diff --git a/demo-test.py b/demo-test.py new file mode 100644 index 000000000..a9f5dd922 --- /dev/null +++ b/demo-test.py @@ -0,0 +1,17 @@ +from guardrails import Guard +from guardrails.utils.telemetry_utils import default_otlp_tracer +from guardrails.validators import RegexMatch +import openai + +guard = Guard.from_string( + validators=[RegexMatch(regex="w.*", on_fail="reask")], + reask_instructions="The response must begin with the letter 'w'", + tracer=default_otlp_tracer() +) + +guard( + llm_api=openai.chat.completions.create, + prompt="Write me a paragraph about computer science" +) + +print(guard.history.last.validated_output) \ No newline at end of file diff --git a/docs/telemetry.md b/docs/telemetry.md new file mode 100644 index 000000000..1dcfa2477 --- /dev/null +++ b/docs/telemetry.md @@ -0,0 +1,5 @@ +1. Go to grafana +2. search for opentelemetry in data sources +3. Create a token, paste in the resulting file to otel-collector-config.yml +4. docker-compose up -f telemetry/docker-compose.yml up +5. run a guard and look for traces \ No newline at end of file diff --git a/guardrails/cli_dir/hub/credentials.py b/guardrails/cli_dir/hub/credentials.py new file mode 100644 index 000000000..2ba3a5748 --- /dev/null +++ b/guardrails/cli_dir/hub/credentials.py @@ -0,0 +1,38 @@ +import os +from dataclasses import dataclass +from os.path import expanduser +from typing import Optional + +from guardrails.cli_dir.logger import logger +from guardrails.cli_dir.server.serializeable import Serializeable + + +@dataclass +class Credentials(Serializeable): + id: Optional[str] = None + client_id: Optional[str] = None + client_secret: Optional[str] = None + no_metrics: Optional[bool] = False + + @staticmethod + def from_rc_file() -> "Credentials": + try: + home = expanduser("~") + guardrails_rc = os.path.join(home, ".guardrailsrc") + with open(guardrails_rc) as rc_file: + lines = rc_file.readlines() + creds = {} + for line in lines: + key, value = line.split("=", 1) + creds[key.strip()] = value.strip() + rc_file.close() + return Credentials.from_dict(creds) + + except FileNotFoundError as e: + logger.error(e) + logger.error( + "Guardrails Hub credentials not found!" + "You will need to sign up to use any authenticated Validators here:" + "{insert url}" + ) + return Credentials() diff --git a/guardrails/cli_dir/logger.py b/guardrails/cli_dir/logger.py new file mode 100644 index 000000000..d29662acc --- /dev/null +++ b/guardrails/cli_dir/logger.py @@ -0,0 +1,20 @@ +import logging +import os + +import coloredlogs + +os.environ[ + "COLOREDLOGS_LEVEL_STYLES" +] = "spam=white,faint;success=green,bold;debug=magenta;verbose=blue;notice=cyan,bold;warning=yellow;error=red;critical=background=red" # noqa +LEVELS = { + "SPAM": 5, + "VERBOSE": 15, + "NOTICE": 25, + "SUCCESS": 35, +} +for key in LEVELS: + logging.addLevelName(LEVELS.get(key), key) # type: ignore + + +logger = logging.getLogger("guardrails-cli") +coloredlogs.install(level="DEBUG", logger=logger) diff --git a/guardrails/cli_dir/server/serializeable.py b/guardrails/cli_dir/server/serializeable.py new file mode 100644 index 000000000..311085200 --- /dev/null +++ b/guardrails/cli_dir/server/serializeable.py @@ -0,0 +1,33 @@ +import inspect +import json +from dataclasses import InitVar, asdict, dataclass, field, is_dataclass +from json import JSONEncoder +from typing import Any, Dict + + +class SerializeableJSONEncoder(JSONEncoder): + def default(self, o): + if is_dataclass(o): + return asdict(o) + return super().default(o) + + +@dataclass +class Serializeable: + encoder: InitVar[JSONEncoder] = field( + kw_only=True, default=SerializeableJSONEncoder # type: ignore + ) + + @classmethod + def from_dict(cls, data: Dict[str, Any]): + annotations = inspect.get_annotations(cls) + attributes = dict.keys(annotations) + kwargs = {k: data.get(k) for k in data if k in attributes} + return cls(**kwargs) # type: ignore + + @property + def __dict__(self) -> Dict[str, Any]: + return asdict(self) + + def to_json(self): + return json.dumps(self, cls=self.encoder) # type: ignore diff --git a/guardrails/guard.py b/guardrails/guard.py index 8824f408f..084bb4c3e 100644 --- a/guardrails/guard.py +++ b/guardrails/guard.py @@ -30,6 +30,7 @@ from guardrails.classes.generic import Stack from guardrails.classes.history import Call from guardrails.classes.history.call_inputs import CallInputs +from guardrails.cli_dir.hub.credentials import Credentials from guardrails.errors import ValidationError from guardrails.llm_providers import get_async_llm_ask, get_llm_ask from guardrails.logger import logger, set_scope @@ -37,6 +38,14 @@ from guardrails.rail import Rail from guardrails.run import AsyncRunner, Runner, StreamRunner from guardrails.schema import Schema, StringSchema +from guardrails.stores.context import ( + Tracer, + get_tracer_context, + set_call_kwargs, + set_tracer, + set_tracer_context, +) +from guardrails.utils.hub_telemetry_utils import HubTelemetry from guardrails.utils.validator_utils import get_validator from guardrails.validator_base import Validator @@ -60,6 +69,11 @@ class Guard(Runnable, Generic[OT]): the LLM and the validated output. """ + _tracer = None + _tracer_context = None + _hub_telemetry = None + _guard_id = None + _user_id = None _validators: List[Validator] def __init__( @@ -67,6 +81,7 @@ def __init__( rail: Optional[Rail] = None, num_reasks: Optional[int] = None, base_model: Optional[Type[BaseModel]] = None, + tracer: Tracer = None, ): """Initialize the Guard with optional Rail instance, num_reasks, and base_model.""" @@ -81,6 +96,25 @@ def __init__( # TODO: Support a sink for history so that it is not solely held in memory self.history: Stack[Call] = Stack() self.base_model = base_model + self._set_tracer(tracer) + + # Get unique id of user from credentials + self._user_id = Credentials.from_rc_file().id + + # Get metrics opt-out from credentials + self._disable_tracer = Credentials.from_rc_file().no_metrics + if self._disable_tracer.strip().lower() == "true": + self._disable_tracer = True + elif self._disable_tracer.strip().lower() == "false": + self._disable_tracer = False + + # Get id of guard object (that is unique) + self._guard_id = id(self) # id of guard object; not the class + + # Initialize Hub Telemetry singleton and get the tracer + # if it is not disabled + if not self._disable_tracer: + self._hub_telemetry = HubTelemetry() self._validators = [] @property @@ -158,8 +192,16 @@ def configure( else 1 ) + def _set_tracer(self, tracer: Tracer = None) -> None: + self._tracer = tracer + set_tracer(tracer) + set_tracer_context() + self._tracer_context = get_tracer_context() + @classmethod - def from_rail(cls, rail_file: str, num_reasks: Optional[int] = None): + def from_rail( + cls, rail_file: str, num_reasks: Optional[int] = None, tracer: Tracer = None + ): """Create a Schema from a `.rail` file. Args: @@ -169,13 +211,23 @@ def from_rail(cls, rail_file: str, num_reasks: Optional[int] = None): Returns: An instance of the `Guard` class. """ + + # We have to set the tracer in the ContextStore before the Rail, + # and therefore the Validators, are initialized + cls._set_tracer(cls, tracer) + rail = Rail.from_file(rail_file) if rail.output_type == "str": return cast(Guard[str], cls(rail=rail, num_reasks=num_reasks)) return cast(Guard[Dict], cls(rail=rail, num_reasks=num_reasks)) @classmethod - def from_rail_string(cls, rail_string: str, num_reasks: Optional[int] = None): + def from_rail_string( + cls, + rail_string: str, + num_reasks: Optional[int] = None, + tracer: Tracer = None, + ): """Create a Schema from a `.rail` string. Args: @@ -185,6 +237,10 @@ def from_rail_string(cls, rail_string: str, num_reasks: Optional[int] = None): Returns: An instance of the `Guard` class. """ + # We have to set the tracer in the ContextStore before the Rail, + # and therefore the Validators, are initialized + cls._set_tracer(cls, tracer) + rail = Rail.from_string(rail_string) if rail.output_type == "str": return cast(Guard[str], cls(rail=rail, num_reasks=num_reasks)) @@ -199,8 +255,13 @@ def from_pydantic( num_reasks: Optional[int] = None, reask_prompt: Optional[str] = None, reask_instructions: Optional[str] = None, + tracer: Tracer = None, ): """Create a Guard instance from a Pydantic model and prompt.""" + # We have to set the tracer in the ContextStore before the Rail, + # and therefore the Validators, are initialized + cls._set_tracer(cls, tracer) + rail = Rail.from_pydantic( output_class=output_class, prompt=prompt, @@ -222,6 +283,7 @@ def from_string( reask_prompt: Optional[str] = None, reask_instructions: Optional[str] = None, num_reasks: Optional[int] = None, + tracer: Tracer = None, ): """Create a Guard instance for a string response with prompt, instructions, and validations. @@ -235,6 +297,9 @@ def from_string( reask_instructions (str, optional): Alternative instructions to use during reasks. Defaults to None. num_reasks (int, optional): The max times to re-ask the LLM for invalid output. """ # noqa + + cls._set_tracer(cls, tracer) + rail = Rail.from_string_validators( validators=validators, description=description, @@ -243,7 +308,7 @@ def from_string( reask_prompt=reask_prompt, reask_instructions=reask_instructions, ) - return cast(Guard[str], cls(rail, num_reasks=num_reasks)) + return cast(Guard[str], cls(rail, num_reasks=num_reasks, tracer=tracer)) @overload def __call__( @@ -313,46 +378,93 @@ def __call__( Returns: The raw text output from the LLM and the validated output. """ - if metadata is None: - metadata = {} - if full_schema_reask is None: - full_schema_reask = self.base_model is not None - if prompt_params is None: - prompt_params = {} - - context = contextvars.ContextVar("kwargs") - context.set(kwargs) - - self.configure(num_reasks) - if self.num_reasks is None: - raise RuntimeError( - "`num_reasks` is `None` after calling `configure()`. " - "This should never happen." - ) - input_prompt = prompt or (self.prompt._source if self.prompt else None) - input_instructions = instructions or ( - self.instructions._source if self.instructions else None - ) - call_inputs = CallInputs( - llm_api=llm_api, - prompt=input_prompt, - instructions=input_instructions, - msg_history=msg_history, - prompt_params=prompt_params, - num_reasks=self.num_reasks, - metadata=metadata, - full_schema_reask=full_schema_reask, - args=list(args), - kwargs=kwargs, - ) - call_log = Call(inputs=call_inputs) - set_scope(str(id(call_log))) - self.history.push(call_log) + def __call( + self, + llm_api: Union[Callable, Callable[[Any], Awaitable[Any]]], + prompt_params: Optional[Dict] = None, + num_reasks: Optional[int] = None, + prompt: Optional[str] = None, + instructions: Optional[str] = None, + msg_history: Optional[List[Dict]] = None, + metadata: Optional[Dict] = None, + full_schema_reask: Optional[bool] = None, + *args, + **kwargs, + ): + if metadata is None: + metadata = {} + if full_schema_reask is None: + full_schema_reask = self.base_model is not None + if prompt_params is None: + prompt_params = {} + + if not self._disable_tracer: + # Create a new span for this guard call + self._hub_telemetry.create_new_span( + span_name="/guard_call", + attributes=[ + ("guard_id", self._guard_id), + ("user_id", self._user_id), + ("llm_api", llm_api.__name__ if llm_api else "None"), + ("custom_reask_prompt", self.reask_prompt is not None), + ( + "custom_reask_instructions", + self.reask_instructions is not None, + ), + ], + is_parent=True, # It will have children + has_parent=False, # Has no parents + ) + + set_call_kwargs(kwargs) + set_tracer(self._tracer) + set_tracer_context(self._tracer_context) - # If the LLM API is async, return a coroutine - if asyncio.iscoroutinefunction(llm_api): - return self._call_async( + self.configure(num_reasks) + if self.num_reasks is None: + raise RuntimeError( + "`num_reasks` is `None` after calling `configure()`. " + "This should never happen." + ) + + input_prompt = prompt or (self.prompt._source if self.prompt else None) + input_instructions = instructions or ( + self.instructions._source if self.instructions else None + ) + call_inputs = CallInputs( + llm_api=llm_api, + prompt=input_prompt, + instructions=input_instructions, + msg_history=msg_history, + prompt_params=prompt_params, + num_reasks=self.num_reasks, + metadata=metadata, + full_schema_reask=full_schema_reask, + args=list(args), + kwargs=kwargs, + ) + call_log = Call(inputs=call_inputs) + set_scope(str(id(call_log))) + self.history.push(call_log) + + # If the LLM API is async, return a coroutine + if asyncio.iscoroutinefunction(llm_api): + return self._call_async( + llm_api, + prompt_params=prompt_params, + num_reasks=self.num_reasks, + prompt=prompt, + instructions=instructions, + msg_history=msg_history, + metadata=metadata, + full_schema_reask=full_schema_reask, + call_log=call_log, + *args, + **kwargs, + ) + # Otherwise, call the LLM synchronously + return self._call_sync( llm_api, prompt_params=prompt_params, num_reasks=self.num_reasks, @@ -365,17 +477,19 @@ def __call__( *args, **kwargs, ) - # Otherwise, call the LLM synchronously - return self._call_sync( + + guard_context = contextvars.Context() + return guard_context.run( + __call, + self, llm_api, - prompt_params=prompt_params, - num_reasks=self.num_reasks, - prompt=prompt, - instructions=instructions, - msg_history=msg_history, - metadata=metadata, - full_schema_reask=full_schema_reask, - call_log=call_log, + prompt_params, + num_reasks, + prompt, + instructions, + msg_history, + metadata, + full_schema_reask, *args, **kwargs, ) @@ -599,44 +713,89 @@ def parse( The validated response. This is either a string or a dictionary, determined by the object schema defined in the RAILspec. """ - final_num_reasks = ( - num_reasks if num_reasks is not None else 0 if llm_api is None else None - ) - self.configure(final_num_reasks) - if self.num_reasks is None: - raise RuntimeError( - "`num_reasks` is `None` after calling `configure()`. " - "This should never happen." + + def __parse( + self, + llm_output: str, + metadata: Optional[Dict] = None, + llm_api: Optional[Callable] = None, + num_reasks: Optional[int] = None, + prompt_params: Optional[Dict] = None, + full_schema_reask: Optional[bool] = None, + *args, + **kwargs, + ): + final_num_reasks = ( + num_reasks if num_reasks is not None else 0 if llm_api is None else None ) - if full_schema_reask is None: - full_schema_reask = self.base_model is not None - metadata = metadata or {} - prompt_params = prompt_params or {} - - context = contextvars.ContextVar("kwargs") - context.set(kwargs) - - input_prompt = self.prompt._source if self.prompt else None - input_instructions = self.instructions._source if self.instructions else None - call_inputs = CallInputs( - llm_api=llm_api, - llm_output=llm_output, - prompt=input_prompt, - instructions=input_instructions, - prompt_params=prompt_params, - num_reasks=self.num_reasks, - metadata=metadata, - full_schema_reask=full_schema_reask, - args=list(args), - kwargs=kwargs, - ) - call_log = Call(inputs=call_inputs) - set_scope(str(id(call_log))) - self.history.push(call_log) - # If the LLM API is async, return a coroutine - if asyncio.iscoroutinefunction(llm_api): - return self._async_parse( + if not self._disable_tracer: + self._hub_telemetry.create_new_span( + span_name="/guard_parse", + attributes=[ + ("guard_id", self._guard_id), + ("user_id", self._user_id), + ("llm_api", llm_api.__name__ if llm_api else "None"), + ("custom_reask_prompt", self.reask_prompt is not None), + ( + "custom_reask_instructions", + self.reask_instructions is not None, + ), + ], + is_parent=True, # It will have children + has_parent=False, # Has no parents + ) + + self.configure(final_num_reasks) + if self.num_reasks is None: + raise RuntimeError( + "`num_reasks` is `None` after calling `configure()`. " + "This should never happen." + ) + if full_schema_reask is None: + full_schema_reask = self.base_model is not None + metadata = metadata or {} + prompt_params = prompt_params or {} + + set_call_kwargs(kwargs) + set_tracer(self._tracer) + set_tracer_context(self._tracer_context) + + input_prompt = self.prompt._source if self.prompt else None + input_instructions = ( + self.instructions._source if self.instructions else None + ) + call_inputs = CallInputs( + llm_api=llm_api, + llm_output=llm_output, + prompt=input_prompt, + instructions=input_instructions, + prompt_params=prompt_params, + num_reasks=self.num_reasks, + metadata=metadata, + full_schema_reask=full_schema_reask, + args=list(args), + kwargs=kwargs, + ) + call_log = Call(inputs=call_inputs) + set_scope(str(id(call_log))) + self.history.push(call_log) + + # If the LLM API is async, return a coroutine + if asyncio.iscoroutinefunction(llm_api): + return self._async_parse( + llm_output, + metadata, + llm_api=llm_api, + num_reasks=self.num_reasks, + prompt_params=prompt_params, + full_schema_reask=full_schema_reask, + call_log=call_log, + *args, + **kwargs, + ) + # Otherwise, call the LLM synchronously + return self._sync_parse( llm_output, metadata, llm_api=llm_api, @@ -647,15 +806,17 @@ def parse( *args, **kwargs, ) - # Otherwise, call the LLM synchronously - return self._sync_parse( + + guard_context = contextvars.Context() + return guard_context.run( + __parse, + self, llm_output, metadata, - llm_api=llm_api, - num_reasks=self.num_reasks, - prompt_params=prompt_params, - full_schema_reask=full_schema_reask, - call_log=call_log, + llm_api, + num_reasks, + prompt_params, + full_schema_reask, *args, **kwargs, ) diff --git a/guardrails/run.py b/guardrails/run.py index 537b3ae6a..410c79ed8 100644 --- a/guardrails/run.py +++ b/guardrails/run.py @@ -6,6 +6,7 @@ from pydantic import BaseModel from guardrails.classes.history import Call, Inputs, Iteration, Outputs +from guardrails.cli_dir.hub.credentials import Credentials from guardrails.datatypes import verify_metadata_requirements from guardrails.errors import ValidationError from guardrails.llm_providers import ( @@ -18,6 +19,7 @@ from guardrails.prompt import Instructions, Prompt from guardrails.schema import Schema, StringSchema from guardrails.utils.exception_utils import UserFacingException +from guardrails.utils.hub_telemetry_utils import HubTelemetry from guardrails.utils.llm_response import LLMResponse from guardrails.utils.openai_utils import OPENAI_VERSION from guardrails.utils.reask_utils import ( @@ -26,6 +28,7 @@ SkeletonReAsk, reasks_to_dict, ) +from guardrails.utils.telemetry_utils import async_trace, trace add_destinations(logger.debug) @@ -102,6 +105,17 @@ def __init__( self.base_model = base_model self.full_schema_reask = full_schema_reask + # Get metrics opt-out from credentials + self._disable_tracer = Credentials.from_rc_file().no_metrics + if self._disable_tracer.strip().lower() == "true": + self._disable_tracer = True + elif self._disable_tracer.strip().lower() == "false": + self._disable_tracer = False + + if not self._disable_tracer: + # Get the HubTelemetry singleton + self._hub_telemetry = HubTelemetry() + def __call__(self, call_log: Call, prompt_params: Optional[Dict] = None) -> Call: """Execute the runner by repeatedly calling step until the reask budget is exhausted. @@ -194,6 +208,17 @@ def __call__(self, call_log: Call, prompt_params: Optional[Dict] = None) -> Call prompt_params=prompt_params, include_instructions=include_instructions, ) + + # Log how many times we reasked + # Use the HubTelemetry singleton + if not self._disable_tracer: + self._hub_telemetry.create_new_span( + span_name="/reasks", + attributes=[("reask_count", index)], + is_parent=False, # This span has no children + has_parent=True, # This span has a parent + ) + except UserFacingException as e: # Because Pydantic v1 doesn't respect property setters call_log._exception = e.original_exception @@ -204,6 +229,7 @@ def __call__(self, call_log: Call, prompt_params: Optional[Dict] = None) -> Call raise e return call_log + @trace(name="step") def step( self, index: int, @@ -509,6 +535,7 @@ def prepare( return instructions, prompt, msg_history + @trace(name="call") def call( self, index: int, @@ -593,7 +620,7 @@ def validate( """Validate the output.""" with start_action(action_type="validate", index=index) as action: validated_output = output_schema.validate( - iteration, parsed_output, self.metadata, **kwargs + iteration, parsed_output, self.metadata, attempt_number=index, **kwargs ) action.log( @@ -783,6 +810,7 @@ async def async_run( return call_log + @async_trace(name="step") async def async_step( self, index: int, @@ -893,6 +921,7 @@ async def async_step( raise e return iteration + @async_trace(name="call") async def async_call( self, index: int, @@ -964,7 +993,7 @@ async def async_validate( """Validate the output.""" with start_action(action_type="validate", index=index) as action: validated_output = await output_schema.async_validate( - iteration, parsed_output, self.metadata + iteration, parsed_output, self.metadata, attempt_number=index ) action.log( diff --git a/guardrails/schema/__init__.py b/guardrails/schema/__init__.py new file mode 100644 index 000000000..b2ec84b9e --- /dev/null +++ b/guardrails/schema/__init__.py @@ -0,0 +1,10 @@ +from guardrails.schema.json_schema import JsonSchema, Schema2Prompt +from guardrails.schema.schema import Schema +from guardrails.schema.string_schema import StringSchema + +__all__ = [ + "Schema", + "StringSchema", + "JsonSchema", + "Schema2Prompt", +] diff --git a/guardrails/schema.py b/guardrails/schema/json_schema.py similarity index 55% rename from guardrails/schema.py rename to guardrails/schema/json_schema.py index 814d526cb..fb5e119cf 100644 --- a/guardrails/schema.py +++ b/guardrails/schema/json_schema.py @@ -1,18 +1,6 @@ import json -import pprint from copy import deepcopy -from typing import ( - TYPE_CHECKING, - Any, - Dict, - List, - Optional, - Sequence, - Set, - Tuple, - Type, - Union, -) +from typing import Any, Dict, List, Optional, Tuple, Type, Union from lxml import etree as ET from pydantic import BaseModel @@ -20,7 +8,7 @@ from guardrails import validator_service from guardrails.classes.history import Iteration -from guardrails.datatypes import Choice, DataType, Object, String +from guardrails.datatypes import Choice, DataType, Object from guardrails.llm_providers import ( AsyncOpenAICallable, AsyncOpenAIChatCallable, @@ -30,6 +18,7 @@ ) from guardrails.logger import logger from guardrails.prompt import Instructions, Prompt +from guardrails.schema.schema import Schema from guardrails.utils.constants import constants from guardrails.utils.json_utils import ( extract_json_from_ouput, @@ -45,177 +34,8 @@ get_pruned_tree, prune_obj_for_reasking, ) -from guardrails.validator_base import ( - FailResult, - ValidatorSpec, - check_refrain_in_dict, - filter_in_dict, -) - -if TYPE_CHECKING: - pass - - -class Schema: - """Schema class that holds a _schema attribute.""" - - reask_prompt_vars: Set[str] - - def __init__( - self, - schema: DataType, - reask_prompt_template: Optional[str] = None, - reask_instructions_template: Optional[str] = None, - ) -> None: - self.root_datatype = schema - - # Setup reask templates - self.reask_prompt_template = reask_prompt_template - self.reask_instructions_template = reask_instructions_template - - @classmethod - def from_xml( - cls, - root: ET._Element, - reask_prompt_template: Optional[str] = None, - reask_instructions_template: Optional[str] = None, - ) -> Self: - """Create a schema from an XML element.""" - raise NotImplementedError - - def __repr__(self) -> str: - # FIXME make sure this is pretty - return f"{self.__class__.__name__}({pprint.pformat(self.root_datatype)})" - - @property - def reask_prompt_template(self) -> Optional[Prompt]: - return self._reask_prompt_template - - @reask_prompt_template.setter - def reask_prompt_template(self, value: Optional[str]) -> None: - self.check_valid_reask_prompt(value) - if value is not None: - self._reask_prompt_template = Prompt(value) - else: - self._reask_prompt_template = None - - @property - def reask_instructions_template(self) -> Optional[Instructions]: - return self._reask_instructions_template - - @reask_instructions_template.setter - def reask_instructions_template(self, value: Optional[str]) -> None: - if value is not None: - self._reask_instructions_template = Instructions(value) - else: - self._reask_instructions_template = None - - def validate( - self, iteration: Iteration, data: Any, metadata: Dict, **kwargs - ) -> Any: - """Validate a dictionary of data against the schema. - - Args: - data: The data to validate. - - Returns: - The validated data. - """ - raise NotImplementedError - - async def async_validate( - self, iteration: Iteration, data: Any, metadata: Dict - ) -> Any: - """Asynchronously validate a dictionary of data against the schema. - - Args: - data: The data to validate. - - Returns: - The validated data. - """ - raise NotImplementedError - - def transpile(self, method: str = "default") -> str: - """Convert the XML schema to a string that is used for prompting a - large language model. - - Returns: - The prompt. - """ - raise NotImplementedError - - def parse(self, output: str, **kwargs) -> Tuple[Any, Optional[Exception]]: - """Parse the output from the large language model. - - Args: - output: The output from the large language model. - - Returns: - The parsed output, and the exception that was raised (if any). - """ - raise NotImplementedError - - def introspect( - self, data: Any - ) -> Tuple[Sequence[ReAsk], Optional[Union[str, Dict]]]: - """Inspect the data for reasks. - - Args: - data: The data to introspect. - - Returns: - A list of ReAsk objects. - """ - raise NotImplementedError - - def get_reask_setup( - self, - reasks: Sequence[ReAsk], - original_response: Any, - use_full_schema: bool, - prompt_params: Optional[Dict[str, Any]] = None, - ) -> Tuple["Schema", Prompt, Instructions]: - """Construct a schema for reasking, and a prompt for reasking. - - Args: - reasks: List of tuples, where each tuple contains the path to the - reasked element, and the ReAsk object (which contains the error - message describing why the reask is necessary). - original_response: The value that was returned from the API, with reasks. - use_full_schema: Whether to use the full schema, or only the schema - for the reasked elements. - - Returns: - The schema for reasking, and the prompt for reasking. - """ - raise NotImplementedError - - def preprocess_prompt( - self, - prompt_callable: PromptCallableBase, - instructions: Optional[Instructions], - prompt: Prompt, - ): - """Preprocess the instructions and prompt before sending it to the - model. - - Args: - prompt_callable: The callable to be used to prompt the model. - instructions: The instructions to preprocess. - prompt: The prompt to preprocess. - """ - raise NotImplementedError - - def check_valid_reask_prompt(self, reask_prompt: Optional[str]) -> None: - if reask_prompt is None: - return - - # Check that the reask prompt has the correct variables - - # TODO decide how to check this - # variables = get_template_variables(reask_prompt) - # assert set(variables) == self.reask_prompt_vars +from guardrails.utils.telemetry_utils import trace_validation_result +from guardrails.validator_base import FailResult, check_refrain_in_dict, filter_in_dict class JsonSchema(Schema): @@ -468,6 +288,7 @@ def validate( iteration: Iteration, data: Optional[Dict[str, Any]], metadata: Dict, + attempt_number: int = 0, **kwargs, ) -> Any: """Validate a dictionary of data against the schema. @@ -525,6 +346,11 @@ def validate( # Remove all keys that have `Filter` values. validated_response = filter_in_dict(validated_response) + # TODO: Capture error messages once Top Level error handling is merged in + trace_validation_result( + validation_logs=iteration.validator_logs, attempt_number=attempt_number + ) + return validated_response async def async_validate( @@ -532,6 +358,7 @@ async def async_validate( iteration: Iteration, data: Optional[Dict[str, Any]], metadata: Dict, + attempt_number: int = 0, ) -> Any: """Validate a dictionary of data against the schema. @@ -588,6 +415,11 @@ async def async_validate( # Remove all keys that have `Filter` values. validated_response = filter_in_dict(validated_response) + # TODO: Capture error messages once Top Level error handling is merged in + trace_validation_result( + validation_logs=iteration.validator_logs, attempt_number=attempt_number + ) + return validated_response def introspect(self, data: Any) -> Tuple[List[ReAsk], Optional[Dict]]: @@ -624,256 +456,6 @@ def transpile(self, method: str = "default") -> str: return transpiler(self) -class StringSchema(Schema): - reask_prompt_vars = {"previous_response", "output_schema", "error_messages"} - - def __init__( - self, - schema: String, - reask_prompt_template: Optional[str] = None, - reask_instructions_template: Optional[str] = None, - ) -> None: - super().__init__( - schema, - reask_prompt_template=reask_prompt_template, - reask_instructions_template=reask_instructions_template, - ) - self.root_datatype = schema - - @classmethod - def from_xml( - cls, - root: ET._Element, - reask_prompt_template: Optional[str] = None, - reask_instructions_template: Optional[str] = None, - ) -> Self: - if len(root) != 0: - raise ValueError("String output schemas must not have children.") - - strict = False - if "strict" in root.attrib and root.attrib["strict"] == "true": - strict = True - - schema = String.from_xml(root, strict=strict) - - return cls( - schema=schema, - reask_prompt_template=reask_prompt_template, - reask_instructions_template=reask_instructions_template, - ) - - @classmethod - def from_string( - cls, - validators: Sequence[ValidatorSpec], - description: Optional[str] = None, - reask_prompt_template: Optional[str] = None, - reask_instructions_template: Optional[str] = None, - ): - strict = False - - schema = String.from_string_rail( - validators, description=description, strict=strict - ) - - return cls( - schema=schema, - reask_prompt_template=reask_prompt_template, - reask_instructions_template=reask_instructions_template, - ) - - def get_reask_setup( - self, - reasks: List[FieldReAsk], - original_response: FieldReAsk, - use_full_schema: bool, - prompt_params: Optional[Dict[str, Any]] = None, - ) -> Tuple[Schema, Prompt, Instructions]: - pruned_tree_string = self.transpile() - - reask_prompt_template = self.reask_prompt_template - if reask_prompt_template is None: - reask_prompt_template = Prompt( - constants["high_level_string_reask_prompt"] - + constants["complete_string_suffix"] - ) - - error_messages = "\n".join( - [ - f"- {fail_result.error_message}" - for reask in reasks - for fail_result in reask.fail_results - ] - ) - - prompt = reask_prompt_template.format( - previous_response=original_response.incorrect_value, - error_messages=error_messages, - output_schema=pruned_tree_string, - **(prompt_params or {}), - ) - - instructions = self.reask_instructions_template - if instructions is None: - instructions = Instructions("You are a helpful assistant.") - instructions = instructions.format(**(prompt_params or {})) - - return self, prompt, instructions - - def parse(self, output: str, **kwargs) -> Tuple[Any, Optional[Exception]]: - return output, None - - def validate( - self, - iteration: Iteration, - data: Any, - metadata: Dict, - **kwargs, - ) -> Any: - """Validate a dictionary of data against the schema. - - Args: - data: The data to validate. - - Returns: - The validated data. - """ - if data is None: - return None - - if not isinstance(data, str): - raise TypeError(f"Argument `data` must be a string, not {type(data)}.") - - # FIXME instead of writing the validation infrastructure for dicts (JSON), - # make it more structure-invariant - dummy_key = "string" - validation = self.root_datatype.collect_validation( - key=dummy_key, - value=data, - schema={ - dummy_key: data, - }, - ) - - validated_response, metadata = validator_service.validate( - value=data, - metadata=metadata, - validator_setup=validation, - iteration=iteration, - ) - - validated_response = {dummy_key: validated_response} - - if check_refrain_in_dict(validated_response): - # If the data contains a `Refain` value, we return an empty - # dictionary. - logger.debug("Refrain detected.") - validated_response = {} - - # Remove all keys that have `Filter` values. - validated_response = filter_in_dict(validated_response) - - if dummy_key in validated_response: - return validated_response[dummy_key] - return None - - async def async_validate( - self, - iteration: Iteration, - data: Any, - metadata: Dict, - ) -> Any: - """Validate a dictionary of data against the schema. - - Args: - data: The data to validate. - - Returns: - The validated data. - """ - if data is None: - return None - - if not isinstance(data, str): - raise TypeError(f"Argument `data` must be a string, not {type(data)}.") - - dummy_key = "string" - validation = self.root_datatype.collect_validation( - key=dummy_key, - value=data, - schema={ - dummy_key: data, - }, - ) - - validated_response, metadata = await validator_service.async_validate( - value=data, - metadata=metadata, - validator_setup=validation, - iteration=iteration, - ) - - validated_response = {dummy_key: validated_response} - - if check_refrain_in_dict(validated_response): - # If the data contains a `Refain` value, we return an empty - # dictionary. - logger.debug("Refrain detected.") - validated_response = {} - - # Remove all keys that have `Filter` values. - validated_response = filter_in_dict(validated_response) - - if dummy_key in validated_response: - return validated_response[dummy_key] - return None - - def introspect( - self, data: Union[ReAsk, Optional[str]] - ) -> Tuple[List[FieldReAsk], Optional[str]]: - if isinstance(data, FieldReAsk): - return [data], None - return [], data # type: ignore - - def preprocess_prompt( - self, - prompt_callable: PromptCallableBase, - instructions: Optional[Instructions], - prompt: Prompt, - ): - if isinstance(prompt_callable, OpenAICallable) or isinstance( - prompt_callable, AsyncOpenAICallable - ): - prompt.source += "\n\nString Output:\n\n" - if ( - isinstance(prompt_callable, OpenAIChatCallable) - or isinstance(prompt_callable, AsyncOpenAIChatCallable) - ) and not instructions: - instructions = Instructions( - "You are a helpful assistant, expressing yourself through a string." - ) - - return instructions, prompt - - def transpile(self, method: str = "default") -> str: - obj = self.root_datatype - schema = "" - if obj.description is not None: - schema += ( - "Here's a description of what I want you to generate: " - f"{obj.description}" - ) - if not obj.validators_attr.empty: - schema += ( - "\n\nYour generated response should satisfy the following properties:" - ) - for validator in obj.validators_attr.validators: - schema += f"\n- {validator.to_prompt()}" - - schema += "\n\nDon't talk; just go." - return schema - - class Schema2Prompt: """Class that contains transpilers to go from a schema to its representation in a prompt. diff --git a/guardrails/schema/schema.py b/guardrails/schema/schema.py new file mode 100644 index 000000000..756767ac8 --- /dev/null +++ b/guardrails/schema/schema.py @@ -0,0 +1,181 @@ +import pprint +from typing import TYPE_CHECKING, Any, Dict, Optional, Sequence, Set, Tuple, Union + +from lxml import etree as ET +from typing_extensions import Self + +from guardrails.classes.history import Iteration +from guardrails.datatypes import DataType +from guardrails.llm_providers import PromptCallableBase +from guardrails.prompt import Instructions, Prompt +from guardrails.utils.reask_utils import ReAsk + +if TYPE_CHECKING: + pass + + +class Schema: + """Schema class that holds a _schema attribute.""" + + reask_prompt_vars: Set[str] + + def __init__( + self, + schema: DataType, + reask_prompt_template: Optional[str] = None, + reask_instructions_template: Optional[str] = None, + ) -> None: + self.root_datatype = schema + + # Setup reask templates + self.reask_prompt_template = reask_prompt_template + self.reask_instructions_template = reask_instructions_template + + @classmethod + def from_xml( + cls, + root: ET._Element, + reask_prompt_template: Optional[str] = None, + reask_instructions_template: Optional[str] = None, + ) -> Self: + """Create a schema from an XML element.""" + raise NotImplementedError + + def __repr__(self) -> str: + # FIXME make sure this is pretty + return f"{self.__class__.__name__}({pprint.pformat(self.root_datatype)})" + + @property + def reask_prompt_template(self) -> Optional[Prompt]: + return self._reask_prompt_template + + @reask_prompt_template.setter + def reask_prompt_template(self, value: Optional[str]) -> None: + self.check_valid_reask_prompt(value) + if value is not None: + self._reask_prompt_template = Prompt(value) + else: + self._reask_prompt_template = None + + @property + def reask_instructions_template(self) -> Optional[Instructions]: + return self._reask_instructions_template + + @reask_instructions_template.setter + def reask_instructions_template(self, value: Optional[str]) -> None: + if value is not None: + self._reask_instructions_template = Instructions(value) + else: + self._reask_instructions_template = None + + def validate( + self, + iteration: Iteration, + data: Any, + metadata: Dict, + attempt_number: int = 0, + **kwargs, + ) -> Any: + """Validate a dictionary of data against the schema. + + Args: + data: The data to validate. + + Returns: + The validated data. + """ + raise NotImplementedError + + async def async_validate( + self, iteration: Iteration, data: Any, metadata: Dict, attempt_number: int = 0 + ) -> Any: + """Asynchronously validate a dictionary of data against the schema. + + Args: + data: The data to validate. + + Returns: + The validated data. + """ + raise NotImplementedError + + def transpile(self, method: str = "default") -> str: + """Convert the XML schema to a string that is used for prompting a + large language model. + + Returns: + The prompt. + """ + raise NotImplementedError + + def parse(self, output: str, **kwargs) -> Tuple[Any, Optional[Exception]]: + """Parse the output from the large language model. + + Args: + output: The output from the large language model. + + Returns: + The parsed output, and the exception that was raised (if any). + """ + raise NotImplementedError + + def introspect( + self, data: Any + ) -> Tuple[Sequence[ReAsk], Optional[Union[str, Dict]]]: + """Inspect the data for reasks. + + Args: + data: The data to introspect. + + Returns: + A list of ReAsk objects. + """ + raise NotImplementedError + + def get_reask_setup( + self, + reasks: Sequence[ReAsk], + original_response: Any, + use_full_schema: bool, + prompt_params: Optional[Dict[str, Any]] = None, + ) -> Tuple["Schema", Prompt, Instructions]: + """Construct a schema for reasking, and a prompt for reasking. + + Args: + reasks: List of tuples, where each tuple contains the path to the + reasked element, and the ReAsk object (which contains the error + message describing why the reask is necessary). + original_response: The value that was returned from the API, with reasks. + use_full_schema: Whether to use the full schema, or only the schema + for the reasked elements. + + Returns: + The schema for reasking, and the prompt for reasking. + """ + raise NotImplementedError + + def preprocess_prompt( + self, + prompt_callable: PromptCallableBase, + instructions: Optional[Instructions], + prompt: Prompt, + ): + """Preprocess the instructions and prompt before sending it to the + model. + + Args: + prompt_callable: The callable to be used to prompt the model. + instructions: The instructions to preprocess. + prompt: The prompt to preprocess. + """ + raise NotImplementedError + + def check_valid_reask_prompt(self, reask_prompt: Optional[str]) -> None: + if reask_prompt is None: + return + + # Check that the reask prompt has the correct variables + + # TODO decide how to check this + # variables = get_template_variables(reask_prompt) + # assert set(variables) == self.reask_prompt_vars diff --git a/guardrails/schema/string_schema.py b/guardrails/schema/string_schema.py new file mode 100644 index 000000000..0c9f1d8b3 --- /dev/null +++ b/guardrails/schema/string_schema.py @@ -0,0 +1,286 @@ +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union + +from lxml import etree as ET +from typing_extensions import Self + +from guardrails import validator_service +from guardrails.classes.history import Iteration +from guardrails.datatypes import String +from guardrails.llm_providers import ( + AsyncOpenAICallable, + AsyncOpenAIChatCallable, + OpenAICallable, + OpenAIChatCallable, + PromptCallableBase, +) +from guardrails.logger import logger +from guardrails.prompt import Instructions, Prompt +from guardrails.schema.schema import Schema +from guardrails.utils.constants import constants +from guardrails.utils.reask_utils import FieldReAsk, ReAsk +from guardrails.utils.telemetry_utils import trace_validation_result +from guardrails.validator_base import ( + ValidatorSpec, + check_refrain_in_dict, + filter_in_dict, +) + + +class StringSchema(Schema): + reask_prompt_vars = {"previous_response", "output_schema", "error_messages"} + + def __init__( + self, + schema: String, + reask_prompt_template: Optional[str] = None, + reask_instructions_template: Optional[str] = None, + ) -> None: + super().__init__( + schema, + reask_prompt_template=reask_prompt_template, + reask_instructions_template=reask_instructions_template, + ) + self.root_datatype = schema + + @classmethod + def from_xml( + cls, + root: ET._Element, + reask_prompt_template: Optional[str] = None, + reask_instructions_template: Optional[str] = None, + ) -> Self: + if len(root) != 0: + raise ValueError("String output schemas must not have children.") + + strict = False + if "strict" in root.attrib and root.attrib["strict"] == "true": + strict = True + + schema = String.from_xml(root, strict=strict) + + return cls( + schema=schema, + reask_prompt_template=reask_prompt_template, + reask_instructions_template=reask_instructions_template, + ) + + @classmethod + def from_string( + cls, + validators: Sequence[ValidatorSpec], + description: Optional[str] = None, + reask_prompt_template: Optional[str] = None, + reask_instructions_template: Optional[str] = None, + ): + strict = False + + schema = String.from_string_rail( + validators, description=description, strict=strict + ) + + return cls( + schema=schema, + reask_prompt_template=reask_prompt_template, + reask_instructions_template=reask_instructions_template, + ) + + def get_reask_setup( + self, + reasks: List[FieldReAsk], + original_response: FieldReAsk, + use_full_schema: bool, + prompt_params: Optional[Dict[str, Any]] = None, + ) -> Tuple[Schema, Prompt, Instructions]: + pruned_tree_string = self.transpile() + + reask_prompt_template = self.reask_prompt_template + if reask_prompt_template is None: + reask_prompt_template = Prompt( + constants["high_level_string_reask_prompt"] + + constants["complete_string_suffix"] + ) + + error_messages = "\n".join( + [ + f"- {fail_result.error_message}" + for reask in reasks + for fail_result in reask.fail_results + ] + ) + + prompt = reask_prompt_template.format( + previous_response=original_response.incorrect_value, + error_messages=error_messages, + output_schema=pruned_tree_string, + **(prompt_params or {}), + ) + + instructions = self.reask_instructions_template + if instructions is None: + instructions = Instructions("You are a helpful assistant.") + instructions = instructions.format(**(prompt_params or {})) + + return self, prompt, instructions + + def parse(self, output: str, **kwargs) -> Tuple[Any, Optional[Exception]]: + return output, None + + def validate( + self, + iteration: Iteration, + data: Any, + metadata: Dict, + attempt_number: int = 0, + **kwargs, + ) -> Any: + """Validate a dictionary of data against the schema. + + Args: + data: The data to validate. + + Returns: + The validated data. + """ + if data is None: + return None + + if not isinstance(data, str): + raise TypeError(f"Argument `data` must be a string, not {type(data)}.") + + # FIXME instead of writing the validation infrastructure for dicts (JSON), + # make it more structure-invariant + dummy_key = "string" + validation = self.root_datatype.collect_validation( + key=dummy_key, + value=data, + schema={ + dummy_key: data, + }, + ) + + validated_response, metadata = validator_service.validate( + value=data, + metadata=metadata, + validator_setup=validation, + iteration=iteration, + ) + + validated_response = {dummy_key: validated_response} + + if check_refrain_in_dict(validated_response): + # If the data contains a `Refain` value, we return an empty + # dictionary. + logger.debug("Refrain detected.") + validated_response = {} + + # Remove all keys that have `Filter` values. + validated_response = filter_in_dict(validated_response) + + trace_validation_result( + validation_logs=iteration.validator_logs, attempt_number=attempt_number + ) + + if dummy_key in validated_response: + return validated_response[dummy_key] + return None + + async def async_validate( + self, + iteration: Iteration, + data: Any, + metadata: Dict, + attempt_number: int = 0, + ) -> Any: + """Validate a dictionary of data against the schema. + + Args: + data: The data to validate. + + Returns: + The validated data. + """ + if data is None: + return None + + if not isinstance(data, str): + raise TypeError(f"Argument `data` must be a string, not {type(data)}.") + + dummy_key = "string" + validation = self.root_datatype.collect_validation( + key=dummy_key, + value=data, + schema={ + dummy_key: data, + }, + ) + + validated_response, metadata = await validator_service.async_validate( + value=data, + metadata=metadata, + validator_setup=validation, + iteration=iteration, + ) + + validated_response = {dummy_key: validated_response} + + if check_refrain_in_dict(validated_response): + # If the data contains a `Refain` value, we return an empty + # dictionary. + logger.debug("Refrain detected.") + validated_response = {} + + # Remove all keys that have `Filter` values. + validated_response = filter_in_dict(validated_response) + + trace_validation_result( + validation_logs=iteration.validator_logs, attempt_number=attempt_number + ) + + if dummy_key in validated_response: + return validated_response[dummy_key] + return None + + def introspect( + self, data: Union[ReAsk, Optional[str]] + ) -> Tuple[List[FieldReAsk], Optional[str]]: + if isinstance(data, FieldReAsk): + return [data], None + return [], data # type: ignore + + def preprocess_prompt( + self, + prompt_callable: PromptCallableBase, + instructions: Optional[Instructions], + prompt: Prompt, + ): + if isinstance(prompt_callable, OpenAICallable) or isinstance( + prompt_callable, AsyncOpenAICallable + ): + prompt.source += "\n\nString Output:\n\n" + if ( + isinstance(prompt_callable, OpenAIChatCallable) + or isinstance(prompt_callable, AsyncOpenAIChatCallable) + ) and not instructions: + instructions = Instructions( + "You are a helpful assistant, expressing yourself through a string." + ) + + return instructions, prompt + + def transpile(self, method: str = "default") -> str: + obj = self.root_datatype + schema = "" + if obj.description is not None: + schema += ( + "Here's a description of what I want you to generate: " + f"{obj.description}" + ) + if not obj.validators_attr.empty: + schema += ( + "\n\nYour generated response should satisfy the following properties:" + ) + for validator in obj.validators_attr.validators: + schema += f"\n- {validator.to_prompt()}" + + schema += "\n\nDon't talk; just go." + return schema diff --git a/guardrails/stores/context.py b/guardrails/stores/context.py new file mode 100644 index 000000000..d55b6d7ee --- /dev/null +++ b/guardrails/stores/context.py @@ -0,0 +1,76 @@ +from contextvars import ContextVar, copy_context +from typing import Any, Dict, Literal, Optional, Union + +try: + from opentelemetry import context + from opentelemetry.context import Context as TracerContext + from opentelemetry.trace import Tracer +except Exception: + + class Tracer: + pass + + class TracerContext: + pass + + context = None + + +TRACER_KEY: Literal["tracer"] = "gr.reserved.tracer" +TRACER_CONTEXT_KEY: Literal["tracer"] = "gr.reserved.tracer.context" +DOCUMENT_STORE_KEY: Literal["document_store"] = "gr.reserved.document_store" +CALL_KWARGS_KEY: Literal["call_kwargs"] = "gr.reserved.call_kwargs" + + +def set_tracer(tracer: Optional[Tracer] = None) -> None: + set_context_var(TRACER_KEY, tracer) + + +def get_tracer() -> Union[Tracer, None]: + return get_context_var(TRACER_KEY) + + +def set_tracer_context(tracer_context: Optional[TracerContext] = None) -> None: + tracer_context = ( + tracer_context + if tracer_context + else (context.get_current() if context is not None else None) + ) + set_context_var(TRACER_CONTEXT_KEY, tracer_context) + + +def get_tracer_context() -> Union[TracerContext, None]: + return get_context_var(TRACER_CONTEXT_KEY) + + +def set_call_kwargs(kwargs: Dict[str, Any]) -> None: + set_context_var(CALL_KWARGS_KEY, kwargs) + + +def get_call_kwargs() -> Dict[str, Any]: + return get_context_var(CALL_KWARGS_KEY) or {} + + +def get_call_kwarg(kwarg_key: str) -> Union[Any, None]: + kwargs = get_call_kwargs() + return kwargs.get(kwarg_key) + + +def _get_contextvar(key): + context = copy_context() + context_var = None + for c_key in context.keys(): + if c_key.name == key: + context_var = c_key + break + return context_var + + +def set_context_var(key, value): + context_var = _get_contextvar(key) or ContextVar(key) + context_var.set(value) + + +def get_context_var(key): + context_var = _get_contextvar(key) + return context_var.get() if context_var else None diff --git a/guardrails/utils/hub_telemetry_utils.py b/guardrails/utils/hub_telemetry_utils.py new file mode 100644 index 000000000..e225758c4 --- /dev/null +++ b/guardrails/utils/hub_telemetry_utils.py @@ -0,0 +1,122 @@ +# Imports +import logging + +from opentelemetry import trace +from opentelemetry.exporter.otlp.proto.http.trace_exporter import ( # HTTP Exporter + OTLPSpanExporter, +) +from opentelemetry.sdk.resources import SERVICE_NAME, Resource +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor +from opentelemetry.trace.propagation.tracecontext import TraceContextTextMapPropagator + + +class HubTelemetry: + """Singleton class for initializing a tracer for Guardrails Hub.""" + + _instance = None + _service_name = None + _endpoint = None + _tracer_name = None + _resource = None + _tracer_provider = None + _processor = None + _tracer = None + _prop = None + _carrier = {} + + def __new__( + cls, + service_name: str = "guardrails-hub", + tracer_name: str = "gr_hub", + export_locally: bool = False, + ): + if cls._instance is None: + logging.debug("Creating HubTelemetry instance...") + cls._instance = super(HubTelemetry, cls).__new__(cls) + logging.debug("Initializing HubTelemetry instance...") + cls._instance.initialize_tracer(service_name, tracer_name, export_locally) + else: + logging.debug("Returning existing HubTelemetry instance...") + return cls._instance + + def initialize_tracer( + self, + service_name: str, + tracer_name: str, + export_locally: bool, + ): + """Initializes a tracer for Guardrails Hub.""" + + self._service_name = service_name + # self._endpoint = "http://localhost:4318/v1/traces" + self._endpoint = ( + "https://hty0gc1ok3.execute-api.us-east-1.amazonaws.com/v1/traces" + ) + self._tracer_name = tracer_name + + # Create a resource + # Service name is required for most backends + self._resource = Resource(attributes={SERVICE_NAME: self._service_name}) + + # Create a tracer provider and a processor + self._tracer_provider = TracerProvider(resource=self._resource) + + if export_locally: + self._processor = SimpleSpanProcessor(ConsoleSpanExporter()) + else: + self._processor = SimpleSpanProcessor( + OTLPSpanExporter(endpoint=self._endpoint) + ) + + # Add the processor to the provider + self._tracer_provider.add_span_processor(self._processor) + + # Set the tracer provider and return a tracer + trace.set_tracer_provider(self._tracer_provider) + self._tracer = trace.get_tracer(self._tracer_name) + + self._prop = TraceContextTextMapPropagator() + + def inject_current_context(self) -> None: + """Injects the current context into the carrier.""" + self._prop.inject(carrier=self._carrier) + + def extract_current_context(self): + """Extracts the current context from the carrier.""" + + context = self._prop.extract(carrier=self._carrier) + return context + + def create_new_span( + self, + span_name: str, + attributes: list, + is_parent: bool, # Inject current context if IS a parent span + has_parent: bool, # Extract current context if HAS a parent span + ): + """Creates a new span within the tracer with the given name and + attributes. + + If it's a parent span, the current context is injected into the carrier. + If it has a parent span, the current context is extracted from the carrier. + Both the conditions can co-exist e.g. a span can be a parent span which + also has a parent span. + + Args: + span_name (str): The name of the span. + attributes (list): A list of attributes to set on the span. + is_parent (bool): True if the span is a parent span. + has_parent (bool): True if the span has a parent span. + """ + + with self._tracer.start_as_current_span( + span_name, + context=self.extract_current_context() if has_parent else None, + ) as span: + if is_parent: + # Inject the current context + self.inject_current_context() + + for attribute in attributes: + span.set_attribute(attribute[0], attribute[1]) diff --git a/guardrails/utils/logs_utils.py b/guardrails/utils/logs_utils.py index e96471ce4..9ffe3e6e3 100644 --- a/guardrails/utils/logs_utils.py +++ b/guardrails/utils/logs_utils.py @@ -1,4 +1,5 @@ from copy import deepcopy +from datetime import datetime from typing import Any, Dict, List, Optional from guardrails.utils.pydantic_utils import ArbitraryModel @@ -10,9 +11,13 @@ class ValidatorLogs(ArbitraryModel): """Logs for a single validator.""" validator_name: str + registered_name: str value_before_validation: Any validation_result: Optional[ValidationResult] = None value_after_validation: Optional[Any] = None + start_time: Optional[datetime] = None + end_time: Optional[datetime] = None + instance_id: Optional[int] = None property_path: str diff --git a/guardrails/utils/telemetry_utils.py b/guardrails/utils/telemetry_utils.py new file mode 100644 index 000000000..33a8464c3 --- /dev/null +++ b/guardrails/utils/telemetry_utils.py @@ -0,0 +1,323 @@ +import logging +import sys +from functools import wraps +from operator import attrgetter +from typing import Any, Dict, List, Optional + +from guardrails.stores.context import Tracer, TracerContext +from guardrails.stores.context import get_tracer as get_context_tracer +from guardrails.stores.context import get_tracer_context +from guardrails.utils.casting_utils import to_string +from guardrails.utils.logs_utils import ValidatorLogs +from guardrails.utils.reask_utils import ReAsk +from guardrails.validator_base import Filter, Refrain + +try: + from opentelemetry import context + from opentelemetry.trace import Span +except ImportError: + + class Span: + pass + + +def get_result_type(before_value: Any, after_value: Any, outcome: str): + try: + if isinstance(after_value, (Filter, Refrain, ReAsk)): + name = after_value.__class__.__name__.lower() + elif after_value != before_value: + name = "fix" + else: + name = outcome + return name + except Exception: + return type(after_value) + + +def get_error_code() -> int: + try: + from opentelemetry.trace import StatusCode + + return StatusCode.ERROR + except Exception as e: + logging.debug(f"Failed to import StatusCode from opentelemetry.trace: {str(e)}") + return 2 + + +def get_tracer(tracer: Tracer = None) -> Tracer: + # TODO: Do we ever need to consider supporting non-otel tracers? + _tracer = tracer if tracer is not None else get_context_tracer() + return _tracer + + +def get_current_context() -> Optional[TracerContext]: + otel_current_context = ( + context.get_current() + if context is not None and hasattr(context, "get_current") + else None + ) + tracer_context = get_tracer_context() + return otel_current_context or tracer_context + + +def get_span(span=None): + if span is not None and hasattr(span, "add_event"): + return span + try: + from opentelemetry import trace + + current_context = get_current_context() + current_span = trace.get_current_span(current_context) + return current_span + except Exception as e: + print(e) + return None + + +def trace_validator_result( + current_span, validator_log: ValidatorLogs, attempt_number: int, **kwargs +): + ( + validator_name, + value_before_validation, + validation_result, + value_after_validation, + start_time, + end_time, + instance_id, + ) = attrgetter( + "registered_name", + "value_before_validation", + "validation_result", + "value_after_validation", + "start_time", + "end_time", + "instance_id", + )( + validator_log + ) + result = ( + validation_result.outcome + if hasattr(validation_result, "outcome") + and validation_result.outcome is not None + else "unknown" + ) + result_type = get_result_type( + value_before_validation, value_after_validation, result + ) + + event: Dict[str, str] = { + "validator_name": validator_name, + "attempt_number": attempt_number, + "result": result, + "result_type": result_type, + "input": to_string(value_before_validation), + "output": to_string(value_after_validation), + "start_time": start_time.isoformat() if start_time else None, + "end_time": end_time.isoformat() if end_time else None, + "instance_id": instance_id, + **kwargs, + } + current_span.add_event( + f"{validator_name}_result", + {k: v for k, v in event.items() if v is not None}, + ) + + +# FIXME: It's creating two of every event +# Might be duplicate validator_logs? +def trace_validation_result( + validation_logs: List[ValidatorLogs], + attempt_number: int, + current_span=None, +): + # Duplicate logs are showing here + # print("validation_logs.validator_logs: ", validation_logs.validator_logs) + _current_span = get_span(current_span) + if _current_span is not None: + for log in validation_logs: + # Duplicate logs are showing here + # print("calling trace_validator_result with: ", log, attempt_number) + trace_validator_result(_current_span, log, attempt_number) + + # CHECKME: disabled these because I think we flattened this structure? + # if validation_logs.children: + # for child in validation_logs.children: + # # print("calling trace_validation_result with child logs") + # trace_validation_result( + # validation_logs.children.get(child), attempt_number, _current_span + # ) + + +def trace_validator( + validator_name: str, + obj_id: int, + # TODO - re-enable once we have namespace support + # namespace: str = None, + on_fail_descriptor: str = None, + tracer: Optional[Tracer] = None, + **init_kwargs, +): + def trace_validator_wrapper(fn): + _tracer = get_tracer(tracer) + + @wraps(fn) + def with_trace(*args, **kwargs): + span_name = ( + # TODO - re-enable once we have namespace support + # f"{namespace}.{validator_name}.validate" + # if namespace is not None + # else f"{validator_name}.validate" + f"{validator_name}.validate" + ) + trace_context = get_current_context() + with _tracer.start_as_current_span( + span_name, trace_context + ) as validator_span: + try: + validator_span.set_attribute( + "on_fail_descriptor", on_fail_descriptor + ) + validator_span.set_attribute( + "args", + to_string({k: to_string(v) for k, v in init_kwargs.items()}), + ) + validator_span.set_attribute("instance_id", to_string(obj_id)) + + # NOTE: Update if Validator.validate method signature ever changes + if args is not None and len(args) > 1: + validator_span.set_attribute("input", to_string(args[1])) + + return fn(*args, **kwargs) + except Exception as e: + validator_span.set_status( + status=get_error_code(), description=str(e) + ) + raise e + + @wraps(fn) + def without_a_trace(*args, **kwargs): + return fn(*args, **kwargs) + + if _tracer is not None and hasattr(_tracer, "start_as_current_span"): + return with_trace + else: + return without_a_trace + + return trace_validator_wrapper + + +def trace(name: str, tracer: Optional[Tracer] = None): + def trace_wrapper(fn): + @wraps(fn) + def to_trace_or_not_to_trace(*args, **kwargs): + _tracer = get_tracer(tracer) + + if _tracer is not None and hasattr(_tracer, "start_as_current_span"): + trace_context = get_current_context() + with _tracer.start_as_current_span(name, trace_context) as trace_span: + try: + # TODO: Capture args and kwargs as attributes? + response = fn(*args, **kwargs) + return response + except Exception as e: + trace_span.set_status( + status=get_error_code(), description=str(e) + ) + raise e + else: + return fn(*args, **kwargs) + + return to_trace_or_not_to_trace + + return trace_wrapper + + +def async_trace(name: str, tracer: Optional[Tracer] = None): + def trace_wrapper(fn): + @wraps(fn) + async def to_trace_or_not_to_trace(*args, **kwargs): + _tracer = get_tracer(tracer) + + if _tracer is not None and hasattr(_tracer, "start_as_current_span"): + trace_context = get_current_context() + with _tracer.start_as_current_span(name, trace_context) as trace_span: + try: + # TODO: Capture args and kwargs as attributes? + response = await fn(*args, **kwargs) + return response + except Exception as e: + trace_span.set_status( + status=get_error_code(), description=str(e) + ) + raise e + else: + response = await fn(*args, **kwargs) + return response + + return to_trace_or_not_to_trace + + return trace_wrapper + + +def default_otel_collector_tracer(resource_name: str = "guardsrails"): + """This is the standard otel tracer set to talk to a grpc open telemetry + collector running on port 4317.""" + + from opentelemetry import trace + from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter + from opentelemetry.sdk.resources import SERVICE_NAME, Resource + from opentelemetry.sdk.trace import TracerProvider + from opentelemetry.sdk.trace.export import BatchSpanProcessor + + resource = Resource(attributes={SERVICE_NAME: resource_name}) + + traceProvider = TracerProvider(resource=resource) + processor = BatchSpanProcessor( + OTLPSpanExporter(endpoint="localhost:4317", insecure=True) + ) + traceProvider.add_span_processor(processor) + trace.set_tracer_provider(traceProvider) + + return trace.get_tracer("gr") + + +def default_otlp_tracer(resource_name: str = "guardsrails"): + """This tracer will emit spans directly to an otlp endpoint, configured by + the following environment variables: + + OTEL_EXPORTER_OTLP_PROTOCOL + OTEL_EXPORTER_OTLP_TRACES_ENDPOINT + OTEL_EXPORTER_OTLP_HEADERS + + We recommend using Grafana to collect your metrics. A full example of how to + do that is in our (docs)[https://docs.guardsrails.com/telemetry] + """ + import os + + from opentelemetry import trace + from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter + from opentelemetry.sdk.resources import SERVICE_NAME, Resource + from opentelemetry.sdk.trace import TracerProvider + from opentelemetry.sdk.trace.export import ( + BatchSpanProcessor, + ConsoleSpanExporter, + SimpleSpanProcessor, + ) + + envvars_exist = os.environ.get("OTEL_EXPORTER_OTLP_PROTOCOL") and os.environ.get( + "OTEL_EXPORTER_OTLP_TRACES_ENDPOINT" + ) + + resource = Resource(attributes={SERVICE_NAME: resource_name}) + + traceProvider = TracerProvider(resource=resource) + if envvars_exist: + processor = BatchSpanProcessor(OTLPSpanExporter()) + else: + processor = SimpleSpanProcessor(ConsoleSpanExporter(out=sys.stderr)) + + traceProvider.add_span_processor(processor) + trace.set_tracer_provider(traceProvider) + + return trace.get_tracer("gr") diff --git a/guardrails/validator_base.py b/guardrails/validator_base.py index 63df6c998..e31c40ac0 100644 --- a/guardrails/validator_base.py +++ b/guardrails/validator_base.py @@ -134,6 +134,18 @@ def filter_in_dict(schema: Dict) -> Dict: types_to_validators = defaultdict(list) +def validator_factory(name: str, validate: Callable): + def validate_wrapper(self, *args, **kwargs): + return validate(*args, **kwargs) + + validator = type( + name, + (Validator,), + {"validate": validate_wrapper, "rail_alias": name}, + ) + return validator + + def register_validator(name: str, data_type: Union[str, List[str]]): """Register a validator for a data type.""" from guardrails.datatypes import registry as types_registry @@ -161,11 +173,7 @@ def decorator(cls_or_func: Union[Type[Validator], Callable]): f"Validator function {func.__name__} must take two arguments." ) # dynamically create Validator subclass with `validate` method as `func` - cls = type( - name, - (Validator,), - {"validate": staticmethod(func), "rail_alias": name}, - ) + cls = validator_factory(name, func) else: raise ValueError( "Only functions and Validator subclasses " diff --git a/guardrails/validator_service.py b/guardrails/validator_service.py index 4e9c7a409..0ea7c5074 100644 --- a/guardrails/validator_service.py +++ b/guardrails/validator_service.py @@ -2,16 +2,28 @@ import itertools import os from concurrent.futures import ProcessPoolExecutor +from datetime import datetime from typing import Any, Dict, List, Optional, Tuple from guardrails.classes.history import Iteration +from guardrails.cli_dir.hub.credentials import Credentials from guardrails.datatypes import FieldValidation from guardrails.errors import ValidationError from guardrails.logger import logger +from guardrails.utils.casting_utils import to_string +from guardrails.utils.hub_telemetry_utils import HubTelemetry from guardrails.utils.logs_utils import ValidatorLogs from guardrails.utils.reask_utils import FieldReAsk, ReAsk from guardrails.utils.safe_get import safe_get -from guardrails.validator_base import FailResult, Filter, PassResult, Refrain, Validator +from guardrails.utils.telemetry_utils import trace_validator +from guardrails.validator_base import ( + FailResult, + Filter, + PassResult, + Refrain, + ValidationResult, + Validator, +) def key_not_empty(key: str) -> bool: @@ -21,6 +33,26 @@ def key_not_empty(key: str) -> bool: class ValidatorServiceBase: """Base class for validator services.""" + # NOTE: This is avoiding an issue with multiprocessing. + # If we wrap the validate methods at the class level or anytime before + # loop.run_in_executor is called, multiprocessing fails with a Pickling error. + # This is a well known issue without any real solutions. + # Using `fork` instead of `spawn` may alleviate the symptom for POSIX systems, + # but is relatively unsupported on Windows. + def execute_validator( + self, validator: Validator, value: Any, metadata: Optional[Dict] + ) -> ValidationResult: + traced_validator = trace_validator( + validator_name=validator.rail_alias, + obj_id=id(validator), + # TODO - re-enable once we have namespace support + # namespace=validator.namespace, + on_fail_descriptor=validator.on_fail_descriptor, + **validator._kwargs, + )(validator.validate) + result = traced_validator(value, metadata) + return result + def perform_correction( self, results: List[FailResult], @@ -35,7 +67,9 @@ def perform_correction( elif on_fail_descriptor == "fix_reask": # FIXME: Same thing here fixed_value = results[0].fix_value - result = validator.validate(fixed_value, results[0].metadata or {}) + result = self.execute_validator( + validator, fixed_value, results[0].metadata or {} + ) if isinstance(result, FailResult): return FieldReAsk( @@ -82,15 +116,48 @@ def run_validator( validator_logs = ValidatorLogs( validator_name=validator_class_name, value_before_validation=value, + registered_name=validator.rail_alias, property_path=property_path, ) iteration.outputs.validator_logs.append(validator_logs) + start_time = datetime.now() + result = self.execute_validator(validator, value, metadata) + end_time = datetime.now() + result = validator.validate(value, metadata) if result is None: result = PassResult() validator_logs.validation_result = result + validator_logs.start_time = start_time + validator_logs.end_time = end_time + # If we ever re-use validator instances across multiple properties, + # this will have to change. + validator_logs.instance_id = to_string(id(validator)) + + # Get metrics opt-out from credentials + disable_tracer = Credentials.from_rc_file().no_metrics + if disable_tracer.strip().lower() == "true": + disable_tracer = True + elif disable_tracer.strip().lower() == "false": + disable_tracer = False + + if not disable_tracer: + # Get HubTelemetry singleton and create a new span to + # log the validator usage + _hub_telemetry = HubTelemetry() + _hub_telemetry.create_new_span( + span_name="/validator_usage", + attributes=[ + ("validator_name", validator.rail_alias), + ("validator_on_fail", validator.on_fail_descriptor), + ("validator_result", result.outcome), + ], + is_parent=False, # This span will have no children + has_parent=True, # This span has a parent + ) + return validator_logs diff --git a/poetry.lock b/poetry.lock index 09612d3b2..1243c6740 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,87 +2,87 @@ [[package]] name = "aiohttp" -version = "3.9.1" +version = "3.9.3" description = "Async http client/server framework (asyncio)" optional = true python-versions = ">=3.8" files = [ - {file = "aiohttp-3.9.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e1f80197f8b0b846a8d5cf7b7ec6084493950d0882cc5537fb7b96a69e3c8590"}, - {file = "aiohttp-3.9.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c72444d17777865734aa1a4d167794c34b63e5883abb90356a0364a28904e6c0"}, - {file = "aiohttp-3.9.1-cp310-cp310-macosx_11_0_arm64.whl", hash = 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-content-hash = "3126d22fc8fa5e89dd262e99a458943596f2d32248be12c7abdb90135e9243a2" +content-hash = "12823e5e225b3a92df951ca4f13a1416682ad46aa10551fe4b46ed64734c3772" diff --git a/pyproject.toml b/pyproject.toml index 1e50ad19d..a6a3d1dbb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,6 +51,9 @@ huggingface_hub = {version = "^0.16.4", optional = true} pydash = "^7.0.6" docspec_python = "2.2.1" pydoc-markdown = "4.8.2" +opentelemetry-sdk = "^1.22.0" +opentelemetry-exporter-otlp-proto-grpc = "^1.22.0" +opentelemetry-exporter-otlp-proto-http = "^1.22.0" langchain-core = "^0.1.18" coloredlogs = "^15.0.1" requests = "^2.31.0" diff --git a/telemetry/docker-compose.yml b/telemetry/docker-compose.yml new file mode 100644 index 000000000..300be4753 --- /dev/null +++ b/telemetry/docker-compose.yml @@ -0,0 +1,13 @@ +version: "2" +services: + # Collector + otel-collector: + image: otel/opentelemetry-collector-contrib + restart: always + command: ["--config=/etc/otel-collector-config.yml"] + volumes: + - ./otel-collector-config.yml:/etc/otel-collector-config.yml + ports: + - "4317:4317" # OTLP gRPC receiver + - "4318:4318" # OTLP HTTP receiver + - "55679:55679" # zpages extension diff --git a/telemetry/otel-collector-config.yml b/telemetry/otel-collector-config.yml new file mode 100644 index 000000000..8b869699a --- /dev/null +++ b/telemetry/otel-collector-config.yml @@ -0,0 +1,89 @@ + + +extensions: + basicauth/grafana_cloud_tempo: + # https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/extension/basicauthextension + client_auth: + username: "734526" + password: "PASSWORD" + basicauth/grafana_cloud_prometheus: + client_auth: + username: "1272753" + password: "PASSWORD" + basicauth/grafana_cloud_loki: + client_auth: + username: "735422" + password: "PASSWORD" + +receivers: + otlp: + # https://github.com/open-telemetry/opentelemetry-collector/tree/main/receiver/otlpreceiver + protocols: + grpc: + http: + hostmetrics: + # Optional. Host Metrics Receiver added as an example of Infra Monitoring capabilities of the OpenTelemetry Collector + # https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/receiver/hostmetricsreceiver + scrapers: + load: + memory: + +processors: + batch: + # https://github.com/open-telemetry/opentelemetry-collector/tree/main/processor/batchprocessor + resourcedetection: + # Enriches telemetry data with resource information from the host + # https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/resourcedetectionprocessor + detectors: ["env", "system"] + override: false + transform/add_resource_attributes_as_metric_attributes: + # https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/transformprocessor + error_mode: ignore + metric_statements: + - context: datapoint + statements: + - set(attributes["deployment.environment"], resource.attributes["deployment.environment"]) + - set(attributes["service.version"], resource.attributes["service.version"]) + +exporters: + logging: + loglevel: debug + + otlp/grafana_cloud_traces: + # https://github.com/open-telemetry/opentelemetry-collector/tree/main/exporter/otlpexporter + endpoint: "tempo-prod-04-prod-us-east-0.grafana.net:443" + auth: + authenticator: basicauth/grafana_cloud_tempo + + loki/grafana_cloud_logs: + # https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/exporter/lokiexporter + endpoint: "https://logs-prod-006.grafana.net/loki/api/v1/push" + auth: + authenticator: basicauth/grafana_cloud_loki + + prometheusremotewrite/grafana_cloud_metrics: + # https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/exporter/prometheusremotewriteexporter + endpoint: "https://prometheus-prod-13-prod-us-east-0.grafana.net/api/prom/push" + add_metric_suffixes: false + auth: + authenticator: basicauth/grafana_cloud_prometheus + + +service: + telemetry: + logs: + level: debug + extensions: [basicauth/grafana_cloud_tempo, basicauth/grafana_cloud_prometheus, basicauth/grafana_cloud_loki] + pipelines: + traces: + receivers: [otlp] + processors: [resourcedetection, batch] + exporters: [otlp/grafana_cloud_traces, logging] + metrics: + receivers: [otlp, hostmetrics] + processors: [resourcedetection, transform/add_resource_attributes_as_metric_attributes, batch] + exporters: [prometheusremotewrite/grafana_cloud_metrics] + logs: + receivers: [otlp] + processors: [resourcedetection, batch] + exporters: [loki/grafana_cloud_logs] diff --git a/tests/integration_tests/test_assets/pydantic/parsing_reask.py b/tests/integration_tests/test_assets/pydantic/parsing_reask.py index 03a8247bd..7ecd7d27a 100644 --- a/tests/integration_tests/test_assets/pydantic/parsing_reask.py +++ b/tests/integration_tests/test_assets/pydantic/parsing_reask.py @@ -12,7 +12,8 @@ \n\nAssistant: """ # noqa -document = """Joe Smith – 1234 5678 / joe@example.com PRIVATE & CONFIDENTIAL +document = ( + """Joe Smith – 1234 5678 / joe@example.com PRIVATE & CONFIDENTIAL 1 Joe Smith Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. @@ -20,7 +21,9 @@ nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia -deserunt mollit anim id est laborum.""" # noqa +deserunt mollit anim id est laborum.""" + "" +) # noqa compiled_prompt = """\n\nHuman:\nGiven the following resume, answer the following questions. If the answer doesn\'t exist in the resume, enter `null`.\n\nJoe Smith – 1234 5678 / joe@example.com PRIVATE & CONFIDENTIAL\n 1 Joe Smith\nLorem ipsum dolor sit amet, consectetur adipiscing elit,\nsed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\nUt enim ad minim veniam, quis nostrud exercitation ullamco laboris\nnisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in\nreprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\nExcepteur sint occaecat cupidatat non proident, sunt in culpa qui officia\ndeserunt mollit anim id est laborum.\n\nExtract information from this resume and return a JSON that follows the correct schema.\n\n\nGiven below is XML that describes the information to extract from this document and the tags to extract it into.\n\n\n \n \n \n\n\n\nONLY return a valid JSON object (no other text is necessary), where the key of the field in JSON is the `name` attribute of the corresponding XML, and the value is of the type specified by the corresponding XML\'s tag. The JSON MUST conform to the XML format, including any types and format requests e.g. requests for lists, objects and specific types. Be correct and concise. If you are unsure anywhere, enter `null`.\n\nHere are examples of simple (XML, JSON) pairs that show the expected behavior:\n- `` => `{\'foo\': \'example one\'}`\n- `` => `{"bar": [\'STRING ONE\', \'STRING TWO\', etc.]}`\n- `` => `{\'baz\': {\'foo\': \'Some String\', \'index\': 1}}`\n\n\n\n\nAssistant:\n""" # noqa diff --git a/tests/unit_tests/classes/history/test_call.py b/tests/unit_tests/classes/history/test_call.py index 2210f9e72..dce43fa0e 100644 --- a/tests/unit_tests/classes/history/test_call.py +++ b/tests/unit_tests/classes/history/test_call.py @@ -101,6 +101,7 @@ def custom_llm(): ReAsk(incorrect_value="Hello there!", fail_results=[first_validation_result]) ] first_validator_log = ValidatorLogs( + registered_name="no-punctuation", validator_name="no-punctuation", value_before_validation="Hello there!", validation_result=first_validation_result, @@ -137,6 +138,7 @@ def custom_llm(): second_validated_output = "Hello there" second_reasks = [] second_validator_log = ValidatorLogs( + registered_name="no-punctuation", validator_name="no-punctuation", value_before_validation="Hello there", validation_result=PassResult(), diff --git a/tests/unit_tests/classes/history/test_iteration.py b/tests/unit_tests/classes/history/test_iteration.py index d8d0fdee4..7ba6a5920 100644 --- a/tests/unit_tests/classes/history/test_iteration.py +++ b/tests/unit_tests/classes/history/test_iteration.py @@ -76,6 +76,7 @@ def test_non_empty_initialization(): reasks = [reask] validator_logs = [ ValidatorLogs( + registered_name="no-punctuation", validator_name="no-punctuation", value_before_validation="Hello there!", validation_result=validation_result, diff --git a/tests/unit_tests/classes/history/test_outputs.py b/tests/unit_tests/classes/history/test_outputs.py index 4a43e1a38..65a0592cc 100644 --- a/tests/unit_tests/classes/history/test_outputs.py +++ b/tests/unit_tests/classes/history/test_outputs.py @@ -36,6 +36,7 @@ def test_non_empty_initialization(): reasks = [ReAsk(incorrect_value="Hello there!", fail_results=[validation_result])] validator_logs = [ ValidatorLogs( + registered_name="no-punctuation", validator_name="no-punctuation", value_before_validation="Hello there!", validation_result=validation_result, @@ -103,6 +104,7 @@ def test_non_empty_initialization(): Outputs( validator_logs=[ ValidatorLogs( + registered_name="no-punctuation", validator_name="no-punctuation", value_before_validation="Hello there!", validation_result=fixable_fail_result, @@ -125,6 +127,7 @@ def test__all_empty(outputs: Outputs, expected_result: bool): def test_failed_validations(): validator_logs = [ ValidatorLogs( + registered_name="no-punctuation", validator_name="no-punctuation", value_before_validation="Hello there!", validation_result=fixable_fail_result, @@ -132,6 +135,7 @@ def test_failed_validations(): property_path="$", ), ValidatorLogs( + registered_name="valid-length", validator_name="valid-length", value_before_validation="Hello there!", validation_result=PassResult(), @@ -153,6 +157,7 @@ def test_failed_validations(): Outputs( validator_logs=[ ValidatorLogs( + registered_name="no-punctuation", validator_name="no-punctuation", value_before_validation="Hello there!", validation_result=non_fixable_fail_result, diff --git a/tests/unit_tests/test_async_validator_service.py b/tests/unit_tests/test_async_validator_service.py index 8b52db16f..ca24dae7a 100644 --- a/tests/unit_tests/test_async_validator_service.py +++ b/tests/unit_tests/test_async_validator_service.py @@ -187,6 +187,7 @@ async def test_run_validators(mocker): def mock_run_validator(iteration, validator, value, metadata, property_path): return ValidatorLogs( + registered_name=validator.name, validator_name=validator.name, value_before_validation=value, validation_result=PassResult(), @@ -253,6 +254,7 @@ async def test_run_validators_with_override(mocker): run_validator_mock = mocker.patch.object(avs, "run_validator") run_validator_mock.return_value = ValidatorLogs( + registered_name="override", validator_name="override", value_before_validation="mock-value", validation_result=PassResult(value_override="override"),