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Jim Bennett d79183139a Add Arize and Phoenix LLM observability skills (#1204 )

* Add 9 Arize LLM observability skills

Add skills for Arize AI platform covering trace export, instrumentation,
datasets, experiments, evaluators, AI provider integrations, annotations,
prompt optimization, and deep linking to the Arize UI.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Add 3 Phoenix AI observability skills

Add skills for Phoenix (Arize open-source) covering CLI debugging,
LLM evaluation workflows, and OpenInference tracing/instrumentation.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Ignoring intentional bad spelling

* Fix CI: remove .DS_Store from generated skills README and add codespell ignore

Remove .DS_Store artifact from winmd-api-search asset listing in generated
README.skills.md so it matches the CI Linux build output. Add queston to
codespell ignore list (intentional misspelling example in arize-dataset skill).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Add arize-ax and phoenix plugins

Bundle the 9 Arize skills into an arize-ax plugin and the 3 Phoenix
skills into a phoenix plugin for easier installation as single packages.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Fix skill folder structures to match source repos

Move arize supporting files from references/ to root level and rename
phoenix references/ to rules/ to exactly match the original source
repository folder structures.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Fixing file locations

* Fixing readme

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-04-02 09:58:55 +11:00

2.3 KiB

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Sessions (Python)

Track multi-turn conversations by grouping traces with session IDs.

Setup

from openinference.instrumentation import using_session

with using_session(session_id="user_123_conv_456"):
    response = llm.invoke(prompt)

Best Practices

Bad: Only parent span gets session ID

from openinference.semconv.trace import SpanAttributes
from opentelemetry import trace

span = trace.get_current_span()
span.set_attribute(SpanAttributes.SESSION_ID, session_id)
response = client.chat.completions.create(...)

Good: All child spans inherit session ID

with using_session(session_id):
    response = client.chat.completions.create(...)
    result = my_custom_function()

Why: using_session() propagates session ID to all nested spans automatically.

Session ID Patterns

import uuid

session_id = str(uuid.uuid4())
session_id = f"user_{user_id}_conv_{conversation_id}"
session_id = f"debug_{timestamp}"

Good: str(uuid.uuid4()), "user_123_conv_456" Bad: "session_1", "test", empty string

Multi-Turn Chatbot Example

import uuid
from openinference.instrumentation import using_session

session_id = str(uuid.uuid4())
messages = []

def send_message(user_input: str) -> str:
    messages.append({"role": "user", "content": user_input})

    with using_session(session_id):
        response = client.chat.completions.create(
            model="gpt-4",
            messages=messages
        )

    assistant_message = response.choices[0].message.content
    messages.append({"role": "assistant", "content": assistant_message})
    return assistant_message

Additional Attributes

from openinference.instrumentation import using_attributes

with using_attributes(
    user_id="user_123",
    session_id="conv_456",
    metadata={"tier": "premium", "region": "us-west"}
):
    response = llm.invoke(prompt)

LangChain Integration

LangChain threads are automatically recognized as sessions:

from langchain.chat_models import ChatOpenAI

response = llm.invoke(
    [HumanMessage(content="Hi!")],
    config={"metadata": {"thread_id": "user_123_thread"}}
)

Phoenix recognizes: thread_id, session_id, conversation_id

2.3 KiB Raw Blame History