Add apps/simple/pinecone-vector-store

apps/simple/pinecone-vector-store/README.md

+# Pinecone Vector Store
+
+There are two scripts available here: load-docs.ts and query.ts
+
+## Prerequisites
+
+You'll need a Pinecone account, project, and index. Pinecone does not allow automatic creation of indexes on the free plan,
+so this vector store does not check and create the index (unlike, e.g., the PGVectorStore)
+
+Set the **PINECONE_API_KEY** and **PINECONE_ENVIRONMENT** environment variables to match your specific values. You will likely also need to set **PINECONE_INDEX_NAME**, unless your
+index is the default value "llama".
+
+You'll also need a value for OPENAI_API_KEY in your environment.
+
+## Setup and Loading Docs
+
+Read and follow the instructions in the README.md file located one directory up to make sure your JS/TS dependencies are set up. The commands listed below are also run from that parent directory.
+
+To import documents and save the embedding vectors to your database:
+
+> `npx ts-node pinecone-vector-store/load-docs.ts data`
+
+where data is the directory containing your input files. Using the _data_ directory in the example above will read all of the files in that directory using the llamaindexTS default readers for each file type.
+
+**NOTE**: Sending text chunks as part of the Pinecone metadata means that upsert API calls can get arbitrarily large. Set the **PINECONE_CHUNK_SIZE** environment variable to a smaller value if the load script fails
+
+## RAG Querying
+
+To query using the resulting vector store:
+
+> `npx ts-node pinecone-vector-store/query.ts`
+
+The script will prompt for a question, then process and present the answer using the PineconeVectorStore data and your OpenAI API key. It will continue to prompt until you enter `q`, `quit` or `exit` as the next query.

apps/simple/pinecone-vector-store/load-docs.ts

+// load-docs.ts
+import fs from "fs/promises";
+import {
+  SimpleDirectoryReader,
+  storageContextFromDefaults,
+  VectorStoreIndex,
+} from "llamaindex";
+import { PineconeVectorStore } from "../../../packages/core/src/storage/vectorStore/PineconeVectorStore";
+
+async function getSourceFilenames(sourceDir: string) {
+  return await fs
+    .readdir(sourceDir)
+    .then((fileNames) => fileNames.map((file) => sourceDir + "/" + file));
+}
+
+function callback(
+  category: string,
+  name: string,
+  status: any,
+  message: string = "",
+): boolean {
+  console.log(category, name, status, message);
+  return true;
+}
+
+async function main(args: any) {
+  const sourceDir: string = args.length > 2 ? args[2] : "../data";
+
+  console.log(`Finding documents in ${sourceDir}`);
+  const fileList = await getSourceFilenames(sourceDir);
+  const count = fileList.length;
+  console.log(`Found ${count} files`);
+
+  console.log(`Importing contents from ${count} files in ${sourceDir}`);
+  var fileName = "";
+  try {
+    // Passing callback fn to the ctor here
+    // will enable looging to console.
+    // See callback fn, defined above.
+    const rdr = new SimpleDirectoryReader(callback);
+    const docs = await rdr.loadData({ directoryPath: sourceDir });
+
+    const pcvs = new PineconeVectorStore();
+
+    const ctx = await storageContextFromDefaults({ vectorStore: pcvs });
+
+    console.debug("  - creating vector store");
+    const index = await VectorStoreIndex.fromDocuments(docs, {
+      storageContext: ctx,
+    });
+    console.debug("  - done.");
+  } catch (err) {
+    console.error(fileName, err);
+    console.log(
+      "If your PineconeVectorStore connection failed, make sure to set env vars for PINECONE_API_KEY and PINECONE_ENVIRONMENT.  If the upserts failed, try setting PINECONE_CHUNK_SIZE to limit the content sent per chunk",
+    );
+    process.exit(1);
+  }
+
+  console.log(
+    "Done. Try running query.ts to ask questions against the imported embeddings.",
+  );
+  process.exit(0);
+}
+
+main(process.argv).catch((err) => console.error(err));

apps/simple/pinecone-vector-store/query.ts

+import { VectorStoreIndex } from "../../../packages/core/src/indices/vectorStore/VectorStoreIndex";
+import { serviceContextFromDefaults } from "../../../packages/core/src/ServiceContext";
+import { PineconeVectorStore } from "../../../packages/core/src/storage/vectorStore/PineconeVectorStore";
+
+async function main() {
+  const readline = require("readline").createInterface({
+    input: process.stdin,
+    output: process.stdout,
+  });
+
+  try {
+    const pcvs = new PineconeVectorStore();
+
+    const ctx = serviceContextFromDefaults();
+    const index = await VectorStoreIndex.fromVectorStore(pcvs, ctx);
+
+    // Query the index
+    const queryEngine = await index.asQueryEngine();
+
+    let question = "";
+    while (!isQuit(question)) {
+      question = await getUserInput(readline);
+
+      if (isQuit(question)) {
+        readline.close();
+        process.exit(0);
+      }
+
+      try {
+        const answer = await queryEngine.query(question);
+        console.log(answer.response);
+      } catch (error) {
+        console.error("Error:", error);
+      }
+    }
+  } catch (err) {
+    console.error(err);
+    console.log(
+      "If your PineconeVectorStore connection failed, make sure to set env vars for PINECONE_API_KEY and PINECONE_ENVIRONMENT.",
+    );
+    process.exit(1);
+  }
+}
+
+function isQuit(question: string) {
+  return ["q", "quit", "exit"].includes(question.trim().toLowerCase());
+}
+
+// Function to get user input as a promise
+function getUserInput(readline: any): Promise<string> {
+  return new Promise((resolve) => {
+    readline.question(
+      "What would you like to know?\n>",
+      (userInput: string) => {
+        resolve(userInput);
+      },
+    );
+  });
+}
+
+main()
+  .catch(console.error)
+  .finally(() => {
+    process.exit(1);
+  });