id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 53 121 |
|---|---|---|
797bac92f742-0 | Source code for langchain.callbacks.streamlit
from __future__ import annotations
from typing import TYPE_CHECKING, Optional
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.streamlit.streamlit_callback_handler import (
LLMThoughtLabeler as LLMThoughtLabeler,
)
from langchain.callbac... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit.html |
797bac92f742-1 | If True, LLM thought expanders will be collapsed when completed.
Defaults to True.
thought_labeler
An optional custom LLMThoughtLabeler instance. If unspecified, the handler
will use the default thought labeling logic. Defaults to None.
Returns
-------
A new StreamlitCallbackHand... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit.html |
d629d7a60d15-0 | Source code for langchain.callbacks.stdout
"""Callback Handler that prints to std out."""
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.input import print_text
from langchain.schema import AgentAction, AgentFinish, LLMResult
[docs]class StdOu... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/stdout.html |
d629d7a60d15-1 | """Print out that we finished a chain."""
print("\n\033[1m> Finished chain.\033[0m")
[docs] def on_chain_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Do nothing."""
pass
[docs] def on_tool_start(
self,
serialized: Dict[str... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/stdout.html |
d629d7a60d15-2 | color: Optional[str] = None,
end: str = "",
**kwargs: Any,
) -> None:
"""Run when agent ends."""
print_text(text, color=color if color else self.color, end=end)
[docs] def on_agent_finish(
self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any
) -> None:... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/stdout.html |
0a0e225c1857-0 | Source code for langchain.callbacks.wandb_callback
import json
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... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
0a0e225c1857-1 | complexity_metrics (bool): Whether to compute complexity metrics.
visualize (bool): Whether to visualize the text.
nlp (spacy.lang): The spacy language model to use for visualization.
output_dir (str): The directory to save the visualization files to.
Returns:
(dict): A dictionary co... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
0a0e225c1857-2 | "crawford": textstat.crawford(text),
"gulpease_index": textstat.gulpease_index(text),
"osman": textstat.osman(text),
}
resp.update(text_complexity_metrics)
if visualize and nlp and output_dir is not None:
doc = nlp(text)
dep_out = spacy.displacy.render( # typ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
0a0e225c1857-3 | return wandb.Html(
f"""
<p style="color:black;">{formatted_prompt}:</p>
<blockquote>
<p style="color:green;">
{formatted_generation}
</p>
</blockquote>
""",
inject=False,
)
[docs]class WandbCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
"""... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
0a0e225c1857-4 | notes: Optional[str] = None,
visualize: bool = False,
complexity_metrics: bool = False,
stream_logs: bool = False,
) -> None:
"""Initialize callback handler."""
wandb = import_wandb()
import_pandas()
import_textstat()
spacy = import_spacy()
sup... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
0a0e225c1857-5 | return {k: None for k in self.callback_columns}
[docs] def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""Run when LLM starts."""
self.step += 1
self.llm_starts += 1
self.starts += 1
resp = self._init_resp()
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
0a0e225c1857-6 | resp.update({"action": "on_llm_end"})
resp.update(flatten_dict(response.llm_output or {}))
resp.update(self.get_custom_callback_meta())
for generations in response.generations:
for generation in generations:
generation_resp = deepcopy(resp)
generation_... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
0a0e225c1857-7 | self.action_records.append(input_resp)
if self.stream_logs:
self.run.log(input_resp)
elif isinstance(chain_input, list):
for inp in chain_input:
input_resp = deepcopy(resp)
input_resp.update(inp)
self.on_chain_start_records.... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
0a0e225c1857-8 | resp.update(flatten_dict(serialized))
resp.update(self.get_custom_callback_meta())
self.on_tool_start_records.append(resp)
self.action_records.append(resp)
if self.stream_logs:
self.run.log(resp)
[docs] def on_tool_end(self, output: str, **kwargs: Any) -> None:
"""... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
0a0e225c1857-9 | self.agent_ends += 1
self.ends += 1
resp = self._init_resp()
resp.update(
{
"action": "on_agent_finish",
"output": finish.return_values["output"],
"log": finish.log,
}
)
resp.update(self.get_custom_callback_m... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
0a0e225c1857-10 | )
complexity_metrics_columns = []
visualizations_columns = []
if self.complexity_metrics:
complexity_metrics_columns = [
"flesch_reading_ease",
"flesch_kincaid_grade",
"smog_index",
"coleman_liau_index",
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
0a0e225c1857-11 | ),
axis=1,
)
return session_analysis_df
[docs] def flush_tracker(
self,
langchain_asset: Any = None,
reset: bool = True,
finish: bool = False,
job_type: Optional[str] = None,
project: Optional[str] = None,
entity: Optional[str] = Non... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
0a0e225c1857-12 | }
)
if langchain_asset:
langchain_asset_path = Path(self.temp_dir.name, "model.json")
model_artifact = wandb.Artifact(name="model", type="model")
model_artifact.add(action_records_table, name="action_records")
model_artifact.add(session_analysis_table, nam... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
47556be87f71-0 | 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 |
47556be87f71-1 | "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 |
47556be87f71-2 | is_completion: bool = False,
) -> 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 ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/openai_info.html |
47556be87f71-3 | [docs]class OpenAICallbackHandler(BaseCallbackHandler):
"""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"Token... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/openai_info.html |
47556be87f71-4 | prompt_tokens = token_usage.get("prompt_tokens", 0)
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
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/openai_info.html |
4d3ab9a1f21f-0 | Source code for langchain.callbacks.whylabs_callback
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import AgentAction, AgentFinish, Generation, LLMResult
from langchain.u... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/whylabs_callback.html |
4d3ab9a1f21f-1 | return langkit
[docs]class WhyLabsCallbackHandler(BaseCallbackHandler):
"""WhyLabs CallbackHandler."""
def __init__(self, logger: Logger):
"""Initiate the rolling logger"""
super().__init__()
self.logger = logger
diagnostic_logger.info(
"Initialized WhyLabs callback h... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/whylabs_callback.html |
4d3ab9a1f21f-2 | """Do nothing."""
[docs] def on_chain_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Do nothing."""
pass
[docs] def on_tool_start(
self,
serialized: Dict[str, Any],
input_str: str,
**kwargs: Any,
) -> None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/whylabs_callback.html |
4d3ab9a1f21f-3 | [docs] def close(self) -> None:
self.logger.close()
diagnostic_logger.info("Closing WhyLabs logger, see you next time!")
def __enter__(self) -> WhyLabsCallbackHandler:
return self
def __exit__(
self, exception_type: Any, exception_value: Any, traceback: Any
) -> None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/whylabs_callback.html |
4d3ab9a1f21f-4 | metric.
"""
# langkit library will import necessary whylogs libraries
import_langkit(sentiment=sentiment, toxicity=toxicity, themes=themes)
import whylogs as why
from whylogs.api.writer.whylabs import WhyLabsWriter
from whylogs.core.schema import DeclarativeSchema
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/whylabs_callback.html |
98e9b284c943-0 | Source code for langchain.callbacks.streaming_aiter
from __future__ import annotations
import asyncio
from typing import Any, AsyncIterator, Dict, List, Literal, Union, cast
from langchain.callbacks.base import AsyncCallbackHandler
from langchain.schema import LLMResult
# TODO If used by two LLM runs in parallel this w... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streaming_aiter.html |
98e9b284c943-1 | done, other = await asyncio.wait(
[
# NOTE: If you add other tasks here, update the code below,
# which assumes each set has exactly one task each
asyncio.ensure_future(self.queue.get()),
asyncio.ensure_future(self.done.wait... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streaming_aiter.html |
1465dd734d2e-0 | Source code for langchain.callbacks.streaming_stdout_final_only
"""Callback Handler streams to stdout on new llm token."""
import sys
from typing import Any, Dict, List, Optional
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
DEFAULT_ANSWER_PREFIX_TOKENS = ["Final", "Answer", ":"]
[docs... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streaming_stdout_final_only.html |
1465dd734d2e-1 | """
super().__init__()
if answer_prefix_tokens is None:
self.answer_prefix_tokens = DEFAULT_ANSWER_PREFIX_TOKENS
else:
self.answer_prefix_tokens = answer_prefix_tokens
if strip_tokens:
self.answer_prefix_tokens_stripped = [
token.strip(... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streaming_stdout_final_only.html |
89d0039e3ef1-0 | Source code for langchain.callbacks.arize_callback
from datetime import datetime
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.utils import import_pandas
from langchain.schema import AgentAction, AgentFinish, LLMResult
[docs]class A... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arize_callback.html |
89d0039e3ef1-1 | self.arize_client = Client(space_key=SPACE_KEY, api_key=API_KEY)
if SPACE_KEY == "SPACE_KEY" or API_KEY == "API_KEY":
raise ValueError("❌ CHANGE SPACE AND API KEYS")
else:
print("✅ Arize client setup done! Now you can start using Arize!")
[docs] def on_llm_start(
self,... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arize_callback.html |
89d0039e3ef1-2 | for generations in response.generations:
for generation in generations:
prompt = self.prompt_records[self.step]
self.step = self.step + 1
prompt_embedding = pd.Series(
self.generator.generate_embeddings(
text_col=pd.... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arize_callback.html |
89d0039e3ef1-3 | "completion_token",
"total_token",
],
prompt_column_names=prompt_columns,
response_column_names=response_columns,
)
response_from_arize = self.arize_client.log(
dataframe=df,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arize_callback.html |
89d0039e3ef1-4 | pass
[docs] def on_tool_end(
self,
output: str,
observation_prefix: Optional[str] = None,
llm_prefix: Optional[str] = None,
**kwargs: Any,
) -> None:
pass
[docs] def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arize_callback.html |
9abc2199714e-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 |
9abc2199714e-1 | 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 |
9abc2199714e-2 | )
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
self.nlp = spacy.load("en_core_web_sm")
def _init_resp(self) -> Dic... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
9abc2199714e-3 | 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."""
self.step += 1
self.llm_ends += 1
self.ends += 1
resp = self._init_resp()
resp.update({"action": "on... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
9abc2199714e-4 | 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)
self.action_records.append(input_resp)
if self.stream_logs:
self... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
9abc2199714e-5 | 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_callback_meta())
self.on_tool_start_records.append(res... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
9abc2199714e-6 | 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 = self._init_resp()
resp.update(
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
9abc2199714e-7 | """
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 |
9abc2199714e-8 | 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", encoding="utf-8").write(dep_out)
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
9abc2199714e-9 | "flesch_kincaid_grade",
"smog_index",
"coleman_liau_index",
"automated_readability_index",
"dale_chall_readability_score",
"difficult_words",
"linsear_write_formula",
"gunning_fog",
"text_stan... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
9abc2199714e-10 | 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 |
9abc2199714e-11 | )
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 |
2537e6d04782-0 | Source code for langchain.callbacks.mlflow_callback
import random
import string
import tempfile
import traceback
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.utils import (
Bas... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
2537e6d04782-1 | "flesch_reading_ease": textstat.flesch_reading_ease(text),
"flesch_kincaid_grade": textstat.flesch_kincaid_grade(text),
"smog_index": textstat.smog_index(text),
"coleman_liau_index": textstat.coleman_liau_index(text),
"automated_readability_index": textstat.automated_readability_index(te... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
2537e6d04782-2 | doc, style="ent", jupyter=False, page=True
)
text_visualizations = {
"dependency_tree": dep_out,
"entities": ent_out,
}
resp.update(text_visualizations)
return resp
def construct_html_from_prompt_and_generation(prompt: str, generation: str) -> Any:
"""Cons... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
2537e6d04782-3 | # User can set other env variables described here
# > https://www.mlflow.org/docs/latest/tracking.html#logging-to-a-tracking-server
experiment_name = get_from_dict_or_env(
kwargs, "experiment_name", "MLFLOW_EXPERIMENT_NAME"
)
self.mlf_exp = self.mlflow.get_experiment_by_name(... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
2537e6d04782-4 | ):
self.mlflow.log_metric(key, value)
def metrics(
self, data: Union[Dict[str, float], Dict[str, int]], step: Optional[int] = 0
) -> None:
"""To log all metrics in the input dict."""
with self.mlflow.start_run(
run_id=self.run.info.run_id, experiment_id=self.mlf_e... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
2537e6d04782-5 | def artifact(self, path: str) -> None:
"""To upload the file from given path as artifact."""
with self.mlflow.start_run(
run_id=self.run.info.run_id, experiment_id=self.mlf_expid
):
self.mlflow.log_artifact(path)
def langchain_artifact(self, chain: Any) -> None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
2537e6d04782-6 | super().__init__()
self.name = name
self.experiment = experiment
self.tags = tags
self.tracking_uri = tracking_uri
self.temp_dir = tempfile.TemporaryDirectory()
self.mlflg = MlflowLogger(
tracking_uri=self.tracking_uri,
experiment_name=self.experim... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
2537e6d04782-7 | self.metrics[k] = 0
for k, v in self.records.items():
self.records[k] = []
[docs] def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""Run when LLM starts."""
self.metrics["step"] += 1
self.metrics["llm_starts"] +=... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
2537e6d04782-8 | self.records["on_llm_token_records"].append(resp)
self.records["action_records"].append(resp)
self.mlflg.jsonf(resp, f"llm_new_tokens_{llm_streams}")
[docs] def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Run when LLM ends running."""
self.metrics["step"] += 1
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
2537e6d04782-9 | dependency_tree = generation_resp["dependency_tree"]
entities = generation_resp["entities"]
self.mlflg.html(dependency_tree, "dep-" + hash_string(generation.text))
self.mlflg.html(entities, "ent-" + hash_string(generation.text))
[docs] def on_llm_error(
self, e... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
2537e6d04782-10 | """Run when chain ends running."""
self.metrics["step"] += 1
self.metrics["chain_ends"] += 1
self.metrics["ends"] += 1
chain_ends = self.metrics["chain_ends"]
resp: Dict[str, Any] = {}
chain_output = ",".join([f"{k}={v}" for k, v in outputs.items()])
resp.update({... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
2537e6d04782-11 | self.records["on_tool_start_records"].append(resp)
self.records["action_records"].append(resp)
self.mlflg.jsonf(resp, f"tool_start_{tool_starts}")
[docs] def on_tool_end(self, output: str, **kwargs: Any) -> None:
"""Run when tool ends running."""
self.metrics["step"] += 1
self... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
2537e6d04782-12 | self.records["on_text_records"].append(resp)
self.records["action_records"].append(resp)
self.mlflg.jsonf(resp, f"on_text_{text_ctr}")
[docs] def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
"""Run when agent ends running."""
self.metrics["step"] += 1
sel... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
2537e6d04782-13 | self.mlflg.metrics(self.metrics, step=self.metrics["step"])
self.records["on_agent_action_records"].append(resp)
self.records["action_records"].append(resp)
self.mlflg.jsonf(resp, f"agent_action_{tool_starts}")
def _create_session_analysis_df(self) -> Any:
"""Create a dataframe with ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
2537e6d04782-14 | [
"step",
"text",
"token_usage_total_tokens",
"token_usage_prompt_tokens",
"token_usage_completion_tokens",
]
+ complexity_metrics_columns
+ visualizations_columns
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
2537e6d04782-15 | try:
langchain_asset.save(langchain_asset_path)
self.mlflg.artifact(langchain_asset_path)
except ValueError:
try:
langchain_asset.save_agent(langchain_asset_path)
self.mlflg.artifact(langchain_ass... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
7c11b81bf6c5-0 | 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 |
7c11b81bf6c5-1 | "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 |
7c11b81bf6c5-2 | # Track success or error flag.
self._send_to_infino("error", self.error)
# Track token usage.
if (response.llm_output is not None) and isinstance(response.llm_output, Dict):
token_usage = response.llm_output["token_usage"]
if token_usage is not None:
promp... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/infino_callback.html |
7c11b81bf6c5-3 | self,
serialized: Dict[str, Any],
input_str: str,
**kwargs: Any,
) -> None:
"""Do nothing when tool starts."""
pass
[docs] def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
"""Do nothing when agent takes a specific action."""
pass
[docs]... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/infino_callback.html |
9dc06427bf9c-0 | Source code for langchain.callbacks.comet_ml_callback
import tempfile
from copy import deepcopy
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Sequence, Union
import langchain
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.utils import (
BaseMetad... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
9dc06427bf9c-1 | "automated_readability_index": textstat.automated_readability_index(text),
"dale_chall_readability_score": textstat.dale_chall_readability_score(text),
"difficult_words": textstat.difficult_words(text),
"linsear_write_formula": textstat.linsear_write_formula(text),
"gunning_fog": textsta... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
9dc06427bf9c-2 | stream_logs (bool): Whether to stream callback actions to Comet
This handler will utilize the associated callback method and formats
the input of each callback function with metadata regarding the state of LLM run,
and adds the response to the list of records for both the {method}_records and
action. It... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
9dc06427bf9c-3 | "based on updates to `langchain`. Please report any issues to "
"https://github.com/comet-ml/issue-tracking/issues with the tag "
"`langchain`."
)
self.comet_ml.LOGGER.warning(warning)
self.callback_columns: list = []
self.action_records: list = []
self.co... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
9dc06427bf9c-4 | self.llm_streams += 1
resp = self._init_resp()
resp.update({"action": "on_llm_new_token", "token": token})
resp.update(self.get_custom_callback_meta())
self.action_records.append(resp)
[docs] def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Run when LLM end... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
9dc06427bf9c-5 | [docs] def on_llm_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Run when LLM errors."""
self.step += 1
self.errors += 1
[docs] def on_chain_start(
self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
) -> Non... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
9dc06427bf9c-6 | if isinstance(chain_output_val, str):
output_resp = deepcopy(resp)
if self.stream_logs:
self._log_stream(chain_output_val, resp, self.step)
output_resp.update({chain_output_key: chain_output_val})
self.action_records.append(output_resp)... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
9dc06427bf9c-7 | resp.update(self.get_custom_callback_meta())
if self.stream_logs:
self._log_stream(output, resp, self.step)
resp.update({"output": output})
self.action_records.append(resp)
[docs] def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> N... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
9dc06427bf9c-8 | """Run on agent action."""
self.step += 1
self.tool_starts += 1
self.starts += 1
tool = action.tool
tool_input = str(action.tool_input)
log = action.log
resp = self._init_resp()
resp.update({"action": "on_agent_action", "log": log, "tool": tool})
r... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
9dc06427bf9c-9 | return resp
[docs] def flush_tracker(
self,
langchain_asset: Any = None,
task_type: Optional[str] = "inference",
workspace: Optional[str] = None,
project_name: Optional[str] = "comet-langchain-demo",
tags: Optional[Sequence] = None,
name: Optional[str] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
9dc06427bf9c-10 | self.experiment.log_text(prompt, metadata=metadata, step=step)
def _log_model(self, langchain_asset: Any) -> None:
model_parameters = self._get_llm_parameters(langchain_asset)
self.experiment.log_parameters(model_parameters, prefix="model")
langchain_asset_path = Path(self.temp_dir.name, "mo... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
9dc06427bf9c-11 | # Log the langchain low-level records as a JSON file directly
self.experiment.log_asset_data(
self.action_records, "langchain-action_records.json", metadata=metadata
)
except Exception:
self.comet_ml.LOGGER.warning(
"Failed to log session data ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
9dc06427bf9c-12 | )
self.experiment.log_asset_data(
html,
name=f"langchain-viz-{visualization}-{idx}.html",
metadata={"prompt": prompt},
step=idx,
)
except Exception as e:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
9dc06427bf9c-13 | self.reset_callback_meta()
self.temp_dir = tempfile.TemporaryDirectory()
def _create_session_analysis_dataframe(self, langchain_asset: Any = None) -> dict:
pd = import_pandas()
llm_parameters = self._get_llm_parameters(langchain_asset)
num_generations_per_prompt = llm_parameters.get(... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
4f6878992750-0 | Source code for langchain.callbacks.streaming_stdout
"""Callback Handler streams to stdout on new llm token."""
import sys
from typing import Any, Dict, List, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import AgentAction, AgentFinish, LLMResult
[docs]class StreamingStdOutCallba... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streaming_stdout.html |
4f6878992750-1 | ) -> None:
"""Run when chain errors."""
[docs] def on_tool_start(
self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
) -> None:
"""Run when tool starts running."""
[docs] def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
"""Run on agent action."... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streaming_stdout.html |
8962eee068ca-0 | Source code for langchain.callbacks.human
from typing import Any, Callable, Dict, Optional
from uuid import UUID
from langchain.callbacks.base import BaseCallbackHandler
def _default_approve(_input: str) -> bool:
msg = (
"Do you approve of the following input? "
"Anything except 'Y'/'Yes' (case-inse... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/human.html |
0e790c0dcca7-0 | 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 |
0e790c0dcca7-1 | wandb_tracing_callback_var: ContextVar[
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 lan... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-2 | 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 |
0e790c0dcca7-3 | 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 |
0e790c0dcca7-4 | )
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 |
0e790c0dcca7-5 | 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 |
0e790c0dcca7-6 | 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:
event(*args, **kw... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-7 | )
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 |
0e790c0dcca7-8 | 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 |
0e790c0dcca7-9 | ) -> 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 |
0e790c0dcca7-10 | """Run when LLM generates a new token.
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,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-11 | 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 |
0e790c0dcca7-12 | "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 |
0e790c0dcca7-13 | 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[Exception, KeyboardInterrupt],
**kwargs: Any,... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
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