252 lines
9.4 KiB
TypeScript
252 lines
9.4 KiB
TypeScript
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import type { ChromaClient as ChromaClientT, Collection, ChromaClientParams, CollectionMetadata, Where } from "chromadb";
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import type { EmbeddingsInterface } from "@langchain/core/embeddings";
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import { VectorStore } from "@langchain/core/vectorstores";
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import { Document } from "@langchain/core/documents";
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type SharedChromaLibArgs = {
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numDimensions?: number;
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collectionName?: string;
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filter?: object;
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collectionMetadata?: CollectionMetadata;
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clientParams?: Omit<ChromaClientParams, "path">;
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};
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/**
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* Defines the arguments that can be passed to the `Chroma` class
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* constructor. It can either contain a `url` for the Chroma database, the
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* number of dimensions for the vectors (`numDimensions`), a
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* `collectionName` for the collection to be used in the database, and a
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* `filter` object; or it can contain an `index` which is an instance of
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* `ChromaClientT`, along with the `numDimensions`, `collectionName`, and
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* `filter`.
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*/
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export type ChromaLibArgs = ({
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url?: string;
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} & SharedChromaLibArgs) | ({
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index?: ChromaClientT;
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} & SharedChromaLibArgs);
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/**
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* Defines the parameters for the `delete` method in the `Chroma` class.
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* It can either contain an array of `ids` of the documents to be deleted
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* or a `filter` object to specify the documents to be deleted.
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*/
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export interface ChromaDeleteParams<T> {
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ids?: string[];
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filter?: T;
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}
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/**
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* Chroma vector store integration.
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*
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* Setup:
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* Install `@langchain/community` and `chromadb`.
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*
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* ```bash
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* npm install @langchain/community chromadb
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* ```
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*
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* ## [Constructor args](https://api.js.langchain.com/classes/langchain_community_vectorstores_chroma.Chroma.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 { Chroma } from '@langchain/community/vectorstores/chroma';
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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 Chroma(
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* embeddings,
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* {
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* collectionName: "foo",
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* url: "http://localhost:8000", // URL of the Chroma server
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* }
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* );
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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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* const ids = ["1", "2", "3"];
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* await vectorStore.addDocuments(documents, { ids });
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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>Delete documents</strong></summary>
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*
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* ```typescript
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* await vectorStore.delete({ ids: ["3"] });
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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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export declare class Chroma extends VectorStore {
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FilterType: Where;
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index?: ChromaClientT;
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collection?: Collection;
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collectionName: string;
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collectionMetadata?: CollectionMetadata;
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numDimensions?: number;
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clientParams?: Omit<ChromaClientParams, "path">;
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url: string;
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filter?: object;
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_vectorstoreType(): string;
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constructor(embeddings: EmbeddingsInterface, args: ChromaLibArgs);
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/**
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* Adds documents to the Chroma database. The documents are first
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* converted to vectors using the `embeddings` instance, and then added to
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* the database.
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* @param documents An array of `Document` instances to be added to the database.
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* @param options Optional. An object containing an array of `ids` for the documents.
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* @returns A promise that resolves when the documents have been added to the database.
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*/
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addDocuments(documents: Document[], options?: {
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ids?: string[];
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}): Promise<string[]>;
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/**
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* Ensures that a collection exists in the Chroma database. If the
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* collection does not exist, it is created.
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* @returns A promise that resolves with the `Collection` instance.
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*/
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ensureCollection(): Promise<Collection>;
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/**
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* Adds vectors to the Chroma database. The vectors are associated with
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* the provided documents.
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* @param vectors An array of vectors to be added to the database.
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* @param documents An array of `Document` instances associated with the vectors.
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* @param options Optional. An object containing an array of `ids` for the vectors.
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* @returns A promise that resolves with an array of document IDs when the vectors have been added to the database.
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*/
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addVectors(vectors: number[][], documents: Document[], options?: {
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ids?: string[];
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}): Promise<string[]>;
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/**
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* Deletes documents from the Chroma database. The documents to be deleted
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* can be specified by providing an array of `ids` or a `filter` object.
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* @param params An object containing either an array of `ids` of the documents to be deleted or a `filter` object to specify the documents to be deleted.
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* @returns A promise that resolves when the specified documents have been deleted from the database.
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*/
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delete(params: ChromaDeleteParams<this["FilterType"]>): Promise<void>;
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/**
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* Searches for vectors in the Chroma database that are similar to the
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* provided query vector. The search can be filtered using the provided
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* `filter` object or the `filter` property of the `Chroma` instance.
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* @param query The query vector.
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* @param k The number of similar vectors to return.
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* @param filter Optional. A `filter` object to filter the search results.
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* @returns A promise that resolves with an array of tuples, each containing a `Document` instance and a similarity score.
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*/
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similaritySearchVectorWithScore(query: number[], k: number, filter?: this["FilterType"]): Promise<[Document<Record<string, any>>, number][]>;
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/**
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* Creates a new `Chroma` instance from an array of text strings. The text
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* strings are converted to `Document` instances and added to the Chroma
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* database.
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* @param texts An array of text strings.
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* @param metadatas An array of metadata objects or a single metadata object. If an array is provided, it must have the same length as the `texts` array.
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* @param embeddings An `Embeddings` instance used to generate embeddings for the documents.
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* @param dbConfig A `ChromaLibArgs` object containing the configuration for the Chroma database.
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* @returns A promise that resolves with a new `Chroma` instance.
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*/
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static fromTexts(texts: string[], metadatas: object[] | object, embeddings: EmbeddingsInterface, dbConfig: ChromaLibArgs): Promise<Chroma>;
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/**
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* Creates a new `Chroma` instance from an array of `Document` instances.
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* The documents are added to the Chroma database.
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* @param docs An array of `Document` instances.
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* @param embeddings An `Embeddings` instance used to generate embeddings for the documents.
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* @param dbConfig A `ChromaLibArgs` object containing the configuration for the Chroma database.
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* @returns A promise that resolves with a new `Chroma` instance.
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*/
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static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, dbConfig: ChromaLibArgs): Promise<Chroma>;
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/**
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* Creates a new `Chroma` instance from an existing collection in the
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* Chroma database.
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* @param embeddings An `Embeddings` instance used to generate embeddings for the documents.
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* @param dbConfig A `ChromaLibArgs` object containing the configuration for the Chroma database.
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* @returns A promise that resolves with a new `Chroma` instance.
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*/
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static fromExistingCollection(embeddings: EmbeddingsInterface, dbConfig: ChromaLibArgs): Promise<Chroma>;
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/** @ignore */
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static imports(): Promise<{
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ChromaClient: typeof ChromaClientT;
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}>;
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}
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export {};
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