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langchain/docs/ecosystem/clearml_tracking.ipynb
Ankush Gola d3ec00b566
Callbacks Refactor [base] (#3256) 
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com>
Co-authored-by: Zander Chase <130414180+vowelparrot@users.noreply.github.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-04-30 11:14:09 -07:00

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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# ClearML Integration\n",
"\n",
"In order to properly keep track of your langchain experiments and their results, you can enable the ClearML integration. ClearML is an experiment manager that neatly tracks and organizes all your experiment runs.\n",
"\n",
"<a target=\"_blank\" href=\"https://colab.research.google.com/github/hwchase17/langchain/blob/master/docs/ecosystem/clearml_tracking.ipynb\">\n",
" <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
"</a>"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting API Credentials\n",
"\n",
"We'll be using quite some APIs in this notebook, here is a list and where to get them:\n",
"\n",
"- ClearML: https://app.clear.ml/settings/workspace-configuration\n",
"- OpenAI: https://platform.openai.com/account/api-keys\n",
"- SerpAPI (google search): https://serpapi.com/dashboard"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"CLEARML_API_ACCESS_KEY\"] = \"\"\n",
"os.environ[\"CLEARML_API_SECRET_KEY\"] = \"\"\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
"os.environ[\"SERPAPI_API_KEY\"] = \"\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setting Up"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install clearml\n",
"!pip install pandas\n",
"!pip install textstat\n",
"!pip install spacy\n",
"!python -m spacy download en_core_web_sm"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The clearml callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/allegroai/clearml/issues with the tag `langchain`.\n"
]
}
],
"source": [
"from datetime import datetime\n",
"from langchain.callbacks import ClearMLCallbackHandler, StdOutCallbackHandler\n",
"from langchain.llms import OpenAI\n",
"\n",
"# Setup and use the ClearML Callback\n",
"clearml_callback = ClearMLCallbackHandler(\n",
" task_type=\"inference\",\n",
" project_name=\"langchain_callback_demo\",\n",
" task_name=\"llm\",\n",
" tags=[\"test\"],\n",
" # Change the following parameters based on the amount of detail you want tracked\n",
" visualize=True,\n",
" complexity_metrics=True,\n",
" stream_logs=True\n",
")\n",
"callbacks = [StdOutCallbackHandler(), clearml_callback]\n",
"# Get the OpenAI model ready to go\n",
"llm = OpenAI(temperature=0, callbacks=callbacks)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scenario 1: Just an LLM\n",
"\n",
"First, let's just run a single LLM a few times and capture the resulting prompt-answer conversation in ClearML"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}\n",
"{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}\n",
"{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}\n",
"{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}\n",
"{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}\n",
"{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}\n",
"{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}\n",
"{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nRoses are red,\\nViolets are blue,\\nSugar is sweet,\\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}\n",
"{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}\n",
"{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nRoses are red,\\nViolets are blue,\\nSugar is sweet,\\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}\n",
"{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}\n",
"{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nRoses are red,\\nViolets are blue,\\nSugar is sweet,\\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}\n",
"{'action_records': action name step starts ends errors text_ctr chain_starts \\\n",
"0 on_llm_start OpenAI 1 1 0 0 0 0 \n",
"1 on_llm_start OpenAI 1 1 0 0 0 0 \n",
"2 on_llm_start OpenAI 1 1 0 0 0 0 \n",
"3 on_llm_start OpenAI 1 1 0 0 0 0 \n",
"4 on_llm_start OpenAI 1 1 0 0 0 0 \n",
"5 on_llm_start OpenAI 1 1 0 0 0 0 \n",
"6 on_llm_end NaN 2 1 1 0 0 0 \n",
"7 on_llm_end NaN 2 1 1 0 0 0 \n",
"8 on_llm_end NaN 2 1 1 0 0 0 \n",
"9 on_llm_end NaN 2 1 1 0 0 0 \n",
"10 on_llm_end NaN 2 1 1 0 0 0 \n",
"11 on_llm_end NaN 2 1 1 0 0 0 \n",
"12 on_llm_start OpenAI 3 2 1 0 0 0 \n",
"13 on_llm_start OpenAI 3 2 1 0 0 0 \n",
"14 on_llm_start OpenAI 3 2 1 0 0 0 \n",
"15 on_llm_start OpenAI 3 2 1 0 0 0 \n",
"16 on_llm_start OpenAI 3 2 1 0 0 0 \n",
"17 on_llm_start OpenAI 3 2 1 0 0 0 \n",
"18 on_llm_end NaN 4 2 2 0 0 0 \n",
"19 on_llm_end NaN 4 2 2 0 0 0 \n",
"20 on_llm_end NaN 4 2 2 0 0 0 \n",
"21 on_llm_end NaN 4 2 2 0 0 0 \n",
"22 on_llm_end NaN 4 2 2 0 0 0 \n",
"23 on_llm_end NaN 4 2 2 0 0 0 \n",
"\n",
" chain_ends llm_starts ... difficult_words linsear_write_formula \\\n",
"0 0 1 ... NaN NaN \n",
"1 0 1 ... NaN NaN \n",
"2 0 1 ... NaN NaN \n",
"3 0 1 ... NaN NaN \n",
"4 0 1 ... NaN NaN \n",
"5 0 1 ... NaN NaN \n",
"6 0 1 ... 0.0 5.5 \n",
"7 0 1 ... 2.0 6.5 \n",
"8 0 1 ... 0.0 5.5 \n",
"9 0 1 ... 2.0 6.5 \n",
"10 0 1 ... 0.0 5.5 \n",
"11 0 1 ... 2.0 6.5 \n",
"12 0 2 ... NaN NaN \n",
"13 0 2 ... NaN NaN \n",
"14 0 2 ... NaN NaN \n",
"15 0 2 ... NaN NaN \n",
"16 0 2 ... NaN NaN \n",
"17 0 2 ... NaN NaN \n",
"18 0 2 ... 0.0 5.5 \n",
"19 0 2 ... 2.0 6.5 \n",
"20 0 2 ... 0.0 5.5 \n",
"21 0 2 ... 2.0 6.5 \n",
"22 0 2 ... 0.0 5.5 \n",
"23 0 2 ... 2.0 6.5 \n",
"\n",
" gunning_fog text_standard fernandez_huerta szigriszt_pazos \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN \n",
"5 NaN NaN NaN NaN \n",
"6 5.20 5th and 6th grade 133.58 131.54 \n",
"7 8.28 6th and 7th grade 115.58 112.37 \n",
"8 5.20 5th and 6th grade 133.58 131.54 \n",
"9 8.28 6th and 7th grade 115.58 112.37 \n",
"10 5.20 5th and 6th grade 133.58 131.54 \n",
"11 8.28 6th and 7th grade 115.58 112.37 \n",
"12 NaN NaN NaN NaN \n",
"13 NaN NaN NaN NaN \n",
"14 NaN NaN NaN NaN \n",
"15 NaN NaN NaN NaN \n",
"16 NaN NaN NaN NaN \n",
"17 NaN NaN NaN NaN \n",
"18 5.20 5th and 6th grade 133.58 131.54 \n",
"19 8.28 6th and 7th grade 115.58 112.37 \n",
"20 5.20 5th and 6th grade 133.58 131.54 \n",
"21 8.28 6th and 7th grade 115.58 112.37 \n",
"22 5.20 5th and 6th grade 133.58 131.54 \n",
"23 8.28 6th and 7th grade 115.58 112.37 \n",
"\n",
" gutierrez_polini crawford gulpease_index osman \n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN \n",
"5 NaN NaN NaN NaN \n",
"6 62.30 -0.2 79.8 116.91 \n",
"7 54.83 1.4 72.1 100.17 \n",
"8 62.30 -0.2 79.8 116.91 \n",
"9 54.83 1.4 72.1 100.17 \n",
"10 62.30 -0.2 79.8 116.91 \n",
"11 54.83 1.4 72.1 100.17 \n",
"12 NaN NaN NaN NaN \n",
"13 NaN NaN NaN NaN \n",
"14 NaN NaN NaN NaN \n",
"15 NaN NaN NaN NaN \n",
"16 NaN NaN NaN NaN \n",
"17 NaN NaN NaN NaN \n",
"18 62.30 -0.2 79.8 116.91 \n",
"19 54.83 1.4 72.1 100.17 \n",
"20 62.30 -0.2 79.8 116.91 \n",
"21 54.83 1.4 72.1 100.17 \n",
"22 62.30 -0.2 79.8 116.91 \n",
"23 54.83 1.4 72.1 100.17 \n",
"\n",
"[24 rows x 39 columns], 'session_analysis': prompt_step prompts name output_step \\\n",
"0 1 Tell me a joke OpenAI 2 \n",
"1 1 Tell me a poem OpenAI 2 \n",
"2 1 Tell me a joke OpenAI 2 \n",
"3 1 Tell me a poem OpenAI 2 \n",
"4 1 Tell me a joke OpenAI 2 \n",
"5 1 Tell me a poem OpenAI 2 \n",
"6 3 Tell me a joke OpenAI 4 \n",
"7 3 Tell me a poem OpenAI 4 \n",
"8 3 Tell me a joke OpenAI 4 \n",
"9 3 Tell me a poem OpenAI 4 \n",
"10 3 Tell me a joke OpenAI 4 \n",
"11 3 Tell me a poem OpenAI 4 \n",
"\n",
" output \\\n",
"0 \\n\\nQ: What did the fish say when it hit the w... \n",
"1 \\n\\nRoses are red,\\nViolets are blue,\\nSugar i... \n",
"2 \\n\\nQ: What did the fish say when it hit the w... \n",
"3 \\n\\nRoses are red,\\nViolets are blue,\\nSugar i... \n",
"4 \\n\\nQ: What did the fish say when it hit the w... \n",
"5 \\n\\nRoses are red,\\nViolets are blue,\\nSugar i... \n",
"6 \\n\\nQ: What did the fish say when it hit the w... \n",
"7 \\n\\nRoses are red,\\nViolets are blue,\\nSugar i... \n",
"8 \\n\\nQ: What did the fish say when it hit the w... \n",
"9 \\n\\nRoses are red,\\nViolets are blue,\\nSugar i... \n",
"10 \\n\\nQ: What did the fish say when it hit the w... \n",
"11 \\n\\nRoses are red,\\nViolets are blue,\\nSugar i... \n",
"\n",
" token_usage_total_tokens token_usage_prompt_tokens \\\n",
"0 162 24 \n",
"1 162 24 \n",
"2 162 24 \n",
"3 162 24 \n",
"4 162 24 \n",
"5 162 24 \n",
"6 162 24 \n",
"7 162 24 \n",
"8 162 24 \n",
"9 162 24 \n",
"10 162 24 \n",
"11 162 24 \n",
"\n",
" token_usage_completion_tokens flesch_reading_ease flesch_kincaid_grade \\\n",
"0 138 109.04 1.3 \n",
"1 138 83.66 4.8 \n",
"2 138 109.04 1.3 \n",
"3 138 83.66 4.8 \n",
"4 138 109.04 1.3 \n",
"5 138 83.66 4.8 \n",
"6 138 109.04 1.3 \n",
"7 138 83.66 4.8 \n",
"8 138 109.04 1.3 \n",
"9 138 83.66 4.8 \n",
"10 138 109.04 1.3 \n",
"11 138 83.66 4.8 \n",
"\n",
" ... difficult_words linsear_write_formula gunning_fog \\\n",
"0 ... 0 5.5 5.20 \n",
"1 ... 2 6.5 8.28 \n",
"2 ... 0 5.5 5.20 \n",
"3 ... 2 6.5 8.28 \n",
"4 ... 0 5.5 5.20 \n",
"5 ... 2 6.5 8.28 \n",
"6 ... 0 5.5 5.20 \n",
"7 ... 2 6.5 8.28 \n",
"8 ... 0 5.5 5.20 \n",
"9 ... 2 6.5 8.28 \n",
"10 ... 0 5.5 5.20 \n",
"11 ... 2 6.5 8.28 \n",
"\n",
" text_standard fernandez_huerta szigriszt_pazos gutierrez_polini \\\n",
"0 5th and 6th grade 133.58 131.54 62.30 \n",
"1 6th and 7th grade 115.58 112.37 54.83 \n",
"2 5th and 6th grade 133.58 131.54 62.30 \n",
"3 6th and 7th grade 115.58 112.37 54.83 \n",
"4 5th and 6th grade 133.58 131.54 62.30 \n",
"5 6th and 7th grade 115.58 112.37 54.83 \n",
"6 5th and 6th grade 133.58 131.54 62.30 \n",
"7 6th and 7th grade 115.58 112.37 54.83 \n",
"8 5th and 6th grade 133.58 131.54 62.30 \n",
"9 6th and 7th grade 115.58 112.37 54.83 \n",
"10 5th and 6th grade 133.58 131.54 62.30 \n",
"11 6th and 7th grade 115.58 112.37 54.83 \n",
"\n",
" crawford gulpease_index osman \n",
"0 -0.2 79.8 116.91 \n",
"1 1.4 72.1 100.17 \n",
"2 -0.2 79.8 116.91 \n",
"3 1.4 72.1 100.17 \n",
"4 -0.2 79.8 116.91 \n",
"5 1.4 72.1 100.17 \n",
"6 -0.2 79.8 116.91 \n",
"7 1.4 72.1 100.17 \n",
"8 -0.2 79.8 116.91 \n",
"9 1.4 72.1 100.17 \n",
"10 -0.2 79.8 116.91 \n",
"11 1.4 72.1 100.17 \n",
"\n",
"[12 rows x 24 columns]}\n",
"2023-03-29 14:00:25,948 - clearml.Task - INFO - Completed model upload to https://files.clear.ml/langchain_callback_demo/llm.988bd727b0e94a29a3ac0ee526813545/models/simple_sequential\n"
]
}
],
"source": [
"# SCENARIO 1 - LLM\n",
"llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\"] * 3)\n",
"# After every generation run, use flush to make sure all the metrics\n",
"# prompts and other output are properly saved separately\n",
"clearml_callback.flush_tracker(langchain_asset=llm, name=\"simple_sequential\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"At this point you can already go to https://app.clear.ml and take a look at the resulting ClearML Task that was created.\n",
"\n",
"Among others, you should see that this notebook is saved along with any git information. The model JSON that contains the used parameters is saved as an artifact, there are also console logs and under the plots section, you'll find tables that represent the flow of the chain.\n",
"\n",
"Finally, if you enabled visualizations, these are stored as HTML files under debug samples."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scenario 2: Creating an agent with tools\n",
"\n",
"To show a more advanced workflow, let's create an agent with access to tools. The way ClearML tracks the results is not different though, only the table will look slightly different as there are other types of actions taken when compared to the earlier, simpler example.\n",
"\n",
"You can now also see the use of the `finish=True` keyword, which will fully close the ClearML Task, instead of just resetting the parameters and prompts for a new conversation."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"{'action': 'on_chain_start', 'name': 'AgentExecutor', 'step': 1, 'starts': 1, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 0, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'input': 'Who is the wife of the person who sang summer of 69?'}\n",
"{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 2, 'starts': 2, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: Who is the wife of the person who sang summer of 69?\\nThought:'}\n",
"{'action': 'on_llm_end', 'token_usage_prompt_tokens': 189, 'token_usage_completion_tokens': 34, 'token_usage_total_tokens': 223, 'model_name': 'text-davinci-003', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': ' I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 91.61, 'flesch_kincaid_grade': 3.8, 'smog_index': 0.0, 'coleman_liau_index': 3.41, 'automated_readability_index': 3.5, 'dale_chall_readability_score': 6.06, 'difficult_words': 2, 'linsear_write_formula': 5.75, 'gunning_fog': 5.4, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 121.07, 'szigriszt_pazos': 119.5, 'gutierrez_polini': 54.91, 'crawford': 0.9, 'gulpease_index': 72.7, 'osman': 92.16}\n",
"\u001b[32;1m\u001b[1;3m I need to find out who sang summer of 69 and then find out who their wife is.\n",
"Action: Search\n",
"Action Input: \"Who sang summer of 69\"\u001b[0m{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who sang summer of 69', 'log': ' I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"', 'step': 4, 'starts': 3, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 1, 'tool_ends': 0, 'agent_ends': 0}\n",
"{'action': 'on_tool_start', 'input_str': 'Who sang summer of 69', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 5, 'starts': 4, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 0, 'agent_ends': 0}\n",
"\n",
"Observation: \u001b[36;1m\u001b[1;3mBryan Adams - Summer Of 69 (Official Music Video).\u001b[0m\n",
"Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams - Summer Of 69 (Official Music Video).', 'step': 6, 'starts': 4, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0}\n",
"{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 7, 'starts': 5, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: Who is the wife of the person who sang summer of 69?\\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"\\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\\nThought:'}\n",
"{'action': 'on_llm_end', 'token_usage_prompt_tokens': 242, 'token_usage_completion_tokens': 28, 'token_usage_total_tokens': 270, 'model_name': 'text-davinci-003', 'step': 8, 'starts': 5, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'text': ' I need to find out who Bryan Adams is married to.\\nAction: Search\\nAction Input: \"Who is Bryan Adams married to\"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 94.66, 'flesch_kincaid_grade': 2.7, 'smog_index': 0.0, 'coleman_liau_index': 4.73, 'automated_readability_index': 4.0, 'dale_chall_readability_score': 7.16, 'difficult_words': 2, 'linsear_write_formula': 4.25, 'gunning_fog': 4.2, 'text_standard': '4th and 5th grade', 'fernandez_huerta': 124.13, 'szigriszt_pazos': 119.2, 'gutierrez_polini': 52.26, 'crawford': 0.7, 'gulpease_index': 74.7, 'osman': 84.2}\n",
"\u001b[32;1m\u001b[1;3m I need to find out who Bryan Adams is married to.\n",
"Action: Search\n",
"Action Input: \"Who is Bryan Adams married to\"\u001b[0m{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who is Bryan Adams married to', 'log': ' I need to find out who Bryan Adams is married to.\\nAction: Search\\nAction Input: \"Who is Bryan Adams married to\"', 'step': 9, 'starts': 6, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 3, 'tool_ends': 1, 'agent_ends': 0}\n",
"{'action': 'on_tool_start', 'input_str': 'Who is Bryan Adams married to', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 10, 'starts': 7, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 1, 'agent_ends': 0}\n",
"\n",
"Observation: \u001b[36;1m\u001b[1;3mBryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...\u001b[0m\n",
"Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...', 'step': 11, 'starts': 7, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0}\n",
"{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 12, 'starts': 8, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: Who is the wife of the person who sang summer of 69?\\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"\\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\\nThought: I need to find out who Bryan Adams is married to.\\nAction: Search\\nAction Input: \"Who is Bryan Adams married to\"\\nObservation: Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...\\nThought:'}\n",
"{'action': 'on_llm_end', 'token_usage_prompt_tokens': 314, 'token_usage_completion_tokens': 18, 'token_usage_total_tokens': 332, 'model_name': 'text-davinci-003', 'step': 13, 'starts': 8, 'ends': 5, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'text': ' I now know the final answer.\\nFinal Answer: Bryan Adams has never been married.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 81.29, 'flesch_kincaid_grade': 3.7, 'smog_index': 0.0, 'coleman_liau_index': 5.75, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 7.37, 'difficult_words': 1, 'linsear_write_formula': 2.5, 'gunning_fog': 2.8, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 115.7, 'szigriszt_pazos': 110.84, 'gutierrez_polini': 49.79, 'crawford': 0.7, 'gulpease_index': 85.4, 'osman': 83.14}\n",
"\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: Bryan Adams has never been married.\u001b[0m\n",
"{'action': 'on_agent_finish', 'output': 'Bryan Adams has never been married.', 'log': ' I now know the final answer.\\nFinal Answer: Bryan Adams has never been married.', 'step': 14, 'starts': 8, 'ends': 6, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1}\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"{'action': 'on_chain_end', 'outputs': 'Bryan Adams has never been married.', 'step': 15, 'starts': 8, 'ends': 7, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 1, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1}\n",
"{'action_records': action name step starts ends errors text_ctr \\\n",
"0 on_llm_start OpenAI 1 1 0 0 0 \n",
"1 on_llm_start OpenAI 1 1 0 0 0 \n",
"2 on_llm_start OpenAI 1 1 0 0 0 \n",
"3 on_llm_start OpenAI 1 1 0 0 0 \n",
"4 on_llm_start OpenAI 1 1 0 0 0 \n",
".. ... ... ... ... ... ... ... \n",
"66 on_tool_end NaN 11 7 4 0 0 \n",
"67 on_llm_start OpenAI 12 8 4 0 0 \n",
"68 on_llm_end NaN 13 8 5 0 0 \n",
"69 on_agent_finish NaN 14 8 6 0 0 \n",
"70 on_chain_end NaN 15 8 7 0 0 \n",
"\n",
" chain_starts chain_ends llm_starts ... gulpease_index osman input \\\n",
"0 0 0 1 ... NaN NaN NaN \n",
"1 0 0 1 ... NaN NaN NaN \n",
"2 0 0 1 ... NaN NaN NaN \n",
"3 0 0 1 ... NaN NaN NaN \n",
"4 0 0 1 ... NaN NaN NaN \n",
".. ... ... ... ... ... ... ... \n",
"66 1 0 2 ... NaN NaN NaN \n",
"67 1 0 3 ... NaN NaN NaN \n",
"68 1 0 3 ... 85.4 83.14 NaN \n",
"69 1 0 3 ... NaN NaN NaN \n",
"70 1 1 3 ... NaN NaN NaN \n",
"\n",
" tool tool_input log \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
".. ... ... ... \n",
"66 NaN NaN NaN \n",
"67 NaN NaN NaN \n",
"68 NaN NaN NaN \n",
"69 NaN NaN I now know the final answer.\\nFinal Answer: B... \n",
"70 NaN NaN NaN \n",
"\n",
" input_str description output \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
".. ... ... ... \n",
"66 NaN NaN Bryan Adams has never married. In the 1990s, h... \n",
"67 NaN NaN NaN \n",
"68 NaN NaN NaN \n",
"69 NaN NaN Bryan Adams has never been married. \n",
"70 NaN NaN NaN \n",
"\n",
" outputs \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
".. ... \n",
"66 NaN \n",
"67 NaN \n",
"68 NaN \n",
"69 NaN \n",
"70 Bryan Adams has never been married. \n",
"\n",
"[71 rows x 47 columns], 'session_analysis': prompt_step prompts name \\\n",
"0 2 Answer the following questions as best you can... OpenAI \n",
"1 7 Answer the following questions as best you can... OpenAI \n",
"2 12 Answer the following questions as best you can... OpenAI \n",
"\n",
" output_step output \\\n",
"0 3 I need to find out who sang summer of 69 and ... \n",
"1 8 I need to find out who Bryan Adams is married... \n",
"2 13 I now know the final answer.\\nFinal Answer: B... \n",
"\n",
" token_usage_total_tokens token_usage_prompt_tokens \\\n",
"0 223 189 \n",
"1 270 242 \n",
"2 332 314 \n",
"\n",
" token_usage_completion_tokens flesch_reading_ease flesch_kincaid_grade \\\n",
"0 34 91.61 3.8 \n",
"1 28 94.66 2.7 \n",
"2 18 81.29 3.7 \n",
"\n",
" ... difficult_words linsear_write_formula gunning_fog \\\n",
"0 ... 2 5.75 5.4 \n",
"1 ... 2 4.25 4.2 \n",
"2 ... 1 2.50 2.8 \n",
"\n",
" text_standard fernandez_huerta szigriszt_pazos gutierrez_polini \\\n",
"0 3rd and 4th grade 121.07 119.50 54.91 \n",
"1 4th and 5th grade 124.13 119.20 52.26 \n",
"2 3rd and 4th grade 115.70 110.84 49.79 \n",
"\n",
" crawford gulpease_index osman \n",
"0 0.9 72.7 92.16 \n",
"1 0.7 74.7 84.20 \n",
"2 0.7 85.4 83.14 \n",
"\n",
"[3 rows x 24 columns]}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Could not update last created model in Task 988bd727b0e94a29a3ac0ee526813545, Task status 'completed' cannot be updated\n"
]
}
],
"source": [
"from langchain.agents import initialize_agent, load_tools\n",
"from langchain.agents import AgentType\n",
"\n",
"# SCENARIO 2 - Agent with Tools\n",
"tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=callbacks)\n",
"agent = initialize_agent(\n",
" tools,\n",
" llm,\n",
" agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
" callbacks=callbacks,\n",
")\n",
"agent.run(\n",
" \"Who is the wife of the person who sang summer of 69?\"\n",
")\n",
"clearml_callback.flush_tracker(langchain_asset=agent, name=\"Agent with Tools\", finish=True)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tips and Next Steps\n",
"\n",
"- Make sure you always use a unique `name` argument for the `clearml_callback.flush_tracker` function. If not, the model parameters used for a run will override the previous run!\n",
"\n",
"- If you close the ClearML Callback using `clearml_callback.flush_tracker(..., finish=True)` the Callback cannot be used anymore. Make a new one if you want to keep logging.\n",
"\n",
"- Check out the rest of the open source ClearML ecosystem, there is a data version manager, a remote execution agent, automated pipelines and much more!\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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"file_extension": ".py",
"mimetype": "text/x-python",
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