forked from Archives/langchain
Harrison/comet ml (#2799)
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com> Co-authored-by: Boris Feld <lothiraldan@gmail.com>fix_agent_callbacks
Harrison Chase
1 year ago
committed by
GitHub
parent
b2bc5ef56a
commit
1bb0706955
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Using Comet with Langchain"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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""
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this guide we will demonstrate how to track your Langchain Experiments, Evaluation Metrics, and LLM Sessions with [Comet](https://www.comet.com/site/?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook). \n",
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"\n",
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"<a target=\"_blank\" href=\"https://colab.research.google.com/github/hwchase17/langchain/blob/master/docs/ecosystem/comet_tracking.ipynb\">\n",
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" <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
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"</a>\n",
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"\n",
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"**Example Project:** [Comet with LangChain](https://www.comet.com/examples/comet-example-langchain/view/b5ZThK6OFdhKWVSP3fDfRtrNF/panels?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img width=\"1280\" alt=\"comet-langchain\" src=\"https://user-images.githubusercontent.com/7529846/230326720-a9711435-9c6f-4edb-a707-94b67271ab25.png\">\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Install Comet and Dependencies"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install comet_ml\n",
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"!pip install langchain\n",
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"!pip install openai\n",
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"!pip install google-search-results"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Initialize Comet and Set your Credentials"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can grab your [Comet API Key here](https://www.comet.com/signup?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook) or click the link after intializing Comet"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import comet_ml\n",
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"\n",
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"comet_ml.init(project_name=\"comet-example-langchain\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Set OpenAI and SerpAPI credentials"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You will need an [OpenAI API Key](https://platform.openai.com/account/api-keys) and a [SerpAPI API Key](https://serpapi.com/dashboard) to run the following examples"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"%env OPENAI_API_KEY=\"...\"\n",
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"%env SERPAPI_API_KEY=\"...\""
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Scenario 1: Using just an LLM"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from datetime import datetime\n",
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"\n",
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"from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
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"from langchain.callbacks.base import CallbackManager\n",
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"from langchain.llms import OpenAI\n",
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"\n",
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"comet_callback = CometCallbackHandler(\n",
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" project_name=\"comet-example-langchain\",\n",
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" complexity_metrics=True,\n",
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" stream_logs=True,\n",
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" tags=[\"llm\"],\n",
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" visualizations=[\"dep\"],\n",
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")\n",
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"manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
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"llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
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"\n",
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"llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\", \"Tell me a fact\"] * 3)\n",
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"print(\"LLM result\", llm_result)\n",
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"comet_callback.flush_tracker(llm, finish=True)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Scenario 2: Using an LLM in a Chain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
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"from langchain.callbacks.base import CallbackManager\n",
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"from langchain.chains import LLMChain\n",
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"from langchain.llms import OpenAI\n",
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"from langchain.prompts import PromptTemplate\n",
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"\n",
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"comet_callback = CometCallbackHandler(\n",
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" complexity_metrics=True,\n",
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" project_name=\"comet-example-langchain\",\n",
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" stream_logs=True,\n",
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" tags=[\"synopsis-chain\"],\n",
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")\n",
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"manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
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"\n",
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"llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
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"\n",
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"template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
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"Title: {title}\n",
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"Playwright: This is a synopsis for the above play:\"\"\"\n",
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"prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
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"synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
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"\n",
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"test_prompts = [{\"title\": \"Documentary about Bigfoot in Paris\"}]\n",
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"synopsis_chain.apply(test_prompts)\n",
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"comet_callback.flush_tracker(synopsis_chain, finish=True)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Scenario 3: Using An Agent with Tools "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import initialize_agent, load_tools\n",
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"from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
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"from langchain.callbacks.base import CallbackManager\n",
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"from langchain.llms import OpenAI\n",
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"\n",
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"comet_callback = CometCallbackHandler(\n",
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" project_name=\"comet-example-langchain\",\n",
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" complexity_metrics=True,\n",
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" stream_logs=True,\n",
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" tags=[\"agent\"],\n",
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")\n",
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"manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
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"llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
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"\n",
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"tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
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"agent = initialize_agent(\n",
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" tools,\n",
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" llm,\n",
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" agent=\"zero-shot-react-description\",\n",
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" callback_manager=manager,\n",
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" verbose=True,\n",
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")\n",
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"agent.run(\n",
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" \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
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")\n",
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"comet_callback.flush_tracker(agent, finish=True)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Scenario 4: Using Custom Evaluation Metrics"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The `CometCallbackManager` also allows you to define and use Custom Evaluation Metrics to assess generated outputs from your model. Let's take a look at how this works. \n",
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"\n",
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"\n",
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"In the snippet below, we will use the [ROUGE](https://huggingface.co/spaces/evaluate-metric/rouge) metric to evaluate the quality of a generated summary of an input prompt. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install rouge-score"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from rouge_score import rouge_scorer\n",
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"\n",
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"from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
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"from langchain.callbacks.base import CallbackManager\n",
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"from langchain.chains import LLMChain\n",
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"from langchain.llms import OpenAI\n",
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"from langchain.prompts import PromptTemplate\n",
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"\n",
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"\n",
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"class Rouge:\n",
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" def __init__(self, reference):\n",
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" self.reference = reference\n",
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" self.scorer = rouge_scorer.RougeScorer([\"rougeLsum\"], use_stemmer=True)\n",
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"\n",
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" def compute_metric(self, generation, prompt_idx, gen_idx):\n",
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" prediction = generation.text\n",
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" results = self.scorer.score(target=self.reference, prediction=prediction)\n",
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"\n",
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" return {\n",
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" \"rougeLsum_score\": results[\"rougeLsum\"].fmeasure,\n",
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" \"reference\": self.reference,\n",
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" }\n",
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"\n",
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"\n",
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"reference = \"\"\"\n",
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"The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building.\n",
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"It was the first structure to reach a height of 300 metres.\n",
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"\n",
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"It is now taller than the Chrysler Building in New York City by 5.2 metres (17 ft)\n",
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"Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France .\n",
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"\"\"\"\n",
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"rouge_score = Rouge(reference=reference)\n",
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"\n",
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"template = \"\"\"Given the following article, it is your job to write a summary.\n",
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"Article:\n",
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"{article}\n",
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"Summary: This is the summary for the above article:\"\"\"\n",
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"prompt_template = PromptTemplate(input_variables=[\"article\"], template=template)\n",
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"\n",
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"comet_callback = CometCallbackHandler(\n",
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" project_name=\"comet-example-langchain\",\n",
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" complexity_metrics=False,\n",
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" stream_logs=True,\n",
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" tags=[\"custom_metrics\"],\n",
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" custom_metrics=rouge_score.compute_metric,\n",
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")\n",
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"manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
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"llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
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"\n",
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"synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
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"\n",
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"test_prompts = [\n",
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" {\n",
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" \"article\": \"\"\"\n",
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" The tower is 324 metres (1,063 ft) tall, about the same height as\n",
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" an 81-storey building, and the tallest structure in Paris. Its base is square,\n",
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" measuring 125 metres (410 ft) on each side.\n",
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" During its construction, the Eiffel Tower surpassed the\n",
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" Washington Monument to become the tallest man-made structure in the world,\n",
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" a title it held for 41 years until the Chrysler Building\n",
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" in New York City was finished in 1930.\n",
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"\n",
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" It was the first structure to reach a height of 300 metres.\n",
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" Due to the addition of a broadcasting aerial at the top of the tower in 1957,\n",
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" it is now taller than the Chrysler Building by 5.2 metres (17 ft).\n",
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"\n",
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" Excluding transmitters, the Eiffel Tower is the second tallest\n",
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" free-standing structure in France after the Millau Viaduct.\n",
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" \"\"\"\n",
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" }\n",
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"]\n",
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"synopsis_chain.apply(test_prompts)\n",
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"comet_callback.flush_tracker(synopsis_chain, finish=True)"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -0,0 +1,627 @@
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import tempfile
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from copy import deepcopy
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from pathlib import Path
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from typing import Any, Callable, Dict, List, Optional, Sequence, Union
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import langchain
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from langchain.callbacks.base import BaseCallbackHandler
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from langchain.callbacks.utils import (
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BaseMetadataCallbackHandler,
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flatten_dict,
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import_pandas,
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import_spacy,
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import_textstat,
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)
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from langchain.schema import AgentAction, AgentFinish, Generation, LLMResult
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LANGCHAIN_MODEL_NAME = "langchain-model"
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def import_comet_ml() -> Any:
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try:
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import comet_ml # noqa: F401
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except ImportError:
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raise ImportError(
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"To use the comet_ml callback manager you need to have the "
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"`comet_ml` python package installed. Please install it with"
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" `pip install comet_ml`"
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)
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return comet_ml
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def _get_experiment(
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workspace: Optional[str] = None, project_name: Optional[str] = None
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) -> Any:
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comet_ml = import_comet_ml()
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experiment = comet_ml.config.get_global_experiment()
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if experiment is None:
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experiment = comet_ml.Experiment( # type: ignore
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workspace=workspace,
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project_name=project_name,
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)
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return experiment
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def _fetch_text_complexity_metrics(text: str) -> dict:
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textstat = import_textstat()
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text_complexity_metrics = {
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"flesch_reading_ease": textstat.flesch_reading_ease(text),
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"flesch_kincaid_grade": textstat.flesch_kincaid_grade(text),
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"smog_index": textstat.smog_index(text),
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"coleman_liau_index": textstat.coleman_liau_index(text),
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"automated_readability_index": textstat.automated_readability_index(text),
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"dale_chall_readability_score": textstat.dale_chall_readability_score(text),
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"difficult_words": textstat.difficult_words(text),
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"linsear_write_formula": textstat.linsear_write_formula(text),
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"gunning_fog": textstat.gunning_fog(text),
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"text_standard": textstat.text_standard(text),
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"fernandez_huerta": textstat.fernandez_huerta(text),
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"szigriszt_pazos": textstat.szigriszt_pazos(text),
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"gutierrez_polini": textstat.gutierrez_polini(text),
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"crawford": textstat.crawford(text),
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"gulpease_index": textstat.gulpease_index(text),
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"osman": textstat.osman(text),
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}
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return text_complexity_metrics
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def _summarize_metrics_for_generated_outputs(metrics: Sequence) -> dict:
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pd = import_pandas()
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metrics_df = pd.DataFrame(metrics)
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metrics_summary = metrics_df.describe()
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return metrics_summary.to_dict()
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class CometCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
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"""Callback Handler that logs to Comet.
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Parameters:
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job_type (str): The type of comet_ml task such as "inference",
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"testing" or "qc"
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project_name (str): The comet_ml project name
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tags (list): Tags to add to the task
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task_name (str): Name of the comet_ml task
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visualize (bool): Whether to visualize the run.
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complexity_metrics (bool): Whether to log complexity metrics
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stream_logs (bool): Whether to stream callback actions to Comet
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This handler will utilize the associated callback method and formats
|
||||
the input of each callback function with metadata regarding the state of LLM run,
|
||||
and adds the response to the list of records for both the {method}_records and
|
||||
action. It then logs the response to Comet.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
task_type: Optional[str] = "inference",
|
||||
workspace: Optional[str] = None,
|
||||
project_name: Optional[str] = "comet-langchain-demo",
|
||||
tags: Optional[Sequence] = None,
|
||||
name: Optional[str] = None,
|
||||
visualizations: Optional[List[str]] = None,
|
||||
complexity_metrics: bool = False,
|
||||
custom_metrics: Optional[Callable] = None,
|
||||
stream_logs: bool = True,
|
||||
) -> None:
|
||||
"""Initialize callback handler."""
|
||||
|
||||
comet_ml = import_comet_ml()
|
||||
super().__init__()
|
||||
|
||||
self.task_type = task_type
|
||||
self.workspace = workspace
|
||||
self.project_name = project_name
|
||||
self.tags = tags
|
||||
self.visualizations = visualizations
|
||||
self.complexity_metrics = complexity_metrics
|
||||
self.custom_metrics = custom_metrics
|
||||
self.stream_logs = stream_logs
|
||||
self.temp_dir = tempfile.TemporaryDirectory()
|
||||
|
||||
self.experiment = _get_experiment(workspace, project_name)
|
||||
self.experiment.log_other("Created from", "langchain")
|
||||
if tags:
|
||||
self.experiment.add_tags(tags)
|
||||
self.name = name
|
||||
if self.name:
|
||||
self.experiment.set_name(self.name)
|
||||
|
||||
warning = (
|
||||
"The comet_ml callback is currently in beta and is subject to change "
|
||||
"based on updates to `langchain`. Please report any issues to "
|
||||
"https://github.com/comet_ml/issue_tracking/issues with the tag "
|
||||
"`langchain`."
|
||||
)
|
||||
comet_ml.LOGGER.warning(warning)
|
||||
|
||||
self.callback_columns: list = []
|
||||
self.action_records: list = []
|
||||
self.complexity_metrics = complexity_metrics
|
||||
if self.visualizations:
|
||||
spacy = import_spacy()
|
||||
self.nlp = spacy.load("en_core_web_sm")
|
||||
else:
|
||||
self.nlp = None
|
||||
|
||||
def _init_resp(self) -> Dict:
|
||||
return {k: None for k in self.callback_columns}
|
||||
|
||||
def on_llm_start(
|
||||
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
|
||||
) -> None:
|
||||
"""Run when LLM starts."""
|
||||
self.step += 1
|
||||
self.llm_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
metadata = self._init_resp()
|
||||
metadata.update({"action": "on_llm_start"})
|
||||
metadata.update(flatten_dict(serialized))
|
||||
metadata.update(self.get_custom_callback_meta())
|
||||
|
||||
for prompt in prompts:
|
||||
prompt_resp = deepcopy(metadata)
|
||||
prompt_resp["prompts"] = prompt
|
||||
self.on_llm_start_records.append(prompt_resp)
|
||||
self.action_records.append(prompt_resp)
|
||||
|
||||
if self.stream_logs:
|
||||
self._log_stream(prompt, metadata, self.step)
|
||||
|
||||
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
|
||||
"""Run when LLM generates a new token."""
|
||||
self.step += 1
|
||||
self.llm_streams += 1
|
||||
|
||||
resp = self._init_resp()
|
||||
resp.update({"action": "on_llm_new_token", "token": token})
|
||||
resp.update(self.get_custom_callback_meta())
|
||||
|
||||
self.action_records.append(resp)
|
||||
|
||||
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
|
||||
"""Run when LLM ends running."""
|
||||
self.step += 1
|
||||
self.llm_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
metadata = self._init_resp()
|
||||
metadata.update({"action": "on_llm_end"})
|
||||
metadata.update(flatten_dict(response.llm_output or {}))
|
||||
metadata.update(self.get_custom_callback_meta())
|
||||
|
||||
output_complexity_metrics = []
|
||||
output_custom_metrics = []
|
||||
|
||||
for prompt_idx, generations in enumerate(response.generations):
|
||||
for gen_idx, generation in enumerate(generations):
|
||||
text = generation.text
|
||||
|
||||
generation_resp = deepcopy(metadata)
|
||||
generation_resp.update(flatten_dict(generation.dict()))
|
||||
|
||||
complexity_metrics = self._get_complexity_metrics(text)
|
||||
if complexity_metrics:
|
||||
output_complexity_metrics.append(complexity_metrics)
|
||||
generation_resp.update(complexity_metrics)
|
||||
|
||||
custom_metrics = self._get_custom_metrics(
|
||||
generation, prompt_idx, gen_idx
|
||||
)
|
||||
if custom_metrics:
|
||||
output_custom_metrics.append(custom_metrics)
|
||||
generation_resp.update(custom_metrics)
|
||||
|
||||
if self.stream_logs:
|
||||
self._log_stream(text, metadata, self.step)
|
||||
|
||||
self.action_records.append(generation_resp)
|
||||
self.on_llm_end_records.append(generation_resp)
|
||||
|
||||
self._log_text_metrics(output_complexity_metrics, step=self.step)
|
||||
self._log_text_metrics(output_custom_metrics, step=self.step)
|
||||
|
||||
def on_llm_error(
|
||||
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
|
||||
) -> None:
|
||||
"""Run when LLM errors."""
|
||||
self.step += 1
|
||||
self.errors += 1
|
||||
|
||||
def on_chain_start(
|
||||
self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
|
||||
) -> None:
|
||||
"""Run when chain starts running."""
|
||||
self.step += 1
|
||||
self.chain_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
resp = self._init_resp()
|
||||
resp.update({"action": "on_chain_start"})
|
||||
resp.update(flatten_dict(serialized))
|
||||
resp.update(self.get_custom_callback_meta())
|
||||
|
||||
comet_ml = import_comet_ml()
|
||||
|
||||
for chain_input_key, chain_input_val in inputs.items():
|
||||
if isinstance(chain_input_val, str):
|
||||
input_resp = deepcopy(resp)
|
||||
if self.stream_logs:
|
||||
self._log_stream(chain_input_val, resp, self.step)
|
||||
input_resp.update({chain_input_key: chain_input_val})
|
||||
self.action_records.append(input_resp)
|
||||
|
||||
else:
|
||||
comet_ml.LOGGER.warning(
|
||||
f"Unexpected data format provided! "
|
||||
f"Input Value for {chain_input_key} will not be logged"
|
||||
)
|
||||
|
||||
def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
|
||||
"""Run when chain ends running."""
|
||||
self.step += 1
|
||||
self.chain_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
resp = self._init_resp()
|
||||
resp.update({"action": "on_chain_end"})
|
||||
resp.update(self.get_custom_callback_meta())
|
||||
|
||||
comet_ml = import_comet_ml()
|
||||
|
||||
for chain_output_key, chain_output_val in outputs.items():
|
||||
if isinstance(chain_output_val, str):
|
||||
output_resp = deepcopy(resp)
|
||||
if self.stream_logs:
|
||||
self._log_stream(chain_output_val, resp, self.step)
|
||||
output_resp.update({chain_output_key: chain_output_val})
|
||||
self.action_records.append(output_resp)
|
||||
else:
|
||||
comet_ml.LOGGER.warning(
|
||||
f"Unexpected data format provided! "
|
||||
f"Output Value for {chain_output_key} will not be logged"
|
||||
)
|
||||
|
||||
def on_chain_error(
|
||||
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
|
||||
) -> None:
|
||||
"""Run when chain errors."""
|
||||
self.step += 1
|
||||
self.errors += 1
|
||||
|
||||
def on_tool_start(
|
||||
self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
|
||||
) -> None:
|
||||
"""Run when tool starts running."""
|
||||
self.step += 1
|
||||
self.tool_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
resp = self._init_resp()
|
||||
resp.update({"action": "on_tool_start"})
|
||||
resp.update(flatten_dict(serialized))
|
||||
resp.update(self.get_custom_callback_meta())
|
||||
if self.stream_logs:
|
||||
self._log_stream(input_str, resp, self.step)
|
||||
|
||||
resp.update({"input_str": input_str})
|
||||
self.action_records.append(resp)
|
||||
|
||||
def on_tool_end(self, output: str, **kwargs: Any) -> None:
|
||||
"""Run when tool ends running."""
|
||||
self.step += 1
|
||||
self.tool_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
resp = self._init_resp()
|
||||
resp.update({"action": "on_tool_end"})
|
||||
resp.update(self.get_custom_callback_meta())
|
||||
if self.stream_logs:
|
||||
self._log_stream(output, resp, self.step)
|
||||
|
||||
resp.update({"output": output})
|
||||
self.action_records.append(resp)
|
||||
|
||||
def on_tool_error(
|
||||
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
|
||||
) -> None:
|
||||
"""Run when tool errors."""
|
||||
self.step += 1
|
||||
self.errors += 1
|
||||
|
||||
def on_text(self, text: str, **kwargs: Any) -> None:
|
||||
"""
|
||||
Run when agent is ending.
|
||||
"""
|
||||
self.step += 1
|
||||
self.text_ctr += 1
|
||||
|
||||
resp = self._init_resp()
|
||||
resp.update({"action": "on_text"})
|
||||
resp.update(self.get_custom_callback_meta())
|
||||
if self.stream_logs:
|
||||
self._log_stream(text, resp, self.step)
|
||||
|
||||
resp.update({"text": text})
|
||||
self.action_records.append(resp)
|
||||
|
||||
def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
|
||||
"""Run when agent ends running."""
|
||||
self.step += 1
|
||||
self.agent_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
resp = self._init_resp()
|
||||
output = finish.return_values["output"]
|
||||
log = finish.log
|
||||
|
||||
resp.update({"action": "on_agent_finish", "log": log})
|
||||
resp.update(self.get_custom_callback_meta())
|
||||
if self.stream_logs:
|
||||
self._log_stream(output, resp, self.step)
|
||||
|
||||
resp.update({"output": output})
|
||||
self.action_records.append(resp)
|
||||
|
||||
def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
|
||||
"""Run on agent action."""
|
||||
self.step += 1
|
||||
self.tool_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
tool = action.tool
|
||||
tool_input = action.tool_input
|
||||
log = action.log
|
||||
|
||||
resp = self._init_resp()
|
||||
resp.update({"action": "on_agent_action", "log": log, "tool": tool})
|
||||
resp.update(self.get_custom_callback_meta())
|
||||
if self.stream_logs:
|
||||
self._log_stream(tool_input, resp, self.step)
|
||||
|
||||
resp.update({"tool_input": tool_input})
|
||||
self.action_records.append(resp)
|
||||
|
||||
def _get_complexity_metrics(self, text: str) -> dict:
|
||||
"""Compute text complexity metrics using textstat.
|
||||
|
||||
Parameters:
|
||||
text (str): The text to analyze.
|
||||
|
||||
Returns:
|
||||
(dict): A dictionary containing the complexity metrics.
|
||||
"""
|
||||
resp = {}
|
||||
if self.complexity_metrics:
|
||||
text_complexity_metrics = _fetch_text_complexity_metrics(text)
|
||||
resp.update(text_complexity_metrics)
|
||||
|
||||
return resp
|
||||
|
||||
def _get_custom_metrics(
|
||||
self, generation: Generation, prompt_idx: int, gen_idx: int
|
||||
) -> dict:
|
||||
"""Compute Custom Metrics for an LLM Generated Output
|
||||
|
||||
Args:
|
||||
generation (LLMResult): Output generation from an LLM
|
||||
prompt_idx (int): List index of the input prompt
|
||||
gen_idx (int): List index of the generated output
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the custom metrics.
|
||||
"""
|
||||
|
||||
resp = {}
|
||||
if self.custom_metrics:
|
||||
custom_metrics = self.custom_metrics(generation, prompt_idx, gen_idx)
|
||||
resp.update(custom_metrics)
|
||||
|
||||
return resp
|
||||
|
||||
def flush_tracker(
|
||||
self,
|
||||
langchain_asset: Any = None,
|
||||
task_type: Optional[str] = "inference",
|
||||
workspace: Optional[str] = None,
|
||||
project_name: Optional[str] = "comet-langchain-demo",
|
||||
tags: Optional[Sequence] = None,
|
||||
name: Optional[str] = None,
|
||||
visualizations: Optional[List[str]] = None,
|
||||
complexity_metrics: bool = False,
|
||||
custom_metrics: Optional[Callable] = None,
|
||||
finish: bool = False,
|
||||
reset: bool = False,
|
||||
) -> None:
|
||||
"""Flush the tracker and setup the session.
|
||||
|
||||
Everything after this will be a new table.
|
||||
|
||||
Args:
|
||||
name: Name of the preformed session so far so it is identifyable
|
||||
langchain_asset: The langchain asset to save.
|
||||
finish: Whether to finish the run.
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
self._log_session(langchain_asset)
|
||||
|
||||
if langchain_asset:
|
||||
self._log_model(langchain_asset)
|
||||
|
||||
if finish:
|
||||
self.experiment.end()
|
||||
|
||||
if reset:
|
||||
self._reset(
|
||||
task_type,
|
||||
workspace,
|
||||
project_name,
|
||||
tags,
|
||||
name,
|
||||
visualizations,
|
||||
complexity_metrics,
|
||||
custom_metrics,
|
||||
)
|
||||
|
||||
def _log_stream(self, prompt: str, metadata: dict, step: int) -> None:
|
||||
self.experiment.log_text(prompt, metadata=metadata, step=step)
|
||||
|
||||
def _log_model(self, langchain_asset: Any) -> None:
|
||||
comet_ml = import_comet_ml()
|
||||
|
||||
model_parameters = self._get_llm_parameters(langchain_asset)
|
||||
self.experiment.log_parameters(model_parameters, prefix="model")
|
||||
|
||||
langchain_asset_path = Path(self.temp_dir.name, "model.json")
|
||||
model_name = self.name if self.name else LANGCHAIN_MODEL_NAME
|
||||
|
||||
try:
|
||||
if hasattr(langchain_asset, "save"):
|
||||
langchain_asset.save(langchain_asset_path)
|
||||
self.experiment.log_model(model_name, str(langchain_asset_path))
|
||||
except (ValueError, AttributeError, NotImplementedError) as e:
|
||||
if hasattr(langchain_asset, "save_agent"):
|
||||
langchain_asset.save_agent(langchain_asset_path)
|
||||
self.experiment.log_model(model_name, str(langchain_asset_path))
|
||||
else:
|
||||
comet_ml.LOGGER.warning(
|
||||
f"{e}"
|
||||
" Could not save Langchain Asset "
|
||||
f"for {langchain_asset.__class__.__name__}"
|
||||
)
|
||||
|
||||
def _log_session(self, langchain_asset: Optional[Any] = None) -> None:
|
||||
llm_session_df = self._create_session_analysis_dataframe(langchain_asset)
|
||||
# Log the cleaned dataframe as a table
|
||||
self.experiment.log_table("langchain-llm-session.csv", llm_session_df)
|
||||
|
||||
metadata = {"langchain_version": str(langchain.__version__)}
|
||||
# Log the langchain low-level records as a JSON file directly
|
||||
self.experiment.log_asset_data(
|
||||
self.action_records, "langchain-action_records.json", metadata=metadata
|
||||
)
|
||||
|
||||
self._log_visualizations(llm_session_df)
|
||||
|
||||
def _log_text_metrics(self, metrics: Sequence[dict], step: int) -> None:
|
||||
if not metrics:
|
||||
return
|
||||
|
||||
metrics_summary = _summarize_metrics_for_generated_outputs(metrics)
|
||||
for key, value in metrics_summary.items():
|
||||
self.experiment.log_metrics(value, prefix=key, step=step)
|
||||
|
||||
def _log_visualizations(self, session_df: Any) -> None:
|
||||
if not (self.visualizations and self.nlp):
|
||||
return
|
||||
|
||||
spacy = import_spacy()
|
||||
comet_ml = import_comet_ml()
|
||||
|
||||
prompts = session_df["prompts"].tolist()
|
||||
outputs = session_df["text"].tolist()
|
||||
|
||||
for idx, (prompt, output) in enumerate(zip(prompts, outputs)):
|
||||
doc = self.nlp(output)
|
||||
sentence_spans = list(doc.sents)
|
||||
|
||||
for visualization in self.visualizations:
|
||||
try:
|
||||
html = spacy.displacy.render(
|
||||
sentence_spans,
|
||||
style=visualization,
|
||||
options={"compact": True},
|
||||
jupyter=False,
|
||||
page=True,
|
||||
)
|
||||
self.experiment.log_asset_data(
|
||||
html,
|
||||
name=f"langchain-viz-{visualization}-{idx}.html",
|
||||
metadata={"prompt": prompt},
|
||||
step=idx,
|
||||
)
|
||||
except Exception as e:
|
||||
comet_ml.LOGGER.warning(e)
|
||||
|
||||
return
|
||||
|
||||
def _reset(
|
||||
self,
|
||||
task_type: Optional[str] = None,
|
||||
workspace: Optional[str] = None,
|
||||
project_name: Optional[str] = None,
|
||||
tags: Optional[Sequence] = None,
|
||||
name: Optional[str] = None,
|
||||
visualizations: Optional[List[str]] = None,
|
||||
complexity_metrics: bool = False,
|
||||
custom_metrics: Optional[Callable] = None,
|
||||
) -> None:
|
||||
_task_type = task_type if task_type else self.task_type
|
||||
_workspace = workspace if workspace else self.workspace
|
||||
_project_name = project_name if project_name else self.project_name
|
||||
_tags = tags if tags else self.tags
|
||||
_name = name if name else self.name
|
||||
_visualizations = visualizations if visualizations else self.visualizations
|
||||
_complexity_metrics = (
|
||||
complexity_metrics if complexity_metrics else self.complexity_metrics
|
||||
)
|
||||
_custom_metrics = custom_metrics if custom_metrics else self.custom_metrics
|
||||
|
||||
self.__init__( # type: ignore
|
||||
task_type=_task_type,
|
||||
workspace=_workspace,
|
||||
project_name=_project_name,
|
||||
tags=_tags,
|
||||
name=_name,
|
||||
visualizations=_visualizations,
|
||||
complexity_metrics=_complexity_metrics,
|
||||
custom_metrics=_custom_metrics,
|
||||
)
|
||||
|
||||
self.reset_callback_meta()
|
||||
self.temp_dir = tempfile.TemporaryDirectory()
|
||||
|
||||
def _create_session_analysis_dataframe(self, langchain_asset: Any = None) -> dict:
|
||||
pd = import_pandas()
|
||||
|
||||
llm_parameters = self._get_llm_parameters(langchain_asset)
|
||||
num_generations_per_prompt = llm_parameters.get("n", 1)
|
||||
|
||||
llm_start_records_df = pd.DataFrame(self.on_llm_start_records)
|
||||
# Repeat each input row based on the number of outputs generated per prompt
|
||||
llm_start_records_df = llm_start_records_df.loc[
|
||||
llm_start_records_df.index.repeat(num_generations_per_prompt)
|
||||
].reset_index(drop=True)
|
||||
llm_end_records_df = pd.DataFrame(self.on_llm_end_records)
|
||||
|
||||
llm_session_df = pd.merge(
|
||||
llm_start_records_df,
|
||||
llm_end_records_df,
|
||||
left_index=True,
|
||||
right_index=True,
|
||||
suffixes=["_llm_start", "_llm_end"],
|
||||
)
|
||||
|
||||
return llm_session_df
|
||||
|
||||
def _get_llm_parameters(self, langchain_asset: Any = None) -> dict:
|
||||
if not langchain_asset:
|
||||
return {}
|
||||
try:
|
||||
if hasattr(langchain_asset, "agent"):
|
||||
llm_parameters = langchain_asset.agent.llm_chain.llm.dict()
|
||||
elif hasattr(langchain_asset, "llm_chain"):
|
||||
llm_parameters = langchain_asset.llm_chain.llm.dict()
|
||||
elif hasattr(langchain_asset, "llm"):
|
||||
llm_parameters = langchain_asset.llm.dict()
|
||||
else:
|
||||
llm_parameters = langchain_asset.dict()
|
||||
except Exception:
|
||||
return {}
|
||||
|
||||
return llm_parameters
|
||||
Loading…
Reference in New Issue