docs(js): add langchain deno notebook (#4848)

js/examples/notebooks/langchain/deno.json

@@ -0,0 +1,3 @@
+{
+    "nodeModulesDir": "manual"
+}

js/examples/notebooks/langchain/package.json

@@ -0,0 +1,11 @@
+{
+    "dependencies": {
+        "@arizeai/openinference-instrumentation-langchain": "^0.2.0",
+        "@arizeai/openinference-semantic-conventions": "^0.10.0",
+        "@langchain/core": "^0.2.30",
+        "@langchain/openai": "^0.2.0",
+        "@opentelemetry/exporter-trace-otlp-proto": "^0.50.0",
+        "@opentelemetry/resources": "^1.25.1",
+        "@opentelemetry/sdk-trace-node": "^1.25.1"
+    }
+}

js/examples/notebooks/langchain/tracing_langchain_node_tutorial.ipynb

@@ -0,0 +1,124 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<center>\n",
+    "    <p style=\"text-align:center\">\n",
+    "        <img alt=\"phoenix logo\" src=\"https://raw.githubusercontent.com/Arize-ai/phoenix-assets/9e6101d95936f4bd4d390efc9ce646dc6937fb2d/images/socal/github-large-banner-phoenix.jpg\" width=\"1000\"/>\n",
+    "        <br>\n",
+    "        <br>\n",
+    "        <a href=\"https://docs.arize.com/phoenix/\">Docs</a>\n",
+    "        |\n",
+    "        <a href=\"https://github.com/Arize-ai/phoenix\">GitHub</a>\n",
+    "        |\n",
+    "        <a href=\"https://join.slack.com/t/arize-ai/shared_invite/zt-1px8dcmlf-fmThhDFD_V_48oU7ALan4Q\">Community</a>\n",
+    "    </p>\n",
+    "</center>\n",
+    "<h1 align=\"center\">LangChain Tracing</h1>\n",
+    "\n",
+    "Let's see how to get started with [LangChain.js](https://js.langchain.com/docs/introduction/) and [OpenInference](https://github.com/Arize-ai/openinference/tree/main/js) to trace your LangChain application using Deno.\n",
+    "\n",
+    "> Note: that this example requires the OPENAI_API_KEY environment variable to be set and assumes you are running the Phoenix server on localhost:6006.\n",
+    "\n",
+    "In order to run this notebook please run the following command from this directory:\n",
+    "```shell\n",
+    "npm install\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import {\n",
+    "  NodeTracerProvider,\n",
+    "  SimpleSpanProcessor,\n",
+    "} from \"@opentelemetry/sdk-trace-node\";\n",
+    "import { Resource } from \"@opentelemetry/resources\";\n",
+    "import { OTLPTraceExporter } from \"@opentelemetry/exporter-trace-otlp-proto\";\n",
+    "import { SEMRESATTRS_PROJECT_NAME } from \"@arizeai/openinference-semantic-conventions\";\n",
+    "\n",
+    "const provider = new NodeTracerProvider({\n",
+    "  resource: new Resource({\n",
+    "    [SEMRESATTRS_PROJECT_NAME]: \"deno-langchain\",\n",
+    "  }),\n",
+    "});\n",
+    "\n",
+    "provider.addSpanProcessor(\n",
+    "  new SimpleSpanProcessor(\n",
+    "    new OTLPTraceExporter({\n",
+    "      url: \"http://localhost:6006/v1/traces\",\n",
+    "    })\n",
+    "  )\n",
+    ");\n",
+    "\n",
+    "provider.register();\n",
+    "\n",
+    "console.log(\"👀 OpenInference initialized\");\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import * as lcCallbackManager from \"@langchain/core/callbacks/manager\";\n",
+    "import { LangChainInstrumentation } from \"@arizeai/openinference-instrumentation-langchain\";\n",
+    "\n",
+    "const lcInstrumentation = new LangChainInstrumentation();\n",
+    "lcInstrumentation.manuallyInstrument(lcCallbackManager);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import { ChatOpenAI } from \"@langchain/openai\";\n",
+    "import { ChatPromptTemplate } from \"@langchain/core/prompts\";\n",
+    "\n",
+    "const PROMPT_TEMPLATE = `\n",
+    "    You are a highly trained AI model that can answer any question. Please answer questions concisely. \n",
+    "    Answer the following question: \n",
+    "    \"{question}\"\n",
+    "`;\n",
+    "\n",
+    "const question = \"What is javascript used for?\";\n",
+    "\n",
+    "const chatPrompt = ChatPromptTemplate.fromTemplate(PROMPT_TEMPLATE)\n",
+    "\n",
+    "const model = new ChatOpenAI({ model: \"gpt-4\" });\n",
+    "\n",
+    "const chain = chatPrompt.pipe(model);\n",
+    "\n",
+    "const response = await chain.invoke({ question });\n",
+    "\n",
+    "console.log(response.content);\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Deno",
+   "language": "typescript",
+   "name": "deno"
+  },
+  "language_info": {
+   "codemirror_mode": "typescript",
+   "file_extension": ".ts",
+   "mimetype": "text/x.typescript",
+   "name": "typescript",
+   "nbconvert_exporter": "script",
+   "pygments_lexer": "typescript",
+   "version": "5.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

js/examples/notebooks/tracing_openai_node_tutorial.ipynb

@@ -18,7 +18,9 @@
     "</center>\n",
     "<h1 align=\"center\">OpenAI Node SDK Tracing</h1>\n",
     "\n",
-    "Let's see how to get started with using the OpenAI Node SDK to trace your LLM calls using Deno. Note that this example requires the OPENAI_API_KEY environment variable to be set and assumes you are running the Phoenix server on localhost:6006."
+    "Let's see how to get started with using the OpenAI Node SDK to trace your LLM calls using Deno. \n",
+    "\n",
+    "> Note: that this example requires the OPENAI_API_KEY environment variable to be set and assumes you are running the Phoenix server on localhost:6006."
    ]
   },
   {
@@ -60,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,13 +97,6 @@
     "\n",
     "await main();"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {