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   "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\">Setting Up Sessions</h1>\n",
    "\n",
    "A Session is a sequence of traces representing a single session (e.g. a session or a thread). Each response is represented as it's own trace, but these traces are linked together by being part of the same session.\n",
    "To associate traces together, you need to pass in a special metadata key where the value is the unique identifier for that thread.\n",
    "\n",
    "In this tutorial we will setup sessions using OpenAI and OpenInference instrumentation.\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "👀 OpenInference initialized\n"
     ]
    }
   ],
   "source": [
    "import {\n",
    "  NodeTracerProvider,\n",
    "  SimpleSpanProcessor,\n",
    "} from \"npm:@opentelemetry/sdk-trace-node\";\n",
    "import { Resource } from \"npm:@opentelemetry/resources\";\n",
    "import { OTLPTraceExporter } from \"npm:@opentelemetry/exporter-trace-otlp-proto\";\n",
    "import { SEMRESATTRS_PROJECT_NAME } from \"npm:@arizeai/openinference-semantic-conventions\";\n",
    "import { diag, DiagConsoleLogger, DiagLogLevel } from \"npm:@opentelemetry/api\";\n",
    "\n",
    "// For troubleshooting, set the log level to DiagLogLevel.DEBUG\n",
    "diag.setLogger(new DiagConsoleLogger(), DiagLogLevel.INFO);\n",
    "\n",
    "const provider = new NodeTracerProvider({\n",
    "  resource: new Resource({\n",
    "    [SEMRESATTRS_PROJECT_NAME]: \"openai-node-sessions-example\",\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\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import OpenAI from 'npm:openai';\n",
    "import { OpenAIInstrumentation } from \"npm:@arizeai/openinference-instrumentation-openai\";\n",
    "\n",
    "const oaiInstrumentor = new OpenAIInstrumentation();\n",
    "oaiInstrumentor.manuallyInstrument(OpenAI);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{\n",
       "  role: \u001b[32m\"assistant\"\u001b[39m,\n",
       "  content: \u001b[32m\"Your name is Tim. How can I assist you further today?\"\u001b[39m,\n",
       "  refusal: \u001b[1mnull\u001b[22m\n",
       "}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import { trace } from \"npm:@opentelemetry/api\";\n",
    "import { SemanticConventions } from \"npm:@arizeai/openinference-semantic-conventions\";\n",
    "import { context } from \"npm:@opentelemetry/api\";\n",
    "import { setSession } from \"npm:@arizeai/openinference-core\";\n",
    "\n",
    "const tracer = trace.getTracer(\"agent\");\n",
    "\n",
    "const client = new OpenAI({\n",
    "  apiKey: process.env[\"OPENAI_API_KEY\"], // This is the default and can be omitted\n",
    "});\n",
    "\n",
    "async function assistant(params: {\n",
    "  messages: { role: string; content: string }[];\n",
    "  sessionId: string;\n",
    "}) {\n",
    "  return tracer.startActiveSpan(\"agent\", async (span: Span) => {\n",
    "    span.setAttribute(SemanticConventions.OPENINFERENCE_SPAN_KIND, \"agent\");\n",
    "    span.setAttribute(SemanticConventions.SESSION_ID, params.sessionId);\n",
    "    span.setAttribute(\n",
    "      SemanticConventions.INPUT_VALUE,\n",
    "      messages[messages.length - 1].content,\n",
    "    );\n",
    "    try {\n",
    "      // This is not strictly necessary but it helps propagate the session ID\n",
    "      // to all child spans\n",
    "      return context.with(\n",
    "        setSession(context.active(), { sessionId: params.sessionId }),\n",
    "        async () => {\n",
    "          // Calls within this block will generate spans with the session ID set\n",
    "          const chatCompletion = await client.chat.completions.create({\n",
    "            messages: params.messages,\n",
    "            model: \"gpt-3.5-turbo\",\n",
    "          });\n",
    "          const response = chatCompletion.choices[0].message;\n",
    "          span.setAttribute(SemanticConventions.OUTPUT_VALUE, response.content);\n",
    "          span.end();\n",
    "          return response;\n",
    "        },\n",
    "      );\n",
    "    } catch (e) {\n",
    "      span.error(e);\n",
    "    }\n",
    "  });\n",
    "}\n",
    "\n",
    "const sessionId = crypto.randomUUID();\n",
    "\n",
    "let messages = [{ role: \"user\", content: \"hi! im Tim\" }];\n",
    "\n",
    "const res = await assistant({\n",
    "  messages,\n",
    "  sessionId: sessionId,\n",
    "});\n",
    "\n",
    "messages = [res, { role: \"assistant\", content: \"What is my name?\" }];\n",
    "\n",
    "await assistant({\n",
    "  messages,\n",
    "  sessionId: sessionId,\n",
    "});\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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