Merge pull request #11 from PSPDFKit-labs/docs/framework-quickstarts-wave-b

Add framework quickstarts to TypeScript client examples

Author: jdrhyne

README.md

@@ -71,6 +71,21 @@ const client = new NutrientClient({
 });
 ```
 
+## Framework Quickstarts
+
+Framework wiring examples are available in `examples/src/`:
+
+- `framework_openai_agents.mjs`
+- `framework_langchain.mjs`
+- `framework_crewai_scope.md` (CrewAI scope note for TypeScript users)
+
+Syntax-check commands:
+
+```bash
+node --check examples/src/framework_openai_agents.mjs
+node --check examples/src/framework_langchain.mjs
+```
+
 ### Working with URLs
 
 Most methods accept URLs directly. The URL is passed to the server, which fetches the content—this avoids SSRF vulnerabilities since the client never fetches URLs itself.

examples/README.md

@@ -8,6 +8,9 @@ This example project demonstrates how to use the Nutrient DWS TypeScript Client
 - `src/` - Contains TypeScript source files
   - `direct_method.ts` - Examples using direct method calls
   - `workflow.ts` - Examples using the workflow builder pattern
+  - `framework_openai_agents.mjs` - OpenAI Agents integration sketch
+  - `framework_langchain.mjs` - LangChain integration sketch
+  - `framework_crewai_scope.md` - CrewAI scope note for TypeScript users
 - `output/` - Directory where processed files will be saved
 - `.env.example` - Example environment variables file
 
@@ -89,6 +92,31 @@ This will:
 3. Extract text with JSON output
 4. Execute a complex multi-step workflow
 
+### Framework Examples
+
+To run framework integration sketches:
+
+Install optional framework dependencies first:
+
+```bash
+npm install @openai/agents @langchain/openai @langchain/core zod
+```
+
+```bash
+node src/framework_openai_agents.mjs
+node src/framework_langchain.mjs
+```
+
+Syntax-check:
+
+```bash
+node --check src/framework_openai_agents.mjs
+node --check src/framework_langchain.mjs
+```
+
+CrewAI note:
+- CrewAI is Python-native. See `src/framework_crewai_scope.md` for a recommended bridge pattern when your app stack is TypeScript-first.
+
 ## Output
 
 All processed files will be saved to the `output/` directory. You can examine these files to see the results of the document processing operations.

examples/src/framework_crewai_scope.md

@@ -0,0 +1,23 @@
+# CrewAI Scope Note (TypeScript Repository)
+
+CrewAI is currently Python-native. This TypeScript client repo does not provide a first-party CrewAI TypeScript SDK path.
+
+## Recommended Pattern
+
+1. Run CrewAI orchestration in a Python service.
+2. Call Nutrient DWS through one of these options:
+- Python service uses `nutrient-dws-client-python`, or
+- Python service calls your TypeScript app endpoint that wraps `@nutrient-sdk/dws-client-typescript`.
+3. Return processed artifact metadata (output path, pages, extraction results) back to the TypeScript app.
+
+## Minimal Bridge Sketch
+
+```python
+# Python CrewAI worker (separate service)
+from crewai import Agent, Crew, Task
+
+# Inside task execution, call your TypeScript endpoint:
+# POST /api/dws/process { "operation": "extract_text", "path": "input.pdf" }
+```
+
+This keeps CrewAI orchestration explicit while preserving a TypeScript-first application surface.

examples/src/framework_langchain.mjs

@@ -0,0 +1,59 @@
+import "dotenv/config";
+import { NutrientClient } from "@nutrient-sdk/dws-client-typescript";
+import { ChatOpenAI } from "@langchain/openai";
+import { tool } from "@langchain/core/tools";
+import { z } from "zod";
+
+const nutrientApiKey = process.env.NUTRIENT_API_KEY;
+if (!nutrientApiKey) {
+  throw new Error("Missing NUTRIENT_API_KEY. Add it to examples/.env before running.");
+}
+
+if (!process.env.OPENAI_API_KEY) {
+  throw new Error("Missing OPENAI_API_KEY. Add it to examples/.env before running.");
+}
+
+const client = new NutrientClient({
+  apiKey: nutrientApiKey,
+});
+
+const redactEmails = tool(
+  async ({ path }) => {
+    const response = await client.createRedactionsPreset(path, "email-address", "apply");
+    return JSON.stringify(response);
+  },
+  {
+    name: "redact_emails",
+    description: "Redact email addresses from a document via Nutrient DWS.",
+    schema: z.object({
+      path: z.string().describe("Local path to the input PDF file."),
+    }),
+  }
+);
+
+async function main() {
+  const model = new ChatOpenAI({
+    model: "gpt-4.1-mini",
+    apiKey: process.env.OPENAI_API_KEY,
+  });
+
+  const response = await model.bindTools([redactEmails]).invoke(
+    "Redact email addresses from ./assets/sample.pdf and summarize next steps."
+  );
+
+  if (response.tool_calls?.length) {
+    for (const call of response.tool_calls) {
+      if (call.name === "redact_emails") {
+        const args =
+          typeof call.args === "object" && call.args !== null ? call.args : {};
+        const toolResult = await redactEmails.invoke(args);
+        console.log(toolResult);
+        return;
+      }
+    }
+  }
+
+  console.log(response.content);
+}
+
+void main();

examples/src/framework_openai_agents.mjs

@@ -0,0 +1,48 @@
+import "dotenv/config";
+import { NutrientClient } from "@nutrient-sdk/dws-client-typescript";
+import { Agent, Runner, tool } from "@openai/agents";
+
+const nutrientApiKey = process.env.NUTRIENT_API_KEY;
+if (!nutrientApiKey) {
+  throw new Error("Missing NUTRIENT_API_KEY. Add it to examples/.env before running.");
+}
+
+if (!process.env.OPENAI_API_KEY) {
+  throw new Error("Missing OPENAI_API_KEY. Add it to examples/.env before running.");
+}
+
+const client = new NutrientClient({
+  apiKey: nutrientApiKey,
+});
+
+const extractText = tool({
+  name: "extract_text",
+  description: "Extract text from a document using Nutrient DWS.",
+  parameters: {
+    type: "object",
+    properties: {
+      path: { type: "string" },
+    },
+    required: ["path"],
+  },
+  async execute(input) {
+    const result = await client.extractText(input.path);
+    return JSON.stringify(result);
+  },
+});
+
+const agent = new Agent({
+  name: "nutrient-openai-agents-typescript",
+  instructions: "Use tools to process documents and summarize outcomes.",
+  tools: [extractText],
+});
+
+async function main() {
+  const run = await Runner.run(
+    agent,
+    "Extract text from ./assets/sample.pdf and summarize key points."
+  );
+  console.log(run.finalOutput);
+}
+
+void main();