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docs: how to use langsmith few shot (#25601)
Requires langsmith 0.1.101 release
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docs/docs/how_to/example_selectors_langsmith.ipynb
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docs/docs/how_to/example_selectors_langsmith.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "4f7e423b",
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"metadata": {},
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"source": [
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"# How to select examples from a LangSmith dataset\n",
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"\n",
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"import Prerequisites from \"@theme/Prerequisites\";\n",
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"import Compatibility from \"@theme/Compatibility\";\n",
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"\n",
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"<Prerequisites titlesAndLinks={[\n",
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" [\"Chat models\", \"/docs/concepts/#chat-models\"],\n",
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" [\"Few-shot-prompting\", \"/docs/concepts/#few-shot-prompting\"],\n",
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" [\"LangSmith\", \"/docs/concepts/#langsmith\"],\n",
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"]} />\n",
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"\n",
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"\n",
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"<Compatibility packagesAndVersions={[\n",
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" [\"langsmith\", \"0.1.101\"],\n",
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"]} />\n",
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"\n",
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"\n",
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"LangSmith datasets have built-in support for similarity search, making them a great tool for building and querying few-shot examples.\n",
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"\n",
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"In this guide we'll see how to use an indexed LangSmith dataset as a few-shot example selector.\n",
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"\n",
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"## Setup\n",
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"\n",
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"Before getting started make sure you've [created a LangSmith account](https://smith.langchain.com/) and set your credentials:"
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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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"id": "85445e0e",
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"metadata": {},
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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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"Set LangSmith API key:\n",
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"\n",
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"········\n"
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]
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}
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],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"if not os.environ.get(\"LANGSMITH_API_KEY\"):\n",
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" os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Set LangSmith API key:\\n\\n\")\n",
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"\n",
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"os.environ[\"LANGSMITH_TRACING\"] = \"true\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "ca899e29",
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"metadata": {},
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"source": [
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"We'll need to install the `langsmith` SDK. In this example we'll also make use of `langchain`, `langchain-openai`, and `langchain-benchmarks`:"
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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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"id": "b4fa7810",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langsmith>=0.1.101 langchain langchain-openai langchain-benchmarks"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fc716e12",
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"metadata": {},
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"source": [
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"Now we'll clone a public dataset and turn on indexing for the dataset. We can also turn on indexing via the [LangSmith UI](https://docs.smith.langchain.com/how_to_guides/datasets/index_datasets_for_dynamic_few_shot_example_selection).\n",
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"\n",
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"We'll clone the [Multiverse math few shot example dataset](https://blog.langchain.dev/few-shot-prompting-to-improve-tool-calling-performance/).\n",
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"\n",
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"This enables searching over the dataset, and will make sure that anytime we update/add examples they are also indexed."
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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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"id": "cf53d280",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langsmith import AsyncClient as AsyncLangSmith\n",
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"from langsmith import Client as LangSmith\n",
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"\n",
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"ls_client = LangSmith()\n",
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"async_ls_client = AsyncLangSmith()\n",
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"\n",
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"dataset_name = \"multiverse-math-examples-for-few-shot\"\n",
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"dataset_public_url = (\n",
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" \"https://smith.langchain.com/public/0df59e49-d226-4ef2-9ecd-8c0fc9cd0288/d\"\n",
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")\n",
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"\n",
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"ls_client.clone_public_dataset(dataset_public_url)\n",
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"\n",
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"dataset_id = ls_client.read_dataset(dataset_name=dataset_name).id\n",
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"ls_client.index_dataset(dataset_id=dataset_id)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5767d171",
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"metadata": {},
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"source": [
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"Indexing can take a few seconds. Once the dataset is indexed, we can search for similar examples like so:"
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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": 29,
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"id": "5013a56f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"3"
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]
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},
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"execution_count": 29,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"examples = ls_client.similar_examples(\n",
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" {\"input\": \"whats the negation of the negation of the negation of 3\"},\n",
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" limit=3,\n",
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" dataset_id=dataset_id,\n",
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")\n",
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"len(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": 34,
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"id": "a142db06",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'evaluate the negation of -100'"
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]
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},
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"execution_count": 34,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"examples[0].inputs[\"input\"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d2627125",
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"metadata": {},
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"source": [
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"For this dataset the outputs are an entire chat history:"
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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": 33,
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"id": "af5b9191",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"9"
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]
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},
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"execution_count": 33,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len(examples[1].outputs[\"output\"])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e852c8ef",
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"metadata": {},
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"source": [
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"The search returns the examples whose inputs are most similar to the query input. We can use this for few-shot prompting a model like so:"
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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": 50,
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"id": "12cba1e1",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chat_models import init_chat_model\n",
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"from langchain_benchmarks.tool_usage.tasks.multiverse_math import (\n",
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" add,\n",
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" cos,\n",
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" divide,\n",
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" log,\n",
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" multiply,\n",
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" negate,\n",
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" pi,\n",
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" power,\n",
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" sin,\n",
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" subtract,\n",
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")\n",
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"from langchain_core.messages import HumanMessage, SystemMessage, convert_to_messages\n",
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"from langchain_core.runnables import RunnableLambda\n",
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"\n",
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"\n",
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"def similar_examples(input_: dict) -> dict:\n",
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" examples = ls_client.similar_examples(input_, limit=5, dataset_id=dataset_id)\n",
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" return {**input_, \"examples\": examples}\n",
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"\n",
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"\n",
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"async def asimilar_examples(input_: dict) -> dict:\n",
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" examples = await async_ls_client.similar_examples(\n",
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" input_, limit=5, dataset_id=dataset_id\n",
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" )\n",
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" return {**input_, \"examples\": examples}\n",
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"\n",
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"\n",
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"def construct_prompt(input_: dict) -> list:\n",
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" instructions = \"\"\"You are great at using mathematical tools.\"\"\"\n",
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" messages = []\n",
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" for ex in input_[\"examples\"]:\n",
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" # For this dataset, a multi-turn conversation is stored as output.\n",
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" messages.extend(convert_to_messages(ex.outputs[\"output\"]))\n",
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" examples = [msg for msg in messages if not isinstance(msg, SystemMessage)]\n",
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" for ex in examples:\n",
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" ex.name = (\n",
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" \"example_user\" if isinstance(ex, HumanMessage) else \"example_assistant\"\n",
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" )\n",
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" return [SystemMessage(instructions), *examples, HumanMessage(input_[\"input\"])]\n",
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"\n",
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"\n",
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"tools = [add, cos, divide, log, multiply, negate, pi, power, sin, subtract]\n",
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"llm = init_chat_model(\"gpt-4o\")\n",
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"llm_with_tools = llm.bind_tools(tools)\n",
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"\n",
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"example_selector = RunnableLambda(func=similar_examples, afunc=asimilar_examples)\n",
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"\n",
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"chain = example_selector | construct_prompt | llm_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": 52,
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"id": "c423b367",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[{'name': 'negate',\n",
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" 'args': {'a': 3},\n",
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" 'id': 'call_ehmx3Z4Cj6HFpI8FV4pYZ5Oo',\n",
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" 'type': 'tool_call'}]"
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]
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},
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"execution_count": 52,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ai_msg = await chain.ainvoke({\"input\": \"whats the negation of the negation of 3\"})\n",
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"ai_msg.tool_calls"
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]
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},
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{
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"cell_type": "markdown",
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"id": "94489b4a",
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"metadata": {},
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"source": [
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"Looking at the LangSmith trace, we can see that relevant examples were pulled in in the `similar_examples` step and passed as messages to ChatOpenAI: https://smith.langchain.com/public/05af2ce8-1a45-4f3a-8d54-6524ff919279/r."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "poetry-venv-311",
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"language": "python",
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"name": "poetry-venv-311"
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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": "3.11.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -69,6 +69,7 @@ These are the core building blocks you can use when building applications.
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- [How to: select examples by semantic similarity](/docs/how_to/example_selectors_similarity)
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- [How to: select examples by semantic ngram overlap](/docs/how_to/example_selectors_ngram)
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- [How to: select examples by maximal marginal relevance](/docs/how_to/example_selectors_mmr)
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- [How to: select examples from LangSmith few-shot datasets](/docs/how_to/example_selectors_langsmith/)
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### Chat models
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