set up versioning and new docs outline (#186)

docusaurus.config.js

@@ -45,6 +45,17 @@ const config = {
       /** @type {import('@docusaurus/preset-classic').Options} */
       ({
         docs: {
+          lastVersion: "current",
+          versions: {
+            current: {
+              label: "1.0",
+              badge: false,
+            },
+            "2.0": {
+              label: "2.0",
+              banner: "unreleased",
+            },
+          },
           sidebarPath: require.resolve("./sidebars.js"),
           remarkPlugins: [
             [require("@docusaurus/remark-plugin-npm2yarn"), { sync: true }],

versioned_docs/version-2.0/concepts.md

@@ -0,0 +1,32 @@
+# Concepts
+
+Explanations, clarification and discussion of key topics in LangSmith.
+
+### Admin
+
+- [ ] Organizations
+- [ ] Workspaces
+- [ ] Users
+- [ ] API Keys
+- [ ] Personal Access Tokens
+- [ ] Roles
+
+### Tracing
+
+- [ ] Projects
+- [ ] Traces
+- [ ] Runs
+- [ ] Tags
+- [ ] Metadata
+
+### Evaluation
+
+- [ ] Prompts
+- [ ] Experiment
+- [ ] Datasets
+- [ ] Feedback
+- [ ] Evaluator
+
+- [ ] Annotation Queue
+- [ ] Prompt
+- [ ] Playground

versioned_docs/version-2.0/how_to_guides.md

@@ -0,0 +1,105 @@
+# How-To Guides
+
+Step-by-step guides that cover key tasks, operations and common problems.
+
+### Setup
+
+- [ ] Create an account and API Key
+- [ ] Setup an organization
+- [ ] Setup a workspace
+- [ ] Setup Billing
+- [ ] Assigning Roles (enterprise only)
+
+### Deployment
+
+- [ ] Self-hosting LangSmith on Kubernetes
+- [ ] Self-hosting LangSmith via Docker Compose
+- [ ] Setting up Auth
+
+### Tracing
+
+- [ ] Annotate code for tracing
+  - [ ] `traceable`
+  - [ ] wrapping OpenAI or Custom LLM
+  - [ ] RunTree
+- [ ] Log traces to specific project
+- [ ] Toggle tracing on and off
+- [ ] Sample traces
+- [ ] Add metadata and tags to traces
+- [ ] Get run_id and trace_id
+- [ ] Mask inputs and outputs
+- [ ] Log a trace using LangChain
+- [ ] Log a trace using instructor
+- [ ] Exporting traces
+  - [ ] Link to data format in reference section
+- [ ] Log multi-modal traces
+
+### Datasets
+
+- [ ] Create a dataset in the application
+- [ ] Create a dataset using the API
+- [ ] Export datasets
+- [ ] Import datasets
+- [ ] Version datasets
+- [ ] Add metadata to examples
+- [ ] Filter examples from a dataset
+- [ ] Add a trace to a dataset
+
+### Evaluation
+
+- [ ] Run an evaluation
+- [ ] Run an evaluation from the playground
+- [ ] Run an evaluation on a particular version of dataset
+- [ ] Run an evaluation on subset of dataset
+- [ ] Use off-the-shelf LangChain evaluators
+- [ ] Use custom evaluators
+- [ ] Evaluate on intermediate steps
+- [ ] Compare experiment results
+- [ ] Track regressions and improvements
+- [ ] Export experiment
+- [ ] Unit test LLM applications
+- [ ] View trace for an evaluation run
+- [ ] Run a pairwise evaluation (coming soon)
+- [ ] Audit evaluation scores (coming soon)
+
+### Human Feedback
+
+- [ ] Attach user feedback from your application to traces
+- [ ] Annotate traces inline
+- [ ] Add trace to annotation queue
+- [ ] Annotate traces in the annotation queue
+
+### Monitoring and Automations
+
+- [ ] Filter for runs
+- [ ] Use a trace filter
+- [ ] View the monitor charts
+- [ ] Slice chart by metadata and tag
+- [ ] Set up a rule
+  - [ ] Online evaluation
+  - [ ] Annotation Queue addition
+  - [ ] Dataset addition
+  - [ ] Webhook action
+- [ ] Group traces as threads
+- [ ] View threads
+
+### Prompt Hub
+
+- [ ] Create a prompt
+- [ ] Update a prompt
+- [ ] Pull prompts in code
+- [ ] Open a prompt from a trace
+- [ ] Open a prompt from an experiment
+
+### Playground
+
+- [ ] Run a prompt in the playground
+- [ ] Run a prompt on a custom model
+
+### Proxy
+
+- [ ] Run proxy
+- [ ] Make a request to the proxy
+- [ ] Turn off caching
+- [ ] Stream results
+- [ ] Turn on tracing

versioned_docs/version-2.0/index.mdx

@@ -0,0 +1,177 @@
+---
+sidebar_label: Quick Start
+sidebar_position: 1
+table_of_contents: true
+---
+
+import Tabs from "@theme/Tabs";
+import CodeBlock from "@theme/CodeBlock";
+import {
+  CodeTabs,
+  PythonBlock,
+  TypeScriptBlock,
+} from "@site/src/components/InstructionsWithCode";
+import {
+  LangChainInstallationCodeTabs,
+  LangChainQuickStartCodeTabs,
+  ConfigureEnvironmentCodeTabs,
+  RunTreeQuickStartCodeTabs,
+  ConfigureSDKEnvironmentCodeTabs,
+  PythonSDKTracingCode,
+  TypeScriptSDKTracingCode,
+} from "@site/src/components/QuickStart";
+import { ClientInstallationCodeTabs } from "@site/src/components/ClientInstallation";
+import DocCardList from "@theme/DocCardList";
+
+# Getting started with LangSmith (New!)
+
+## Introduction
+
+**LangSmith** is a platform for building production-grade LLM applications. It allows you to closely monitor and evaluate your application, so you can ship quickly and with confidence. Use of LangChain is not necessary - LangSmith works on its own!
+
+## Install LangSmith
+
+We offer Python and Typescript SDKs for all your LangSmith needs.
+
+<CodeTabs
+  tabs={[
+    {
+      value: "python",
+      label: "Python",
+      language: "bash",
+      content: `pip install -U langsmith`,
+    },
+    {
+      value: "typescript",
+      label: "TypeScript",
+      language: "bash",
+      content: `yarn add langchain langsmith`,
+    },
+  ]}
+  groupId="client-language"
+/>
+
+## Create an API key
+
+To create an API key head to the [setting pages](https://smith.langchain.com/settings). Then click **Create API Key.**
+
+## Setup your environment
+
+<ConfigureSDKEnvironmentCodeTabs />
+
+## Log your first trace
+
+<p>
+  We provide multiple ways to log traces to LangSmith. Below, we'll highlight
+  how to use <code>traceable</code>. See more on the{" "}
+  <a href="./tracing/integrations">Integrations</a> page.
+</p>
+<CodeTabs
+  tabs={[
+    {
+      value: "python",
+      label: "Python",
+      language: "python",
+      content: PythonSDKTracingCode(),
+    },
+    {
+      value: "typescript",
+      label: "TypeScript",
+      language: "typescript",
+      content: TypeScriptSDKTracingCode(),
+    },
+  ]}
+  groupId="client-language"
+/>
+
+- View a [sample output trace](https://smith.langchain.com/public/b37ca9b1-60cd-4a2a-817e-3c4e4443fdc0/r).
+- Learn more about tracing on the [tracing page](/tracing).
+
+## Create your first evaluation
+
+Evalution requires a system to test, [data](evaluation/faq) to serve as test cases, and optionally evaluators to grade the results. Here we use a built-in accuracy evaluator.
+
+<CodeTabs
+  tabs={[
+    {
+      value: "python",
+      label: "Python",
+      language: "python",
+      content: `from langsmith import Client
+from langsmith.evaluation import evaluate\n
+client = Client()\n
+# Define dataset: these are your test cases
+dataset_name = "Sample Dataset"
+dataset = client.create_dataset(dataset_name, description="A sample dataset in LangSmith.")
+client.create_examples(
+    inputs=[
+        {"postfix": "to LangSmith"},
+        {"postfix": "to Evaluations in LangSmith"},
+    ],
+    outputs=[
+        {"output": "Welcome to LangSmith"},
+        {"output": "Welcome to Evaluations in LangSmith"},
+    ],
+    dataset_id=dataset.id,
+)\n
+# Define your evaluator
+def exact_match(run, example):
+    return {"score": run.outputs["output"] == example.outputs["output"]}\n
+experiment_results = evaluate(
+    lambda input: "Welcome " + input['postfix'], # Your AI system goes here
+    data=dataset_name, # The data to predict and grade over
+    evaluators=[exact_match], # The evaluators to score the results
+    experiment_prefix="sample-experiment", # The name of the experiment
+    metadata={
+      "version": "1.0.0",
+      "revision_id": "beta"
+    },
+)
+`,
+    },
+    {
+      value: "typescript",
+      label: "TypeScript",
+      language: "typescript",
+      content: `import { Client, Run, Example } from 'langsmith';
+import { runOnDataset } from 'langchain/smith';
+import { EvaluationResult } from 'langsmith/evaluation';\n
+const client = new Client();\n
+// Define dataset: these are your test cases
+const datasetName = "Sample Dataset";
+const dataset = await client.createDataset(datasetName, {
+    description: "A sample dataset in LangSmith."
+});
+await client.createExamples({
+    inputs: [
+        { postfix: "to LangSmith" },
+        { postfix: "to Evaluations in LangSmith" },
+    ],
+    outputs: [
+        { output: "Welcome to LangSmith" },
+        { output: "Welcome to Evaluations in LangSmith" },
+    ],
+    datasetId: dataset.id,
+});\n
+// Define your evaluator
+const exactMatch = async ({ run, example }: { run: Run; example?: Example; }): Promise<EvaluationResult> => {
+    return {
+        key: 'exact_match',
+        score: run.outputs?.output === example?.outputs?.output ? 1 : 0,
+    };
+};\n
+await runOnDataset(
+    (input: { postfix: string }) => ({ output: \`Welcome $\{input.postfix\}\` }), // Your AI system goes here
+    datasetName, // The data to predict and grade over
+    {
+        evaluationConfig: { customEvaluators: [exactMatch] },
+        projectMetadata: {
+            version: "1.0.0",
+            revision_id: "beta",
+        },
+    }
+);`,
+    },
+  ]}
+  groupId="client-language"
+/>