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Install

npm install vectorview

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Use

Get your key by logging in to the vectorview dashboard

import Vectorview from "vectorview";
const vv = new Vectorview(key)
vv.event(query, docsWithScore)

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Example

import { FaissStore } from "langchain/vectorstores/faiss";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { TokenTextSplitter } from "langchain/text_splitter";
import Vectorview from "vectorview";

// Setup
const vv = new Vectorview(key);

//  Do semantic search
const text = await fs.readFile('./text.txt', 'utf8'); 
const splitter = new TokenTextSplitter({ 
  chunkSize: 40, 
  chunkOverlap: 0,
});
const docs = await splitter.createDocuments([text]);
const vectorStore = await FaissStore.fromDocuments(docs, new OpenAIEmbeddings()); 

const query = "What did the president say about Ketanji Brown Jackson";
const docsWithScore = await vectorStore.similaritySearchWithScore(query, 3);

// Log vv event
vv.event(query, docsWithScore)

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Advanced Use

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Query metadata

Assign custom metadata to an event() by passing a dict as the third argument.

vv.event(query, docsWithScore, {"foo": "bar"})

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Document metadata

Documents in vectorview are langchain Documents which has a metadata field. Any metadata added will show up in your vectorview dashboard.

docsWithScore[0][0].metadata = {"foo": "bar"}
vv.event(query, docsWithScore)

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Custom document id

Vectorview assigns an id to each document used in an event(). To assign a custom id, add an id field in the Document’s metadata.

docsWithScore[0][0].metadata = {"id": new_id}
vv.event(query, docsWithScore)
Python API Reference