feat(community): Support google bigquery vector store

docs/core_docs/docs/integrations/vectorstores/google_bigquery.mdx

@@ -0,0 +1,169 @@
+---
+sidebar_class_name: node-only
+---
+
+import CodeBlock from "@theme/CodeBlock";
+
+# Google BigQuery Vector Store
+
+:::tip Compatibility
+Only available on Node.js.
+:::
+
+The Google BigQuery provide the search embeddings stored in BigQuery Table.
+This guide provides a quick overview for getting started with `GoogleBigQueryVectorSearch`
+
+## Setup
+
+To use Google BigQuery stores, you'll need to launch a Google BigQuery service on GCP and install the `@langchain/google-bigquery` integration package.  
+You also need to install the [`@google-cloud/bigquery`](https://github.com/googleapis/nodejs-bigquery) package to initialize a `Table` instance via `BigQuery`.
+
+This guide will use [OpenAI embeddings](/docs/integrations/text_embedding/openai), which require you to install the `@langchain/openai` integration package. You can also use [other supported embeddings models](/docs/integrations/text_embedding) if you wish.
+
+```{=mdx}
+import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
+import Npm2Yarn from "@theme/Npm2Yarn";
+
+<IntegrationInstallTooltip></IntegrationInstallTooltip>
+
+<Npm2Yarn>
+  @langchain/google-bigquery @langchain/core @google-cloud/bigquery @langchain/openai
+</Npm2Yarn>
+```
+
+You can set up a BigQuery service locally by [`bigquery-emulator`](https://github.com/goccy/bigquery-emulator).
+
+If you are using OpenAI embeddings for this guide, you'll need to set your OpenAI key as well:
+
+```typescript
+process.env.OPENAI_API_KEY = "YOUR_API_KEY";
+```
+
+If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:
+
+```typescript
+// process.env.LANGSMITH_TRACING="true"
+// process.env.LANGSMITH_API_KEY="your-api-key"
+```
+
+## Usage
+
+### Instantiation
+
+Following provide the minimal initialization for setting up the `GoogleBigQueryVectorSearch`:
+
+```typescript
+import { BigQuery } from "@google-cloud/bigquery";
+import { GoogleBigQueryVectorSearch } from "@langchain/google-bigquery";
+import { OpenAIEmbeddings } from "@langchain/openai";
+
+const embeddings = new OpenAIEmbeddings({
+  model: "text-embedding-3-small",
+});
+
+// Create a BigQuery client that uses Application Default Credentials(ADC)
+// see https://cloud.google.com/docs/authentication/production#providing_credentials_to_your_application for all the authentication options
+const bigQueryClient = new BigQuery();
+const table = bigQueryClient.dataset().table();
+
+const vectorStore = new GoogleBigQueryVectorSearch(embeddings, {
+  table,
+});
+```
+
+Apart from `table` param, you can custom the `GoogleBigQueryVectorSearch` by your needs
+
+```typescript
+const vectorStore = new GoogleBigQueryVectorSearch(embeddings, {
+  table,
+  textKey: "text_field", // The name of the table field containing the raw content. Defaults to "text"
+  embeddingKey: "embedding_field", // The name of the table field containing the embedding text. Defaults to "embedding"
+  documentKey: "id_field", // The name of the table field representing an unique id. Defaults to "_id"
+  distanceType: "COSINE", // Only used when vector search, which specifies the type of metric to use to compute the distance between two vectors. Default is "EUCLIDEAN"
+  useBruteForce: true, // Only used when vector search, which determines whether to use brute force search by skipping the vector index if one is available. Default is false
+  fractionListsToSearch: 0.005, // Only used when vector search, which specifies the percentage of lists to search. No default value.
+});
+```
+
+### Adding documents
+
+```typescript
+const documents = [
+  new Document({
+    pageContent: "this apple",
+    metadata: {
+      color: "red",
+      category: "edible",
+    },
+  }),
+  new Document({
+    pageContent: "this blueberry",
+    metadata: {
+      color: "blue",
+      category: "edible",
+    },
+  }),
+  new Document({
+    pageContent: "this firetruck",
+    metadata: {
+      color: "red",
+      category: "machine",
+    },
+  }),
+];
+
+// Add all the documents to store
+await vectorStore.addDocuments(documents);
+```
+
+### Querying Documents
+
+Doing a straightforward search which returns all results by searching a keyword
+
+```typescript
+const results = await vectorStore.similaritySearch("this", 3);
+for (const doc of results) {
+  console.log(` ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
+}
+```
+
+You can also get the corrsponding score by `similaritySearchWithScore` method
+
+```typescript
+const results = await vectorStore.similaritySearchWithScore("this", 3);
+for (const [doc, score] of results) {
+  console.log(
+    `${score}: ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`
+  );
+}
+```
+
+### Querying documents with filter
+
+The vectorstore supports two methods for applying filters to metadata fields when performing document searches
+
+#### Object-based Filters
+
+You can pass a Json object where the keys represent metadata fields and the values specify the filter condition.  
+This method applies an equality filter between the key and the corresponding value. When multiple key-value pairs are provided, they are combined using a logical AND operation.
+
+```typescript
+const filters = {
+  namespace: "color",
+  category: "machine",
+};
+const result = await vectorStore.similaritySearch("this", 2, filters);
+```
+
+#### SQL-based Filters
+
+Comming soon
+
+## Related
+
+- Vector store [conceptual guide](/docs/concepts/#vectorstores)
+- Vector store [how-to guides](/docs/how_to/#vectorstores)
+
+## API reference
+
+For detailed documentation of all `GoogleBigQueryVectorSearch` features and configurations head to the [API reference](https://api.js.langchain.com/classes/langchain_google-bigquery.GoogleBigQueryVectorSearch.html).

docs/core_docs/src/theme/FeatureTables.js

@@ -660,6 +660,19 @@ const FEATURE_TABLES = {
         local: true,
         idsInAddDocuments: false,
       },
+      {
+        name: "Google BigQuery",
+        link: "google_bigquery",
+        deleteById: false,
+        filtering: true,
+        searchByVector: true,
+        searchWithScore: true,
+        async: true,
+        passesStandardTests: true,
+        multiTenancy: false,
+        local: true,
+        idsInAddDocuments: false,
+      },
       {
         name: "InMemoryVectorStore",
         link: "in_memory",

libs/langchain-google-bigquery/.eslintrc.cjs

@@ -0,0 +1,74 @@
+module.exports = {
+  extends: [
+    "airbnb-base",
+    "eslint:recommended",
+    "prettier",
+    "plugin:@typescript-eslint/recommended",
+  ],
+  parserOptions: {
+    ecmaVersion: 12,
+    parser: "@typescript-eslint/parser",
+    project: "./tsconfig.json",
+    sourceType: "module",
+  },
+  plugins: ["@typescript-eslint", "no-instanceof"],
+  ignorePatterns: [
+    ".eslintrc.cjs",
+    "scripts",
+    "node_modules",
+    "dist",
+    "dist-cjs",
+    "*.js",
+    "*.cjs",
+    "*.d.ts",
+  ],
+  rules: {
+    "no-process-env": 2,
+    "no-instanceof/no-instanceof": 2,
+    "@typescript-eslint/explicit-module-boundary-types": 0,
+    "@typescript-eslint/no-empty-function": 0,
+    "@typescript-eslint/no-shadow": 0,
+    "@typescript-eslint/no-empty-interface": 0,
+    "@typescript-eslint/no-use-before-define": ["error", "nofunc"],
+    "@typescript-eslint/no-unused-vars": ["warn", { args: "none" }],
+    "@typescript-eslint/no-floating-promises": "error",
+    "@typescript-eslint/no-misused-promises": "error",
+    camelcase: 0,
+    "class-methods-use-this": 0,
+    "import/extensions": [2, "ignorePackages"],
+    "import/no-extraneous-dependencies": [
+      "error",
+      { devDependencies: ["**/*.test.ts"] },
+    ],
+    "import/no-unresolved": 0,
+    "import/prefer-default-export": 0,
+    "keyword-spacing": "error",
+    "max-classes-per-file": 0,
+    "max-len": 0,
+    "no-await-in-loop": 0,
+    "no-bitwise": 0,
+    "no-console": 0,
+    "no-restricted-syntax": 0,
+    "no-shadow": 0,
+    "no-continue": 0,
+    "no-void": 0,
+    "no-underscore-dangle": 0,
+    "no-use-before-define": 0,
+    "no-useless-constructor": 0,
+    "no-return-await": 0,
+    "consistent-return": 0,
+    "no-else-return": 0,
+    "func-names": 0,
+    "no-lonely-if": 0,
+    "prefer-rest-params": 0,
+    "new-cap": ["error", { properties: false, capIsNew: false }],
+  },
+  overrides: [
+    {
+      files: ["**/*.test.ts"],
+      rules: {
+        "@typescript-eslint/no-unused-vars": "off",
+      },
+    },
+  ],
+};

libs/langchain-google-bigquery/.gitignore

@@ -0,0 +1,7 @@
+index.cjs
+index.js
+index.d.ts
+index.d.cts
+node_modules
+dist
+.yarn

libs/langchain-google-bigquery/.prettierrc

@@ -0,0 +1,19 @@
+{
+  "$schema": "https://json.schemastore.org/prettierrc",
+  "printWidth": 80,
+  "tabWidth": 2,
+  "useTabs": false,
+  "semi": true,
+  "singleQuote": false,
+  "quoteProps": "as-needed",
+  "jsxSingleQuote": false,
+  "trailingComma": "es5",
+  "bracketSpacing": true,
+  "arrowParens": "always",
+  "requirePragma": false,
+  "insertPragma": false,
+  "proseWrap": "preserve",
+  "htmlWhitespaceSensitivity": "css",
+  "vueIndentScriptAndStyle": false,
+  "endOfLine": "lf"
+}

libs/langchain-google-bigquery/.release-it.json

@@ -0,0 +1,10 @@
+{
+  "github": {
+    "release": true,
+    "autoGenerate": true,
+    "tokenRef": "GITHUB_TOKEN_RELEASE"
+  },
+  "npm": {
+    "versionArgs": ["--workspaces-update=false"]
+  }
+}

libs/langchain-google-bigquery/LICENSE

@@ -0,0 +1,21 @@
+The MIT License
+
+Copyright (c) 2025 LangChain
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

libs/langchain-google-bigquery/README.md

@@ -0,0 +1,59 @@
+# @langchain/google-bigquery
+
+This package contains the LangChain.js integrations for Google BigQuery.
+
+## Installation
+
+```bash npm2yarn
+npm install @langchain/google-bigquery @langchain/core
+```
+
+## Development
+
+To develop the BigQuery package, you'll need to follow these instructions:
+
+### Install dependencies
+
+```bash
+yarn install
+```
+
+### Build the package
+
+```bash
+yarn build
+```
+
+Or from the repo root:
+
+```bash
+yarn build --filter=@langchain/google-bigquery
+```
+
+### Run tests
+
+Test files should live within a `tests/` file in the `src/` folder. Unit tests should end in `.test.ts` and integration tests should
+end in `.int.test.ts`:
+
+```bash
+$ yarn test
+$ yarn test:int
+```
+
+### Lint & Format
+
+Run the linter & formatter to ensure your code is up to standard:
+
+```bash
+yarn lint && yarn format
+```
+
+### Adding new entrypoints
+
+If you add a new file to be exported, either import & re-export from `src/index.ts`, or add it to the `entrypoints` field in the `config` variable located inside `langchain.config.js` and run `yarn build` to generate the new entrypoint.
+
+### Todo
+- [ ] Create table for vector store if not existing
+- [ ] Create index for vector store if not existing
+- [ ] Support SQL based filter
+- [ ] Support delete document