From d4a3903a36740ed2159913e6f3d02ce5a4b6c1cf Mon Sep 17 00:00:00 2001
From: Anuran Roy <76481787+anuran-roy@users.noreply.github.com>
Date: Fri, 3 Oct 2025 01:37:29 +0000
Subject: [PATCH] [ADD] Added code snippets for Alchemyst Retriever

---
 .../retrievers/alchemystai-retriever.mdx      | 33 +++++++++++++++++
 .../integrations/retrievers/index.mdx         |  7 ++++
 .../examples/retrievers/alchemystai.mdx       | 36 +++++++++++++++++++
 3 files changed, 76 insertions(+)
 create mode 100644 src/oss/javascript/integrations/retrievers/alchemystai-retriever.mdx
 create mode 100644 src/snippets/javascript-integrations/examples/retrievers/alchemystai.mdx

diff --git a/src/oss/javascript/integrations/retrievers/alchemystai-retriever.mdx b/src/oss/javascript/integrations/retrievers/alchemystai-retriever.mdx
new file mode 100644
index 000000000..bf784c840
--- /dev/null
+++ b/src/oss/javascript/integrations/retrievers/alchemystai-retriever.mdx
@@ -0,0 +1,33 @@
+---
+title: Alchemyst AI Retriever
+---
+
+# Alchemyst AI Retriever
+
+[**Alchemyst AI**](https://getalchemystai.com) is the context cloud for LLMs and AI Agents, purpose-built to serve as the connective tissue of your entire AI stack. It provides a unified memory layer that bridges data ingestion, context management, orchestration, inference, and deployment. By acting as a central hub for context and knowledge, Alchemyst enables your AI systems to access, share, and persist information across tools, sessions, and workflows.
+
+With seamless integrations at every layer, Alchemyst empowers you to:
+
+- Aggregate and enrich data from diverse sources, making it accessible to LLMs and agents in real time.
+- Maintain persistent, context-aware memory that enhances reasoning, retrieval, and generation.
+- Orchestrate complex workflows by connecting specialized frameworks, libraries, and platforms.
+- Deploy AI solutions that are modular, scalable, and easy to maintain.
+
+## Setup
+
+Goto [**Alchemyst Platform**](https://platform.getalchemystai.com/settings) to get API keys. Sign in with Google to sign up with a new account.
+
+import IntegrationInstallTooltip from '/snippets/javascript-integrations/integration-install-tooltip.mdx';
+
+<IntegrationInstallTooltip></IntegrationInstallTooltip>
+
+```bash npm2yarn
+npm install @langchain/openai @langchain/core @langchain/community
+```
+
+## Usage
+
+import AlchemystAiRetrieverExample from "/snippets/javascript-integrations/examples/retrievers/alchemystai.mdx";
+import CodeBlock from "@theme/CodeBlock";
+
+<CodeBlock language="typescript">{AlchemystAiRetrieverExample}</CodeBlock>
diff --git a/src/oss/javascript/integrations/retrievers/index.mdx b/src/oss/javascript/integrations/retrievers/index.mdx
index db8f539db..47b85f7dc 100644
--- a/src/oss/javascript/integrations/retrievers/index.mdx
+++ b/src/oss/javascript/integrations/retrievers/index.mdx
@@ -20,6 +20,13 @@ If you'd like to contribute an integration, see [Contributing integrations](/oss
 ## All retrievers
 
 <Columns cols={3}>
+  <Card
+    title="Alchemyst AI Retriever"
+    icon="link"
+    href="/oss/integrations/retrievers/alchemystai-retriever"
+    arrow="true"
+    cta="View guide"
+  />
   <Card
     title="ArxivRetriever"
     icon="link"
diff --git a/src/snippets/javascript-integrations/examples/retrievers/alchemystai.mdx b/src/snippets/javascript-integrations/examples/retrievers/alchemystai.mdx
new file mode 100644
index 000000000..074848706
--- /dev/null
+++ b/src/snippets/javascript-integrations/examples/retrievers/alchemystai.mdx
@@ -0,0 +1,36 @@
+```typescript
+import { AlchemystRetriever } from "@alchemystai/langchain-js";
+import { StringOutputParser } from "@langchain/core/output_parsers";
+import { RunnableSequence } from "@langchain/core/runnables";
+import dotenv from "dotenv";
+// filepath: /Users/anuran/Alchemyst/integrations/playground/example.ts
+
+dotenv.config();
+
+// Instantiate the retriever with your API key and optional config
+const retriever = new AlchemystRetriever({
+  apiKey: process.env.ALCHEMYST_AI_API_KEY!,
+  similarityThreshold: 0.8,
+  minimumSimilarityThreshold: 0.5,
+  scope: "internal"
+});
+
+// Example: Use the retriever in a LangChain pipeline
+async function main() {
+  // Create a simple pipeline that retrieves documents and outputs their content
+  const pipeline = RunnableSequence.from([
+    async (input: string) => {
+      const docs = await retriever.getRelevantDocuments(input);
+      return docs.map(doc => doc.pageContent).join("\n---\n");
+    },
+    new StringOutputParser()
+  ]);
+
+  const query = "Tell me about Quantum Entanglement"; // Put your query here
+  const result = await pipeline.invoke(query);
+
+  console.log("Retrieved Documents:\n", result);
+}
+
+main().catch(console.error);
+```