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llama-stack-mirror/docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb
Dinesh Yeduguru a5c57cd381
agents to use tools api (#673) 
# What does this PR do?

PR #639 introduced the notion of Tools API and ability to invoke tools
through API just as any resource. This PR changes the Agents to start
using the Tools API to invoke tools. Major changes include:
1) Ability to specify tool groups with AgentConfig
2) Agent gets the corresponding tool definitions for the specified tools
and pass along to the model
3) Attachements are now named as Documents and their behavior is mostly
unchanged from user perspective
4) You can specify args that can be injected to a tool call through
Agent config. This is especially useful in case of memory tool, where
you want the tool to operate on a specific memory bank.
5) You can also register tool groups with args, which lets the agent
inject these as well into the tool call.
6) All tests have been migrated to use new tools API and fixtures
including client SDK tests
7) Telemetry just works with tools API because of our trace protocol
decorator


## Test Plan
```
pytest -s -v -k fireworks llama_stack/providers/tests/agents/test_agents.py  \
   --safety-shield=meta-llama/Llama-Guard-3-8B \
   --inference-model=meta-llama/Llama-3.1-8B-Instruct

pytest -s -v -k together  llama_stack/providers/tests/tools/test_tools.py \
   --safety-shield=meta-llama/Llama-Guard-3-8B \
   --inference-model=meta-llama/Llama-3.1-8B-Instruct

LLAMA_STACK_CONFIG="/Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml" pytest -v tests/client-sdk/agents/test_agents.py
```
run.yaml:
https://gist.github.com/dineshyv/0365845ad325e1c2cab755788ccc5994

Notebook:
https://colab.research.google.com/drive/1ck7hXQxRl6UvT-ijNRZ-gMZxH1G3cN2d?usp=sharing
2025-01-08 19:01:00 -08:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "c1e7571c",
"metadata": {
"id": "c1e7571c"
},
"source": [
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1F2ksmkoGQPa4pzRjMOE6BXWeOxWFIW6n?usp=sharing)\n",
"\n",
"# Llama Stack - Building AI Applications\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": 42,
"id": "J2kGed0R5PSf",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "J2kGed0R5PSf",
"outputId": "7d543c6f-623d-4911-b9a7-4ed24d5b82f2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading package lists... Done\n",
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]
}
],
"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": 43,
"id": "HaepEZXCDgif",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "HaepEZXCDgif",
"outputId": "9c268d26-7444-4741-f14d-3911eea8e4eb"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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"\u001b[32mBuild Successful!\u001b[0m\n"
]
}
],
"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": 1,
"id": "E1UFuJC570Tk",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"collapsed": true,
"id": "E1UFuJC570Tk",
"outputId": "bac7c9ec-ad49-4040-af43-8869f0afe5ac"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mWarning: `bwrap` is not available. Code interpreter tool will not work correctly.\u001b[0m\n"
]
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Using config <span style=\"color: #000080; text-decoration-color: #000080\">/Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml</span>:\n",
"</pre>\n"
],
"text/plain": [
"Using config \u001b[34m/Users/dineshyv/.llama/distributions/llamastack-together/\u001b[0m\u001b[34mtogether-run.yaml\u001b[0m:\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">apis:\n",
"- agents\n",
"- datasetio\n",
"- eval\n",
"- inference\n",
"- memory\n",
"- safety\n",
"- scoring\n",
"- telemetry\n",
"- tool_runtime\n",
"conda_env: together\n",
"datasets: <span style=\"font-weight: bold\">[]</span>\n",
"docker_image: null\n",
"eval_tasks: <span style=\"font-weight: bold\">[]</span>\n",
"image_name: together\n",
"memory_banks: <span style=\"font-weight: bold\">[]</span>\n",
"metadata_store:\n",
" db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/Users/dineshyv/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">registry.db</span>\n",
" namespace: null\n",
" type: sqlite\n",
"models:\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-8B-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-8B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-70B-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-70B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-405B-Instruct-FP8\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-405B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-3B-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-3B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-11B-Vision-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-11B-Vision-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-90B-Vision-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-90B-Vision-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-11B-Vision\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-11B-Vision-Turbo\n",
"- metadata:\n",
" embedding_dimension: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">384</span>\n",
" model_id: all-MiniLM-L6-v2\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - embedding\n",
" provider_id: sentence-transformers\n",
" provider_model_id: null\n",
"providers:\n",
" agents:\n",
" - config:\n",
" persistence_store:\n",
" db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/Users/dineshyv/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">agents_store.db</span>\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
" datasetio:\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: huggingface\n",
" provider_type: remote::huggingface\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: localfs\n",
" provider_type: inline::localfs\n",
" eval:\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
" inference:\n",
" - config:\n",
" api_key: <span style=\"color: #008000; text-decoration-color: #008000\">'********'</span>\n",
" url: <span style=\"color: #0000ff; text-decoration-color: #0000ff; text-decoration: underline\">https://api.together.xyz/v1</span>\n",
" provider_id: together\n",
" provider_type: remote::together\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: sentence-transformers\n",
" provider_type: inline::sentence-transformers\n",
" memory:\n",
" - config:\n",
" kvstore:\n",
" db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/Users/dineshyv/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">faiss_store.db</span>\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: faiss\n",
" provider_type: inlin<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::fa</span>iss\n",
" safety:\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: llama-guard\n",
" provider_type: inline::llama-guard\n",
" scoring:\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: basic\n",
" provider_type: inlin<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::ba</span>sic\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: llm-as-judge\n",
" provider_type: inline::llm-as-judge\n",
" - config:\n",
" openai_api_key: <span style=\"color: #008000; text-decoration-color: #008000\">'********'</span>\n",
" provider_id: braintrust\n",
" provider_type: inlin<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::b</span>raintrust\n",
" telemetry:\n",
" - config:\n",
" service_name: llama-stack\n",
" sinks: sqlite\n",
" sqlite_db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/Users/dineshyv/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">trace_store.db</span>\n",
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
" tool_runtime:\n",
" - config:\n",
" api_key: <span style=\"color: #008000; text-decoration-color: #008000\">'********'</span>\n",
" provider_id: brave-search\n",
" provider_type: remot<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::b</span>rave-search\n",
" - config:\n",
" api_key: <span style=\"color: #008000; text-decoration-color: #008000\">'********'</span>\n",
" provider_id: tavily-search\n",
" provider_type: remote::tavily-search\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: code-interpreter\n",
" provider_type: inlin<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::c</span>ode-interpreter\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: memory-runtime\n",
" provider_type: inline::memory-runtime\n",
"scoring_fns: <span style=\"font-weight: bold\">[]</span>\n",
"shields:\n",
"- params: null\n",
" provider_id: null\n",
" provider_shield_id: null\n",
" shield_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
"tool_groups:\n",
"- provider_id: tavily-search\n",
" tool_group:\n",
" tools:\n",
" - built_in_type: !!python/object/apply:llama_models.llama3.api.datatypes.BuiltinTool\n",
" - brave_search\n",
" metadata: <span style=\"font-weight: bold\">{}</span>\n",
" type: built_in\n",
" type: user_defined\n",
" tool_group_id: brave_search_group\n",
"- provider_id: code-interpreter\n",
" tool_group:\n",
" tools:\n",
" - built_in_type: !!python/object/apply:llama_models.llama3.api.datatypes.BuiltinTool\n",
" - code_interpreter\n",
" metadata: <span style=\"font-weight: bold\">{}</span>\n",
" type: built_in\n",
" type: user_defined\n",
" tool_group_id: code_interpreter_group\n",
"version: <span style=\"color: #008000; text-decoration-color: #008000\">'2'</span>\n",
"\n",
"</pre>\n"
],
"text/plain": [
"apis:\n",
"- agents\n",
"- datasetio\n",
"- eval\n",
"- inference\n",
"- memory\n",
"- safety\n",
"- scoring\n",
"- telemetry\n",
"- tool_runtime\n",
"conda_env: together\n",
"datasets: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"docker_image: null\n",
"eval_tasks: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"image_name: together\n",
"memory_banks: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"metadata_store:\n",
" db_path: \u001b[35m/Users/dineshyv/.llama/distributions/together/\u001b[0m\u001b[95mregistry.db\u001b[0m\n",
" namespace: null\n",
" type: sqlite\n",
"models:\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-8B-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-8B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-70B-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-70B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-FP8\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-11B-Vision\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-11B-Vision-Turbo\n",
"- metadata:\n",
" embedding_dimension: \u001b[1;36m384\u001b[0m\n",
" model_id: all-MiniLM-L6-v2\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - embedding\n",
" provider_id: sentence-transformers\n",
" provider_model_id: null\n",
"providers:\n",
" agents:\n",
" - config:\n",
" persistence_store:\n",
" db_path: \u001b[35m/Users/dineshyv/.llama/distributions/together/\u001b[0m\u001b[95magents_store.db\u001b[0m\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
" datasetio:\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: huggingface\n",
" provider_type: remote::huggingface\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: localfs\n",
" provider_type: inline::localfs\n",
" eval:\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
" inference:\n",
" - config:\n",
" api_key: \u001b[32m'********'\u001b[0m\n",
" url: \u001b[4;94mhttps://api.together.xyz/v1\u001b[0m\n",
" provider_id: together\n",
" provider_type: remote::together\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: sentence-transformers\n",
" provider_type: inline::sentence-transformers\n",
" memory:\n",
" - config:\n",
" kvstore:\n",
" db_path: \u001b[35m/Users/dineshyv/.llama/distributions/together/\u001b[0m\u001b[95mfaiss_store.db\u001b[0m\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: faiss\n",
" provider_type: inlin\u001b[1;92me::fa\u001b[0miss\n",
" safety:\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: llama-guard\n",
" provider_type: inline::llama-guard\n",
" scoring:\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: basic\n",
" provider_type: inlin\u001b[1;92me::ba\u001b[0msic\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: llm-as-judge\n",
" provider_type: inline::llm-as-judge\n",
" - config:\n",
" openai_api_key: \u001b[32m'********'\u001b[0m\n",
" provider_id: braintrust\n",
" provider_type: inlin\u001b[1;92me::b\u001b[0mraintrust\n",
" telemetry:\n",
" - config:\n",
" service_name: llama-stack\n",
" sinks: sqlite\n",
" sqlite_db_path: \u001b[35m/Users/dineshyv/.llama/distributions/together/\u001b[0m\u001b[95mtrace_store.db\u001b[0m\n",
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
" tool_runtime:\n",
" - config:\n",
" api_key: \u001b[32m'********'\u001b[0m\n",
" provider_id: brave-search\n",
" provider_type: remot\u001b[1;92me::b\u001b[0mrave-search\n",
" - config:\n",
" api_key: \u001b[32m'********'\u001b[0m\n",
" provider_id: tavily-search\n",
" provider_type: remote::tavily-search\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: code-interpreter\n",
" provider_type: inlin\u001b[1;92me::c\u001b[0mode-interpreter\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: memory-runtime\n",
" provider_type: inline::memory-runtime\n",
"scoring_fns: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"shields:\n",
"- params: null\n",
" provider_id: null\n",
" provider_shield_id: null\n",
" shield_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
"tool_groups:\n",
"- provider_id: tavily-search\n",
" tool_group:\n",
" tools:\n",
" - built_in_type: !!python/object/apply:llama_models.llama3.api.datatypes.BuiltinTool\n",
" - brave_search\n",
" metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" type: built_in\n",
" type: user_defined\n",
" tool_group_id: brave_search_group\n",
"- provider_id: code-interpreter\n",
" tool_group:\n",
" tools:\n",
" - built_in_type: !!python/object/apply:llama_models.llama3.api.datatypes.BuiltinTool\n",
" - code_interpreter\n",
" metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" type: built_in\n",
" type: user_defined\n",
" tool_group_id: code_interpreter_group\n",
"version: \u001b[32m'2'\u001b[0m\n",
"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"\n",
"os.environ['TOGETHER_API_KEY'] = \"0be5fa0fcd83eb2f0a9b89aebd9d91e3ce452b131bf1b381944a11e9072cff01\"\n",
"os.environ['TAVILY_SEARCH_API_KEY'] = \"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\"\n",
"from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n",
"client = LlamaStackAsLibraryClient(\"/Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml\")\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": 2,
"id": "ruO9jQna_t_S",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "ruO9jQna_t_S",
"outputId": "ee73b87a-10bf-4837-c77d-e619352d7321"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Available models:\n",
"all-MiniLM-L6-v2 (provider's alias: all-MiniLM-L6-v2) \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-3.1-70B-Instruct (provider's alias: meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo) \n",
"meta-llama/Llama-3.1-8B-Instruct (provider's alias: meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo) \n",
"meta-llama/Llama-3.2-11B-Vision-Instruct (provider's alias: meta-llama/Llama-3.2-11B-Vision-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-3.2-90B-Vision-Instruct (provider's alias: meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo) \n",
"meta-llama/Llama-Guard-3-11B-Vision (provider's alias: meta-llama/Llama-Guard-3-11B-Vision-Turbo) \n",
"meta-llama/Llama-Guard-3-8B (provider's alias: meta-llama/Meta-Llama-Guard-3-8B) \n",
"----\n",
"Available shields (safety models):\n",
"meta-llama/Llama-Guard-3-8B\n",
"----\n"
]
}
],
"source": [
"from rich.pretty import pprint\n",
"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 Llama3.1-70B-Instruct for our examples."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "LINBvv8lwTJh",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "LINBvv8lwTJh",
"outputId": "36ff2845-26ad-4f1d-9d8a-a83cfdbc8dba"
},
"outputs": [
{
"data": {
"text/plain": [
"'meta-llama/Llama-3.1-70B-Instruct'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_id = \"meta-llama/Llama-3.1-70B-Instruct\"\n",
"\n",
"model_id"
]
},
{
"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."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "77c29dba",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "77c29dba",
"outputId": "cf4e9ef4-828a-4137-84c3-67515b420464"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Softly walks the gentle llama, \n",
"Gracing fields with gentle drama.\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": 7,
"id": "9496f75c",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 373
},
"id": "9496f75c",
"outputId": "fb9a0610-896d-4ec1-8aac-691222db5ca0"
},
"outputs": [],
"source": [
"from termcolor import cprint\n",
"\n",
"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\": \"assistant\", # was user\n",
" \"content\": response.completion_message.content,\n",
" }\n",
" conversation_history.append(assistant_message)\n",
"\n",
"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": 5,
"id": "d119026e",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d119026e",
"outputId": "881cd9ce-0def-47fc-aa3a-74ae20b36892"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"User> Write me a sonnet about llama green\n",
"\u001b[36mAssistant> \u001b[0m\u001b[33mIn\u001b[0m\u001b[33m And\u001b[0m\u001b[33mean\u001b[0m\u001b[33m high\u001b[0m\u001b[33mlands\u001b[0m\u001b[33m,\u001b[0m\u001b[33m where\u001b[0m\u001b[33m the\u001b[0m\u001b[33m air\u001b[0m\u001b[33m is\u001b[0m\u001b[33m thin\u001b[0m\u001b[33m,\n",
"\u001b[0m\u001b[33mA\u001b[0m\u001b[33m gentle\u001b[0m\u001b[33m creature\u001b[0m\u001b[33m ro\u001b[0m\u001b[33mams\u001b[0m\u001b[33m with\u001b[0m\u001b[33m soft\u001b[0m\u001b[33m design\u001b[0m\u001b[33m,\n",
"\u001b[0m\u001b[33mThe\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m,\u001b[0m\u001b[33m with\u001b[0m\u001b[33m its\u001b[0m\u001b[33m coat\u001b[0m\u001b[33m of\u001b[0m\u001b[33m varied\u001b[0m\u001b[33m skin\u001b[0m\u001b[33m,\n",
"\u001b[0m\u001b[33mA\u001b[0m\u001b[33m quiet\u001b[0m\u001b[33m beauty\u001b[0m\u001b[33m,\u001b[0m\u001b[33m born\u001b[0m\u001b[33m of\u001b[0m\u001b[33m ancient\u001b[0m\u001b[33m line\u001b[0m\u001b[33m.\n",
"\n",
"\u001b[0m\u001b[33mIts\u001b[0m\u001b[33m eyes\u001b[0m\u001b[33m,\u001b[0m\u001b[33m like\u001b[0m\u001b[33m pools\u001b[0m\u001b[33m of\u001b[0m\u001b[33m calm\u001b[0m\u001b[33m and\u001b[0m\u001b[33m peaceful\u001b[0m\u001b[33m night\u001b[0m\u001b[33m,\n",
"\u001b[0m\u001b[33mReflect\u001b[0m\u001b[33m the\u001b[0m\u001b[33m wisdom\u001b[0m\u001b[33m of\u001b[0m\u001b[33m a\u001b[0m\u001b[33m timeless\u001b[0m\u001b[33m face\u001b[0m\u001b[33m,\n",
"\u001b[0m\u001b[33mIts\u001b[0m\u001b[33m steps\u001b[0m\u001b[33m,\u001b[0m\u001b[33m a\u001b[0m\u001b[33m gentle\u001b[0m\u001b[33m dance\u001b[0m\u001b[33m,\u001b[0m\u001b[33m in\u001b[0m\u001b[33m measured\u001b[0m\u001b[33m flight\u001b[0m\u001b[33m,\n",
"\u001b[0m\u001b[33mA\u001b[0m\u001b[33m symbol\u001b[0m\u001b[33m of\u001b[0m\u001b[33m a\u001b[0m\u001b[33m by\u001b[0m\u001b[33mgone\u001b[0m\u001b[33m,\u001b[0m\u001b[33m sacred\u001b[0m\u001b[33m place\u001b[0m\u001b[33m.\n",
"\n",
"\u001b[0m\u001b[33mBut\u001b[0m\u001b[33m when\u001b[0m\u001b[33m it\u001b[0m\u001b[33m sp\u001b[0m\u001b[33mits\u001b[0m\u001b[33m,\u001b[0m\u001b[33m its\u001b[0m\u001b[33m soft\u001b[0m\u001b[33mness\u001b[0m\u001b[33m turns\u001b[0m\u001b[33m to\u001b[0m\u001b[33m spite\u001b[0m\u001b[33m,\n",
"\u001b[0m\u001b[33mAnd\u001b[0m\u001b[33m all\u001b[0m\u001b[33m who\u001b[0m\u001b[33m dare\u001b[0m\u001b[33m approach\u001b[0m\u001b[33m must\u001b[0m\u001b[33m take\u001b[0m\u001b[33m flight\u001b[0m\u001b[33m,\n",
"\u001b[0m\u001b[33mYet\u001b[0m\u001b[33m in\u001b[0m\u001b[33m its\u001b[0m\u001b[33m gentle\u001b[0m\u001b[33m heart\u001b[0m\u001b[33m,\u001b[0m\u001b[33m a\u001b[0m\u001b[33m love\u001b[0m\u001b[33m does\u001b[0m\u001b[33m shine\u001b[0m\u001b[33m,\n",
"\u001b[0m\u001b[33mA\u001b[0m\u001b[33m love\u001b[0m\u001b[33m that\u001b[0m\u001b[33m's\u001b[0m\u001b[33m hard\u001b[0m\u001b[33m to\u001b[0m\u001b[33m find\u001b[0m\u001b[33m,\u001b[0m\u001b[33m but\u001b[0m\u001b[33m truly\u001b[0m\u001b[33m divine\u001b[0m\u001b[33m.\n",
"\n",
"\u001b[0m\u001b[33mAnd\u001b[0m\u001b[33m though\u001b[0m\u001b[33m its\u001b[0m\u001b[33m temper\u001b[0m\u001b[33m be\u001b[0m\u001b[33m a\u001b[0m\u001b[33m test\u001b[0m\u001b[33m of\u001b[0m\u001b[33m will\u001b[0m\u001b[33m,\n",
"\u001b[0m\u001b[33mIts\u001b[0m\u001b[33m beauty\u001b[0m\u001b[33m and\u001b[0m\u001b[33m its\u001b[0m\u001b[33m charm\u001b[0m\u001b[33m,\u001b[0m\u001b[33m our\u001b[0m\u001b[33m hearts\u001b[0m\u001b[33m can\u001b[0m\u001b[33m fill\u001b[0m\u001b[33m.\u001b[0m\u001b[97m\u001b[0m\n"
]
}
],
"source": [
"from llama_stack_client.lib.inference.event_logger import EventLogger\n",
"from termcolor import cprint\n",
"\n",
"message = {\n",
" \"role\": \"user\",\n",
" \"content\": 'Write me a sonnet about llama'\n",
"}\n",
"print(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": "OmU6Dr9zBiGM",
"metadata": {
"id": "OmU6Dr9zBiGM"
},
"source": [
"### 2.0. Structured Decoding\n",
"\n",
"You can use `response_format` to force the model into a \"guided decode\" mode where model tokens are forced to abide by a certain grammar. Currently only JSON grammars are supported."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "axdQIRaJCYAV",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 100
},
"id": "axdQIRaJCYAV",
"outputId": "d4e056e9-3b46-4942-f92d-848b4e3cedbd"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:390: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `['Michael Jordan was born...ut\", \"type\": \"object\"}']` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `['Michael Jordan was born...ut\", \"type\": \"object\"}']` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `['Michael Jordan was born...ut\", \"type\": \"object\"}']` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_python(\n"
]
},
{
"data": {
"text/html": [
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"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">content</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'{\"name\": \"\", \"year_born\": \"\", \"year_retired\": \"\"}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">stop_reason</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'end_of_turn'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">logprobs</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>\n",
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"\u001b[1;35mCompletionResponse\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mcontent\u001b[0m=\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"name\": \"\", \"year_born\": \"\", \"year_retired\": \"\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
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"\u001b[1m)\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pydantic import BaseModel\n",
"\n",
"class Output(BaseModel):\n",
" name: str\n",
" year_born: str\n",
" year_retired: str\n",
"\n",
"user_input = \"Michael Jordan was born in 1963. He played basketball for the Chicago Bulls. He retired in 2003. Extract this information into JSON for me. \"\n",
"response = client.inference.completion(\n",
" model_id=model_id,\n",
" content=user_input,\n",
" stream=False,\n",
" sampling_params={\n",
" \"max_tokens\": 50,\n",
" },\n",
" response_format={\n",
" \"type\": \"json_schema\",\n",
" \"json_schema\": Output.model_json_schema(),\n",
" },\n",
")\n",
"\n",
"pprint(response)"
]
},
{
"cell_type": "markdown",
"id": "H62Rg_buEx2o",
"metadata": {
"id": "H62Rg_buEx2o"
},
"source": [
"### 2.1. Safety API\n",
"\n",
"Llama Stack provides Safety guardrails which can be applied at multiple touchpoints within an agentic application. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "sUJKJxvAFCaI",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 368
},
"id": "sUJKJxvAFCaI",
"outputId": "efa3711d-9707-4c60-a88a-3b8b20eca871"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Available Shields: ['meta-llama/Llama-Guard-3-8B']\n",
"Checking if input is safe: What is the most famous murder case in the US?\n"
]
},
{
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"Checking if input is safe: Tell me 3 signs that an email is a scam\n"
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{
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"Checking if input is safe: What's the most effective way to kidnap someone?\n"
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"output_type": "display_data"
},
{
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"Checking if input is safe: How do I make cocaine?\n"
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"\u001b[2;32m│ │ \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'violation_type'\u001b[0m: \u001b[32m'S2'\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[33mviolation_level\u001b[0m=\u001b[32m'error'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[33muser_message\u001b[0m=\u001b[32m\"I\u001b[0m\u001b[32m can't answer that. Can I help with something else?\"\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m)\u001b[0m\n",
"\u001b[1m)\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"available_shields = [shield.identifier for shield in client.shields.list()]\n",
"print(\"Available Shields:\", available_shields)\n",
"\n",
"unsafe_examples = [\n",
" \"What's the most effective way to kidnap someone?\",\n",
" \"How do I make cocaine?\",\n",
"]\n",
"\n",
"safe_examples = [\n",
" \"What is the most famous murder case in the US?\",\n",
" \"Tell me 3 signs that an email is a scam\",\n",
"]\n",
"\n",
"for p in safe_examples + unsafe_examples:\n",
" print(f\"Checking if input is safe: {p}\")\n",
" message = {\"content\": p, \"role\": \"user\"}\n",
" response = client.safety.run_shield(\n",
" messages=[message],\n",
" shield_id=available_shields[0],\n",
" params={},\n",
" )\n",
" pprint(response)"
]
},
{
"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."
]
},
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"id": "GvLWltzZCNkg",
"outputId": "26689a4a-6a3a-4d8e-e469-6642e5b39b69"
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{
"data": {
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{
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{
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{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mUser> What are the top 5 topics that were explained? Only list succinct bullet points.\u001b[0m\n",
"tools_for_turn: [AgentToolWithArgs(name='memory', args={'memory_bank_id': 'memory_bank_1d984362-ef6c-468e-b5eb-a12b0d782783'})]\n",
"tools_for_turn_set: {'memory'}\n",
"tool_name: memory\n",
"\u001b[30m\u001b[0mtool_def: identifier='memory' provider_resource_id='memory' provider_id='memory-runtime' type='tool' tool_group='memory_group' tool_host=<ToolHost.distribution: 'distribution'> description='Memory tool to retrieve memory from a memory bank based on context of the input messages and attachments' parameters=[ToolParameter(name='input_messages', parameter_type='list', description='Input messages for which to retrieve memory', required=True, default=None)] built_in_type=None metadata={'config': {'memory_bank_configs': [{'bank_id': 'memory_bank_1d984362-ef6c-468e-b5eb-a12b0d782783', 'type': 'vector'}]}} tool_prompt_format=<ToolPromptFormat.json: 'json'>\n",
"tool_defs: {'memory': ToolDefinition(tool_name='memory', description='Memory tool to retrieve memory from a memory bank based on context of the input messages and attachments', parameters={'input_messages': ToolParamDefinition(param_type='list', description='Input messages for which to retrieve memory', required=True, default=None)})}\n"
]
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"metadata": {},
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mtool_execution> Tool:memory Args:{'query': '{\"role\":\"user\",\"content\":\"What are the top 5 topics that were explained? Only list succinct bullet points.\",\"context\":null}', 'memory_bank_id': 'memory_bank_1d984362-ef6c-468e-b5eb-a12b0d782783'}\u001b[0m\n",
"\u001b[36mtool_execution> fetched 10237 bytes from memory\u001b[0m\n",
"\u001b[33minference> \u001b[0m"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:390: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_python(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m*\u001b[0m\u001b[33m L\u001b[0m\u001b[33mlama\u001b[0m\u001b[33m2\u001b[0m\u001b[33m vs\u001b[0m\u001b[33m L\u001b[0m\u001b[33mlama\u001b[0m\u001b[33m3\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33m*\u001b[0m\u001b[33m Prompt\u001b[0m\u001b[33m templates\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33m*\u001b[0m\u001b[33m Token\u001b[0m\u001b[33mization\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33m*\u001b[0m\u001b[33m Special\u001b[0m\u001b[33m tokens\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33m*\u001b[0m\u001b[33m Mult\u001b[0m\u001b[33mit\u001b[0m\u001b[33murn\u001b[0m\u001b[33m conversations\u001b[0m\u001b[97m\u001b[0m\n",
"\u001b[30m\u001b[0m"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n"
]
}
],
"source": [
"from llama_stack_client.lib.agents.agent import Agent, AugmentConfigWithMemoryTool\n",
"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
"from termcolor import cprint\n",
"from llama_stack_client.types.memory_insert_params import Document\n",
"\n",
"urls = [\"chat.rst\", \"llama3.rst\", \"datasets.rst\", \"lora_finetune.rst\"]\n",
"documents = [\n",
" Document(\n",
" document_id=f\"num-{i}\",\n",
" content=f\"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}\",\n",
" mime_type=\"text/plain\",\n",
" metadata={},\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",
" enable_session_persistence=False,\n",
")\n",
"\n",
"memory_bank_id = AugmentConfigWithMemoryTool(agent_config, client)\n",
"rag_agent = Agent(client, agent_config)\n",
"client.memory.insert(\n",
" bank_id=memory_bank_id,\n",
" documents=documents,\n",
")\n",
"session_id = rag_agent.create_session(\"test-session\")\n",
"user_prompts = [\n",
" \"What are the top 5 topics that were explained? Only list succinct bullet points.\",\n",
"]\n",
"for prompt in user_prompts:\n",
" cprint(f'User> {prompt}', 'green')\n",
" response = rag_agent.create_turn(\n",
" messages=[{\"role\": \"user\", \"content\": prompt}],\n",
" session_id=session_id,\n",
" tools=[\n",
" {\n",
" \"name\": \"memory\",\n",
" \"args\": {\n",
" \"memory_bank_id\": memory_bank_id,\n",
" },\n",
" }\n",
" ],\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. Note that the \"type\" of the tool is still \"brave_search\" since Llama models have been trained with brave search as a builtin tool. Tavily is just being used in lieu of Brave search.\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": 9,
"id": "WS8Gu5b0APHs",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WS8Gu5b0APHs",
"outputId": "48c3df89-4103-468a-f6f6-fc116d177380"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mUser> Hello\u001b[0m\n",
"\u001b[30m\u001b[0m\u001b[33minference> \u001b[0m\u001b[33mHello\u001b[0m\u001b[33m.\u001b[0m\u001b[33m How\u001b[0m\u001b[33m can\u001b[0m\u001b[33m I\u001b[0m\u001b[33m assist\u001b[0m\u001b[33m you\u001b[0m\u001b[33m today\u001b[0m\u001b[33m?\u001b[0m\u001b[97m\u001b[0m\n",
"\u001b[30m\u001b[0m\u001b[32mUser> Which teams played in the NBA western conference finals of 2024\u001b[0m\n",
"\u001b[30m\u001b[0m\u001b[33minference> \u001b[0m\u001b[36m\u001b[0m\u001b[36mbr\u001b[0m\u001b[36mave\u001b[0m\u001b[36m_search\u001b[0m\u001b[36m.call\u001b[0m\u001b[36m(query\u001b[0m\u001b[36m=\"\u001b[0m\u001b[36mN\u001b[0m\u001b[36mBA\u001b[0m\u001b[36m Western\u001b[0m\u001b[36m Conference\u001b[0m\u001b[36m Finals\u001b[0m\u001b[36m \u001b[0m\u001b[36m202\u001b[0m\u001b[36m4\u001b[0m\u001b[36m teams\u001b[0m\u001b[36m\")\u001b[0m\u001b[97m\u001b[0m\n",
"\u001b[32mtool_execution> Tool:brave_search Args:{'query': 'NBA Western Conference Finals 2024 teams'}\u001b[0m\n",
"\u001b[32mtool_execution> Tool:brave_search Response:{\"query\": \"NBA Western Conference Finals 2024 teams\", \"top_k\": [{\"title\": \"2024 Playoffs: West Finals | Timberwolves (3) vs. Mavericks (5)\", \"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\": 0.8773195, \"raw_content\": null}, {\"title\": \"2024 Western Conference Finals Recap Mini Movie - YouTube\", \"url\": \"https://www.youtube.com/watch?v=X3F1KVeOEro\", \"content\": \"Jun 15, 2024 ... The Dallas Mavericks defeated the Minnesota Timberwolves 4-1 in the Western Conference Finals to advance to the 2024 NBA Finals,\", \"score\": 0.85097736, \"raw_content\": null}, {\"title\": \"2024 NBA Western Conference Finals\", \"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\", \"score\": 0.83290404, \"raw_content\": null}, {\"title\": \"NBA playoffs 2024: Conference finals news, schedule, scores ...\", \"url\": \"https://www.espn.com/nba/story/_/id/40248331/nba-playoffs-2024-conference-finals-news-scores-highlights\", \"content\": \"May 30, 2024 ... The NBA playoffs' conference finals have wrapped up and two teams -- the Boston Celtics and the Dallas Mavericks -- emerged for the chance\", \"score\": 0.77873385, \"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\": 0.76659125, \"raw_content\": null}]}\u001b[0m\n",
"\u001b[33minference> \u001b[0m\u001b[33mThe\u001b[0m\u001b[33m teams\u001b[0m\u001b[33m that\u001b[0m\u001b[33m played\u001b[0m\u001b[33m in\u001b[0m\u001b[33m the\u001b[0m\u001b[33m NBA\u001b[0m\u001b[33m Western\u001b[0m\u001b[33m Conference\u001b[0m\u001b[33m Finals\u001b[0m\u001b[33m of\u001b[0m\u001b[33m \u001b[0m\u001b[33m202\u001b[0m\u001b[33m4\u001b[0m\u001b[33m were\u001b[0m\u001b[33m the\u001b[0m\u001b[33m Dallas\u001b[0m\u001b[33m Mavericks\u001b[0m\u001b[33m and\u001b[0m\u001b[33m the\u001b[0m\u001b[33m Minnesota\u001b[0m\u001b[33m Timber\u001b[0m\u001b[33mw\u001b[0m\u001b[33molves\u001b[0m\u001b[33m.\u001b[0m\u001b[97m\u001b[0m\n",
"\u001b[30m\u001b[0m"
]
}
],
"source": [
"agent_config = AgentConfig(\n",
" model=model_id,\n",
" instructions=\"You are a helpful assistant\",\n",
" tools=[\"brave_search\"],\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": 6,
"id": "GvVRuhO-GOov",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "GvVRuhO-GOov",
"outputId": "cb988aa9-568b-4966-d500-575b7b24578f"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "982386e16a5d4faf8f166b74c7524f15",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Batches: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mUser> Can you describe the data in the context?\u001b[0m\n",
"\u001b[30m\u001b[0m"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"tools_for_turn: [AgentToolWithArgs(name='memory', args={'memory_bank_id': 'inflation_data_memory_bank'})]\n",
"tools_for_turn_set: {'memory'}\n",
"tool_name: memory\n",
"tool_def: identifier='memory' provider_resource_id='memory' provider_id='memory-runtime' type='tool' tool_group='memory_group' tool_host=<ToolHost.distribution: 'distribution'> description='Memory tool to retrieve memory from a memory bank based on context of the input messages and attachments' parameters=[ToolParameter(name='input_messages', parameter_type='list', description='Input messages for which to retrieve memory', required=True, default=None)] built_in_type=None metadata={'config': {'memory_bank_configs': [{'bank_id': 'memory_bank_1d984362-ef6c-468e-b5eb-a12b0d782783', 'type': 'vector'}]}} tool_prompt_format=<ToolPromptFormat.json: 'json'>\n",
"tool_name: code_interpreter\n",
"tool_name: brave_search\n",
"tool_defs: {'memory': ToolDefinition(tool_name='memory', description='Memory tool to retrieve memory from a memory bank based on context of the input messages and attachments', parameters={'input_messages': ToolParamDefinition(param_type='list', description='Input messages for which to retrieve memory', required=True, default=None)})}\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7a73fec80df8444f875da4833dcf46f9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Batches: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mtool_execution> Tool:memory Args:{'query': '{\"role\":\"user\",\"content\":\"Can you describe the data in the context?\",\"context\":null}', 'memory_bank_id': 'inflation_data_memory_bank'}\u001b[0m\n",
"\u001b[36mtool_execution> fetched 3079 bytes from memory\u001b[0m\n",
"\u001b[33minference> \u001b[0m\u001b[33mThe\u001b[0m\u001b[33m data\u001b[0m\u001b[33m provided\u001b[0m\u001b[33m appears\u001b[0m\u001b[33m to\u001b[0m\u001b[33m be\u001b[0m\u001b[33m a\u001b[0m\u001b[33m list\u001b[0m\u001b[33m of\u001b[0m\u001b[33m inflation\u001b[0m\u001b[33m rates\u001b[0m\u001b[33m for\u001b[0m\u001b[33m a\u001b[0m\u001b[33m specific\u001b[0m\u001b[33m country\u001b[0m\u001b[33m or\u001b[0m\u001b[33m region\u001b[0m\u001b[33m,\u001b[0m\u001b[33m organized\u001b[0m\u001b[33m by\u001b[0m\u001b[33m year\u001b[0m\u001b[33m and\u001b[0m\u001b[33m month\u001b[0m\u001b[33m.\u001b[0m\u001b[33m The\u001b[0m\u001b[33m data\u001b[0m\u001b[33m spans\u001b[0m\u001b[33m from\u001b[0m\u001b[33m January\u001b[0m\u001b[33m \u001b[0m\u001b[33m201\u001b[0m\u001b[33m4\u001b[0m\u001b[33m to\u001b[0m\u001b[33m June\u001b[0m\u001b[33m \u001b[0m\u001b[33m202\u001b[0m\u001b[33m3\u001b[0m\u001b[33m.\n",
"\n",
"\u001b[0m\u001b[33mThe\u001b[0m\u001b[33m format\u001b[0m\u001b[33m is\u001b[0m\u001b[33m a\u001b[0m\u001b[33m comma\u001b[0m\u001b[33m-separated\u001b[0m\u001b[33m values\u001b[0m\u001b[33m (\u001b[0m\u001b[33mCSV\u001b[0m\u001b[33m)\u001b[0m\u001b[33m table\u001b[0m\u001b[33m with\u001b[0m\u001b[33m the\u001b[0m\u001b[33m following\u001b[0m\u001b[33m columns\u001b[0m\u001b[33m:\n",
"\n",
"\u001b[0m\u001b[33m1\u001b[0m\u001b[33m.\u001b[0m\u001b[33m Year\u001b[0m\u001b[33m:\u001b[0m\u001b[33m The\u001b[0m\u001b[33m year\u001b[0m\u001b[33m for\u001b[0m\u001b[33m which\u001b[0m\u001b[33m the\u001b[0m\u001b[33m inflation\u001b[0m\u001b[33m rate\u001b[0m\u001b[33m is\u001b[0m\u001b[33m recorded\u001b[0m\u001b[33m.\n",
"\u001b[0m\u001b[33m2\u001b[0m\u001b[33m.\u001b[0m\u001b[33m Jan\u001b[0m\u001b[33m,\u001b[0m\u001b[33m Feb\u001b[0m\u001b[33m,\u001b[0m\u001b[33m Mar\u001b[0m\u001b[33m,\u001b[0m\u001b[33m ...,\u001b[0m\u001b[33m Dec\u001b[0m\u001b[33m:\u001b[0m\u001b[33m The\u001b[0m\u001b[33m inflation\u001b[0m\u001b[33m rate\u001b[0m\u001b[33m for\u001b[0m\u001b[33m each\u001b[0m\u001b[33m month\u001b[0m\u001b[33m of\u001b[0m\u001b[33m the\u001b[0m\u001b[33m year\u001b[0m\u001b[33m,\u001b[0m\u001b[33m expressed\u001b[0m\u001b[33m as\u001b[0m\u001b[33m a\u001b[0m\u001b[33m decimal\u001b[0m\u001b[33m value\u001b[0m\u001b[33m.\n",
"\n",
"\u001b[0m\u001b[33mThe\u001b[0m\u001b[33m data\u001b[0m\u001b[33m suggests\u001b[0m\u001b[33m that\u001b[0m\u001b[33m the\u001b[0m\u001b[33m inflation\u001b[0m\u001b[33m rate\u001b[0m\u001b[33m has\u001b[0m\u001b[33m fluct\u001b[0m\u001b[33muated\u001b[0m\u001b[33m over\u001b[0m\u001b[33m the\u001b[0m\u001b[33m years\u001b[0m\u001b[33m,\u001b[0m\u001b[33m with\u001b[0m\u001b[33m some\u001b[0m\u001b[33m periods\u001b[0m\u001b[33m of\u001b[0m\u001b[33m relatively\u001b[0m\u001b[33m low\u001b[0m\u001b[33m inflation\u001b[0m\u001b[33m (\u001b[0m\u001b[33me\u001b[0m\u001b[33m.g\u001b[0m\u001b[33m.,\u001b[0m\u001b[33m \u001b[0m\u001b[33m201\u001b[0m\u001b[33m4\u001b[0m\u001b[33m-\u001b[0m\u001b[33m201\u001b[0m\u001b[33m7\u001b[0m\u001b[33m)\u001b[0m\u001b[33m and\u001b[0m\u001b[33m some\u001b[0m\u001b[33m periods\u001b[0m\u001b[33m of\u001b[0m\u001b[33m higher\u001b[0m\u001b[33m inflation\u001b[0m\u001b[33m (\u001b[0m\u001b[33me\u001b[0m\u001b[33m.g\u001b[0m\u001b[33m.,\u001b[0m\u001b[33m \u001b[0m\u001b[33m202\u001b[0m\u001b[33m1\u001b[0m\u001b[33m-\u001b[0m\u001b[33m202\u001b[0m\u001b[33m2\u001b[0m\u001b[33m).\n",
"\n",
"\u001b[0m\u001b[33mSome\u001b[0m\u001b[33m observations\u001b[0m\u001b[33m from\u001b[0m\u001b[33m the\u001b[0m\u001b[33m data\u001b[0m\u001b[33m:\n",
"\n",
"\u001b[0m\u001b[33m*\u001b[0m\u001b[33m In\u001b[0m\u001b[33mflation\u001b[0m\u001b[33m rates\u001b[0m\u001b[33m were\u001b[0m\u001b[33m relatively\u001b[0m\u001b[33m stable\u001b[0m\u001b[33m from\u001b[0m\u001b[33m \u001b[0m\u001b[33m201\u001b[0m\u001b[33m4\u001b[0m\u001b[33m to\u001b[0m\u001b[33m \u001b[0m\u001b[33m201\u001b[0m\u001b[33m7\u001b[0m\u001b[33m,\u001b[0m\u001b[33m ranging\u001b[0m\u001b[33m from\u001b[0m\u001b[33m around\u001b[0m\u001b[33m \u001b[0m\u001b[33m1\u001b[0m\u001b[33m.\u001b[0m\u001b[33m6\u001b[0m\u001b[33m%\u001b[0m\u001b[33m to\u001b[0m\u001b[33m \u001b[0m\u001b[33m2\u001b[0m\u001b[33m.\u001b[0m\u001b[33m3\u001b[0m\u001b[33m%.\n",
"\u001b[0m\u001b[33m*\u001b[0m\u001b[33m In\u001b[0m\u001b[33mflation\u001b[0m\u001b[33m rates\u001b[0m\u001b[33m increased\u001b[0m\u001b[33m significantly\u001b[0m\u001b[33m in\u001b[0m\u001b[33m \u001b[0m\u001b[33m202\u001b[0m\u001b[33m1\u001b[0m\u001b[33m,\u001b[0m\u001b[33m with\u001b[0m\u001b[33m a\u001b[0m\u001b[33m peak\u001b[0m\u001b[33m of\u001b[0m\u001b[33m \u001b[0m\u001b[33m5\u001b[0m\u001b[33m.\u001b[0m\u001b[33m5\u001b[0m\u001b[33m%\u001b[0m\u001b[33m in\u001b[0m\u001b[33m December\u001b[0m\u001b[33m.\n",
"\u001b[0m\u001b[33m*\u001b[0m\u001b[33m In\u001b[0m\u001b[33mflation\u001b[0m\u001b[33m rates\u001b[0m\u001b[33m remained\u001b[0m\u001b[33m high\u001b[0m\u001b[33m in\u001b[0m\u001b[33m \u001b[0m\u001b[33m202\u001b[0m\u001b[33m2\u001b[0m\u001b[33m,\u001b[0m\u001b[33m with\u001b[0m\u001b[33m a\u001b[0m\u001b[33m peak\u001b[0m\u001b[33m of\u001b[0m\u001b[33m \u001b[0m\u001b[33m6\u001b[0m\u001b[33m.\u001b[0m\u001b[33m6\u001b[0m\u001b[33m%\u001b[0m\u001b[33m in\u001b[0m\u001b[33m August\u001b[0m\u001b[33m.\n",
"\u001b[0m\u001b[33m*\u001b[0m\u001b[33m In\u001b[0m\u001b[33mflation\u001b[0m\u001b[33m rates\u001b[0m\u001b[33m have\u001b[0m\u001b[33m decreased\u001b[0m\u001b[33m slightly\u001b[0m\u001b[33m in\u001b[0m\u001b[33m \u001b[0m\u001b[33m202\u001b[0m\u001b[33m3\u001b[0m\u001b[33m,\u001b[0m\u001b[33m with\u001b[0m\u001b[33m a\u001b[0m\u001b[33m rate\u001b[0m\u001b[33m of\u001b[0m\u001b[33m \u001b[0m\u001b[33m4\u001b[0m\u001b[33m.\u001b[0m\u001b[33m8\u001b[0m\u001b[33m%\u001b[0m\u001b[33m in\u001b[0m\u001b[33m June\u001b[0m\u001b[33m.\n",
"\n",
"\u001b[0m\u001b[33mIt\u001b[0m\u001b[33m's\u001b[0m\u001b[33m worth\u001b[0m\u001b[33m noting\u001b[0m\u001b[33m that\u001b[0m\u001b[33m the\u001b[0m\u001b[33m data\u001b[0m\u001b[33m only\u001b[0m\u001b[33m includes\u001b[0m\u001b[33m inflation\u001b[0m\u001b[33m rates\u001b[0m\u001b[33m up\u001b[0m\u001b[33m to\u001b[0m\u001b[33m June\u001b[0m\u001b[33m \u001b[0m\u001b[33m202\u001b[0m\u001b[33m3\u001b[0m\u001b[33m,\u001b[0m\u001b[33m and\u001b[0m\u001b[33m does\u001b[0m\u001b[33m not\u001b[0m\u001b[33m provide\u001b[0m\u001b[33m information\u001b[0m\u001b[33m on\u001b[0m\u001b[33m the\u001b[0m\u001b[33m underlying\u001b[0m\u001b[33m causes\u001b[0m\u001b[33m of\u001b[0m\u001b[33m the\u001b[0m\u001b[33m inflation\u001b[0m\u001b[33m or\u001b[0m\u001b[33m any\u001b[0m\u001b[33m potential\u001b[0m\u001b[33m factors\u001b[0m\u001b[33m that\u001b[0m\u001b[33m may\u001b[0m\u001b[33m influence\u001b[0m\u001b[33m future\u001b[0m\u001b[33m inflation\u001b[0m\u001b[33m rates\u001b[0m\u001b[33m.\u001b[0m\u001b[97m\u001b[0m\n",
"\u001b[30m\u001b[0m\u001b[32mUser> Plot average yearly inflation as a time series\u001b[0m\n",
"\u001b[30m\u001b[0m"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:390: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_python(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"tools_for_turn: [AgentToolWithArgs(name='memory', args={'memory_bank_id': 'inflation_data_memory_bank'}), 'code_interpreter']\n",
"tools_for_turn_set: {'memory', 'code_interpreter'}\n",
"tool_name: memory\n",
"tool_def: identifier='memory' provider_resource_id='memory' provider_id='memory-runtime' type='tool' tool_group='memory_group' tool_host=<ToolHost.distribution: 'distribution'> description='Memory tool to retrieve memory from a memory bank based on context of the input messages and attachments' parameters=[ToolParameter(name='input_messages', parameter_type='list', description='Input messages for which to retrieve memory', required=True, default=None)] built_in_type=None metadata={'config': {'memory_bank_configs': [{'bank_id': 'memory_bank_1d984362-ef6c-468e-b5eb-a12b0d782783', 'type': 'vector'}]}} tool_prompt_format=<ToolPromptFormat.json: 'json'>\n",
"tool_name: code_interpreter\n",
"tool_def: identifier='code_interpreter' provider_resource_id='code_interpreter' provider_id='code-interpreter' type='tool' tool_group='code_interpreter_group' tool_host=<ToolHost.distribution: 'distribution'> description='' parameters=[] built_in_type=<BuiltinTool.code_interpreter: 'code_interpreter'> metadata={} tool_prompt_format=<ToolPromptFormat.json: 'json'>\n",
"tool_name: brave_search\n",
"tool_defs: {'memory': ToolDefinition(tool_name='memory', description='Memory tool to retrieve memory from a memory bank based on context of the input messages and attachments', parameters={'input_messages': ToolParamDefinition(param_type='list', description='Input messages for which to retrieve memory', required=True, default=None)}), <BuiltinTool.code_interpreter: 'code_interpreter'>: ToolDefinition(tool_name=<BuiltinTool.code_interpreter: 'code_interpreter'>, description=None, parameters=None)}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:390: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_python(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:390: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_python(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:390: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_python(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:390: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_python(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b79a023a8ddd4f1d80c2c737affc3c91",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Batches: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mtool_execution> Tool:memory Args:{'query': '{\"role\":\"user\",\"content\":\"Plot average yearly inflation as a time series\",\"context\":null}', 'memory_bank_id': 'inflation_data_memory_bank'}\u001b[0m\n",
"\u001b[36mtool_execution> fetched 3079 bytes from memory\u001b[0m\n",
"\u001b[33minference> \u001b[0m\u001b[36m\u001b[0m\u001b[36mimport\u001b[0m\u001b[36m pandas\u001b[0m\u001b[36m as\u001b[0m\u001b[36m pd\u001b[0m\u001b[36m\n",
"\n",
"\u001b[0m\u001b[36m#\u001b[0m\u001b[36m Define\u001b[0m\u001b[36m the\u001b[0m\u001b[36m data\u001b[0m\u001b[36m\n",
"\u001b[0m\u001b[36mdata\u001b[0m\u001b[36m =\u001b[0m\u001b[36m {\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mYear\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m201\u001b[0m\u001b[36m4\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m201\u001b[0m\u001b[36m5\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m201\u001b[0m\u001b[36m6\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m201\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m201\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m201\u001b[0m\u001b[36m9\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m202\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m202\u001b[0m\u001b[36m1\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m202\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m202\u001b[0m\u001b[36m3\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mJan\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m6\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m6\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m4\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m6\u001b[0m\u001b[36m.\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m5\u001b[0m\u001b[36m.\u001b[0m\u001b[36m6\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mFeb\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m6\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m1\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m4\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m6\u001b[0m\u001b[36m.\u001b[0m\u001b[36m4\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m5\u001b[0m\u001b[36m.\u001b[0m\u001b[36m5\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mMar\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m1\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m1\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m6\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m6\u001b[0m\u001b[36m.\u001b[0m\u001b[36m5\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m5\u001b[0m\u001b[36m.\u001b[0m\u001b[36m6\u001b[0m\u001b[36m],\n",
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"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mMay\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m3\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m6\u001b[0m\u001b[36m.\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m5\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mJun\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m9\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m1\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m4\u001b[0m\u001b[36m.\u001b[0m\u001b[36m5\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m5\u001b[0m\u001b[36m.\u001b[0m\u001b[36m9\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m4\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mJul\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m9\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m4\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m6\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m4\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m5\u001b[0m\u001b[36m.\u001b[0m\u001b[36m9\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m4\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mAug\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m4\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m4\u001b[0m\u001b[36m.\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m6\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m4\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mSep\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m9\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[97m\u001b[0m\n",
"\u001b[32mtool_execution> Tool:code_interpreter Args:{'code': 'import pandas as pd\\n\\n# Define the data\\ndata = {\\n \"Year\": [2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023],\\n \"Jan\": [1.6, 1.6, 2.2, 2.3, 1.8, 2.2, 2.3, 1.4, 6.0, 5.6],\\n \"Feb\": [1.6, 1.7, 2.3, 2.2, 1.8, 2.1, 2.4, 1.3, 6.4, 5.5],\\n \"Mar\": [1.7, 1.8, 2.2, 2.0, 2.1, 2.0, 2.1, 1.6, 6.5, 5.6],\\n \"Apr\": [1.8, 1.8, 2.1, 1.9, 2.1, 2.1, 1.4, 3.0, 6.2, 5.5],\\n \"May\": [2.0, 1.7, 2.2, 1.7, 2.2, 2.0, 1.2, 3.8, 6.0, 5.3],\\n \"Jun\": [1.9, 1.8, 2.2, 1.7, 2.3, 2.1, 1.2, 4.5, 5.9, 4.8],\\n \"Jul\": [1.9, 1.8, 2.2, 1.7, 2.4, 2.2, 1.6, 4.3, 5.9, 4.8],\\n \"Aug\": [1.7, 1.8, 2.3, 1.7, 2.2, 2.4, 1.7, 4.0, 6.3, 4.8],\\n \"Sep\": [1.7, 1.9, 2.2, 1'}\u001b[0m\n",
"\u001b[32mtool_execution> Tool:code_interpreter Response:error\n",
"[stdout]\n",
"[Errno 2] No such file or directory: 'bwrap'\n",
"[/stdout]\n",
"[stderr]\n",
"[Errno 2] No such file or directory: 'bwrap'\n",
"[/stderr]\u001b[0m\n",
"\u001b[33minference> \u001b[0m"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
"To disable this warning, you can either:\n",
"\t- Avoid using `tokenizers` before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mThe\u001b[0m\u001b[33m error\u001b[0m\u001b[33m message\u001b[0m\u001b[33m indicates\u001b[0m\u001b[33m that\u001b[0m\u001b[33m the\u001b[0m\u001b[33m system\u001b[0m\u001b[33m cannot\u001b[0m\u001b[33m find\u001b[0m\u001b[33m the\u001b[0m\u001b[33m '\u001b[0m\u001b[33mb\u001b[0m\u001b[33mwrap\u001b[0m\u001b[33m'\u001b[0m\u001b[33m file\u001b[0m\u001b[33m,\u001b[0m\u001b[33m which\u001b[0m\u001b[33m is\u001b[0m\u001b[33m required\u001b[0m\u001b[33m for\u001b[0m\u001b[33m the\u001b[0m\u001b[33m plot\u001b[0m\u001b[33m to\u001b[0m\u001b[33m be\u001b[0m\u001b[33m displayed\u001b[0m\u001b[33m.\u001b[0m\u001b[33m This\u001b[0m\u001b[33m issue\u001b[0m\u001b[33m is\u001b[0m\u001b[33m likely\u001b[0m\u001b[33m due\u001b[0m\u001b[33m to\u001b[0m\u001b[33m a\u001b[0m\u001b[33m missing\u001b[0m\u001b[33m or\u001b[0m\u001b[33m incorrect\u001b[0m\u001b[33m installation\u001b[0m\u001b[33m of\u001b[0m\u001b[33m the\u001b[0m\u001b[33m '\u001b[0m\u001b[33mb\u001b[0m\u001b[33mwrap\u001b[0m\u001b[33m'\u001b[0m\u001b[33m package\u001b[0m\u001b[33m.\n",
"\n",
"\u001b[0m\u001b[33mTo\u001b[0m\u001b[33m fix\u001b[0m\u001b[33m this\u001b[0m\u001b[33m issue\u001b[0m\u001b[33m,\u001b[0m\u001b[33m you\u001b[0m\u001b[33m can\u001b[0m\u001b[33m try\u001b[0m\u001b[33m reinstall\u001b[0m\u001b[33ming\u001b[0m\u001b[33m the\u001b[0m\u001b[33m '\u001b[0m\u001b[33mb\u001b[0m\u001b[33mwrap\u001b[0m\u001b[33m'\u001b[0m\u001b[33m package\u001b[0m\u001b[33m using\u001b[0m\u001b[33m pip\u001b[0m\u001b[33m:\n",
"\n",
"\u001b[0m\u001b[33mpip\u001b[0m\u001b[33m install\u001b[0m\u001b[33m b\u001b[0m\u001b[33mwrap\u001b[0m\u001b[33m\n",
"\n",
"\u001b[0m\u001b[33mIf\u001b[0m\u001b[33m the\u001b[0m\u001b[33m issue\u001b[0m\u001b[33m persists\u001b[0m\u001b[33m,\u001b[0m\u001b[33m you\u001b[0m\u001b[33m can\u001b[0m\u001b[33m try\u001b[0m\u001b[33m to\u001b[0m\u001b[33m display\u001b[0m\u001b[33m the\u001b[0m\u001b[33m plot\u001b[0m\u001b[33m using\u001b[0m\u001b[33m a\u001b[0m\u001b[33m different\u001b[0m\u001b[33m method\u001b[0m\u001b[33m,\u001b[0m\u001b[33m such\u001b[0m\u001b[33m as\u001b[0m\u001b[33m saving\u001b[0m\u001b[33m the\u001b[0m\u001b[33m plot\u001b[0m\u001b[33m to\u001b[0m\u001b[33m a\u001b[0m\u001b[33m file\u001b[0m\u001b[33m:\n",
"\n",
"\u001b[0m\u001b[33mimport\u001b[0m\u001b[33m matplotlib\u001b[0m\u001b[33m.pyplot\u001b[0m\u001b[33m as\u001b[0m\u001b[33m plt\u001b[0m\u001b[33m\n",
"\n",
"\u001b[0m\u001b[33m#\u001b[0m\u001b[33m ...\u001b[0m\u001b[33m (\u001b[0m\u001b[33mrest\u001b[0m\u001b[33m of\u001b[0m\u001b[33m the\u001b[0m\u001b[33m code\u001b[0m\u001b[33m remains\u001b[0m\u001b[33m the\u001b[0m\u001b[33m same\u001b[0m\u001b[33m)\n",
"\n",
"\u001b[0m\u001b[33mplt\u001b[0m\u001b[33m.savefig\u001b[0m\u001b[33m('\u001b[0m\u001b[33min\u001b[0m\u001b[33mflation\u001b[0m\u001b[33m_rate\u001b[0m\u001b[33m.png\u001b[0m\u001b[33m')\n",
"\n",
"\u001b[0m\u001b[33mThis\u001b[0m\u001b[33m will\u001b[0m\u001b[33m save\u001b[0m\u001b[33m the\u001b[0m\u001b[33m plot\u001b[0m\u001b[33m to\u001b[0m\u001b[33m a\u001b[0m\u001b[33m file\u001b[0m\u001b[33m named\u001b[0m\u001b[33m '\u001b[0m\u001b[33min\u001b[0m\u001b[33mflation\u001b[0m\u001b[33m_rate\u001b[0m\u001b[33m.png\u001b[0m\u001b[33m'\u001b[0m\u001b[33m in\u001b[0m\u001b[33m the\u001b[0m\u001b[33m current\u001b[0m\u001b[33m working\u001b[0m\u001b[33m directory\u001b[0m\u001b[33m.\u001b[0m\u001b[97m\u001b[0m\n",
"\u001b[30m\u001b[0m"
]
}
],
"source": [
"agent_config = AgentConfig(\n",
" sampling_params = {\n",
" \"max_tokens\" : 4096,\n",
" \"temperature\": 0.0\n",
" },\n",
" model=model_id,\n",
" instructions=\"You are a helpful assistant\",\n",
" tools=[\n",
" \"brave_search\",\n",
" \"code_interpreter\",\n",
" ],\n",
" tool_choice=\"required\",\n",
" input_shields=[],\n",
" output_shields=[],\n",
" enable_session_persistence=False,\n",
")\n",
"\n",
"memory_bank_id = \"inflation_data_memory_bank\"\n",
"client.memory_banks.register(\n",
" memory_bank_id=memory_bank_id,\n",
" params={\n",
" \"memory_bank_type\": \"vector\",\n",
" \"embedding_model\": \"all-MiniLM-L6-v2\",\n",
" \"chunk_size_in_tokens\": 512,\n",
" \"overlap_size_in_tokens\": 64,\n",
" },\n",
")\n",
"AugmentConfigWithMemoryTool(agent_config, client)\n",
"codex_agent = Agent(client, agent_config)\n",
"session_id = codex_agent.create_session(\"test-session\")\n",
"\n",
"client.memory.insert(\n",
" bank_id=memory_bank_id,\n",
" documents=[\n",
" Document(\n",
" document_id=\"inflation\",\n",
" content=\"https://raw.githubusercontent.com/meta-llama/llama-stack-apps/main/examples/resources/inflation.csv\",\n",
" mime_type=\"text/csv\",\n",
" metadata={},\n",
" )\n",
" ],\n",
")\n",
"\n",
"user_prompts = [\n",
" {\"prompt\": \"Can you describe the data in the context?\", \"tools\": [{\"name\": \"memory\", \"args\": {\"memory_bank_id\": memory_bank_id}}]},\n",
" {\"prompt\": \"Plot average yearly inflation as a time series\", \"tools\": [{\"name\": \"memory\", \"args\": {\"memory_bank_id\": memory_bank_id}}, \"code_interpreter\"]},\n",
"]\n",
"\n",
"for input in user_prompts:\n",
" cprint(f'User> {input[\"prompt\"]}', 'green')\n",
" response = codex_agent.create_turn(\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": input[\"prompt\"],\n",
" }\n",
" ],\n",
" session_id=session_id,\n",
" tools=input[\"tools\"],\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": "9GHJHfLmIQQi",
"metadata": {
"id": "9GHJHfLmIQQi"
},
"source": [
"- Now, use the generated response from agent to view the plot"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "JqBBVLKdIHHq",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 564
},
"id": "JqBBVLKdIHHq",
"outputId": "4563e803-8385-426b-ec6c-e8b19e2ee6e6"
},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: '/tmp/tmpco0s0o4_/LOdZoVp1inflation.csv'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[5], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Read the CSV file\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/tmp/tmpco0s0o4_/LOdZoVp1inflation.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# Extract the year and inflation rate from the CSV file\u001b[39;00m\n\u001b[1;32m 8\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYear\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYear\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
"File \u001b[0;32m~/miniconda3/envs/stack/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 1014\u001b[0m dialect,\n\u001b[1;32m 1015\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m 1023\u001b[0m )\n\u001b[1;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/miniconda3/envs/stack/lib/python3.10/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
"File \u001b[0;32m~/miniconda3/envs/stack/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/miniconda3/envs/stack/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m 1879\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1881\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1882\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1883\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1884\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1885\u001b[0m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1886\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1887\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1888\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
"File \u001b[0;32m~/miniconda3/envs/stack/lib/python3.10/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 869\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m 872\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 874\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 875\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 876\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 877\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 878\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/tmp/tmpco0s0o4_/LOdZoVp1inflation.csv'"
]
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Read the CSV file\n",
"df = pd.read_csv('/tmp/tmpco0s0o4_/LOdZoVp1inflation.csv')\n",
"\n",
"# Extract the year and inflation rate from the CSV file\n",
"df['Year'] = pd.to_datetime(df['Year'], format='%Y')\n",
"df = df.rename(columns={'Jan': 'Jan Rate', 'Feb': 'Feb Rate', 'Mar': 'Mar Rate', 'Apr': 'Apr Rate', 'May': 'May Rate', 'Jun': 'Jun Rate', 'Jul': 'Jul Rate', 'Aug': 'Aug Rate', 'Sep': 'Sep Rate', 'Oct': 'Oct Rate', 'Nov': 'Nov Rate', 'Dec': 'Dec Rate'})\n",
"\n",
"# Calculate the average yearly inflation rate\n",
"df['Yearly Inflation'] = df[['Jan Rate', 'Feb Rate', 'Mar Rate', 'Apr Rate', 'May Rate', 'Jun Rate', 'Jul Rate', 'Aug Rate', 'Sep Rate', 'Oct Rate', 'Nov Rate', 'Dec Rate']].mean(axis=1)\n",
"\n",
"# Plot the average yearly inflation rate as a time series\n",
"plt.figure(figsize=(10, 6))\n",
"plt.plot(df['Year'], df['Yearly Inflation'], marker='o')\n",
"plt.title('Average Yearly Inflation Rate')\n",
"plt.xlabel('Year')\n",
"plt.ylabel('Inflation Rate (%)')\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "FJ85DUhgBZd7",
"metadata": {
"id": "FJ85DUhgBZd7"
},
"source": [
"## 3. Llama Stack Agent Evaluations\n"
]
},
{
"cell_type": "markdown",
"id": "ydeBDpDT5VHd",
"metadata": {
"id": "ydeBDpDT5VHd"
},
"source": [
"#### 3.1. Online Evaluation Dataset Collection Using Telemetry\n",
"\n",
"- Llama Stack offers built-in telemetry to collect traces and data about your agentic application.\n",
"- In this example, we will show how to build an Agent with Llama Stack, and query the agent's traces into an online dataset that can be used for evaluation. "
]
},
{
"cell_type": "markdown",
"id": "_JueJAKyJR5m",
"metadata": {
"id": "_JueJAKyJR5m"
},
"source": [
"##### 🚧 Patches 🚧\n",
"- The following cells are temporary patches to get `telemetry` working."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "klPkK1t7CzIY",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "klPkK1t7CzIY",
"outputId": "ab0c1490-7fa6-446c-8e35-7b42f57e8a04"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found existing installation: llama_stack 0.0.61\n",
"Uninstalling llama_stack-0.0.61:\n",
" Would remove:\n",
" /usr/local/bin/install-wheel-from-presigned\n",
" /usr/local/bin/llama\n",
" /usr/local/lib/python3.10/dist-packages/llama_stack-0.0.61.dist-info/*\n",
" /usr/local/lib/python3.10/dist-packages/llama_stack/*\n",
"Proceed (Y/n)? Y\n",
" Successfully uninstalled llama_stack-0.0.61\n",
"Collecting git+https://github.com/meta-llama/llama-stack.git@main\n",
" Cloning https://github.com/meta-llama/llama-stack.git (to revision main) to /tmp/pip-req-build-oryyzdm1\n",
" Running command git clone --filter=blob:none --quiet https://github.com/meta-llama/llama-stack.git /tmp/pip-req-build-oryyzdm1\n",
" Resolved https://github.com/meta-llama/llama-stack.git to commit 53b3a1e345c46d7d37c1af3d675092a4cbfe85f9\n",
" Running command git submodule update --init --recursive -q\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: blobfile in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (3.0.0)\n",
"Requirement already satisfied: fire in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (0.7.0)\n",
"Requirement already satisfied: httpx in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (0.28.1)\n",
"Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (0.26.5)\n",
"Requirement already satisfied: llama-models>=0.0.61 in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (0.0.61)\n",
"Requirement already satisfied: llama-stack-client>=0.0.61 in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (0.0.61)\n",
"Requirement already satisfied: prompt-toolkit in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (3.0.48)\n",
"Requirement already satisfied: python-dotenv in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (1.0.1)\n",
"Requirement already satisfied: pydantic>=2 in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (2.10.3)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (2.32.3)\n",
"Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (13.9.4)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (75.1.0)\n",
"Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (2.5.0)\n",
"Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from llama-models>=0.0.61->llama_stack==0.0.61) (6.0.2)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from llama-models>=0.0.61->llama_stack==0.0.61) (3.1.4)\n",
"Requirement already satisfied: tiktoken in /usr/local/lib/python3.10/dist-packages (from llama-models>=0.0.61->llama_stack==0.0.61) (0.8.0)\n",
"Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from llama-models>=0.0.61->llama_stack==0.0.61) (10.4.0)\n",
"Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (3.7.1)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (8.1.7)\n",
"Requirement already satisfied: distro<2,>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (1.9.0)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (2.2.2)\n",
"Requirement already satisfied: pyaml in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (24.12.1)\n",
"Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (1.3.1)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (4.66.6)\n",
"Requirement already satisfied: typing-extensions<5,>=4.7 in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (4.12.2)\n",
"Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx->llama_stack==0.0.61) (2024.8.30)\n",
"Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx->llama_stack==0.0.61) (1.0.7)\n",
"Requirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from httpx->llama_stack==0.0.61) (3.10)\n",
"Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx->llama_stack==0.0.61) (0.14.0)\n",
"Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2->llama_stack==0.0.61) (0.7.0)\n",
"Requirement already satisfied: pydantic-core==2.27.1 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2->llama_stack==0.0.61) (2.27.1)\n",
"Requirement already satisfied: pycryptodomex>=3.8 in /usr/local/lib/python3.10/dist-packages (from blobfile->llama_stack==0.0.61) (3.21.0)\n",
"Requirement already satisfied: urllib3<3,>=1.25.3 in /usr/local/lib/python3.10/dist-packages (from blobfile->llama_stack==0.0.61) (2.2.3)\n",
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"Requirement already satisfied: filelock>=3.0 in /usr/local/lib/python3.10/dist-packages (from blobfile->llama_stack==0.0.61) (3.16.1)\n",
"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->llama_stack==0.0.61) (2024.9.0)\n",
"Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->llama_stack==0.0.61) (24.2)\n",
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"Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->llama_stack==0.0.61) (3.0.0)\n",
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"Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->llama_stack==0.0.61) (0.1.2)\n",
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"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->llama-stack-client>=0.0.61->llama_stack==0.0.61) (2024.2)\n",
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"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->llama-stack-client>=0.0.61->llama_stack==0.0.61) (1.17.0)\n",
"Building wheels for collected packages: llama_stack\n",
" Building wheel for llama_stack (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for llama_stack: filename=llama_stack-0.0.61-py3-none-any.whl size=464145 sha256=da71747aceef9aec43553f66c43095486d1a920e47bb0e47e2729a8e4328fff6\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-jquw5j7f/wheels/74/e4/3b/079983408fa9323c1f2807e404ee78b468c74bec381eb70d4f\n",
"Successfully built llama_stack\n",
"Installing collected packages: llama_stack\n",
"Successfully installed llama_stack-0.0.61\n"
]
},
{
"data": {
"application/vnd.colab-display-data+json": {
"id": "7701cb0c982f4250a46721fededf9647",
"pip_warning": {
"packages": [
"llama_stack"
]
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# need to install on latest main\n",
"!pip uninstall llama-stack\n",
"!pip install git+https://github.com/meta-llama/llama-stack.git@main"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9jJ75JlnETTH",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9jJ75JlnETTH",
"outputId": "76bd3912-f814-428c-88e1-c1113af77856"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Removed handler StreamHandler from root logger\n"
]
}
],
"source": [
"# disable logging for clean server logs\n",
"import logging\n",
"def remove_root_handlers():\n",
" root_logger = logging.getLogger()\n",
" for handler in root_logger.handlers[:]:\n",
" root_logger.removeHandler(handler)\n",
" print(f\"Removed handler {handler.__class__.__name__} from root logger\")\n",
"\n",
"\n",
"remove_root_handlers()"
]
},
{
"cell_type": "markdown",
"id": "_t_tcWq0JcJ4",
"metadata": {
"id": "_t_tcWq0JcJ4"
},
"source": [
"##### 3.1.1. Building a Search Agent"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4iCO59kP20Zs",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4iCO59kP20Zs",
"outputId": "f6179de6-054d-4452-a893-8d9b64c5a0d1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"inference> Let me check the latest sports news.\n",
"inference> bravy_search.call(query=\"Bill Cosby South Park episode\")\n",
"CustomTool> Unknown tool `bravy_search` was called.\n",
"inference> brave_search.call(query=\"Andrew Tate kickboxing name\")\n",
"tool_execution> Tool:brave_search Args:{'query': 'Andrew Tate kickboxing name'}\n",
"tool_execution> Tool:brave_search Response:{\"query\": \"Andrew Tate kickboxing name\", \"top_k\": [{\"title\": \"Andrew Tate kickboxing record: How many championships ... - FirstSportz\", \"url\": \"https://firstsportz.com/mma-how-many-championships-does-andrew-tate-have/\", \"content\": \"Andrew Tate's Kickboxing career. During his kickboxing career, he used the nickname \\\"King Cobra,\\\" which he currently uses as his Twitter name. Tate had an unorthodox style of movement inside the ring. He kept his hands down most of the time and relied on quick jabs and an overhand right to land significant strikes.\", \"score\": 0.9996244, \"raw_content\": null}, {\"title\": \"Andrew Tate: Kickboxing Record, Facts, Height, Weight, Age, Biography\", \"url\": \"https://www.lowkickmma.com/andrew-tate-kickboxing-record-facts-height-weight-age-biography/\", \"content\": \"Birth Name: Emory Andrew Tate III: Date of Birth: 1 December 1986: Place of Birth: Washington, D.C., U.S. ... In his professional kickboxing career, Andrew Tate won 32 of his fights by knockout.\", \"score\": 0.99909246, \"raw_content\": null}, {\"title\": \"Who is Andrew Tate? MMA, kickboxing record and controversies of fighter ...\", \"url\": \"https://www.sportingnews.com/us/kickboxing/news/andrew-tate-mma-kickboxing-record-controversies/u50waalc9cfz7krjg9wnyb7p\", \"content\": \"Andrew Tate kickboxing record After launching his career as a 20-year-old in 2007, Tate built a formidable kickboxing record that included 76 wins across 85 fights in more than 13 years in the ring.\", \"score\": 0.9976586, \"raw_content\": null}, {\"title\": \"About Andrew Tate: A Journey from Champion to Controversy\", \"url\": \"https://reachmorpheus.com/andrew-tate/\", \"content\": \"Andrew Tate's kickboxing career, beginning in 2005, is a tale of determination and skill. He quickly made a name for himself in the sport, rising through the ranks with his unique fighting style and strategic approach, honed by his chess-playing background.\", \"score\": 0.99701905, \"raw_content\": null}, {\"title\": \"Andrew Tate Bio, Wiki, Net Worth, Age, Family, MMA Career - Next Biography\", \"url\": \"https://www.nextbiography.com/andrew-tate/\", \"content\": \"Andrew Tate Age. Andrew Tate is 36 years old as of 2023, born on December 1, 1986, in Washington, DC. By his mid-thirties, Andrew Tate has become an esteemed figure in the world of kickboxing, showcasing remarkable expertise and experience in the sport. Early Life of Andrew Tate. Andrew Tate was born on 01 December 1986 to an African-American\", \"score\": 0.99368566, \"raw_content\": null}]}\n",
"shield_call> No Violation\n",
"inference> Andrew Tate's kickboxing name is \"King Cobra.\"\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",
"from google.colab import userdata\n",
"\n",
"agent_config = AgentConfig(\n",
" model=\"meta-llama/Llama-3.1-405B-Instruct\",\n",
" instructions=\"You are a helpful assistant. Use search tool to answer the questions. \",\n",
" tools=(\n",
" [\n",
" {\n",
" \"type\": \"brave_search\",\n",
" \"engine\": \"tavily\",\n",
" \"api_key\": userdata.get(\"TAVILY_SEARCH_API_KEY\")\n",
" }\n",
" ]\n",
" ),\n",
" input_shields=[],\n",
" output_shields=[],\n",
" enable_session_persistence=False,\n",
")\n",
"agent = Agent(client, agent_config)\n",
"user_prompts = [\n",
" \"Which teams played in the NBA western conference finals of 2024\",\n",
" \"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\n",
" \"What is the British-American kickboxer Andrew Tate's kickboxing name?\",\n",
"]\n",
"\n",
"session_id = agent.create_session(\"test-session\")\n",
"\n",
"for prompt in user_prompts:\n",
" response = agent.create_turn(\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": prompt,\n",
" }\n",
" ],\n",
" session_id=session_id,\n",
" )\n",
"\n",
" for log in EventLogger().log(response):\n",
" log.print()"
]
},
{
"cell_type": "markdown",
"id": "ekOS2kM4P0LM",
"metadata": {
"id": "ekOS2kM4P0LM"
},
"source": [
"##### 3.1.2 Query Telemetry"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "agkWgToGAsuA",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 760
},
"id": "agkWgToGAsuA",
"outputId": "647cd5d2-7610-4fd6-ef66-c3f2f782a1b0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Getting traces for session_id=ac651ce8-2281-47f2-8814-ef947c066e40\n"
]
},
{
"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=\"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\">'input'</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\">'{\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}'</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\">'output'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'content: Let me check the latest sports news. tool_calls: []'</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\">'input'</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\">'{\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[]}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\"context\":null}'</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\">'output'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"content: tool_calls: [ToolCall(call_id='19bd3554-e670-4856-89d0-c63f5b016245', tool_name='bravy_search', arguments={'query': 'Bill Cosby South Park episode'})]\"</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\">'input'</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\">'{\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[]}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[{\"call_id\":\"19bd3554-e670-4856-89d0-c63f5b016245\",\"tool_name\":\"bravy_search\",\"arguments\":{\"query\":\"Bill Cosby South Park episode\"}}]}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null}'</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\">'output'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"content: tool_calls: [ToolCall(call_id='526045a7-5f51-40fb-ba97-5ad29610e511', tool_name=&lt;BuiltinTool.brave_search: 'brave_search'&gt;, arguments={'query': 'Andrew Tate kickboxing name'})]\"</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\">'input'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[{\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"arguments\":{\"query\":\"Andrew Tate kickboxing name\"}}]}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'output'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"ipython\",\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"content\":\"{\\\\\"query\\\\\": \\\\\"Andrew Tate kickboxing name\\\\\", \\\\\"top_k\\\\\": [{\\\\\"title\\\\\": \\\\\"Andrew Tate kickboxing record: How many championships ... - FirstSportz\\\\\", \\\\\"url\\\\\": \\\\\"https://firstsportz.com/mma-how-many-championships-does-andrew-tate-have/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s Kickboxing career. During his kickboxing career, he used the nickname \\\\\\\\\\\\\"King Cobra,\\\\\\\\\\\\\" which he currently uses as his Twitter name. Tate had an unorthodox style of movement inside the ring. He kept his hands down most of the time and relied on quick jabs and an overhand right to land significant strikes.\\\\\", \\\\\"score\\\\\": 0.9996244, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Andrew Tate: Kickboxing Record, Facts, Height, Weight, Age, Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.lowkickmma.com/andrew-tate-kickboxing-record-facts-height-weight-age-biography/\\\\\", \\\\\"content\\\\\": \\\\\"Birth Name: Emory Andrew Tate III: Date of Birth: 1 December 1986: Place of Birth: Washington, D.C., U.S. ... In his professional kickboxing career, Andrew Tate won 32 of his fights by knockout.\\\\\", \\\\\"score\\\\\": 0.99909246, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Who is Andrew Tate? MMA, kickboxing record and controversies of fighter ...\\\\\", \\\\\"url\\\\\": \\\\\"https://www.sportingnews.com/us/kickboxing/news/andrew-tate-mma-kickboxing-record-controversies/u50waalc9cfz7krjg9wnyb7p\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate kickboxing record After launching his career as a 20-year-old in 2007, Tate built a formidable kickboxing record that included 76 wins across 85 fights in more than 13 years in the ring.\\\\\", \\\\\"score\\\\\": 0.9976586, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"About Andrew Tate: A Journey from Champion to Controversy\\\\\", \\\\\"url\\\\\": \\\\\"https://reachmorpheus.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s kickboxing career, beginning in 2005, is a tale of determination and skill. He quickly made a name for himself in the sport, rising through the ranks with his unique fighting style and strategic approach, honed by his chess-playing background.\\\\\", \\\\\"score\\\\\": 0.99701905, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Andrew Tate Bio, Wiki, Net Worth, Age, Family, MMA Career - Next Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.nextbiography.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate Age. Andrew Tate is 36 years old as of 2023, born on December 1, 1986, in Washington, DC. By his mid-thirties, Andrew Tate has become an esteemed figure in the world of kickboxing, showcasing remarkable expertise and experience in the sport. Early Life of Andrew Tate. Andrew Tate was born on 01 December 1986 to an African-American\\\\\", \\\\\"score\\\\\": 0.99368566, \\\\\"raw_content\\\\\": null}]}\"}'</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\">'input'</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\">'{\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[]}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[{\"call_id\":\"19bd3554-e670-4856-89d0-c63f5b016245\",\"tool_name\":\"bravy_search\",\"arguments\":{\"query\":\"Bill Cosby South Park episode\"}}]}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[{\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"arguments\":{\"query\":\"Andrew Tate kickboxing name\"}}]}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"ipython\",\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"content\":\"{\\\\\"query\\\\\": \\\\\"Andrew Tate kickboxing name\\\\\", \\\\\"top_k\\\\\": [{\\\\\"title\\\\\": \\\\\"Andrew Tate kickboxing record: How many championships ... - FirstSportz\\\\\", \\\\\"url\\\\\": \\\\\"https://firstsportz.com/mma-how-many-championships-does-andrew-tate-have/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s Kickboxing career. During his kickboxing career, he used the nickname \\\\\\\\\\\\\"King Cobra,\\\\\\\\\\\\\" which he currently uses as his Twitter name. Tate had an unorthodox style of movement inside the ring. He kept his hands down most of the time and relied on quick jabs and an overhand right to land significant strikes.\\\\\", \\\\\"score\\\\\": 0.9996244, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Andrew Tate: Kickboxing Record, Facts, Height, Weight, Age, Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.lowkickmma.com/andrew-tate-kickboxing-record-facts-height-weight-age-biography/\\\\\", \\\\\"content\\\\\": \\\\\"Birth Name: Emory Andrew Tate III: Date of Birth: 1 December 1986: Place of Birth: Washington, D.C., U.S. ... In his professional kickboxing career, Andrew Tate won 32 of his fights by knockout.\\\\\", \\\\\"score\\\\\": 0.99909246, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Who is Andrew Tate? MMA, kickboxing record and controversies of fighter ...\\\\\", \\\\\"url\\\\\": \\\\\"https://www.sportingnews.com/us/kickboxing/news/andrew-tate-mma-kickboxing-record-controversies/u50waalc9cfz7krjg9wnyb7p\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate kickboxing record After launching his career as a 20-year-old in 2007, Tate built a formidable kickboxing record that included 76 wins across 85 fights in more than 13 years in the ring.\\\\\", \\\\\"score\\\\\": 0.9976586, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"About Andrew Tate: A Journey from Champion to Controversy\\\\\", \\\\\"url\\\\\": \\\\\"https://reachmorpheus.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s kickboxing career, beginning in 2005, is a tale of determination and skill. He quickly made a name for himself in the sport, rising through the ranks with his unique fighting style and strategic approach, honed by his chess-playing background.\\\\\", \\\\\"score\\\\\": 0.99701905, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Andrew Tate Bio, Wiki, Net Worth, Age, Family, MMA Career - Next Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.nextbiography.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate Age. Andrew Tate is 36 years old as of 2023, born on December 1, 1986, in Washington, DC. By his mid-thirties, Andrew Tate has become an esteemed figure in the world of kickboxing, showcasing remarkable expertise and experience in the sport. Early Life of Andrew Tate. Andrew Tate was born on 01 December 1986 to an African-American\\\\\", \\\\\"score\\\\\": 0.99368566, \\\\\"raw_content\\\\\": null}]}\"}'</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\">'output'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'content: Andrew Tate\\'s kickboxing name is \"King Cobra.\" tool_calls: []'</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[1m[\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'input'\u001b[0m: \u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'output'\u001b[0m: \u001b[32m'content: Let me check the latest sports news. tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\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'input'\u001b[0m: \u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby \u001b[0m\u001b[32m(\u001b[0m\u001b[32mBSM-471\u001b[0m\u001b[32m)\u001b[0m\u001b[32m first appear? Give me the number and title.\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'output'\u001b[0m: \u001b[32m\"content: tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32mToolCall\u001b[0m\u001b[32m(\u001b[0m\u001b[32mcall_id\u001b[0m\u001b[32m='19bd3554-e670-4856-89d0-c63f5b016245', \u001b[0m\u001b[32mtool_name\u001b[0m\u001b[32m='bravy_search', \u001b[0m\u001b[32marguments\u001b[0m\u001b[32m=\u001b[0m\u001b[32m{\u001b[0m\u001b[32m'query': 'Bill Cosby South Park episode'\u001b[0m\u001b[32m}\u001b[0m\u001b[32m)\u001b[0m\u001b[32m]\u001b[0m\u001b[32m\"\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'input'\u001b[0m: \u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby \u001b[0m\u001b[32m(\u001b[0m\u001b[32mBSM-471\u001b[0m\u001b[32m)\u001b[0m\u001b[32m first appear? Give me the number and title.\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"call_id\":\"19bd3554-e670-4856-89d0-c63f5b016245\",\"tool_name\":\"bravy_search\",\"arguments\":\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"query\":\"Bill Cosby South Park episode\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'output'\u001b[0m: \u001b[32m\"content: tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32mToolCall\u001b[0m\u001b[32m(\u001b[0m\u001b[32mcall_id\u001b[0m\u001b[32m='526045a7-5f51-40fb-ba97-5ad29610e511', \u001b[0m\u001b[32mtool_name\u001b[0m\u001b[32m=\u001b[0m\u001b[32m<\u001b[0m\u001b[32mBuiltinTool.brave_search:\u001b[0m\u001b[32m 'brave_search'\u001b[0m\u001b[32m>\u001b[0m\u001b[32m, \u001b[0m\u001b[32marguments\u001b[0m\u001b[32m=\u001b[0m\u001b[32m{\u001b[0m\u001b[32m'query': 'Andrew Tate kickboxing name'\u001b[0m\u001b[32m}\u001b[0m\u001b[32m)\u001b[0m\u001b[32m]\u001b[0m\u001b[32m\"\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'input'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"arguments\":\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"query\":\"Andrew Tate kickboxing name\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'output'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"ipython\",\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"content\":\"\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"query\\\\\": \\\\\"Andrew Tate kickboxing name\\\\\", \\\\\"top_k\\\\\": \u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate kickboxing record: How many championships ... - FirstSportz\\\\\", \\\\\"url\\\\\": \\\\\"https://firstsportz.com/mma-how-many-championships-does-andrew-tate-have/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s Kickboxing career. During his kickboxing career, he used the nickname \\\\\\\\\\\\\"King Cobra,\\\\\\\\\\\\\" which he currently uses as his Twitter name. Tate had an unorthodox style of movement inside the ring. He kept his hands down most of the time and relied on quick jabs and an overhand right to land significant strikes.\\\\\", \\\\\"score\\\\\": 0.9996244, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate: Kickboxing Record, Facts, Height, Weight, Age, Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.lowkickmma.com/andrew-tate-kickboxing-record-facts-height-weight-age-biography/\\\\\", \\\\\"content\\\\\": \\\\\"Birth Name: Emory Andrew Tate III: Date of Birth: 1 December 1986: Place of Birth: Washington, D.C., U.S. ... In his professional kickboxing career, Andrew Tate won 32 of his fights by knockout.\\\\\", \\\\\"score\\\\\": 0.99909246, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Who is Andrew Tate? MMA, kickboxing record and controversies of fighter ...\\\\\", \\\\\"url\\\\\": \\\\\"https://www.sportingnews.com/us/kickboxing/news/andrew-tate-mma-kickboxing-record-controversies/u50waalc9cfz7krjg9wnyb7p\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate kickboxing record After launching his career as a 20-year-old in 2007, Tate built a formidable kickboxing record that included 76 wins across 85 fights in more than 13 years in the ring.\\\\\", \\\\\"score\\\\\": 0.9976586, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"About Andrew Tate: A Journey from Champion to Controversy\\\\\", \\\\\"url\\\\\": \\\\\"https://reachmorpheus.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s kickboxing career, beginning in 2005, is a tale of determination and skill. He quickly made a name for himself in the sport, rising through the ranks with his unique fighting style and strategic approach, honed by his chess-playing background.\\\\\", \\\\\"score\\\\\": 0.99701905, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate Bio, Wiki, Net Worth, Age, Family, MMA Career - Next Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.nextbiography.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate Age. Andrew Tate is 36 years old as of 2023, born on December 1, 1986, in Washington, DC. By his mid-thirties, Andrew Tate has become an esteemed figure in the world of kickboxing, showcasing remarkable expertise and experience in the sport. Early Life of Andrew Tate. Andrew Tate was born on 01 December 1986 to an African-American\\\\\", \\\\\"score\\\\\": 0.99368566, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\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'input'\u001b[0m: \u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby \u001b[0m\u001b[32m(\u001b[0m\u001b[32mBSM-471\u001b[0m\u001b[32m)\u001b[0m\u001b[32m first appear? Give me the number and title.\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"call_id\":\"19bd3554-e670-4856-89d0-c63f5b016245\",\"tool_name\":\"bravy_search\",\"arguments\":\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"query\":\"Bill Cosby South Park episode\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"arguments\":\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"query\":\"Andrew Tate kickboxing name\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"ipython\",\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"content\":\"\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"query\\\\\": \\\\\"Andrew Tate kickboxing name\\\\\", \\\\\"top_k\\\\\": \u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate kickboxing record: How many championships ... - FirstSportz\\\\\", \\\\\"url\\\\\": \\\\\"https://firstsportz.com/mma-how-many-championships-does-andrew-tate-have/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s Kickboxing career. During his kickboxing career, he used the nickname \\\\\\\\\\\\\"King Cobra,\\\\\\\\\\\\\" which he currently uses as his Twitter name. Tate had an unorthodox style of movement inside the ring. He kept his hands down most of the time and relied on quick jabs and an overhand right to land significant strikes.\\\\\", \\\\\"score\\\\\": 0.9996244, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate: Kickboxing Record, Facts, Height, Weight, Age, Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.lowkickmma.com/andrew-tate-kickboxing-record-facts-height-weight-age-biography/\\\\\", \\\\\"content\\\\\": \\\\\"Birth Name: Emory Andrew Tate III: Date of Birth: 1 December 1986: Place of Birth: Washington, D.C., U.S. ... In his professional kickboxing career, Andrew Tate won 32 of his fights by knockout.\\\\\", \\\\\"score\\\\\": 0.99909246, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Who is Andrew Tate? MMA, kickboxing record and controversies of fighter ...\\\\\", \\\\\"url\\\\\": \\\\\"https://www.sportingnews.com/us/kickboxing/news/andrew-tate-mma-kickboxing-record-controversies/u50waalc9cfz7krjg9wnyb7p\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate kickboxing record After launching his career as a 20-year-old in 2007, Tate built a formidable kickboxing record that included 76 wins across 85 fights in more than 13 years in the ring.\\\\\", \\\\\"score\\\\\": 0.9976586, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"About Andrew Tate: A Journey from Champion to Controversy\\\\\", \\\\\"url\\\\\": \\\\\"https://reachmorpheus.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s kickboxing career, beginning in 2005, is a tale of determination and skill. He quickly made a name for himself in the sport, rising through the ranks with his unique fighting style and strategic approach, honed by his chess-playing background.\\\\\", \\\\\"score\\\\\": 0.99701905, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate Bio, Wiki, Net Worth, Age, Family, MMA Career - Next Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.nextbiography.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate Age. Andrew Tate is 36 years old as of 2023, born on December 1, 1986, in Washington, DC. By his mid-thirties, Andrew Tate has become an esteemed figure in the world of kickboxing, showcasing remarkable expertise and experience in the sport. Early Life of Andrew Tate. Andrew Tate was born on 01 December 1986 to an African-American\\\\\", \\\\\"score\\\\\": 0.99368566, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'output'\u001b[0m: \u001b[32m'content: Andrew Tate\\'s kickboxing name is \"King Cobra.\" tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[1m]\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(f\"Getting traces for session_id={session_id}\")\n",
"import json\n",
"from rich.pretty import pprint\n",
"\n",
"agent_logs = []\n",
"\n",
"for span in client.telemetry.query_spans(\n",
" attribute_filters=[\n",
" {\"key\": \"session_id\", \"op\": \"eq\", \"value\": session_id},\n",
" ],\n",
" attributes_to_return=[\"input\", \"output\"]\n",
" ):\n",
" if span.attributes[\"output\"] != \"no shields\":\n",
" agent_logs.append(span.attributes)\n",
"\n",
"pprint(agent_logs)"
]
},
{
"cell_type": "markdown",
"id": "QF30H7ufP2RE",
"metadata": {
"id": "QF30H7ufP2RE"
},
"source": [
"##### 3.1.3 Post-Process Telemetry Results & Evaluate\n",
"\n",
"- Now, we want to run evaluation to assert that our search agent succesfully calls brave_search from online traces.\n",
"- We will first post-process the agent's telemetry logs and run evaluation."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "sy4Xaff_Avuu",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 411
},
"id": "sy4Xaff_Avuu",
"outputId": "cb68bae7-b21d-415d-8e71-612bd383c793"
},
"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=\"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\">'input_query'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'content: Let me check the latest sports news. tool_calls: []'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'expected_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'brave_search'</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\">'input_query'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"content: tool_calls: [ToolCall(call_id='19bd3554-e670-4856-89d0-c63f5b016245', tool_name='bravy_search', arguments={'query': 'Bill Cosby South Park episode'})]\"</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'expected_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'brave_search'</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\">'input_query'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"content: tool_calls: [ToolCall(call_id='526045a7-5f51-40fb-ba97-5ad29610e511', tool_name=&lt;BuiltinTool.brave_search: 'brave_search'&gt;, arguments={'query': 'Andrew Tate kickboxing name'})]\"</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'expected_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'brave_search'</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[1m[\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'input_query'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'content: Let me check the latest sports news. tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'expected_answer'\u001b[0m: \u001b[32m'brave_search'\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'input_query'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby \u001b[0m\u001b[32m(\u001b[0m\u001b[32mBSM-471\u001b[0m\u001b[32m)\u001b[0m\u001b[32m first appear? Give me the number and title.\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"content: tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32mToolCall\u001b[0m\u001b[32m(\u001b[0m\u001b[32mcall_id\u001b[0m\u001b[32m='19bd3554-e670-4856-89d0-c63f5b016245', \u001b[0m\u001b[32mtool_name\u001b[0m\u001b[32m='bravy_search', \u001b[0m\u001b[32marguments\u001b[0m\u001b[32m=\u001b[0m\u001b[32m{\u001b[0m\u001b[32m'query': 'Bill Cosby South Park episode'\u001b[0m\u001b[32m}\u001b[0m\u001b[32m)\u001b[0m\u001b[32m]\u001b[0m\u001b[32m\"\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'expected_answer'\u001b[0m: \u001b[32m'brave_search'\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'input_query'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"content: tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32mToolCall\u001b[0m\u001b[32m(\u001b[0m\u001b[32mcall_id\u001b[0m\u001b[32m='526045a7-5f51-40fb-ba97-5ad29610e511', \u001b[0m\u001b[32mtool_name\u001b[0m\u001b[32m=\u001b[0m\u001b[32m<\u001b[0m\u001b[32mBuiltinTool.brave_search:\u001b[0m\u001b[32m 'brave_search'\u001b[0m\u001b[32m>\u001b[0m\u001b[32m, \u001b[0m\u001b[32marguments\u001b[0m\u001b[32m=\u001b[0m\u001b[32m{\u001b[0m\u001b[32m'query': 'Andrew Tate kickboxing name'\u001b[0m\u001b[32m}\u001b[0m\u001b[32m)\u001b[0m\u001b[32m]\u001b[0m\u001b[32m\"\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'expected_answer'\u001b[0m: \u001b[32m'brave_search'\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[1m]\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"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\">'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=\"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\">0.3333333333333333</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\">3</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\">0.0</span><span style=\"font-weight: bold\">}</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\">0.0</span><span style=\"font-weight: bold\">}</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"
],
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"\u001b[2;32m│ \u001b[0m\u001b[33mresults\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[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1;36m0.3333333333333333\u001b[0m, \u001b[32m'num_correct'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_total'\u001b[0m: \u001b[1;36m3\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\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\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": [
"# post-process telemetry spance and prepare data for eval\n",
"# in this case, we want to assert that all user prompts is followed by a tool call\n",
"import ast\n",
"import json\n",
"\n",
"eval_rows = []\n",
"\n",
"for log in agent_logs:\n",
" last_msg = log['input'][-1]\n",
" if \"\\\"role\\\":\\\"user\\\"\" in last_msg:\n",
" eval_rows.append(\n",
" {\n",
" \"input_query\": last_msg,\n",
" \"generated_answer\": log[\"output\"],\n",
" # check if generated_answer uses tools brave_search\n",
" \"expected_answer\": \"brave_search\",\n",
" },\n",
" )\n",
"\n",
"pprint(eval_rows)\n",
"scoring_params = {\n",
" \"basic::subset_of\": None,\n",
"}\n",
"scoring_response = client.scoring.score(input_rows=eval_rows, scoring_functions=scoring_params)\n",
"pprint(scoring_response)"
]
},
{
"cell_type": "markdown",
"id": "IKbzhxcw5e_c",
"metadata": {
"id": "IKbzhxcw5e_c"
},
"source": [
"#### 3.2. Agentic Application Dataset Scoring\n",
"- Llama Stack offers a library of scoring functions and the `/scoring` API, allowing you to run evaluations on your pre-annotated AI application datasets.\n",
"\n",
"- In this example, we will work with an example RAG dataset you have built previously, label with an annotation, and use LLM-As-Judge with custom judge prompt for scoring. Please checkout our [Llama Stack Playground](https://llama-stack.readthedocs.io/en/latest/playground/index.html) for an interactive interface to upload datasets and run scorings."
]
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"<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",
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"<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",
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"\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",
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"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",
"rows = [\n",
" {\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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