@langchain/community
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Third-party integrations for LangChain.js
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JavaScript
const require_rolldown_runtime = require('../_virtual/rolldown_runtime.cjs');
const __langchain_core_utils_env = require_rolldown_runtime.__toESM(require("@langchain/core/utils/env"));
const __langchain_core_documents = require_rolldown_runtime.__toESM(require("@langchain/core/documents"));
const __langchain_core_vectorstores = require_rolldown_runtime.__toESM(require("@langchain/core/vectorstores"));
const mariadb = require_rolldown_runtime.__toESM(require("mariadb"));
//#region src/vectorstores/mariadb.ts
var mariadb_exports = {};
require_rolldown_runtime.__export(mariadb_exports, { MariaDBStore: () => MariaDBStore });
const STANDARD_SIMPLE_OPERATOR = new Map([
["$eq", "="],
["$ne", "!="],
["$lt", "<"],
["$lte", "<="],
["$gt", ">"],
["$gte", ">="]
]);
const STANDARD_LIST_OPERATOR = new Map([["$in", "in"], ["$nin", "not in"]]);
const STANDARD_BETWEEN_OPERATOR = new Map([["$like", "like"], ["$nlike", "no like"]]);
const GROUP_OPERATORS = new Map([
["$or", "or"],
["$and", "and"],
["$not", "not"]
]);
const SUPPORTED_OPERATORS = new Map([
...STANDARD_SIMPLE_OPERATOR,
...STANDARD_LIST_OPERATOR,
...STANDARD_BETWEEN_OPERATOR,
...GROUP_OPERATORS
]);
/**
* MariaDB vector store integration.
*
* Setup:
* Install `@langchain/community` and `mariadb`.
*
* If you wish to generate ids, you should also install the `uuid` package.
*
* ```bash
* npm install @langchain/community mariadb uuid
* ```
*
* ## [Constructor args](https://api.js.langchain.com/classes/_langchain_community.vectorstores_mariadb.MariaDB.html#constructor)
*
* <details open>
* <summary><strong>Instantiate</strong></summary>
*
* ```typescript
* import {
* MariaDBStore,
* DistanceStrategy,
* } from "@langchain/community/vectorstores/mariadb";
*
* // Or other embeddings
* import { OpenAIEmbeddings } from "@langchain/openai";
* import { PoolConfig } from "mariadb";
*
* const embeddings = new OpenAIEmbeddings({
* model: "text-embedding-3-small",
* });
*
* // Sample config
* const config = {
* connectionOptions: {
* host: "127.0.0.1",
* port: 3306,
* user: "myuser",
* password: "ChangeMe",
* database: "api",
* } as PoolConfig,
* tableName: "testlangchainjs",
* columns: {
* idColumnName: "id",
* vectorColumnName: "vector",
* contentColumnName: "content",
* metadataColumnName: "metadata",
* },
* // supported distance strategies: COSINE (default) or EUCLIDEAN
* distanceStrategy: "COSINE" as DistanceStrategy,
* };
*
* const vectorStore = await MariaDBStore.initialize(embeddings, config);
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Add documents</strong></summary>
*
* ```typescript
* import type { Document } from '@langchain/core/documents';
*
* const document1 = { pageContent: "foo", metadata: { baz: "bar" } };
* const document2 = { pageContent: "thud", metadata: { bar: "baz" } };
* const document3 = { pageContent: "i will be deleted :(", metadata: {} };
*
* const documents: Document[] = [document1, document2, document3];
* const ids = ["1", "2", "3"];
* await vectorStore.addDocuments(documents, { ids });
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Delete documents</strong></summary>
*
* ```typescript
* await vectorStore.delete({ ids: ["3"] });
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Similarity search</strong></summary>
*
* ```typescript
* const results = await vectorStore.similaritySearch("thud", 1);
* for (const doc of results) {
* console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
* }
* // Output: * thud [{"baz":"bar"}]
* ```
* </details>
*
* <br />
*
*
* <details>
* <summary><strong>Similarity search with filter</strong></summary>
*
* ```typescript
* const resultsWithFilter = await vectorStore.similaritySearch("thud", 1, {"country": "BG"});
*
* for (const doc of resultsWithFilter) {
* console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
* }
* // Output: * foo [{"baz":"bar"}]
* ```
* </details>
*
* <br />
*
*
* <details>
* <summary><strong>Similarity search with score</strong></summary>
*
* ```typescript
* const resultsWithScore = await vectorStore.similaritySearchWithScore("qux", 1);
* for (const [doc, score] of resultsWithScore) {
* console.log(`* [SIM=${score.toFixed(6)}] ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
* }
* // Output: * [SIM=0.000000] qux [{"bar":"baz","baz":"bar"}]
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>As a retriever</strong></summary>
*
* ```typescript
* const retriever = vectorStore.asRetriever({
* searchType: "mmr", // Leave blank for standard similarity search
* k: 1,
* });
* const resultAsRetriever = await retriever.invoke("thud");
* console.log(resultAsRetriever);
*
* // Output: [Document({ metadata: { "baz":"bar" }, pageContent: "thud" })]
* ```
* </details>
*
* <br />
*/
var MariaDBStore = class MariaDBStore extends __langchain_core_vectorstores.VectorStore {
tableName;
collectionTableName;
collectionName = "langchain";
collectionId;
collectionMetadata;
schemaName;
idColumnName;
vectorColumnName;
contentColumnName;
metadataColumnName;
_verbose;
pool;
chunkSize = 500;
distanceStrategy;
constructor(embeddings, config) {
super(embeddings, config);
this.tableName = this.escapeId(config.tableName ?? "langchain", false);
if (config.collectionName !== void 0 && config.collectionTableName === void 0) throw new Error(`If supplying a "collectionName", you must also supply a "collectionTableName".`);
this.collectionTableName = config.collectionTableName ? this.escapeId(config.collectionTableName, false) : void 0;
this.collectionName = config.collectionName ? this.escapeId(config.collectionName, false) : "langchaincol";
this.collectionMetadata = config.collectionMetadata ?? null;
this.schemaName = config.schemaName ? this.escapeId(config.schemaName, false) : null;
this.vectorColumnName = this.escapeId(config.columns?.vectorColumnName ?? "embedding", false);
this.contentColumnName = this.escapeId(config.columns?.contentColumnName ?? "text", false);
this.idColumnName = this.escapeId(config.columns?.idColumnName ?? "id", false);
this.metadataColumnName = this.escapeId(config.columns?.metadataColumnName ?? "metadata", false);
if (!config.connectionOptions && !config.pool) throw new Error("You must provide either a `connectionOptions` object or a `pool` instance.");
const langchainVerbose = (0, __langchain_core_utils_env.getEnvironmentVariable)("LANGCHAIN_VERBOSE");
if (langchainVerbose === "true") this._verbose = true;
else if (langchainVerbose === "false") this._verbose = false;
else this._verbose = config.verbose;
if (config.pool) this.pool = config.pool;
else {
const poolConf = {
...config.connectionOptions,
rowsAsArray: true
};
if (this._verbose) poolConf.logger = { query: console.log };
this.pool = mariadb.default.createPool(poolConf);
}
this.chunkSize = config.chunkSize ?? 500;
this.distanceStrategy = config.distanceStrategy ?? "COSINE";
}
get computedTableName() {
return this.schemaName == null ? this.tableName : `${this.schemaName}.${this.tableName}`;
}
get computedCollectionTableName() {
return this.schemaName == null ? `${this.collectionTableName}` : `"${this.schemaName}"."${this.collectionTableName}"`;
}
/**
* Escape identifier
*
* @param identifier identifier value
* @param alwaysQuote must identifier be quoted if not required
*/
escapeId(identifier, alwaysQuote) {
if (!identifier || identifier === "") throw new Error("Identifier is required");
const len = identifier.length;
const simpleIdentifier = /^[0-9a-zA-Z$_]*$/;
if (simpleIdentifier.test(identifier)) {
if (len < 1 || len > 64) throw new Error("Invalid identifier length");
if (alwaysQuote) return `\`${identifier}\``;
if (/^\d+$/.test(identifier)) return `\`${identifier}\``;
return identifier;
} else {
if (identifier.includes("\0")) throw new Error("Invalid name - containing u0000 character");
let ident = identifier;
if (/^`.+`$/.test(identifier)) ident = identifier.substring(1, identifier.length - 1);
if (len < 1 || len > 64) throw new Error("Invalid identifier length");
return `\`${ident.replace(/`/g, "``")}\``;
}
}
printable(definition) {
return definition.replaceAll(/[^0-9a-zA-Z_]/g, "");
}
/**
* Static method to create a new `MariaDBStore` instance from a
* connection. It creates a table if one does not exist, and calls
* `connect` to return a new instance of `MariaDBStore`.
*
* @param embeddings - Embeddings instance.
* @param fields - `MariaDBStoreArgs` instance
* @param fields.dimensions Number of dimensions in your vector data type. default to 1536.
* @returns A new instance of `MariaDBStore`.
*/
static async initialize(embeddings, config) {
const { dimensions,...rest } = config;
const mariadbStore = new MariaDBStore(embeddings, rest);
await mariadbStore.ensureTableInDatabase(dimensions);
await mariadbStore.ensureCollectionTableInDatabase();
await mariadbStore.loadCollectionId();
return mariadbStore;
}
/**
* Static method to create a new `MariaDBStore` instance from an
* array of texts and their metadata. It converts the texts into
* `Document` instances and adds them to the store.
*
* @param texts - Array of texts.
* @param metadatas - Array of metadata objects or a single metadata object.
* @param embeddings - Embeddings instance.
* @param dbConfig - `MariaDBStoreArgs` instance.
* @returns Promise that resolves with a new instance of `MariaDBStore`.
*/
static async fromTexts(texts, metadatas, embeddings, dbConfig) {
const docs = [];
for (let i = 0; i < texts.length; i += 1) {
const metadata = Array.isArray(metadatas) ? metadatas[i] : metadatas;
const newDoc = new __langchain_core_documents.Document({
pageContent: texts[i],
metadata
});
docs.push(newDoc);
}
return MariaDBStore.fromDocuments(docs, embeddings, dbConfig);
}
/**
* Static method to create a new `MariaDBStore` instance from an
* array of `Document` instances. It adds the documents to the store.
*
* @param docs - Array of `Document` instances.
* @param embeddings - Embeddings instance.
* @param dbConfig - `MariaDBStoreArgs` instance.
* @returns Promise that resolves with a new instance of `MariaDBStore`.
*/
static async fromDocuments(docs, embeddings, dbConfig) {
const instance = await MariaDBStore.initialize(embeddings, dbConfig);
await instance.addDocuments(docs, { ids: dbConfig.ids });
return instance;
}
_vectorstoreType() {
return "mariadb";
}
/**
* Method to add documents to the vector store. It converts the documents into
* vectors, and adds them to the store.
*
* @param documents - Array of `Document` instances.
* @param options - Optional arguments for adding documents
* @returns Promise that resolves when the documents have been added.
*/
async addDocuments(documents, options) {
const texts = documents.map(({ pageContent }) => pageContent);
return this.addVectors(await this.embeddings.embedDocuments(texts), documents, options);
}
/**
* Inserts a row for the collectionName provided at initialization if it does not
* exist and set the collectionId.
*/
async loadCollectionId() {
if (this.collectionId) return;
if (this.collectionTableName) {
const queryResult = await this.pool.query({
sql: `SELECT uuid from ${this.computedCollectionTableName} WHERE label = ?`,
rowsAsArray: true
}, [this.collectionName]);
if (queryResult.length > 0) this.collectionId = queryResult[0][0];
else {
const insertString = `INSERT INTO ${this.computedCollectionTableName}(label, cmetadata) VALUES (?, ?) RETURNING uuid`;
const insertResult = await this.pool.query({
sql: insertString,
rowsAsArray: true
}, [this.collectionName, this.collectionMetadata]);
this.collectionId = insertResult[0][0];
}
}
}
/**
* Method to add vectors to the vector store. It converts the vectors into
* rows and inserts them into the database.
*
* @param vectors - Array of vectors.
* @param documents - Array of `Document` instances.
* @param options - Optional arguments for adding documents
* @returns Promise that resolves when the vectors have been added.
*/
async addVectors(vectors, documents, options) {
const ids = options?.ids;
if (ids !== void 0 && ids.length !== vectors.length) throw new Error("The number of ids must match the number of vectors provided.");
await this.loadCollectionId();
const insertQuery = `INSERT INTO ${this.computedTableName}(${this.idColumnName},${this.contentColumnName},${this.metadataColumnName},${this.vectorColumnName}${this.collectionId ? ",collection_id" : ""}) VALUES (${ids ? "?" : "UUID_v7()"}, ?, ?, ?${this.collectionId ? ", ?" : ""})`;
try {
const batchParams = [];
for (let i = 0; i < vectors.length; i += 1) {
const param = [
ids ? ids[i] : null,
documents[i].pageContent,
documents[i].metadata,
this.getFloat32Buffer(vectors[i]),
this.collectionId
];
if (!ids) param.shift();
if (!this.collectionId) param.pop();
batchParams.push(param);
}
await this.pool.batch(insertQuery, batchParams);
} catch (e) {
console.error(e);
throw new Error(`Error inserting: ${e.message}`);
}
}
/**
* Convert float array to binary value
* @param vector embedding value
* @private
*/
getFloat32Buffer(vector) {
return Buffer.from(new Float32Array(vector).buffer);
}
/**
* Method to delete documents from the vector store. It deletes the
* documents that match the provided ids
*
* @param ids - array of ids
* @returns Promise that resolves when the documents have been deleted.
* @example
* await vectorStore.delete(["id1", "id2"]);
*/
async delete(params) {
const { ids, filter } = params;
if (!(ids || filter)) throw new Error("You must specify either ids or a filter when deleting documents.");
await this.loadCollectionId();
if (ids) await this.pool.query(`DELETE FROM ${this.computedTableName} WHERE ${this.idColumnName} IN (?) ${this.collectionId ? " AND collection_id = ?" : ""}`, [ids, this.collectionId]);
else if (filter) {
const [filterPart, params$1] = this.filterConverter(filter);
if (filterPart.length === 0) throw new Error("Wrong filter.");
await this.pool.query(`DELETE FROM ${this.computedTableName} WHERE ${filterPart} ${this.collectionId ? " AND collection_id = ?" : ""}`, [...params$1, this.collectionId]);
}
}
filterConverter(filter) {
if (!filter) return ["", []];
const _filter = filter ?? {};
const parameters = [];
let sqlFilter = this.subFilterConverter(_filter, parameters, "$and");
if (sqlFilter.charAt(0) === "(") sqlFilter = sqlFilter.substring(1, sqlFilter.length - 1);
return [sqlFilter, parameters];
}
subFilterConverter(filter, parameters, groupOperator) {
const sqlFilterPart = [];
for (const [key, value] of Object.entries(filter)) if (typeof value === "object" && value !== null) {
const _value = value;
for (const [type, subvalue] of Object.entries(_value)) {
let realvalue = subvalue;
if (STANDARD_LIST_OPERATOR.has(type)) {
if (!Array.isArray(realvalue)) {
if (typeof realvalue !== "string" || typeof realvalue !== "number") throw new Error("value for in/not in filter are expected to be an array type");
realvalue = [realvalue];
}
const placeholders = realvalue.map(() => "?").join(",");
sqlFilterPart.push(`JSON_VALUE(${this.metadataColumnName}, '$.${key}') ${STANDARD_LIST_OPERATOR.get(type)} (${placeholders})`);
parameters.push(...realvalue);
} else if (GROUP_OPERATORS.has(type)) sqlFilterPart.push(this.subFilterConverter(realvalue, parameters, type));
else if (SUPPORTED_OPERATORS.has(type)) {
sqlFilterPart.push(`JSON_VALUE(${this.metadataColumnName}, '$.${key}') ${SUPPORTED_OPERATORS.get(type)} ?`);
parameters.push(realvalue);
} else throw new Error(`unknown type operation, must be in ${SUPPORTED_OPERATORS.keys()}`);
}
} else {
sqlFilterPart.push(`JSON_VALUE(${this.metadataColumnName}, '$.${key}') = ?`);
parameters.push(value);
}
if (sqlFilterPart.length > 1) return `(${sqlFilterPart.join(` ${GROUP_OPERATORS.get(groupOperator)} `)})`;
else return sqlFilterPart[0];
}
/**
* Method to perform a similarity search in the vector store. It returns
* the `k` most similar documents to the query vector, along with their
* similarity scores.
*
* @param query - Query vector.
* @param k - Number of most similar documents to return.
* @param filter - Optional filter to apply to the search.
* @returns Promise that resolves with an array of tuples, each containing a `Document` and its similarity score.
*/
async similaritySearchVectorWithScore(query, k, filter) {
const parameters = [this.getFloat32Buffer(query)];
const whereClauses = [];
await this.loadCollectionId();
if (this.collectionId) {
whereClauses.push("collection_id = ?");
parameters.push(this.collectionId);
}
if (filter) {
const [filterPart, params] = this.filterConverter(filter);
whereClauses.push(filterPart);
parameters.push(...params);
}
parameters.push(k);
const whereClause = whereClauses.length ? `WHERE ${whereClauses.join(" AND ")}` : "";
const queryString = `SELECT ${this.idColumnName},${this.contentColumnName},${this.metadataColumnName},VEC_DISTANCE_${this.distanceStrategy}(${this.vectorColumnName}, ?) as distance FROM ${this.computedTableName} ${whereClause} ORDER BY distance ASC LIMIT ?`;
const documents = await this.pool.execute({
sql: queryString,
rowsAsArray: true
}, parameters);
const results = [];
for (const doc of documents) if (doc[3] != null && doc[1] != null) {
const document = new __langchain_core_documents.Document({
id: doc[0],
pageContent: doc[1],
metadata: doc[2]
});
results.push([document, doc[3]]);
}
return results;
}
/**
* Method to ensure the existence of the table in the database. It creates
* the table if it does not already exist.
* @param dimensions Number of dimensions in your vector data type. Default to 1536.
* @returns Promise that resolves when the table has been ensured.
*/
async ensureTableInDatabase(dimensions = 1536) {
const tableQuery = `CREATE TABLE IF NOT EXISTS ${this.computedTableName}(${this.idColumnName} UUID NOT NULL DEFAULT UUID_v7() PRIMARY KEY,${this.contentColumnName} TEXT,${this.metadataColumnName} JSON,${this.vectorColumnName} VECTOR(${dimensions}) NOT NULL, VECTOR INDEX ${this.printable(`${this.tableName}_${this.vectorColumnName}`)}_idx (${this.vectorColumnName}) ) ENGINE=InnoDB`;
await this.pool.query(tableQuery);
}
/**
* Method to ensure the existence of the collection table in the database.
* It creates the table if it does not already exist.
*
* @returns Promise that resolves when the collection table has been ensured.
*/
async ensureCollectionTableInDatabase() {
try {
if (this.collectionTableName != null) await Promise.all([this.pool.query(`CREATE TABLE IF NOT EXISTS ${this.computedCollectionTableName}(uuid UUID NOT NULL DEFAULT UUID_v7() PRIMARY KEY,
label VARCHAR(256), cmetadata JSON, UNIQUE KEY idx_${this.printable(this.collectionTableName)}_label
(label))`), this.pool.query(`ALTER TABLE ${this.computedTableName}
ADD COLUMN IF NOT EXISTS collection_id uuid,
ADD CONSTRAINT FOREIGN KEY IF NOT EXISTS ${this.printable(this.tableName)}_collection_id_fkey (collection_id)
REFERENCES ${this.computedCollectionTableName}(uuid) ON DELETE CASCADE`)]);
} catch (e) {
console.error(e);
throw new Error(`Error adding column or creating index: ${e.message}`);
}
}
/**
* Close the pool.
*
* @returns Promise that resolves when the pool is terminated.
*/
async end() {
return this.pool.end();
}
};
//#endregion
exports.MariaDBStore = MariaDBStore;
Object.defineProperty(exports, 'mariadb_exports', {
enumerable: true,
get: function () {
return mariadb_exports;
}
});
//# sourceMappingURL=mariadb.cjs.map