Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

docs: Add human feedback notebook tutorial #4257

Merged
merged 2 commits into from
Aug 16, 2024
Merged

docs: Add human feedback notebook tutorial #4257

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
aeb44c5
Add human feedback example
anticorrelator Aug 15, 2024
1012124
Ruff 🐶
anticorrelator Aug 15, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
263 changes: 263 additions & 0 deletions tutorials/human_feedback/chatbot_with_human_feedback.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,263 @@
{
"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\">Instrumenting a chatbot with human feedback</h1>\n",
"\n",
"Phoenix provides endpoints to associate user-provided feedback directly with OpenInference spans as annotations.\n",
"\n",
"In this tutorial, we will create a manually-instrument chatbot with user-triggered \"👍\" and \"👎\" feedback buttons. We will have those buttons trigger a callback that sends the user feedback to Phoenix and is viewable alongside the span. Automating associating feedback with spans is a powerful way to quickly focus on traces of your application that are not behaving as expected."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install \"phoenix\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import os\n",
"import warnings\n",
"from typing import Any, Dict\n",
"from uuid import uuid4\n",
"\n",
"import httpx\n",
"import ipywidgets as widgets\n",
"import phoenix as px\n",
"from IPython.display import display\n",
"from openinference.semconv.trace import (\n",
" OpenInferenceMimeTypeValues,\n",
" OpenInferenceSpanKindValues,\n",
" SpanAttributes,\n",
")\n",
"from opentelemetry import trace as trace_api\n",
"from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\n",
"from opentelemetry.sdk import trace as trace_sdk\n",
"from opentelemetry.sdk.trace.export import SimpleSpanProcessor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from getpass import getpass\n",
"\n",
"if not (openai_api_key := os.getenv(\"OPENAI_API_KEY\")):\n",
" openai_api_key = getpass(\"🔑 Enter your OpenAI API key: \")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define endpoints and configure OpenTelemetry tracing"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ENDPOINT = \"http://localhost:6006/v1\"\n",
"FEEDBACK_ENDPOINT = f\"{ENDPOINT}/span_annotations\"\n",
anticorrelator marked this conversation as resolved.
Show resolved Hide resolved
"OPENAI_API_URL = \"https://api.openai.com/v1/chat/completions\"\n",
"\n",
"tracer_provider = trace_sdk.TracerProvider()\n",
"tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(f\"{ENDPOINT}/traces\")))\n",
"trace_api.set_tracer_provider(tracer_provider)\n",
"TRACER = trace_api.get_tracer(__name__)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"px.launch_app()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define and instrument chat service backend\n",
"\n",
"Here we define two functions:\n",
"\n",
"`generate_response` is a function that contains the chatbot logic for responding to a user query. `generate_response` is manually instrumented using the `OpenInference` semantic conventions. More information on how to manually instrument an application can be found [here](https://docs.arize.com/phoenix/tracing/how-to-tracing/manual-instrumentation). `generate_response` also returns the OpenTelemetry spanID, a hex-encoded string that is used to associate feedback with a specific trace.\n",
"\n",
"`send_feedback` is a function that sends user feedback to Phoenix via the `span_annotations` REST route."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"http_client = httpx.Client()\n",
"\n",
"\n",
"def generate_response(\n",
" input_text: str, model: str = \"gpt-4o\", temperature: float = 0.1\n",
") -> Dict[str, Any]:\n",
" user_message = {\"role\": \"user\", \"content\": input_text, \"uuid\": str(uuid4())}\n",
" invocation_parameters = {\"temperature\": temperature}\n",
" payload = {\n",
" \"model\": model,\n",
" **invocation_parameters,\n",
" \"messages\": [user_message],\n",
" }\n",
" headers = {\n",
" \"Content-Type\": \"application/json\",\n",
" \"Authorization\": f\"Bearer {openai_api_key}\",\n",
" }\n",
" with TRACER.start_as_current_span(\"chatbot with feedback example\") as span:\n",
" span.set_attribute(\n",
" SpanAttributes.OPENINFERENCE_SPAN_KIND, OpenInferenceSpanKindValues.LLM.value\n",
" )\n",
" span.set_attribute(SpanAttributes.LLM_MODEL_NAME, payload[\"model\"])\n",
" span.set_attribute(SpanAttributes.INPUT_VALUE, json.dumps(payload[\"messages\"][0]))\n",
" span.set_attribute(SpanAttributes.INPUT_MIME_TYPE, OpenInferenceMimeTypeValues.JSON.value)\n",
"\n",
" # get the active hex-encoded spanID\n",
" span_id = span.get_span_context().span_id.to_bytes(8, \"big\").hex()\n",
" print(span_id)\n",
"\n",
" response = http_client.post(OPENAI_API_URL, headers=headers, json=payload)\n",
"\n",
" if not (200 <= response.status_code < 300):\n",
" raise Exception(f\"Failed to call OpenAI API: {response.text}\")\n",
" response_json = response.json()\n",
"\n",
" span.set_attribute(SpanAttributes.OUTPUT_VALUE, json.dumps(response_json))\n",
" span.set_attribute(SpanAttributes.OUTPUT_MIME_TYPE, OpenInferenceMimeTypeValues.JSON.value)\n",
"\n",
" return response_json, span_id\n",
"\n",
"\n",
"def send_feedback(span_id: str, feedback: int) -> None:\n",
" label = \"👍\" if feedback == 1 else \"👎\"\n",
" request_body = {\n",
" \"data\": [\n",
" {\n",
" \"span_id\": span_id,\n",
" \"name\": \"user_feedback\",\n",
" \"annotator_kind\": \"HUMAN\",\n",
" \"result\": {\"label\": label, \"score\": feedback},\n",
" \"metadata\": {},\n",
" }\n",
" ]\n",
" }\n",
"\n",
" try:\n",
" response = http_client.post(FEEDBACK_ENDPOINT, json=request_body)\n",
" if not (200 <= response.status_code < 300):\n",
" raise Exception(f\"Failed to send feedback: {response.text}\")\n",
" print(f\"Feedback sent for span_id {span_id}: {label}\")\n",
" except httpx.ConnectError:\n",
" warnings.warn(\"Could not connect to feedback server.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Chat Widget\n",
"\n",
"We create a simple chat application using IPython widgets. Alongside the chatbot responses we provide feedback buttons that a user can click to provide feedback. These can be seen inside the Phoenix UI!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def send_message(_):\n",
" input_text = input_box.value\n",
"\n",
" # Send the message to the OpenAI API and get the response\n",
" response_data, span_id = generate_response(input_text)\n",
" assistant_content = response_data[\"choices\"][0][\"message\"][\"content\"]\n",
"\n",
" # Create thumbs up and thumbs down buttons\n",
" thumbs_up = widgets.Button(description=\"👍\", layout=widgets.Layout(width=\"30px\"))\n",
" thumbs_down = widgets.Button(description=\"👎\", layout=widgets.Layout(width=\"30px\"))\n",
"\n",
" # Set up the callbacks for the buttons\n",
" thumbs_up.on_click(lambda _: send_feedback(span_id, 1))\n",
" thumbs_down.on_click(lambda _: send_feedback(span_id, 0))\n",
"\n",
" # Create a horizontal box to hold the response and the buttons\n",
" response_box = widgets.HBox(\n",
" [widgets.Label(f\"Bot: {assistant_content}\"), thumbs_up, thumbs_down]\n",
" )\n",
"\n",
" # Add the user's message and the response to the chat history\n",
" chat_history.children += (widgets.Label(f\"You: {input_text}\"), response_box)\n",
"\n",
" # Clear the input box\n",
" input_box.value = \"\"\n",
"\n",
"\n",
"# Set up the chat interface\n",
"chat_history = widgets.VBox()\n",
"input_box = widgets.Text(placeholder=\"Type your message here...\")\n",
"send_button = widgets.Button(description=\"Send\")\n",
"send_button.on_click(send_message)\n",
"\n",
"# Display the chat interface\n",
"display(chat_history, input_box, send_button)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "phoenix",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Loading