id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
705eaf3c22a2-2 | llm._deploy()
call_result = llm._call(input)
"""
model_name: str = ""
name: str = ""
cpu: str = ""
memory: str = ""
gpu: str = ""
python_version: str = ""
python_packages: List[str] = []
max_length: str = ""
url: str = ""
"""model endpoint to use"""
model_kwar... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
705eaf3c22a2-3 | @root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = {field.alias for field in cls.__fields__.values()}
extra = values.get("model_kwargs", {})
for fiel... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
705eaf3c22a2-4 | """Validate that api key and python package exists in environment."""
beam_client_id = get_from_dict_or_env(
values, "beam_client_id", "BEAM_CLIENT_ID"
)
beam_client_secret = get_from_dict_or_env(
values, "beam_client_secret", "BEAM_CLIENT_SECRET"
)
values... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
705eaf3c22a2-5 | "python_packages": self.python_packages,
"max_length": self.max_length,
"model_kwargs": self.model_kwargs,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "beam"
[docs] def app_creation(self) -> None:
"""Creates a Python file wh... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
705eaf3c22a2-6 | outputs={{"text": beam.Types.String()}},
handler="run.py:beam_langchain",
)
"""
)
script_name = "app.py"
with open(script_name, "w") as file:
file.write(
script.format(
name=self.name,
cpu=self.cpu,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
705eaf3c22a2-7 | prompt = inputs["prompt"]
length = inputs["max_length"]
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
encodedPrompt = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(encodedProm... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
705eaf3c22a2-8 | if beam.__path__ == "":
raise ImportError
except ImportError:
raise ImportError(
"Could not import beam python package. "
"Please install it with `curl "
"https://raw.githubusercontent.com/slai-labs"
"/get-beam/main/get-... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
705eaf3c22a2-9 | self.url = line.split(":")[1].strip()
return self.app_id
raise ValueError(
f"""Failed to retrieve the appID from the deployment output.
Deployment output: {output}"""
)
else:
raise ValueError(f"Deployment failed. Error: {pro... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
705eaf3c22a2-10 | ) -> str:
"""Call to Beam."""
url = "https://apps.beam.cloud/" + self.app_id if self.app_id else self.url
payload = {"prompt": prompt, "max_length": self.max_length}
payload.update(kwargs)
headers = {
"Accept": "*/*",
"Accept-Encoding": "gzip, deflate",
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
8326587f1e62-0 | Source code for langchain.callbacks.clearml_callback
import tempfile
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.utils import (
BaseMetadataCallbackHandler,
flat... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-1 | "package installed. Please install it with `pip install clearml`"
)
return clearml
[docs]class ClearMLCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
"""Callback Handler that logs to ClearML.
Parameters:
job_type (str): The type of clearml task such as "inference", "testin... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-2 | and adds the response to the list of records for both the {method}_records and
action. It then logs the response to the ClearML console.
"""
def __init__(
self,
task_type: Optional[str] = "inference",
project_name: Optional[str] = "langchain_callback_demo",
tags: Optional[Seq... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-3 | self.visualize = visualize
self.complexity_metrics = complexity_metrics
self.stream_logs = stream_logs
self.temp_dir = tempfile.TemporaryDirectory()
# Check if ClearML task already exists (e.g. in pipeline)
if clearml.Task.current_task():
self.task = clearml.Task.curr... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-4 | "https://github.com/allegroai/clearml/issues with the tag `langchain`."
)
self.logger.report_text(warning, level=30, print_console=True)
self.callback_columns: list = []
self.action_records: list = []
self.complexity_metrics = complexity_metrics
self.visualize = visualize... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-5 | resp = self._init_resp()
resp.update({"action": "on_llm_start"})
resp.update(flatten_dict(serialized))
resp.update(self.get_custom_callback_meta())
for prompt in prompts:
prompt_resp = deepcopy(resp)
prompt_resp["prompts"] = prompt
self.on_llm_start_re... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-6 | resp.update(self.get_custom_callback_meta())
self.on_llm_token_records.append(resp)
self.action_records.append(resp)
if self.stream_logs:
self.logger.report_text(resp)
[docs] def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Run when LLM ends running."""... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-7 | self.on_llm_end_records.append(generation_resp)
self.action_records.append(generation_resp)
if self.stream_logs:
self.logger.report_text(generation_resp)
[docs] def on_llm_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-8 | resp.update(flatten_dict(serialized))
resp.update(self.get_custom_callback_meta())
chain_input = inputs["input"]
if isinstance(chain_input, str):
input_resp = deepcopy(resp)
input_resp["input"] = chain_input
self.on_chain_start_records.append(input_resp)
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-9 | """Run when chain ends running."""
self.step += 1
self.chain_ends += 1
self.ends += 1
resp = self._init_resp()
resp.update({"action": "on_chain_end", "outputs": outputs["output"]})
resp.update(self.get_custom_callback_meta())
self.on_chain_end_records.append(resp)... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-10 | ) -> None:
"""Run when tool starts running."""
self.step += 1
self.tool_starts += 1
self.starts += 1
resp = self._init_resp()
resp.update({"action": "on_tool_start", "input_str": input_str})
resp.update(flatten_dict(serialized))
resp.update(self.get_custom... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-11 | resp.update(self.get_custom_callback_meta())
self.on_tool_end_records.append(resp)
self.action_records.append(resp)
if self.stream_logs:
self.logger.report_text(resp)
[docs] def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-12 | self.action_records.append(resp)
if self.stream_logs:
self.logger.report_text(resp)
[docs] def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
"""Run when agent ends running."""
self.step += 1
self.agent_ends += 1
self.ends += 1
resp = se... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-13 | """Run on agent action."""
self.step += 1
self.tool_starts += 1
self.starts += 1
resp = self._init_resp()
resp.update(
{
"action": "on_agent_action",
"tool": action.tool,
"tool_input": action.tool_input,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-14 | """
resp = {}
textstat = import_textstat()
spacy = import_spacy()
if self.complexity_metrics:
text_complexity_metrics = {
"flesch_reading_ease": textstat.flesch_reading_ease(text),
"flesch_kincaid_grade": textstat.flesch_kincaid_grade(text),
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-15 | "linsear_write_formula": textstat.linsear_write_formula(text),
"gunning_fog": textstat.gunning_fog(text),
"text_standard": textstat.text_standard(text),
"fernandez_huerta": textstat.fernandez_huerta(text),
"szigriszt_pazos": textstat.szigriszt_pazos(text),... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-16 | doc = self.nlp(text)
dep_out = spacy.displacy.render( # type: ignore
doc, style="dep", jupyter=False, page=True
)
dep_output_path = Path(
self.temp_dir.name, hash_string(f"dep-{text}") + ".html"
)
dep_output_path.open("w", enco... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-17 | )
self.logger.report_media("Entities Plot", text, local_path=ent_output_path)
return resp
def _create_session_analysis_df(self) -> Any:
"""Create a dataframe with all the information from the session."""
pd = import_pandas()
on_llm_start_records_df = pd.DataFrame(self.on_... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-18 | "flesch_reading_ease",
"flesch_kincaid_grade",
"smog_index",
"coleman_liau_index",
"automated_readability_index",
"dale_chall_readability_score",
"difficult_words",
"linsear_write_formula",
"g... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-19 | ]
+ complexity_metrics_columns
+ visualizations_columns
]
.dropna(axis=1)
.rename({"step": "output_step", "text": "output"}, axis=1)
)
session_analysis_df = pd.concat([llm_input_prompts_df, llm_outputs_df], axis=1)
# session_ana... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-20 | finish: bool = False,
) -> None:
"""Flush the tracker and setup the session.
Everything after this will be a new table.
Args:
name: Name of the preformed session so far so it is identifyable
langchain_asset: The langchain asset to save.
finish: Whether to ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-21 | self.logger.report_text(
{
"action_records": pd.DataFrame(self.action_records),
"session_analysis": session_analysis_df,
}
)
if langchain_asset:
langchain_asset_path = Path(self.temp_dir.name, "model.json")
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
8326587f1e62-22 | )
output_model.update_weights(
weights_filename=str(langchain_asset_path),
auto_delete_file=False,
target_filename=name,
)
except NotImplementedError as e:
print("Could not save model.")
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
60c255bb80c6-0 | Source code for langchain.callbacks.manager
from __future__ import annotations
import asyncio
import functools
import logging
import os
import warnings
from contextlib import asynccontextmanager, contextmanager
from contextvars import ContextVar
from typing import (
Any,
AsyncGenerator,
Dict,
Generator,... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-1 | from langchain.callbacks.tracers.langchain_v1 import LangChainTracerV1, TracerSessionV1
from langchain.callbacks.tracers.stdout import ConsoleCallbackHandler
from langchain.callbacks.tracers.wandb import WandbTracer
from langchain.schema import (
AgentAction,
AgentFinish,
BaseMessage,
LLMResult,
get... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-2 | Optional[WandbTracer]
] = ContextVar( # noqa: E501
"tracing_wandb_callback", default=None
)
tracing_v2_callback_var: ContextVar[
Optional[LangChainTracer]
] = ContextVar( # noqa: E501
"tracing_callback_v2", default=None
)
def _get_debug() -> bool:
return langchain.debug
[docs]@contextmanager
def get_o... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-3 | """
cb = OpenAICallbackHandler()
openai_callback_var.set(cb)
yield cb
openai_callback_var.set(None)
[docs]@contextmanager
def tracing_enabled(
session_name: str = "default",
) -> Generator[TracerSessionV1, None, None]:
"""Get the Deprecated LangChainTracer in a context manager.
Args:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-4 | tracing_callback_var.set(cb)
yield session
tracing_callback_var.set(None)
[docs]@contextmanager
def wandb_tracing_enabled(
session_name: str = "default",
) -> Generator[None, None, None]:
"""Get the WandbTracer in a context manager.
Args:
session_name (str, optional): The name of the session... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-5 | *,
example_id: Optional[Union[str, UUID]] = None,
) -> Generator[None, None, None]:
"""Instruct LangChain to log all runs in context to LangSmith.
Args:
project_name (str, optional): The name of the project.
Defaults to "default".
example_id (str or UUID, optional): The ID of the... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-6 | example_id = UUID(example_id)
cb = LangChainTracer(
example_id=example_id,
project_name=project_name,
)
tracing_v2_callback_var.set(cb)
yield
tracing_v2_callback_var.set(None)
@contextmanager
def trace_as_chain_group(
group_name: str,
*,
project_name: Optional[str] = None... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-7 | project_name (str, optional): The name of the project.
Defaults to None.
example_id (str or UUID, optional): The ID of the example.
Defaults to None.
tags (List[str], optional): The inheritable tags to apply to all runs.
Defaults to None.
Returns:
Callback... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-8 | )
run_manager = cm.on_chain_start({"name": group_name}, {})
yield run_manager.get_child()
run_manager.on_chain_end({})
@asynccontextmanager
async def atrace_as_chain_group(
group_name: str,
*,
project_name: Optional[str] = None,
example_id: Optional[Union[str, UUID]] = None,
tags: Option... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-9 | Defaults to None.
example_id (str or UUID, optional): The ID of the example.
Defaults to None.
tags (List[str], optional): The inheritable tags to apply to all runs.
Defaults to None.
Returns:
AsyncCallbackManager: The async callback manager for the chain group.
E... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-10 | try:
yield run_manager.get_child()
finally:
await run_manager.on_chain_end({})
def _handle_event(
handlers: List[BaseCallbackHandler],
event_name: str,
ignore_condition_name: Optional[str],
*args: Any,
**kwargs: Any,
) -> None:
"""Generic event handler for CallbackManager."""... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-11 | [handler],
"on_llm_start",
"ignore_llm",
args[0],
message_strings,
*args[2:],
**kwargs,
)
else:
logger.warning(
f"Error in {handler.__cl... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-12 | ) -> None:
try:
if ignore_condition_name is None or not getattr(handler, ignore_condition_name):
event = getattr(handler, event_name)
if asyncio.iscoroutinefunction(event):
await event(*args, **kwargs)
else:
if handler.run_inline:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-13 | **kwargs,
)
else:
logger.warning(
f"Error in {handler.__class__.__name__}.{event_name} callback: {e}"
)
except Exception as e:
logger.warning(
f"Error in {handler.__class__.__name__}.{event_name} callback: {e}"
)
if hand... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-14 | )
await asyncio.gather(
*(
_ahandle_event_for_handler(
handler, event_name, ignore_condition_name, *args, **kwargs
)
for handler in handlers
if not handler.run_inline
)
)
BRM = TypeVar("BRM", bound="BaseRunManager")
class BaseRunMan... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-15 | Args:
run_id (UUID): The ID of the run.
handlers (List[BaseCallbackHandler]): The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]):
The list of inheritable handlers.
parent_run_id (UUID, optional): The ID of the parent run.
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-16 | Returns:
BaseRunManager: The noop manager.
"""
return cls(
run_id=uuid4(),
handlers=[],
inheritable_handlers=[],
tags=[],
inheritable_tags=[],
)
class RunManager(BaseRunManager):
"""Sync Run Manager."""
def on_text(
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-17 | class AsyncRunManager(BaseRunManager):
"""Async Run Manager."""
async def on_text(
self,
text: str,
**kwargs: Any,
) -> Any:
"""Run when text is received.
Args:
text (str): The received text.
Returns:
Any: The result of the callback.
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-18 | ) -> None:
"""Run when LLM generates a new token.
Args:
token (str): The new token.
"""
_handle_event(
self.handlers,
"on_llm_new_token",
"ignore_llm",
token=token,
run_id=self.run_id,
parent_run_id=self.... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-19 | **kwargs,
)
def on_llm_error(
self,
error: Union[Exception, KeyboardInterrupt],
**kwargs: Any,
) -> None:
"""Run when LLM errors.
Args:
error (Exception or KeyboardInterrupt): The error.
"""
_handle_event(
self.handlers,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-20 | Args:
token (str): The new token.
"""
await _ahandle_event(
self.handlers,
"on_llm_new_token",
"ignore_llm",
token,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
async def on_l... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-21 | async def on_llm_error(
self,
error: Union[Exception, KeyboardInterrupt],
**kwargs: Any,
) -> None:
"""Run when LLM errors.
Args:
error (Exception or KeyboardInterrupt): The error.
"""
await _ahandle_event(
self.handlers,
"o... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-22 | Defaults to None.
Returns:
CallbackManager: The child callback manager.
"""
manager = CallbackManager(handlers=[], parent_run_id=self.run_id)
manager.set_handlers(self.inheritable_handlers)
manager.add_tags(self.inheritable_tags)
if tag is not None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-23 | )
def on_chain_error(
self,
error: Union[Exception, KeyboardInterrupt],
**kwargs: Any,
) -> None:
"""Run when chain errors.
Args:
error (Exception or KeyboardInterrupt): The error.
"""
_handle_event(
self.handlers,
"on_c... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-24 | "on_agent_action",
"ignore_agent",
action,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
"""Run when agent finish is received.
Args:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-25 | """Async callback manager for chain run."""
def get_child(self, tag: Optional[str] = None) -> AsyncCallbackManager:
"""Get a child callback manager.
Args:
tag (str, optional): The tag for the child callback manager.
Defaults to None.
Returns:
AsyncCall... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-26 | """
await _ahandle_event(
self.handlers,
"on_chain_end",
"ignore_chain",
outputs,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
async def on_chain_error(
self,
error: Union[Excepti... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-27 | """Run when agent action is received.
Args:
action (AgentAction): The agent action.
Returns:
Any: The result of the callback.
"""
await _ahandle_event(
self.handlers,
"on_agent_action",
"ignore_agent",
action,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-28 | run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
class CallbackManagerForToolRun(RunManager, ToolManagerMixin):
"""Callback manager for tool run."""
def get_child(self, tag: Optional[str] = None) -> CallbackManager:
"""Get a child callback manager.
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-29 | self,
output: str,
**kwargs: Any,
) -> None:
"""Run when tool ends running.
Args:
output (str): The output of the tool.
"""
_handle_event(
self.handlers,
"on_tool_end",
"ignore_agent",
output,
run... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-30 | "ignore_agent",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
class AsyncCallbackManagerForToolRun(AsyncRunManager, ToolManagerMixin):
"""Async callback manager for tool run."""
def get_child(self, tag: Optional[str] = None) -> A... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-31 | return manager
async def on_tool_end(self, output: str, **kwargs: Any) -> None:
"""Run when tool ends running.
Args:
output (str): The output of the tool.
"""
await _ahandle_event(
self.handlers,
"on_tool_end",
"ignore_agent",
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-32 | "ignore_agent",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
class CallbackManager(BaseCallbackManager):
"""Callback manager that can be used to handle callbacks from langchain."""
def on_llm_start(
self,
seriali... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-33 | """
managers = []
for prompt in prompts:
run_id_ = uuid4()
_handle_event(
self.handlers,
"on_llm_start",
"ignore_llm",
serialized,
[prompt],
run_id=run_id_,
parent_run_... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-34 | messages: List[List[BaseMessage]],
**kwargs: Any,
) -> List[CallbackManagerForLLMRun]:
"""Run when LLM starts running.
Args:
serialized (Dict[str, Any]): The serialized LLM.
messages (List[List[BaseMessage]]): The list of messages.
run_id (UUID, optional):... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-35 | run_id=run_id_,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
managers.append(
CallbackManagerForLLMRun(
run_id=run_id_,
handlers=self.handlers,
inheritable... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-36 | inputs (Dict[str, Any]): The inputs to the chain.
run_id (UUID, optional): The ID of the run. Defaults to None.
Returns:
CallbackManagerForChainRun: The callback manager for the chain run.
"""
if run_id is None:
run_id = uuid4()
_handle_event(
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-37 | )
def on_tool_start(
self,
serialized: Dict[str, Any],
input_str: str,
run_id: Optional[UUID] = None,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> CallbackManagerForToolRun:
"""Run when tool starts running.
Args:
serialized... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-38 | _handle_event(
self.handlers,
"on_tool_start",
"ignore_agent",
serialized,
input_str,
run_id=run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
return CallbackManagerForToolRun(... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-39 | ) -> CallbackManager:
"""Configure the callback manager.
Args:
inheritable_callbacks (Optional[Callbacks], optional): The inheritable
callbacks. Defaults to None.
local_callbacks (Optional[Callbacks], optional): The local callbacks.
Defaults to Non... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-40 | class AsyncCallbackManager(BaseCallbackManager):
"""Async callback manager that can be used to handle callbacks from LangChain."""
@property
def is_async(self) -> bool:
"""Return whether the handler is async."""
return True
async def on_llm_start(
self,
serialized: Dict[s... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-41 | to each prompt.
"""
tasks = []
managers = []
for prompt in prompts:
run_id_ = uuid4()
tasks.append(
_ahandle_event(
self.handlers,
"on_llm_start",
"ignore_llm",
seriali... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-42 | return managers
async def on_chat_model_start(
self,
serialized: Dict[str, Any],
messages: List[List[BaseMessage]],
**kwargs: Any,
) -> Any:
"""Run when LLM starts running.
Args:
serialized (Dict[str, Any]): The serialized LLM.
messages (Li... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-43 | self.handlers,
"on_chat_model_start",
"ignore_chat_model",
serialized,
[message_list],
run_id=run_id_,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwarg... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-44 | **kwargs: Any,
) -> AsyncCallbackManagerForChainRun:
"""Run when chain starts running.
Args:
serialized (Dict[str, Any]): The serialized chain.
inputs (Dict[str, Any]): The inputs to the chain.
run_id (UUID, optional): The ID of the run. Defaults to None.
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-45 | return AsyncCallbackManagerForChainRun(
run_id=run_id,
handlers=self.handlers,
inheritable_handlers=self.inheritable_handlers,
parent_run_id=self.parent_run_id,
tags=self.tags,
inheritable_tags=self.inheritable_tags,
)
async def on_tool... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-46 | parent_run_id (UUID, optional): The ID of the parent run.
Defaults to None.
Returns:
AsyncCallbackManagerForToolRun: The async callback manager
for the tool run.
"""
if run_id is None:
run_id = uuid4()
await _ahandle_event(
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-47 | )
@classmethod
def configure(
cls,
inheritable_callbacks: Callbacks = None,
local_callbacks: Callbacks = None,
verbose: bool = False,
inheritable_tags: Optional[List[str]] = None,
local_tags: Optional[List[str]] = None,
) -> AsyncCallbackManager:
"""Co... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-48 | Defaults to None.
Returns:
AsyncCallbackManager: The configured async callback manager.
"""
return _configure(
cls,
inheritable_callbacks,
local_callbacks,
verbose,
inheritable_tags,
local_tags,
)
T = Typ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-49 | callback_manager_cls: Type[T],
inheritable_callbacks: Callbacks = None,
local_callbacks: Callbacks = None,
verbose: bool = False,
inheritable_tags: Optional[List[str]] = None,
local_tags: Optional[List[str]] = None,
) -> T:
"""Configure the callback manager.
Args:
callback_manager_cl... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-50 | Returns:
T: The configured callback manager.
"""
callback_manager = callback_manager_cls(handlers=[])
if inheritable_callbacks or local_callbacks:
if isinstance(inheritable_callbacks, list) or inheritable_callbacks is None:
inheritable_callbacks_ = inheritable_callbacks or []
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-51 | if isinstance(local_callbacks, list)
else (local_callbacks.handlers if local_callbacks else [])
)
for handler in local_handlers_:
callback_manager.add_handler(handler, False)
if inheritable_tags or local_tags:
callback_manager.add_tags(inheritable_tags or [])
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-52 | )
tracer_v2 = tracing_v2_callback_var.get()
tracing_v2_enabled_ = (
env_var_is_set("LANGCHAIN_TRACING_V2") or tracer_v2 is not None
)
tracer_project = os.environ.get(
"LANGCHAIN_PROJECT", os.environ.get("LANGCHAIN_SESSION", "default")
)
debug = _get_debug()
if (
verbo... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-53 | if debug and not any(
isinstance(handler, ConsoleCallbackHandler)
for handler in callback_manager.handlers
):
callback_manager.add_handler(ConsoleCallbackHandler(), True)
if tracing_enabled_ and not any(
isinstance(handler, LangChainTracerV1)
f... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
60c255bb80c6-54 | callback_manager.add_handler(handler, True)
if tracing_v2_enabled_ and not any(
isinstance(handler, LangChainTracer)
for handler in callback_manager.handlers
):
if tracer_v2:
callback_manager.add_handler(tracer_v2, True)
else:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
63318f9c36e3-0 | Source code for langchain.callbacks.openai_info
"""Callback Handler that prints to std out."""
from typing import Any, Dict, List
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import LLMResult
MODEL_COST_PER_1K_TOKENS = {
# GPT-4 input
"gpt-4": 0.03,
"gpt-4-0314": 0.03,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/openai_info.html |
63318f9c36e3-1 | "gpt-4-0314-completion": 0.06,
"gpt-4-0613-completion": 0.06,
"gpt-4-32k-completion": 0.12,
"gpt-4-32k-0314-completion": 0.12,
"gpt-4-32k-0613-completion": 0.12,
# GPT-3.5 input
"gpt-3.5-turbo": 0.0015,
"gpt-3.5-turbo-0301": 0.0015,
"gpt-3.5-turbo-0613": 0.0015,
"gpt-3.5-turbo-16k": ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/openai_info.html |
63318f9c36e3-2 | "gpt-3.5-turbo-16k-0613": 0.003,
# GPT-3.5 output
"gpt-3.5-turbo-completion": 0.002,
"gpt-3.5-turbo-0301-completion": 0.002,
"gpt-3.5-turbo-0613-completion": 0.002,
"gpt-3.5-turbo-16k-completion": 0.004,
"gpt-3.5-turbo-16k-0613-completion": 0.004,
# Others
"gpt-35-turbo": 0.002, # Azure... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/openai_info.html |
63318f9c36e3-3 | "ada": 0.0004,
"text-babbage-001": 0.0005,
"babbage": 0.0005,
"text-curie-001": 0.002,
"curie": 0.002,
"text-davinci-003": 0.02,
"text-davinci-002": 0.02,
"code-davinci-002": 0.02,
"ada-finetuned": 0.0016,
"babbage-finetuned": 0.0024,
"curie-finetuned": 0.012,
"davinci-finetu... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/openai_info.html |
63318f9c36e3-4 | ) -> str:
"""
Standardize the model name to a format that can be used in the OpenAI API.
Args:
model_name: Model name to standardize.
is_completion: Whether the model is used for completion or not.
Defaults to False.
Returns:
Standardized model name.
"""
model... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/openai_info.html |
63318f9c36e3-5 | ) -> float:
"""
Get the cost in USD for a given model and number of tokens.
Args:
model_name: Name of the model
num_tokens: Number of tokens.
is_completion: Whether the model is used for completion or not.
Defaults to False.
Returns:
Cost in USD.
"""
m... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/openai_info.html |
63318f9c36e3-6 | """Callback Handler that tracks OpenAI info."""
total_tokens: int = 0
prompt_tokens: int = 0
completion_tokens: int = 0
successful_requests: int = 0
total_cost: float = 0.0
def __repr__(self) -> str:
return (
f"Tokens Used: {self.total_tokens}\n"
f"\tPrompt Tokens... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/openai_info.html |
63318f9c36e3-7 | self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""Print out the prompts."""
pass
[docs] def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""Print out the token."""
pass
[docs] def on_llm_end(self, response: LLMResult, **kwargs: Any)... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/openai_info.html |
63318f9c36e3-8 | model_name = standardize_model_name(response.llm_output.get("model_name", ""))
if model_name in MODEL_COST_PER_1K_TOKENS:
completion_cost = get_openai_token_cost_for_model(
model_name, completion_tokens, is_completion=True
)
prompt_cost = get_openai_token_cost... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/openai_info.html |
655999c04061-0 | Source code for langchain.callbacks.infino_callback
import time
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import AgentAction, AgentFinish, LLMResult
def import_infino() -> Any:
try:
from infinopy import InfinoClient
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/infino_callback.html |
655999c04061-1 | verbose: bool = False,
) -> None:
# Set Infino client
self.client = import_infino()
self.model_id = model_id
self.model_version = model_version
self.verbose = verbose
def _send_to_infino(
self,
key: str,
value: Any,
is_ts: bool = True,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/infino_callback.html |
655999c04061-2 | "labels": {
"model_id": self.model_id,
"model_version": self.model_version,
},
}
if self.verbose:
print(f"Tracking {key} with Infino: {payload}")
# Append to Infino time series only if is_ts is True, otherwise
# append to Infino log... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/infino_callback.html |
655999c04061-3 | # Set the error flag to indicate no error (this will get overridden
# in on_llm_error if an error occurs).
self.error = 0
# Set the start time (so that we can calculate the request
# duration in on_llm_end).
self.start_time = time.time()
[docs] def on_llm_new_token(self, token... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/infino_callback.html |
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