ci[minor]: add lint/format to ci (#169)

cc @hinthornw

.eslintrc.js

@@ -0,0 +1,57 @@
+/**
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ *
+ * @format
+ */
+
+const OFF = 0;
+const WARNING = 1;
+const ERROR = 2;
+
+module.exports = {
+  root: true,
+  env: {
+    browser: true,
+    commonjs: true,
+    jest: true,
+    node: true,
+  },
+  parser: "@babel/eslint-parser",
+  parserOptions: {
+    allowImportExportEverywhere: true,
+  },
+  extends: ["airbnb", "prettier"],
+  plugins: ["react-hooks", "header"],
+  ignorePatterns: [
+    "build",
+    "docs/api",
+    "node_modules",
+    "docs/_static",
+    "static",
+  ],
+  rules: {
+    // Ignore certain webpack alias because it can't be resolved
+    "import/no-unresolved": [
+      ERROR,
+      { ignore: ["^@theme", "^@docusaurus", "^@generated"] },
+    ],
+    "import/extensions": OFF,
+    "react/jsx-filename-extension": OFF,
+    "react-hooks/rules-of-hooks": ERROR,
+    "react/prop-types": OFF, // PropTypes aren't used much these days.
+    "react/function-component-definition": [
+      WARNING,
+      {
+        namedComponents: "function-declaration",
+        unnamedComponents: "arrow-function",
+      },
+    ],
+    "no-unused-vars": WARNING,
+    "import/prefer-default-export": WARNING,
+    "react/jsx-props-no-spreading": OFF,
+    "no-empty-pattern": WARNING,
+  },
+};

.github/workflows/format-lint.yml

@@ -0,0 +1,52 @@
+# This workflow will do a clean installation of node dependencies, cache/restore them, build the source code and run tests across different versions of node
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-nodejs
+
+name: CI (Lint/Format)
+
+on:
+  push:
+    branches: ["main"]
+  pull_request:
+  workflow_dispatch:  # Allows triggering the workflow manually in GitHub UI
+
+
+# If another push to the same PR or branch happens while this workflow is still running,
+# cancel the earlier run in favor of the next run.
+#
+# There's no point in testing an outdated version of the code. GitHub only allows
+# a limited number of job runners to be active at the same time, so it's better to cancel
+# pointless jobs early so that more useful jobs can run sooner.
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  lint:
+    name: Check linting
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Use Node.js 18.x
+        uses: actions/setup-node@v3
+        with:
+          node-version: 18.x
+          cache: "yarn"
+      - name: Install dependencies
+        run: yarn install --immutable --mode=skip-build
+      - name: Check linting
+        run: yarn run lint
+
+  format:
+    name: Check formatting
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Use Node.js 18.x
+        uses: actions/setup-node@v3
+        with:
+          node-version: 18.x
+          cache: "yarn"
+      - name: Install dependencies
+        run: yarn install --immutable --mode=skip-build
+      - name: Check formatting
+        run: yarn run format:check

docs/evaluation/faq/evaluator-implementations.mdx

@@ -53,10 +53,10 @@ Three QA evaluators you can load are: `"qa"`, `"context_qa"`, `"cot_qa"`. Based
 - The `"qa"` evaluator ([reference](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html#langchain-evaluation-qa-eval-chain-qaevalchain)) instructs an llm to directly grade a response as "correct" or "incorrect" based on the reference answer.
 - The `"context_qa"` evaluator ([reference](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html#langchain.evaluation.qa.eval_chain.ContextQAEvalChain)) instructs the LLM chain to use reference "context" (provided throught the example outputs) in determining correctness. This is useful if you have a larger corpus of grounding docs but don't have ground truth answers to a query.
 - The `"cot_qa"` evaluator ([reference](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html#langchain.evaluation.qa.eval_chain.CotQAEvalChain)) is similar to the "context_qa" evaluator, except it instructs the LLMChain to use chain of thought "reasoning" before determining a final verdict. This tends to lead to responses that better correlate with human labels, for a slightly higher token and runtime cost.
-  {" "}
-  <CodeTabs
-    tabs={[
-      PythonBlock(`from langsmith import Client
+
+<CodeTabs
+  tabs={[
+    PythonBlock(`from langsmith import Client
 from langsmith.evaluation import LangChainStringEvaluator, evaluate\n
 qa_evaluator = LangChainStringEvaluator("qa")  
 context_qa_evaluator = LangChainStringEvaluator("context_qa")
@@ -68,17 +68,16 @@ evaluate(
     evaluators=[qa_evaluator, context_qa_evaluator, cot_qa_evaluator],
     metadata={"revision_id": "the version of your pipeline you are testing"},
 )`),
-    ]}
-    groupId="client-language"
-  />
-  You can customize the evaluator by specifying the LLM used to power its LLM
-  chain or even by customizing the prompt itself. Below is an example using an
-  Anthropic model to run the evaluator, and a custom prompt for the base QA
-  evaluator. Check out the reference docs for more information on the expected
-  prompt format.
-  <CodeTabs
-    tabs={[
-      PythonBlock(`from langchain.chat_models import ChatAnthropic
+  ]}
+  groupId="client-language"
+/>
+You can customize the evaluator by specifying the LLM used to power its LLM chain
+or even by customizing the prompt itself. Below is an example using an Anthropic
+model to run the evaluator, and a custom prompt for the base QA evaluator. Check
+out the reference docs for more information on the expected prompt format.
+<CodeTabs
+  tabs={[
+    PythonBlock(`from langchain.chat_models import ChatAnthropic
 from langchain_core.prompts.prompt import PromptTemplate
 from langsmith.evaluation import LangChainStringEvaluator\n
 _PROMPT_TEMPLATE = """You are an expert professor specialized in grading students' answers to questions.
@@ -104,9 +103,9 @@ evaluate(
     evaluators=[qa_evaluator, context_qa_evaluator, cot_qa_evaluator],  
 )
 `),
-    ]}
-    groupId="client-language"
-  />
+  ]}
+  groupId="client-language"
+/>
 
 ## Criteria Evaluators (No Labels)
 

docs/tracing/faq/logging_and_viewing.mdx

@@ -52,6 +52,7 @@ Additionally, you will need to set `LANGCHAIN_TRACING_V2='true'` if you plan to
 
 - LangChain (Python or JS)
 - `@traceable` decorator or `wrap_openai` method in the Python SDK
+
 :::
 
 <CodeTabs

src/components/ClientInstallation.js

@@ -1,34 +1,36 @@
 import React from "react";
 import { CodeTabs } from "./InstructionsWithCode";
 
-export const ClientInstallationCodeTabs = () => (
-  <CodeTabs
-    groupId="client-language"
-    tabs={[
-      {
-        value: "python",
-        label: "pip",
-        language: "bash",
-        content: `pip install -U langsmith`,
-      },
-      {
-        value: "typescript",
-        label: "yarn",
-        language: "bash",
-        content: `yarn add langsmith`,
-      },
-      {
-        value: "npm",
-        label: "npm",
-        language: "bash",
-        content: `npm install -S langsmith`,
-      },
-      {
-        value: "pnpm",
-        label: "pnpm",
-        language: "bash",
-        content: `pnpm add langsmith`,
-      },
-    ]}
-  />
-);
+export function ClientInstallationCodeTabs() {
+  return (
+    <CodeTabs
+      groupId="client-language"
+      tabs={[
+        {
+          value: "python",
+          label: "pip",
+          language: "bash",
+          content: `pip install -U langsmith`,
+        },
+        {
+          value: "typescript",
+          label: "yarn",
+          language: "bash",
+          content: `yarn add langsmith`,
+        },
+        {
+          value: "npm",
+          label: "npm",
+          language: "bash",
+          content: `npm install -S langsmith`,
+        },
+        {
+          value: "pnpm",
+          label: "pnpm",
+          language: "bash",
+          content: `pnpm add langsmith`,
+        },
+      ]}
+    />
+  );
+}

src/components/Hub.js

@@ -10,39 +10,41 @@ import {
   ShellBlock,
 } from "./InstructionsWithCode";
 
-export const HubInstallationCodeTabs = () => (
-  <CodeTabs
-    groupId="client-language"
-    tabs={[
-      {
-        value: "python",
-        label: "pip",
-        language: "bash",
-        content: `pip install -U langchain langchainhub`,
-      },
-      {
-        value: "typescript",
-        label: "yarn",
-        language: "bash",
-        content: `yarn add langchain`,
-      },
-      {
-        value: "npm",
-        label: "npm",
-        language: "bash",
-        content: `npm install -S langchain`,
-      },
-      {
-        value: "pnpm",
-        label: "pnpm",
-        language: "bash",
-        content: `pnpm add langchain`,
-      },
-    ]}
-  />
-);
+export function HubInstallationCodeTabs() {
+  return (
+    <CodeTabs
+      groupId="client-language"
+      tabs={[
+        {
+          value: "python",
+          label: "pip",
+          language: "bash",
+          content: `pip install -U langchain langchainhub`,
+        },
+        {
+          value: "typescript",
+          label: "yarn",
+          language: "bash",
+          content: `yarn add langchain`,
+        },
+        {
+          value: "npm",
+          label: "npm",
+          language: "bash",
+          content: `npm install -S langchain`,
+        },
+        {
+          value: "pnpm",
+          label: "pnpm",
+          language: "bash",
+          content: `pnpm add langchain`,
+        },
+      ]}
+    />
+  );
+}
 
-export const HubPullCodeTabs = ({}) => {
+export function HubPullCodeTabs() {
   const pyBlock = `from langchain import hub
 
 # pull a chat prompt
@@ -94,9 +96,9 @@ console.log(result);`;
       </TabItem>
     </Tabs>
   );
-};
+}
 
-export const HubPushCodeTabs = ({}) => {
+export function HubPushCodeTabs() {
   const pyBlock = `from langchain import hub
 from langchain.prompts.chat import ChatPromptTemplate
 
@@ -131,4 +133,4 @@ await hub.push("<handle>/my-first-prompt", prompt);`;
       </TabItem>
     </Tabs>
   );
-};
+}