mirror of https://github.com/hwchase17/langchain
DOCS: format notebooks (#13371)
Bagatur
7 months ago
committed by
GitHub
parent
d63d4994c0
commit
3596be5210
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Comparing Chain Outputs\n",
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"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/examples/comparisons.ipynb)\n",
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"\n",
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"Suppose you have two different prompts (or LLMs). How do you know which will generate \"better\" results?\n",
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"\n",
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"One automated way to predict the preferred configuration is to use a `PairwiseStringEvaluator` like the `PairwiseStringEvalChain`<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1). This chain prompts an LLM to select which output is preferred, given a specific input.\n",
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"\n",
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"For this evaluation, we will need 3 things:\n",
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"1. An evaluator\n",
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"2. A dataset of inputs\n",
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"3. 2 (or more) LLMs, Chains, or Agents to compare\n",
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"\n",
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"Then we will aggregate the results to determine the preferred model.\n",
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"\n",
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"### Step 1. Create the Evaluator\n",
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"\n",
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"In this example, you will use gpt-4 to select which output is preferred."
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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": 1,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.evaluation import load_evaluator\n",
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"\n",
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"eval_chain = load_evaluator(\"pairwise_string\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Step 2. Select Dataset\n",
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"\n",
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"If you already have real usage data for your LLM, you can use a representative sample. More examples\n",
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"provide more reliable results. We will use some example queries someone might have about how to use langchain here."
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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": 2,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Found cached dataset parquet (/Users/wfh/.cache/huggingface/datasets/LangChainDatasets___parquet/LangChainDatasets--langchain-howto-queries-bbb748bbee7e77aa/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7)\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "a2358d37246640ce95e0f9940194590a",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/1 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from langchain.evaluation.loading import load_dataset\n",
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"\n",
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"dataset = load_dataset(\"langchain-howto-queries\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Step 3. Define Models to Compare\n",
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"\n",
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"We will be comparing two agents in this case."
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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": 3,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.utilities import SerpAPIWrapper\n",
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"from langchain.agents import initialize_agent, Tool\n",
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"from langchain.agents import AgentType\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"\n",
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"\n",
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"# Initialize the language model\n",
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"# You can add your own OpenAI API key by adding openai_api_key=\"<your_api_key>\"\n",
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"llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")\n",
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"\n",
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"# Initialize the SerpAPIWrapper for search functionality\n",
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"# Replace <your_api_key> in openai_api_key=\"<your_api_key>\" with your actual SerpAPI key.\n",
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"search = SerpAPIWrapper()\n",
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"\n",
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"# Define a list of tools offered by the agent\n",
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"tools = [\n",
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" Tool(\n",
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" name=\"Search\",\n",
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" func=search.run,\n",
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" coroutine=search.arun,\n",
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" description=\"Useful when you need to answer questions about current events. You should ask targeted questions.\",\n",
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" ),\n",
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"]"
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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": 4,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"functions_agent = initialize_agent(\n",
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" tools, llm, agent=AgentType.OPENAI_MULTI_FUNCTIONS, verbose=False\n",
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")\n",
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"conversations_agent = initialize_agent(\n",
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" tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=False\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Step 4. Generate Responses\n",
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"\n",
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"We will generate outputs for each of the models before evaluating them."
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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": 5,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "87277cb39a1a4726bb7cc533a24e2ea4",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/20 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from tqdm.notebook import tqdm\n",
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"import asyncio\n",
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"\n",
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"results = []\n",
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"agents = [functions_agent, conversations_agent]\n",
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"concurrency_level = 6 # How many concurrent agents to run. May need to decrease if OpenAI is rate limiting.\n",
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"\n",
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"# We will only run the first 20 examples of this dataset to speed things up\n",
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"# This will lead to larger confidence intervals downstream.\n",
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"batch = []\n",
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"for example in tqdm(dataset[:20]):\n",
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" batch.extend([agent.acall(example[\"inputs\"]) for agent in agents])\n",
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" if len(batch) >= concurrency_level:\n",
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" batch_results = await asyncio.gather(*batch, return_exceptions=True)\n",
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" results.extend(list(zip(*[iter(batch_results)] * 2)))\n",
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" batch = []\n",
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"if batch:\n",
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" batch_results = await asyncio.gather(*batch, return_exceptions=True)\n",
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" results.extend(list(zip(*[iter(batch_results)] * 2)))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 5. Evaluate Pairs\n",
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"\n",
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"Now it's time to evaluate the results. For each agent response, run the evaluation chain to select which output is preferred (or return a tie).\n",
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"\n",
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"Randomly select the input order to reduce the likelihood that one model will be preferred just because it is presented first."
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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": 6,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import random\n",
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"\n",
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"\n",
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"def predict_preferences(dataset, results) -> list:\n",
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" preferences = []\n",
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"\n",
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" for example, (res_a, res_b) in zip(dataset, results):\n",
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" input_ = example[\"inputs\"]\n",
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" # Flip a coin to reduce persistent position bias\n",
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" if random.random() < 0.5:\n",
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" pred_a, pred_b = res_a, res_b\n",
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" a, b = \"a\", \"b\"\n",
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" else:\n",
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" pred_a, pred_b = res_b, res_a\n",
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" a, b = \"b\", \"a\"\n",
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" eval_res = eval_chain.evaluate_string_pairs(\n",
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" prediction=pred_a[\"output\"] if isinstance(pred_a, dict) else str(pred_a),\n",
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" prediction_b=pred_b[\"output\"] if isinstance(pred_b, dict) else str(pred_b),\n",
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" input=input_,\n",
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" )\n",
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" if eval_res[\"value\"] == \"A\":\n",
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" preferences.append(a)\n",
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" elif eval_res[\"value\"] == \"B\":\n",
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" preferences.append(b)\n",
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" else:\n",
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" preferences.append(None) # No preference\n",
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" return preferences"
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"preferences = predict_preferences(dataset, results)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": []
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},
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"source": [
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"**Print out the ratio of preferences.**"
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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": 8,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"OpenAI Functions Agent: 95.00%\n",
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"None: 5.00%\n"
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]
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}
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],
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"source": [
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"from collections import Counter\n",
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"\n",
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"name_map = {\n",
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" \"a\": \"OpenAI Functions Agent\",\n",
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" \"b\": \"Structured Chat Agent\",\n",
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"}\n",
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"counts = Counter(preferences)\n",
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"pref_ratios = {k: v / len(preferences) for k, v in counts.items()}\n",
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"for k, v in pref_ratios.items():\n",
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" print(f\"{name_map.get(k)}: {v:.2%}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Estimate Confidence Intervals\n",
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"\n",
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"The results seem pretty clear, but if you want to have a better sense of how confident we are, that model \"A\" (the OpenAI Functions Agent) is the preferred model, we can calculate confidence intervals. \n",
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"\n",
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"Below, use the Wilson score to estimate the confidence interval."
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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": 9,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from math import sqrt\n",
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"\n",
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"\n",
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"def wilson_score_interval(\n",
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" preferences: list, which: str = \"a\", z: float = 1.96\n",
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") -> tuple:\n",
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" \"\"\"Estimate the confidence interval using the Wilson score.\n",
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"\n",
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" See: https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Wilson_score_interval\n",
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" for more details, including when to use it and when it should not be used.\n",
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" \"\"\"\n",
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" total_preferences = preferences.count(\"a\") + preferences.count(\"b\")\n",
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" n_s = preferences.count(which)\n",
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"\n",
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" if total_preferences == 0:\n",
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" return (0, 0)\n",
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"\n",
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" p_hat = n_s / total_preferences\n",
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"\n",
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" denominator = 1 + (z**2) / total_preferences\n",
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" adjustment = (z / denominator) * sqrt(\n",
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" p_hat * (1 - p_hat) / total_preferences\n",
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" + (z**2) / (4 * total_preferences * total_preferences)\n",
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" )\n",
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" center = (p_hat + (z**2) / (2 * total_preferences)) / denominator\n",
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" lower_bound = min(max(center - adjustment, 0.0), 1.0)\n",
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" upper_bound = min(max(center + adjustment, 0.0), 1.0)\n",
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"\n",
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" return (lower_bound, upper_bound)"
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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": 10,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The \"OpenAI Functions Agent\" would be preferred between 83.18% and 100.00% percent of the time (with 95% confidence).\n",
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"The \"Structured Chat Agent\" would be preferred between 0.00% and 16.82% percent of the time (with 95% confidence).\n"
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]
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}
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],
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"source": [
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"for which_, name in name_map.items():\n",
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" low, high = wilson_score_interval(preferences, which=which_)\n",
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" print(\n",
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" f'The \"{name}\" would be preferred between {low:.2%} and {high:.2%} percent of the time (with 95% confidence).'\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Print out the p-value.**"
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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": 11,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The p-value is 0.00000. If the null hypothesis is true (i.e., if the selected eval chain actually has no preference between the models),\n",
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"then there is a 0.00038% chance of observing the OpenAI Functions Agent be preferred at least 19\n",
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"times out of 19 trials.\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/ipykernel_15978/384907688.py:6: DeprecationWarning: 'binom_test' is deprecated in favour of 'binomtest' from version 1.7.0 and will be removed in Scipy 1.12.0.\n",
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" p_value = stats.binom_test(successes, n, p=0.5, alternative=\"two-sided\")\n"
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]
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}
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],
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"source": [
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"from scipy import stats\n",
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"\n",
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"preferred_model = max(pref_ratios, key=pref_ratios.get)\n",
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"successes = preferences.count(preferred_model)\n",
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"n = len(preferences) - preferences.count(None)\n",
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"p_value = stats.binom_test(successes, n, p=0.5, alternative=\"two-sided\")\n",
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"print(\n",
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" f\"\"\"The p-value is {p_value:.5f}. If the null hypothesis is true (i.e., if the selected eval chain actually has no preference between the models),\n",
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"then there is a {p_value:.5%} chance of observing the {name_map.get(preferred_model)} be preferred at least {successes}\n",
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"times out of {n} trials.\"\"\"\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a name=\"cite_note-1\"></a>_1. Note: Automated evals are still an open research topic and are best used alongside other evaluation approaches. \n",
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"LLM preferences exhibit biases, including banal ones like the order of outputs.\n",
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"In choosing preferences, \"ground truth\" may not be taken into account, which may lead to scores that aren't grounded in utility._"
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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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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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}
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Comparing Chain Outputs\n",
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"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/examples/comparisons.ipynb)\n",
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"\n",
|
||||
"Suppose you have two different prompts (or LLMs). How do you know which will generate \"better\" results?\n",
|
||||
"\n",
|
||||
"One automated way to predict the preferred configuration is to use a `PairwiseStringEvaluator` like the `PairwiseStringEvalChain`<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1). This chain prompts an LLM to select which output is preferred, given a specific input.\n",
|
||||
"\n",
|
||||
"For this evaluation, we will need 3 things:\n",
|
||||
"1. An evaluator\n",
|
||||
"2. A dataset of inputs\n",
|
||||
"3. 2 (or more) LLMs, Chains, or Agents to compare\n",
|
||||
"\n",
|
||||
"Then we will aggregate the results to determine the preferred model.\n",
|
||||
"\n",
|
||||
"### Step 1. Create the Evaluator\n",
|
||||
"\n",
|
||||
"In this example, you will use gpt-4 to select which output is preferred."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.evaluation import load_evaluator\n",
|
||||
"\n",
|
||||
"eval_chain = load_evaluator(\"pairwise_string\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Step 2. Select Dataset\n",
|
||||
"\n",
|
||||
"If you already have real usage data for your LLM, you can use a representative sample. More examples\n",
|
||||
"provide more reliable results. We will use some example queries someone might have about how to use langchain here."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Found cached dataset parquet (/Users/wfh/.cache/huggingface/datasets/LangChainDatasets___parquet/LangChainDatasets--langchain-howto-queries-bbb748bbee7e77aa/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7)\n"
|
||||
]
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "a2358d37246640ce95e0f9940194590a",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
" 0%| | 0/1 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain.evaluation.loading import load_dataset\n",
|
||||
"\n",
|
||||
"dataset = load_dataset(\"langchain-howto-queries\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Step 3. Define Models to Compare\n",
|
||||
"\n",
|
||||
"We will be comparing two agents in this case."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.agents import AgentType, Tool, initialize_agent\n",
|
||||
"from langchain.chat_models import ChatOpenAI\n",
|
||||
"from langchain.utilities import SerpAPIWrapper\n",
|
||||
"\n",
|
||||
"# Initialize the language model\n",
|
||||
"# You can add your own OpenAI API key by adding openai_api_key=\"<your_api_key>\"\n",
|
||||
"llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")\n",
|
||||
"\n",
|
||||
"# Initialize the SerpAPIWrapper for search functionality\n",
|
||||
"# Replace <your_api_key> in openai_api_key=\"<your_api_key>\" with your actual SerpAPI key.\n",
|
||||
"search = SerpAPIWrapper()\n",
|
||||
"\n",
|
||||
"# Define a list of tools offered by the agent\n",
|
||||
"tools = [\n",
|
||||
" Tool(\n",
|
||||
" name=\"Search\",\n",
|
||||
" func=search.run,\n",
|
||||
" coroutine=search.arun,\n",
|
||||
" description=\"Useful when you need to answer questions about current events. You should ask targeted questions.\",\n",
|
||||
" ),\n",
|
||||
"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"functions_agent = initialize_agent(\n",
|
||||
" tools, llm, agent=AgentType.OPENAI_MULTI_FUNCTIONS, verbose=False\n",
|
||||
")\n",
|
||||
"conversations_agent = initialize_agent(\n",
|
||||
" tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=False\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Step 4. Generate Responses\n",
|
||||
"\n",
|
||||
"We will generate outputs for each of the models before evaluating them."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "87277cb39a1a4726bb7cc533a24e2ea4",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
" 0%| | 0/20 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import asyncio\n",
|
||||
"\n",
|
||||
"from tqdm.notebook import tqdm\n",
|
||||
"\n",
|
||||
"results = []\n",
|
||||
"agents = [functions_agent, conversations_agent]\n",
|
||||
"concurrency_level = 6 # How many concurrent agents to run. May need to decrease if OpenAI is rate limiting.\n",
|
||||
"\n",
|
||||
"# We will only run the first 20 examples of this dataset to speed things up\n",
|
||||
"# This will lead to larger confidence intervals downstream.\n",
|
||||
"batch = []\n",
|
||||
"for example in tqdm(dataset[:20]):\n",
|
||||
" batch.extend([agent.acall(example[\"inputs\"]) for agent in agents])\n",
|
||||
" if len(batch) >= concurrency_level:\n",
|
||||
" batch_results = await asyncio.gather(*batch, return_exceptions=True)\n",
|
||||
" results.extend(list(zip(*[iter(batch_results)] * 2)))\n",
|
||||
" batch = []\n",
|
||||
"if batch:\n",
|
||||
" batch_results = await asyncio.gather(*batch, return_exceptions=True)\n",
|
||||
" results.extend(list(zip(*[iter(batch_results)] * 2)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step 5. Evaluate Pairs\n",
|
||||
"\n",
|
||||
"Now it's time to evaluate the results. For each agent response, run the evaluation chain to select which output is preferred (or return a tie).\n",
|
||||
"\n",
|
||||
"Randomly select the input order to reduce the likelihood that one model will be preferred just because it is presented first."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import random\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def predict_preferences(dataset, results) -> list:\n",
|
||||
" preferences = []\n",
|
||||
"\n",
|
||||
" for example, (res_a, res_b) in zip(dataset, results):\n",
|
||||
" input_ = example[\"inputs\"]\n",
|
||||
" # Flip a coin to reduce persistent position bias\n",
|
||||
" if random.random() < 0.5:\n",
|
||||
" pred_a, pred_b = res_a, res_b\n",
|
||||
" a, b = \"a\", \"b\"\n",
|
||||
" else:\n",
|
||||
" pred_a, pred_b = res_b, res_a\n",
|
||||
" a, b = \"b\", \"a\"\n",
|
||||
" eval_res = eval_chain.evaluate_string_pairs(\n",
|
||||
" prediction=pred_a[\"output\"] if isinstance(pred_a, dict) else str(pred_a),\n",
|
||||
" prediction_b=pred_b[\"output\"] if isinstance(pred_b, dict) else str(pred_b),\n",
|
||||
" input=input_,\n",
|
||||
" )\n",
|
||||
" if eval_res[\"value\"] == \"A\":\n",
|
||||
" preferences.append(a)\n",
|
||||
" elif eval_res[\"value\"] == \"B\":\n",
|
||||
" preferences.append(b)\n",
|
||||
" else:\n",
|
||||
" preferences.append(None) # No preference\n",
|
||||
" return preferences"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"preferences = predict_preferences(dataset, results)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"source": [
|
||||
"**Print out the ratio of preferences.**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"OpenAI Functions Agent: 95.00%\n",
|
||||
"None: 5.00%\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from collections import Counter\n",
|
||||
"\n",
|
||||
"name_map = {\n",
|
||||
" \"a\": \"OpenAI Functions Agent\",\n",
|
||||
" \"b\": \"Structured Chat Agent\",\n",
|
||||
"}\n",
|
||||
"counts = Counter(preferences)\n",
|
||||
"pref_ratios = {k: v / len(preferences) for k, v in counts.items()}\n",
|
||||
"for k, v in pref_ratios.items():\n",
|
||||
" print(f\"{name_map.get(k)}: {v:.2%}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Estimate Confidence Intervals\n",
|
||||
"\n",
|
||||
"The results seem pretty clear, but if you want to have a better sense of how confident we are, that model \"A\" (the OpenAI Functions Agent) is the preferred model, we can calculate confidence intervals. \n",
|
||||
"\n",
|
||||
"Below, use the Wilson score to estimate the confidence interval."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from math import sqrt\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def wilson_score_interval(\n",
|
||||
" preferences: list, which: str = \"a\", z: float = 1.96\n",
|
||||
") -> tuple:\n",
|
||||
" \"\"\"Estimate the confidence interval using the Wilson score.\n",
|
||||
"\n",
|
||||
" See: https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Wilson_score_interval\n",
|
||||
" for more details, including when to use it and when it should not be used.\n",
|
||||
" \"\"\"\n",
|
||||
" total_preferences = preferences.count(\"a\") + preferences.count(\"b\")\n",
|
||||
" n_s = preferences.count(which)\n",
|
||||
"\n",
|
||||
" if total_preferences == 0:\n",
|
||||
" return (0, 0)\n",
|
||||
"\n",
|
||||
" p_hat = n_s / total_preferences\n",
|
||||
"\n",
|
||||
" denominator = 1 + (z**2) / total_preferences\n",
|
||||
" adjustment = (z / denominator) * sqrt(\n",
|
||||
" p_hat * (1 - p_hat) / total_preferences\n",
|
||||
" + (z**2) / (4 * total_preferences * total_preferences)\n",
|
||||
" )\n",
|
||||
" center = (p_hat + (z**2) / (2 * total_preferences)) / denominator\n",
|
||||
" lower_bound = min(max(center - adjustment, 0.0), 1.0)\n",
|
||||
" upper_bound = min(max(center + adjustment, 0.0), 1.0)\n",
|
||||
"\n",
|
||||
" return (lower_bound, upper_bound)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"The \"OpenAI Functions Agent\" would be preferred between 83.18% and 100.00% percent of the time (with 95% confidence).\n",
|
||||
"The \"Structured Chat Agent\" would be preferred between 0.00% and 16.82% percent of the time (with 95% confidence).\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for which_, name in name_map.items():\n",
|
||||
" low, high = wilson_score_interval(preferences, which=which_)\n",
|
||||
" print(\n",
|
||||
" f'The \"{name}\" would be preferred between {low:.2%} and {high:.2%} percent of the time (with 95% confidence).'\n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Print out the p-value.**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"The p-value is 0.00000. If the null hypothesis is true (i.e., if the selected eval chain actually has no preference between the models),\n",
|
||||
"then there is a 0.00038% chance of observing the OpenAI Functions Agent be preferred at least 19\n",
|
||||
"times out of 19 trials.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/ipykernel_15978/384907688.py:6: DeprecationWarning: 'binom_test' is deprecated in favour of 'binomtest' from version 1.7.0 and will be removed in Scipy 1.12.0.\n",
|
||||
" p_value = stats.binom_test(successes, n, p=0.5, alternative=\"two-sided\")\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from scipy import stats\n",
|
||||
"\n",
|
||||
"preferred_model = max(pref_ratios, key=pref_ratios.get)\n",
|
||||
"successes = preferences.count(preferred_model)\n",
|
||||
"n = len(preferences) - preferences.count(None)\n",
|
||||
"p_value = stats.binom_test(successes, n, p=0.5, alternative=\"two-sided\")\n",
|
||||
"print(\n",
|
||||
" f\"\"\"The p-value is {p_value:.5f}. If the null hypothesis is true (i.e., if the selected eval chain actually has no preference between the models),\n",
|
||||
"then there is a {p_value:.5%} chance of observing the {name_map.get(preferred_model)} be preferred at least {successes}\n",
|
||||
"times out of {n} trials.\"\"\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a name=\"cite_note-1\"></a>_1. Note: Automated evals are still an open research topic and are best used alongside other evaluation approaches. \n",
|
||||
"LLM preferences exhibit biases, including banal ones like the order of outputs.\n",
|
||||
"In choosing preferences, \"ground truth\" may not be taken into account, which may lead to scores that aren't grounded in utility._"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
@ -1,209 +1,209 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "4460f924-1738-4dc5-999f-c26383aba0a4",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Custom String Evaluator\n",
|
||||
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/custom.ipynb)\n",
|
||||
"\n",
|
||||
"You can make your own custom string evaluators by inheriting from the `StringEvaluator` class and implementing the `_evaluate_strings` (and `_aevaluate_strings` for async support) methods.\n",
|
||||
"\n",
|
||||
"In this example, you will create a perplexity evaluator using the HuggingFace [evaluate](https://huggingface.co/docs/evaluate/index) library.\n",
|
||||
"[Perplexity](https://en.wikipedia.org/wiki/Perplexity) is a measure of how well the generated text would be predicted by the model used to compute the metric."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "90ec5942-4b14-47b1-baff-9dd2a9f17a4e",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# %pip install evaluate > /dev/null"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "54fdba68-0ae7-4102-a45b-dabab86c97ac",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from typing import Any, Optional\n",
|
||||
"\n",
|
||||
"from langchain.evaluation import StringEvaluator\n",
|
||||
"from evaluate import load\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class PerplexityEvaluator(StringEvaluator):\n",
|
||||
" \"\"\"Evaluate the perplexity of a predicted string.\"\"\"\n",
|
||||
"\n",
|
||||
" def __init__(self, model_id: str = \"gpt2\"):\n",
|
||||
" self.model_id = model_id\n",
|
||||
" self.metric_fn = load(\n",
|
||||
" \"perplexity\", module_type=\"metric\", model_id=self.model_id, pad_token=0\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" def _evaluate_strings(\n",
|
||||
" self,\n",
|
||||
" *,\n",
|
||||
" prediction: str,\n",
|
||||
" reference: Optional[str] = None,\n",
|
||||
" input: Optional[str] = None,\n",
|
||||
" **kwargs: Any,\n",
|
||||
" ) -> dict:\n",
|
||||
" results = self.metric_fn.compute(\n",
|
||||
" predictions=[prediction], model_id=self.model_id\n",
|
||||
" )\n",
|
||||
" ppl = results[\"perplexities\"][0]\n",
|
||||
" return {\"score\": ppl}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "52767568-8075-4f77-93c9-80e1a7e5cba3",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"evaluator = PerplexityEvaluator()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "697ee0c0-d1ae-4a55-a542-a0f8e602c28a",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Using pad_token, but it is not set yet.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
|
||||
"To disable this warning, you can either:\n",
|
||||
"\t- Avoid using `tokenizers` before the fork if possible\n",
|
||||
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "467109d44654486e8b415288a319fc2c",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
" 0%| | 0/1 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 190.3675537109375}"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"evaluator.evaluate_strings(prediction=\"The rains in Spain fall mainly on the plain.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "5089d9d1-eae6-4d47-b4f6-479e5d887d74",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Using pad_token, but it is not set yet.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "d3266f6f06d746e1bb03ce4aca07d9b9",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
" 0%| | 0/1 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 1982.0709228515625}"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# The perplexity is much higher since LangChain was introduced after 'gpt-2' was released and because it is never used in the following context.\n",
|
||||
"evaluator.evaluate_strings(prediction=\"The rains in Spain fall mainly on LangChain.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "5eaa178f-6ba3-47ae-b3dc-1b196af6d213",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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": "3.11.2"
|
||||
}
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "4460f924-1738-4dc5-999f-c26383aba0a4",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Custom String Evaluator\n",
|
||||
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/custom.ipynb)\n",
|
||||
"\n",
|
||||
"You can make your own custom string evaluators by inheriting from the `StringEvaluator` class and implementing the `_evaluate_strings` (and `_aevaluate_strings` for async support) methods.\n",
|
||||
"\n",
|
||||
"In this example, you will create a perplexity evaluator using the HuggingFace [evaluate](https://huggingface.co/docs/evaluate/index) library.\n",
|
||||
"[Perplexity](https://en.wikipedia.org/wiki/Perplexity) is a measure of how well the generated text would be predicted by the model used to compute the metric."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "90ec5942-4b14-47b1-baff-9dd2a9f17a4e",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# %pip install evaluate > /dev/null"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "54fdba68-0ae7-4102-a45b-dabab86c97ac",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from typing import Any, Optional\n",
|
||||
"\n",
|
||||
"from evaluate import load\n",
|
||||
"from langchain.evaluation import StringEvaluator\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class PerplexityEvaluator(StringEvaluator):\n",
|
||||
" \"\"\"Evaluate the perplexity of a predicted string.\"\"\"\n",
|
||||
"\n",
|
||||
" def __init__(self, model_id: str = \"gpt2\"):\n",
|
||||
" self.model_id = model_id\n",
|
||||
" self.metric_fn = load(\n",
|
||||
" \"perplexity\", module_type=\"metric\", model_id=self.model_id, pad_token=0\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" def _evaluate_strings(\n",
|
||||
" self,\n",
|
||||
" *,\n",
|
||||
" prediction: str,\n",
|
||||
" reference: Optional[str] = None,\n",
|
||||
" input: Optional[str] = None,\n",
|
||||
" **kwargs: Any,\n",
|
||||
" ) -> dict:\n",
|
||||
" results = self.metric_fn.compute(\n",
|
||||
" predictions=[prediction], model_id=self.model_id\n",
|
||||
" )\n",
|
||||
" ppl = results[\"perplexities\"][0]\n",
|
||||
" return {\"score\": ppl}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "52767568-8075-4f77-93c9-80e1a7e5cba3",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"evaluator = PerplexityEvaluator()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "697ee0c0-d1ae-4a55-a542-a0f8e602c28a",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Using pad_token, but it is not set yet.\n"
|
||||
]
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
|
||||
"To disable this warning, you can either:\n",
|
||||
"\t- Avoid using `tokenizers` before the fork if possible\n",
|
||||
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "467109d44654486e8b415288a319fc2c",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
" 0%| | 0/1 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 190.3675537109375}"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"evaluator.evaluate_strings(prediction=\"The rains in Spain fall mainly on the plain.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "5089d9d1-eae6-4d47-b4f6-479e5d887d74",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Using pad_token, but it is not set yet.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "d3266f6f06d746e1bb03ce4aca07d9b9",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
" 0%| | 0/1 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 1982.0709228515625}"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# The perplexity is much higher since LangChain was introduced after 'gpt-2' was released and because it is never used in the following context.\n",
|
||||
"evaluator.evaluate_strings(prediction=\"The rains in Spain fall mainly on LangChain.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "5eaa178f-6ba3-47ae-b3dc-1b196af6d213",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,142 +1,143 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "db9d627f-b234-4f7f-ab96-639fae474122",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Custom Trajectory Evaluator\n",
|
||||
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/trajectory/custom.ipynb)\n",
|
||||
"\n",
|
||||
"You can make your own custom trajectory evaluators by inheriting from the [AgentTrajectoryEvaluator](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.AgentTrajectoryEvaluator.html#langchain.evaluation.schema.AgentTrajectoryEvaluator) class and overwriting the `_evaluate_agent_trajectory` (and `_aevaluate_agent_action`) method.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"In this example, you will make a simple trajectory evaluator that uses an LLM to determine if any actions were unnecessary."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "ca84ab0c-e7e2-4c03-bd74-9cc4e6338eec",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from typing import Any, Optional, Sequence, Tuple\n",
|
||||
"from langchain.chat_models import ChatOpenAI\n",
|
||||
"from langchain.chains import LLMChain\n",
|
||||
"from langchain.schema import AgentAction\n",
|
||||
"from langchain.evaluation import AgentTrajectoryEvaluator\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class StepNecessityEvaluator(AgentTrajectoryEvaluator):\n",
|
||||
" \"\"\"Evaluate the perplexity of a predicted string.\"\"\"\n",
|
||||
"\n",
|
||||
" def __init__(self) -> None:\n",
|
||||
" llm = ChatOpenAI(model=\"gpt-4\", temperature=0.0)\n",
|
||||
" template = \"\"\"Are any of the following steps unnecessary in answering {input}? Provide the verdict on a new line as a single \"Y\" for yes or \"N\" for no.\n",
|
||||
"\n",
|
||||
" DATA\n",
|
||||
" ------\n",
|
||||
" Steps: {trajectory}\n",
|
||||
" ------\n",
|
||||
"\n",
|
||||
" Verdict:\"\"\"\n",
|
||||
" self.chain = LLMChain.from_string(llm, template)\n",
|
||||
"\n",
|
||||
" def _evaluate_agent_trajectory(\n",
|
||||
" self,\n",
|
||||
" *,\n",
|
||||
" prediction: str,\n",
|
||||
" input: str,\n",
|
||||
" agent_trajectory: Sequence[Tuple[AgentAction, str]],\n",
|
||||
" reference: Optional[str] = None,\n",
|
||||
" **kwargs: Any,\n",
|
||||
" ) -> dict:\n",
|
||||
" vals = [\n",
|
||||
" f\"{i}: Action=[{action.tool}] returned observation = [{observation}]\"\n",
|
||||
" for i, (action, observation) in enumerate(agent_trajectory)\n",
|
||||
" ]\n",
|
||||
" trajectory = \"\\n\".join(vals)\n",
|
||||
" response = self.chain.run(dict(trajectory=trajectory, input=input), **kwargs)\n",
|
||||
" decision = response.split(\"\\n\")[-1].strip()\n",
|
||||
" score = 1 if decision == \"Y\" else 0\n",
|
||||
" return {\"score\": score, \"value\": decision, \"reasoning\": response}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "297dea4b-fb28-4292-b6e0-1c769cfb9cbd",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The example above will return a score of 1 if the language model predicts that any of the actions were unnecessary, and it returns a score of 0 if all of them were predicted to be necessary. It returns the string 'decision' as the 'value', and includes the rest of the generated text as 'reasoning' to let you audit the decision.\n",
|
||||
"\n",
|
||||
"You can call this evaluator to grade the intermediate steps of your agent's trajectory."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "a3fbcc1d-249f-4e00-8841-b6872c73c486",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 1, 'value': 'Y', 'reasoning': 'Y'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"evaluator = StepNecessityEvaluator()\n",
|
||||
"\n",
|
||||
"evaluator.evaluate_agent_trajectory(\n",
|
||||
" prediction=\"The answer is pi\",\n",
|
||||
" input=\"What is today?\",\n",
|
||||
" agent_trajectory=[\n",
|
||||
" (\n",
|
||||
" AgentAction(tool=\"ask\", tool_input=\"What is today?\", log=\"\"),\n",
|
||||
" \"tomorrow's yesterday\",\n",
|
||||
" ),\n",
|
||||
" (\n",
|
||||
" AgentAction(tool=\"check_tv\", tool_input=\"Watch tv for half hour\", log=\"\"),\n",
|
||||
" \"bzzz\",\n",
|
||||
" ),\n",
|
||||
" ],\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "77353528-723e-4075-939e-aebdb17c1e4f",
|
||||
"metadata": {},
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "db9d627f-b234-4f7f-ab96-639fae474122",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Custom Trajectory Evaluator\n",
|
||||
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/trajectory/custom.ipynb)\n",
|
||||
"\n",
|
||||
"You can make your own custom trajectory evaluators by inheriting from the [AgentTrajectoryEvaluator](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.AgentTrajectoryEvaluator.html#langchain.evaluation.schema.AgentTrajectoryEvaluator) class and overwriting the `_evaluate_agent_trajectory` (and `_aevaluate_agent_action`) method.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"In this example, you will make a simple trajectory evaluator that uses an LLM to determine if any actions were unnecessary."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "ca84ab0c-e7e2-4c03-bd74-9cc4e6338eec",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from typing import Any, Optional, Sequence, Tuple\n",
|
||||
"\n",
|
||||
"from langchain.chains import LLMChain\n",
|
||||
"from langchain.chat_models import ChatOpenAI\n",
|
||||
"from langchain.evaluation import AgentTrajectoryEvaluator\n",
|
||||
"from langchain.schema import AgentAction\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class StepNecessityEvaluator(AgentTrajectoryEvaluator):\n",
|
||||
" \"\"\"Evaluate the perplexity of a predicted string.\"\"\"\n",
|
||||
"\n",
|
||||
" def __init__(self) -> None:\n",
|
||||
" llm = ChatOpenAI(model=\"gpt-4\", temperature=0.0)\n",
|
||||
" template = \"\"\"Are any of the following steps unnecessary in answering {input}? Provide the verdict on a new line as a single \"Y\" for yes or \"N\" for no.\n",
|
||||
"\n",
|
||||
" DATA\n",
|
||||
" ------\n",
|
||||
" Steps: {trajectory}\n",
|
||||
" ------\n",
|
||||
"\n",
|
||||
" Verdict:\"\"\"\n",
|
||||
" self.chain = LLMChain.from_string(llm, template)\n",
|
||||
"\n",
|
||||
" def _evaluate_agent_trajectory(\n",
|
||||
" self,\n",
|
||||
" *,\n",
|
||||
" prediction: str,\n",
|
||||
" input: str,\n",
|
||||
" agent_trajectory: Sequence[Tuple[AgentAction, str]],\n",
|
||||
" reference: Optional[str] = None,\n",
|
||||
" **kwargs: Any,\n",
|
||||
" ) -> dict:\n",
|
||||
" vals = [\n",
|
||||
" f\"{i}: Action=[{action.tool}] returned observation = [{observation}]\"\n",
|
||||
" for i, (action, observation) in enumerate(agent_trajectory)\n",
|
||||
" ]\n",
|
||||
" trajectory = \"\\n\".join(vals)\n",
|
||||
" response = self.chain.run(dict(trajectory=trajectory, input=input), **kwargs)\n",
|
||||
" decision = response.split(\"\\n\")[-1].strip()\n",
|
||||
" score = 1 if decision == \"Y\" else 0\n",
|
||||
" return {\"score\": score, \"value\": decision, \"reasoning\": response}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "297dea4b-fb28-4292-b6e0-1c769cfb9cbd",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The example above will return a score of 1 if the language model predicts that any of the actions were unnecessary, and it returns a score of 0 if all of them were predicted to be necessary. It returns the string 'decision' as the 'value', and includes the rest of the generated text as 'reasoning' to let you audit the decision.\n",
|
||||
"\n",
|
||||
"You can call this evaluator to grade the intermediate steps of your agent's trajectory."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "a3fbcc1d-249f-4e00-8841-b6872c73c486",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 1, 'value': 'Y', 'reasoning': 'Y'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"evaluator = StepNecessityEvaluator()\n",
|
||||
"\n",
|
||||
"evaluator.evaluate_agent_trajectory(\n",
|
||||
" prediction=\"The answer is pi\",\n",
|
||||
" input=\"What is today?\",\n",
|
||||
" agent_trajectory=[\n",
|
||||
" (\n",
|
||||
" AgentAction(tool=\"ask\", tool_input=\"What is today?\", log=\"\"),\n",
|
||||
" \"tomorrow's yesterday\",\n",
|
||||
" ),\n",
|
||||
" (\n",
|
||||
" AgentAction(tool=\"check_tv\", tool_input=\"Watch tv for half hour\", log=\"\"),\n",
|
||||
" \"bzzz\",\n",
|
||||
" ),\n",
|
||||
" ],\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "77353528-723e-4075-939e-aebdb17c1e4f",
|
||||
"metadata": {},
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue