272 lines
10 KiB
JavaScript
272 lines
10 KiB
JavaScript
"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.MemoryVectorStore = void 0;
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const vectorstores_1 = require("@langchain/core/vectorstores");
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const documents_1 = require("@langchain/core/documents");
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const similarities_js_1 = require("../util/ml-distance/similarities.cjs");
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const math_js_1 = require("../util/math.cjs");
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/**
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* In-memory, ephemeral vector store.
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*
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* Setup:
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* Install `langchain`:
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*
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* ```bash
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* npm install langchain
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* ```
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*
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* ## [Constructor args](https://api.js.langchain.com/classes/langchain.vectorstores_memory.MemoryVectorStore.html#constructor)
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*
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* <details open>
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* <summary><strong>Instantiate</strong></summary>
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*
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* ```typescript
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* import { MemoryVectorStore } from 'langchain/vectorstores/memory';
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* // Or other embeddings
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* import { OpenAIEmbeddings } from '@langchain/openai';
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*
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* const embeddings = new OpenAIEmbeddings({
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* model: "text-embedding-3-small",
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* });
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*
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* const vectorStore = new MemoryVectorStore(embeddings);
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Add documents</strong></summary>
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*
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* ```typescript
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* import type { Document } from '@langchain/core/documents';
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*
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* const document1 = { pageContent: "foo", metadata: { baz: "bar" } };
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* const document2 = { pageContent: "thud", metadata: { bar: "baz" } };
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* const document3 = { pageContent: "i will be deleted :(", metadata: {} };
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*
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* const documents: Document[] = [document1, document2, document3];
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*
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* await vectorStore.addDocuments(documents);
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Similarity search</strong></summary>
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*
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* ```typescript
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* const results = await vectorStore.similaritySearch("thud", 1);
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* for (const doc of results) {
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* console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
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* }
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* // Output: * thud [{"baz":"bar"}]
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* ```
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* </details>
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*
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* <br />
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*
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*
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* <details>
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* <summary><strong>Similarity search with filter</strong></summary>
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*
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* ```typescript
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* const resultsWithFilter = await vectorStore.similaritySearch("thud", 1, { baz: "bar" });
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*
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* for (const doc of resultsWithFilter) {
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* console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
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* }
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* // Output: * foo [{"baz":"bar"}]
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* ```
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* </details>
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*
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* <br />
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*
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*
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* <details>
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* <summary><strong>Similarity search with score</strong></summary>
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*
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* ```typescript
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* const resultsWithScore = await vectorStore.similaritySearchWithScore("qux", 1);
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* for (const [doc, score] of resultsWithScore) {
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* console.log(`* [SIM=${score.toFixed(6)}] ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
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* }
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* // Output: * [SIM=0.000000] qux [{"bar":"baz","baz":"bar"}]
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>As a retriever</strong></summary>
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*
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* ```typescript
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* const retriever = vectorStore.asRetriever({
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* searchType: "mmr", // Leave blank for standard similarity search
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* k: 1,
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* });
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* const resultAsRetriever = await retriever.invoke("thud");
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* console.log(resultAsRetriever);
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*
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* // Output: [Document({ metadata: { "baz":"bar" }, pageContent: "thud" })]
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* ```
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* </details>
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*
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* <br />
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*/
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class MemoryVectorStore extends vectorstores_1.VectorStore {
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_vectorstoreType() {
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return "memory";
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}
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constructor(embeddings, { similarity, ...rest } = {}) {
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super(embeddings, rest);
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Object.defineProperty(this, "memoryVectors", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: []
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});
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Object.defineProperty(this, "similarity", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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this.similarity = similarity ?? similarities_js_1.cosine;
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}
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/**
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* Method to add documents to the memory vector store. It extracts the
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* text from each document, generates embeddings for them, and adds the
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* resulting vectors to the store.
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* @param documents Array of `Document` instances to be added to the store.
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* @returns Promise that resolves when all documents have been added.
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*/
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async addDocuments(documents) {
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const texts = documents.map(({ pageContent }) => pageContent);
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return this.addVectors(await this.embeddings.embedDocuments(texts), documents);
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}
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/**
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* Method to add vectors to the memory vector store. It creates
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* `MemoryVector` instances for each vector and document pair and adds
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* them to the store.
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* @param vectors Array of vectors to be added to the store.
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* @param documents Array of `Document` instances corresponding to the vectors.
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* @returns Promise that resolves when all vectors have been added.
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*/
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async addVectors(vectors, documents) {
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const memoryVectors = vectors.map((embedding, idx) => ({
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content: documents[idx].pageContent,
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embedding,
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metadata: documents[idx].metadata,
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id: documents[idx].id,
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}));
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this.memoryVectors = this.memoryVectors.concat(memoryVectors);
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}
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async _queryVectors(query, k, filter) {
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const filterFunction = (memoryVector) => {
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if (!filter) {
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return true;
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}
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const doc = new documents_1.Document({
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metadata: memoryVector.metadata,
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pageContent: memoryVector.content,
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id: memoryVector.id,
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});
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return filter(doc);
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};
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const filteredMemoryVectors = this.memoryVectors.filter(filterFunction);
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return filteredMemoryVectors
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.map((vector, index) => ({
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similarity: this.similarity(query, vector.embedding),
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index,
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metadata: vector.metadata,
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content: vector.content,
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embedding: vector.embedding,
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id: vector.id,
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}))
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.sort((a, b) => (a.similarity > b.similarity ? -1 : 0))
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.slice(0, k);
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}
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/**
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* Method to perform a similarity search in the memory vector store. It
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* calculates the similarity between the query vector and each vector in
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* the store, sorts the results by similarity, and returns the top `k`
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* results along with their scores.
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* @param query Query vector to compare against the vectors in the store.
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* @param k Number of top results to return.
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* @param filter Optional filter function to apply to the vectors before performing the search.
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* @returns Promise that resolves with an array of tuples, each containing a `Document` and its similarity score.
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*/
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async similaritySearchVectorWithScore(query, k, filter) {
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const searches = await this._queryVectors(query, k, filter);
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const result = searches.map((search) => [
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new documents_1.Document({
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metadata: search.metadata,
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pageContent: search.content,
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id: search.id,
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}),
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search.similarity,
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]);
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return result;
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}
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async maxMarginalRelevanceSearch(query, options) {
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const queryEmbedding = await this.embeddings.embedQuery(query);
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const searches = await this._queryVectors(queryEmbedding, options.fetchK ?? 20, options.filter);
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const embeddingList = searches.map((searchResp) => searchResp.embedding);
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const mmrIndexes = (0, math_js_1.maximalMarginalRelevance)(queryEmbedding, embeddingList, options.lambda, options.k);
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return mmrIndexes.map((idx) => new documents_1.Document({
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metadata: searches[idx].metadata,
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pageContent: searches[idx].content,
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id: searches[idx].id,
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}));
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}
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/**
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* Static method to create a `MemoryVectorStore` instance from an array of
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* texts. It creates a `Document` for each text and metadata pair, and
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* adds them to the store.
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* @param texts Array of texts to be added to the store.
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* @param metadatas Array or single object of metadata corresponding to the texts.
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* @param embeddings `Embeddings` instance used to generate embeddings for the texts.
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* @param dbConfig Optional `MemoryVectorStoreArgs` to configure the `MemoryVectorStore` instance.
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* @returns Promise that resolves with a new `MemoryVectorStore` instance.
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*/
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static async fromTexts(texts, metadatas, embeddings, dbConfig) {
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const docs = [];
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for (let i = 0; i < texts.length; i += 1) {
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const metadata = Array.isArray(metadatas) ? metadatas[i] : metadatas;
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const newDoc = new documents_1.Document({
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pageContent: texts[i],
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metadata,
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});
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docs.push(newDoc);
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}
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return MemoryVectorStore.fromDocuments(docs, embeddings, dbConfig);
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}
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/**
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* Static method to create a `MemoryVectorStore` instance from an array of
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* `Document` instances. It adds the documents to the store.
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* @param docs Array of `Document` instances to be added to the store.
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* @param embeddings `Embeddings` instance used to generate embeddings for the documents.
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* @param dbConfig Optional `MemoryVectorStoreArgs` to configure the `MemoryVectorStore` instance.
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* @returns Promise that resolves with a new `MemoryVectorStore` instance.
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*/
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static async fromDocuments(docs, embeddings, dbConfig) {
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const instance = new this(embeddings, dbConfig);
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await instance.addDocuments(docs);
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return instance;
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}
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/**
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* Static method to create a `MemoryVectorStore` instance from an existing
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* index. It creates a new `MemoryVectorStore` instance without adding any
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* documents or vectors.
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* @param embeddings `Embeddings` instance used to generate embeddings for the documents.
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* @param dbConfig Optional `MemoryVectorStoreArgs` to configure the `MemoryVectorStore` instance.
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* @returns Promise that resolves with a new `MemoryVectorStore` instance.
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*/
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static async fromExistingIndex(embeddings, dbConfig) {
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const instance = new this(embeddings, dbConfig);
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return instance;
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}
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}
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exports.MemoryVectorStore = MemoryVectorStore;
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