mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
61858c5a08
This PR fixes the document structure in the [Ecosystem](https://python.langchain.com/en/latest/ecosystem.html) page. Also adds a fix for the heading on the [Comet](https://python.langchain.com/en/latest/ecosystem/comet_tracking.html) page for more consistency with other ecosystem tools. ## Screenshot <img width="878" alt="image" src="https://user-images.githubusercontent.com/6207830/231674921-9bf25376-cf14-4dba-be3c-08e0abda6154.png"> <img width="869" alt="image" src="https://user-images.githubusercontent.com/6207830/231675105-d8e42df4-2d01-435b-9e09-3371522fd2ce.png">
353 lines
12 KiB
Plaintext
353 lines
12 KiB
Plaintext
{
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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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"# Comet"
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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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