langchain/docs/integrations/mlflow_tracking.ipynb

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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)"
   ]
  }
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