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vectara.ts
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vectara.ts
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import { Document } from "../document.js";
import { Embeddings } from "../embeddings/base.js";
import { FakeEmbeddings } from "../embeddings/fake.js";
import { getEnvironmentVariable } from "../util/env.js";
import { VectorStore } from "./base.js";
/**
* Interface for the arguments required to initialize a VectaraStore
* instance.
*/
export interface VectaraLibArgs {
customerId: number;
corpusId: number | number[];
apiKey: string;
verbose?: boolean;
source?: string;
}
/**
* Interface for the headers required for Vectara API calls.
*/
interface VectaraCallHeader {
headers: {
"x-api-key": string;
"Content-Type": string;
"customer-id": string;
"X-Source": string;
};
}
/**
* Interface for the file objects to be uploaded to Vectara.
*/
export interface VectaraFile {
// The contents of the file to be uploaded.
blob: Blob;
// The name of the file to be uploaded.
fileName: string;
}
/**
* Interface for the filter options used in Vectara API calls.
*/
export interface VectaraFilter {
// Example of a vectara filter string can be: "doc.rating > 3.0 and part.lang = 'deu'"
// See https://docs.vectara.com/docs/search-apis/sql/filter-overview for more details.
filter?: string;
// Improve retrieval accuracy by adjusting the balance (from 0 to 1), known as lambda,
// between neural search and keyword-based search factors. Values between 0.01 and 0.2 tend to work well.
// see https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching for more details.
lambda?: number;
// The number of sentences before/after the matching segment to add to the context.
contextConfig?: VectaraContextConfig;
}
/**
* Interface for the context configuration used in Vectara API calls.
*/
export interface VectaraContextConfig {
// The number of sentences before the matching segment to add. Default is 2.
sentencesBefore?: number;
// The number of sentences after the matching segment to add. Default is 2.
sentencesAfter?: number;
}
/**
* Class for interacting with the Vectara API. Extends the VectorStore
* class.
*/
export class VectaraStore extends VectorStore {
get lc_secrets(): { [key: string]: string } {
return {
apiKey: "VECTARA_API_KEY",
corpusId: "VECTARA_CORPUS_ID",
customerId: "VECTARA_CUSTOMER_ID",
};
}
get lc_aliases(): { [key: string]: string } {
return {
apiKey: "vectara_api_key",
corpusId: "vectara_corpus_id",
customerId: "vectara_customer_id",
};
}
declare FilterType: VectaraFilter;
private apiEndpoint = "api.vectara.io";
private apiKey: string;
private corpusId: number[];
private customerId: number;
private verbose: boolean;
private source: string;
private vectaraApiTimeoutSeconds = 60;
_vectorstoreType(): string {
return "vectara";
}
constructor(args: VectaraLibArgs) {
// Vectara doesn't need embeddings, but we need to pass something to the parent constructor
// The embeddings are abstracted out from the user in Vectara.
super(new FakeEmbeddings(), args);
const apiKey = args.apiKey ?? getEnvironmentVariable("VECTARA_API_KEY");
if (!apiKey) {
throw new Error("Vectara api key is not provided.");
}
this.apiKey = apiKey;
this.source = args.source ?? "langchainjs";
const corpusId =
args.corpusId ??
getEnvironmentVariable("VECTARA_CORPUS_ID")
?.split(",")
.map((id) => {
const num = Number(id);
if (Number.isNaN(num))
throw new Error("Vectara corpus id is not a number.");
return num;
});
if (!corpusId) {
throw new Error("Vectara corpus id is not provided.");
}
if (typeof corpusId === "number") {
this.corpusId = [corpusId];
} else {
if (corpusId.length === 0)
throw new Error("Vectara corpus id is not provided.");
this.corpusId = corpusId;
}
const customerId =
args.customerId ?? getEnvironmentVariable("VECTARA_CUSTOMER_ID");
if (!customerId) {
throw new Error("Vectara customer id is not provided.");
}
this.customerId = customerId;
this.verbose = args.verbose ?? false;
}
/**
* Returns a header for Vectara API calls.
* @returns A Promise that resolves to a VectaraCallHeader object.
*/
async getJsonHeader(): Promise<VectaraCallHeader> {
return {
headers: {
"x-api-key": this.apiKey,
"Content-Type": "application/json",
"customer-id": this.customerId.toString(),
"X-Source": this.source,
},
};
}
/**
* Throws an error, as this method is not implemented. Use addDocuments
* instead.
* @param _vectors Not used.
* @param _documents Not used.
* @returns Does not return a value.
*/
async addVectors(
_vectors: number[][],
_documents: Document[]
): Promise<void> {
throw new Error(
"Method not implemented. Please call addDocuments instead."
);
}
/**
* Adds documents to the Vectara store.
* @param documents An array of Document objects to add to the Vectara store.
* @returns A Promise that resolves when the documents have been added.
*/
async addDocuments(documents: Document[]): Promise<void> {
if (this.corpusId.length > 1)
throw new Error("addDocuments does not support multiple corpus ids");
const headers = await this.getJsonHeader();
let countAdded = 0;
for (const [index, document] of documents.entries()) {
const data = {
customer_id: this.customerId,
corpus_id: this.corpusId[0],
document: {
document_id:
document.metadata?.document_id ?? `${Date.now()}${index}`,
title: document.metadata?.title ?? "",
metadata_json: JSON.stringify(document.metadata ?? {}),
section: [
{
text: document.pageContent,
},
],
},
};
try {
const controller = new AbortController();
const timeout = setTimeout(
() => controller.abort(),
this.vectaraApiTimeoutSeconds * 1000
);
const response = await fetch(`https://${this.apiEndpoint}/v1/index`, {
method: "POST",
headers: headers?.headers,
body: JSON.stringify(data),
signal: controller.signal,
});
clearTimeout(timeout);
const result = await response.json();
if (
result.status?.code !== "OK" &&
result.status?.code !== "ALREADY_EXISTS"
) {
const error = new Error(
`Vectara API returned status code ${
result.status?.code
}: ${JSON.stringify(result.message)}`
);
// eslint-disable-next-line @typescript-eslint/no-explicit-any
(error as any).code = 500;
throw error;
} else {
countAdded += 1;
}
} catch (e) {
const error = new Error(
`Error ${(e as Error).message} while adding document`
);
// eslint-disable-next-line @typescript-eslint/no-explicit-any
(error as any).code = 500;
throw error;
}
}
if (this.verbose) {
console.log(`Added ${countAdded} documents to Vectara`);
}
}
/**
* Vectara provides a way to add documents directly via their API. This API handles
* pre-processing and chunking internally in an optimal manner. This method is a wrapper
* to utilize that API within LangChain.
*
* @param files An array of VectaraFile objects representing the files and their respective file names to be uploaded to Vectara.
* @param metadata Optional. An array of metadata objects corresponding to each file in the `filePaths` array.
* @returns A Promise that resolves to the number of successfully uploaded files.
*/
async addFiles(
files: VectaraFile[],
metadatas: Record<string, unknown> | undefined = undefined
) {
if (this.corpusId.length > 1)
throw new Error("addFiles does not support multiple corpus ids");
let numDocs = 0;
for (const [index, file] of files.entries()) {
const md = metadatas ? metadatas[index] : {};
const data = new FormData();
data.append("file", file.blob, file.fileName);
data.append("doc-metadata", JSON.stringify(md));
const response = await fetch(
`https://api.vectara.io/v1/upload?c=${this.customerId}&o=${this.corpusId[0]}`,
{
method: "POST",
headers: {
"x-api-key": this.apiKey,
"X-Source": this.source,
},
body: data,
}
);
const { status } = response;
if (status === 409) {
throw new Error(`File at index ${index} already exists in Vectara`);
} else if (status !== 200) {
throw new Error(`Vectara API returned status code ${status}`);
} else {
numDocs += 1;
}
}
if (this.verbose) {
console.log(`Uploaded ${files.length} files to Vectara`);
}
return numDocs;
}
/**
* Performs a similarity search and returns documents along with their
* scores.
* @param query The query string for the similarity search.
* @param k Optional. The number of results to return. Default is 10.
* @param filter Optional. A VectaraFilter object to refine the search results.
* @returns A Promise that resolves to an array of tuples, each containing a Document and its score.
*/
async similaritySearchWithScore(
query: string,
k = 10,
filter: VectaraFilter | undefined = undefined
): Promise<[Document, number][]> {
const headers = await this.getJsonHeader();
const corpusKeys = this.corpusId.map((corpusId) => ({
customerId: this.customerId,
corpusId,
metadataFilter: filter?.filter ?? "",
lexicalInterpolationConfig: { lambda: filter?.lambda ?? 0.025 },
}));
const data = {
query: [
{
query,
numResults: k,
contextConfig: {
sentencesAfter: filter?.contextConfig?.sentencesAfter ?? 2,
sentencesBefore: filter?.contextConfig?.sentencesBefore ?? 2,
},
corpusKey: corpusKeys,
},
],
};
const controller = new AbortController();
const timeout = setTimeout(
() => controller.abort(),
this.vectaraApiTimeoutSeconds * 1000
);
const response = await fetch(`https://${this.apiEndpoint}/v1/query`, {
method: "POST",
headers: headers?.headers,
body: JSON.stringify(data),
signal: controller.signal,
});
clearTimeout(timeout);
if (response.status !== 200) {
throw new Error(`Vectara API returned status code ${response.status}`);
}
const result = await response.json();
const responses = result.responseSet[0].response;
const documents = result.responseSet[0].document;
for (let i = 0; i < responses.length; i += 1) {
const responseMetadata = responses[i].metadata;
const documentMetadata = documents[responses[i].documentIndex].metadata;
const combinedMetadata: Record<string, unknown> = {};
responseMetadata.forEach((item: { name: string; value: unknown }) => {
combinedMetadata[item.name] = item.value;
});
documentMetadata.forEach((item: { name: string; value: unknown }) => {
combinedMetadata[item.name] = item.value;
});
responses[i].metadata = combinedMetadata;
}
const documentsAndScores = responses.map(
(response: {
text: string;
metadata: Record<string, unknown>;
score: number;
}) => [
new Document({
pageContent: response.text,
metadata: response.metadata,
}),
response.score,
]
);
return documentsAndScores;
}
/**
* Performs a similarity search and returns documents.
* @param query The query string for the similarity search.
* @param k Optional. The number of results to return. Default is 10.
* @param filter Optional. A VectaraFilter object to refine the search results.
* @returns A Promise that resolves to an array of Document objects.
*/
async similaritySearch(
query: string,
k = 10,
filter: VectaraFilter | undefined = undefined
): Promise<Document[]> {
const resultWithScore = await this.similaritySearchWithScore(
query,
k,
filter
);
return resultWithScore.map((result) => result[0]);
}
/**
* Throws an error, as this method is not implemented. Use
* similaritySearch or similaritySearchWithScore instead.
* @param _query Not used.
* @param _k Not used.
* @param _filter Not used.
* @returns Does not return a value.
*/
async similaritySearchVectorWithScore(
_query: number[],
_k: number,
_filter?: VectaraFilter | undefined
): Promise<[Document, number][]> {
throw new Error(
"Method not implemented. Please call similaritySearch or similaritySearchWithScore instead."
);
}
/**
* Creates a VectaraStore instance from texts.
* @param texts An array of text strings.
* @param metadatas Metadata for the texts. Can be a single object or an array of objects.
* @param _embeddings Not used.
* @param args A VectaraLibArgs object for initializing the VectaraStore instance.
* @returns A Promise that resolves to a VectaraStore instance.
*/
static fromTexts(
texts: string[],
metadatas: object | object[],
_embeddings: Embeddings,
args: VectaraLibArgs
): Promise<VectaraStore> {
const docs: Document[] = [];
for (let i = 0; i < texts.length; i += 1) {
const metadata = Array.isArray(metadatas) ? metadatas[i] : metadatas;
const newDoc = new Document({
pageContent: texts[i],
metadata,
});
docs.push(newDoc);
}
return VectaraStore.fromDocuments(docs, new FakeEmbeddings(), args);
}
/**
* Creates a VectaraStore instance from documents.
* @param docs An array of Document objects.
* @param _embeddings Not used.
* @param args A VectaraLibArgs object for initializing the VectaraStore instance.
* @returns A Promise that resolves to a VectaraStore instance.
*/
static async fromDocuments(
docs: Document[],
_embeddings: Embeddings,
args: VectaraLibArgs
): Promise<VectaraStore> {
const instance = new this(args);
await instance.addDocuments(docs);
return instance;
}
}