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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# MLflow\n",
"\n",
"This notebook goes over how to track your LangChain experiments into your MLflow Server"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install azureml-mlflow\n",
"!pip install pandas\n",
"!pip install textstat\n",
"!pip install spacy\n",
"!pip install openai\n",
"!pip install google-search-results\n",
"!python -m spacy download en_core_web_sm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"MLFLOW_TRACKING_URI\"] = \"\"\n",
"os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
"os.environ[\"SERPAPI_API_KEY\"] = \"\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.callbacks import MlflowCallbackHandler\n",
"from langchain.llms import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"Main function.\n",
"\n",
"This function is used to try the callback handler.\n",
"Scenarios:\n",
"1. OpenAI LLM\n",
"2. Chain with multiple SubChains on multiple generations\n",
"3. Agent with Tools\n",
"\"\"\"\n",
"mlflow_callback = MlflowCallbackHandler()\n",
"llm = OpenAI(model_name=\"gpt-3.5-turbo\", temperature=0, callbacks=[mlflow_callback], verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# SCENARIO 1 - LLM\n",
"llm_result = llm.generate([\"Tell me a joke\"])\n",
"\n",
"mlflow_callback.flush_tracker(llm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts import PromptTemplate\n",
"from langchain.chains import LLMChain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# SCENARIO 2 - Chain\n",
"template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
"Title: {title}\n",
"Playwright: This is a synopsis for the above play:\"\"\"\n",
"prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
"synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=[mlflow_callback])\n",
"\n",
"test_prompts = [\n",
" {\n",
" \"title\": \"documentary about good video games that push the boundary of game design\"\n",
" },\n",
"]\n",
"synopsis_chain.apply(test_prompts)\n",
"mlflow_callback.flush_tracker(synopsis_chain)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_jN73xcPVEpI"
},
"outputs": [],
"source": [
"from langchain.agents import initialize_agent, load_tools\n",
"from langchain.agents import AgentType"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Gpq4rk6VT9cu"
},
"outputs": [],
"source": [
"# SCENARIO 3 - Agent with Tools\n",
"tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=[mlflow_callback])\n",
"agent = initialize_agent(\n",
" tools,\n",
" llm,\n",
" agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
" callbacks=[mlflow_callback],\n",
" verbose=True,\n",
")\n",
"agent.run(\n",
" \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
")\n",
"mlflow_callback.flush_tracker(agent, finish=True)"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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