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Arthur Callback (#6972)

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Co-authored-by: Max Cembalest <115359769+arthuractivemodeling@users.noreply.github.com>
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docs/extras/ecosystem/integrations/arthur_tracking.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "944e4194",
"metadata": {},
"source": [
"# Arthur LangChain integration"
]
},
{
"cell_type": "markdown",
"id": "b1ccdfe8",
"metadata": {},
"source": [
"[Arthur](https://www.arthur.ai/) is a model monitoring and observability platform.\n",
"\n",
"This notebook shows how to register LLMs (chat and non-chat) as models with the Arthur platform. Then we show how to set up langchain LLMs with an Arthur callback that will automatically log model inferences to Arthur.\n",
"\n",
"For more information about how to use the Arthur SDK, visit our [docs](http://docs.arthur.ai), in particular our [model onboarding guide](https://docs.arthur.ai/user-guide/walkthroughs/model-onboarding/index.html)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "961c6691",
"metadata": {},
"outputs": [],
"source": [
"from langchain.callbacks import ArthurCallbackHandler\n",
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
"from langchain.chat_models import ChatOpenAI, ChatAnthropic\n",
"from langchain.schema import HumanMessage\n",
"from langchain.llms import OpenAI, Cohere, HuggingFacePipeline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a23d1963",
"metadata": {},
"outputs": [],
"source": [
"from arthurai import ArthurAI\n",
"from arthurai.common.constants import InputType, OutputType, Stage, ValueType\n",
"from arthurai.core.attributes import ArthurAttribute, AttributeCategory"
]
},
{
"cell_type": "markdown",
"id": "4d1b90c0",
"metadata": {},
"source": [
"# ArthurModel for chatbot with only input text and output text attributes"
]
},
{
"cell_type": "markdown",
"id": "1a4a4a8a",
"metadata": {},
"source": [
"Connect to Arthur client"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f49e9b79",
"metadata": {},
"outputs": [],
"source": [
"arthur_url = \"https://app.arthur.ai\"\n",
"arthur_login = \"your-username-here\"\n",
"arthur = ArthurAI(url=arthur_url, login=arthur_login)"
]
},
{
"cell_type": "markdown",
"id": "c6e063bf",
"metadata": {},
"source": [
"Before you can register model inferences to Arthur, you must have a registered model with an ID in the Arthur platform. We will provide this ID to the ArthurCallbackHandler.\n",
"\n",
"You can register a model with Arthur here in the notebook using this `register_chat_llm()` function. This function returns the ID of the model saved to the platform. To use the function, uncomment `arthur_model_chatbot_id = register_chat_llm()` in the cell below."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "31b17b5e",
"metadata": {},
"outputs": [],
"source": [
"def register_chat_llm():\n",
"\n",
" arthur_model = arthur.model(\n",
" display_name=\"LangChainChat\",\n",
" input_type=InputType.NLP,\n",
" output_type=OutputType.TokenSequence\n",
" )\n",
"\n",
" arthur_model._add_attribute_to_model(ArthurAttribute(\n",
" name=\"my_input_text\",\n",
" stage=Stage.ModelPipelineInput,\n",
" value_type=ValueType.Unstructured_Text,\n",
" categorical=True,\n",
" is_unique=True\n",
" ))\n",
" arthur_model._add_attribute_to_model(ArthurAttribute(\n",
" name=\"my_output_text\",\n",
" stage=Stage.PredictedValue,\n",
" value_type=ValueType.Unstructured_Text,\n",
" categorical=True,\n",
" is_unique=False,\n",
" ))\n",
" \n",
" return arthur_model.save()\n",
"# arthur_model_chatbot_id = register_chat_llm()"
]
},
{
"cell_type": "markdown",
"id": "0d1d1e60",
"metadata": {},
"source": [
"Alternatively, you can set the `arthur_model_chatbot_id` variable to be the ID of your model on your [model dashboard](https://app.arthur.ai/)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "cdfa02c8",
"metadata": {},
"outputs": [],
"source": [
"arthur_model_chatbot_id = \"your-model-id-here\""
]
},
{
"cell_type": "markdown",
"id": "58be5234",
"metadata": {},
"source": [
"This function creates a Langchain chat LLM with the ArthurCallbackHandler to log inferences to Arthur. We provide our `arthur_model_chatbot_id`, as well as the Arthur url and login we are using."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "448a8fee",
"metadata": {},
"outputs": [],
"source": [
"def make_langchain_chat_llm(chat_model=ChatOpenAI):\n",
" if chat_model not in [ChatOpenAI, ChatAnthropic]:\n",
" raise ValueError(\"For this notebook, use one of the chat models imported from langchain.chat_models\")\n",
" return chat_model(\n",
" streaming=True, \n",
" temperature=0.1,\n",
" callbacks=[\n",
" StreamingStdOutCallbackHandler(), \n",
" ArthurCallbackHandler.from_credentials(arthur_model_chatbot_id, arthur_url=arthur_url, arthur_login=arthur_login)\n",
" ])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "17c182da",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2dfc00ed",
"metadata": {},
"outputs": [],
"source": [
"chat_llm = make_langchain_chat_llm()"
]
},
{
"cell_type": "markdown",
"id": "139291f2",
"metadata": {},
"source": [
"Run the chatbot (it will save the chat history in the `history` list so that the conversation can reference earlier messages)\n",
"\n",
"Type `q` to quit"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "7480a443",
"metadata": {},
"outputs": [],
"source": [
"def run_langchain_chat_llm(llm):\n",
" history = []\n",
" while True:\n",
" user_input = input(\"\\n>>> input >>>\\n>>>: \")\n",
" if user_input == 'q': break\n",
" history.append(HumanMessage(content=user_input))\n",
" history.append(llm(history))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "6868ce71",
"metadata": {},
"outputs": [],
"source": [
"run_langchain_chat_llm(chat_llm)"
]
},
{
"cell_type": "markdown",
"id": "a0be7d01",
"metadata": {},
"source": [
"# ArthurModel with input text, output text, token likelihoods, finish reason, and amount of token usage attributes"
]
},
{
"cell_type": "markdown",
"id": "1ee4b741",
"metadata": {},
"source": [
"This function registers an LLM with additional metadata attributes to log to Arthur with each inference\n",
"\n",
"As above, you can register your callback handler for an LLM using this function here in the notebook or by pasting the ID of an already-registered model from your [model dashboard](https://app.arthur.ai/)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "e671836c",
"metadata": {},
"outputs": [],
"source": [
"def register_llm():\n",
"\n",
" arthur_model = arthur.model(\n",
" display_name=\"LangChainLLM\",\n",
" input_type=InputType.NLP,\n",
" output_type=OutputType.TokenSequence\n",
" )\n",
" arthur_model._add_attribute_to_model(ArthurAttribute(\n",
" name=\"my_input_text\",\n",
" stage=Stage.ModelPipelineInput,\n",
" value_type=ValueType.Unstructured_Text,\n",
" categorical=True,\n",
" is_unique=True\n",
" ))\n",
" arthur_model._add_attribute_to_model(ArthurAttribute(\n",
" name=\"my_output_text\",\n",
" stage=Stage.PredictedValue,\n",
" value_type=ValueType.Unstructured_Text,\n",
" categorical=True,\n",
" is_unique=False,\n",
" token_attribute_link=\"my_output_likelihoods\"\n",
" ))\n",
" arthur_model._add_attribute_to_model(ArthurAttribute(\n",
" name=\"my_output_likelihoods\",\n",
" stage=Stage.PredictedValue,\n",
" value_type=ValueType.TokenLikelihoods,\n",
" token_attribute_link=\"my_output_text\"\n",
" ))\n",
" arthur_model._add_attribute_to_model(ArthurAttribute(\n",
" name=\"finish_reason\",\n",
" stage=Stage.NonInputData,\n",
" value_type=ValueType.String,\n",
" categorical=True,\n",
" categories=[\n",
" AttributeCategory(value='stop'),\n",
" AttributeCategory(value='length'),\n",
" AttributeCategory(value='content_filter'),\n",
" AttributeCategory(value='null')\n",
" ]\n",
" ))\n",
" arthur_model._add_attribute_to_model(ArthurAttribute(\n",
" name=\"prompt_tokens\",\n",
" stage=Stage.NonInputData,\n",
" value_type=ValueType.Integer\n",
" ))\n",
" arthur_model._add_attribute_to_model(ArthurAttribute(\n",
" name=\"completion_tokens\",\n",
" stage=Stage.NonInputData,\n",
" value_type=ValueType.Integer\n",
" ))\n",
" arthur_model._add_attribute_to_model(ArthurAttribute(\n",
" name=\"duration\",\n",
" stage=Stage.NonInputData,\n",
" value_type=ValueType.Float\n",
" ))\n",
" \n",
" return arthur_model.save()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "2a6686f7",
"metadata": {},
"outputs": [],
"source": [
"arthur_model_llm_id = \"your-model-id-here\""
]
},
{
"cell_type": "markdown",
"id": "2dcacb96",
"metadata": {},
"source": [
"These functions create Langchain LLMs with the ArthurCallbackHandler to log inferences to Arthur.\n",
"\n",
"There are small differences in the underlying Langchain integrations with these libraries and the available metadata for model inputs & outputs"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "34cf0072",
"metadata": {},
"outputs": [],
"source": [
"def make_langchain_openai_llm():\n",
" return OpenAI(\n",
" temperature=0.1,\n",
" model_kwargs = {'logprobs': 3},\n",
" callbacks=[\n",
" ArthurCallbackHandler.from_credentials(arthur_model_llm_id, arthur_url=arthur_url, arthur_login=arthur_login)\n",
" ])\n",
"\n",
"def make_langchain_cohere_llm():\n",
" return Cohere(\n",
" temperature=0.1,\n",
" callbacks=[\n",
" ArthurCallbackHandler.from_credentials(arthur_model_chatbot_id, arthur_url=arthur_url, arthur_login=arthur_login)\n",
" ])\n",
"\n",
"def make_langchain_huggingface_llm():\n",
" llm = HuggingFacePipeline.from_model_id(\n",
" model_id=\"bert-base-uncased\", \n",
" task=\"text-generation\", \n",
" model_kwargs={\"temperature\":2.5, \"max_length\":64})\n",
" llm.callbacks = [\n",
" ArthurCallbackHandler.from_credentials(arthur_model_chatbot_id, arthur_url=arthur_url, arthur_login=arthur_login)\n",
" ]\n",
" return llm"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "f40c3ce0",
"metadata": {},
"outputs": [],
"source": [
"openai_llm = make_langchain_openai_llm()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "8476d531",
"metadata": {},
"outputs": [],
"source": [
"cohere_llm = make_langchain_cohere_llm()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7483b9d3",
"metadata": {},
"outputs": [],
"source": [
"huggingface_llm = make_langchain_huggingface_llm()"
]
},
{
"cell_type": "markdown",
"id": "c17d8e86",
"metadata": {},
"source": [
"Run the LLM (each completion is independent, no chat history is saved as we were doing above with the chat llms)\n",
"\n",
"Type `q` to quit"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "72ee0790",
"metadata": {},
"outputs": [],
"source": [
"def run_langchain_llm(llm):\n",
" while True:\n",
" print(\"Type your text for completion:\\n\")\n",
" user_input = input(\"\\n>>> input >>>\\n>>>: \")\n",
" if user_input == 'q': break\n",
" print(llm(user_input), \"\\n================\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "fb864057",
"metadata": {},
"outputs": [],
"source": [
"run_langchain_llm(openai_llm)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "e6673769",
"metadata": {},
"outputs": [],
"source": [
"run_langchain_llm(cohere_llm)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "85541f1c",
"metadata": {},
"outputs": [],
"source": [
"run_langchain_llm(huggingface_llm)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

8
langchain/callbacks/__init__.py
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from langchain.callbacks.aim_callback import AimCallbackHandler
from langchain.callbacks.argilla_callback import ArgillaCallbackHandler
from langchain.callbacks.arize_callback import ArizeCallbackHandler
from langchain.callbacks.arthur_callback import ArthurCallbackHandler
from langchain.callbacks.clearml_callback import ClearMLCallbackHandler
from langchain.callbacks.comet_ml_callback import CometCallbackHandler
from langchain.callbacks.file import FileCallbackHandler
@ -29,19 +30,20 @@ __all__ = [
"AimCallbackHandler",
"ArgillaCallbackHandler",
"ArizeCallbackHandler",
"AsyncIteratorCallbackHandler",
"ArthurCallbackHandler",
"ClearMLCallbackHandler",
"CometCallbackHandler",
"FileCallbackHandler",
"FinalStreamingStdOutCallbackHandler",
"HumanApprovalCallbackHandler",
"InfinoCallbackHandler",
"MlflowCallbackHandler",
"OpenAICallbackHandler",
"StdOutCallbackHandler",
"AsyncIteratorCallbackHandler",
"StreamingStdOutCallbackHandler",
"StreamlitCallbackHandler",
"FinalStreamingStdOutCallbackHandler",
"LLMThoughtLabeler",
"StreamlitCallbackHandler",
"WandbCallbackHandler",
"WhyLabsCallbackHandler",
"get_openai_callback",

297
langchain/callbacks/arthur_callback.py
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"""ArthurAI's Callback Handler."""
from __future__ import annotations
import os
import uuid
from collections import defaultdict
from datetime import datetime
from time import time
from typing import TYPE_CHECKING, Any, DefaultDict, Dict, List, Optional, Union
import numpy as np
import pytz
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import AgentAction, AgentFinish, LLMResult
if TYPE_CHECKING:
import arthurai
from arthurai.core.models import ArthurModel
PROMPT_TOKENS = "prompt_tokens"
COMPLETION_TOKENS = "completion_tokens"
TOKEN_USAGE = "token_usage"
FINISH_REASON = "finish_reason"
DURATION = "duration"
def _lazy_load_arthur() -> arthurai:
"""Lazy load Arthur."""
try:
import arthurai
except ImportError as e:
raise ImportError(
"To use the ArthurCallbackHandler you need the"
" `arthurai` package. Please install it with"
" `pip install arthurai`.",
e,
)
return arthurai
class ArthurCallbackHandler(BaseCallbackHandler):
"""Callback Handler that logs to Arthur platform.
Arthur helps enterprise teams optimize model operations
and performance at scale. The Arthur API tracks model
performance, explainability, and fairness across tabular,
NLP, and CV models. Our API is model- and platform-agnostic,
and continuously scales with complex and dynamic enterprise needs.
To learn more about Arthur, visit our website at
https://www.arthur.ai/ or read the Arthur docs at
https://docs.arthur.ai/
"""
def __init__(
self,
arthur_model: ArthurModel,
) -> None:
"""Initialize callback handler."""
super().__init__()
arthurai = _lazy_load_arthur()
Stage = arthurai.common.constants.Stage
ValueType = arthurai.common.constants.ValueType
self.arthur_model = arthur_model
# save the attributes of this model to be used when preparing
# inferences to log to Arthur in on_llm_end()
self.attr_names = set([a.name for a in self.arthur_model.get_attributes()])
self.input_attr = [
x
for x in self.arthur_model.get_attributes()
if x.stage == Stage.ModelPipelineInput
and x.value_type == ValueType.Unstructured_Text
][0].name
self.output_attr = [
x
for x in self.arthur_model.get_attributes()
if x.stage == Stage.PredictedValue
and x.value_type == ValueType.Unstructured_Text
][0].name
self.token_likelihood_attr = None
if (
len(
[
x
for x in self.arthur_model.get_attributes()
if x.value_type == ValueType.TokenLikelihoods
]
)
> 0
):
self.token_likelihood_attr = [
x
for x in self.arthur_model.get_attributes()
if x.value_type == ValueType.TokenLikelihoods
][0].name
self.run_map: DefaultDict[str, Any] = defaultdict(dict)
@classmethod
def from_credentials(
cls,
model_id: str,
arthur_url: Optional[str] = "https://app.arthur.ai",
arthur_login: Optional[str] = None,
arthur_password: Optional[str] = None,
) -> ArthurCallbackHandler:
"""Initialize callback handler from Arthur credentials.
Args:
model_id (str): The ID of the arthur model to log to.
arthur_url (str, optional): The URL of the Arthur instance to log to.
Defaults to "https://app.arthur.ai".
arthur_login (str, optional): The login to use to connect to Arthur.
Defaults to None.
arthur_password (str, optional): The password to use to connect to
Arthur. Defaults to None.
Returns:
ArthurCallbackHandler: The initialized callback handler.
"""
arthurai = _lazy_load_arthur()
ArthurAI = arthurai.ArthurAI
ResponseClientError = arthurai.common.exceptions.ResponseClientError
# connect to Arthur
if arthur_login is None:
try:
arthur_api_key = os.environ["ARTHUR_API_KEY"]
except KeyError:
raise ValueError(
"No Arthur authentication provided. Either give"
" a login to the ArthurCallbackHandler"
" or set an ARTHUR_API_KEY as an environment variable."
)
arthur = ArthurAI(url=arthur_url, access_key=arthur_api_key)
else:
if arthur_password is None:
arthur = ArthurAI(url=arthur_url, login=arthur_login)
else:
arthur = ArthurAI(
url=arthur_url, login=arthur_login, password=arthur_password
)
# get model from Arthur by the provided model ID
try:
arthur_model = arthur.get_model(model_id)
except ResponseClientError:
raise ValueError(
f"Was unable to retrieve model with id {model_id} from Arthur."
" Make sure the ID corresponds to a model that is currently"
" registered with your Arthur account."
)
return cls(arthur_model)
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""On LLM start, save the input prompts"""
run_id = kwargs["run_id"]
self.run_map[run_id]["input_texts"] = prompts
self.run_map[run_id]["start_time"] = time()
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""On LLM end, send data to Arthur."""
run_id = kwargs["run_id"]
# get the run params from this run ID,
# or raise an error if this run ID has no corresponding metadata in self.run_map
try:
run_map_data = self.run_map[run_id]
except KeyError as e:
raise KeyError(
"This function has been called with a run_id"
" that was never registered in on_llm_start()."
" Restart and try running the LLM again"
) from e
# mark the duration time between on_llm_start() and on_llm_end()
time_from_start_to_end = time() - run_map_data["start_time"]
# create inferences to log to Arthur
inferences = []
for i, generations in enumerate(response.generations):
for generation in generations:
inference = {
"partner_inference_id": str(uuid.uuid4()),
"inference_timestamp": datetime.now(tz=pytz.UTC),
self.input_attr: run_map_data["input_texts"][i],
self.output_attr: generation.text,
}
if generation.generation_info is not None:
# add finish reason to the inference
# if generation info contains a finish reason and
# if the ArthurModel was registered to monitor finish_reason
if (
FINISH_REASON in generation.generation_info
and FINISH_REASON in self.attr_names
):
inference[FINISH_REASON] = generation.generation_info[
FINISH_REASON
]
# add token likelihoods data to the inference if the ArthurModel
# was registered to monitor token likelihoods
logprobs_data = generation.generation_info["logprobs"]
if (
logprobs_data is not None
and self.token_likelihood_attr is not None
):
logprobs = logprobs_data["top_logprobs"]
likelihoods = [
{k: np.exp(v) for k, v in logprobs[i].items()}
for i in range(len(logprobs))
]
inference[self.token_likelihood_attr] = likelihoods
# add token usage counts to the inference if the
# ArthurModel was registered to monitor token usage
if (
isinstance(response.llm_output, dict)
and TOKEN_USAGE in response.llm_output
):
token_usage = response.llm_output[TOKEN_USAGE]
if (
PROMPT_TOKENS in token_usage
and PROMPT_TOKENS in self.attr_names
):
inference[PROMPT_TOKENS] = token_usage[PROMPT_TOKENS]
if (
COMPLETION_TOKENS in token_usage
and COMPLETION_TOKENS in self.attr_names
):
inference[COMPLETION_TOKENS] = token_usage[COMPLETION_TOKENS]
# add inference duration to the inference if the ArthurModel
# was registered to monitor inference duration
if DURATION in self.attr_names:
inference[DURATION] = time_from_start_to_end
inferences.append(inference)
# send inferences to arthur
self.arthur_model.send_inferences(inferences)
def on_chain_start(
self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
) -> None:
"""On chain start, do nothing."""
def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
"""On chain end, do nothing."""
def on_llm_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Do nothing when LLM outputs an error."""
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""On new token, pass."""
def on_chain_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Do nothing when LLM chain outputs an error."""
def on_tool_start(
self,
serialized: Dict[str, Any],
input_str: str,
**kwargs: Any,
) -> None:
"""Do nothing when tool starts."""
def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
"""Do nothing when agent takes a specific action."""
def on_tool_end(
self,
output: str,
observation_prefix: Optional[str] = None,
llm_prefix: Optional[str] = None,
**kwargs: Any,
) -> None:
"""Do nothing when tool ends."""
def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Do nothing when tool outputs an error."""
def on_text(self, text: str, **kwargs: Any) -> None:
"""Do nothing"""
def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
"""Do nothing"""

556
poetry.lock generated
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1
pyproject.toml
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@ -199,6 +199,7 @@ momento = "^1.5.0"
ruff = "^0.0.249"
types-toml = "^0.10.8.1"
types-redis = "^4.3.21.6"
types-pytz = "^2023.3.0.0"
black = "^23.1.0"
types-chardet = "^5.0.4.6"
mypy-protobuf = "^3.0.0"

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