diff --git a/experiments/max_tool_per_agent/README.md b/experiments/max_tool_per_agent/README.md index 743a234..3a81758 100644 --- a/experiments/max_tool_per_agent/README.md +++ b/experiments/max_tool_per_agent/README.md @@ -4,41 +4,85 @@ Assess LlamaStack’s ability to handle an increasing number of tools, evaluate ## Key Research Questions * Scalability: What is the maximum number of tools an agent can handle before performance degrades? * Tool Selection Accuracy: How well does the agent pick the correct tool for a given query? -* Guided Tool Selection: Does providing a relevant subset of tools in the prompt improve accuracy? -* Model factor: How does model architecture (series, size, temperature type of hyperparameter) affect LlamaStack's tool execution and selection performance? +* Model factor: How does model architecture (series, size, context length, temperature type of hyperparameter) affect LlamaStack's tool execution and selection performance? + * Guided Tool Selection: Does providing a relevant subset of tools in the prompt improve accuracy? + +## Definition Of Done: +Must: Visualise results about max tool number over a range of models. +optional: A blog at https://next.redhat.com/blog/ + ## Methodology -* Tools: - - 3 built-in tools (websearch, wolfram_alpha, code_interpreter). - - Dynamically generated N client tools to test scalability (N = 5, 10, 20, 50). - - also mcp tools (less priority) -* Queries: -A diverse set of tasks designed to require specific tools. -* **Metrics**: - - Scalability Limit (max tool count before performance drops). - - Tool Selection Accuracy (% correct, incorrect, or missing tool calls by assert response.steps). - - Context size vs tool count. (token size is a strict limitation.) - - Latency (response time vs. tool count). - -* Logging: -Structured logs in CSV format capturing the query, available tools, selected tool, expected tool, execution success, and latency. - -Currently, we have multiple built-in tool scripts available. A more systematic evaluation is still a work in progress. - -## TODOs +* Scope: + - focus on client tools. + +`maxtool.ipynb` script tests how well LlamaStack handles increasing numbers of tools by measuring **tool selection accuracy, execution success, and latency**. +### Experiment Setup +- **5 Real Tools**: Weather info, word count, string reversal, uppercase conversion, insurance scoring. +- **Fake Tools**: Dynamically generated tools with random outputs. +- **5 Fixed Queries**: Each mapped to a ground truth tool. +- **Scaling**: Start with 5 tools, increase 1 each time until model fail to select correct tool. +- **Metrics Logged**: + - Exception Rate (how many exception occurs out of 5 queries) + - Tool Execution Success Rate (how many time tools are actually executed out of 5 queries) + - Correct Tool Selection Rate (how many time correct tool is selected out of 5 queries) + - Average Latency (average time taken to respond 5 queries) + +## πŸ“ Structure +- `maxtool.ipynb`: Automates multi-run tool testing. +- `experiment_logs/`: Contains CSVs, logs for each model run with timestamp and key hyperparameters. +- `count_token.ipynb`: early attemp in counting tool set tokens. +- `README.md`: This document. + +## πŸ’‘ Key Insights So Far +- (**26 Mar**) Ruled out temperature as a factor. Even with the temperature set to 0.001, we observed a maximum of 11, 16, and 15 tools in three runs. Temporarily shifting focus to MCP tasks. Will wrap up updates and revisit later. + +- (**24 Mar**) The 3B model last week consistently handled 24 tools. However, this week with v0.1.8, it handled 11, 18, and 23 tools in three different runs. Suspect temperature-related parameters were changed for the 3B model. The 8B model will be tested to see if it follows the same pattern. A draft token count script `count_token.ipynb` has been created. + - Findings: Currently, v1.8 supports token metrics but only for the `client.inference.chat_completion` function. It is only the first step out of 3 for `response = agent.create_turn(` when involving tool calls + - still working on how to proper count token used for tool sets following llamastack way. + +- (**20 Mar**) + - Improved the maxtool test script with a diverse fake tool generation method. + - Refined the script for later scale experiments with logs and switched to an IPython notebook for better visualization. + - findings: + - LLaMA-8B can handle around **21 tools** (3B is about 24) before misidentifying the correct one. + - **Extending tool descriptions** reduced that number to **18**, suggesting performance is bound by docstring. + - **Extending tool name** reduced that number further to **17**, suggesting performance is bound by tool name. + - **Extending tool return message** does not affect. + - (suspect) Models may either: + - Prioritize **later tools** in prompt context (due to recency bias). + - Or, after exceeding a threshold, **fail to abstract and match** any tools, even among the first few. + - Even when inference still returns a response, the selected tool may be incorrect or invalid. + - leading to investigate token size for tools. + - **Local vs. cluster-hosted models** (e.g., on NERC) behave differentlyβ€”even for identical 3B modelsβ€”likely due to variations in runtime or configuration (e.g., token limit in VLLM's `run.yaml`). + +- (**by 11 Mar**) Developed the initial max tool test script. However, the fake tools lacked diversity, resulting in overly optimistic max tool counts. Spent time reading and finding existing benchmark literature. + +## TODOs +- [ ] Add token usage tracking to confirm max tool tokens for each model. (its not the token budge for model like max context length, its the max token size that model can call correct tool from a tool set.) +- [ ] Draw graphs comparing model size vs. tool capacity vs. tool token budget. +- [ ] Expand testing to additional models (e.g., 13B, possibly 70B via cluster). +- [ ] Compare local vs. hosted model behavior in a controlled setting. +less priority +- [ ] investigate if given similar tools how this affect? +- [ ] whats the minimum token size for describing a tool? - [ ] Identify and summarize suitable benchmark datasets. - [ ] Calculate accuracy using appropriate benchmarks. - [ ] Continue refining metrics and identifying influencing factors. - [ ] Develop a suitable system prompt wrapper for user queries to ensure the correct tool is executed. +#### Limitations +- **Fake tools are highly similar**, making them easy to distinguish from real tools, also no parameter. +- **Only 5 queries**, limiting diversity in tool usage. +- **Model may perform better here** than in real-world scenarios with more diverse tools. -### Test multi-builtin-tool +## Test multi-builtin-tool This is a initial test about having multiple buildin tools configed for one agent. The current version works with 0.1.6. If running with previous versions, ensure that `run.yaml` has all three tools configured. -step 1. `ollama run llama3.2:3b-instruct-fp16 --keepalive 60m` +step 1. `ollama run llama3.2:3b-instruct-fp16 --keepalive 60m` step 2. ``` export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" export LLAMA_STACK_PORT=8321 -``` -step 3: `llama stack run --image-type conda ~/llama-stack/llama_stack/templates/ollama/run.yaml` (I'm using conda env, follow this(https://llama-stack.readthedocs.io/en/latest/distributions/building_distro.html) if not using conda) +``` +step 3: `llama stack run --image-type conda ~/llama-stack/llama_stack/templates/ollama/run.yaml` (I'm using conda env, follow this(https://llama-stack.readthedocs.io/en/latest/distributions/building_distro.html) if not using conda) step 4: run `python multi-builtintools.py` \ No newline at end of file diff --git a/experiments/max_tool_per_agent/count_token.ipynb b/experiments/max_tool_per_agent/count_token.ipynb new file mode 100644 index 0000000..5ac3b18 --- /dev/null +++ b/experiments/max_tool_per_agent/count_token.ipynb @@ -0,0 +1,972 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name: llama_stack_client\n", + "Version: 0.1.8\n", + "Summary: The official Python library for the llama-stack-client API\n", + "Home-page: https://github.com/meta-llama/llama-stack-client-python\n", + "Author: \n", + "Author-email: Llama Stack Client \n", + "License-Expression: Apache-2.0\n", + "Location: /opt/anaconda3/envs/stack-client/lib/python3.10/site-packages\n", + "Requires: anyio, click, distro, httpx, pandas, prompt-toolkit, pyaml, pydantic, rich, sniffio, termcolor, tqdm, typing-extensions\n", + "Required-by: llama_stack\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip show llama-stack-client" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import random\n", + "import types\n", + "from llama_stack_client import LlamaStackClient\n", + "from llama_stack_client.lib.agents.client_tool import client_tool\n", + "from llama_stack_client.lib.agents.agent import Agent\n", + "from llama_stack_client.lib.agents.event_logger import EventLogger\n", + "from dotenv import load_dotenv\n", + "from rich.pretty import pprint\n", + "load_dotenv()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Define real tools\n", + "@client_tool\n", + "def weather_info(loc: str):\n", + " \"\"\"Fetches the current weather for a given location.\n", + " \n", + " :param loc: The location for which weather information is requested.\n", + " :returns: A dictionary containing success status and the weather result.\n", + " \"\"\"\n", + " return {\"success\": True, \"result\": f\"Weather in {loc} is sunny.\"}\n", + "\n", + "@client_tool\n", + "def word_count(text: str):\n", + " \"\"\"Counts the number of words in the given text.\n", + " \n", + " :param text: The input text to analyze.\n", + " :returns: A dictionary containing success status and the word count.\n", + " \"\"\"\n", + " return {\"success\": True, \"result\": len(text.split())}\n", + "\n", + "@client_tool\n", + "def reverse_string(text: str):\n", + " \"\"\"Reverses the given string.\n", + " \n", + " :param text: The input text to reverse.\n", + " :returns: A dictionary containing success status and the reversed string.\n", + " \"\"\"\n", + " return {\"success\": True, \"result\": text[::-1]}\n", + "\n", + "@client_tool\n", + "def uppercase(text: str):\n", + " \"\"\"Converts the given string to uppercase.\n", + " \n", + " :param text: The input text to convert.\n", + " :returns: A dictionary containing success status and the uppercase text.\n", + " \"\"\"\n", + " return {\"success\": True, \"result\": text.upper()}\n", + "\n", + "@client_tool\n", + "def insurance_scorer(text: str):\n", + " \"\"\"Generates a insurance score between 1 and 100.\n", + " :param text: The input text to eval.\n", + " :returns: A dictionary containing success status and the generated number.\n", + " \"\"\"\n", + " return {\"success\": True, \"result\": random.randint(1, 100)}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate fake tools using `types.FunctionType`\n", + "def generate_fake_tools(n):\n", + " tools = []\n", + " \n", + " for i in range(n):\n", + " tool_name = f\"tool_{i}_{generate_random_text(2)}\"\n", + " tool_doc = f\"\"\"Tool {i} performs a unique operation on the input data. {generate_random_text(10)}\n", + " \n", + " :param input_data: The input data for the tool.\n", + " :returns: A dictionary with success status and a unique response.\n", + " \"\"\"\n", + " \n", + " def fake_tool(input_data: str, tool_id=i):\n", + " responses = [\n", + " f\"Tool {tool_id} processed input: {input_data}\",\n", + " f\"Tool {tool_id} received: {input_data}\",\n", + " f\"Input {input_data} was handled by tool {tool_id}\",\n", + " ]\n", + " return {\"success\": True, \"result\": random.choice(responses)}\n", + " \n", + " fake_tool_fn = types.FunctionType(fake_tool.__code__, globals(), tool_name)\n", + " fake_tool_fn.__doc__ = tool_doc\n", + " print(tool_name)\n", + " print(tool_doc[:100])\n", + " fake_tool_fn = client_tool(fake_tool_fn)\n", + " \n", + " tools.append(fake_tool_fn)\n", + " \n", + " return tools\n", + "\n", + "def generate_random_text(length=10):\n", + " words = [\"alpha\", \"bravo\", \"charlie\", \"delta\", \"echo\", \"foxtrot\", \"golf\", \"hotel\", \"india\", \"juliet\", \"kilo\", \"lima\", \"mike\", \"november\", \"oscar\", \"papa\", \"quebec\", \"romeo\", \"sierra\", \"tango\", \"uniform\", \"victor\", \"whiskey\", \"x-ray\", \"yankee\", \"zulu\"]\n", + " return \" \".join(random.choices(words, k=length))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "meta-llama/Llama-3.2-3B-Instruct\n", + "http://localhost:8321\n" + ] + } + ], + "source": [ + "model_id = os.getenv(\"INFERENCE_MODEL\")\n", + "# model_id = \"meta-llama/Llama-3.2-3B-Instruct\"\n", + "print(model_id)\n", + "inference_model = model_id.split(\"/\")[1]\n", + "environment = \"local\" # \"nerc\" or \"local\"\n", + "\n", + "base_url = f\"http://localhost:{os.getenv('LLAMA_STACK_PORT')}\" if environment == \"local\" else os.getenv(\"LLAMA_STACK_ENDPOINT\")\n", + "print(base_url)\n", + "client = LlamaStackClient(\n", + " base_url = base_url\n", + ")\n", + "\n", + "real_tools = [weather_info, word_count, reverse_string, uppercase, insurance_scorer]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n", + "Fetches the current weather for a given location.\n", + " \n", + " :param loc: The location for which weather information is requested.\n", + " :returns: A dictionary containing success status and the weather result.\n", + " \n", + "weather_info\n" + ] + } + ], + "source": [ + "print(len(real_tools))\n", + "print(real_tools[0].__doc__)\n", + "print(real_tools[0].__name__)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ChatCompletionResponse(\n",
+       "β”‚   completion_message=CompletionMessage(\n",
+       "β”‚   β”‚   content='',\n",
+       "β”‚   β”‚   role='assistant',\n",
+       "β”‚   β”‚   stop_reason='end_of_turn',\n",
+       "β”‚   β”‚   tool_calls=[\n",
+       "β”‚   β”‚   β”‚   ToolCall(\n",
+       "β”‚   β”‚   β”‚   β”‚   arguments={'text': 'I am a responsible driver with good grades and no accidents'},\n",
+       "β”‚   β”‚   β”‚   β”‚   call_id='3ccb2eaa-43bc-4c91-9635-c3e371cf0d03',\n",
+       "β”‚   β”‚   β”‚   β”‚   tool_name='insurance_scorer',\n",
+       "β”‚   β”‚   β”‚   β”‚   arguments_json='{\"text\": \"I am a responsible driver with good grades and no accidents\"}'\n",
+       "β”‚   β”‚   β”‚   )\n",
+       "β”‚   β”‚   ]\n",
+       "β”‚   ),\n",
+       "β”‚   logprobs=None,\n",
+       "β”‚   metrics=[\n",
+       "β”‚   β”‚   Metric(metric='prompt_tokens', value=90.0, unit=None),\n",
+       "β”‚   β”‚   Metric(metric='completion_tokens', value=44.0, unit=None),\n",
+       "β”‚   β”‚   Metric(metric='total_tokens', value=134.0, unit=None)\n",
+       "β”‚   ]\n",
+       ")\n",
+       "
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Must use the correct tool for each query.\n", + " When using the tools:\n", + " 1. Extract the relevant number or values from the user's request.\n", + " 2. Use the correct tool to perform the operation.\n", + " 3. Present the result clearly.\n", + " 4. Handle errors gracefully.\"\"\"},\n", + " {\"role\": \"user\", \"content\": \"Give me an insurance evaluation score\"}\n", + " ],\n", + " model_id=model_id,\n", + " stream=False,\n", + " tools=json_tool\n", + ")\n", + "pprint(response)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "227\n" + ] + } + ], + "source": [ + "# try to manually calculate token size for tool sets.\n", + "from llama_models.llama3.api.chat_format import ChatFormat\n", + "from llama_models.llama3.api.tokenizer import Tokenizer\n", + "import json\n", + "tokenizer = Tokenizer.get_instance() # this is how pull 1300 calculate token size. not sure how it works with other models. https://github.com/meta-llama/llama-stack/pull/1300/files#diff-bfab1a9cce8bb39b87f331653f4bec3fa2c83302337416acafb3be17ac34d73e\n", + "formatter = ChatFormat(tokenizer)\n", + "encoded = formatter.encode_content(json.dumps(json_tool))\n", + "print(len(encoded.tokens))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Investigate how llama stack deal with tools, so that could count tokens properly. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
[\n",
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+       "β”‚   β”‚   β”‚   'description': 'Fetches the current weather for a given location.',\n",
+       "β”‚   β”‚   β”‚   'parameters': {\n",
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+       "β”‚   β”‚   β”‚   β”‚   β”‚   β”‚   'type': 'object',\n",
+       "β”‚   β”‚   β”‚   β”‚   β”‚   β”‚   'description': 'The location for which weather information is requested.'\n",
+       "β”‚   β”‚   β”‚   β”‚   β”‚   }\n",
+       "β”‚   β”‚   β”‚   β”‚   },\n",
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+       "β”‚   β”‚   β”‚   'description': 'Counts the number of words in the given text.',\n",
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+       "β”‚   β”‚   β”‚   β”‚   'type': 'object',\n",
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+       "β”‚   β”‚   β”‚   'name': 'reverse_string',\n",
+       "β”‚   β”‚   β”‚   'description': 'Reverses the given string.',\n",
+       "β”‚   β”‚   β”‚   'parameters': {\n",
+       "β”‚   β”‚   β”‚   β”‚   'type': 'object',\n",
+       "β”‚   β”‚   β”‚   β”‚   'properties': {'param text': {'type': 'object', 'description': 'The input text to reverse.'}},\n",
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+       "β”‚   β”‚   β”‚   'description': 'Converts the given string to uppercase.',\n",
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+       "β”‚   β”‚   β”‚   β”‚   'type': 'object',\n",
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+       "β”‚   {\n",
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+       "β”‚   β”‚   β”‚   'name': 'insurance_scorer',\n",
+       "β”‚   β”‚   β”‚   'description': 'Generates a insurance score between 1 and 100.',\n",
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\u001b[0m\u001b[1m{\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[32m'type'\u001b[0m: \u001b[32m'function'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[32m'function'\u001b[0m: \u001b[1m{\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[32m'name'\u001b[0m: \u001b[32m'reverse_string'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[32m'description'\u001b[0m: \u001b[32m'Reverses the given string.'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[32m'parameters'\u001b[0m: \u001b[1m{\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ β”‚ \u001b[0m\u001b[32m'type'\u001b[0m: \u001b[32m'object'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ β”‚ \u001b[0m\u001b[32m'properties'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'param text'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'type'\u001b[0m: \u001b[32m'object'\u001b[0m, \u001b[32m'description'\u001b[0m: \u001b[32m'The input text to reverse.'\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ β”‚ \u001b[0m\u001b[32m'required'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'param text'\u001b[0m\u001b[1m]\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[1m}\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[1m}\u001b[0m\n", + "\u001b[2;32mβ”‚ \u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32mβ”‚ \u001b[0m\u001b[1m{\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[32m'type'\u001b[0m: \u001b[32m'function'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[32m'function'\u001b[0m: \u001b[1m{\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[32m'name'\u001b[0m: \u001b[32m'uppercase'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[32m'description'\u001b[0m: \u001b[32m'Converts the given string to uppercase.'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[32m'parameters'\u001b[0m: \u001b[1m{\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ β”‚ \u001b[0m\u001b[32m'type'\u001b[0m: \u001b[32m'object'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ β”‚ \u001b[0m\u001b[32m'properties'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'param text'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'type'\u001b[0m: \u001b[32m'object'\u001b[0m, \u001b[32m'description'\u001b[0m: \u001b[32m'The input text to convert.'\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ β”‚ \u001b[0m\u001b[32m'required'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'param text'\u001b[0m\u001b[1m]\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[1m}\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[1m}\u001b[0m\n", + "\u001b[2;32mβ”‚ \u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32mβ”‚ \u001b[0m\u001b[1m{\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[32m'type'\u001b[0m: \u001b[32m'function'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[32m'function'\u001b[0m: \u001b[1m{\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[32m'name'\u001b[0m: \u001b[32m'insurance_scorer'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[32m'description'\u001b[0m: \u001b[32m'Generates a insurance score between 1 and 100.'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[32m'parameters'\u001b[0m: \u001b[1m{\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ β”‚ \u001b[0m\u001b[32m'type'\u001b[0m: \u001b[32m'object'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ β”‚ \u001b[0m\u001b[32m'properties'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'param text'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'type'\u001b[0m: \u001b[32m'object'\u001b[0m, \u001b[32m'description'\u001b[0m: \u001b[32m'The input text to eval.'\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ β”‚ \u001b[0m\u001b[32m'required'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'param text'\u001b[0m\u001b[1m]\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[1m}\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[1m}\u001b[0m\n", + "\u001b[2;32mβ”‚ \u001b[0m\u001b[1m}\u001b[0m\n", + "\u001b[1m]\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# found two ways to convert tool to json format, all use tool definition. try see how it actually works.\n", + "# one by from llama_stack.providers.utils.inference.openai_compat import convert_tooldef_to_openai_tool\n", + "from llama_stack.models.llama.datatypes import ToolDefinition, ToolParamDefinition\n", + "\n", + "def convert_tool_to_tool_definition(tool_func) -> ToolDefinition:\n", + " docstring = tool_func.__doc__\n", + " lines = docstring.strip().split('\\n')\n", + " description = lines[0]\n", + " param_lines = [line.strip() for line in lines if line.strip().startswith(':param')]\n", + "\n", + " parameters = {}\n", + " for line in param_lines:\n", + " parts = line.split(':')\n", + " param_name = parts[1].strip()\n", + " param_desc = parts[2].strip()\n", + " parameters[param_name] = ToolParamDefinition(\n", + " param_type=\"object\",\n", + " description=param_desc,\n", + " required=True\n", + " )\n", + "\n", + " return ToolDefinition(\n", + " tool_name=tool_func.__name__,\n", + " description=description,\n", + " parameters=parameters\n", + " )\n", + "\n", + "# Convert your tools\n", + "tool_definitions = [convert_tool_to_tool_definition(tool) for tool in real_tools]\n", + "\n", + "# Now convert ToolDefinition to JSON using convert_tooldef_to_openai_tool function\n", + "from llama_stack.providers.utils.inference.openai_compat import convert_tooldef_to_openai_tool\n", + "\n", + "json_tools = [convert_tooldef_to_openai_tool(tool_def) for tool_def in tool_definitions]\n", + "pprint(json_tools)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Answer the user's question by making use of the following functions if needed.\n", + "If none of the function can be used, please say so.\n", + "Here is a list of functions in JSON format:\n", + "{\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"weather_info\",\n", + " \"description\": \"Fetches the current weather for a given location.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": [\n", + " {\n", + " \"param loc\": {\n", + " \"type\": \"object\",\n", + " \"description\": \"The location for which weather information is requested.\"\n", + " }\n", + " }\n", + " ],\n", + " \"required\": [\"param loc\"]\n", + " }\n", + " }\n", + "}\n", + "{\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"word_count\",\n", + " \"description\": \"Counts the number of words in the given text.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": [\n", + " {\n", + " \"param text\": {\n", + " \"type\": \"object\",\n", + " \"description\": \"The input text to analyze.\"\n", + " }\n", + " }\n", + " ],\n", + " \"required\": [\"param text\"]\n", + " }\n", + " }\n", + "}\n", + "{\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"reverse_string\",\n", + " \"description\": \"Reverses the given string.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": [\n", + " {\n", + " \"param text\": {\n", + " \"type\": \"object\",\n", + " \"description\": \"The input text to reverse.\"\n", + " }\n", + " }\n", + " ],\n", + " \"required\": [\"param text\"]\n", + " }\n", + " }\n", + "}\n", + "{\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"uppercase\",\n", + " \"description\": \"Converts the given string to uppercase.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": [\n", + " {\n", + " \"param text\": {\n", + " \"type\": \"object\",\n", + " \"description\": \"The input text to convert.\"\n", + " }\n", + " }\n", + " ],\n", + " \"required\": [\"param text\"]\n", + " }\n", + " }\n", + "}\n", + "{\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"insurance_scorer\",\n", + " \"description\": \"Generates a insurance score between 1 and 100.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": [\n", + " {\n", + " \"param text\": {\n", + " \"type\": \"object\",\n", + " \"description\": \"The input text to eval.\"\n", + " }\n", + " }\n", + " ],\n", + " \"required\": [\"param text\"]\n", + " }\n", + " }\n", + "}\n", + "\n", + "Return function calls in JSON format.\n" + ] + } + ], + "source": [ + "# another way to convert tool to json format, by using JsonCustomToolGenerator\n", + "\n", + "from llama_stack.models.llama.llama3.prompt_templates.system_prompts import JsonCustomToolGenerator\n", + "\n", + "prompt_template = JsonCustomToolGenerator().gen(tool_definitions)\n", + "print(prompt_template.render())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# total_tools = 6\n", + "# tools = real_tools + generate_fake_tools(total_tools - len(real_tools)-1)\n", + "# print(len(tools))\n", + "\n", + "# agent = Agent(\n", + "# client=client,\n", + "# model=model_id,\n", + "# instructions=\"\"\"You are an AI tool calling assistant. Must use the correct tool for each query.\n", + "# When using the tools:\n", + "# 1. Extract the relevant number or values from the user's request.\n", + "# 2. Use the correct tool to perform the operation.\n", + "# 3. Present the result clearly.\n", + "# 4. Handle errors gracefully.\"\"\",\n", + "# tools=tools,\n", + "# )\n", + "# query = \"Give me an insurance evaluation score\"\n", + "# i = 1\n", + "# print(f\"\\nUser: {query}\")\n", + "# start_time = time.time()\n", + "# print(f\"Agent id is {agent.agent_id}\")\n", + "# session_id = agent.create_session(f\"tool-experiment-session-{i+1}\")\n", + "# print(f'session id is {session_id}')\n", + "\n", + "# response = agent.create_turn(\n", + "# messages=[\n", + "# {\"role\": \"user\", \"content\": query}\n", + "# ],\n", + "# session_id=session_id,\n", + "# stream=False,\n", + "# )\n", + "# session_response = client.agents.session.retrieve(\n", + "# session_id=session_id,\n", + "# agent_id=agent.agent_id,\n", + "# )\n", + "# pprint(session_response)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# https://github.com/meta-llama/llama-stack/blob/441016bee8c6b3b7ce89e7809a903d3343b705e2/tests/integration/inference/test_text_inference.py#L316C1-L331C81\n", + "# def test_text_chat_completion_with_tool_calling_and_non_streaming(client_with_models, text_model_id, test_case):\n", + "# tc = TestCase(test_case)\n", + "\n", + "# response = client_with_models.inference.chat_completion(\n", + "# model_id=text_model_id,\n", + "# messages=tc[\"messages\"],\n", + "# tools=tc[\"tools\"],\n", + "# tool_choice=\"auto\",\n", + "# stream=False,\n", + "# )\n", + "# # some models can return content for the response in addition to the tool call\n", + "# assert response.completion_message.role == \"assistant\"\n", + "\n", + "# assert len(response.completion_message.tool_calls) == 1\n", + "# assert response.completion_message.tool_calls[0].tool_name == tc[\"tools\"][0][\"tool_name\"]\n", + "# assert response.completion_message.tool_calls[0].arguments == tc[\"expected\"]\n", + "\n", + "# aiming to convert my tools to json format as llama stack do natually and then pass it to client.inference.chat_completion so that i can get some token size" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
ChatCompletionResponse(\n",
+       "β”‚   completion_message=CompletionMessage(\n",
+       "β”‚   β”‚   content='',\n",
+       "β”‚   β”‚   role='assistant',\n",
+       "β”‚   β”‚   stop_reason='end_of_turn',\n",
+       "β”‚   β”‚   tool_calls=[\n",
+       "β”‚   β”‚   β”‚   ToolCall(\n",
+       "β”‚   β”‚   β”‚   β”‚   arguments={'text': 'I am a responsible driver with good grades and no accidents'},\n",
+       "β”‚   β”‚   β”‚   β”‚   call_id='dce6a136-25a8-4833-9395-09779cb82cc2',\n",
+       "β”‚   β”‚   β”‚   β”‚   tool_name='insurance_scorer',\n",
+       "β”‚   β”‚   β”‚   β”‚   arguments_json='{\"text\": \"I am a responsible driver with good grades and no accidents\"}'\n",
+       "β”‚   β”‚   β”‚   )\n",
+       "β”‚   β”‚   ]\n",
+       "β”‚   ),\n",
+       "β”‚   logprobs=None,\n",
+       "β”‚   metrics=[\n",
+       "β”‚   β”‚   Metric(metric='prompt_tokens', value=90.0, unit=None),\n",
+       "β”‚   β”‚   Metric(metric='completion_tokens', value=44.0, unit=None),\n",
+       "β”‚   β”‚   Metric(metric='total_tokens', value=134.0, unit=None)\n",
+       "β”‚   ]\n",
+       ")\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;35mChatCompletionResponse\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32mβ”‚ \u001b[0m\u001b[33mcompletion_message\u001b[0m=\u001b[1;35mCompletionMessage\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[33mcontent\u001b[0m=\u001b[32m''\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[33mrole\u001b[0m=\u001b[32m'assistant'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[33mstop_reason\u001b[0m=\u001b[32m'end_of_turn'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[33mtool_calls\u001b[0m=\u001b[1m[\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[1;35mToolCall\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ β”‚ \u001b[0m\u001b[33marguments\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'text'\u001b[0m: \u001b[32m'I am a responsible driver with good grades and no accidents'\u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ β”‚ \u001b[0m\u001b[33mcall_id\u001b[0m=\u001b[32m'dce6a136-25a8-4833-9395-09779cb82cc2'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ β”‚ \u001b[0m\u001b[33mtool_name\u001b[0m=\u001b[32m'insurance_scorer'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ β”‚ \u001b[0m\u001b[33marguments_json\u001b[0m=\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"text\": \"I am a responsible driver with good grades and no accidents\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[1m)\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[1m]\u001b[0m\n", + "\u001b[2;32mβ”‚ \u001b[0m\u001b[1m)\u001b[0m,\n", + "\u001b[2;32mβ”‚ \u001b[0m\u001b[33mlogprobs\u001b[0m=\u001b[3;35mNone\u001b[0m,\n", + "\u001b[2;32mβ”‚ \u001b[0m\u001b[33mmetrics\u001b[0m=\u001b[1m[\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[1;35mMetric\u001b[0m\u001b[1m(\u001b[0m\u001b[33mmetric\u001b[0m=\u001b[32m'prompt_tokens'\u001b[0m, \u001b[33mvalue\u001b[0m=\u001b[1;36m90\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[33munit\u001b[0m=\u001b[3;35mNone\u001b[0m\u001b[1m)\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[1;35mMetric\u001b[0m\u001b[1m(\u001b[0m\u001b[33mmetric\u001b[0m=\u001b[32m'completion_tokens'\u001b[0m, \u001b[33mvalue\u001b[0m=\u001b[1;36m44\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[33munit\u001b[0m=\u001b[3;35mNone\u001b[0m\u001b[1m)\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[1;35mMetric\u001b[0m\u001b[1m(\u001b[0m\u001b[33mmetric\u001b[0m=\u001b[32m'total_tokens'\u001b[0m, \u001b[33mvalue\u001b[0m=\u001b[1;36m134\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[33munit\u001b[0m=\u001b[3;35mNone\u001b[0m\u001b[1m)\u001b[0m\n", + "\u001b[2;32mβ”‚ \u001b[0m\u001b[1m]\u001b[0m\n", + "\u001b[1m)\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "response = client.inference.chat_completion(\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"\"\"You are an AI tool calling assistant. Must use the correct tool for each query.\n", + " When using the tools:\n", + " 1. Extract the relevant number or values from the user's request.\n", + " 2. Use the correct tool to perform the operation.\n", + " 3. Present the result clearly.\n", + " 4. Handle errors gracefully.\"\"\"},\n", + " {\"role\": \"user\", \"content\": \"Give me an insurance evaluation score\"}\n", + " ],\n", + " model_id=model_id,\n", + " stream=False,\n", + " tools=json_tool\n", + ")\n", + "pprint(response)\n", + "\n", + "assert response.completion_message.role == \"assistant\"\n", + "assert len(response.completion_message.tool_calls) == 1\n", + "assert response.completion_message.tool_calls[0].tool_name == \"insurance_scorer\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "stack-client", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.1-8B-Instruct_local_20250319_195333.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.1-8B-Instruct_local_20250319_195333.csv new file mode 100644 index 0000000..db97d87 --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.1-8B-Instruct_local_20250319_195333.csv @@ -0,0 +1,18 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,7.168120718002319 +6,0.0,1.0,1.0,5.706745910644531 +7,0.0,1.0,1.0,5.765239620208741 +8,0.0,1.0,1.0,5.657627248764038 +9,0.0,1.0,1.0,5.875210905075074 +10,0.0,1.0,1.0,5.7309229373931885 +11,0.0,1.0,1.0,5.963864517211914 +12,0.0,1.0,1.0,6.3765569686889645 +13,0.0,1.0,1.0,6.2285350322723385 +14,0.0,1.0,1.0,6.2883015155792235 +15,0.0,1.0,1.0,7.525290584564209 +16,0.0,1.0,1.0,7.0993012428283695 +17,0.0,1.0,1.0,6.762645149230957 +18,0.0,1.0,1.0,6.543761110305786 +19,0.0,1.0,1.0,6.474275779724121 +20,0.0,1.0,1.0,18.422873973846436 +21,0.0,0.0,0.0,12.641533184051514 diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.1-8B-Instruct_local_20250319_201538.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.1-8B-Instruct_local_20250319_201538.csv new file mode 100644 index 0000000..3f1d85d --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.1-8B-Instruct_local_20250319_201538.csv @@ -0,0 +1,18 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,5.9044859409332275 +6,0.0,1.0,1.0,5.604955244064331 +7,0.0,1.0,1.0,5.33694863319397 +8,0.0,1.0,1.0,5.688683986663818 +9,0.0,1.0,1.0,5.474440670013427 +10,0.0,1.0,1.0,6.431410408020019 +11,0.0,1.0,1.0,5.6390461921691895 +12,0.0,1.0,1.0,5.6270452499389645 +13,0.0,1.0,1.0,7.1502515316009525 +14,0.0,1.0,1.0,6.310414361953735 +15,0.0,1.0,1.0,6.158989143371582 +16,0.0,1.0,1.0,6.190614366531372 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+15,0.0,1.0,1.0,6.28502779006958 +16,0.0,1.0,1.0,6.42034330368042 +17,0.0,1.0,1.0,6.773412990570068 +18,0.0,1.0,1.0,6.839489889144898 +19,0.0,1.0,1.0,6.61051664352417 +20,0.0,1.0,1.0,10.407156705856323 +21,0.0,0.0,0.0,10.343230390548706 diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.1-8B-Instruct_local_20250320_140540.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.1-8B-Instruct_local_20250320_140540.csv new file mode 100644 index 0000000..b73fb65 --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.1-8B-Instruct_local_20250320_140540.csv @@ -0,0 +1,18 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,7.1565908908844 +6,0.0,1.0,1.0,5.619954681396484 +7,0.0,1.0,1.0,5.554626321792602 +8,0.0,1.0,1.0,5.301392412185669 +9,0.0,1.0,1.0,5.610034990310669 +10,0.0,1.0,1.0,7.514761304855346 +11,0.0,1.0,1.0,5.92407193183899 +12,0.0,1.0,1.0,6.148418283462524 +13,0.0,1.0,1.0,6.222324991226197 +14,0.0,1.0,1.0,6.336846446990966 +15,0.0,1.0,1.0,6.335723352432251 +16,0.0,1.0,1.0,7.065677642822266 +17,0.0,1.0,1.0,6.502049970626831 +18,0.0,1.0,1.0,6.536556816101074 +19,0.0,1.0,1.0,6.672611999511719 +20,0.0,1.0,1.0,10.43622579574585 +21,0.0,0.0,0.0,16.805323457717897 diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.1-8B-Instruct_local_20250320_141653_extenddocstring.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.1-8B-Instruct_local_20250320_141653_extenddocstring.csv new file mode 100644 index 0000000..045adde --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.1-8B-Instruct_local_20250320_141653_extenddocstring.csv @@ -0,0 +1,15 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,6.324452209472656 +6,0.0,1.0,1.0,6.517477655410767 +7,0.0,1.0,1.0,5.676704216003418 +8,0.0,1.0,1.0,6.57601261138916 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+6,0.0,1.0,1.0,6.162015581130982 +7,0.0,1.0,1.0,5.518191003799439 +8,0.0,1.0,1.0,5.77085394859314 +9,0.0,1.0,1.0,5.962193727493286 +10,0.0,1.0,1.0,6.137166547775268 +11,0.0,1.0,1.0,5.96928219795227 +12,0.0,1.0,1.0,6.386389112472534 +13,0.0,1.0,1.0,7.214794778823853 +14,0.0,1.0,1.0,6.898428344726563 +15,0.0,1.0,1.0,7.058244752883911 +16,0.0,1.0,1.0,18.302621269226073 +17,0.0,0.0,0.0,12.695356369018555 diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.1-8B-Instruct_local_20250324_130106.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.1-8B-Instruct_local_20250324_130106.csv new file mode 100644 index 0000000..d0f8f6a --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.1-8B-Instruct_local_20250324_130106.csv @@ -0,0 +1,18 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,5.8334942817687985 +6,0.0,1.0,1.0,6.188730144500733 +7,0.0,1.0,1.0,6.305921363830566 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b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250319_192337.csv @@ -0,0 +1,21 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,1.9605723857879638 +6,0.0,1.0,1.0,1.9430059909820556 +7,0.0,1.0,1.0,2.0025591373443605 +8,0.0,1.0,1.0,2.4212567806243896 +9,0.0,1.0,1.0,2.1397215843200685 +10,0.0,1.0,1.0,2.4745744705200194 +11,0.0,1.0,1.0,2.185876560211182 +12,0.0,1.0,1.0,2.024741268157959 +13,0.0,1.0,1.0,2.5074799060821533 +14,0.0,1.0,1.0,2.274612236022949 +15,0.0,1.0,1.0,2.5944032192230226 +16,0.0,1.0,1.0,2.5398499011993407 +17,0.0,1.0,1.0,2.373843050003052 +18,0.0,1.0,1.0,2.681550645828247 +19,0.0,1.0,1.0,2.715683650970459 +20,0.0,1.0,1.0,2.727094268798828 +21,0.0,1.0,1.0,2.520679998397827 +22,0.0,1.0,1.0,2.9902826309204102 +23,0.0,1.0,1.0,8.307605171203614 +24,0.0,0.0,0.0,6.446945095062256 diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250319_193310.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250319_193310.csv new file mode 100644 index 0000000..daf2c7c --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250319_193310.csv @@ -0,0 +1,21 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,1.956993532180786 +6,0.0,1.0,1.0,2.010591650009155 +7,0.0,1.0,1.0,2.040410375595093 +8,0.0,1.0,1.0,2.1072919845581053 +9,0.0,1.0,1.0,2.1609561920166014 +10,0.0,1.0,1.0,2.273438549041748 +11,0.0,1.0,1.0,2.102739667892456 +12,0.0,1.0,1.0,2.0397234916687013 +13,0.0,1.0,1.0,2.8388110637664794 +14,0.0,1.0,1.0,2.5578278064727784 +15,0.0,1.0,1.0,2.763335371017456 +16,0.0,1.0,1.0,2.12097053527832 +17,0.0,1.0,1.0,2.350267028808594 +18,0.0,1.0,1.0,2.716095209121704 +19,0.0,1.0,1.0,2.4806845664978026 +20,0.0,1.0,1.0,2.368894338607788 +21,0.0,1.0,1.0,2.8748481273651123 +22,0.0,1.0,1.0,2.9112053871154786 +23,0.0,1.0,1.0,8.163079261779785 +24,0.0,0.0,0.0,5.315383005142212 diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250324_133356.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250324_133356.csv new file mode 100644 index 0000000..0ae2185 --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250324_133356.csv @@ -0,0 +1,8 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,2.5925121307373047 +6,0.0,1.0,1.0,2.211979055404663 +7,0.0,1.0,1.0,2.0513830184936523 +8,0.0,1.0,1.0,2.1493160724639893 +9,0.0,1.0,1.0,2.7783721446990968 +10,0.0,1.0,1.0,2.462459182739258 +11,0.0,0.8,0.8,2.0407893657684326 diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250324_134029.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250324_134029.csv new file mode 100644 index 0000000..0061828 --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250324_134029.csv @@ -0,0 +1,20 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,1.8828797817230225 +6,0.0,1.0,1.0,2.201099491119385 +7,0.0,1.0,1.0,1.9882763385772706 +8,0.0,1.0,1.0,2.6543992519378663 +9,0.0,1.0,1.0,2.435856819152832 +10,0.0,1.0,1.0,2.0422706604003906 +11,0.0,1.0,1.0,3.048834228515625 +12,0.0,1.0,1.0,2.253065299987793 +13,0.0,1.0,1.0,3.222274589538574 +14,0.0,1.0,1.0,2.370079517364502 +15,0.0,1.0,1.0,3.0646922111511232 +16,0.0,1.0,1.0,3.894747591018677 +17,0.0,1.0,1.0,2.941934585571289 +18,0.0,1.0,1.0,2.649833583831787 +19,0.0,1.0,1.0,2.6706958293914793 +20,0.0,1.0,1.0,2.6951428413391114 +21,0.0,1.0,1.0,2.714418077468872 +22,0.0,1.0,1.0,3.809346342086792 +23,0.0,0.0,0.0,4.6187090396881105 diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250324_134905.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250324_134905.csv new file mode 100644 index 0000000..d8b66f1 --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250324_134905.csv @@ -0,0 +1,15 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,2.0433825492858886 +6,0.0,1.0,1.0,1.791330623626709 +7,0.0,1.0,1.0,2.0724017143249513 +8,0.0,1.0,1.0,2.657204818725586 +9,0.0,1.0,1.0,2.413788938522339 +10,0.0,1.0,1.0,2.4713243007659913 +11,0.0,1.0,1.0,2.2381070613861085 +12,0.0,1.0,1.0,2.511775541305542 +13,0.0,1.0,1.0,2.9209228515625 +14,0.0,1.0,1.0,2.358298683166504 +15,0.0,1.0,1.0,2.576124668121338 +16,0.0,1.0,1.0,3.155591869354248 +17,0.0,1.0,1.0,2.81975417137146 +18,0.0,0.8,0.8,2.599167251586914 diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250324_153706.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250324_153706.csv new file mode 100644 index 0000000..4a332a5 --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_20250324_153706.csv @@ -0,0 +1,15 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,2.3763480186462402 +6,0.0,1.0,1.0,1.8460272789001464 +7,0.0,1.0,1.0,1.9013474941253663 +8,0.0,1.0,1.0,1.9353102684020995 +9,0.0,1.0,1.0,2.657464361190796 +10,0.0,1.0,1.0,2.334258222579956 +11,0.0,1.0,1.0,2.681872081756592 +12,0.0,1.0,1.0,2.8686394691467285 +13,0.0,1.0,1.0,2.0044567584991455 +14,0.0,1.0,1.0,2.994369077682495 +15,0.0,1.0,1.0,2.7436699867248535 +16,0.0,1.0,1.0,3.1351931571960447 +17,0.0,1.0,1.0,2.3304506301879884 +18,0.0,0.8,0.8,2.413019371032715 diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.001_20250326_104445.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.001_20250326_104445.csv new file mode 100644 index 0000000..92f636c --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.001_20250326_104445.csv @@ -0,0 +1,14 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,2.8157065391540526 +6,0.0,1.0,1.0,1.8637529850006103 +7,0.0,1.0,1.0,2.1495183944702148 +8,0.0,1.0,1.0,2.0987974643707275 +9,0.0,1.0,1.0,2.1226954460144043 +10,0.0,1.0,1.0,2.8045509338378904 +11,0.0,1.0,1.0,2.1693278312683106 +12,0.0,1.0,1.0,2.4525922775268554 +13,0.0,1.0,1.0,2.518063259124756 +14,0.0,1.0,1.0,2.534862422943115 +15,0.0,1.0,1.0,2.427891159057617 +16,0.0,1.0,1.0,2.569189214706421 +17,0.0,0.8,0.8,2.0277631759643553 diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.001_20250326_104942.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.001_20250326_104942.csv new file mode 100644 index 0000000..6b4a8a3 --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.001_20250326_104942.csv @@ -0,0 +1,9 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,2.0422457218170167 +6,0.0,1.0,1.0,1.92123122215271 +7,0.0,1.0,1.0,1.934469223022461 +8,0.0,1.0,1.0,2.044729995727539 +9,0.0,1.0,1.0,2.3833662033081056 +10,0.0,1.0,1.0,2.524967575073242 +11,0.0,1.0,1.0,2.811110591888428 +12,0.0,0.8,0.8,1.8967945098876953 diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.001_20250326_112532.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.001_20250326_112532.csv new file mode 100644 index 0000000..db9d2a5 --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.001_20250326_112532.csv @@ -0,0 +1,13 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,2.053250026702881 +6,0.0,1.0,1.0,1.8666814327239991 +7,0.0,1.0,1.0,2.4705746173858643 +8,0.0,1.0,1.0,2.013365602493286 +9,0.0,1.0,1.0,2.071187162399292 +10,0.0,1.0,1.0,2.365837812423706 +11,0.0,1.0,1.0,2.1435930728912354 +12,0.0,1.0,1.0,2.229608678817749 +13,0.0,1.0,1.0,2.207880067825317 +14,0.0,1.0,1.0,2.376689338684082 +15,0.0,1.0,1.0,2.4068315029144287 +16,0.0,0.8,0.8,2.357902240753174 diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.001_20250326_112532.log b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.001_20250326_112532.log new file mode 100644 index 0000000..d1fa9d7 --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.001_20250326_112532.log @@ -0,0 +1,516 @@ +http://localhost:8321 +5 + +User: What is the weather in New York? +Agent id is dc3270ea-cdd9-4096-8853-489f0c99d066 +session id is 79cef771-ac27-4619-9f3d-97843c535560 +Inference: Note: The actual output may vary based on the current weather conditions. This response is a placeholder. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is 09741f7b-17a4-4060-bd06-93db58c2411c +session id is 921b8aca-2c92-4da0-aa03-80a966809215 +Inference: The text contains 7 words. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 5dc404e1-50cb-42c1-aebe-9e4cb9b9fe5b +session id is 12ed9795-9279-4663-9c23-30b26d224b84 +Inference: The reversed text is "tnemirepxE nohtyP". +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 6b3300cb-dc4e-45a1-81d9-a04aca79635f +session id is 5b9c0400-075a-4dd4-a78e-990d2d3a0feb +Inference: The word "llamastack" has been successfully converted to uppercase. The result is LLAMASTACK. +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is ccd13547-605b-428b-a255-a517fe648fd8 +session id is db7cf277-a1da-4149-ab91-d86f15717409 +Inference: The insurance scorer has generated a score of 91 based on the provided text. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 5, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.0533s +5 + +User: What is the weather in New York? +Agent id is 450454ee-9811-4f6a-816c-bffde5dbab5b +session id is c56fe2c1-13e1-48c2-baa0-17136bab31c3 +Inference: Note: The actual output may vary based on the current weather conditions. This response is a placeholder. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is 964555ca-8bb8-4df9-9347-d109c9151966 +session id is db12301d-59d2-454f-92ac-4146ae8e87c7 +Inference: The text contains 7 words. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 93fa73cd-fadd-41e5-bf0d-79dd6f76d15d +session id is 9bc0328c-7cba-4ffe-b196-c75c6e533685 +Inference: The reversed text is "tnemirepxE nohtyP". +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 1dccf5b3-1dd9-43ae-a91f-0d775a43d05a +session id is a38ded09-102b-4c62-9034-269b910871ed +Inference: The word "llamastack" has been successfully converted to uppercase. The result is LLAMASTACK. +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is ce55ffd2-c4d6-4e48-b2f5-2d8015fc7cb5 +session id is 6bd6e943-225f-462c-b599-c72ff3ee5572 +Inference: The insurance scorer has generated a score of 63 based on the provided text. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 6, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 1.8667s +tool_0_golf delta +Tool 0 performs a unique operation on the input data. hotel golf papa zulu alpha foxtrot victor char +6 + +User: What is the weather in New York? +Agent id is b9819f36-b5af-43d5-908c-af028ed34960 +session id is 53f923aa-de57-4b05-be9b-f10fa5f4764e +Inference: Note: The actual output may vary based on the current weather conditions. This response is a placeholder. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is 9d395f72-e1da-4bd8-8884-6a8c221eae5d +session id is d6c759b6-079d-432d-9abd-588af10d20e8 +Inference: The word count for the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 958e2767-a1ed-4141-b89e-d43c0123143c +session id is a3491257-52de-4f8d-b25c-eca082cdced6 +Inference: What would you like to do next? +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 05e75d0e-2d9b-403d-8ccc-3c8e9ca06246 +session id is 764482d7-f358-448d-8db9-dd7460b63667 +Inference: The input string "llamastack" has been successfully converted to uppercase and the result is "LLAMASTACK". +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is 6cd30939-38e5-480e-b875-3b268cf6f8fc +session id is 17277195-a32f-499c-a1e9-b740aed6f7c6 +Inference: I've generated a random insurance score of 7. Please note that this is not an actual insurance score and should not be used for any real-world decision-making. The `insurance_scorer` function is just a placeholder for demonstration purposes. If you need an actual insurance evaluation, please consult with a licensed insurance professional or use a real insurance scoring tool. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 7, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.4706s +tool_0_mike quebec +Tool 0 performs a unique operation on the input data. foxtrot mike whiskey whiskey delta alpha india +tool_1_oscar tango +Tool 1 performs a unique operation on the input data. romeo mike lima india oscar hotel oscar quebec +7 + +User: What is the weather in New York? +Agent id is cc720f5b-f1e5-46f8-8006-2534c93f2ee0 +session id is db5d2f8d-ee78-412b-8c9b-440474897d88 +Inference: Note: The actual output of the `weather_info` function may vary based on the current weather conditions. This response is a placeholder. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is d8ab2e7a-158a-426a-863d-89cec99e2b33 +session id is 0d901947-5383-41a0-a9af-638b45dc167c +Inference: The word count for the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 06628bb9-8720-4616-a6c0-7af41068fb5d +session id is 6a34ba70-1733-4b12-b05f-2b12f71be26a +Inference: What would you like to do next? +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is c28d9f56-87aa-40b5-a617-cecb2adec39d +session id is 2e68a3d5-75ac-4060-9254-67c29bc4b2af +Inference: The word "llamastack" has been successfully converted to uppercase. The result is LLAMASTACK. +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is b7c21ed1-57b4-4148-b565-04e08eec9459 +session id is 0c4d0981-241c-4125-8f5c-6a2a4680356a +Inference: I need to know the input text for the insurance scorer. Please provide it and I'll give you the result. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 8, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.0134s +tool_0_charlie mike +Tool 0 performs a unique operation on the input data. tango india yankee mike sierra mike x-ray vict +tool_1_foxtrot sierra +Tool 1 performs a unique operation on the input data. papa echo mike charlie echo alpha uniform yank +tool_2_lima charlie +Tool 2 performs a unique operation on the input data. hotel foxtrot kilo hotel india tango hotel pap +8 + +User: What is the weather in New York? +Agent id is 8f91ad32-0c18-4b25-9c65-829c0677b2b4 +session id is 88dbbbe8-6ca7-4b81-813b-0b5a4d448ae2 +Inference: Note: The actual output may vary based on the current weather conditions. This response is a placeholder. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is 133d10cf-5adb-42bd-9ed7-d7ce224469c5 +session id is 5e1b45cf-de72-448b-8725-b99fabee7b09 +Inference: The word count for the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 70f08b67-015c-4088-b787-c484271d90fe +session id is 9324efdb-c64a-40fa-ab29-269e5ea739b9 +Inference: What would you like to do next? +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 900375c3-4dc7-4838-b50a-cfe7250ea3b7 +session id is ef16a51a-070b-4279-b92f-34e4118427d8 +Inference: I used the `uppercase` function to convert the input string "llamastack" to uppercase. The result is "LLAMASTACK". +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is dd754c85-552f-4fd6-97a2-dddff090ba35 +session id is d17c29f7-07f0-47ac-94d8-4443bd89d200 +Inference: It seems like you didn't provide the required information for the insurance scorer. Please provide your insurance policy details to get an accurate score. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 9, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.0712s +tool_0_sierra victor +Tool 0 performs a unique operation on the input data. lima juliet quebec victor papa golf foxtrot ta +tool_1_tango alpha +Tool 1 performs a unique operation on the input data. november delta oscar juliet x-ray juliet x-ray +tool_2_india lima +Tool 2 performs a unique operation on the input data. x-ray hotel zulu juliet delta kilo victor delt +tool_3_tango romeo +Tool 3 performs a unique operation on the input data. charlie quebec charlie romeo tango papa papa w +9 + +User: What is the weather in New York? +Agent id is 823be703-047f-40a0-a5b6-005fb1e637f2 +session id is 721303a5-b718-4049-9df6-9f553b2fb44f +Inference: Note: Since I don't have real-time access to current weather information, I provided a generic response. In a real-world scenario, you would replace the result with actual weather data from a reliable source. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is d7fb82b2-44eb-4744-a1d6-0e7f95adfbc0 +session id is 40f632d8-8932-4fe6-a770-eafaa9808785 +Inference: The word count for the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 6e92ded6-c6c0-47ba-ae8a-6ae0dc74a5f8 +session id is cdd3862c-6416-46bf-8c3a-7a1a162207da +Inference: What would you like to do next? +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is b87ed4ba-5e01-4aa9-8f1f-5e742941aaea +session id is 8d3ab1cb-9c7d-43c9-b9f4-ca8f5fd27e43 +Inference: I used the `uppercase` function to convert the input string "llamastack" to uppercase. The result is "LLAMASTACK". +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is fff96e5e-61ac-425d-9833-0238d6ca76da +session id is 109830d9-066c-49aa-b14f-cdb2d4eff836 +Inference: It seems like you didn't provide the required information for the insurance scorer. Please provide your insurance policy details to get an accurate score. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 10, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.3658s +tool_0_quebec sierra +Tool 0 performs a unique operation on the input data. echo papa delta lima sierra alpha mike oscar j +tool_1_quebec papa +Tool 1 performs a unique operation on the input data. juliet golf sierra delta foxtrot juliet juliet +tool_2_whiskey whiskey +Tool 2 performs a unique operation on the input data. mike romeo delta tango victor echo kilo yankee +tool_3_uniform foxtrot +Tool 3 performs a unique operation on the input data. kilo yankee india golf papa mike zulu juliet o +tool_4_golf echo +Tool 4 performs a unique operation on the input data. delta whiskey mike november echo foxtrot lima +10 + +User: What is the weather in New York? +Agent id is dfe8957c-faa1-459f-b5a2-55a6e19c744b +session id is f5c3c286-8e07-42d1-80b7-8e636f58ba10 +Inference: Note: The actual output may vary based on the current weather conditions. This response is a placeholder. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is ea3f8a22-c876-4aa4-9b46-6ec0f80513f4 +session id is e8f6364c-b079-421c-a69d-9a6457212049 +Inference: The word count for the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is c90ecac8-885a-42f5-9042-7cf7c8b5fb2c +session id is 48f20d2f-95d3-45e2-bbbe-5e769bd1192c +Inference: What would you like to do next? +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 20bbca3d-5574-4244-b993-7444b577c4f3 +session id is 34baf039-1f42-4a30-b725-ff12a904195a +Inference: I used the `uppercase` function to convert the input string "llamastack" to uppercase. The result is "LLAMASTACK". +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is c807df15-f6d6-4377-a761-6231f5dfff7d +session id is 1c176cfe-2b6a-4958-a2eb-ac2aab15b70d +Inference: It seems like you didn't provide the required information for the insurance scorer. Please provide your insurance policy details to get an accurate score. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 11, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.1436s +tool_0_echo india +Tool 0 performs a unique operation on the input data. delta victor papa bravo charlie charlie yankee +tool_1_delta yankee +Tool 1 performs a unique operation on the input data. hotel echo bravo oscar echo alpha uniform alph +tool_2_charlie zulu +Tool 2 performs a unique operation on the input data. delta oscar kilo november x-ray bravo sierra u +tool_3_november mike +Tool 3 performs a unique operation on the input data. echo bravo lima november alpha uniform kilo pa +tool_4_zulu oscar +Tool 4 performs a unique operation on the input data. sierra delta kilo alpha tango mike zulu foxtro +tool_5_tango bravo +Tool 5 performs a unique operation on the input data. november echo tango romeo quebec delta papa pa +11 + +User: What is the weather in New York? +Agent id is 62bd2892-d8b1-40d5-882a-afdf270639ac +session id is f4366658-91ef-462c-bd32-e32c18313467 +Inference: Note: The actual output may vary based on the current weather conditions. This response is a placeholder. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is dacb3f2a-33c2-497c-b36b-351a97bac6f8 +session id is 58147fd7-2e6b-4c27-b462-0c00d6955dc5 +Inference: The word count for the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 202edbec-8b13-4bf2-9509-a37dd5e07e68 +session id is d355d2a1-43db-4689-aa63-1133802db467 +Inference: What would you like to do next? +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 8f0c3d7b-c820-4fd3-b3e0-a698140810ab +session id is df7d2f3a-5492-424d-a073-e0e07c6be246 +Inference: I used the `uppercase` function to convert the input string "llamastack" to uppercase. The result is "LLAMASTACK". +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is 30dbb82c-7ff8-4ed7-8d1b-9228a7ac3663 +session id is c70cc916-3a91-47b5-ba02-fae4f5d4a30e +Inference: This is a successful response from the `insurance_scorer` tool. The output indicates that the input text was processed successfully and an insurance score of 96 was generated. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 12, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.2296s +tool_0_victor charlie +Tool 0 performs a unique operation on the input data. x-ray mike echo papa golf tango victor papa un +tool_1_mike sierra +Tool 1 performs a unique operation on the input data. mike kilo x-ray victor charlie oscar oscar jul +tool_2_delta x-ray +Tool 2 performs a unique operation on the input data. november zulu alpha zulu bravo bravo charlie p +tool_3_mike oscar +Tool 3 performs a unique operation on the input data. uniform victor papa delta x-ray golf juliet x- +tool_4_kilo echo +Tool 4 performs a unique operation on the input data. juliet victor kilo quebec bravo whiskey x-ray +tool_5_hotel mike +Tool 5 performs a unique operation on the input data. kilo oscar quebec golf india echo mike quebec +tool_6_echo golf +Tool 6 performs a unique operation on the input data. november juliet foxtrot charlie alpha yankee t +12 + +User: What is the weather in New York? +Agent id is 1e9d8f9d-a6a1-4da1-ad02-e36a4cd49ec6 +session id is 287c125a-57f2-4f62-8482-186dcddf0258 +Inference: Note: The actual output may vary based on the current weather conditions. This response is a placeholder. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is 77d0c8fe-6b0b-46d9-966e-34c75b7e8ee1 +session id is e7c2bc29-0939-44e0-97c4-5d767eb77bdc +Inference: The word count for the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 308392ac-eded-436d-8b7e-ea55312efd6b +session id is fe94d846-bdfa-40f8-8eba-e703e3435d9f +Inference: What would you like to do next? +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 65106c68-ff50-474d-86df-ffb581042df7 +session id is 29039655-ea72-42d3-9535-f7262af36c89 +Inference: I used the `uppercase` function to convert the input string "llamastack" to uppercase. The result is "LLAMASTACK". +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is d448aa61-1368-4ca1-88e7-3530eebeec75 +session id is 178e03a2-01d5-4011-b19f-0bffa4e8bdec +Inference: It seems like you didn't provide the required information for the insurance evaluation. Please provide your insurance policy details so I can assist you further. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 13, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.2079s +tool_0_kilo kilo +Tool 0 performs a unique operation on the input data. kilo sierra lima foxtrot charlie mike november +tool_1_echo victor +Tool 1 performs a unique operation on the input data. golf kilo tango kilo lima india echo romeo zul +tool_2_delta india +Tool 2 performs a unique operation on the input data. alpha uniform bravo victor charlie victor kilo +tool_3_mike yankee +Tool 3 performs a unique operation on the input data. charlie x-ray mike bravo juliet tango charlie +tool_4_papa sierra +Tool 4 performs a unique operation on the input data. oscar x-ray x-ray foxtrot yankee echo mike pap +tool_5_charlie lima +Tool 5 performs a unique operation on the input data. lima uniform echo echo india romeo lima hotel +tool_6_yankee hotel +Tool 6 performs a unique operation on the input data. juliet tango november yankee victor juliet zul +tool_7_yankee hotel +Tool 7 performs a unique operation on the input data. whiskey hotel yankee india juliet tango alpha +13 + +User: What is the weather in New York? +Agent id is 88949f74-fc69-478d-9fe7-c786f3c51a10 +session id is c24934ca-63e5-40b0-a951-637fbe2a29ed +Inference: Note: The actual output may vary based on the current weather conditions. This response is a placeholder. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is a50cdcb4-c44f-4dab-b740-a998eb89fe95 +session id is d1557fde-26c9-4875-bfe2-28f6af5b38aa +Inference: The word count for the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 107295e6-454a-4cbe-a880-3b0d5c3eb391 +session id is f7d2d818-cd58-4529-8796-a50a5a9da963 +Inference: Here is the result of using tool 5_charlie lima on input "Python Experiment". +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 7679440f-cc18-4dcb-9059-a48e34e63879 +session id is e18ba799-2f8f-4e19-9517-4b6c37d5e16c +Inference: I used the `uppercase` function to convert the input string "llamastack" to uppercase. The result is "LLAMASTACK". +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is 1e49eccc-90c2-4511-8882-3c5e7cecbbe9 +session id is b1009988-193e-4605-8554-8aca60c95911 +Inference: It seems like you didn't provide the required information for the insurance policy details. Please provide the necessary details to get an accurate score. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 14, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.3767s +tool_0_victor victor +Tool 0 performs a unique operation on the input data. hotel alpha romeo juliet lima victor kilo unif +tool_1_victor quebec +Tool 1 performs a unique operation on the input data. kilo whiskey delta oscar whiskey sierra oscar +tool_2_bravo delta +Tool 2 performs a unique operation on the input data. lima quebec x-ray mike papa papa november juli +tool_3_sierra bravo +Tool 3 performs a unique operation on the input data. romeo echo echo x-ray papa golf victor bravo t +tool_4_sierra bravo +Tool 4 performs a unique operation on the input data. echo zulu whiskey mike mike kilo charlie lima +tool_5_quebec foxtrot +Tool 5 performs a unique operation on the input data. foxtrot kilo india bravo hotel oscar golf indi +tool_6_golf kilo +Tool 6 performs a unique operation on the input data. mike zulu yankee quebec golf echo victor delta +tool_7_romeo yankee +Tool 7 performs a unique operation on the input data. juliet sierra papa lima mike uniform yankee de +tool_8_whiskey echo +Tool 8 performs a unique operation on the input data. foxtrot lima x-ray mike yankee hotel india del +14 + +User: What is the weather in New York? +Agent id is acbf1fc4-1011-4c25-8737-923b8dbbba4e +session id is c21d1be2-4fcc-4078-8b98-bcb8824012af +Inference: Note: Since I don't have real-time access to current weather information, the result is a placeholder. In a real scenario, you would replace it with the actual weather data for New York. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is e7d43811-4d1e-4b7e-973b-e2e2fdf69dbf +session id is 4b9ced36-2a6a-4a73-aec8-0e8d84ea43ad +Inference: The word count for the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is bdbfadca-7828-41cf-b891-48050a35e366 +session id is 82b0f74d-cfb7-4a4a-8131-07692b9f72c4 +Inference: Here is the result of using tool 0 on the input "Python Experiment". +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is b224cbdf-1501-4ffd-a73b-9aa61eb0eb0b +session id is bf3fe203-8913-455e-b168-0fa92a914a4e +Inference: The word "llamastack" in uppercase is "LLAMASTACK". +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is 950412dd-5baf-460b-83ef-71adafa4e3ce +session id is fd72ae4e-2467-4ef1-966c-175adaeb0212 +Inference: It seems like you didn't provide the required information for the insurance scorer. Please provide your insurance policy details to get an accurate score. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 15, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.4068s +tool_0_foxtrot echo +Tool 0 performs a unique operation on the input data. tango papa uniform lima yankee juliet papa lim +tool_1_victor victor +Tool 1 performs a unique operation on the input data. oscar hotel november november kilo sierra whis +tool_2_sierra foxtrot +Tool 2 performs a unique operation on the input data. kilo yankee delta mike tango oscar golf hotel +tool_3_lima quebec diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.5_20250326_121319.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.5_20250326_121319.csv new file mode 100644 index 0000000..62221ad --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.5_20250326_121319.csv @@ -0,0 +1,9 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,2.7309218406677247 +6,0.0,1.0,1.0,1.8516632080078126 +7,0.0,1.0,1.0,2.943095350265503 +8,0.0,1.0,1.0,1.9520610332489015 +9,0.0,1.0,1.0,2.3773645877838137 +10,0.0,1.0,1.0,2.5030558109283447 +11,0.0,1.0,1.0,2.751538705825806 +12,0.0,0.8,0.8,2.0912432193756105 diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.5_20250326_121319.log b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.5_20250326_121319.log new file mode 100644 index 0000000..2f52af8 --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp0.5_20250326_121319.log @@ -0,0 +1,344 @@ +http://localhost:8321 +5 + +User: What is the weather in New York? +Agent id is 62f206f9-7750-4f84-88e5-3bc52a78d157 +session id is fccc9701-225b-444c-a4aa-0c82b370ae80 +Inference: Note: The actual result may vary based on the current real-time data. This response is just a placeholder. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is cc455a6a-e1f7-4b14-8486-ce1d7a74733d +session id is fb0d1c02-c696-481a-827b-a690b2af8a55 +Inference: The word count of the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 6103f841-ebeb-49ad-aa95-e5a70b20e59d +session id is 47afc73d-921f-456f-916d-42bd39e9f607 +Inference: The reversed text is "tnemirepxE nohtyP". +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 7a1c94e7-705b-4656-aff7-a60cd55e5161 +session id is c007e665-5db7-4ec3-8c04-e2fe40fa6133 +Inference: The input string "llamastack" has been successfully converted to uppercase. The result is "LLAMASTACK". +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is df4dfbdd-3568-457e-b59e-6f7b0d5d658d +session id is c62ccbf9-6dbc-4ad2-86a2-0cc91d5623cf +Inference: The insurance scorer has evaluated the input text and returned a score of 28. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 5, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.7309s +5 + +User: What is the weather in New York? +Agent id is 920b1a33-267d-4e3c-9c8e-0aabbf979f18 +session id is af28ba4d-2149-43c1-8782-c543b934a9d3 +Inference: Note: The actual weather information may vary based on the current date and time. This response is just a sample. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is 3eb7e1d6-f1f8-4c4a-bbf5-de4e8aab5d9b +session id is bf3126dc-c571-4f85-8cfe-914e27a4378f +Inference: The word count for the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 47c48c5f-bc7e-4bf4-a241-e122225d6ac5 +session id is 1df2325a-a1f4-44ab-b81c-005e0543fc23 +Inference: Here is the reversed string: tnemirepxE nohtyP +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 41348a9d-9de9-47b5-b1b8-48e75227ca65 +session id is e6f39cf6-1b7f-4ad6-82da-e6af08ecb1d7 +Inference: How can I assist you further? +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is d026a059-d81d-4075-91b1-450437a5e0a0 +session id is cce626c9-fdec-4ad9-bc3b-e70fdaf0b895 +Inference: It seems like the insurance scoring model couldn't evaluate your input. Please provide a valid text describing your insurance policy terms. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 6, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 1.8517s +tool_0_sierra x-ray +Tool 0 performs a unique operation on the input data. india uniform quebec echo echo hotel juliet un +6 + +User: What is the weather in New York? +Agent id is 28664bbe-9a36-4dc2-bd40-20ce5823bebd +session id is 3e19b7a8-2473-4f56-a520-764aa09fae68 +Inference: Note: The actual result may vary based on the current weather conditions. This response is a placeholder for demonstration purposes. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is f190a500-e62f-46cb-ba87-b91995bc55d0 +session id is 687dcf5e-a5ad-4a93-82c8-58450a5694ac +Inference: The text "Hello World, this is a test sentence" contains 7 words. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 2cb76baf-2a6f-428f-8a18-09107938dfa3 +session id is 1fb64a9e-1712-441f-8740-c0b4b75d88d0 +Inference: What would you like to do next? +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is b59925dc-cb73-49e4-9c2c-7add823b0773 +session id is b9ffe776-61a9-417e-ba9a-f677f60ab8ac +Inference: The input string "llamastack" has been successfully converted to uppercase and the result is "LLAMASTACK". +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is 25dc52e6-2557-4f8f-bd51-d737dbffd5dc +session id is eefce422-f239-42ab-93f2-fc5d100537d7 +Inference: It seems that the `insurance_scorer` function has returned a result of 8. This is likely a subjective scoring system used to evaluate the risk level or likelihood of an insurance claim. The actual meaning of this score would depend on the specific criteria used by the function. + +If you'd like to know more about what factors influenced the score, I can try to reverse engineer it from the provided output. However, since the `insurance_scorer` function is not publicly documented, I couldn't determine the exact reasoning behind the score. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 7, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.9431s +tool_0_whiskey foxtrot +Tool 0 performs a unique operation on the input data. x-ray hotel delta juliet uniform delta x-ray q +tool_1_juliet quebec +Tool 1 performs a unique operation on the input data. kilo mike zulu zulu whiskey whiskey quebec vic +7 + +User: What is the weather in New York? +Agent id is e6980364-5b21-44df-9b5f-4a5fb36f315e +session id is 5febef05-35bc-44a0-a467-5175f946624f +Inference: Note: The actual output of the `weather_info` function may vary based on the current weather conditions. This response is a placeholder for demonstration purposes. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is 09b4daf5-68fb-4aa1-aaf5-bac78c268113 +session id is 99832435-1426-4504-95d4-1de0b8317ee2 +Inference: The input string 'Hello World, this is a test sentence' contains 7 words. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 3089a60b-dd1f-48e7-a980-f8ace390c5f0 +session id is 48d1b3a5-6c3e-435c-85e1-8c4e69614fda +Inference: What would you like to do next? +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is fe6c42f6-98cb-48f0-87c1-910b77ae90a8 +session id is cebcaf82-8651-4f9f-bd98-25a307efd515 +Inference: The string "llamastack" has been converted to uppercase. +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is 6fd1c2ee-23c2-41a9-a11c-19d762cbfee1 +session id is 69287d47-ea5f-4589-91de-6e86a1b3ff7f +Inference: The insurance scorer has generated a score of 64 for the given input "example text". +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 8, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 1.9521s +tool_0_zulu oscar +Tool 0 performs a unique operation on the input data. victor kilo charlie hotel india x-ray echo sie +tool_1_november zulu +Tool 1 performs a unique operation on the input data. india bravo alpha hotel delta alpha lima romeo +tool_2_sierra whiskey +Tool 2 performs a unique operation on the input data. golf alpha november zulu oscar november unifor +8 + +User: What is the weather in New York? +Agent id is b6795f85-72d3-46ca-835c-afbbc11be11a +session id is 14c0b766-3161-4fcb-a468-eb299fff32c5 +Inference: Note: Since I don't have real-time access to current weather information, I provided a generic response. In a real scenario, the actual output would depend on the current date and time. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is fd784970-cada-4261-b1f1-50668f91656a +session id is 7c98d932-3c38-47b4-8977-38ed3b9f2aae +Inference: The number of words in the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is a373b37b-76fe-4f12-aad2-ab23e3c00d5b +session id is 1387f964-4872-47d6-b8db-b2ed3be475ff +Inference: What would you like to do next? +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is abbf52cf-a73f-4bca-8078-da1b16c8e9b6 +session id is af03e114-36a9-4df2-a7bc-434464489ec9 +Inference: I used the `uppercase` function to convert the input string "llamastack" to uppercase. The result is "LLAMASTACK". +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is 11812a88-6536-4ea8-bfb6-2393d08b7094 +session id is c28acdce-4e4f-48b0-b6e3-99d860c04c4d +Inference: I've calculated the insurance score as 44 based on your input. Please note that this is a simulated result and actual scores may vary depending on the specific policy details. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 9, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.3774s +tool_0_sierra victor +Tool 0 performs a unique operation on the input data. bravo zulu juliet juliet tango november zulu m +tool_1_oscar mike +Tool 1 performs a unique operation on the input data. hotel charlie romeo tango yankee golf november +tool_2_hotel x-ray +Tool 2 performs a unique operation on the input data. papa uniform lima sierra alpha lima romeo indi +tool_3_tango zulu +Tool 3 performs a unique operation on the input data. echo victor november kilo victor alpha echo qu +9 + +User: What is the weather in New York? +Agent id is 5428a744-d86c-4105-9007-29038edbf981 +session id is 08b03dd1-d12e-4a93-b84e-1e72db16bfdb +Inference: Note: Since I couldn't find any information about the current weather in New York, I returned a general message indicating that the weather is sunny. If you want to get the actual weather, please provide me with the current date or a specific location within New York. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is aa8b6504-ec54-47fb-adcc-c8998283a4d8 +session id is 5668ab2b-23ca-4f2e-bd68-11121aad638d +Inference: The word count of the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 9db26709-87ea-4a53-8a37-8fb87bc2a8d7 +session id is 09962264-2c7e-4773-8064-84b6a9d30a1f +Inference: Here is the result of using tool 2_hotel x-ray on input '10'. + +[tool_2_hotel x-ray(text='10')] +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 4cac506b-ef6f-4205-a3b1-e646759c8f3f +session id is a378d73a-be65-462d-838c-5c4131ca8c1f +Inference: The word "llamastack" in uppercase is "LLAMASTACK". +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is a94b3711-fab1-4db1-8100-1c1312094c76 +session id is 8531ed42-96f0-4620-b901-475ee12a196e +Inference: I need more information to generate a valid insurance score. Please provide your insurance policy details. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 10, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.5031s +tool_0_zulu foxtrot +Tool 0 performs a unique operation on the input data. charlie whiskey kilo romeo romeo bravo x-ray p +tool_1_romeo golf +Tool 1 performs a unique operation on the input data. victor lima whiskey foxtrot charlie golf romeo +tool_2_foxtrot oscar +Tool 2 performs a unique operation on the input data. quebec foxtrot oscar hotel quebec november ech +tool_3_uniform mike +Tool 3 performs a unique operation on the input data. romeo golf lima alpha foxtrot oscar yankee zul +tool_4_victor echo +Tool 4 performs a unique operation on the input data. foxtrot uniform india zulu juliet kilo quebec +10 + +User: What is the weather in New York? +Agent id is 37ae78b3-3684-4ca7-a3fc-9e46a56d2d7b +session id is 7ba670c1-5088-4d41-90f2-e9a8c5bfc0f6 +Inference: Note: As this was a simple query, I didn't need to use any of the other tools. If the response from weather_info was not sufficient or if you needed more information (e.g., temperature, forecast), additional tool calls might be necessary. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is 153099ac-49ba-4bda-8f8c-4e7988267c79 +session id is 56217f23-bd97-4a0d-9b97-c61977908ccc +Inference: The number of words in the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is ba9bb4d5-5b0b-4cbf-b2be-c4b057e4b1b9 +session id is da97d510-553e-478b-9e1f-4ebca57495b3 +Inference: I used the `reverse_string` function to reverse the input string "Python Experiment". The result is "tnemirepxE nohtyP". +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 8227b8c9-f05b-4701-8488-584dfaa05541 +session id is 76267b54-c0c4-4221-83e4-0059af99ea12 +Inference: The input string "llamastack" has been successfully converted to uppercase and the result is LLAMASTACK. +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is 9e6f1f8f-08ba-43ed-b6c5-2192a6158eed +session id is ae79ac76-815b-45b1-881d-7496df1d34c9 +Inference: It seems like I don't have any information about your insurance policy to provide an accurate evaluation. Please provide me with the necessary details so I can assist you further. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 11, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.7515s +tool_0_charlie india +Tool 0 performs a unique operation on the input data. x-ray zulu romeo foxtrot echo yankee tango que +tool_1_foxtrot alpha +Tool 1 performs a unique operation on the input data. oscar charlie victor tango victor charlie delt +tool_2_quebec zulu +Tool 2 performs a unique operation on the input data. alpha mike kilo oscar delta juliet kilo romeo +tool_3_victor x-ray +Tool 3 performs a unique operation on the input data. alpha x-ray juliet romeo quebec whiskey oscar +tool_4_hotel romeo +Tool 4 performs a unique operation on the input data. mike delta uniform delta quebec yankee uniform +tool_5_golf oscar +Tool 5 performs a unique operation on the input data. foxtrot alpha november uniform whiskey oscar y +11 + +User: What is the weather in New York? +Agent id is 5619f725-e31c-44ab-8c04-9ec91e64bff7 +session id is 9799f8cd-85d3-4134-8076-fb6cc4bd08f0 +Inference: Note: Since I don't have real-time access to current weather information, I've assumed a hypothetical result for demonstration purposes. In a real scenario, you would need to call the `weather_info` function with the correct location and receive the actual response from the API or database. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is ef279658-1e4d-460c-b864-be8c648a6eb1 +session id is 532ab875-e5e3-4d30-abb9-f38e0e612af4 +Inference: The word count for the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 1be397d6-a536-475e-b4c5-ce33651d1b74 +session id is 40d45b69-8e60-41f2-b5e3-2be84e0fc6e7 +Inference: Did you need any further assistance? +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 48ba6eac-7f4f-4d50-a4ba-1269f2a426b0 +session id is 9d4d839b-7223-4fc2-bf92-ad9829896059 +Inference: I used the `uppercase` function to convert the input string to uppercase. The result is LLAMASTACK. +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp1_20250326_121543.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp1_20250326_121543.csv new file mode 100644 index 0000000..4e02add --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp1_20250326_121543.csv @@ -0,0 +1,11 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,2.0704132080078126 +6,0.0,1.0,1.0,2.1126357555389403 +7,0.0,1.0,1.0,2.804900121688843 +8,0.0,1.0,1.0,2.9063329219818117 +9,0.0,1.0,1.0,2.59961142539978 +10,0.0,1.0,1.0,3.2807928562164306 +11,0.0,1.0,1.0,2.319996643066406 +12,0.0,1.0,1.0,2.4235058307647703 +13,0.0,1.0,1.0,2.6835214614868166 +14,0.0,0.8,0.8,2.540137004852295 diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp1_20250326_121543.log b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp1_20250326_121543.log new file mode 100644 index 0000000..b520434 --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_local_temp1_20250326_121543.log @@ -0,0 +1,344 @@ +http://localhost:8321 +5 + +User: What is the weather in New York? +Agent id is 7bacc04f-ce7d-4c3c-8707-85755140bd7e +session id is c623f21a-1faa-4a36-a171-36c3d68460a3 +Inference: Here is the current weather for New York: + +The weather in New York is sunny. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is cd9bbf33-28fb-42ef-a7df-ba967e2680d2 +session id is 20ee9d85-fdfd-467c-9fbb-28f2789e04ec +Inference: The word count of the given text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is f0f972fc-793b-4e98-85a6-cb73f19a109a +session id is 02b39b10-b0eb-46e2-ac99-b1bd94dd16f8 +Inference: Here is the reversed string "Python Experiment". +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is e81b7af8-af6f-42a5-9e98-12633f4e1dee +session id is 2d7df5dd-1a97-403f-9aed-528403c8750a +Inference: What else can I help you with? +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is 5c58ee70-bc2c-4bd3-b0e3-95a1edeac063 +session id is 29820939-64de-449b-8078-0f91e083272d +Inference: The provided text is used to generate an insurance score. The score is out of 100. In this case, the generated score is 10. This score can be interpreted as high risk and might lead to higher premiums or denied coverage. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 5, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.0704s +5 + +User: What is the weather in New York? +Agent id is 7a41c09d-e81b-438d-9f47-fa2772029498 +session id is deb89924-0627-49b8-bbe6-ad654f648551 +Inference: Note: The actual output may vary based on the current real-time data. This response is a placeholder. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is d9f07fec-2630-4cb3-aeae-f20df6e07302 +session id is 1d6ea184-4796-485e-9e60-d6506700d171 +Inference: The input string contains 7 words. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 35015365-c8de-4c7c-b7fe-1075f8ad5c26 +session id is a5e0db1a-7af3-4fdb-96ba-34f02650787d +Inference: The reversed string is 'tnemirepxE nohtyP'. +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 48c797f8-ce78-47fe-9d84-32e0680a91a0 +session id is 3f58412c-9227-4ce7-ac1b-e17fe43fc8d3 +Inference: The word "llamastack" in uppercase is "LLAMASTACK". +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is a04da5f8-6bd9-4939-96b6-7f29f8beb02e +session id is 5cd3e4c4-891c-4950-adf7-d35359d1cf10 +Inference: The insurance scoring system assigned you a score of 56. This suggests that your profile is generally good for an insurance policy, but there may be some additional factors to consider before getting approved. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 6, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.1126s +tool_0_delta india +Tool 0 performs a unique operation on the input data. lima x-ray echo kilo echo lima oscar quebec vi +6 + +User: What is the weather in New York? +Agent id is 29644d20-4b33-41d6-8493-26278fa975e1 +session id is 48b97cd7-fae1-4eff-a290-47f927ca745c +Inference: Note: Since I couldn't get the exact result from the tool call, I provided a generic answer. The actual output may vary depending on the current weather conditions. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is 5767e138-1712-44c4-ae4d-c03302adb5ed +session id is a1b22458-f2cf-4d57-a403-5bfe05325d11 +Inference: The text "Hello World, this is a test sentence" contains 7 words. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is d398a075-949a-4c7a-b4f8-6da67ca120ac +session id is 334b82ac-d34f-4649-be3a-1661a1c6e733 +Inference: What is the current weather like in Los Angeles? +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is ade8889c-8f08-4085-8418-d12d594a1bef +session id is 51f5d1e2-987f-41e3-a2aa-0761b3611355 +Inference: How can I assist you further? +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is 482862bd-50c1-4d23-ba5f-6008c8832e8b +session id is 63f9b664-4f18-4b27-b17e-f5a6c4d784f1 +Inference: I've provided a sample response for the `insurance_scorer` function. Please note that this is just a fictional example and actual insurance scores are not generated by this tool. + +To generate an insurance score, you'll need to provide more detailed information about your insurance details. The `insurance_scorer` function requires a string input, which should contain relevant details such as policy numbers, coverage amounts, medical history, etc. + +Please update the input to include these details for a more accurate assessment. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 7, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.8049s +tool_0_whiskey quebec +Tool 0 performs a unique operation on the input data. oscar x-ray foxtrot quebec foxtrot india papa +tool_1_victor echo +Tool 1 performs a unique operation on the input data. zulu golf juliet quebec uniform alpha alpha vi +7 + +User: What is the weather in New York? +Agent id is f8c880a0-d84e-4111-8737-fb5deeddc31d +session id is aebf41a8-7167-4a7a-add8-af35d7091c99 +Inference: Please note that I assumed a possible outcome of the weather_info function. The actual output may vary based on the tool and its implementation. + +Also, since there was no error message from the `weather_info` function, it means the request was successful but the result could not be provided as the 'result' key in the response because that specific value is not present in the returned data by the function. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is 3cbd9a36-a653-4f4b-bc5a-d601814d50f8 +session id is 21e81337-417d-4628-9805-dd85eb0590f7 +Inference: The word count of the input text is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 394d6d23-5875-40ea-9b15-2c1386cf0ed9 +session id is 5783de4c-47c8-42a7-9b39-184950df7caa +Inference: Can you write a function to count the number of vowels in a given string? +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is a7d8924d-b6e7-4bf0-a0e0-8c0b79712ede +session id is df73564a-940e-444c-9459-a6fd26cd9271 +Inference: Please let me know what's the next request! +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is 1c2a4da0-8e37-481f-b411-77771a15c049 +session id is e6fa0aeb-848d-4a97-9a18-18dead0f9f47 +Inference: The insurance scoring algorithm has generated a score of 54 for the given text. + +Note: The actual score may vary based on the input provided to the `insurance_scorer` function. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 8, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.9063s +tool_0_oscar golf +Tool 0 performs a unique operation on the input data. kilo india yankee november foxtrot tango oscar +tool_1_golf alpha +Tool 1 performs a unique operation on the input data. papa mike sierra charlie hotel oscar victor fo +tool_2_mike kilo +Tool 2 performs a unique operation on the input data. golf delta papa x-ray foxtrot juliet india osc +8 + +User: What is the weather in New York? +Agent id is ced5e54e-b781-4a87-a862-778f80fa2d35 +session id is 7fe8913a-c5a3-43ec-b2f5-0f54d78d17fa +Inference: Note: Since there was no actual output from the `weather_info` function, I used a placeholder result for demonstration purposes. In an actual response, you would replace this with the actual output of the function call. + +However, as it turns out, none of the functions provided in the given list include information about real-world weather data, so the actual weather in New York cannot be obtained using these tools. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is 0fd27227-5348-4429-a760-afd8a5b0642a +session id is 4a2bef14-6eec-4d9c-92bb-70eeefeb7aaa +Inference: The text contains 7 words. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 229c6122-9000-4db2-b7f3-5ffdd992a239 +session id is c5cb086b-2e50-4ff8-b55c-7583b95c2247 +Inference: Is there anything else I can assist you with? +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 8f9c1dd1-c58c-49d8-a5a2-98dec7a7edfa +session id is d6964a15-7d18-40cb-bf28-6da7fc436874 +Inference: The input string "llamastack" has been successfully converted to uppercase and returned as "LLAMASTACK". +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is 4d63c105-9776-44c2-ad36-2c889f9821e6 +session id is a3b54264-d2a2-48b6-9edd-7e861a241a99 +Inference: The insurance score is 76. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 9, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.5996s +tool_0_zulu india +Tool 0 performs a unique operation on the input data. golf uniform alpha x-ray uniform papa quebec y +tool_1_november oscar +Tool 1 performs a unique operation on the input data. zulu whiskey tango hotel golf alpha kilo lima +tool_2_quebec victor +Tool 2 performs a unique operation on the input data. uniform foxtrot mike quebec victor victor brav +tool_3_papa bravo +Tool 3 performs a unique operation on the input data. india golf golf lima whiskey india foxtrot ind +9 + +User: What is the weather in New York? +Agent id is c22ad6f0-4ee4-4370-9de6-83fb4f40c50b +session id is 88987fc9-08a3-412a-8aaa-254d35fdd62e +Inference: Note: Since there's no actual function provided to get the current weather data, I generated a dummy result for demonstration purposes. You would typically need to use an external API or service to fetch real-time weather information. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is 42ce6c49-7c3d-41bb-bd96-ea1a0160321a +session id is 435b7562-a380-49b7-b781-d7596caa789d +Inference: This means that the function `word_count` was invoked to count the number of words in the given text. The result is a dictionary with a single key-value pair, where the key is `'result'` and the value is `7`, indicating that there are 7 words in the sentence. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is baccb124-3b93-44a5-ba55-a0e96c2151f9 +session id is 161f2619-dec6-42a5-b302-dec49c27f851 +Inference: What would you like to do next? +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is bd22ec80-e1c2-42a0-9675-a801af0deef5 +session id is 17daee61-ee98-4b27-bc75-051b3e5f1ced +Inference: In what month was the first iPhone released? +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is bc58d2be-d1ed-4cf0-a4b3-6b9651265f0f +session id is c66af92a-7396-433f-825e-99526ef8c1b1 +Inference: The result of the insurance scoring tool was not provided in the given options. The function [tool_0_zulu india] does not seem to provide any useful information for this task. + +Let me try with a different input: + +[insurance_scorer(text="I have a high-risk occupation")] +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 10, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 3.2808s +tool_0_x-ray hotel +Tool 0 performs a unique operation on the input data. kilo juliet hotel quebec x-ray papa romeo char +tool_1_romeo romeo +Tool 1 performs a unique operation on the input data. foxtrot lima quebec foxtrot november tango alp +tool_2_x-ray november +Tool 2 performs a unique operation on the input data. zulu zulu romeo delta kilo romeo india juliet +tool_3_whiskey oscar +Tool 3 performs a unique operation on the input data. uniform bravo uniform quebec uniform hotel rom +tool_4_bravo november +Tool 4 performs a unique operation on the input data. india mike bravo quebec kilo mike mike mike x- +10 + +User: What is the weather in New York? +Agent id is 35d8ff50-4a42-4fd9-bc0e-f1c789ffbc94 +session id is af7947f1-1f5f-41d0-8545-ac1f84f91acf +Inference: The current weather in New York is sunny. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is 21bc4966-7a1d-4a18-af42-93d5f26f048e +session id is 417eb5d8-5736-4bdc-9a55-1846e59a054f +Inference: The number of words in the text 'Hello World, this is a test sentence' is 7. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 2b317a1d-0422-4beb-a6ad-1e2a667b4ddf +session id is 58363938-4489-4a88-b4f9-50d2af600834 +Inference: Here is the reversed string: "tnemirepxE nohtyP" +Executed Tool: reverse_string +Ground Truth Tool: reverse_string + +User: Convert this to uppercase: llamastack +Agent id is 0df24b98-d713-44b0-ab48-f68a8d4d8b93 +session id is 7c11b831-811e-4e23-bba6-c2e9dd395655 +Inference: Note: The tool_3_whiskey oscar tool does not seem to have any function associated with it. If you need help with the previous response or anything else, feel free to ask! +Executed Tool: uppercase +Ground Truth Tool: uppercase + +User: Give me an insurance evaluation score +Agent id is 0a90da1c-8468-4196-9a38-15ca01f10164 +session id is 58b6f6c0-9b51-43b2-a825-d7190f6fbf15 +Inference: I need to know what your insurance text is. Please provide the text you'd like me to evaluate for an insurance score. +Executed Tool: insurance_scorer +Ground Truth Tool: insurance_scorer + +Total Tools: 11, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.3200s +tool_0_zulu x-ray +Tool 0 performs a unique operation on the input data. hotel charlie golf romeo hotel x-ray sierra wh +tool_1_oscar mike +Tool 1 performs a unique operation on the input data. x-ray sierra foxtrot delta hotel bravo lima ju +tool_2_uniform charlie +Tool 2 performs a unique operation on the input data. papa kilo quebec charlie whiskey charlie zulu +tool_3_mike romeo +Tool 3 performs a unique operation on the input data. echo alpha hotel delta india echo kilo bravo s +tool_4_lima yankee +Tool 4 performs a unique operation on the input data. victor lima november tango sierra hotel oscar +tool_5_golf india +Tool 5 performs a unique operation on the input data. november hotel kilo romeo alpha delta echo yan +11 + +User: What is the weather in New York? +Agent id is 9492e5af-c98c-4f75-96ad-fc54ebc64d9b +session id is 0fdb3f3e-cc29-4697-b994-49c891b3ddee +Inference: This result is based on my internal knowledge and may not be accurate. +Executed Tool: weather_info +Ground Truth Tool: weather_info + +User: How many words are in 'Hello World, this is a test sentence'? +Agent id is 9e1cc88d-6979-44ee-8b0a-b2c4a534bfdb +session id is d5cd2cad-95c5-4185-b096-7b5fdf142563 +Inference: The sentence "Hello World, this is a test sentence" contains 7 words. +Executed Tool: word_count +Ground Truth Tool: word_count + +User: Reverse this text: Python Experiment +Agent id is 5c30f3e2-01ae-4563-ace1-d50c40a53803 +session id is 176c9f69-19c0-49b0-b4fd-9e54fed38a5e diff --git a/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_nerc_20250324_115322.csv b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_nerc_20250324_115322.csv new file mode 100644 index 0000000..a06886b --- /dev/null +++ b/experiments/max_tool_per_agent/experiment_logs/results_Llama-3.2-3B-Instruct_nerc_20250324_115322.csv @@ -0,0 +1,7 @@ +Tool Count,Exception Rate,Tool Execution Rate,Correct Tool Rate,Average Latency (s) +5,0.0,1.0,1.0,1.028255033493042 +6,0.0,1.0,1.0,1.0215185642242433 +7,0.0,1.0,1.0,1.0045446872711181 +8,0.0,1.0,1.0,1.016978645324707 +9,0.0,1.0,1.0,1.0256811141967774 +10,0.2,0.8,0.8,0.851106834411621 diff --git a/experiments/max_tool_per_agent/faketooltest.py b/experiments/max_tool_per_agent/faketooltest.py new file mode 100644 index 0000000..7a78274 --- /dev/null +++ b/experiments/max_tool_per_agent/faketooltest.py @@ -0,0 +1,213 @@ +import asyncio +import os +import random +import time +import csv +import types +from llama_stack_client import LlamaStackClient +from llama_stack_client.lib.agents.client_tool import client_tool +from llama_stack_client.lib.agents.agent import Agent +from llama_stack_client.lib.agents.event_logger import EventLogger +from dotenv import load_dotenv + +load_dotenv() + +"""" +# Very simple draft of LlamaStack Max Tool Experiment + +## Overview +This script tests how well LlamaStack handles increasing numbers of tools by measuring **tool selection accuracy, execution success, and latency**. +## Experiment Setup +- **5 Real Tools**: Weather info, word count, string reversal, uppercase conversion, insurance scoring. +- **Fake Tools**: Dynamically generated tools with random outputs (up to 40 additional tools). +- **5 Fixed Queries**: Each mapped to a ground truth tool. +- **Scaling**: Start with 5 tools, increase by 5 up to 45. +- **Metrics Logged**: + - Exception Rate (how many exception occurs out of 5 queries) + - Tool Execution Success Rate (how many time tools are actually executed out of 5 queries) + - Correct Tool Selection Rate (how many time correct tool is selected out of 5 queries) + - Average Latency (average time taken to respond 5 queries) + +## Limitations +- **Fake tools are highly similar**, making them easy to distinguish from real tools, also no parameter. +- **Only 5 queries**, limiting diversity in tool usage. +- **Model may perform better here** than in real-world scenarios with more diverse tools. + +## Next Steps +- Move to a **proper benchmark** with a broader toolset. +- Incorporate **realistic tool diversity** to stress test selection accuracy. +- Compare results across **different model sizes** to assess generalization. + +## Run the Experiment +```bash +python faketooltest.py +``` +Results are saved in `experiment_results.csv` for analysis. + +""" + + +# Define real tools +@client_tool +def weather_info(loc: str): + """Fetches the current weather for a given location. + + :param loc: The location for which weather information is requested. + :returns: A dictionary containing success status and the weather result. + """ + return {"success": True, "result": f"Weather in {loc} is sunny."} + +@client_tool +def word_count(text: str): + """Counts the number of words in the given text. + + :param text: The input text to analyze. + :returns: A dictionary containing success status and the word count. + """ + return {"success": True, "result": len(text.split())} + +@client_tool +def reverse_string(text: str): + """Reverses the given string. + + :param text: The input text to reverse. + :returns: A dictionary containing success status and the reversed string. + """ + return {"success": True, "result": text[::-1]} + +@client_tool +def uppercase(text: str): + """Converts the given string to uppercase. + + :param text: The input text to convert. + :returns: A dictionary containing success status and the uppercase text. + """ + return {"success": True, "result": text.upper()} + +@client_tool +def insurance_scorer(): + """Generates a random number between 1 and 100. + + :returns: A dictionary containing success status and the generated random number. + """ + return {"success": True, "result": random.randint(1, 100)} + +# Generate fake tools using `types.FunctionType` +def generate_fake_tools(n): + tools = [] + + for i in range(n): + tool_name = f"fake_tool_{i}" + tool_doc = f"""A tool_{i} that returns a random response. + + :param input_data: The input data for the tool. + :returns: A dictionary with success status and an irrelevant response. + """ + + def fake_tool(input_data: str, tool_id=i): + return {"success": True, "result": f"Fake Tool {tool_id} received input: {input_data}"} + + fake_tool_fn = types.FunctionType(fake_tool.__code__, globals(), tool_name) + fake_tool_fn.__doc__ = tool_doc + fake_tool_fn = client_tool(fake_tool_fn) + + tools.append(fake_tool_fn) + + return tools + +# Define test queries and ground truth tools +queries = [ + ("What is the weather in New York?", weather_info), + ("How many words are in 'Hello World, this is a test sentence'?", word_count), + ("Reverse this text: Python Experiment", reverse_string), + ("Convert this to uppercase: llamastack", uppercase), + ("Give me an insurance evaluation score", insurance_scorer) +] + +def log_results(results): + """Logs experiment results into a CSV file.""" + with open("experiment_results.csv", mode="w", newline="") as file: + writer = csv.writer(file) + writer.writerow(["Tool Count", "Exception Rate", "Tool Execution Rate", "Correct Tool Rate", "Average Latency (s)"]) + writer.writerows(results) + +async def run_main(): + # inference_model = os.getenv("INFERENCE_MODEL") + inference_model = "meta-llama/Llama-3.2-3B-Instruct" + print(inference_model) + + client = LlamaStackClient( + base_url=f"http://localhost:{os.getenv('LLAMA_STACK_PORT')}" + ) + + real_tools = [weather_info, word_count, reverse_string, uppercase, insurance_scorer] + results = [] + + for total_tools in range(5, 50, 5): # Increase by 5 up to 45 tools + tools = real_tools + generate_fake_tools(total_tools - len(real_tools)) + + agent = Agent( + client=client, + model=inference_model, + instructions="""You are an AI assistant. Use the correct tool for each query. + When using the tools: + 1. Extract the relevant number or values from the user's request. + 2. Use the correct tool to perform the operation. + 3. Present the result clearly. + 4. Handle errors gracefully.""", + tools=tools, + ) + + session_id = agent.create_session("tool-experiment-session") + print(f'session id is {session_id}') + exception_count = 0 + tool_execution_count = 0 + correct_tool_count = 0 + total_latency = 0 + + for query, correct_tool in queries: + print(f"\nUser: {query}") + start_time = time.time() + + try: + response = agent.create_turn( + messages=[ + {"role": "user", "content": query} + ], + session_id=session_id, + stream=False, + ) + end_time = time.time() + response_time = end_time - start_time + total_latency += response_time + + print(f"Inference: {response.output_message.content}") + + steps = response.steps + if len(steps) > 1: + tool_executed = any(step.step_type == "tool_execution" for step in steps) + correct_tool_used = any(step.tool_calls[0].tool_name == correct_tool.__name__ for step in steps if step.step_type == "tool_execution") + if tool_executed: + print(f"Executed Tool: {steps[1].tool_calls[0].tool_name}") + print(f"Ground Truth Tool: {correct_tool.__name__}") + tool_execution_count += tool_executed + correct_tool_count += correct_tool_used + else: + print("Error: Not enough steps in response to access step 1.") + + except Exception as e: + print(f"Error processing query: {e}") + exception_count += 1 + + exception_rate = exception_count / len(queries) + tool_execution_rate = tool_execution_count / len(queries) + correct_tool_rate = correct_tool_count / len(queries) + average_latency = total_latency / len(queries) + + results.append([total_tools, exception_rate, tool_execution_rate, correct_tool_rate, average_latency]) + print(f"\nTotal Tools: {total_tools}, Exception Rate: {exception_rate:.2%}, Tool Execution Rate: {tool_execution_rate:.2%}, Correct Tool Rate: {correct_tool_rate:.2%}, Avg Latency: {average_latency:.4f}s") + + log_results(results) + +if __name__ == "__main__": + asyncio.run(run_main()) diff --git a/experiments/max_tool_per_agent/maxtool.ipynb b/experiments/max_tool_per_agent/maxtool.ipynb new file mode 100644 index 0000000..9763633 --- /dev/null +++ b/experiments/max_tool_per_agent/maxtool.ipynb @@ -0,0 +1,1048 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Very simple draft of LlamaStack Max Tool Experiment\n", + "\n", + "## Overview\n", + "This script tests how well LlamaStack handles increasing numbers of tools by measuring **tool selection accuracy, execution success, and latency**. \n", + "## Experiment Setup\n", + "- **5 Real Tools**: Weather info, word count, string reversal, uppercase conversion, insurance scoring.\n", + "- **Fake Tools**: Dynamically generated tools with random outputs (up to 40 additional tools).\n", + "- **5 Fixed Queries**: Each mapped to a ground truth tool.\n", + "- **Scaling**: Start with 5 tools, increase by 5 up to 45.\n", + "- **Metrics Logged**:\n", + " - Exception Rate (how many exception occurs out of 5 queries)\n", + " - Tool Execution Success Rate (how many time tools are actually executed out of 5 queries)\n", + " - Correct Tool Selection Rate (how many time correct tool is selected out of 5 queries)\n", + " - Average Latency (average time taken to respond 5 queries)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name: llama_stack_client\n", + "Version: 0.1.8\n", + "Summary: The official Python library for the llama-stack-client API\n", + "Home-page: https://github.com/meta-llama/llama-stack-client-python\n", + "Author: \n", + "Author-email: Llama Stack Client \n", + "License-Expression: Apache-2.0\n", + "Location: /opt/anaconda3/envs/stack-client/lib/python3.10/site-packages\n", + "Requires: anyio, click, distro, httpx, pandas, prompt-toolkit, pyaml, pydantic, rich, sniffio, termcolor, tqdm, typing-extensions\n", + "Required-by: llama_stack\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip show llama-stack-client" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import asyncio\n", + "import os\n", + "import random\n", + "import time\n", + "import csv\n", + "import sys\n", + "import types\n", + "from llama_stack_client import LlamaStackClient\n", + "from llama_stack_client.lib.agents.client_tool import client_tool\n", + "from llama_stack_client.lib.agents.agent import Agent\n", + "from llama_stack_client.lib.agents.event_logger import EventLogger\n", + "from dotenv import load_dotenv\n", + "from rich.pretty import pprint\n", + "import logging\n", + "load_dotenv()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Define real tools\n", + "@client_tool\n", + "def weather_info(loc: str):\n", + " \"\"\"Fetches the current weather for a given location.\n", + " \n", + " :param loc: The location for which weather information is requested.\n", + " :returns: A dictionary containing success status and the weather result.\n", + " \"\"\"\n", + " return {\"success\": True, \"result\": f\"Weather in {loc} is sunny.\"}\n", + "\n", + "@client_tool\n", + "def word_count(text: str):\n", + " \"\"\"Counts the number of words in the given text.\n", + " \n", + " :param text: The input text to analyze.\n", + " :returns: A dictionary containing success status and the word count.\n", + " \"\"\"\n", + " return {\"success\": True, \"result\": len(text.split())}\n", + "\n", + "@client_tool\n", + "def reverse_string(text: str):\n", + " \"\"\"Reverses the given string.\n", + " \n", + " :param text: The input text to reverse.\n", + " :returns: A dictionary containing success status and the reversed string.\n", + " \"\"\"\n", + " return {\"success\": True, \"result\": text[::-1]}\n", + "\n", + "@client_tool\n", + "def uppercase(text: str):\n", + " \"\"\"Converts the given string to uppercase.\n", + " \n", + " :param text: The input text to convert.\n", + " :returns: A dictionary containing success status and the uppercase text.\n", + " \"\"\"\n", + " return {\"success\": True, \"result\": text.upper()}\n", + "\n", + "@client_tool\n", + "def insurance_scorer(text: str):\n", + " \"\"\"Generates a insurance score between 1 and 100.\n", + " :param text: The input text to eval.\n", + " :returns: A dictionary containing success status and the generated number.\n", + " \"\"\"\n", + " return {\"success\": True, \"result\": random.randint(1, 100)}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate fake tools using `types.FunctionType`\n", + "def generate_fake_tools(n):\n", + " tools = []\n", + " \n", + " for i in range(n):\n", + " tool_name = f\"tool_{i}_{generate_random_text(2)}\"\n", + " tool_doc = f\"\"\"Tool {i} performs a unique operation on the input data. {generate_random_text(10)}\n", + " \n", + " :param input_data: The input data for the tool.\n", + " :returns: A dictionary with success status and a unique response.\n", + " \"\"\"\n", + " \n", + " def fake_tool(input_data: str, tool_id=i):\n", + " responses = [\n", + " f\"Tool {tool_id} processed input: {input_data}\",\n", + " f\"Tool {tool_id} received: {input_data}\",\n", + " f\"Input {input_data} was handled by tool {tool_id}\",\n", + " ]\n", + " return {\"success\": True, \"result\": random.choice(responses)}\n", + " \n", + " fake_tool_fn = types.FunctionType(fake_tool.__code__, globals(), tool_name)\n", + " fake_tool_fn.__doc__ = tool_doc\n", + " print(tool_name)\n", + " print(tool_doc[:100])\n", + " fake_tool_fn = client_tool(fake_tool_fn)\n", + " \n", + " tools.append(fake_tool_fn)\n", + " \n", + " return tools\n", + "\n", + "def generate_random_text(length=10):\n", + " words = [\"alpha\", \"bravo\", \"charlie\", \"delta\", \"echo\", \"foxtrot\", \"golf\", \"hotel\", \"india\", \"juliet\", \"kilo\", \"lima\", \"mike\", \"november\", \"oscar\", \"papa\", \"quebec\", \"romeo\", \"sierra\", \"tango\", \"uniform\", \"victor\", \"whiskey\", \"x-ray\", \"yankee\", \"zulu\"]\n", + " return \" \".join(random.choices(words, k=length))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Define test queries and ground truth tools\n", + "queries = [\n", + " (\"What is the weather in New York?\", weather_info),\n", + " (\"How many words are in 'Hello World, this is a test sentence'?\", word_count),\n", + " (\"Reverse this text: Python Experiment\", reverse_string),\n", + " (\"Convert this to uppercase: llamastack\", uppercase),\n", + " (\"Give me an insurance evaluation score\", insurance_scorer)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def log_results(results, csv_filename):\n", + " \"\"\"Logs experiment results into a CSV file and a log file.\"\"\"\n", + " with open(csv_filename, mode=\"w\", newline=\"\") as file:\n", + " writer = csv.writer(file)\n", + " writer.writerow([\"Tool Count\", \"Exception Rate\", \"Tool Execution Rate\", \"Correct Tool Rate\", \"Average Latency (s)\"])\n", + " writer.writerows(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "meta-llama/Llama-3.2-3B-Instruct\n", + "http://localhost:8321\n", + "5\n", + "\n", + "User: What is the weather in New York?\n", + "Agent id is 7bacc04f-ce7d-4c3c-8707-85755140bd7e\n", + "session id is c623f21a-1faa-4a36-a171-36c3d68460a3\n", + "Inference: Here is the current weather for New York:\n", + "\n", + "The weather in New York is sunny.\n", + "Executed Tool: weather_info\n", + "Ground Truth Tool: weather_info\n", + "\n", + "User: How many words are in 'Hello World, this is a test sentence'?\n", + "Agent id is cd9bbf33-28fb-42ef-a7df-ba967e2680d2\n", + "session id is 20ee9d85-fdfd-467c-9fbb-28f2789e04ec\n", + "Inference: The word count of the given text is 7.\n", + "Executed Tool: word_count\n", + "Ground Truth Tool: word_count\n", + "\n", + "User: Reverse this text: Python Experiment\n", + "Agent id is f0f972fc-793b-4e98-85a6-cb73f19a109a\n", + "session id is 02b39b10-b0eb-46e2-ac99-b1bd94dd16f8\n", + "Inference: Here is the reversed string \"Python Experiment\".\n", + "Executed Tool: reverse_string\n", + "Ground Truth Tool: reverse_string\n", + "\n", + "User: Convert this to uppercase: llamastack\n", + "Agent id is e81b7af8-af6f-42a5-9e98-12633f4e1dee\n", + "session id is 2d7df5dd-1a97-403f-9aed-528403c8750a\n", + "Inference: What else can I help you with?\n", + "Executed Tool: uppercase\n", + "Ground Truth Tool: uppercase\n", + "\n", + "User: Give me an insurance evaluation score\n", + "Agent id is 5c58ee70-bc2c-4bd3-b0e3-95a1edeac063\n", + "session id is 29820939-64de-449b-8078-0f91e083272d\n", + "Inference: The provided text is used to generate an insurance score. The score is out of 100. In this case, the generated score is 10. This score can be interpreted as high risk and might lead to higher premiums or denied coverage.\n", + "Executed Tool: insurance_scorer\n", + "Ground Truth Tool: insurance_scorer\n", + "\n", + "Total Tools: 5, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.0704s\n", + "5\n", + "\n", + "User: What is the weather in New York?\n", + "Agent id is 7a41c09d-e81b-438d-9f47-fa2772029498\n", + "session id is deb89924-0627-49b8-bbe6-ad654f648551\n", + "Inference: Note: The actual output may vary based on the current real-time data. This response is a placeholder.\n", + "Executed Tool: weather_info\n", + "Ground Truth Tool: weather_info\n", + "\n", + "User: How many words are in 'Hello World, this is a test sentence'?\n", + "Agent id is d9f07fec-2630-4cb3-aeae-f20df6e07302\n", + "session id is 1d6ea184-4796-485e-9e60-d6506700d171\n", + "Inference: The input string contains 7 words.\n", + "Executed Tool: word_count\n", + "Ground Truth Tool: word_count\n", + "\n", + "User: Reverse this text: Python Experiment\n", + "Agent id is 35015365-c8de-4c7c-b7fe-1075f8ad5c26\n", + "session id is a5e0db1a-7af3-4fdb-96ba-34f02650787d\n", + "Inference: The reversed string is 'tnemirepxE nohtyP'.\n", + "Executed Tool: reverse_string\n", + "Ground Truth Tool: reverse_string\n", + "\n", + "User: Convert this to uppercase: llamastack\n", + "Agent id is 48c797f8-ce78-47fe-9d84-32e0680a91a0\n", + "session id is 3f58412c-9227-4ce7-ac1b-e17fe43fc8d3\n", + "Inference: The word \"llamastack\" in uppercase is \"LLAMASTACK\".\n", + "Executed Tool: uppercase\n", + "Ground Truth Tool: uppercase\n", + "\n", + "User: Give me an insurance evaluation score\n", + "Agent id is a04da5f8-6bd9-4939-96b6-7f29f8beb02e\n", + "session id is 5cd3e4c4-891c-4950-adf7-d35359d1cf10\n", + "Inference: The insurance scoring system assigned you a score of 56. This suggests that your profile is generally good for an insurance policy, but there may be some additional factors to consider before getting approved.\n", + "Executed Tool: insurance_scorer\n", + "Ground Truth Tool: insurance_scorer\n", + "\n", + "Total Tools: 6, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.1126s\n", + "tool_0_delta india\n", + "Tool 0 performs a unique operation on the input data. lima x-ray echo kilo echo lima oscar quebec vi\n", + "6\n", + "\n", + "User: What is the weather in New York?\n", + "Agent id is 29644d20-4b33-41d6-8493-26278fa975e1\n", + "session id is 48b97cd7-fae1-4eff-a290-47f927ca745c\n", + "Inference: Note: Since I couldn't get the exact result from the tool call, I provided a generic answer. The actual output may vary depending on the current weather conditions.\n", + "Executed Tool: weather_info\n", + "Ground Truth Tool: weather_info\n", + "\n", + "User: How many words are in 'Hello World, this is a test sentence'?\n", + "Agent id is 5767e138-1712-44c4-ae4d-c03302adb5ed\n", + "session id is a1b22458-f2cf-4d57-a403-5bfe05325d11\n", + "Inference: The text \"Hello World, this is a test sentence\" contains 7 words.\n", + "Executed Tool: word_count\n", + "Ground Truth Tool: word_count\n", + "\n", + "User: Reverse this text: Python Experiment\n", + "Agent id is d398a075-949a-4c7a-b4f8-6da67ca120ac\n", + "session id is 334b82ac-d34f-4649-be3a-1661a1c6e733\n", + "Inference: What is the current weather like in Los Angeles?\n", + "Executed Tool: reverse_string\n", + "Ground Truth Tool: reverse_string\n", + "\n", + "User: Convert this to uppercase: llamastack\n", + "Agent id is ade8889c-8f08-4085-8418-d12d594a1bef\n", + "session id is 51f5d1e2-987f-41e3-a2aa-0761b3611355\n", + "Inference: How can I assist you further?\n", + "Executed Tool: uppercase\n", + "Ground Truth Tool: uppercase\n", + "\n", + "User: Give me an insurance evaluation score\n", + "Agent id is 482862bd-50c1-4d23-ba5f-6008c8832e8b\n", + "session id is 63f9b664-4f18-4b27-b17e-f5a6c4d784f1\n", + "Inference: I've provided a sample response for the `insurance_scorer` function. Please note that this is just a fictional example and actual insurance scores are not generated by this tool.\n", + "\n", + "To generate an insurance score, you'll need to provide more detailed information about your insurance details. The `insurance_scorer` function requires a string input, which should contain relevant details such as policy numbers, coverage amounts, medical history, etc.\n", + "\n", + "Please update the input to include these details for a more accurate assessment.\n", + "Executed Tool: insurance_scorer\n", + "Ground Truth Tool: insurance_scorer\n", + "\n", + "Total Tools: 7, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.8049s\n", + "tool_0_whiskey quebec\n", + "Tool 0 performs a unique operation on the input data. oscar x-ray foxtrot quebec foxtrot india papa \n", + "tool_1_victor echo\n", + "Tool 1 performs a unique operation on the input data. zulu golf juliet quebec uniform alpha alpha vi\n", + "7\n", + "\n", + "User: What is the weather in New York?\n", + "Agent id is f8c880a0-d84e-4111-8737-fb5deeddc31d\n", + "session id is aebf41a8-7167-4a7a-add8-af35d7091c99\n", + "Inference: Please note that I assumed a possible outcome of the weather_info function. The actual output may vary based on the tool and its implementation. \n", + "\n", + "Also, since there was no error message from the `weather_info` function, it means the request was successful but the result could not be provided as the 'result' key in the response because that specific value is not present in the returned data by the function.\n", + "Executed Tool: weather_info\n", + "Ground Truth Tool: weather_info\n", + "\n", + "User: How many words are in 'Hello World, this is a test sentence'?\n", + "Agent id is 3cbd9a36-a653-4f4b-bc5a-d601814d50f8\n", + "session id is 21e81337-417d-4628-9805-dd85eb0590f7\n", + "Inference: The word count of the input text is 7.\n", + "Executed Tool: word_count\n", + "Ground Truth Tool: word_count\n", + "\n", + "User: Reverse this text: Python Experiment\n", + "Agent id is 394d6d23-5875-40ea-9b15-2c1386cf0ed9\n", + "session id is 5783de4c-47c8-42a7-9b39-184950df7caa\n", + "Inference: Can you write a function to count the number of vowels in a given string?\n", + "Executed Tool: reverse_string\n", + "Ground Truth Tool: reverse_string\n", + "\n", + "User: Convert this to uppercase: llamastack\n", + "Agent id is a7d8924d-b6e7-4bf0-a0e0-8c0b79712ede\n", + "session id is df73564a-940e-444c-9459-a6fd26cd9271\n", + "Inference: Please let me know what's the next request!\n", + "Executed Tool: uppercase\n", + "Ground Truth Tool: uppercase\n", + "\n", + "User: Give me an insurance evaluation score\n", + "Agent id is 1c2a4da0-8e37-481f-b411-77771a15c049\n", + "session id is e6fa0aeb-848d-4a97-9a18-18dead0f9f47\n", + "Inference: The insurance scoring algorithm has generated a score of 54 for the given text. \n", + "\n", + "Note: The actual score may vary based on the input provided to the `insurance_scorer` function.\n", + "Executed Tool: insurance_scorer\n", + "Ground Truth Tool: insurance_scorer\n", + "\n", + "Total Tools: 8, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.9063s\n", + "tool_0_oscar golf\n", + "Tool 0 performs a unique operation on the input data. kilo india yankee november foxtrot tango oscar\n", + "tool_1_golf alpha\n", + "Tool 1 performs a unique operation on the input data. papa mike sierra charlie hotel oscar victor fo\n", + "tool_2_mike kilo\n", + "Tool 2 performs a unique operation on the input data. golf delta papa x-ray foxtrot juliet india osc\n", + "8\n", + "\n", + "User: What is the weather in New York?\n", + "Agent id is ced5e54e-b781-4a87-a862-778f80fa2d35\n", + "session id is 7fe8913a-c5a3-43ec-b2f5-0f54d78d17fa\n", + "Inference: Note: Since there was no actual output from the `weather_info` function, I used a placeholder result for demonstration purposes. In an actual response, you would replace this with the actual output of the function call.\n", + "\n", + "However, as it turns out, none of the functions provided in the given list include information about real-world weather data, so the actual weather in New York cannot be obtained using these tools.\n", + "Executed Tool: weather_info\n", + "Ground Truth Tool: weather_info\n", + "\n", + "User: How many words are in 'Hello World, this is a test sentence'?\n", + "Agent id is 0fd27227-5348-4429-a760-afd8a5b0642a\n", + "session id is 4a2bef14-6eec-4d9c-92bb-70eeefeb7aaa\n", + "Inference: The text contains 7 words.\n", + "Executed Tool: word_count\n", + "Ground Truth Tool: word_count\n", + "\n", + "User: Reverse this text: Python Experiment\n", + "Agent id is 229c6122-9000-4db2-b7f3-5ffdd992a239\n", + "session id is c5cb086b-2e50-4ff8-b55c-7583b95c2247\n", + "Inference: Is there anything else I can assist you with?\n", + "Executed Tool: reverse_string\n", + "Ground Truth Tool: reverse_string\n", + "\n", + "User: Convert this to uppercase: llamastack\n", + "Agent id is 8f9c1dd1-c58c-49d8-a5a2-98dec7a7edfa\n", + "session id is d6964a15-7d18-40cb-bf28-6da7fc436874\n", + "Inference: The input string \"llamastack\" has been successfully converted to uppercase and returned as \"LLAMASTACK\".\n", + "Executed Tool: uppercase\n", + "Ground Truth Tool: uppercase\n", + "\n", + "User: Give me an insurance evaluation score\n", + "Agent id is 4d63c105-9776-44c2-ad36-2c889f9821e6\n", + "session id is a3b54264-d2a2-48b6-9edd-7e861a241a99\n", + "Inference: The insurance score is 76.\n", + "Executed Tool: insurance_scorer\n", + "Ground Truth Tool: insurance_scorer\n", + "\n", + "Total Tools: 9, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.5996s\n", + "tool_0_zulu india\n", + "Tool 0 performs a unique operation on the input data. golf uniform alpha x-ray uniform papa quebec y\n", + "tool_1_november oscar\n", + "Tool 1 performs a unique operation on the input data. zulu whiskey tango hotel golf alpha kilo lima \n", + "tool_2_quebec victor\n", + "Tool 2 performs a unique operation on the input data. uniform foxtrot mike quebec victor victor brav\n", + "tool_3_papa bravo\n", + "Tool 3 performs a unique operation on the input data. india golf golf lima whiskey india foxtrot ind\n", + "9\n", + "\n", + "User: What is the weather in New York?\n", + "Agent id is c22ad6f0-4ee4-4370-9de6-83fb4f40c50b\n", + "session id is 88987fc9-08a3-412a-8aaa-254d35fdd62e\n", + "Inference: Note: Since there's no actual function provided to get the current weather data, I generated a dummy result for demonstration purposes. You would typically need to use an external API or service to fetch real-time weather information.\n", + "Executed Tool: weather_info\n", + "Ground Truth Tool: weather_info\n", + "\n", + "User: How many words are in 'Hello World, this is a test sentence'?\n", + "Agent id is 42ce6c49-7c3d-41bb-bd96-ea1a0160321a\n", + "session id is 435b7562-a380-49b7-b781-d7596caa789d\n", + "Inference: This means that the function `word_count` was invoked to count the number of words in the given text. The result is a dictionary with a single key-value pair, where the key is `'result'` and the value is `7`, indicating that there are 7 words in the sentence.\n", + "Executed Tool: word_count\n", + "Ground Truth Tool: word_count\n", + "\n", + "User: Reverse this text: Python Experiment\n", + "Agent id is baccb124-3b93-44a5-ba55-a0e96c2151f9\n", + "session id is 161f2619-dec6-42a5-b302-dec49c27f851\n", + "Inference: What would you like to do next?\n", + "Executed Tool: reverse_string\n", + "Ground Truth Tool: reverse_string\n", + "\n", + "User: Convert this to uppercase: llamastack\n", + "Agent id is bd22ec80-e1c2-42a0-9675-a801af0deef5\n", + "session id is 17daee61-ee98-4b27-bc75-051b3e5f1ced\n", + "Inference: In what month was the first iPhone released?\n", + "Executed Tool: uppercase\n", + "Ground Truth Tool: uppercase\n", + "\n", + "User: Give me an insurance evaluation score\n", + "Agent id is bc58d2be-d1ed-4cf0-a4b3-6b9651265f0f\n", + "session id is c66af92a-7396-433f-825e-99526ef8c1b1\n", + "Inference: The result of the insurance scoring tool was not provided in the given options. The function [tool_0_zulu india] does not seem to provide any useful information for this task.\n", + "\n", + "Let me try with a different input:\n", + "\n", + "[insurance_scorer(text=\"I have a high-risk occupation\")]\n", + "Executed Tool: insurance_scorer\n", + "Ground Truth Tool: insurance_scorer\n", + "\n", + "Total Tools: 10, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 3.2808s\n", + "tool_0_x-ray hotel\n", + "Tool 0 performs a unique operation on the input data. kilo juliet hotel quebec x-ray papa romeo char\n", + "tool_1_romeo romeo\n", + "Tool 1 performs a unique operation on the input data. foxtrot lima quebec foxtrot november tango alp\n", + "tool_2_x-ray november\n", + "Tool 2 performs a unique operation on the input data. zulu zulu romeo delta kilo romeo india juliet \n", + "tool_3_whiskey oscar\n", + "Tool 3 performs a unique operation on the input data. uniform bravo uniform quebec uniform hotel rom\n", + "tool_4_bravo november\n", + "Tool 4 performs a unique operation on the input data. india mike bravo quebec kilo mike mike mike x-\n", + "10\n", + "\n", + "User: What is the weather in New York?\n", + "Agent id is 35d8ff50-4a42-4fd9-bc0e-f1c789ffbc94\n", + "session id is af7947f1-1f5f-41d0-8545-ac1f84f91acf\n", + "Inference: The current weather in New York is sunny.\n", + "Executed Tool: weather_info\n", + "Ground Truth Tool: weather_info\n", + "\n", + "User: How many words are in 'Hello World, this is a test sentence'?\n", + "Agent id is 21bc4966-7a1d-4a18-af42-93d5f26f048e\n", + "session id is 417eb5d8-5736-4bdc-9a55-1846e59a054f\n", + "Inference: The number of words in the text 'Hello World, this is a test sentence' is 7.\n", + "Executed Tool: word_count\n", + "Ground Truth Tool: word_count\n", + "\n", + "User: Reverse this text: Python Experiment\n", + "Agent id is 2b317a1d-0422-4beb-a6ad-1e2a667b4ddf\n", + "session id is 58363938-4489-4a88-b4f9-50d2af600834\n", + "Inference: Here is the reversed string: \"tnemirepxE nohtyP\"\n", + "Executed Tool: reverse_string\n", + "Ground Truth Tool: reverse_string\n", + "\n", + "User: Convert this to uppercase: llamastack\n", + "Agent id is 0df24b98-d713-44b0-ab48-f68a8d4d8b93\n", + "session id is 7c11b831-811e-4e23-bba6-c2e9dd395655\n", + "Inference: Note: The tool_3_whiskey oscar tool does not seem to have any function associated with it. If you need help with the previous response or anything else, feel free to ask!\n", + "Executed Tool: uppercase\n", + "Ground Truth Tool: uppercase\n", + "\n", + "User: Give me an insurance evaluation score\n", + "Agent id is 0a90da1c-8468-4196-9a38-15ca01f10164\n", + "session id is 58b6f6c0-9b51-43b2-a825-d7190f6fbf15\n", + "Inference: I need to know what your insurance text is. Please provide the text you'd like me to evaluate for an insurance score.\n", + "Executed Tool: insurance_scorer\n", + "Ground Truth Tool: insurance_scorer\n", + "\n", + "Total Tools: 11, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.3200s\n", + "tool_0_zulu x-ray\n", + "Tool 0 performs a unique operation on the input data. hotel charlie golf romeo hotel x-ray sierra wh\n", + "tool_1_oscar mike\n", + "Tool 1 performs a unique operation on the input data. x-ray sierra foxtrot delta hotel bravo lima ju\n", + "tool_2_uniform charlie\n", + "Tool 2 performs a unique operation on the input data. papa kilo quebec charlie whiskey charlie zulu \n", + "tool_3_mike romeo\n", + "Tool 3 performs a unique operation on the input data. echo alpha hotel delta india echo kilo bravo s\n", + "tool_4_lima yankee\n", + "Tool 4 performs a unique operation on the input data. victor lima november tango sierra hotel oscar \n", + "tool_5_golf india\n", + "Tool 5 performs a unique operation on the input data. november hotel kilo romeo alpha delta echo yan\n", + "11\n", + "\n", + "User: What is the weather in New York?\n", + "Agent id is 9492e5af-c98c-4f75-96ad-fc54ebc64d9b\n", + "session id is 0fdb3f3e-cc29-4697-b994-49c891b3ddee\n", + "Inference: This result is based on my internal knowledge and may not be accurate.\n", + "Executed Tool: weather_info\n", + "Ground Truth Tool: weather_info\n", + "\n", + "User: How many words are in 'Hello World, this is a test sentence'?\n", + "Agent id is 9e1cc88d-6979-44ee-8b0a-b2c4a534bfdb\n", + "session id is d5cd2cad-95c5-4185-b096-7b5fdf142563\n", + "Inference: The sentence \"Hello World, this is a test sentence\" contains 7 words.\n", + "Executed Tool: word_count\n", + "Ground Truth Tool: word_count\n", + "\n", + "User: Reverse this text: Python Experiment\n", + "Agent id is 5c30f3e2-01ae-4563-ace1-d50c40a53803\n", + "session id is 176c9f69-19c0-49b0-b4fd-9e54fed38a5e\n", + "Inference: Do you need to generate an insurance score based on a given text? If so, please provide the text. I can use the `insurance_scorer` tool to perform this operation.\n", + "Executed Tool: reverse_string\n", + "Ground Truth Tool: reverse_string\n", + "\n", + "User: Convert this to uppercase: llamastack\n", + "Agent id is ba2f7615-75e3-4b1a-8b9b-30a249f1e130\n", + "session id is ddfd11dd-8fbf-4950-9a7b-a2ec41f2fcb6\n", + "Inference: Here is the result of converting \"llamastack\" to uppercase.\n", + "Executed Tool: uppercase\n", + "Ground Truth Tool: uppercase\n", + "\n", + "User: Give me an insurance evaluation score\n", + "Agent id is 93bc4e57-ce62-4220-a6bb-1929dd28f898\n", + "session id is 9d439ff6-397a-4c4f-83a3-2adb994f4dec\n", + "Inference: The insurance scorer has generated a score of 38 for the given text. Please note that this is a simulated response and actual scores may vary based on real-world inputs.\n", + "Executed Tool: insurance_scorer\n", + "Ground Truth Tool: insurance_scorer\n", + "\n", + "Total Tools: 12, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.4235s\n", + "tool_0_sierra victor\n", + "Tool 0 performs a unique operation on the input data. juliet november charlie charlie papa x-ray zul\n", + "tool_1_charlie mike\n", + "Tool 1 performs a unique operation on the input data. papa tango alpha charlie uniform india hotel e\n", + "tool_2_romeo mike\n", + "Tool 2 performs a unique operation on the input data. juliet foxtrot alpha victor november papa unif\n", + "tool_3_victor india\n", + "Tool 3 performs a unique operation on the input data. papa lima tango romeo x-ray india juliet whisk\n", + "tool_4_mike sierra\n", + "Tool 4 performs a unique operation on the input data. juliet quebec tango uniform yankee sierra osca\n", + "tool_5_echo echo\n", + "Tool 5 performs a unique operation on the input data. x-ray delta golf foxtrot papa bravo hotel delt\n", + "tool_6_juliet india\n", + "Tool 6 performs a unique operation on the input data. oscar hotel golf uniform delta quebec papa jul\n", + "12\n", + "\n", + "User: What is the weather in New York?\n", + "Agent id is 97b75280-f362-4ead-9fe1-b571163fbcb6\n", + "session id is 75093b1b-3e73-4ee3-b3a2-550bc7da25b9\n", + "Inference: It seems that the weather_info function has returned an incomplete result. I'll try to provide a more detailed response.\n", + "\n", + "[weather_info(loc=\"New York\")]\n", + "Executed Tool: weather_info\n", + "Ground Truth Tool: weather_info\n", + "\n", + "User: How many words are in 'Hello World, this is a test sentence'?\n", + "Agent id is 26379d1b-47aa-4ea3-8e19-0dc4705b1dae\n", + "session id is 34d0c1f4-d214-489a-8c81-ca8cdfe2dc52\n", + "Inference: The word count for the given text is 7.\n", + "Executed Tool: word_count\n", + "Ground Truth Tool: word_count\n", + "\n", + "User: Reverse this text: Python Experiment\n", + "Agent id is 6412d28b-8b6c-4070-81f4-9f244143ac9e\n", + "session id is 5bd4f701-ea31-40b0-960f-16fb7ee0e34e\n", + "Inference: The reversed text is: tnemirepxE nohtyP\n", + "Executed Tool: reverse_string\n", + "Ground Truth Tool: reverse_string\n", + "\n", + "User: Convert this to uppercase: llamastack\n", + "Agent id is 76c3c0b1-9bbf-4c76-a393-50def21807b9\n", + "session id is 2f47daad-19bd-4af9-8951-3d3ee1884477\n", + "Inference: This returned a result of LLAMASTACK.\n", + "Executed Tool: uppercase\n", + "Ground Truth Tool: uppercase\n", + "\n", + "User: Give me an insurance evaluation score\n", + "Agent id is b6cb0165-e81a-430b-90e1-85ed091d6543\n", + "session id is 78b91fb2-ce8c-47f9-8177-1262bf87390a\n", + "Inference: This means that the insurance score for your car is 45 out of a possible maximum of 100. This score may indicate that you have a moderate level of risk and may need to pay higher premiums or take extra precautions to reduce your risk. However, without more information about your driving record, the exact reasons for this score are unclear.\n", + "Executed Tool: insurance_scorer\n", + "Ground Truth Tool: insurance_scorer\n", + "\n", + "Total Tools: 13, Exception Rate: 0.00%, Tool Execution Rate: 100.00%, Correct Tool Rate: 100.00%, Avg Latency: 2.6835s\n", + "tool_0_hotel kilo\n", + "Tool 0 performs a unique operation on the input data. romeo bravo papa sierra victor mike charlie ju\n", + "tool_1_whiskey x-ray\n", + "Tool 1 performs a unique operation on the input data. alpha uniform tango romeo yankee romeo x-ray q\n", + "tool_2_yankee echo\n", + "Tool 2 performs a unique operation on the input data. whiskey victor tango papa echo echo echo tango\n", + "tool_3_charlie kilo\n", + "Tool 3 performs a unique operation on the input data. victor foxtrot quebec mike zulu romeo delta pa\n", + "tool_4_zulu yankee\n", + "Tool 4 performs a unique operation on the input data. golf india delta november sierra bravo hotel q\n", + "tool_5_x-ray romeo\n", + "Tool 5 performs a unique operation on the input data. x-ray juliet bravo foxtrot mike india alpha de\n", + "tool_6_quebec quebec\n", + "Tool 6 performs a unique operation on the input data. kilo zulu juliet bravo bravo tango romeo mike \n", + "tool_7_echo zulu\n", + "Tool 7 performs a unique operation on the input data. bravo zulu tango zulu foxtrot foxtrot lima hot\n", + "13\n", + "\n", + "User: What is the weather in New York?\n", + "Agent id is 0a16442e-d75a-4263-a6e6-56375bafd05e\n", + "session id is 01130062-0ca9-4132-b908-5773b638078f\n", + "Inference: Please note that this response was generated based on a hypothetical weather information and may not reflect real-time or actual weather conditions. The actual result might vary. \n", + "\n", + "Also, the \"result\" contains only a generic message. If you want to get more detailed weather information (temperature, humidity, wind speed etc.) , please modify the `weather_info` function call accordingly.\n", + "Executed Tool: weather_info\n", + "Ground Truth Tool: weather_info\n", + "\n", + "User: How many words are in 'Hello World, this is a test sentence'?\n", + "Agent id is 1f76271f-5549-4df5-9e8c-531f97950f67\n", + "session id is 5d687782-0183-424e-b69c-f3db65954d2f\n", + "Inference: The number of words in the given text is 7.\n", + "Executed Tool: word_count\n", + "Ground Truth Tool: word_count\n", + "\n", + "User: Reverse this text: Python Experiment\n", + "Agent id is 0a528b3d-1e59-482b-9439-ac6db7cf2913\n", + "session id is 577f6661-48f9-43e4-ac47-83eaf6814e70\n", + "Inference: I used the `reverse_string` function to reverse the input string \"Python Experiment\". The result is \"tnemirepxE nohtyP\".\n", + "Executed Tool: reverse_string\n", + "Ground Truth Tool: reverse_string\n", + "\n", + "User: Convert this to uppercase: llamastack\n", + "Agent id is 4cf4893a-24c1-4cea-8089-d5c96f2cb4b2\n", + "session id is 97d64ed6-aabf-4ee6-8f16-d659d730534d\n", + "Inference: The word 'llamastack' has been converted to uppercase.\n", + "Executed Tool: uppercase\n", + "Ground Truth Tool: uppercase\n", + "\n", + "User: Give me an insurance evaluation score\n", + "Agent id is 69de5f4e-08bd-4a21-9347-4ad7070dec5e\n", + "session id is 88227a8d-b728-49fa-997a-7e1cd5203dc2\n" + ] + }, + { + "data": { + "text/html": [ + "
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Session(\n",
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+       "β”‚   β”‚   Turn(\n",
+       "β”‚   β”‚   β”‚   input_messages=[\n",
+       "β”‚   β”‚   β”‚   β”‚   UserMessage(content='Give me an insurance evaluation score', role='user', context=None)\n",
+       "β”‚   β”‚   β”‚   ],\n",
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+       "β”‚   β”‚   β”‚   β”‚   stop_reason='end_of_turn',\n",
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+       "β”‚   β”‚   β”‚   steps=[\n",
+       "β”‚   β”‚   β”‚   β”‚   InferenceStep(\n",
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"\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[33mturn_id\u001b[0m=\u001b[32m'697a2bc3-cfa5-4d16-97e4-741a38844cfe'\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[33mcompleted_at\u001b[0m=\u001b[1;35mdatetime\u001b[0m\u001b[1;35m.datetime\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2025\u001b[0m, \u001b[1;36m3\u001b[0m, \u001b[1;36m26\u001b[0m, \u001b[1;36m12\u001b[0m, \u001b[1;36m17\u001b[0m, \u001b[1;36m53\u001b[0m, \u001b[1;36m231839\u001b[0m, \u001b[33mtzinfo\u001b[0m=\u001b[1;35mTzInfo\u001b[0m\u001b[1m(\u001b[0mUTC\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m,\n", + "\u001b[2;32mβ”‚ β”‚ β”‚ \u001b[0m\u001b[33moutput_attachments\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n", + "\u001b[2;32mβ”‚ β”‚ \u001b[0m\u001b[1m)\u001b[0m\n", + "\u001b[2;32mβ”‚ \u001b[0m\u001b[1m]\u001b[0m\n", + "\u001b[1m)\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inference: [tool_5_x-ray romeo]\n", + "Error: Not enough steps in response to access step 1.\n", + "\n", + "Total Tools: 14, Exception Rate: 0.00%, Tool Execution Rate: 80.00%, Correct Tool Rate: 80.00%, Avg Latency: 2.5401s\n", + "Max correct tool count: 13\n", + "Max tool execution count: 13\n" + ] + } + ], + "source": [ + "# Run the experiment\n", + "model_id = os.getenv(\"INFERENCE_MODEL\")\n", + "# model_id = \"meta-llama/Llama-3.2-3B-Instruct\"\n", + "print(model_id)\n", + "inference_model = model_id.split(\"/\")[1]\n", + "environment = \"local\" # \"nerc\" or \"local\"\n", + "temperature = 1\n", + "\n", + "# Setup logging to a file\n", + "output_dir = \"experiment_logs\"\n", + "os.makedirs(output_dir, exist_ok=True)\n", + "experiment_date = time.strftime(\"%Y%m%d_%H%M%S\")\n", + "subname = f\"{inference_model}_{environment}_temp{temperature}_{experiment_date}\"\n", + "log_file = os.path.join(output_dir, f\"results_{subname}.log\")\n", + "csv_filename = os.path.join(output_dir, f\"results_{subname}.csv\")\n", + "\n", + "# Redirect print statements to a log file\n", + "class Logger(object):\n", + " def __init__(self, filename):\n", + " self.terminal = sys.stdout\n", + " self.log = open(filename, \"a\")\n", + "\n", + " def write(self, message):\n", + " self.terminal.write(message)\n", + " self.log.write(message)\n", + "\n", + " def flush(self):\n", + " pass\n", + "\n", + "sys.stdout = Logger(log_file)\n", + "\n", + "base_url = f\"http://localhost:{os.getenv('LLAMA_STACK_PORT')}\" if environment == \"local\" else os.getenv(\"LLAMA_STACK_ENDPOINT\")\n", + "print(base_url)\n", + "client = LlamaStackClient(\n", + " base_url = base_url\n", + ")\n", + "\n", + "real_tools = [weather_info, word_count, reverse_string, uppercase, insurance_scorer]\n", + "results = []\n", + "\n", + "for total_tools in range(5, 100, 1): # Increase by 5 up to 50 tools\n", + " tools = real_tools + generate_fake_tools(total_tools - len(real_tools)-1)\n", + " print(len(tools))\n", + " \n", + " exception_count = 0\n", + " tool_execution_count = 0\n", + " correct_tool_count = 0\n", + " total_latency = 0\n", + " max_correct_tool_count = -1\n", + " max_tool_exe_count = -1\n", + "\n", + " for i, (query, correct_tool) in enumerate(queries):\n", + " agent = Agent(\n", + " client=client,\n", + " model=model_id,\n", + " instructions=\"\"\"You are an AI tool calling assistant. Must use the correct tool for each query.\n", + " When using the tools:\n", + " 1. Extract the relevant number or values from the user's request.\n", + " 2. Use the correct tool to perform the operation.\n", + " 3. Present the result clearly.\n", + " 4. Handle errors gracefully.\"\"\",\n", + " tools=tools,\n", + " sampling_params = { # Todo, test how temperature affect the results. \n", + " \"strategy\": {\n", + " \"type\": \"top_p\",\n", + " \"temperature\": temperature,\n", + " \"top_p\": 0.9,\n", + " }\n", + " },\n", + " \n", + " )\n", + "\n", + " print(f\"\\nUser: {query}\")\n", + " start_time = time.time()\n", + " print(f\"Agent id is {agent.agent_id}\")\n", + " session_id = agent.create_session(f\"tool-experiment-session-{i+1}\")\n", + " print(f'session id is {session_id}')\n", + " \n", + " try:\n", + " response = agent.create_turn(\n", + " messages=[\n", + " {\"role\": \"user\", \"content\": query}\n", + " ],\n", + " session_id=session_id,\n", + " stream=False,\n", + " )\n", + " \n", + " end_time = time.time()\n", + " response_time = end_time - start_time\n", + " total_latency += response_time\n", + " # pprint(response)\n", + " \n", + " print(f\"Inference: {response.output_message.content}\")\n", + "\n", + " steps = response.steps\n", + " if len(steps) > 1:\n", + " tool_executed = any(step.step_type == \"tool_execution\" for step in steps)\n", + " correct_tool_used = any(step.tool_calls[0].tool_name == correct_tool.__name__ for step in steps if step.step_type == \"tool_execution\")\n", + " if tool_executed:\n", + " print(f\"Executed Tool: {steps[1].tool_calls[0].tool_name}\")\n", + " print(f\"Ground Truth Tool: {correct_tool.__name__}\")\n", + " tool_execution_count += tool_executed\n", + " correct_tool_count += correct_tool_used\n", + " else:\n", + " print(\"Error: Not enough steps in response to access step 1.\")\n", + " \n", + " except Exception as e:\n", + " print(f\"Error processing query: {e}\")\n", + " exception_count += 1\n", + "\n", + " exception_rate = exception_count / len(queries)\n", + " tool_execution_rate = tool_execution_count / len(queries)\n", + " correct_tool_rate = correct_tool_count / len(queries)\n", + " average_latency = total_latency / len(queries)\n", + " \n", + " results.append([total_tools, exception_rate, tool_execution_rate, correct_tool_rate, average_latency])\n", + " print(f\"\\nTotal Tools: {total_tools}, Exception Rate: {exception_rate:.2%}, Tool Execution Rate: {tool_execution_rate:.2%}, Correct Tool Rate: {correct_tool_rate:.2%}, Avg Latency: {average_latency:.4f}s\")\n", + " \n", + " if correct_tool_rate < 1 and max_correct_tool_count== -1:\n", + " max_correct_tool_count = total_tools-1\n", + " session_response = client.agents.session.retrieve(\n", + " session_id=session_id,\n", + " agent_id=agent.agent_id,\n", + " )\n", + " pprint(session_response)\n", + " if tool_execution_rate < 1 and max_tool_exe_count == -1:\n", + " max_tool_exe_count = total_tools-1\n", + " session_response = client.agents.session.retrieve(\n", + " session_id=session_id,\n", + " agent_id=agent.agent_id,\n", + " )\n", + " pprint(session_response)\n", + " break\n", + "log_results(results, csv_filename)\n", + "print(f\"Max correct tool count: {max_correct_tool_count}\")\n", + "print(f\"Max tool execution count: {max_tool_exe_count}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# session_response = client.agents.session.retrieve(\n", + "# session_id=\"31822cbb-c4af-4032-ac45-9c7d5628cce7\",\n", + "# agent_id=\"c65548f1-0e6b-4a70-aed2-ae7249640b23\",\n", + "# )\n", + "# pprint(session_response)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "stack-client", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}