"from llama_stack_client.lib.agents.agent import Agent\n",
"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
"\n",
"agent_config = AgentConfig(\n",
" model=model_id,\n",
" instructions=\"You are a helpful assistant\",\n",
" tools=[search_tool],\n",
" input_shields=[],\n",
" output_shields=[],\n",
" enable_session_persistence=False,\n",
")\n",
"agent = Agent(client, agent_config)\n",
"user_prompts = [\n",
" \"Hello\",\n",
" \"Which teams played in the NBA western conference finals of 2024\",\n",
"]\n",
"\n",
"session_id = agent.create_session(\"test-session\")\n",
"for prompt in user_prompts:\n",
" cprint(f'User> {prompt}', 'green')\n",
" response = agent.create_turn(\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": prompt,\n",
" }\n",
" ],\n",
" session_id=session_id,\n",
" )\n",
" for log in EventLogger().log(response):\n",
" log.print()\n"
]
},
{
"cell_type": "markdown",
"id": "yRzRwu8qxyl0",
"metadata": {
"id": "yRzRwu8qxyl0"
},
"source": [
"### 2.3. Code Execution Agent\n",
"\n",
"In this example, we will show how multiple tools can be called by the model - including web search and code execution. It will use bubblewrap that we installed earlier to execute the generated code."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "GvVRuhO-GOov",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "GvVRuhO-GOov",
"outputId": "301bffc9-73ad-4f70-e3d3-1151c7e84c54"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"User> ('Here is a csv, can you describe it ?', [Attachment(content='https://raw.githubusercontent.com/meta-llama/llama-stack-apps/main/examples/resources/inflation.csv', mime_type='test/csv')])\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:httpx:HTTP Request: GET https://raw.githubusercontent.com/meta-llama/llama-stack-apps/main/examples/resources/inflation.csv \"HTTP/1.1 200 OK\"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"inference> import pandas as pd\n",
"\n",
"df = pd.read_csv('/tmp/tmpxpfv53gh/3Dv9UVZ0inflation.csv')\n",
"\n",
"print(df.head())\n",
"tool_execution> Tool:code_interpreter Args:{'code': \"import pandas as pd\\n\\ndf = pd.read_csv('/tmp/tmpxpfv53gh/3Dv9UVZ0inflation.csv')\\n\\nprint(df.head())\"}\n",
"tool_execution> Tool:code_interpreter Response:completed\n",
"[stdout]\n",
"Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec\n",
"0 2014 1.6 1.6 1.7 1.8 2.0 1.9 1.9 1.7 1.7 1.8 1.7 1.6\n",
"1 2015 1.6 1.7 1.8 1.8 1.7 1.8 1.8 1.8 1.9 1.9 2.0 2.1\n",
"2 2016 2.2 2.3 2.2 2.1 2.2 2.2 2.2 2.3 2.2 2.1 2.1 2.2\n",
"3 2017 2.3 2.2 2.0 1.9 1.7 1.7 1.7 1.7 1.7 1.8 1.7 1.8\n",
"4 2018 1.8 1.8 2.1 2.1 2.2 2.3 2.4 2.2 2.2 2.1 2.2 2.2\n",
"[/stdout]\n",
"shield_call> No Violation\n",
"inference> The CSV file contains information about inflation rates for each month from 2014 to 2018. The data is organized into a table with 4 columns (Year, Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec) and 5 rows (2014-2018).\n",
"\n",
"The data shows a gradual decrease in inflation rates from 2014 to 2018, with some fluctuations in certain months. The inflation rates are displayed in percentage values.\n",
"\n",
"Please note that the actual data in the CSV file may vary depending on the source and the specific values. The above description is based on a hypothetical example and may not reflect the actual data in the file.\n",
"User> ('Which year ended with the highest inflation ?', None)\n",
"inference> Based on the provided data, the year 2014 had the highest inflation rates for several months, with an average inflation rate of around 1.7-1.8% for January to December.\n",
"User> ('What macro economic situations that led to such high inflation in that period?', None)\n",
"inference> Based on general macroeconomic principles, several factors could have contributed to high inflation in the period from 2014 to 2018:\n",
"\n",
"1. **Low Interest Rates**: The Federal Reserve in the United States maintained low interest rates during this period, which increased the money supply and fueled inflation. Lower interest rates made borrowing cheaper, leading to increased consumption and investment, which in turn pushed up prices.\n",
"2. **Quantitative Easing (QE)**: The Fed's quantitative easing program, which involved purchasing government bonds and other securities, increased the money supply and reduced the value of the US dollar. This led to higher prices for imported goods and services, contributing to inflation.\n",
"3. **Weakened Currency**: A weaker US dollar made imports cheaper, which can lead to higher inflation as foreign goods and services become more affordable.\n",
"4. **Monetary Policy in Emerging Markets**: Some emerging economies, like China and India, implemented expansionary monetary policies, such as cutting interest rates and increasing liquidity, which fueled growth and inflation in those regions.\n",
"5. **Commodity Price Shocks**: Increases in oil prices, particularly in 2014, contributed to higher inflation in some economies, as higher energy costs are passed on to consumers.\n",
"6. **Fiscal Policy Stimulation**: In some countries, governments implemented fiscal policies, such as tax cuts and increased government spending, which can stimulate economic growth and lead to inflation.\n",
"7. **Supply Chain Disruptions**: Global supply chain disruptions, such as the Ebola outbreak in West Africa, can lead to shortages and higher prices for certain goods, contributing to inflation.\n",
"\n",
"Please note that these factors are general explanations and may not specifically apply to the inflation rates in the provided CSV data. The actual causes of inflation may be more complex and specific to the economic context of the time.\n",
"User> ('Plot average yearly inflation as a time series', None)\n",
"inference> import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Load the data\n",
"df = pd.read_csv('/tmp/tmpxpfv53gh/3Dv9UVZ0inflation.csv')\n",
"\n",
"# Calculate the average yearly inflation\n",
"df['Average Yearly Inflation'] = df.iloc[:, 1:].mean(axis=1)\n",
"\n",
"# Plot the time series\n",
"plt.figure(figsize=(10,6))\n",
"plt.plot(df['Year'], df['Average Yearly Inflation'], marker='o')\n",
"plt.title('Average Yearly Inflation')\n",
"plt.xlabel('Year')\n",
"plt.ylabel('Inflation Rate (%)')\n",
"plt.grid(True)\n",
"plt.show()\n",
"tool_execution> Tool:code_interpreter Args:{'code': \"import pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n# Load the data\\ndf = pd.read_csv('/tmp/tmpxpfv53gh/3Dv9UVZ0inflation.csv')\\n\\n# Calculate the average yearly inflation\\ndf['Average Yearly Inflation'] = df.iloc[:, 1:].mean(axis=1)\\n\\n# Plot the time series\\nplt.figure(figsize=(10,6))\\nplt.plot(df['Year'], df['Average Yearly Inflation'], marker='o')\\nplt.title('Average Yearly Inflation')\\nplt.xlabel('Year')\\nplt.ylabel('Inflation Rate (%)')\\nplt.grid(True)\\nplt.show()\"}\n",
"tool_execution> Tool:code_interpreter Response:completed\n",
"shield_call> No Violation\n",
"inference> This code loads the data from the CSV file, calculates the average yearly inflation rate for each year, and then plots the time series of average yearly inflation rates.\n",
"\n",
"Please note that this is a simple example and might not be suitable for all cases. For example, it assumes that all the months in each year have the same inflation rate, which might not be the case in real-world scenarios. Additionally, the plot might not be very informative if the inflation rates are not uniformly distributed throughout the years.\n"
]
}
],
"source": [
"agent_config = AgentConfig(\n",
" model=model_id,\n",
" instructions=\"You are a helpful assistant\",\n",
" tools=[\n",
" search_tool,\n",
" {\n",
" \"type\": \"code_interpreter\",\n",
" }\n",
" ],\n",
" tool_choice=\"required\",\n",
" input_shields=[],\n",
" output_shields=[],\n",
" enable_session_persistence=False,\n",
")\n",
"\n",
"codex_agent = Agent(client, agent_config)\n",
"session_id = codex_agent.create_session(\"test-session\")\n",
"\n",
"user_prompts = [\n",
" (\n",
" \"Here is a csv, can you describe it ?\",\n",
" [\n",
" Attachment(\n",
" content=\"https://raw.githubusercontent.com/meta-llama/llama-stack-apps/main/examples/resources/inflation.csv\",\n",
" mime_type=\"test/csv\",\n",
" )\n",
" ],\n",
" ),\n",
" (\"Which year ended with the highest inflation ?\", None),\n",
" (\n",
" \"What macro economic situations that led to such high inflation in that period?\",\n",
" None,\n",
" ),\n",
" (\"Plot average yearly inflation as a time series\", None),\n",
"]\n",
"\n",
"for prompt in user_prompts:\n",
" cprint(f'User> {prompt}', 'green')\n",
" response = codex_agent.create_turn(\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": prompt[0],\n",
" }\n",
" ],\n",
" attachments=prompt[1],\n",
" session_id=session_id,\n",
" )\n",
" # for chunk in response:\n",
" # print(chunk)\n",
"\n",
" for log in EventLogger().log(response):\n",
" log.print()\n"
]
},
{
"cell_type": "markdown",
"id": "FJ85DUhgBZd7",
"metadata": {
"id": "FJ85DUhgBZd7"
},
"source": [
"## 3. Llama Stack Agent Evaluations\n",
"\n",
"Llama Stack offers `/eval` and `/scoring` APIs, which allows you to run evaluations on generated responses."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "xG4Y84VQBb0g",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 298
},
"id": "xG4Y84VQBb0g",
"outputId": "f61cebdf-f614-440c-d170-f1e873b542ef"
},
"outputs": [
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ScoringScoreResponse</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">results</span>=<span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'llm-as-judge::base'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ScoringResult</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">aggregated_results</span>=<span style=\"font-weight: bold\">{}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">score_rows</span>=<span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'B'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Answer: B, Explanation: The GENERATED_RESPONSE is a superset of the EXPECTED_RESPONSE and is fully consistent with it. The GENERATED_RESPONSE provides more detailed information about the top 5 topics related to LoRA, while the EXPECTED_RESPONSE only mentions \"LoRA\". The GENERATED_RESPONSE expands on the topic, but does not conflict with the EXPECTED_RESPONSE.'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"font-weight: bold\">]</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">)</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'basic::subset_of'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ScoringResult</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">aggregated_results</span>=<span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'accuracy'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'num_correct'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'num_total'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">score_rows</span>=<span style=\"font-weight: bold\">[{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span><span style=\"font-weight: bold\">}]</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">)</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"font-weight: bold\">)</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[1;35mScoringScoreResponse\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mresults\u001b[0m=\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'llm-as-judge::base'\u001b[0m: \u001b[1;35mScoringResult\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ │ │ \u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'B'\u001b[0m,\n",
"\u001b[2;32m│ │ │ │ │ \u001b[0m\u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'Answer: B, Explanation: The GENERATED_RESPONSE is a superset of the EXPECTED_RESPONSE and is fully consistent with it. The GENERATED_RESPONSE provides more detailed information about the top 5 topics related to LoRA, while the EXPECTED_RESPONSE only mentions \"LoRA\". The GENERATED_RESPONSE expands on the topic, but does not conflict with the EXPECTED_RESPONSE.'\u001b[0m\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m)\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'basic::subset_of'\u001b[0m: \u001b[1;35mScoringResult\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_correct'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_total'\u001b[0m: \u001b[1;36m1.0\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m1.0\u001b[0m\u001b[1m}\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m)\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[1m)\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import rich\n",
"from rich.pretty import pprint\n",
"\n",
"judge_model_id = \"meta-llama/Llama-3.1-405B-Instruct-FP8\"\n",
"\n",
"JUDGE_PROMPT = \"\"\"\n",
"Given a QUESTION and GENERATED_RESPONSE and EXPECTED_RESPONSE.\n",
"\n",
"Compare the factual content of the GENERATED_RESPONSE with the EXPECTED_RESPONSE. Ignore any differences in style, grammar, or punctuation.\n",
" The GENERATED_RESPONSE may either be a subset or superset of the EXPECTED_RESPONSE, or it may conflict with it. Determine which case applies. Answer the question by selecting one of the following options:\n",
" (A) The GENERATED_RESPONSE is a subset of the EXPECTED_RESPONSE and is fully consistent with it.\n",
" (B) The GENERATED_RESPONSE is a superset of the EXPECTED_RESPONSE and is fully consistent with it.\n",
" (C) The GENERATED_RESPONSE contains all the same details as the EXPECTED_RESPONSE.\n",
" (D) There is a disagreement between the GENERATED_RESPONSE and the EXPECTED_RESPONSE.\n",
" (E) The answers differ, but these differences don't matter from the perspective of factuality.\n",
"\n",
"Give your answer in the format \"Answer: One of ABCDE, Explanation: \".\n",
"\n",
"Your actual task:\n",
"\n",
"QUESTION: {input_query}\n",
"GENERATED_RESPONSE: {generated_answer}\n",
"EXPECTED_RESPONSE: {expected_answer}\n",
"\"\"\"\n",
"\n",
"input_query = \"What are the top 5 topics that were explained? Only list succinct bullet points.\"\n",
"generated_answer = \"\"\"\n",
"Here are the top 5 topics that were explained in the documentation for Torchtune:\n",
"\n",
"* What is LoRA and how does it work?\n",
"* Fine-tuning with LoRA: memory savings and parameter-efficient finetuning\n",
"* Running a LoRA finetune with Torchtune: overview and recipe\n",
"* Experimenting with different LoRA configurations: rank, alpha, and attention modules\n",
"* LoRA finetuning\n",
"\"\"\"\n",
"expected_answer = \"\"\"LoRA\"\"\"\n",
"\n",
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" {\n",
" \"input_query\": input_query,\n",
" \"generated_answer\": generated_answer,\n",
" \"expected_answer\": expected_answer,\n",
" },\n",
"]\n",
"\n",
"scoring_params = {\n",
" \"llm-as-judge::base\": {\n",
" \"judge_model\": judge_model_id,\n",
" \"prompt_template\": JUDGE_PROMPT,\n",
" \"type\": \"llm_as_judge\",\n",
" \"judge_score_regexes\": [\"Answer: (A|B|C|D|E)\"],\n",
" },\n",
" \"basic::subset_of\": None,\n",
"}\n",
"\n",
"response = client.scoring.score(input_rows=rows, scoring_functions=scoring_params)\n",
"pprint(response)"
]
},
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