@langchain/qdrant - npm Package Compare versions

Comparing version 0.0.4 to 0.0.5

dist/vectorstores.d.ts

 import { QdrantClient } from "@qdrant/js-client-rest";
 import type { Schemas as QdrantSchemas } from "@qdrant/js-client-rest";
 import type { EmbeddingsInterface } from "@langchain/core/embeddings";
-import { VectorStore } from "@langchain/core/vectorstores";
+import { type MaxMarginalRelevanceSearchOptions, VectorStore } from "@langchain/core/vectorstores";
 import { Document } from "@langchain/core/documents";
@@ -75,2 +75,17 @@ /**
     /**
+     * Return documents selected using the maximal marginal relevance.
+     * Maximal marginal relevance optimizes for similarity to the query AND diversity
+     * among selected documents.
+     *
+     * @param {string} query - Text to look up documents similar to.
+     * @param {number} options.k - Number of documents to return.
+     * @param {number} options.fetchK - Number of documents to fetch before passing to the MMR algorithm. Defaults to 20.
+     * @param {number} options.lambda - Number between 0 and 1 that determines the degree of diversity among the results,
+     *                 where 0 corresponds to maximum diversity and 1 to minimum diversity.
+     * @param {this["FilterType"]} options.filter - Optional filter to apply to the search results.
+     *
+     * @returns {Promise<Document[]>} - List of documents selected by maximal marginal relevance.
+     */
+    maxMarginalRelevanceSearch(query: string, options: MaxMarginalRelevanceSearchOptions<this["FilterType"]>): Promise<Document[]>;
+    /**
      * Method to ensure the existence of a collection in the Qdrant database.
@@ -77,0 +92,0 @@ * If the collection does not exist, it is created.

dist/vectorstores.js

 import { QdrantClient } from "@qdrant/js-client-rest";
 import { v4 as uuid } from "uuid";
-import { VectorStore } from "@langchain/core/vectorstores";
+import { VectorStore, } from "@langchain/core/vectorstores";
 import { Document } from "@langchain/core/documents";
 import { getEnvironmentVariable } from "@langchain/core/utils/env";
+import { maximalMarginalRelevance } from "@langchain/core/utils/math";
 const CONTENT_KEY = "content";
@@ -137,2 +138,4 @@ const METADATA_KEY = "metadata";
             filter,
+            with_payload: [this.metadataPayloadKey, this.contentPayloadKey],
+            with_vector: false,
         });
@@ -150,2 +153,39 @@ const result = results.map((res) => [
     /**
+     * Return documents selected using the maximal marginal relevance.
+     * Maximal marginal relevance optimizes for similarity to the query AND diversity
+     * among selected documents.
+     *
+     * @param {string} query - Text to look up documents similar to.
+     * @param {number} options.k - Number of documents to return.
+     * @param {number} options.fetchK - Number of documents to fetch before passing to the MMR algorithm. Defaults to 20.
+     * @param {number} options.lambda - Number between 0 and 1 that determines the degree of diversity among the results,
+     *                 where 0 corresponds to maximum diversity and 1 to minimum diversity.
+     * @param {this["FilterType"]} options.filter - Optional filter to apply to the search results.
+     *
+     * @returns {Promise<Document[]>} - List of documents selected by maximal marginal relevance.
+     */
+    async maxMarginalRelevanceSearch(query, options) {
+        if (!query) {
+            return [];
+        }
+        const queryEmbedding = await this.embeddings.embedQuery(query);
+        await this.ensureCollection();
+        const results = await this.client.search(this.collectionName, {
+            vector: queryEmbedding,
+            limit: options?.fetchK ?? 20,
+            filter: options?.filter,
+            with_payload: [this.metadataPayloadKey, this.contentPayloadKey],
+            with_vector: true,
+        });
+        const embeddingList = results.map((res) => res.vector);
+        const mmrIndexes = maximalMarginalRelevance(queryEmbedding, embeddingList, options?.lambda, options.k);
+        const topMmrMatches = mmrIndexes.map((idx) => results[idx]);
+        const result = topMmrMatches.map((res) => new Document({
+            // eslint-disable-next-line @typescript-eslint/no-explicit-any
+            metadata: res.payload[this.metadataPayloadKey],
+            pageContent: res.payload[this.contentPayloadKey],
+        }));
+        return result;
+    }
+    /**
      * Method to ensure the existence of a collection in the Qdrant database.
@@ -152,0 +192,0 @@ * If the collection does not exist, it is created.

package.json

 {
   "name": "@langchain/qdrant",
-  "version": "0.0.4",
+  "version": "0.0.5",
   "description": "LangChain.js integration for the Qdrant vector database",
@@ -17,3 +17,4 @@ "type": "module",
   "scripts": {
-    "build": "yarn clean && yarn build:esm && yarn build:cjs && yarn build:scripts",
+    "build": "yarn turbo:command build:internal --filter=@langchain/qdrant",
+    "build:internal": "yarn lc-build:v2 --create-entrypoints --pre --tree-shaking",
     "build:esm": "NODE_OPTIONS=--max-old-space-size=4096 tsc --outDir dist/ && rm -rf dist/tests dist/**/tests",
@@ -27,3 +28,3 @@ "build:cjs": "NODE_OPTIONS=--max-old-space-size=4096 tsc --outDir dist-cjs/ -p tsconfig.cjs.json && yarn move-cjs-to-dist && rm -rf dist-cjs",
     "lint:fix": "yarn lint:eslint --fix && yarn lint:dpdm",
-    "clean": "rm -rf dist/ && NODE_OPTIONS=--max-old-space-size=4096 yarn lc-build --config ./langchain.config.js --create-entrypoints --pre",
+    "clean": "rm -rf .turbo dist/",
     "prepack": "yarn build",
@@ -43,3 +44,3 @@ "test": "NODE_OPTIONS=--experimental-vm-modules jest --testPathIgnorePatterns=\\.int\\.test.ts --testTimeout 30000 --maxWorkers=50%",
   "dependencies": {
-    "@langchain/core": ">0.1.0  <0.3.0",
+    "@langchain/core": ">0.1.0 <0.3.0",
     "@qdrant/js-client-rest": "^1.9.0",
@@ -51,3 +52,3 @@ "uuid": "^9.0.1"
     "@jest/globals": "^29.5.0",
-    "@langchain/scripts": "~0.0",
+    "@langchain/scripts": "~0.0.14",
     "@swc/core": "^1.3.90",
@@ -54,0 +55,0 @@ "@swc/jest": "^0.2.29",

New alerts

New author

Supply chain risk

A new npm collaborator published a version of the package for the first time. New collaborators are usually benign additions to a project, but do indicate a change to the security surface area of a package.

Found 1 instance in 1 package

Improved metrics

Total package byte prevSize

37911

increased by16.37%

Lines of code

668

increased by16.58%

Worsened metrics

Number of medium supply chain risk alerts

1

increased byInfinity%

Dependency changes

• Updated@langchain/core@>0.1.0 <0.3.0