From 95dd0e01585622270533be21c7a04c5480cc7848 Mon Sep 17 00:00:00 2001
From: Marcus Schiesser <mail@marcusschiesser.de>
Date: Thu, 16 Nov 2023 18:38:57 +0700
Subject: [PATCH] feat: add mongo db vector support with example
---
apps/mongodb/2_load_and_index.ts | 31 +
apps/mongodb/3_query.ts | 28 +
apps/mongodb/README.md | 123 +
apps/mongodb/docs/1_signup.png | Bin 0 -> 144738 bytes
apps/mongodb/docs/2_connection_string.png | Bin 0 -> 109324 bytes
apps/mongodb/docs/3_vectors_in_db.png | Bin 0 -> 368172 bytes
apps/mongodb/docs/4_search_tab.png | Bin 0 -> 235568 bytes
apps/mongodb/docs/5_json_editor.png | Bin 0 -> 284844 bytes
apps/mongodb/docs/7_index_created.png | Bin 0 -> 215981 bytes
apps/mongodb/package.json | 17 +
apps/mongodb/tinytweets.json | 57482 ++++++++++++++++
packages/core/src/Node.ts | 9 +-
packages/core/src/index.ts | 1 +
packages/core/src/storage/index.ts | 1 +
.../vectorStore/MongoDBAtlasVectorStore.ts | 164 +
.../core/src/storage/vectorStore/types.ts | 4 +-
.../core/src/storage/vectorStore/utils.ts | 64 +
pnpm-lock.yaml | 83 +-
18 files changed, 57972 insertions(+), 35 deletions(-)
create mode 100644 apps/mongodb/2_load_and_index.ts
create mode 100644 apps/mongodb/3_query.ts
create mode 100644 apps/mongodb/README.md
create mode 100644 apps/mongodb/docs/1_signup.png
create mode 100644 apps/mongodb/docs/2_connection_string.png
create mode 100644 apps/mongodb/docs/3_vectors_in_db.png
create mode 100644 apps/mongodb/docs/4_search_tab.png
create mode 100644 apps/mongodb/docs/5_json_editor.png
create mode 100644 apps/mongodb/docs/7_index_created.png
create mode 100644 apps/mongodb/package.json
create mode 100644 apps/mongodb/tinytweets.json
create mode 100644 packages/core/src/storage/vectorStore/MongoDBAtlasVectorStore.ts
create mode 100644 packages/core/src/storage/vectorStore/utils.ts
diff --git a/apps/mongodb/2_load_and_index.ts b/apps/mongodb/2_load_and_index.ts
new file mode 100644
index 000000000..3c92ab714
--- /dev/null
+++ b/apps/mongodb/2_load_and_index.ts
@@ -0,0 +1,31 @@
+/* eslint-disable turbo/no-undeclared-env-vars */
+import "dotenv/config";
+import {
+ MongoDBAtlasVectorSearch,
+ SimpleMongoReader,
+ VectorStoreIndex,
+ storageContextFromDefaults,
+} from "llamaindex";
+import { MongoClient } from "mongodb";
+
+// Create a new client and connect to the server
+const client = new MongoClient(process.env.MONGODB_URI!);
+// load objects from mongo and convert them into LlamaIndex Document objects
+// llamaindex has a special class that does this for you
+// it pulls every object in a given collection
+const MR = new SimpleMongoReader(client);
+const documents = await MR.loadData("data", "posts");
+
+// create Atlas as a vector store
+const vectorStore = new MongoDBAtlasVectorSearch({
+ mongodbClient: client,
+ dbName: process.env.MONGODB_DATABASE!,
+ collectionName: process.env.MONGODB_VECTORS!, // this is where your embeddings will be stored
+ indexName: process.env.MONGODB_VECTOR_INDEX, // this is the name of the index you will need to create
+});
+
+// now create an index from all the Documents and store them in Atlas
+const storageContext = await storageContextFromDefaults({ vectorStore });
+VectorStoreIndex.fromDocuments(documents, { storageContext });
+
+// you can't query your index yet because you need to create a vector search index in mongodb's UI now
diff --git a/apps/mongodb/3_query.ts b/apps/mongodb/3_query.ts
new file mode 100644
index 000000000..82a074980
--- /dev/null
+++ b/apps/mongodb/3_query.ts
@@ -0,0 +1,28 @@
+/* eslint-disable turbo/no-undeclared-env-vars */
+import "dotenv/config";
+import {
+ MongoDBAtlasVectorSearch,
+ VectorStoreIndex,
+ serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+ const serviceContext = serviceContextFromDefaults();
+ const store = new MongoDBAtlasVectorSearch({
+ dbName: process.env.MONGODB_DATABASE!,
+ collectionName: process.env.MONGODB_VECTORS!,
+ indexName: process.env.MONGODB_VECTOR_INDEX!,
+ });
+
+ const index = await VectorStoreIndex.fromVectorStore(store, serviceContext);
+
+ const retriever = index.asRetriever({ similarityTopK: 20 });
+ const queryEngine = index.asQueryEngine({ retriever });
+ const response = await queryEngine.query(
+ "What does the author think of web frameworks?",
+ );
+ console.log(response);
+ process.exit(0);
+}
+
+main();
diff --git a/apps/mongodb/README.md b/apps/mongodb/README.md
new file mode 100644
index 000000000..03d15b14c
--- /dev/null
+++ b/apps/mongodb/README.md
@@ -0,0 +1,123 @@
+# LlamaIndexTS retrieval augmented generation with MongoDB
+
+### Prepare Environment
+
+Make sure to run `pnpm install` and set your OpenAI environment variable before running these examples.
+
+```
+pnpm install
+export OPENAI_API_KEY="sk-..."
+```
+
+### Sign up for MongoDB Atlas
+
+We'll be using MongoDB's hosted database service, [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/register). You can sign up for free and get a small hosted cluster for free:
+
+
+
+The signup process will walk you through the process of creating your cluster and ensuring it's configured for you to access. Once the cluster is created, choose "Connect" and then "Connect to your application". Choose Python, and you'll be presented with a connection string that looks like this:
+
+
+
+### Set up environment variables
+
+Copy the connection string (make sure you include your password) and put it into a file called `.env` in the root of this repo. It should look like this:
+
+```
+MONGODB_URI=mongodb+srv://seldo:xxxxxxxxxxx@llamaindexdemocluster.xfrdhpz.mongodb.net/?retryWrites=true&w=majority
+```
+
+You will also need to choose a name for your database, and the collection where we will store the tweets, and also include them in .env. They can be any string, but this is what we used:
+
+```
+MONGODB_DATABASE=tiny_tweets_db
+MONGODB_COLLECTION=tiny_tweets_collection
+```
+
+### Import tweets into MongoDB
+
+You are now ready to import our ready-made data set into Mongo. This is the file `tinytweets.json`, a selection of approximately 1000 tweets from @seldo on Twitter in mid-2019. With your environment set up you can do this by running
+
+```
+pnpm ts-node 1_import.ts
+```
+
+If you don't want to use tweets, you can replace `json_file` with any other array of JSON objects, but you will need to modify some code later to make sure the correct field gets indexed. There is no LlamaIndex-specific code here; you can load your data into Mongo any way you want to.
+
+### Load and index your data
+
+Now we're ready to index our data. To do this, LlamaIndex will pull your text out of Mongo, split it into chunks, and then send those chunks to OpenAI to be turned into [vector embeddings](https://docs.llamaindex.ai/en/stable/understanding/indexing/indexing.html#what-is-an-embedding). The embeddings will then be stored in a new collection in Mongo. This will take a while depending how much text you have, but the good news is that once it's done you will be able to query quickly without needing to re-index.
+
+We'll be using OpenAI to do the embedding, so now is when you need to [generate an OpenAI API key](https://platform.openai.com/account/api-keys) if you haven't already and add it to your `.env` file like this:
+
+```
+OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+```
+
+You'll also need to pick a name for the new collection where the embeddings will be stored, and add it to `.env`, along with the name of a vector search index (we'll be creating this in the next step, after you've indexed your data):
+
+```
+MONGODB_VECTORS=tiny_tweets_vectors
+MONGODB_VECTOR_INDEX=tiny_tweets_vector_index
+```
+
+If the data you're indexing is the tweets we gave you, you're ready to go:
+
+```bash
+pnpm ts-node 2_load_and_index.ts
+```
+
+What you're doing here is creating a Reader which loads the data out of Mongo in the collection and database specified. It looks for text in a set of specific keys in each object. In this case we've given it just one key, "full_text". The final parameter is a mongo [query document](https://www.mongodb.com/docs/manual/tutorial/query-documents/), a JSON object you can use to filter your objects down to a subset. We're leaving it empty because we want all the tweets in the collection.
+
+Now you're creating a vector search client for Mongo. In addition to a MongoDB client object, you again tell it what database everything is in. This time you give it the name of the collection where you'll store the vector embeddings, and the name of the vector search index you'll create in the next step.
+
+### Create a vector search index
+
+Now if all has gone well you should be able to log in to the Mongo Atlas UI and see two collections in your database: the original data in `tiny_tweets_collection`, and the vector embeddings in `tiny_tweets_vectors`.
+
+
+
+Now it's time to create the vector search index so that you can query the data. First, click the Search tab, and then click "Create Search Index":
+
+
+
+It's not yet possible to create a vector search index using the Visual Editor, so select JSON editor:
+
+
+
+Now under "database and collection" select `tiny_tweets_db` and within that select `tiny_tweets_vectors`. Then under "Index name" enter `tiny_tweets_vector_index` (or whatever value you put for MONGODB_VECTOR_INDEX in `.env`). Under that, you'll want to enter this JSON object:
+
+```json
+{
+ "mappings": {
+ "dynamic": true,
+ "fields": {
+ "embedding": {
+ "dimensions": 1536,
+ "similarity": "cosine",
+ "type": "knnVector"
+ }
+ }
+ }
+}
+```
+
+This tells Mongo that the `embedding` field in each document (in the `tiny_tweets_vectors` collection) is a vector of 1536 dimensions (this is the size of embeddings used by OpenAI), and that we want to use cosine similarity to compare vectors. You don't need to worry too much about these values unless you want to use a different LLM to OpenAI entirely.
+
+The UI will ask you to review and confirm your choices, then you need to wait a minute or two while it generates the index. If all goes well, you should see something like this screen:
+
+
+
+Now you're ready to query your data!
+
+### Run a test query
+
+You can do this by running
+
+```bash
+pnpm ts-node 3_query.ts
+```
+
+This sets up a connection to Atlas just like `2_load_and_index.ts` did, then it creates a [query engine](https://docs.llamaindex.ai/en/stable/understanding/querying/querying.html#getting-started) and runs a query against it.
+
+If all is well, you should get a nuanced opinion about web frameworks.
diff --git a/apps/mongodb/docs/1_signup.png b/apps/mongodb/docs/1_signup.png
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diff --git a/apps/mongodb/docs/7_index_created.png b/apps/mongodb/docs/7_index_created.png
new file mode 100644
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new file mode 100644
index 000000000..5791c3200
--- /dev/null
+++ b/apps/mongodb/package.json
@@ -0,0 +1,17 @@
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+ }
+}
\ No newline at end of file
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+ "created_at": "Wed Jan 03 11:23:51 +0000 2018",
+ "favorited": false,
+ "full_text": "@adamadzp The whole series is \"the dysfunctional Skywalker family fucks up the entire galaxy in their attempts to get laid\".",
+ "lang": "en",
+ "in_reply_to_screen_name": "adamadzp",
+ "in_reply_to_user_id_str": "21006323"
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+ "created_at": "Wed Jan 03 01:49:10 +0000 2018",
+ "favorited": false,
+ "full_text": "RT @jaketapper: \"Every man, woman and child lives under a nuclear sword of Damocles, hanging by the slenderest of threads, capable of being…",
+ "lang": "en"
+ },
+ {
+ "edit_info": {
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+ }
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+ "retweeted": false,
+ "source": "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>",
+ "entities": {
+ "hashtags": [],
+ "symbols": [],
+ "user_mentions": [
+ {
+ "name": "Burrito Justice",
+ "screen_name": "burritojustice",
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+ "created_at": "Wed Jan 03 01:35:18 +0000 2018",
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+ "created_at": "Wed Jan 03 01:35:13 +0000 2018",
+ "favorited": false,
+ "full_text": "@burritojustice It's not subtle.",
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+ "source": "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>",
+ "entities": {
+ "hashtags": [],
+ "symbols": [],
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+ {
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+ "retweet_count": "0",
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+ "in_reply_to_status_id": "948361444180897792",
+ "created_at": "Wed Jan 03 01:15:12 +0000 2018",
+ "favorited": false,
+ "full_text": "@oliviacpu Damn guuuurl",
+ "lang": "tl",
+ "in_reply_to_screen_name": "oliviacpu",
+ "in_reply_to_user_id_str": "861116848686809088"
+ },
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+ "isEditEligible": true
+ }
+ },
+ "retweeted": false,
+ "source": "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>",
+ "entities": {
+ "hashtags": [],
+ "symbols": [],
+ "user_mentions": [
+ {
+ "name": "ðŸ³ï¸â€ðŸŒˆðŸ³ï¸â€âš§ï¸ðŸ‘ŠMarco👊ðŸ³ï¸â€âš§ï¸ðŸ³ï¸â€ðŸŒˆ",
+ "screen_name": "TheMarco",
+ "indices": [
+ "0",
+ "9"
+ ],
+ "id_str": "6875652",
+ "id": "6875652"
+ }
+ ],
+ "urls": []
+ },
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+ ],
+ "favorite_count": "2",
+ "in_reply_to_status_id_str": "948360023347900416",
+ "id_str": "948360380551782400",
+ "in_reply_to_user_id": "6875652",
+ "truncated": false,
+ "retweet_count": "0",
+ "id": "948360380551782400",
+ "in_reply_to_status_id": "948360023347900416",
+ "created_at": "Wed Jan 03 01:08:29 +0000 2018",
+ "favorited": false,
+ "full_text": "@TheMarco I started thinking carefully about non-US concerned parties and voting adults etc and gave up and picked a number.",
+ "lang": "en",
+ "in_reply_to_screen_name": "TheMarco",
+ "in_reply_to_user_id_str": "6875652"
+ },
+ {
+ "edit_info": {
+ "initial": {
+ "editTweetIds": [
+ "948359245786992640"
+ ],
+ "editableUntil": "2018-01-03T02:03:58.515Z",
+ "editsRemaining": "5",
+ "isEditEligible": true
+ }
+ },
+ "retweeted": false,
+ "source": "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>",
+ "entities": {
+ "hashtags": [],
+ "symbols": [],
+ "user_mentions": [],
+ "urls": [
+ {
+ "url": "https://t.co/f1i4S4iUKX",
+ "expanded_url": "https://twitter.com/realdonaldtrump/status/948355557022420992",
+ "display_url": "twitter.com/realdonaldtrum…",
+ "indices": [
+ "82",
+ "105"
+ ]
+ }
+ ]
+ },
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+ "truncated": false,
+ "retweet_count": "9",
+ "id": "948359245786992640",
+ "possibly_sensitive": false,
+ "created_at": "Wed Jan 03 01:03:58 +0000 2018",
+ "favorited": false,
+ "full_text": "That noise you just heard was 300 million people all facepalming at the same time https://t.co/f1i4S4iUKX",
+ "lang": "en"
+ },
+ {
+ "edit_info": {
+ "initial": {
+ "editTweetIds": [
+ "948352227122524161"
+ ],
+ "editableUntil": "2018-01-03T01:36:05.135Z",
+ "editsRemaining": "5",
+ "isEditEligible": true
+ }
+ },
+ "retweeted": false,
+ "source": "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>",
+ "entities": {
+ "hashtags": [],
+ "symbols": [],
+ "user_mentions": [
+ {
+ "name": "Matthew Green",
+ "screen_name": "matthew_d_green",
+ "indices": [
+ "0",
+ "16"
+ ],
+ "id_str": "106234268",
+ "id": "106234268"
+ }
+ ],
+ "urls": []
+ },
+ "display_text_range": [
+ "0",
+ "177"
+ ],
+ "favorite_count": "2",
+ "in_reply_to_status_id_str": "948351505761030149",
+ "id_str": "948352227122524161",
+ "in_reply_to_user_id": "106234268",
+ "truncated": false,
+ "retweet_count": "1",
+ "id": "948352227122524161",
+ "in_reply_to_status_id": "948351505761030149",
+ "created_at": "Wed Jan 03 00:36:05 +0000 2018",
+ "favorited": false,
+ "full_text": "@matthew_d_green There's overhead in each city, and not all rides are profitable. Especially for Uberpool, you need a density of customers to make the thing worth anyone's time.",
+ "lang": "en",
+ "in_reply_to_screen_name": "matthew_d_green",
+ "in_reply_to_user_id_str": "106234268"
+ },
+ {
+ "edit_info": {
+ "initial": {
+ "editTweetIds": [
+ "948341074262667264"
+ ],
+ "editableUntil": "2018-01-03T00:51:46.086Z",
+ "editsRemaining": "5",
+ "isEditEligible": true
+ }
+ },
+ "retweeted": false,
+ "source": "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>",
+ "entities": {
+ "hashtags": [],
+ "symbols": [],
+ "user_mentions": [
+ {
+ "name": "kenneth 🌞",
+ "screen_name": "kpk",
+ "indices": [
+ "0",
+ "4"
+ ],
+ "id_str": "22915745",
+ "id": "22915745"
+ }
+ ],
+ "urls": []
+ },
+ "display_text_range": [
+ "0",
+ "169"
+ ],
+ "favorite_count": "4",
+ "in_reply_to_status_id_str": "948340637333622784",
+ "id_str": "948341074262667264",
+ "in_reply_to_user_id": "22915745",
+ "truncated": false,
+ "retweet_count": "0",
+ "id": "948341074262667264",
+ "in_reply_to_status_id": "948340637333622784",
+ "created_at": "Tue Jan 02 23:51:46 +0000 2018",
+ "favorited": false,
+ "full_text": "@kpk Uber is a very, very poorly run company. I do not think they can pull off something this complicated unless Dara manages to change the fundamental DNA of the place.",
+ "lang": "en",
+ "in_reply_to_screen_name": "kpk",
+ "in_reply_to_user_id_str": "22915745"
+ },
+ {
+ "edit_info": {
+ "initial": {
+ "editTweetIds": [
+ "948338658997923840"
+ ],
+ "editableUntil": "2018-01-03T00:42:10.242Z",
+ "editsRemaining": "5",
+ "isEditEligible": true
+ }
+ },
+ "retweeted": false,
+ "source": "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>",
+ "entities": {
+ "hashtags": [],
+ "symbols": [],
+ "user_mentions": [],
+ "urls": []
+ },
+ "display_text_range": [
+ "0",
+ "240"
+ ],
+ "favorite_count": "4",
+ "in_reply_to_status_id_str": "948337520735150080",
+ "id_str": "948338658997923840",
+ "in_reply_to_user_id": "15453",
+ "truncated": false,
+ "retweet_count": "1",
+ "id": "948338658997923840",
+ "in_reply_to_status_id": "948337520735150080",
+ "created_at": "Tue Jan 02 23:42:10 +0000 2018",
+ "favorited": false,
+ "full_text": "If profitability does require self-driving cars then it's unlikely the high fidelity mapping needed will be profitable outside of, yes, large, dense cities. So the self-driving car outcome is my \"smaller, profitable, lower valuation\" model.",
+ "lang": "en",
+ "in_reply_to_screen_name": "seldo",
+ "in_reply_to_user_id_str": "15453"
+ },
+ {
+ "edit_info": {
+ "initial": {
+ "editTweetIds": [
+ "948337520735150080"
+ ],
+ "editableUntil": "2018-01-03T00:37:38.859Z",
+ "editsRemaining": "5",
+ "isEditEligible": true
+ }
+ },
+ "retweeted": false,
+ "source": "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>",
+ "entities": {
+ "hashtags": [],
+ "symbols": [],
+ "user_mentions": [],
+ "urls": []
+ },
+ "display_text_range": [
+ "0",
+ "273"
+ ],
+ "favorite_count": "10",
+ "in_reply_to_status_id_str": "948337158183628800",
+ "id_str": "948337520735150080",
+ "in_reply_to_user_id": "15453",
+ "truncated": false,
+ "retweet_count": "1",
+ "id": "948337520735150080",
+ "in_reply_to_status_id": "948337158183628800",
+ "created_at": "Tue Jan 02 23:37:38 +0000 2018",
+ "favorited": false,
+ "full_text": "An even more pessimistic model would be that the service can only be profitable if Uber invents self-driving cars. I don't think they know how to do this -- they fired the guy who did and it turns out he stole the tech anyway. Uber is not going to invent self-driving cars.",
+ "lang": "en",
+ "in_reply_to_screen_name": "seldo",
+ "in_reply_to_user_id_str": "15453"
+ },
+ {
+ "edit_info": {
+ "initial": {
+ "editTweetIds": [
+ "948337158183628800"
+ ],
+ "editableUntil": "2018-01-03T00:36:12.420Z",
+ "editsRemaining": "5",
+ "isEditEligible": true
+ }
+ },
+ "retweeted": false,
+ "source": "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>",
+ "entities": {
+ "hashtags": [],
+ "symbols": [],
+ "user_mentions": [],
+ "urls": []
+ },
+ "display_text_range": [
+ "0",
+ "277"
+ ],
+ "favorite_count": "4",
+ "in_reply_to_status_id_str": "948336620138352645",
+ "id_str": "948337158183628800",
+ "in_reply_to_user_id": "15453",
+ "truncated": false,
+ "retweet_count": "0",
+ "id": "948337158183628800",
+ "in_reply_to_status_id": "948336620138352645",
+ "created_at": "Tue Jan 02 23:36:12 +0000 2018",
+ "favorited": false,
+ "full_text": "This model means Uber matures into a real, steady, profitable service available in major cities around the world. It's a good outcome but it doesn't justify their current massive valuation - they better IPO before that reality becomes apparent, and buying at IPO would be dumb.",
+ "lang": "en",
+ "in_reply_to_screen_name": "seldo",
+ "in_reply_to_user_id_str": "15453"
+ }
+]
diff --git a/packages/core/src/Node.ts b/packages/core/src/Node.ts
index 1908e6724..25fa75e1f 100644
--- a/packages/core/src/Node.ts
+++ b/packages/core/src/Node.ts
@@ -272,12 +272,13 @@ export class Document<T extends Metadata = Metadata> extends TextNode<T> {
}
}
-export function jsonToNode(json: any) {
- if (!json.type) {
+export function jsonToNode(json: any, type?: ObjectType) {
+ if (!json.type && !type) {
throw new Error("Node type not found");
}
+ const nodeType = type || json.type;
- switch (json.type) {
+ switch (nodeType) {
case ObjectType.TEXT:
return new TextNode(json);
case ObjectType.INDEX:
@@ -285,7 +286,7 @@ export function jsonToNode(json: any) {
case ObjectType.DOCUMENT:
return new Document(json);
default:
- throw new Error(`Invalid node type: ${json.type}`);
+ throw new Error(`Invalid node type: ${nodeType}`);
}
}
diff --git a/packages/core/src/index.ts b/packages/core/src/index.ts
index 85c078602..20ab46297 100644
--- a/packages/core/src/index.ts
+++ b/packages/core/src/index.ts
@@ -25,5 +25,6 @@ export * from "./readers/MarkdownReader";
export * from "./readers/NotionReader";
export * from "./readers/PDFReader";
export * from "./readers/SimpleDirectoryReader";
+export * from "./readers/SimpleMongoReader";
export * from "./readers/base";
export * from "./storage";
diff --git a/packages/core/src/storage/index.ts b/packages/core/src/storage/index.ts
index b97ba0411..96f8743be 100644
--- a/packages/core/src/storage/index.ts
+++ b/packages/core/src/storage/index.ts
@@ -7,5 +7,6 @@ export { SimpleIndexStore } from "./indexStore/SimpleIndexStore";
export * from "./indexStore/types";
export { SimpleKVStore } from "./kvStore/SimpleKVStore";
export * from "./kvStore/types";
+export { MongoDBAtlasVectorSearch } from "./vectorStore/MongoDBAtlasVectorStore";
export { SimpleVectorStore } from "./vectorStore/SimpleVectorStore";
export * from "./vectorStore/types";
diff --git a/packages/core/src/storage/vectorStore/MongoDBAtlasVectorStore.ts b/packages/core/src/storage/vectorStore/MongoDBAtlasVectorStore.ts
new file mode 100644
index 000000000..6f2ec478b
--- /dev/null
+++ b/packages/core/src/storage/vectorStore/MongoDBAtlasVectorStore.ts
@@ -0,0 +1,164 @@
+import { BulkWriteOptions, Collection, MongoClient } from "mongodb";
+import { BaseNode, MetadataMode } from "../../Node";
+import {
+ MetadataFilters,
+ VectorStore,
+ VectorStoreQuery,
+ VectorStoreQueryResult,
+} from "./types";
+import { metadataDictToNode, nodeToMetadata } from "./utils";
+
+// Utility function to convert metadata filters to MongoDB filter
+function toMongoDBFilter(
+ standardFilters: MetadataFilters,
+): Record<string, any> {
+ const filters: Record<string, any> = {};
+ for (const filter of standardFilters.filters) {
+ filters[filter.key] = filter.value;
+ }
+ return filters;
+}
+
+// MongoDB Atlas Vector Store class implementing VectorStore
+export class MongoDBAtlasVectorSearch implements VectorStore {
+ storesText: boolean = true;
+ flatMetadata: boolean = true;
+
+ mongodbClient: MongoClient;
+ indexName: string;
+ embeddingKey: string;
+ idKey: string;
+ textKey: string;
+ metadataKey: string;
+ insertOptions?: BulkWriteOptions;
+ private collection: Collection;
+
+ constructor(
+ init: Partial<MongoDBAtlasVectorSearch> & {
+ dbName: string;
+ collectionName: string;
+ },
+ ) {
+ if (init.mongodbClient) {
+ this.mongodbClient = init.mongodbClient;
+ } else {
+ const mongoUri = process.env.MONGODB_URI;
+ if (!mongoUri) {
+ throw new Error(
+ "Must specify MONGODB_URI via env variable if not directly passing in client.",
+ );
+ }
+ this.mongodbClient = new MongoClient(mongoUri);
+ }
+
+ this.collection = this.mongodbClient
+ .db(init.dbName ?? "default_db")
+ .collection(init.collectionName ?? "default_collection");
+ this.indexName = init.indexName ?? "default";
+ this.embeddingKey = init.embeddingKey ?? "embedding";
+ this.idKey = init.idKey ?? "id";
+ this.textKey = init.textKey ?? "text";
+ this.metadataKey = init.metadataKey ?? "metadata";
+ this.insertOptions = init.insertOptions;
+ }
+
+ async add(nodes: BaseNode[]): Promise<string[]> {
+ if (!nodes || nodes.length === 0) {
+ return [];
+ }
+ const dataToInsert = nodes.map((node) => {
+ const metadata = nodeToMetadata(
+ node,
+ true,
+ this.textKey,
+ this.flatMetadata,
+ );
+
+ return {
+ [this.idKey]: node.id_,
+ [this.embeddingKey]: node.getEmbedding(),
+ [this.textKey]: node.getContent(MetadataMode.NONE) || "",
+ [this.metadataKey]: metadata,
+ };
+ });
+
+ console.debug("Inserting data into MongoDB: ", dataToInsert);
+ const insertResult = await this.collection.insertMany(
+ dataToInsert,
+ this.insertOptions,
+ );
+ console.debug("Result of insert: ", insertResult);
+ return nodes.map((node) => node.id_);
+ }
+
+ async delete(refDocId: string, deleteOptions?: any): Promise<void> {
+ await this.collection.deleteOne(
+ {
+ [`${this.metadataKey}.ref_doc_id`]: refDocId,
+ },
+ deleteOptions,
+ );
+ }
+
+ get client(): any {
+ return this.mongodbClient;
+ }
+
+ async query(
+ query: VectorStoreQuery,
+ options?: any,
+ ): Promise<VectorStoreQueryResult> {
+ const params: any = {
+ queryVector: query.queryEmbedding,
+ path: this.embeddingKey,
+ numCandidates: query.similarityTopK * 10,
+ limit: query.similarityTopK,
+ index: this.indexName,
+ };
+
+ if (query.filters) {
+ params.filter = toMongoDBFilter(query.filters);
+ }
+
+ const queryField = { $vectorSearch: params };
+ const pipeline = [
+ queryField,
+ {
+ $project: {
+ score: { $meta: "vectorSearchScore" },
+ [this.embeddingKey]: 0,
+ },
+ },
+ ];
+
+ console.debug("Running query pipeline: ", pipeline);
+ const cursor = await this.collection.aggregate(pipeline);
+
+ const nodes: BaseNode[] = [];
+ const ids: string[] = [];
+ const similarities: number[] = [];
+
+ for await (const res of await cursor) {
+ const text = res[this.textKey];
+ const score = res.score;
+ const id = res[this.idKey];
+ const metadata = res[this.metadataKey];
+
+ const node = metadataDictToNode(metadata);
+ node.setContent(text);
+
+ ids.push(id);
+ nodes.push(node);
+ similarities.push(score);
+ }
+
+ const result = {
+ nodes,
+ similarities,
+ ids,
+ };
+
+ console.debug("Result of query (ids):", ids);
+ return result;
+ }
+}
diff --git a/packages/core/src/storage/vectorStore/types.ts b/packages/core/src/storage/vectorStore/types.ts
index cf98afbbb..be4bb0968 100644
--- a/packages/core/src/storage/vectorStore/types.ts
+++ b/packages/core/src/storage/vectorStore/types.ts
@@ -1,5 +1,4 @@
import { BaseNode } from "../../Node";
-import { GenericFileSystem } from "../FileSystem";
export interface VectorStoreQueryResult {
nodes?: BaseNode[];
@@ -62,10 +61,9 @@ export interface VectorStore {
isEmbeddingQuery?: boolean;
client(): any;
add(embeddingResults: BaseNode[]): Promise<string[]>;
- delete(refDocId: string, deleteKwargs?: any): Promise<void>;
+ delete(refDocId: string, deleteOptions?: any): Promise<void>;
query(
query: VectorStoreQuery,
options?: any,
): Promise<VectorStoreQueryResult>;
- persist(persistPath: string, fs?: GenericFileSystem): Promise<void>;
}
diff --git a/packages/core/src/storage/vectorStore/utils.ts b/packages/core/src/storage/vectorStore/utils.ts
new file mode 100644
index 000000000..ab31b8cc3
--- /dev/null
+++ b/packages/core/src/storage/vectorStore/utils.ts
@@ -0,0 +1,64 @@
+import {
+ BaseNode,
+ IndexNode,
+ Metadata,
+ ObjectType,
+ jsonToNode,
+} from "../../Node";
+
+const DEFAULT_TEXT_KEY = "text";
+
+export function validateIsFlat(obj: { [key: string]: any }): void {
+ for (let key in obj) {
+ if (typeof obj[key] === "object" && obj[key] !== null) {
+ throw new Error(`Value for metadata ${key} must not be another object`);
+ }
+ }
+}
+
+export function nodeToMetadata(
+ node: BaseNode,
+ removeText: boolean = false,
+ textField: string = DEFAULT_TEXT_KEY,
+ flatMetadata: boolean = false,
+): Metadata {
+ const nodeObj = node.toJSON();
+ const metadata = node.metadata;
+
+ if (flatMetadata) {
+ validateIsFlat(node.metadata);
+ }
+
+ if (removeText) {
+ nodeObj[textField] = "";
+ }
+
+ nodeObj["embedding"] = null;
+
+ metadata["_node_content"] = JSON.stringify(nodeObj);
+ metadata["_node_type"] = node.constructor.name;
+
+ metadata["document_id"] = node.sourceNode?.nodeId || "None";
+ metadata["doc_id"] = node.sourceNode?.nodeId || "None";
+ metadata["ref_doc_id"] = node.sourceNode?.nodeId || "None";
+
+ return metadata;
+}
+
+export function metadataDictToNode(metadata: Metadata): BaseNode {
+ const nodeObj = metadata["_node_content"];
+ if (!nodeObj) {
+ throw new Error("Node content not found in metadata.");
+ }
+
+ // Note: we're using the name of the class stored in `_node_type`
+ // and not the type attribute to reconstruct
+ // the node. This way we're compatible with LlamaIndex Python
+ const node_type = metadata["_node_type"];
+ switch (node_type) {
+ case IndexNode.name:
+ return jsonToNode(nodeObj, ObjectType.INDEX);
+ default:
+ return jsonToNode(nodeObj, ObjectType.TEXT);
+ }
+}
diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml
index 50f0fdef5..47d34a3a5 100644
--- a/pnpm-lock.yaml
+++ b/pnpm-lock.yaml
@@ -17,7 +17,7 @@ importers:
version: 2.26.2
'@turbo/gen':
specifier: ^1.10.16
- version: 1.10.16(@types/node@20.9.0)(typescript@5.2.2)
+ version: 1.10.16(@types/node@18.18.8)(typescript@5.2.2)
'@types/jest':
specifier: ^29.5.8
version: 29.5.8
@@ -32,7 +32,7 @@ importers:
version: 8.0.3
jest:
specifier: ^29.7.0
- version: 29.7.0(@types/node@20.9.0)
+ version: 29.7.0(@types/node@18.18.8)
lint-staged:
specifier: ^15.1.0
version: 15.1.0
@@ -104,6 +104,25 @@ importers:
specifier: ^4.9.5
version: 4.9.5
+ apps/mongodb:
+ dependencies:
+ dotenv:
+ specifier: ^16.3.1
+ version: 16.3.1
+ llamaindex:
+ specifier: workspace:*
+ version: link:../../packages/core
+ mongodb:
+ specifier: ^6.2.0
+ version: 6.2.0
+ devDependencies:
+ '@types/node':
+ specifier: ^18.18.6
+ version: 18.18.8
+ ts-node:
+ specifier: ^10.9.1
+ version: 10.9.1(@types/node@18.18.8)(typescript@5.2.2)
+
apps/simple:
dependencies:
'@notionhq/client':
@@ -3366,14 +3385,14 @@ packages:
'@jest/test-result': 29.7.0
'@jest/transform': 29.7.0
'@jest/types': 29.6.3
- '@types/node': 20.9.0
+ '@types/node': 18.18.8
ansi-escapes: 4.3.2
chalk: 4.1.2
ci-info: 3.9.0
exit: 0.1.2
graceful-fs: 4.2.11
jest-changed-files: 29.7.0
- jest-config: 29.7.0(@types/node@20.9.0)
+ jest-config: 29.7.0(@types/node@18.18.8)
jest-haste-map: 29.7.0
jest-message-util: 29.7.0
jest-regex-util: 29.6.3
@@ -3548,7 +3567,7 @@ packages:
'@jest/schemas': 29.6.3
'@types/istanbul-lib-coverage': 2.0.6
'@types/istanbul-reports': 3.0.4
- '@types/node': 20.9.0
+ '@types/node': 18.18.8
'@types/yargs': 17.0.31
chalk: 4.1.2
@@ -3572,7 +3591,7 @@ packages:
resolution: {integrity: sha512-UTYAUj/wviwdsMfzoSJspJxbkH5o1snzwX0//0ENX1u/55kkZZkcTZP6u9bwKGkv+dkk9at4m1Cpt0uY80kcpQ==}
dependencies:
'@jridgewell/gen-mapping': 0.3.3
- '@jridgewell/trace-mapping': 0.3.19
+ '@jridgewell/trace-mapping': 0.3.20
/@jridgewell/sourcemap-codec@1.4.15:
resolution: {integrity: sha512-eF2rxCRulEKXHTRiDrDy6erMYWqNw4LPdQ8UQA4huuxaQsVeRPFl2oM8oDGxMFhJUWZf9McpLtJasDDZb/Bpeg==}
@@ -3789,7 +3808,7 @@ packages:
dependencies:
'@edge-runtime/types': 2.2.4
'@sinclair/typebox': 0.29.6
- '@types/node': 18.18.7
+ '@types/node': 18.18.8
ajv: 8.12.0
cross-fetch: 3.1.8(encoding@0.1.13)
encoding: 0.1.13
@@ -4067,7 +4086,7 @@ packages:
resolution: {integrity: sha512-vxhUy4J8lyeyinH7Azl1pdd43GJhZH/tP2weN8TntQblOY+A0XbT8DJk1/oCPuOOyg/Ja757rG0CgHcWC8OfMA==}
dev: true
- /@turbo/gen@1.10.16(@types/node@20.9.0)(typescript@5.2.2):
+ /@turbo/gen@1.10.16(@types/node@18.18.8)(typescript@5.2.2):
resolution: {integrity: sha512-PzyluADjVuy5OcIi+/aRcD70OElQpRVRDdfZ9fH8G5Fv75lQcNrjd1bBGKmhjSw+g+eTEkXMGnY7s6gsCYjYTQ==}
hasBin: true
dependencies:
@@ -4079,7 +4098,7 @@ packages:
minimatch: 9.0.3
node-plop: 0.26.3
proxy-agent: 6.3.1
- ts-node: 10.9.1(@types/node@20.9.0)(typescript@5.2.2)
+ ts-node: 10.9.1(@types/node@18.18.8)(typescript@5.2.2)
update-check: 1.5.4
validate-npm-package-name: 5.0.0
transitivePeerDependencies:
@@ -4185,7 +4204,7 @@ packages:
/@types/cross-spawn@6.0.0:
resolution: {integrity: sha512-evp2ZGsFw9YKprDbg8ySgC9NA15g3YgiI8ANkGmKKvvi0P2aDGYLPxQIC5qfeKNUOe3TjABVGuah6omPRpIYhg==}
dependencies:
- '@types/node': 20.9.0
+ '@types/node': 18.18.8
dev: true
/@types/eslint-scope@3.7.5:
@@ -4362,7 +4381,7 @@ packages:
/@types/node-fetch@2.6.6:
resolution: {integrity: sha512-95X8guJYhfqiuVVhRFxVQcf4hW/2bCuoPwDasMf/531STFoNoWTT7YDnWdXHEZKqAGUigmpG31r2FE70LwnzJw==}
dependencies:
- '@types/node': 18.18.7
+ '@types/node': 18.18.8
form-data: 4.0.0
dev: false
@@ -4392,6 +4411,7 @@ packages:
resolution: {integrity: sha512-bw+lEsxis6eqJYW8Ql6+yTqkE6RuFtsQPSe5JxXbqYRFQEER5aJA9a5UH9igqDWm3X4iLHIKOHlnAXLM4mi7uQ==}
dependencies:
undici-types: 5.26.5
+ dev: true
/@types/node@18.18.8:
resolution: {integrity: sha512-OLGBaaK5V3VRBS1bAkMVP2/W9B+H8meUfl866OrMNQqt7wDgdpWPp5o6gmIc9pB+lIQHSq4ZL8ypeH1vPxcPaQ==}
@@ -4402,6 +4422,7 @@ packages:
resolution: {integrity: sha512-nekiGu2NDb1BcVofVcEKMIwzlx4NjHlcjhoxxKBNLtz15Y1z7MYf549DFvkHSId02Ax6kGwWntIBPC3l/JZcmw==}
dependencies:
undici-types: 5.26.5
+ dev: true
/@types/normalize-package-data@2.4.4:
resolution: {integrity: sha512-37i+OaWTh9qeK4LSHPsyRC7NahnGotNuZvjLSgcPzblpHB3rrCJxAOgI5gCdKm7coonsaX1Of0ILiTcnZjbfxA==}
@@ -4527,7 +4548,7 @@ packages:
/@types/tar@6.1.5:
resolution: {integrity: sha512-qm2I/RlZij5RofuY7vohTpYNaYcrSQlN2MyjucQc7ZweDwaEWkdN/EeNh6e9zjK6uEm6PwjdMXkcj05BxZdX1Q==}
dependencies:
- '@types/node': 20.9.0
+ '@types/node': 18.18.8
minipass: 4.2.8
dev: true
@@ -4790,6 +4811,7 @@ packages:
/acorn-walk@8.2.0:
resolution: {integrity: sha512-k+iyHEuPgSw6SbuDpGQM+06HQUa04DZ3o+F6CSzXMvvI5KMvnaEqXe+YVe555R9nn6GPt404fos4wcgpw12SDA==}
engines: {node: '>=0.4.0'}
+ dev: false
/acorn-walk@8.3.0:
resolution: {integrity: sha512-FS7hV565M5l1R08MXqo8odwMTB02C2UqzB17RVgu9EyuYFBqJZ3/ZY97sQD5FewVu1UyDFc1yztUDrAwT0EypA==}
@@ -6334,7 +6356,7 @@ packages:
sha.js: 2.4.11
dev: true
- /create-jest@29.7.0(@types/node@20.9.0):
+ /create-jest@29.7.0(@types/node@18.18.8):
resolution: {integrity: sha512-Adz2bdH0Vq3F53KEMJOoftQFutWCukm6J24wbPWRO4k1kMY7gS7ds/uoJkNuV8wDCtWWnuwGcJwpWcih+zEW1Q==}
engines: {node: ^14.15.0 || ^16.10.0 || >=18.0.0}
hasBin: true
@@ -6343,7 +6365,7 @@ packages:
chalk: 4.1.2
exit: 0.1.2
graceful-fs: 4.2.11
- jest-config: 29.7.0(@types/node@20.9.0)
+ jest-config: 29.7.0(@types/node@18.18.8)
jest-util: 29.7.0
prompts: 2.4.2
transitivePeerDependencies:
@@ -7074,6 +7096,11 @@ packages:
is-obj: 2.0.0
dev: true
+ /dotenv@16.3.1:
+ resolution: {integrity: sha512-IPzF4w4/Rd94bA9imS68tZBaYyBWSCE47V1RGuMrB94iyTOIEwRmVL2x/4An+6mETpLrKJ5hQkB8W4kFAadeIQ==}
+ engines: {node: '>=12'}
+ dev: false
+
/duck@0.1.12:
resolution: {integrity: sha512-wkctla1O6VfP89gQ+J/yDesM0S7B7XLXjKGzXxMDVFg7uEn706niAtyYovKbyq1oT9YwDcly721/iUWoc8MVRg==}
dependencies:
@@ -9649,7 +9676,7 @@ packages:
- supports-color
dev: true
- /jest-cli@29.7.0(@types/node@20.9.0):
+ /jest-cli@29.7.0(@types/node@18.18.8):
resolution: {integrity: sha512-OVVobw2IubN/GSYsxETi+gOe7Ka59EFMR/twOU3Jb2GnKKeMGJB5SGUUrEz3SFVmJASUdZUzy83sLNNQ2gZslg==}
engines: {node: ^14.15.0 || ^16.10.0 || >=18.0.0}
hasBin: true
@@ -9663,10 +9690,10 @@ packages:
'@jest/test-result': 29.7.0
'@jest/types': 29.6.3
chalk: 4.1.2
- create-jest: 29.7.0(@types/node@20.9.0)
+ create-jest: 29.7.0(@types/node@18.18.8)
exit: 0.1.2
import-local: 3.1.0
- jest-config: 29.7.0(@types/node@20.9.0)
+ jest-config: 29.7.0(@types/node@18.18.8)
jest-util: 29.7.0
jest-validate: 29.7.0
yargs: 17.7.2
@@ -9677,7 +9704,7 @@ packages:
- ts-node
dev: true
- /jest-config@29.7.0(@types/node@20.9.0):
+ /jest-config@29.7.0(@types/node@18.18.8):
resolution: {integrity: sha512-uXbpfeQ7R6TZBqI3/TxCU4q4ttk3u0PJeC+E0zbfSoSjq6bJ7buBPxzQPL0ifrkY4DNu4JUdk0ImlBUYi840eQ==}
engines: {node: ^14.15.0 || ^16.10.0 || >=18.0.0}
peerDependencies:
@@ -9692,7 +9719,7 @@ packages:
'@babel/core': 7.23.3
'@jest/test-sequencer': 29.7.0
'@jest/types': 29.6.3
- '@types/node': 20.9.0
+ '@types/node': 18.18.8
babel-jest: 29.7.0(@babel/core@7.23.3)
chalk: 4.1.2
ci-info: 3.9.0
@@ -9957,7 +9984,7 @@ packages:
engines: {node: ^14.15.0 || ^16.10.0 || >=18.0.0}
dependencies:
'@jest/types': 29.6.3
- '@types/node': 20.9.0
+ '@types/node': 18.18.8
chalk: 4.1.2
ci-info: 3.9.0
graceful-fs: 4.2.11
@@ -10006,7 +10033,7 @@ packages:
merge-stream: 2.0.0
supports-color: 8.1.1
- /jest@29.7.0(@types/node@20.9.0):
+ /jest@29.7.0(@types/node@18.18.8):
resolution: {integrity: sha512-NIy3oAFp9shda19hy4HK0HRTWKtPJmGdnvywu01nOqNC2vZg+Z+fvJDxpMQA88eb2I9EcafcdjYgsDthnYTvGw==}
engines: {node: ^14.15.0 || ^16.10.0 || >=18.0.0}
hasBin: true
@@ -10019,7 +10046,7 @@ packages:
'@jest/core': 29.7.0
'@jest/types': 29.6.3
import-local: 3.1.0
- jest-cli: 29.7.0(@types/node@20.9.0)
+ jest-cli: 29.7.0(@types/node@18.18.8)
transitivePeerDependencies:
- '@types/node'
- babel-plugin-macros
@@ -13989,7 +14016,7 @@ packages:
hasBin: true
dependencies:
'@jridgewell/source-map': 0.3.5
- acorn: 8.10.0
+ acorn: 8.11.2
commander: 2.20.0
source-map-support: 0.5.21
@@ -14171,7 +14198,7 @@ packages:
'@babel/core': 7.23.3
bs-logger: 0.2.6
fast-json-stable-stringify: 2.1.0
- jest: 29.7.0(@types/node@20.9.0)
+ jest: 29.7.0(@types/node@18.18.8)
jest-util: 29.7.0
json5: 2.2.3
lodash.memoize: 4.1.2
@@ -14201,8 +14228,8 @@ packages:
'@tsconfig/node14': 1.0.3
'@tsconfig/node16': 1.0.4
'@types/node': 18.18.7
- acorn: 8.10.0
- acorn-walk: 8.2.0
+ acorn: 8.11.2
+ acorn-walk: 8.3.0
arg: 4.1.3
create-require: 1.1.1
diff: 4.0.2
@@ -14212,7 +14239,7 @@ packages:
yn: 3.1.1
dev: true
- /ts-node@10.9.1(@types/node@20.9.0)(typescript@5.2.2):
+ /ts-node@10.9.1(@types/node@18.18.8)(typescript@5.2.2):
resolution: {integrity: sha512-NtVysVPkxxrwFGUUxGYhfux8k78pQB3JqYBXlLRZgdGUqTO5wU/UyHop5p70iEbGhB7q5KmiZiU0Y3KlJrScEw==}
hasBin: true
peerDependencies:
@@ -14231,7 +14258,7 @@ packages:
'@tsconfig/node12': 1.0.11
'@tsconfig/node14': 1.0.3
'@tsconfig/node16': 1.0.4
- '@types/node': 20.9.0
+ '@types/node': 18.18.8
acorn: 8.11.2
acorn-walk: 8.3.0
arg: 4.1.3
--
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