id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
53ee3b064a1c-1 | llm_ends (int): The number of times the llm end method has been called.
llm_streams (int): The number of times the text method has been called.
tool_starts (int): The number of times the tool start method has been called.
tool_ends (int): The number of times the tool end method has been called.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
53ee3b064a1c-2 | """Whether to ignore agent callbacks."""
return self.ignore_agent_
@property
def ignore_retriever(self) -> bool:
"""Whether to ignore retriever callbacks."""
return self.ignore_retriever_
[docs] def get_custom_callback_meta(self) -> Dict[str, Any]:
return {
"step":... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
53ee3b064a1c-3 | """Callback Handler that logs to Aim.
Parameters:
repo (:obj:`str`, optional): Aim repository path or Repo object to which
Run object is bound. If skipped, default Repo is used.
experiment_name (:obj:`str`, optional): Sets Run's `experiment` property.
'default' if not specifi... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
53ee3b064a1c-4 | self._run_hash = self._run.hash
self.action_records: list = []
[docs] def setup(self, **kwargs: Any) -> None:
aim = import_aim()
if not self._run:
if self._run_hash:
self._run = aim.Run(
self._run_hash,
repo=self.repo,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
53ee3b064a1c-5 | self.llm_ends += 1
self.ends += 1
resp = {"action": "on_llm_end"}
resp.update(self.get_custom_callback_meta())
response_res = deepcopy(response)
generated = [
aim.Text(generation.text)
for generations in response_res.generations
for generation ... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
53ee3b064a1c-6 | aim = import_aim()
self.step += 1
self.chain_ends += 1
self.ends += 1
resp = {"action": "on_chain_end"}
resp.update(self.get_custom_callback_meta())
outputs_res = deepcopy(outputs)
self._run.track(
aim.Text(outputs_res["output"]), name="on_chain_end", ... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
53ee3b064a1c-7 | """Run when tool errors."""
self.step += 1
self.errors += 1
[docs] def on_text(self, text: str, **kwargs: Any) -> None:
"""
Run when agent is ending.
"""
self.step += 1
self.text_ctr += 1
[docs] def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
53ee3b064a1c-8 | [docs] def flush_tracker(
self,
repo: Optional[str] = None,
experiment_name: Optional[str] = None,
system_tracking_interval: Optional[int] = 10,
log_system_params: bool = True,
langchain_asset: Any = None,
reset: bool = True,
finish: bool = False,
)... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
53ee3b064a1c-9 | repo=repo if repo else self.repo,
experiment_name=experiment_name
if experiment_name
else self.experiment_name,
system_tracking_interval=system_tracking_interval
if system_tracking_interval
else self.system_tracking_interval... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
b9dd31a821f5-0 | Source code for langchain.callbacks.promptlayer_callback
"""Callback handler for promptlayer."""
from __future__ import annotations
import datetime
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple
from uuid import UUID
from langchain.callbacks.base import BaseCallbackHandler
from langchain.s... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/promptlayer_callback.html |
b9dd31a821f5-1 | tags: Optional[List[str]] = None,
**kwargs: Any,
) -> Any:
self.runs[run_id] = {
"messages": [self._create_message_dicts(m)[0] for m in messages],
"invocation_params": kwargs.get("invocation_params", {}),
"name": ".".join(serialized["id"]),
"request_st... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/promptlayer_callback.html |
b9dd31a821f5-2 | generation = response.generations[i][0]
resp = {
"text": generation.text,
"llm_output": response.llm_output,
}
model_params = run_info.get("invocation_params", {})
is_chat_model = run_info.get("messages", None) is not None
model... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/promptlayer_callback.html |
b9dd31a821f5-3 | elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "content": message.content}
elif isinstance(message, ChatMessage):
message_dict = {"role": message.role, "content": message.content}
else:
raise ValueError(f"Got unknown type {message}")
... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/promptlayer_callback.html |
db9948796294-0 | Source code for langchain.callbacks.streamlit.mutable_expander
from __future__ import annotations
from enum import Enum
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional
if TYPE_CHECKING:
from streamlit.delta_generator import DeltaGenerator
from streamlit.type_util import SupportsStr
[docs... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/mutable_expander.html |
db9948796294-1 | def label(self) -> str:
"""The expander's label string."""
return self._label
@property
def expanded(self) -> bool:
"""True if the expander was created with `expanded=True`."""
return self._expanded
[docs] def clear(self) -> None:
"""Remove the container and its conten... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/mutable_expander.html |
db9948796294-2 | [docs] def markdown(
self,
body: SupportsStr,
unsafe_allow_html: bool = False,
*,
help: Optional[str] = None,
index: Optional[int] = None,
) -> int:
"""Add a Markdown element to the container and return its index."""
kwargs = {"body": body, "unsafe_... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/mutable_expander.html |
db9948796294-3 | the existing record at that index. Otherwise, append the record to the
end of the list.
Return the index of the added record.
"""
if index is not None:
# Replace existing child
self._child_records[index] = record
return index
# Append new child... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/mutable_expander.html |
d770c79a4540-0 | Source code for langchain.callbacks.streamlit.streamlit_callback_handler
"""Callback Handler that prints to streamlit."""
from __future__ import annotations
from enum import Enum
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional
from langchain.callbacks.base import BaseCallbackHandler
from langcha... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
d770c79a4540-1 | labeling logic.
"""
[docs] def get_initial_label(self) -> str:
"""Return the markdown label for a new LLMThought that doesn't have
an associated tool yet.
"""
return f"{THINKING_EMOJI} **Thinking...**"
[docs] def get_tool_label(self, tool: ToolRecord, is_complete: bool) -> str:... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
d770c79a4540-2 | a tool.
"""
return f"{CHECKMARK_EMOJI} **Complete!**"
[docs]class LLMThought:
"""A thought in the LLM's thought stream."""
[docs] def __init__(
self,
parent_container: DeltaGenerator,
labeler: LLMThoughtLabeler,
expanded: bool,
collapse_on_complete: bool,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
d770c79a4540-3 | self._reset_llm_token_stream()
[docs] def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
# This is only called when the LLM is initialized with `streaming=True`
self._llm_token_stream += _convert_newlines(token)
self._llm_token_writer_idx = self._container.markdown(
se... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
d770c79a4540-4 | )
[docs] def on_tool_end(
self,
output: str,
color: Optional[str] = None,
observation_prefix: Optional[str] = None,
llm_prefix: Optional[str] = None,
**kwargs: Any,
) -> None:
self._container.markdown(f"**{output}**")
[docs] def on_tool_error(self, error... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
d770c79a4540-5 | else:
self._container.update(new_label=final_label)
[docs] def clear(self) -> None:
"""Remove the thought from the screen. A cleared thought can't be reused."""
self._container.clear()
[docs]class StreamlitCallbackHandler(BaseCallbackHandler):
"""A callback handler that writes to a St... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
d770c79a4540-6 | self._parent_container = parent_container
self._history_parent = parent_container.container()
self._history_container: Optional[MutableExpander] = None
self._current_thought: Optional[LLMThought] = None
self._completed_thoughts: List[LLMThought] = []
self._max_thought_containers ... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
d770c79a4540-7 | if self._current_thought is not None:
count += 1
return count
def _complete_current_thought(self, final_label: Optional[str] = None) -> None:
"""Complete the current thought, optionally assigning it a new label.
Add it to our _completed_thoughts list.
"""
thought ... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
d770c79a4540-8 | self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
if self._current_thought is None:
self._current_thought = LLMThought(
parent_container=self._parent_container,
expanded=self._expand_new_thoughts,
collapse_on_complete=s... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
d770c79a4540-9 | [docs] def on_tool_end(
self,
output: str,
color: Optional[str] = None,
observation_prefix: Optional[str] = None,
llm_prefix: Optional[str] = None,
**kwargs: Any,
) -> None:
self._require_current_thought().on_tool_end(
output, color, observation... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
d770c79a4540-10 | [docs] def on_agent_finish(
self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any
) -> None:
if self._current_thought is not None:
self._current_thought.complete(
self._thought_labeler.get_final_agent_thought_label()
)
self._curr... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
44f36e397620-0 | Source code for langchain.callbacks.tracers.wandb
"""A Tracer Implementation that records activity to Weights & Biases."""
from __future__ import annotations
import json
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
Optional,
Sequence,
Tuple,
TypedDict,
Union,
)
from langchain... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/tracers/wandb.html |
44f36e397620-1 | [docs] def process_span(self, run: Run) -> Optional["Span"]:
"""Converts a LangChain Run into a W&B Trace Span.
:param run: The LangChain Run to convert.
:return: The converted W&B Trace Span.
"""
try:
span = self._convert_lc_run_to_wb_span(run)
return ... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/tracers/wandb.html |
44f36e397620-2 | """Converts a LangChain LLM Run into a W&B Trace Span.
:param run: The LangChain LLM Run to convert.
:return: The converted W&B Trace Span.
"""
base_span = self._convert_run_to_wb_span(run)
if base_span.attributes is None:
base_span.attributes = {}
base_span.a... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/tracers/wandb.html |
44f36e397620-3 | ]
base_span.span_kind = (
self.trace_tree.SpanKind.AGENT
if "agent" in run.name.lower()
else self.trace_tree.SpanKind.CHAIN
)
return base_span
def _convert_tool_run_to_wb_span(self, run: Run) -> "Span":
"""Converts a LangChain Tool Run into a W&B T... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/tracers/wandb.html |
44f36e397620-4 | else:
return self._convert_run_to_wb_span(run)
[docs] def process_model(self, run: Run) -> Optional[Dict[str, Any]]:
"""Utility to process a run for wandb model_dict serialization.
:param run: The run to process.
:return: The convert model_dict to pass to WBTraceTree.
"""
... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/tracers/wandb.html |
44f36e397620-5 | :param child_runs: The list of child runs to flatten.
:return: The flattened list of runs.
"""
if child_runs is None:
return []
result = []
for item in child_runs:
child_runs = item.pop("child_runs", [])
result.a... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/tracers/wandb.html |
44f36e397620-6 | """Utility to modify the serialized field of a list of runs dictionaries.
removes any keys that match the exact_keys and any keys that contain any of the
partial_keys.
recursively moves the dictionaries under the kwargs key to the top level.
changes the "id" field to a string "_kind" fie... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/tracers/wandb.html |
44f36e397620-7 | changes the id field to a string "_kind" field that tells WBTraceTree how
to visualize the run. recursively moves the dictionaries under the kwargs
key to the top level.
:param obj: a run dictionary with id and kwargs fields.
:param root: whether this is the root dictiona... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/tracers/wandb.html |
44f36e397620-8 | """Transforms a run dictionary to be compatible with WBTraceTree.
:param run: The run dictionary to transform.
:return: The transformed run dictionary.
"""
transformed_dict = transform_serialized(run)
serialized = transformed_dict.pop("serialized")
... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/tracers/wandb.html |
44f36e397620-9 | parent_dict[next(iter(parent_dict))][
next(iter(id_to_data[child_id]))
] = id_to_data[child_id][next(iter(id_to_data[child_id]))]
root_dict = next(
data for id_val, data in id_to_data.items() if id_val not in child_to_parent
)
return root_dict
[docs]class ... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/tracers/wandb.html |
44f36e397620-10 | """
_run: Optional[WBRun] = None
_run_args: Optional[WandbRunArgs] = None
[docs] def __init__(self, run_args: Optional[WandbRunArgs] = None, **kwargs: Any) -> None:
"""Initializes the WandbTracer.
Parameters:
run_args: (dict, optional) Arguments to pass to `wandb.init()`. If not
... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/tracers/wandb.html |
44f36e397620-11 | """
self._wandb.finish()
def _log_trace_from_run(self, run: Run) -> None:
"""Logs a LangChain Run to W*B as a W&B Trace."""
self._ensure_run()
root_span = self.run_processor.process_span(run)
model_dict = self.run_processor.process_model(run)
if root_span is None:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/tracers/wandb.html |
44f36e397620-12 | "`langchain`."
)
self._wandb.run._label(repo="langchain")
def _persist_run(self, run: "Run") -> None:
"""Persist a run."""
self._log_trace_from_run(run) | lang/api.python.langchain.com/en/latest/_modules/langchain/callbacks/tracers/wandb.html |
8fb1c6a656ef-0 | Source code for langchain.docstore.base
"""Interface to access to place that stores documents."""
from abc import ABC, abstractmethod
from typing import Dict, List, Union
from langchain.docstore.document import Document
[docs]class Docstore(ABC):
"""Interface to access to place that stores documents."""
[docs] @... | lang/api.python.langchain.com/en/latest/_modules/langchain/docstore/base.html |
99a87cd34bba-0 | Source code for langchain.docstore.wikipedia
"""Wrapper around wikipedia API."""
from typing import Union
from langchain.docstore.base import Docstore
from langchain.docstore.document import Document
[docs]class Wikipedia(Docstore):
"""Wrapper around wikipedia API."""
[docs] def __init__(self) -> None:
"... | lang/api.python.langchain.com/en/latest/_modules/langchain/docstore/wikipedia.html |
4ef954a81e65-0 | Source code for langchain.docstore.arbitrary_fn
from typing import Callable, Union
from langchain.docstore.base import Docstore
from langchain.schema import Document
[docs]class DocstoreFn(Docstore):
"""Langchain Docstore via arbitrary lookup function.
This is useful when:
* it's expensive to construct an ... | lang/api.python.langchain.com/en/latest/_modules/langchain/docstore/arbitrary_fn.html |
54cf80ba11d3-0 | Source code for langchain.docstore.in_memory
"""Simple in memory docstore in the form of a dict."""
from typing import Dict, List, Optional, Union
from langchain.docstore.base import AddableMixin, Docstore
from langchain.docstore.document import Document
[docs]class InMemoryDocstore(Docstore, AddableMixin):
"""Simp... | lang/api.python.langchain.com/en/latest/_modules/langchain/docstore/in_memory.html |
54cf80ba11d3-1 | """
if search not in self._dict:
return f"ID {search} not found."
else:
return self._dict[search] | lang/api.python.langchain.com/en/latest/_modules/langchain/docstore/in_memory.html |
9767901d2013-0 | Source code for langchain.smith.evaluation.string_run_evaluator
"""Run evaluator wrapper for string evaluators."""
from __future__ import annotations
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from langsmith import EvaluationResult, RunEvaluator
from langsmith.schemas import DataType, E... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/string_run_evaluator.html |
9767901d2013-1 | return self.map(run)
[docs]class LLMStringRunMapper(StringRunMapper):
"""Extract items to evaluate from the run object."""
[docs] def serialize_chat_messages(self, messages: List[Dict]) -> str:
"""Extract the input messages from the run."""
if isinstance(messages, list) and messages:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/string_run_evaluator.html |
9767901d2013-2 | first_generation: Dict = generations[0]
if isinstance(first_generation, list):
# Runs from Tracer have generations as a list of lists of dicts
# Whereas Runs from the API have a list of dicts
first_generation = first_generation[0]
if "message" in first_generation:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/string_run_evaluator.html |
9767901d2013-3 | """The key from the model Run's inputs to use as the eval input.
If not provided, will use the only input key or raise an
error if there are multiple."""
prediction_key: Optional[str] = None
"""The key from the model Run's outputs to use as the eval prediction.
If not provided, will use the only out... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/string_run_evaluator.html |
9767901d2013-4 | available_keys = ", ".join(run.outputs.keys())
raise ValueError(
f"Run with ID {run.id} doesn't have the expected prediction key"
f" '{self.prediction_key}'. Available prediction keys in this Run are:"
f" {available_keys}. Adjust the evaluator's prediction_key... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/string_run_evaluator.html |
9767901d2013-5 | """Maps the Example, or dataset row to a dictionary."""
if not example.outputs:
raise ValueError(
f"Example {example.id} has no outputs to use as a reference."
)
if self.reference_key is None:
if len(example.outputs) > 1:
raise ValueErr... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/string_run_evaluator.html |
9767901d2013-6 | """The name of the evaluation metric."""
string_evaluator: StringEvaluator
"""The evaluation chain."""
@property
def input_keys(self) -> List[str]:
return ["run", "example"]
@property
def output_keys(self) -> List[str]:
return ["feedback"]
def _prepare_input(self, inputs: Dic... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/string_run_evaluator.html |
9767901d2013-7 | """Call the evaluation chain."""
evaluate_strings_inputs = self._prepare_input(inputs)
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
chain_output = self.string_evaluator.evaluate_strings(
**evaluate_strings_in... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/string_run_evaluator.html |
9767901d2013-8 | return EvaluationResult(
key=self.string_evaluator.evaluation_name,
comment=f"Error evaluating run {run.id}: {e}",
# TODO: Add run ID once we can declare it via callbacks
)
[docs] async def aevaluate_run(
self, run: Run, example: Optional[Example] =... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/string_run_evaluator.html |
9767901d2013-9 | data_type (DataType): The type of dataset used in the run.
input_key (str, optional): The key used to map the input from the run.
prediction_key (str, optional): The key used to map the prediction from the run.
reference_key (str, optional): The key used to map the reference from the... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/string_run_evaluator.html |
9767901d2013-10 | )
else:
example_mapper = None
return cls(
name=evaluator.evaluation_name,
run_mapper=run_mapper,
example_mapper=example_mapper,
string_evaluator=evaluator,
tags=tags,
) | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/string_run_evaluator.html |
c128777ea886-0 | Source code for langchain.smith.evaluation.name_generation
import random
adjectives = [
"abandoned",
"aching",
"advanced",
"ample",
"artistic",
"back",
"best",
"bold",
"brief",
"clear",
"cold",
"complicated",
"cooked",
"crazy",
"crushing",
"damp",
"dea... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/name_generation.html |
c128777ea886-1 | "sunny",
"tart",
"terrific",
"timely",
"unique",
"upbeat",
"vacant",
"virtual",
"warm",
"weary",
"whispered",
"worthwhile",
"yellow",
]
nouns = [
"account",
"acknowledgment",
"address",
"advertising",
"airplane",
"animal",
"appointment",
"a... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/name_generation.html |
c128777ea886-2 | "cheek",
"cheese",
"chef",
"cherry",
"chicken",
"child",
"church",
"circle",
"class",
"clay",
"click",
"clock",
"cloth",
"cloud",
"clove",
"club",
"coach",
"coal",
"coast",
"coat",
"cod",
"coffee",
"collar",
"color",
"comb",... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/name_generation.html |
c128777ea886-3 | "discussion",
"disease",
"disgust",
"distance",
"distribution",
"division",
"doctor",
"dog",
"door",
"drain",
"drawer",
"dress",
"drink",
"driving",
"dust",
"ear",
"earth",
"edge",
"education",
"effect",
"egg",
"end",
"energy",
... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/name_generation.html |
c128777ea886-4 | "group",
"growth",
"guide",
"guitar",
"hair",
"hall",
"hand",
"harbor",
"harmony",
"hat",
"head",
"health",
"heart",
"heat",
"hill",
"history",
"hobbies",
"hole",
"hope",
"horn",
"horse",
"hospital",
"hour",
"house",
"humor"... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/name_generation.html |
c128777ea886-5 | "list",
"look",
"loss",
"love",
"lunch",
"machine",
"man",
"manager",
"map",
"marble",
"mark",
"market",
"mass",
"match",
"meal",
"measure",
"meat",
"meeting",
"memory",
"metal",
"middle",
"milk",
"mind",
"mine",
"minute",
... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/name_generation.html |
c128777ea886-6 | "pear",
"pen",
"pencil",
"person",
"pest",
"pet",
"picture",
"pie",
"pin",
"pipe",
"pizza",
"place",
"plane",
"plant",
"plastic",
"plate",
"play",
"pleasure",
"plot",
"plough",
"pocket",
"point",
"poison",
"police",
"polluti... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/name_generation.html |
c128777ea886-7 | "rice",
"river",
"road",
"roll",
"room",
"root",
"rose",
"route",
"rub",
"rule",
"run",
"sack",
"sail",
"salt",
"sand",
"scale",
"scarecrow",
"scarf",
"scene",
"scent",
"school",
"science",
"scissors",
"screw",
"sea",
"s... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/name_generation.html |
c128777ea886-8 | "sound",
"soup",
"space",
"spark",
"speed",
"sponge",
"spoon",
"spray",
"spring",
"spy",
"square",
"stamp",
"star",
"start",
"statement",
"station",
"steam",
"steel",
"stem",
"step",
"stew",
"stick",
"stitch",
"stocking",
"s... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/name_generation.html |
c128777ea886-9 | "toad",
"toe",
"tooth",
"toothpaste",
"touch",
"town",
"toy",
"trade",
"train",
"transport",
"tray",
"treatment",
"tree",
"trick",
"trip",
"trouble",
"trousers",
"truck",
"tub",
"turkey",
"turn",
"twist",
"umbrella",
"uncle",
... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/name_generation.html |
c128777ea886-10 | "wrist",
"writer",
"yard",
"yoke",
"zebra",
"zinc",
"zipper",
"zone",
]
[docs]def random_name(prefix: str = "test") -> str:
"""Generate a random name."""
adjective = random.choice(adjectives)
noun = random.choice(nouns)
number = random.randint(1, 100)
return f"{prefix}-{a... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/name_generation.html |
4c966120638b-0 | Source code for langchain.smith.evaluation.progress
"""A simple progress bar for the console."""
import threading
from typing import Any, Dict, Optional, Sequence
from uuid import UUID
from langchain.callbacks import base as base_callbacks
from langchain.schema.document import Document
from langchain.schema.output impo... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/progress.html |
4c966120638b-1 | ) -> Any:
if parent_run_id is None:
self.increment()
[docs] def on_chain_end(
self,
outputs: Dict[str, Any],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
if parent_run_id is None:
self.incre... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/progress.html |
4c966120638b-2 | self.increment()
[docs] def on_tool_error(
self,
error: BaseException,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
if parent_run_id is None:
self.increment()
[docs] def on_tool_end(
self,
ou... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/progress.html |
2dfea7e2b96b-0 | Source code for langchain.smith.evaluation.config
"""Configuration for run evaluators."""
from typing import Any, Dict, List, Optional, Union
from langsmith import RunEvaluator
from langchain.evaluation.criteria.eval_chain import CRITERIA_TYPE
from langchain.evaluation.embedding_distance.base import (
EmbeddingDist... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/config.html |
2dfea7e2b96b-1 | If not provided, we will attempt to infer automatically."""
prediction_key: Optional[str] = None
"""The key from the traced run's outputs dictionary to use to
represent the prediction. If not provided, it will be inferred
automatically."""
input_key: Optional[str] = None
"""The key from the trac... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/config.html |
2dfea7e2b96b-2 | The key from the traced run's outputs dictionary to use to
represent the prediction. If not provided, it will be inferred
automatically.
input_key : Optional[str]
The key from the traced run's inputs dictionary to use to represent the
input. If not provided, it will be inferred autom... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/config.html |
2dfea7e2b96b-3 | """The key from the traced run's inputs dictionary to use to represent the
input. If not provided, it will be inferred automatically."""
eval_llm: Optional[BaseLanguageModel] = None
"""The language model to pass to any evaluators that require one."""
class Config:
arbitrary_types_allowed = True
... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/config.html |
2dfea7e2b96b-4 | ) -> None:
super().__init__(criteria=criteria, **kwargs)
[docs] class EmbeddingDistance(SingleKeyEvalConfig):
"""Configuration for an embedding distance evaluator.
Parameters
----------
embeddings : Optional[Embeddings]
The embeddings to use for computing the d... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/config.html |
2dfea7e2b96b-5 | The prompt template to use for generating the question.
llm : Optional[BaseLanguageModel]
The language model to use for the evaluation chain.
"""
evaluator_type: EvaluatorType = EvaluatorType.QA
llm: Optional[BaseLanguageModel] = None
prompt: Optional[BasePromptTempla... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/config.html |
2dfea7e2b96b-6 | """Configuration for a json equality evaluator.
Parameters
----------
"""
evaluator_type: EvaluatorType = EvaluatorType.JSON_EQUALITY
[docs] class ExactMatch(SingleKeyEvalConfig):
"""Configuration for an exact match string evaluator.
Parameters
----------
... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/config.html |
2dfea7e2b96b-7 | If not provided, the score will be between 1 and 10 (by default).
prompt : Optional[BasePromptTemplate]
"""
evaluator_type: EvaluatorType = EvaluatorType.SCORE_STRING
criteria: Optional[CRITERIA_TYPE] = None
llm: Optional[BaseLanguageModel] = None
normalize_by: Optional[f... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/config.html |
94b373b81797-0 | Source code for langchain.smith.evaluation.runner_utils
"""Utilities for running language models or Chains over datasets."""
from __future__ import annotations
import functools
import inspect
import logging
import uuid
from enum import Enum
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Li... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-1 | if TYPE_CHECKING:
import pandas as pd
logger = logging.getLogger(__name__)
MODEL_OR_CHAIN_FACTORY = Union[
Callable[[], Union[Chain, Runnable]],
BaseLanguageModel,
Callable[[dict], Any],
Runnable,
Chain,
]
MCF = Union[Callable[[], Union[Chain, Runnable]], BaseLanguageModel]
[docs]class InputForm... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-2 | raise ImportError(
"Pandas is required to convert the results to a dataframe."
" to install pandas, run `pip install pandas`."
) from e
indices = []
records = []
for example_id, result in self["results"].items():
feedback = result["feedback... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-3 | f" new_memory = {memory_class}(...)\n"
f" return {chain_class}"
"(memory=new_memory, ...)\n\n"
f'run_on_dataset("{dataset_name}", chain_constructor, ...)'
)
return lambda: chain
elif isinstance(llm_or_chain_factory, BaseLanguageModel):
... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-4 | return lambda: runnable_
elif not isinstance(_model, Runnable):
# This is unlikely to happen - a constructor for a model function
return lambda: RunnableLambda(constructor)
else:
# Typical correct case
return constructor # noqa
return llm_or_chain_fac... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-5 | raise InputFormatError(f"LLM Run expects string prompt input. Got {inputs}")
else:
raise InputFormatError(
f"LLM Run expects 'prompt' or 'prompts' in inputs. Got {inputs}"
)
if len(prompts) == 1:
return prompts[0]
else:
raise InputFormatError(
f"LLM Ru... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-6 | f" 'messages' key input. Got {inputs}"
)
if len(raw_messages) == 1:
return messages_from_dict(raw_messages[0])
else:
raise InputFormatError(
f"Chat Run expects single List[dict] or List[List[dict]] 'messages'"
f" input. Got {len(raw_messages)} messages from inputs... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-7 | " for the llm or chat model you wish to evaluate."
)
def _validate_example_inputs_for_chain(
first_example: Example,
chain: Chain,
input_mapper: Optional[Callable[[Dict], Any]],
) -> None:
"""Validate that the example inputs match the chain input keys."""
if input_mapper:
fir... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-8 | example: Example,
llm_or_chain_factory: MCF,
input_mapper: Optional[Callable[[Dict], Any]],
) -> None:
"""Validate that the example inputs are valid for the model."""
if isinstance(llm_or_chain_factory, BaseLanguageModel):
_validate_example_inputs_for_language_model(example, input_mapper)
el... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-9 | run_outputs = chain.output_keys if isinstance(chain, Chain) else None
run_evaluators = _load_run_evaluators(
evaluation,
run_type,
data_type,
list(examples[0].outputs) if examples[0].outputs else None,
run_inputs,
run_outputs,
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-10 | if run_outputs and prediction_key not in run_outputs:
logger.warning(
f"Prediction key {prediction_key} not in chain's specified"
f" output keys {run_outputs}. Evaluation behavior may be undefined."
)
elif run_outputs and len(run_outputs) == 1:
predict... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-11 | if not isinstance(eval_config, EvaluatorType):
eval_config = EvaluatorType(eval_config)
evaluator_ = load_evaluator(eval_config, llm=eval_llm)
eval_type_tag = eval_config.value
else:
kwargs = {"llm": eval_llm, **eval_config.get_kwargs()}
evaluator_ = load_evaluator(eval_c... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-12 | " Did you mean to use a StringEvaluator instead?"
"\nSee: https://python.langchain.com/docs/guides/evaluation/string/"
)
else:
raise NotImplementedError(
f"Run evaluator for {eval_type_tag} is not implemented"
)
return run_evaluator
def _get_keys(
config: smit... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-13 | )
):
input_key, prediction_key, reference_key = _get_keys(
config, run_inputs, run_outputs, example_outputs
)
for eval_config in config.evaluators:
run_evaluator = _construct_run_evaluator(
eval_config,
config.eval_llm,
run_type,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-14 | llm: The language model to run.
inputs: The input dictionary.
tags: Optional tags to add to the run.
callbacks: Optional callbacks to use during the run.
input_mapper: Optional function to map inputs to the expected format.
Returns:
The LLMResult or ChatResult.
Raises:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-15 | inputs: Dict[str, Any],
callbacks: Callbacks,
*,
tags: Optional[List[str]] = None,
input_mapper: Optional[Callable[[Dict], Any]] = None,
) -> Union[dict, str]:
"""Run a chain asynchronously on inputs."""
inputs_ = inputs if input_mapper is None else input_mapper(inputs)
if (
isinstan... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-16 | )
result = None
try:
if isinstance(llm_or_chain_factory, BaseLanguageModel):
output: Any = await _arun_llm(
llm_or_chain_factory,
example.inputs,
tags=config["tags"],
callbacks=config["callbacks"],
input_mapper=i... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-17 | InputFormatError: If the input format is invalid.
"""
if input_mapper is not None:
prompt_or_messages = input_mapper(inputs)
if isinstance(prompt_or_messages, str):
llm_output: Union[str, BaseMessage] = llm.predict(
prompt_or_messages, callbacks=callbacks, tags=tags
... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-18 | and len(inputs_) == 1
and chain.input_keys
):
val = next(iter(inputs_.values()))
output = chain(val, callbacks=callbacks, tags=tags)
else:
runnable_config = RunnableConfig(tags=tags or [], callbacks=callbacks)
output = chain.invoke(inputs_, config=runnable_config)
ret... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-19 | chain,
example.inputs,
config["callbacks"],
tags=config["tags"],
input_mapper=input_mapper,
)
result = output
except Exception as e:
error_type = type(e).__name__
logger.warning(
f"{chain_or_llm} failed f... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
94b373b81797-20 | f"\n\n{example_msg}"
)
print(
f"View the evaluation results for project '{project_name}'"
f" at:\n{project.url}?eval=true\n\n"
f"View all tests for Dataset {dataset_name} at:\n{dataset.url}",
flush=True,
)
examples = list(client.list_examples(dataset_id=dataset.id))
... | lang/api.python.langchain.com/en/latest/_modules/langchain/smith/evaluation/runner_utils.html |
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