chore: add qdrant readme (#444)

.changeset/funny-mice-fail.md

+---
+"llamaindex": minor
+"docs": patch
+---
+
+chore: add qdrant readme

apps/docs/docs/modules/documents_and_nodes.md

 ---
-sidebar_position: 0
+sidebar_position: 1
 ---
 
 # Documents and Nodes

apps/docs/docs/modules/llm.md

 ---
-sidebar_position: 0
+sidebar_position: 1
 ---
 
 # LLM

apps/docs/docs/modules/vectorStores/_category_.yml

+label: "Vector Stores"
+position: 0

apps/docs/docs/modules/vectorStores/qdrant.md

+# Qdrant Vector Store
+
+To run this example, you need to have a Qdrant instance running. You can run it with Docker:
+
+```bash
+docker pull qdrant/qdrant
+docker run -p 6333:6333 qdrant/qdrant
+```
+
+## Importing the modules
+
+```ts
+import fs from "node:fs/promises";
+import { Document, VectorStoreIndex, QdrantVectorStore } from "llamaindex";
+```
+
+## Load the documents
+
+```ts
+const path = "node_modules/llamaindex/examples/abramov.txt";
+const essay = await fs.readFile(path, "utf-8");
+```
+
+## Setup Qdrant
+
+```ts
+const vectorStore = new QdrantVectorStore({
+  url: "http://localhost:6333",
+  port: 6333,
+});
+```
+
+## Setup the index
+
+```ts
+const document = new Document({ text: essay, id_: path });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  vectorStore,
+});
+```
+
+## Query the index
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const response = await queryEngine.query({
+  query: "What did the author do in college?",
+});
+
+// Output response
+console.log(response.toString());
+```
+
+## Full code
+
+```ts
+import fs from "node:fs/promises";
+import { Document, VectorStoreIndex, QdrantVectorStore } from "llamaindex";
+
+async function main() {
+  const path = "node_modules/llamaindex/examples/abramov.txt";
+  const essay = await fs.readFile(path, "utf-8");
+
+  const vectorStore = new QdrantVectorStore({
+    url: "http://localhost:6333",
+    port: 6333,
+  });
+
+  const document = new Document({ text: essay, id_: path });
+
+  const index = await VectorStoreIndex.fromDocuments([document], {
+    vectorStore,
+  });
+
+  const queryEngine = index.asQueryEngine();
+
+  const response = await queryEngine.query({
+    query: "What did the author do in college?",
+  });
+
+  // Output response
+  console.log(response.toString());
+}
+
+main().catch(console.error);
+```

packages/core/src/storage/vectorStore/QdrantVectorStore.ts

@@ -54,11 +54,9 @@ export class QdrantVectorStore implements VectorStore {
     apiKey,
     batchSize,
   }: QdrantParams) {
-    if (!client && (!url || !apiKey)) {
-      if (!url || !apiKey || !collectionName) {
-        throw new Error(
-          "QdrantVectorStore requires url, apiKey and collectionName",
-        );
+    if (!client && !url) {
+      if (!url || !collectionName) {
+        throw new Error("QdrantVectorStore requires url and collectionName");
       }
     }