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docs/docs/integrations/document_loaders/langsmith.ipynb
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294
docs/docs/integrations/document_loaders/langsmith.ipynb
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@ -0,0 +1,294 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: LangSmith\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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"# LangSmithLoader\n",
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"\n",
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"This notebook provides a quick overview for getting started with the LangSmith [document loader](https://python.langchain.com/v0.2/docs/concepts/#document-loaders). For detailed documentation of all LangSmithLoader features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/document_loaders/langchain_core.document_loaders.langsmith.LangSmithLoader.html).\n",
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"\n",
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"## Overview\n",
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"### Integration details\n",
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"\n",
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"| Class | Package | Local | Serializable | JS support|\n",
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"| :--- | :--- | :---: | :---: | :---: |\n",
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"| [LangSmithLoader](https://api.python.langchain.com/en/latest/document_loaders/langchain_core.document_loaders.langsmith.LangSmithLoader.html) | [langchain-core](https://api.python.langchain.com/en/latest/core_api_reference.html) | ❌ | ❌ | ❌ | \n",
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"\n",
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"### Loader features\n",
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"| Source | Lazy loading | Native async\n",
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"| :---: | :---: | :---: | \n",
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"| LangSmithLoader | ✅ | ❌ | \n",
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"\n",
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"## Setup\n",
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"\n",
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"To access the LangSmith document loader you'll need to install `langchain-core`, create a [LangSmith](https://langsmith.com) account and get an API key.\n",
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"\n",
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"### Credentials\n",
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"\n",
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"Sign up at https://langsmith.com and generate an API key. Once you've done this set the LANGSMITH_API_KEY environment variable:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import 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(\"Enter your LangSmith API key: \")"
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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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"If you want to get automated best-in-class tracing, you can also turn on LangSmith tracing:"
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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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"outputs": [],
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"source": [
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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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"metadata": {},
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"source": [
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"### Installation\n",
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"\n",
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"Install `langchain-core`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langchain-core"
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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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"### Clone example dataset\n",
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"\n",
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"For this example, we'll clone and load a public LangSmith dataset. Cloning creates a copy of this dataset on our personal LangSmith account. You can only load datasets that you have a personal copy of."
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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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"outputs": [],
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"source": [
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"from langsmith import Client as LangSmithClient\n",
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"\n",
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"ls_client = LangSmithClient()\n",
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"\n",
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"dataset_name = \"LangSmith Few Shot Datasets Notebook\"\n",
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"dataset_public_url = (\n",
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" \"https://smith.langchain.com/public/55658626-124a-4223-af45-07fb774a6212/d\"\n",
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")\n",
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"\n",
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"ls_client.clone_public_dataset(dataset_public_url)"
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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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"## Initialization\n",
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"\n",
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"Now we can instantiate our document loader and load documents:"
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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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"outputs": [],
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"source": [
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"from langchain_core.document_loaders import LangSmithLoader\n",
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"\n",
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"loader = LangSmithLoader(\n",
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" dataset_name=dataset_name,\n",
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" content_key=\"question\",\n",
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" limit=50,\n",
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" # format_content=...,\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": "markdown",
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"metadata": {},
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"source": [
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"## Load"
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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": 12,
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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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"Show me an example using Weaviate, but customizing the vectorStoreRetriever to return the top 10 k nearest neighbors. \n"
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]
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}
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],
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"source": [
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"docs = loader.load()\n",
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"print(docs[0].page_content)"
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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": 15,
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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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"{'question': 'Show me an example using Weaviate, but customizing the vectorStoreRetriever to return the top 10 k nearest neighbors. '}\n"
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]
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}
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],
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"source": [
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"print(docs[0].metadata[\"inputs\"])"
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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": 16,
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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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"{'answer': 'To customize the Weaviate client and return the top 10 k nearest neighbors, you can utilize the `as_retriever` method with the appropriate parameters. Here\\'s how you can achieve this:\\n\\n```python\\n# Assuming you have imported the necessary modules and classes\\n\\n# Create the Weaviate client\\nclient = weaviate.Client(url=os.environ[\"WEAVIATE_URL\"], ...)\\n\\n# Initialize the Weaviate wrapper\\nweaviate = Weaviate(client, index_name, text_key)\\n\\n# Customize the client to return top 10 k nearest neighbors using as_retriever\\ncustom_retriever = weaviate.as_retriever(\\n search_type=\"similarity\",\\n search_kwargs={\\n \\'k\\': 10 # Customize the value of k as needed\\n }\\n)\\n\\n# Now you can use the custom_retriever to perform searches\\nresults = custom_retriever.search(query, ...)\\n```'}\n"
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]
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}
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],
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"source": [
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"print(docs[0].metadata[\"outputs\"])"
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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": 19,
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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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"['dataset_id',\n",
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" 'inputs',\n",
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" 'outputs',\n",
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" 'metadata',\n",
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" 'id',\n",
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" 'created_at',\n",
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" 'modified_at',\n",
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" 'runs',\n",
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" 'source_run_id']"
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]
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},
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"execution_count": 19,
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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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"list(docs[0].metadata.keys())"
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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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"## Lazy Load"
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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": 20,
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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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"10"
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]
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},
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"execution_count": 20,
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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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"page = []\n",
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"for doc in loader.lazy_load():\n",
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" page.append(doc)\n",
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" if len(page) >= 10:\n",
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" # do some paged operation, e.g.\n",
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" # index.upsert(page)\n",
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" # page = []\n",
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" break\n",
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"len(page)"
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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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"## API reference\n",
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"\n",
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"For detailed documentation of all LangSmithLoader features and configurations head to the API reference: https://api.python.langchain.com/en/latest/document_loaders/langchain_core.document_loaders.langsmith.LangSmithLoader.html"
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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": 4
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}
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@ -1,5 +1,6 @@
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from langchain_core.document_loaders.base import BaseBlobParser, BaseLoader
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from langchain_core.document_loaders.blob_loaders import Blob, BlobLoader, PathLike
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from langchain_core.document_loaders.langsmith import LangSmithLoader
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__all__ = [
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"BaseBlobParser",
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@ -7,4 +8,5 @@ __all__ = [
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"Blob",
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"BlobLoader",
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"PathLike",
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"LangSmithLoader",
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]
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128
libs/core/langchain_core/document_loaders/langsmith.py
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128
libs/core/langchain_core/document_loaders/langsmith.py
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@ -0,0 +1,128 @@
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import datetime
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import json
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import uuid
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from typing import Any, Callable, Iterator, Optional, Sequence, Union
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from langsmith import Client as LangSmithClient
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from langchain_core.document_loaders.base import BaseLoader
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from langchain_core.documents import Document
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class LangSmithLoader(BaseLoader):
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"""Load LangSmith Dataset examples as Documents.
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Loads the example inputs as the Document page content and places the entire example
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into the Document metadata. This allows you to easily create few-shot example
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retrievers from the loaded documents.
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.. dropdown:: Lazy load
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.. code-block:: python
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from langchain_core.document_loaders import LangSmithLoader
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loader = LangSmithLoader(dataset_id="...", limit=100)
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docs = []
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for doc in loader.lazy_load():
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docs.append(doc)
|
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.. code-block:: pycon
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# -> [Document("...", metadata={"inputs": {...}, "outputs": {...}, ...}), ...]
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.. versionadded:: 0.2.34
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""" # noqa: E501
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def __init__(
|
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self,
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*,
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dataset_id: Optional[Union[uuid.UUID, str]] = None,
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dataset_name: Optional[str] = None,
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example_ids: Optional[Sequence[Union[uuid.UUID, str]]] = None,
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as_of: Optional[Union[datetime.datetime, str]] = None,
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splits: Optional[Sequence[str]] = None,
|
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inline_s3_urls: bool = True,
|
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offset: int = 0,
|
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limit: Optional[int] = None,
|
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metadata: Optional[dict] = None,
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filter: Optional[str] = None,
|
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content_key: str = "",
|
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format_content: Optional[Callable[..., str]] = None,
|
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client: Optional[LangSmithClient] = None,
|
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**client_kwargs: Any,
|
||||
) -> None:
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||||
"""
|
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Args:
|
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dataset_id: The ID of the dataset to filter by. Defaults to None.
|
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dataset_name: The name of the dataset to filter by. Defaults to None.
|
||||
content_key: The inputs key to set as Document page content. ``"."`` characters
|
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are interpreted as nested keys. E.g. ``content_key="first.second"`` will
|
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result in
|
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``Document(page_content=format_content(example.inputs["first"]["second"]))``
|
||||
format_content: Function for converting the content extracted from the example
|
||||
inputs into a string. Defaults to JSON-encoding the contents.
|
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example_ids: The IDs of the examples to filter by. Defaults to None.
|
||||
as_of: The dataset version tag OR
|
||||
timestamp to retrieve the examples as of.
|
||||
Response examples will only be those that were present at the time
|
||||
of the tagged (or timestamped) version.
|
||||
splits: A list of dataset splits, which are
|
||||
divisions of your dataset such as 'train', 'test', or 'validation'.
|
||||
Returns examples only from the specified splits.
|
||||
inline_s3_urls: Whether to inline S3 URLs. Defaults to True.
|
||||
offset: The offset to start from. Defaults to 0.
|
||||
limit: The maximum number of examples to return.
|
||||
filter: A structured fileter string to apply to the examples.
|
||||
client: LangSmith Client. If not provided will be initialized from below args.
|
||||
client_kwargs: Keyword args to pass to LangSmith client init. Should only be
|
||||
specified if ``client`` isn't.
|
||||
""" # noqa: E501
|
||||
if client and client_kwargs:
|
||||
raise ValueError
|
||||
self._client = client or LangSmithClient(**client_kwargs)
|
||||
self.content_key = list(content_key.split(".")) if content_key else []
|
||||
self.format_content = format_content or _stringify
|
||||
self.dataset_id = dataset_id
|
||||
self.dataset_name = dataset_name
|
||||
self.example_ids = example_ids
|
||||
self.as_of = as_of
|
||||
self.splits = splits
|
||||
self.inline_s3_urls = inline_s3_urls
|
||||
self.offset = offset
|
||||
self.limit = limit
|
||||
self.metadata = metadata
|
||||
self.filter = filter
|
||||
|
||||
def lazy_load(self) -> Iterator[Document]:
|
||||
for example in self._client.list_examples(
|
||||
dataset_id=self.dataset_id,
|
||||
dataset_name=self.dataset_name,
|
||||
example_ids=self.example_ids,
|
||||
as_of=self.as_of,
|
||||
splits=self.splits,
|
||||
inline_s3_urls=self.inline_s3_urls,
|
||||
offset=self.offset,
|
||||
limit=self.limit,
|
||||
metadata=self.metadata,
|
||||
filter=self.filter,
|
||||
):
|
||||
content: Any = example.inputs
|
||||
for key in self.content_key:
|
||||
content = content[key]
|
||||
content_str = self.format_content(content)
|
||||
metadata = example.dict()
|
||||
# Stringify datetime and UUID types.
|
||||
for k in ("dataset_id", "created_at", "modified_at", "source_run_id", "id"):
|
||||
metadata[k] = str(metadata[k]) if metadata[k] else metadata[k]
|
||||
yield Document(content_str, metadata=metadata)
|
||||
|
||||
|
||||
def _stringify(x: Union[str, dict]) -> str:
|
||||
if isinstance(x, str):
|
||||
return x
|
||||
else:
|
||||
try:
|
||||
return json.dumps(x, indent=2)
|
||||
except Exception:
|
||||
return str(x)
|
||||
@ -0,0 +1,58 @@
|
||||
import datetime
|
||||
import uuid
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
from langsmith.schemas import Example
|
||||
|
||||
from langchain_core.document_loaders import LangSmithLoader
|
||||
from langchain_core.documents import Document
|
||||
|
||||
|
||||
def test_init() -> None:
|
||||
LangSmithLoader(api_key="secret")
|
||||
|
||||
|
||||
EXAMPLES = [
|
||||
Example(
|
||||
inputs={"first": {"second": "foo"}},
|
||||
outputs={"res": "a"},
|
||||
dataset_id=uuid.uuid4(),
|
||||
id=uuid.uuid4(),
|
||||
created_at=datetime.datetime.now(),
|
||||
),
|
||||
Example(
|
||||
inputs={"first": {"second": "bar"}},
|
||||
outputs={"res": "b"},
|
||||
dataset_id=uuid.uuid4(),
|
||||
id=uuid.uuid4(),
|
||||
created_at=datetime.datetime.now(),
|
||||
),
|
||||
Example(
|
||||
inputs={"first": {"second": "baz"}},
|
||||
outputs={"res": "c"},
|
||||
dataset_id=uuid.uuid4(),
|
||||
id=uuid.uuid4(),
|
||||
created_at=datetime.datetime.now(),
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
@patch("langsmith.Client.list_examples", MagicMock(return_value=iter(EXAMPLES)))
|
||||
def test_lazy_load() -> None:
|
||||
loader = LangSmithLoader(
|
||||
api_key="dummy",
|
||||
dataset_id="mock",
|
||||
content_key="first.second",
|
||||
format_content=(lambda x: x.upper()),
|
||||
)
|
||||
expected = []
|
||||
for example in EXAMPLES:
|
||||
metadata = {
|
||||
k: v if not v or isinstance(v, dict) else str(v)
|
||||
for k, v in example.dict().items()
|
||||
}
|
||||
expected.append(
|
||||
Document(example.inputs["first"]["second"].upper(), metadata=metadata)
|
||||
)
|
||||
actual = [doc for doc in loader.lazy_load()]
|
||||
assert expected == actual
|
||||
Loading…
Reference in New Issue
Block a user