Bump @pinecone-database to v2.0.0

.env.example

@@ -1,3 +1,4 @@
 PINECONE_API_KEY=
-PINECONE_ENVIRONMENT=
-PINECONE_INDEX=
+PINECONE_INDEX=
+PINECONE_CLOUD="aws"
+PINECONE_REGION="us-west-2"

.gitignore

@@ -4,4 +4,5 @@ dist
 .turbo
 data/
 all-the-news-2-1.zip
-all-the-news-2-1.csv
+all-the-news-2-1.csv
+.DS_Store

README.md

@@ -10,24 +10,27 @@ npm install
 
 ## Required configuration
 
-In order to run this example, you have to supply the Pinecone credentials needed to interact with the Pinecone API. You can find these credentials in the [Pinecone web console](https://app.pinecone.io) under **API Keys**. This project uses `dotenv` to easily load values from the `.env` file into the environment when executing. 
+In order to run this example, you have to supply the Pinecone credentials needed to interact with the Pinecone API. You can find these credentials in the [Pinecone web console](https://app.pinecone.io) under **API Keys**. This project uses `dotenv` to easily load values from the `.env` file into the environment when executing.
 
 Copy the template file:
 
 ```sh
 cp .env.example .env
 ```
 
-And fill in your API key and environment details:
+And fill in your API key and index name:
 
 ```sh
 PINECONE_API_KEY=<your-api-key>
-PINECONE_ENVIRONMENT=<your-environment>
-PINECONE_INDEX=article-recommendations
+PINECONE_INDEX="article-recommendations"
+PINECONE_CLOUD="aws"
+PINECONE_REGION="us-west-2"
 ```
 
 `PINECONE_INDEX` is the name of the index where this demo will store and query embeddings. You can change `PINECONE_INDEX` to any name you like, but make sure the name not going to collide with any indexes you are already using.
 
+`PINECONE_CLOUD` and `PINECONE_REGION` define where the index should be deployed. Currently, this is the only available cloud and region combination (`aws` and `us-west-2`), so it's recommended to leave them defaulted.
+
 ## Data preparation
 
 Next, we will prepare data for the Pinecone vector index, and insert it in batches.
@@ -97,27 +100,35 @@ Next we'll create a function that will generate the embeddings and upsert them i
 
 ```typescript
 async function embedAndUpsert(dataFrame: dfd.DataFrame, chunkSize: number) {
-  const chunkGenerator = processInChunks<ArticleRecord, 'section' | 'url' | 'title' | 'publication' | 'author' | 'article', 'article'>(
+  const chunkGenerator = processInChunks<
+    ArticleRecord,
+    "section" | "url" | "title" | "publication" | "author" | "article",
+    "article"
+  >(
     dataFrame,
     100,
-    ['section', 'url', 'title', 'publication', 'author', 'article'],
-    'article'
+    ["section", "url", "title", "publication", "author", "article"],
+    "article"
   );
   const index = pinecone.index(indexName);
 
   for await (const documents of chunkGenerator) {
-    await embedder.embedBatch(documents, chunkSize, async (embeddings: PineconeRecord[]) => {
-      await chunkedUpsert(index, embeddings, "default");
-      progressBar.increment(embeddings.length);
-    });
+    await embedder.embedBatch(
+      documents,
+      chunkSize,
+      async (embeddings: PineconeRecord[]) => {
+        await chunkedUpsert(index, embeddings, "default");
+        progressBar.increment(embeddings.length);
+      }
+    );
   }
 }
 ```
 
 We'll use the `splitFile` utility function to split the CSV file we downloaded into chunks of 100k parts each. For the purposes of this example, we'll only use the first 100k records.
 
 ```typescript
-const fileParts = await splitFile("./data/all-the-news-2-1.csv", 1000000);
+const fileParts = await splitFile("./data/all-the-news-2-1.csv", 100000);
 const firstFile = fileParts[0];
 ```
 
@@ -133,8 +144,13 @@ Now we'll create the Pinecone index and kick off the embedding and upserting pro
 ```typescript
 // Create the index if it doesn't already exist
 const indexList = await pinecone.listIndexes();
-if (indexList.indexOf({ name: indexName }) === -1) {
-  await pinecone.createIndex({ name: indexName, dimension: 384, waitUntilReady: true })
+if (!indexList.indexes?.some((index) => index.name === indexName)) {
+  await pinecone.createIndex({
+    name: indexName,
+    dimension: 384,
+    spec: { serverless: { region: indexRegion, cloud: indexCloud } },
+    waitUntilReady: true,
+  });
 }
 
 progressBar.start(clean.shape[0], 0);
@@ -155,45 +171,52 @@ const pinecone = new Pinecone();
 try {
   const description = await pinecone.describeIndex(indexName);
   if (!description.status?.ready) {
-    throw `Index not ready, description was ${JSON.stringify(description)}`
+    throw new Error(
+      `Index not ready, description was ${JSON.stringify(description)}`
+    );
   }
 } catch (e) {
-  console.log('An error occurred. Run "npm run index" to load data into the index before querying.')
+  console.log(
+    'An error occurred. Run "npm run index" to load data into the index before querying.'
+  );
   throw e;
 }
 
-const index = pinecone.index<ArticleRecord>(indexName).namespace('default');
+const index = pinecone.index<ArticleRecord>(indexName).namespace("default");
 
 await embedder.init("Xenova/all-MiniLM-L6-v2");
 
 const { query, section } = getQueryingCommandLineArguments();
 
 // We create a simulated user with an interest given a query and a specific section
-const queryEmbedding = await embedder.embed(query)
+const queryEmbedding = await embedder.embed(query);
 const queryResult = await index.query({
-    vector: queryEmbedding.values,
-    includeMetadata: true,
-    includeValues: true,
-    filter: {
-      section: { "$eq": section }
-    },
-    topK: 10
+  vector: queryEmbedding.values,
+  includeMetadata: true,
+  includeValues: true,
+  filter: {
+    section: { $eq: section },
+  },
+  topK: 10,
 });
 ```
 
 We'll calculate the **mean** vector given the results of the query. The mean vector represents the user's interests based on the articles they've read.
 
 ```typescript
 // We extract the vectors of the results
-const userVectors = queryResult?.matches?.map((result: ScoredPineconeRecord<ArticleRecord>) => result.values);
+const userVectors = queryResult?.matches?.map(
+  (result: ScoredPineconeRecord<ArticleRecord>) => result.values
+);
 
 // A couple of functions to calculate mean vector
-const mean = (arr: number[]): number => arr.reduce((a, b) => a + b, 0) / arr.length;
+const mean = (arr: number[]): number =>
+  arr.reduce((a, b) => a + b, 0) / arr.length;
 const meanVector = (vectors: number[][]): number[] => {
   const { length } = vectors[0];
 
   return Array.from({ length }).map((_, i) =>
-    mean(vectors.map(vec => vec[i]))
+    mean(vectors.map((vec) => vec[i]))
   );
 };
 
@@ -205,14 +228,11 @@ const meanVec = meanVector(userVectors!);
 To resolve the recommendations, we'll query the index with the mean vector and filter out the articles that the user has already read.
 
 ```typescript
-const recommendations = await pineconeIndex.query({
-  queryRequest: {
-    vector: meanVec,
-    includeMetadata: true,
-    includeValues: true,
-    namespace: "default",
-    topK: 10,
-  },
+const recommendations = await index.query({
+  vector: meanVec,
+  includeMetadata: true,
+  includeValues: true,
+  topK: 10,
 });
 ```
 
@@ -236,10 +256,11 @@ const userRecommendations = new Table({
 });
 
 queryResult?.matches?.slice(0, 10).forEach((result: any) => {
-  const { title, author, section } = result.metadata;
+  const { title, article, publication, section } = result.metadata;
   userPreferences.addRow({
     title,
-    author,
+    article: `${article.slice(0, 70)}...`,
+    publication,
     section,
   });
 });
@@ -248,10 +269,11 @@ console.log("========== User Preferences ==========");
 userPreferences.printTable();
 
 recommendations?.matches?.slice(0, 10).forEach((result: any) => {
-  const { title, author, section } = result.metadata;
+  const { title, article, publication, section } = result.metadata;
   userRecommendations.addRow({
     title,
-    author,
+    article: `${article.slice(0, 70)}...`,
+    publication,
     section,
   });
 });

package.json

@@ -28,7 +28,7 @@
   "author": "",
   "license": "MIT",
   "dependencies": {
-    "@pinecone-database/pinecone": "^1.0.0",
+    "@pinecone-database/pinecone": "^2.0.0",
     "@xenova/transformers": "^2.2.0",
     "chalk": "^5.2.0",
     "cli-progress": "^3.12.0",