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Feat: typesense vector store (#1244)

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* chore(langchain): add typesense

* feat(langchain/vectorstores): add typesense vectorstore

* tests(langchain/vectorstore): add tests transform between docs & typesense records

* docs(examples): add typesense vectorstore examples

* feat(vectorstores/typesense): add fromTexts

* docs(examples): add typesense client to example

* docs(examples): add import and similarity search examples

* chore(package): typesense as peerDependency

* chore(create-entrypoints): add vectorstores/typesense

* chore(package): typesense optional dep

* lint(vectorstores/typesense): fix errors

* fix(vectorstores/typesense): throw error instead of catch it

* refactor(vectorstores/typesense): use asyncCaller in default import

* refactor(vectorstores/typesense): impor types with import type

* feat(vectorstores/typesense): add similaritySearchVectorWithScore & similaritySearchWithScore methods

* Fix entrypoints

* Remove unnecessary TypeSense initialisation

* fix(examples/typesense): add missing properties

* feat(typesense): add documentation

* fix deleted entry points by error

* fix deleted entry points by error

* Some fixes

* Fix bug

* refactor(typesense): remove modifySearchParams, use filters

* fix on docs

* feat(typesense): addVectors method implemented

* fix(typesense): change test after change

* Updates

* Update docs

---------

Co-authored-by: Tat Dat Duong <david@duong.cz>
Co-authored-by: Nuno Campos <nuno@boringbits.io>
This commit is contained in:
Tomás Mercado
2023-06-14 10:44:49 -03:00
committed by GitHub
parent 590e21a07d
commit 687f2c8047
13 changed files with 689 additions and 0 deletions
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docs/docs/modules/indexes/vector_stores/integrations/typesense.md
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@@ -0,0 +1,129 @@
# Typesense
Vector store that utilizes the Typesense search engine.
### Basic Usage
```typescript
import { Typesense, TypesenseConfig } from "langchain/vectorstores/typesense";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { Client } from "typesense";
import { Document } from "langchain/document";
const vectorTypesenseClient = new Client({
nodes: [
{
// Ideally should come from your .env file
host: "...",
port: 123,
protocol: "https",
},
],
// Ideally should come from your .env file
apiKey: "...",
numRetries: 3,
connectionTimeoutSeconds: 60,
});
const typesenseVectorStoreConfig = {
// Typesense client
typesenseClient: vectorTypesenseClient,
// Name of the collection to store the vectors in
schemaName: "your_schema_name",
// Optional column names to be used in Typesense
columnNames: {
// "vec" is the default name for the vector column in Typesense but you can change it to whatever you want
vector: "vec",
// "text" is the default name for the text column in Typesense but you can change it to whatever you want
pageContent: "text",
// Names of the columns that you will save in your typesense schema and need to be retrieved as metadata when searching
metadataColumnNames: ["foo", "bar", "baz"],
},
// Optional search parameters to be passed to Typesense when searching
searchParams: {
q: "*",
filter_by: "foo:[fooo]",
query_by: "",
},
// You can override the default Typesense import function if you want to do something more complex
// Default import function:
// async importToTypesense<
// T extends Record<string, unknown> = Record<string, unknown>
// >(data: T[], collectionName: string) {
// const chunkSize = 2000;
// for (let i = 0; i < data.length; i += chunkSize) {
// const chunk = data.slice(i, i + chunkSize);
// await this.caller.call(async () => {
// await this.client
// .collections<T>(collectionName)
// .documents()
// .import(chunk, { action: "emplace", dirty_values: "drop" });
// });
// }
// }
import: async (data, collectionName) => {
await vectorTypesenseClient
.collections(collectionName)
.documents()
.import(data, { action: "emplace", dirty_values: "drop" });
},
} satisfies TypesenseConfig;
/**
* Creates a Typesense vector store from a list of documents.
* Will update documents if there is a document with the same id, at least with the default import function.
* @param documents list of documents to create the vector store from
* @returns Typesense vector store
*/
const createVectorStoreWithTypesense = async (documents: Document[] = []) =>
Typesense.fromDocuments(
documents,
new OpenAIEmbeddings(),
typesenseVectorStoreConfig
);
/**
* Returns a Typesense vector store from an existing index.
* @returns Typesense vector store
*/
const getVectorStoreWithTypesense = async () =>
new Typesense(new OpenAIEmbeddings(), typesenseVectorStoreConfig);
// Do a similarity search
const vectorStore = await getVectorStoreWithTypesense();
const documents = await vectorStore.similaritySearch("hello world");
// Add filters based on metadata with the search parameters of Typesense
// will exclude documents with author:JK Rowling, so if Joe Rowling & JK Rowling exists, only Joe Rowling will be returned
vectorStore.similaritySearch("Rowling", undefined, {
filter_by: "author:!=JK Rowling",
});
// Delete a document
vectorStore.deleteDocuments(["document_id_1", "document_id_2"]);
```
### Constructor
Before starting, create a schema in Typesense with an id, a field for the vector and a field for the text. Add as many other fields as needed for the metadata.
- `constructor(embeddings: Embeddings, config: TypesenseConfig)`: Constructs a new instance of the `Typesense` class.
- `embeddings`: An instance of the `Embeddings` class used for embedding documents.
- `config`: Configuration object for the Typesense vector store.
- `typesenseClient`: Typesense client instance.
- `schemaName`: Name of the Typesense schema in which documents will be stored and searched.
- `searchParams` (optional): Typesense search parameters. Default is `{ q: '*', per_page: 5, query_by: '' }`.
- `columnNames` (optional): Column names configuration.
- `vector` (optional): Vector column name. Default is `'vec'`.
- `pageContent` (optional): Page content column name. Default is `'text'`.
- `metadataColumnNames` (optional): Metadata column names. Default is an empty array `[]`.
- `import` (optional): Replace the default import function for importing data to Typesense. This can affect the functionality of updating documents.
### Methods
- `async addDocuments(documents: Document[]): Promise<void>`: Adds documents to the vector store. The documents will be updated if there is a document with the same ID.
- `static async fromDocuments(docs: Document[], embeddings: Embeddings, config: TypesenseConfig): Promise<Typesense>`: Creates a Typesense vector store from a list of documents. Documents are added to the vector store during construction.
- `static async fromTexts(texts: string[], metadatas: object[], embeddings: Embeddings, config: TypesenseConfig): Promise<Typesense>`: Creates a Typesense vector store from a list of texts and associated metadata. Texts are converted to documents and added to the vector store during construction.
- `async similaritySearch(query: string, k?: number, filter?: Record<string, unknown>): Promise<Document[]>`: Searches for similar documents based on a query. Returns an array of similar documents.
- `async deleteDocuments(documentIds: string[]): Promise<void>`: Deletes documents from the vector store based on their IDs.
examples/package.json
+1
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@@ -48,6 +48,7 @@
"redis": "^4.6.6",
"sqlite3": "^5.1.4",
"typeorm": "^0.3.12",
"typesense": "^1.5.3",
"weaviate-ts-client": "^1.0.0",
"zod": "^3.21.4"
},
examples/src/indexes/vector_stores/typesense.ts
+97
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@@ -0,0 +1,97 @@
import { Typesense, TypesenseConfig } from "langchain/vectorstores/typesense";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { Client } from "typesense";
import { Document } from "langchain/document";
const vectorTypesenseClient = new Client({
nodes: [
{
// Ideally should come from your .env file
host: "...",
port: 123,
protocol: "https",
},
],
// Ideally should come from your .env file
apiKey: "...",
numRetries: 3,
connectionTimeoutSeconds: 60,
});
const typesenseVectorStoreConfig = {
// Typesense client
typesenseClient: vectorTypesenseClient,
// Name of the collection to store the vectors in
schemaName: "your_schema_name",
// Optional column names to be used in Typesense
columnNames: {
// "vec" is the default name for the vector column in Typesense but you can change it to whatever you want
vector: "vec",
// "text" is the default name for the text column in Typesense but you can change it to whatever you want
pageContent: "text",
// Names of the columns that you will save in your typesense schema and need to be retrieved as metadata when searching
metadataColumnNames: ["foo", "bar", "baz"],
},
// Optional search parameters to be passed to Typesense when searching
searchParams: {
q: "*",
filter_by: "foo:[fooo]",
query_by: "",
},
// You can override the default Typesense import function if you want to do something more complex
// Default import function:
// async importToTypesense<
// T extends Record<string, unknown> = Record<string, unknown>
// >(data: T[], collectionName: string) {
// const chunkSize = 2000;
// for (let i = 0; i < data.length; i += chunkSize) {
// const chunk = data.slice(i, i + chunkSize);
// await this.caller.call(async () => {
// await this.client
// .collections<T>(collectionName)
// .documents()
// .import(chunk, { action: "emplace", dirty_values: "drop" });
// });
// }
// }
import: async (data, collectionName) => {
await vectorTypesenseClient
.collections(collectionName)
.documents()
.import(data, { action: "emplace", dirty_values: "drop" });
},
} satisfies TypesenseConfig;
/**
* Creates a Typesense vector store from a list of documents.
* Will update documents if there is a document with the same id, at least with the default import function.
* @param documents list of documents to create the vector store from
* @returns Typesense vector store
*/
const createVectorStoreWithTypesense = async (documents: Document[] = []) =>
Typesense.fromDocuments(
documents,
new OpenAIEmbeddings(),
typesenseVectorStoreConfig
);
/**
* Returns a Typesense vector store from an existing index.
* @returns Typesense vector store
*/
const getVectorStoreWithTypesense = async () =>
new Typesense(new OpenAIEmbeddings(), typesenseVectorStoreConfig);
// Do a similarity search
const vectorStore = await getVectorStoreWithTypesense();
const documents = await vectorStore.similaritySearch("hello world");
// Add filters based on metadata with the search parameters of Typesense
// will exclude documents with author:JK Rowling, so if Joe Rowling & JK Rowling exists, only Joe Rowling will be returned
vectorStore.similaritySearch("Rowling", undefined, {
filter_by: "author:!=JK Rowling",
});
// Delete a document
vectorStore.deleteDocuments(["document_id_1", "document_id_2"]);
langchain/.gitignore
+3
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@@ -154,6 +154,9 @@ vectorstores/myscale.d.ts
vectorstores/redis.cjs
vectorstores/redis.js
vectorstores/redis.d.ts
vectorstores/typesense.cjs
vectorstores/typesense.js
vectorstores/typesense.d.ts
vectorstores/singlestore.cjs
vectorstores/singlestore.js
vectorstores/singlestore.d.ts
langchain/package.json
+13
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@@ -166,6 +166,9 @@
"vectorstores/redis.cjs",
"vectorstores/redis.js",
"vectorstores/redis.d.ts",
"vectorstores/typesense.cjs",
"vectorstores/typesense.js",
"vectorstores/typesense.d.ts",
"vectorstores/singlestore.cjs",
"vectorstores/singlestore.js",
"vectorstores/singlestore.d.ts",
@@ -508,6 +511,7 @@
"ts-jest": "^29.1.0",
"typeorm": "^0.3.12",
"typescript": "^5.0.0",
"typesense": "^1.5.3",
"weaviate-ts-client": "^1.0.0"
},
"peerDependencies": {
@@ -557,6 +561,7 @@
"replicate": "^0.9.0",
"srt-parser-2": "^1.2.2",
"typeorm": "^0.3.12",
"typesense": "^1.5.3",
"weaviate-ts-client": "^1.0.0"
},
"peerDependenciesMeta": {
@@ -698,6 +703,9 @@
"typeorm": {
"optional": true
},
"typesense": {
"optional": true
},
"weaviate-ts-client": {
"optional": true
}
@@ -1012,6 +1020,11 @@
"import": "./vectorstores/redis.js",
"require": "./vectorstores/redis.cjs"
},
"./vectorstores/typesense": {
"types": "./vectorstores/typesense.d.ts",
"import": "./vectorstores/typesense.js",
"require": "./vectorstores/typesense.cjs"
},
"./vectorstores/singlestore": {
"types": "./vectorstores/singlestore.d.ts",
"import": "./vectorstores/singlestore.js",
langchain/scripts/create-entrypoints.js
+2
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@@ -68,6 +68,7 @@ const entrypoints = {
"vectorstores/typeorm": "vectorstores/typeorm",
"vectorstores/myscale": "vectorstores/myscale",
"vectorstores/redis": "vectorstores/redis",
"vectorstores/typesense": "vectorstores/typesense",
"vectorstores/singlestore": "vectorstores/singlestore",
"vectorstores/tigris": "vectorstores/tigris",
// text_splitter
@@ -210,6 +211,7 @@ const requiresOptionalDependency = [
"vectorstores/myscale",
"vectorstores/redis",
"vectorstores/singlestore",
"vectorstores/typesense",
"vectorstores/tigris",
"memory/zep",
"document_loaders/web/apify_dataset",
langchain/src/load/import_constants.ts
+1
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@@ -33,6 +33,7 @@ export const optionalImportEntrypoints = [
"langchain/vectorstores/typeorm",
"langchain/vectorstores/myscale",
"langchain/vectorstores/redis",
"langchain/vectorstores/typesense",
"langchain/vectorstores/singlestore",
"langchain/vectorstores/tigris",
"langchain/memory/zep",
langchain/src/load/import_type.d.ts
Vendored
+3
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@@ -97,6 +97,9 @@ export interface OptionalImportMap {
"langchain/vectorstores/redis"?:
| typeof import("../vectorstores/redis.js")
| Promise<typeof import("../vectorstores/redis.js")>;
"langchain/vectorstores/typesense"?:
| typeof import("../vectorstores/typesense.js")
| Promise<typeof import("../vectorstores/typesense.js")>;
"langchain/vectorstores/singlestore"?:
| typeof import("../vectorstores/singlestore.js")
| Promise<typeof import("../vectorstores/singlestore.js")>;
langchain/src/schema/index.ts
+1
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@@ -168,6 +168,7 @@ export type AgentFinish = {
returnValues: Record<string, any>;
log: string;
};
export type AgentStep = {
action: AgentAction;
observation: string;
langchain/src/vectorstores/tests/typesense.test.ts
+115
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@@ -0,0 +1,115 @@
import { Client } from "typesense";
import { Document } from "../../document.js";
import { FakeEmbeddings } from "../../embeddings/fake.js";
import { Typesense } from "../typesense.js";
test("documentsToTypesenseRecords should return the correct typesense records", async () => {
const embeddings = new FakeEmbeddings();
const vectorstore = new Typesense(embeddings, {
schemaName: "test",
typesenseClient: {} as unknown as Client,
columnNames: {
vector: "vec",
pageContent: "text",
metadataColumnNames: ["foo", "bar", "baz"],
},
});
const documents: Document[] = [
{
metadata: {
id: "1",
foo: "fooo",
bar: "barr",
baz: "bazz",
},
pageContent: "hello world",
},
{
metadata: {
id: "2",
foo: "foooo",
bar: "barrr",
baz: "bazzz",
},
pageContent: "hello world 2",
},
];
const expected = [
{
text: "hello world",
foo: "fooo",
bar: "barr",
baz: "bazz",
vec: await embeddings.embedQuery("hello world"),
},
{
text: "hello world 2",
foo: "foooo",
bar: "barrr",
baz: "bazzz",
vec: await embeddings.embedQuery("hello world 2"),
},
];
expect(
await vectorstore._documentsToTypesenseRecords(
documents,
await embeddings.embedDocuments(["hello world", "hello world 2"])
)
).toEqual(expected);
});
test("typesenseRecordsToDocuments should return the correct langchain documents", async () => {
const embeddings = new FakeEmbeddings();
const vectorstore = new Typesense(embeddings, {
schemaName: "test",
typesenseClient: {} as unknown as Client,
columnNames: {
vector: "vec",
pageContent: "text",
metadataColumnNames: ["foo", "bar", "baz"],
},
});
const typesenseRecords = [
{
text: "hello world",
foo: "fooo",
bar: "barr",
baz: "bazz",
vec: await embeddings.embedQuery("hello world"),
},
{
text: "hello world 2",
foo: "foooo",
bar: "barrr",
baz: "bazzz",
vec: await embeddings.embedQuery("hello world 2"),
},
];
const expected = [
{
metadata: {
foo: "fooo",
bar: "barr",
baz: "bazz",
},
pageContent: "hello world",
},
{
metadata: {
foo: "foooo",
bar: "barrr",
baz: "bazzz",
},
pageContent: "hello world 2",
},
];
expect(vectorstore._typesenseRecordsToDocuments(typesenseRecords)).toEqual(
expected
);
});
langchain/src/vectorstores/typesense.ts
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@@ -0,0 +1,299 @@
import type { Client } from "typesense";
import type { MultiSearchRequestSchema } from "typesense/lib/Typesense/MultiSearch.js";
import type { Document } from "../document.js";
import { Embeddings } from "../embeddings/base.js";
import { VectorStore } from "./base.js";
import { AsyncCaller, AsyncCallerParams } from "../util/async_caller.js";
/**
* Typesense vector store configuration.
*/
export interface TypesenseConfig extends AsyncCallerParams {
/**
* Typesense client.
*/
typesenseClient: Client;
/**
* Typesense schema name in which documents will be stored and searched.
*/
schemaName: string;
/**
* Typesense search parameters.
* @default { q: '*', per_page: 5, query_by: '' }
*/
searchParams?: MultiSearchRequestSchema;
/**
* Column names.
*/
columnNames?: {
/**
* Vector column name.
* @default 'vec'
*/
vector?: string;
/**
* Page content column name.
* @default 'text'
*/
pageContent?: string;
/**
* Metadata column names.
* @default []
*/
metadataColumnNames?: string[];
};
/**
* Replace default import function.
* Default import function will update documents if there is a document with the same id.
* @param data
* @param collectionName
*/
import?<T extends Record<string, unknown> = Record<string, unknown>>(
data: T[],
collectionName: string
): Promise<void>;
}
/**
* Typesense vector store.
*/
export class Typesense extends VectorStore {
declare FilterType: Partial<MultiSearchRequestSchema>;
private client: Client;
private schemaName: string;
private searchParams: MultiSearchRequestSchema;
private vectorColumnName: string;
private pageContentColumnName: string;
private metadataColumnNames: string[];
private caller: AsyncCaller;
private import: (
data: Record<string, unknown>[],
collectionName: string
) => Promise<void>;
constructor(embeddings: Embeddings, config: TypesenseConfig) {
super(embeddings, config);
// Assign config values to class properties.
this.client = config.typesenseClient;
this.schemaName = config.schemaName;
this.searchParams = config.searchParams || {
q: "*",
per_page: 5,
query_by: "",
};
this.vectorColumnName = config.columnNames?.vector || "vec";
this.pageContentColumnName = config.columnNames?.pageContent || "text";
this.metadataColumnNames = config.columnNames?.metadataColumnNames || [];
// Assign import function.
this.import = config.import || this.importToTypesense.bind(this);
this.caller = new AsyncCaller(config);
}
/**
* Default function to import data to typesense
* @param data
* @param collectionName
*/
private async importToTypesense<
T extends Record<string, unknown> = Record<string, unknown>
>(data: T[], collectionName: string) {
const chunkSize = 2000;
for (let i = 0; i < data.length; i += chunkSize) {
const chunk = data.slice(i, i + chunkSize);
await this.caller.call(async () => {
await this.client
.collections<T>(collectionName)
.documents()
.import(chunk, { action: "emplace", dirty_values: "drop" });
});
}
}
/**
* Transform documents to Typesense records.
* @param documents
* @returns Typesense records.
*/
_documentsToTypesenseRecords(
documents: Document[],
vectors: number[][]
): Record<string, unknown>[] {
const metadatas = documents.map((doc) => doc.metadata);
const typesenseDocuments = documents.map((doc, index) => {
const metadata = metadatas[index];
const objectWithMetadatas: Record<string, unknown> = {};
this.metadataColumnNames.forEach((metadataColumnName) => {
objectWithMetadatas[metadataColumnName] = metadata[metadataColumnName];
});
return {
[this.pageContentColumnName]: doc.pageContent,
[this.vectorColumnName]: vectors[index],
...objectWithMetadatas,
};
});
return typesenseDocuments;
}
/**
* Transform the Typesense records to documents.
* @param typesenseRecords
* @returns documents
*/
_typesenseRecordsToDocuments(
typesenseRecords: Record<string, unknown>[] | undefined
): Document[] {
const documents =
typesenseRecords?.map((hit) => {
const objectWithMetadatas: Record<string, unknown> = {};
this.metadataColumnNames.forEach((metadataColumnName) => {
objectWithMetadatas[metadataColumnName] = hit[metadataColumnName];
});
const document: Document = {
pageContent: (hit[this.pageContentColumnName] as string) || "",
metadata: objectWithMetadatas,
};
return document;
}) || [];
return documents;
}
/**
* Add documents to the vector store.
* Will be updated if in the metadata there is a document with the same id if is using the default import function.
* Metadata will be added in the columns of the schema based on metadataColumnNames.
* @param documents Documents to add.
*/
async addDocuments(documents: Document[]) {
const typesenseDocuments = this._documentsToTypesenseRecords(
documents,
await this.embeddings.embedDocuments(
documents.map((doc) => doc.pageContent)
)
);
await this.import(typesenseDocuments, this.schemaName);
}
async addVectors(vectors: number[][], documents: Document[]) {
const typesenseDocuments = this._documentsToTypesenseRecords(
documents,
vectors
);
await this.import(typesenseDocuments, this.schemaName);
}
/**
* Search for similar documents with their similarity score.
* All the documents have 0 as similarity score because Typesense API
* does not return the similarity score.
* @param vectorPrompt vector to search for
* @param k amount of results to return
* @returns similar documents with their similarity score
*/
async similaritySearchVectorWithScore(
vectorPrompt: number[],
k?: number,
filter: this["FilterType"] = {}
) {
const amount = k || this.searchParams.per_page || 5;
const vector_query = `${this.vectorColumnName}:([${vectorPrompt}], k:${amount})`;
const typesenseResponse = await this.client.multiSearch.perform(
{
searches: [
{
...this.searchParams,
...filter,
per_page: amount,
vector_query,
collection: this.schemaName,
},
],
},
{}
);
const results = typesenseResponse.results[0].hits;
const hits = results?.map((hit) => hit.document) as
| Record<string, unknown>[]
| undefined;
const documents = this._typesenseRecordsToDocuments(hits).map(
(doc) => [doc, 0] as [Document<Record<string, unknown>>, number]
);
return documents;
}
/**
* Delete documents from the vector store.
* @param documentIds ids of the documents to delete
*/
async deleteDocuments(documentIds: string[]) {
await this.client
.collections(this.schemaName)
.documents()
.delete({
filter_by: `id:=${documentIds.join(",")}`,
});
}
/**
* Create a vector store from documents.
* @param docs documents
* @param embeddings embeddings
* @param config Typesense configuration
* @returns Typesense vector store
* @warning You can omit this method, and only use the constructor and addDocuments.
*/
static async fromDocuments(
docs: Document[],
embeddings: Embeddings,
config: TypesenseConfig
): Promise<Typesense> {
const instance = new Typesense(embeddings, config);
await instance.addDocuments(docs);
return instance;
}
/**
* Create a vector store from texts.
* @param texts
* @param metadatas
* @param embeddings
* @param config
* @returns Typesense vector store
*/
static async fromTexts(
texts: string[],
metadatas: object[],
embeddings: Embeddings,
config: TypesenseConfig
) {
const instance = new Typesense(embeddings, config);
const documents: Document[] = texts.map((text, i) => ({
pageContent: text,
metadata: metadatas[i] || {},
}));
await instance.addDocuments(documents);
return instance;
}
}
langchain/tsconfig.json
+1
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@@ -81,6 +81,7 @@
"src/vectorstores/typeorm.ts",
"src/vectorstores/myscale.ts",
"src/vectorstores/redis.ts",
"src/vectorstores/typesense.ts",
"src/vectorstores/singlestore.ts",
"src/vectorstores/tigris.ts",
"src/text_splitter.ts",
yarn.lock
+24
View File
@@ -14278,6 +14278,7 @@ __metadata:
tsx: ^3.12.3
typeorm: ^0.3.12
typescript: ^5.0.0
typesense: ^1.5.3
weaviate-ts-client: ^1.0.0
zod: ^3.21.4
languageName: unknown
@@ -18680,6 +18681,7 @@ __metadata:
ts-jest: ^29.1.0
typeorm: ^0.3.12
typescript: ^5.0.0
typesense: ^1.5.3
uuid: ^9.0.0
weaviate-ts-client: ^1.0.0
yaml: ^2.2.1
@@ -18732,6 +18734,7 @@ __metadata:
replicate: ^0.9.0
srt-parser-2: ^1.2.2
typeorm: ^0.3.12
typesense: ^1.5.3
weaviate-ts-client: ^1.0.0
peerDependenciesMeta:
"@aws-sdk/client-dynamodb":
@@ -18826,6 +18829,8 @@ __metadata:
optional: true
typeorm:
optional: true
typesense:
optional: true
weaviate-ts-client:
optional: true
languageName: unknown
@@ -19203,6 +19208,13 @@ __metadata:
languageName: node
linkType: hard
"loglevel@npm:^1.8.0":
version: 1.8.1
resolution: "loglevel@npm:1.8.1"
checksum: a1a62db40291aaeaef2f612334c49e531bff71cc1d01a2acab689ab80d59e092f852ab164a5aedc1a752fdc46b7b162cb097d8a9eb2cf0b299511106c29af61d
languageName: node
linkType: hard
"long@npm:4.0.0, long@npm:^4.0.0":
version: 4.0.0
resolution: "long@npm:4.0.0"
@@ -26674,6 +26686,18 @@ __metadata:
languageName: node
linkType: hard
"typesense@npm:^1.5.3":
version: 1.5.3
resolution: "typesense@npm:1.5.3"
dependencies:
axios: ^0.26.0
loglevel: ^1.8.0
peerDependencies:
"@babel/runtime": ^7.17.2
checksum: 6315139ca0be12f558a8ffb78e8055497941ba625fd61729ebe05d4af7d69524d89d1bd56a4a3f38df32fe41216ab6fde2e97bcddddce8af8328d5cddba85f82
languageName: node
linkType: hard
"ua-parser-js@npm:^0.7.30":
version: 0.7.34
resolution: "ua-parser-js@npm:0.7.34"