id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
0416b27147b9-10 | self.mlflow.log_artifact(path)
def langchain_artifact(self, chain: Any) -> None:
with self.mlflow.start_run(
run_id=self.run.info.run_id, experiment_id=self.mlf_expid
):
self.mlflow.langchain.log_model(chain, "langchain-model")
[docs]class MlflowCallbackHandler(BaseMetadataCa... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-11 | and adds the response to the list of records for both the {method}_records and
action. It then logs the response to mlflow server.
"""
def __init__(
self,
name: Optional[str] = "langchainrun-%",
experiment: Optional[str] = "langchain",
tags: Optional[Dict] = {},
track... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-12 | tracking_uri=self.tracking_uri,
experiment_name=self.experiment,
run_name=self.name,
run_tags=self.tags,
)
self.action_records: list = []
self.nlp = spacy.load("en_core_web_sm")
self.metrics = {
"step": 0,
"starts": 0,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-13 | "on_llm_start_records": [],
"on_llm_token_records": [],
"on_llm_end_records": [],
"on_chain_start_records": [],
"on_chain_end_records": [],
"on_tool_start_records": [],
"on_tool_end_records": [],
"on_text_records": [],
"on_a... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-14 | """Run when LLM starts."""
self.metrics["step"] += 1
self.metrics["llm_starts"] += 1
self.metrics["starts"] += 1
llm_starts = self.metrics["llm_starts"]
resp: Dict[str, Any] = {}
resp.update({"action": "on_llm_start"})
resp.update(flatten_dict(serialized))
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-15 | [docs] def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""Run when LLM generates a new token."""
self.metrics["step"] += 1
self.metrics["llm_streams"] += 1
llm_streams = self.metrics["llm_streams"]
resp: Dict[str, Any] = {}
resp.update({"action": "on_llm... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-16 | """Run when LLM ends running."""
self.metrics["step"] += 1
self.metrics["llm_ends"] += 1
self.metrics["ends"] += 1
llm_ends = self.metrics["llm_ends"]
resp: Dict[str, Any] = {}
resp.update({"action": "on_llm_end"})
resp.update(flatten_dict(response.llm_output or {... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-17 | )
)
complexity_metrics: Dict[str, float] = generation_resp.pop("text_complexity_metrics") # type: ignore # noqa: E501
self.mlflg.metrics(
complexity_metrics,
step=self.metrics["step"],
)
self.record... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-18 | self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Run when LLM errors."""
self.metrics["step"] += 1
self.metrics["errors"] += 1
[docs] def on_chain_start(
self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
) -> None:
"""... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-19 | chain_input = ",".join([f"{k}={v}" for k, v in inputs.items()])
input_resp = deepcopy(resp)
input_resp["inputs"] = chain_input
self.records["on_chain_start_records"].append(input_resp)
self.records["action_records"].append(input_resp)
self.mlflg.jsonf(input_resp, f"chain_start_{c... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-20 | chain_output = ",".join([f"{k}={v}" for k, v in outputs.items()])
resp.update({"action": "on_chain_end", "outputs": chain_output})
resp.update(self.metrics)
self.mlflg.metrics(self.metrics, step=self.metrics["step"])
self.records["on_chain_end_records"].append(resp)
self.records[... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-21 | ) -> None:
"""Run when tool starts running."""
self.metrics["step"] += 1
self.metrics["tool_starts"] += 1
self.metrics["starts"] += 1
tool_starts = self.metrics["tool_starts"]
resp: Dict[str, Any] = {}
resp.update({"action": "on_tool_start", "input_str": input_str... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-22 | """Run when tool ends running."""
self.metrics["step"] += 1
self.metrics["tool_ends"] += 1
self.metrics["ends"] += 1
tool_ends = self.metrics["tool_ends"]
resp: Dict[str, Any] = {}
resp.update({"action": "on_tool_end", "output": output})
resp.update(self.metrics)
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-23 | self.metrics["step"] += 1
self.metrics["errors"] += 1
[docs] def on_text(self, text: str, **kwargs: Any) -> None:
"""
Run when agent is ending.
"""
self.metrics["step"] += 1
self.metrics["text_ctr"] += 1
text_ctr = self.metrics["text_ctr"]
resp: Dict[st... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-24 | [docs] def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
"""Run when agent ends running."""
self.metrics["step"] += 1
self.metrics["agent_ends"] += 1
self.metrics["ends"] += 1
agent_ends = self.metrics["agent_ends"]
resp: Dict[str, Any] = {}
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-25 | [docs] def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
"""Run on agent action."""
self.metrics["step"] += 1
self.metrics["tool_starts"] += 1
self.metrics["starts"] += 1
tool_starts = self.metrics["tool_starts"]
resp: Dict[str, Any] = {}
re... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-26 | 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.records["on_llm_start_records"])
on_llm_end_records_df = pd.DataFrame(self.records["on_llm_end_records"])
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-27 | "automated_readability_index",
"dale_chall_readability_score",
"difficult_words",
"linsear_write_formula",
"gunning_fog",
# "text_standard",
"fernandez_huerta",
"szigriszt_pazos",
"gutierrez_polini",
"crawford",
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-28 | .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_analysis_df["chat_html"] = session_analysis_df[
["prompt", "output"]
].apply(
lambda ro... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-29 | chat_html = session_analysis_df.pop("chat_html")
chat_html = chat_html.replace("\n", "", regex=True)
self.mlflg.table("session_analysis", pd.DataFrame(session_analysis_df))
self.mlflg.html("".join(chat_html.tolist()), "chat_html")
if langchain_asset:
# To avoid circular impor... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
0416b27147b9-30 | self.mlflg.artifact(langchain_asset_path)
except AttributeError:
print("Could not save model.")
traceback.print_exc()
pass
except NotImplementedError:
print("Could not save model.")
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
5ceddaa19b29-0 | Source code for langchain.callbacks.argilla_callback
import os
import warnings
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import AgentAction, AgentFinish, LLMResult
[docs]class ArgillaCallbackHandler(BaseCallbackHandler):
"""Cal... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-1 | default workspace will be used.
api_url: URL of the Argilla Server that we want to use, and where the
`FeedbackDataset` lives in. Defaults to `None`, which means that either
`ARGILLA_API_URL` environment variable or the default http://localhost:6900
will be used.
api_... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-2 | >>> argilla_callback = ArgillaCallbackHandler(
... dataset_name="my-dataset",
... workspace_name="my-workspace",
... api_url="http://localhost:6900",
... api_key="argilla.apikey",
... )
>>> llm = OpenAI(
... temperature=0,
... callb... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-3 | api_url: Optional[str] = None,
api_key: Optional[str] = None,
) -> None:
"""Initializes the `ArgillaCallbackHandler`.
Args:
dataset_name: name of the `FeedbackDataset` in Argilla. Note that it must
exist in advance. If you need help on how to create a `FeedbackDat... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-4 | `ARGILLA_API_URL` environment variable or the default
http://localhost:6900 will be used.
api_key: API Key to connect to the Argilla Server. Defaults to `None`, which
means that either `ARGILLA_API_KEY` environment variable or the default
`argilla.apikey` will... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-5 | "Python package installed. Please install it with `pip install argilla`"
)
# Show a warning message if Argilla will assume the default values will be used
if api_url is None and os.getenv("ARGILLA_API_URL") is None:
warnings.warn(
(
"Since `api... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-6 | ),
)
# Connect to Argilla with the provided credentials, if applicable
try:
rg.init(
api_key=api_key,
api_url=api_url,
)
except Exception as e:
raise ConnectionError(
f"Could not connect to Argilla wi... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-7 | try:
self.dataset = rg.FeedbackDataset.from_argilla(
name=self.dataset_name,
workspace=self.workspace_name,
with_records=False,
)
except Exception as e:
raise FileNotFoundError(
"`FeedbackDataset` retrieval from ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-8 | " https://github.com/argilla-io/argilla/issues with the label"
" `langchain`."
) from e
supported_fields = ["prompt", "response"]
if supported_fields != [field.name for field in self.dataset.fields]:
raise ValueError(
f"`FeedbackDataset` with name=... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-9 | " https://docs.argilla.io/en/latest/guides/llms/practical_guides/use_argilla_callback_in_langchain.html." # noqa: E501
)
self.prompts: Dict[str, List[str]] = {}
warnings.warn(
(
"The `ArgillaCallbackHandler` is currently in beta and is subject to "
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-10 | self.prompts.update({str(kwargs["parent_run_id"] or kwargs["run_id"]): prompts})
[docs] def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""Do nothing when a new token is generated."""
pass
[docs] def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Log record... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-11 | records=[
{
"fields": {
"prompt": prompt,
"response": generation.text.strip(),
},
}
for generation in generations
]
)
# ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-12 | either the `parent_run_id` or the `run_id` as the key. This is done so that
we don't log the same input prompt twice, once when the LLM starts and once
when the chain starts.
"""
if "input" in inputs:
self.prompts.update(
{
str(kwargs["pare... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-13 | differs if the output is a list or not.
"""
if not any(
key in self.prompts
for key in [str(kwargs["parent_run_id"]), str(kwargs["run_id"])]
):
return
prompts = self.prompts.get(str(kwargs["parent_run_id"])) or self.prompts.get(
str(kwargs[... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-14 | )
]
)
else:
# Creates the records and adds them to the `FeedbackDataset`
self.dataset.add_records(
records=[
{
"fields": {
"prompt": " "... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-15 | ) -> None:
"""Do nothing when LLM chain outputs an error."""
pass
[docs] def on_tool_start(
self,
serialized: Dict[str, Any],
input_str: str,
**kwargs: Any,
) -> None:
"""Do nothing when tool starts."""
pass
[docs] def on_agent_action(self, actio... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5ceddaa19b29-16 | [docs] def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Do nothing when tool outputs an error."""
pass
[docs] def on_text(self, text: str, **kwargs: Any) -> None:
"""Do nothing"""
pass
[docs] def on_agent_finish(self, f... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
5e069f085165-0 | 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 |
5e069f085165-1 | " `pip install comet_ml`"
)
return comet_ml
def _get_experiment(
workspace: Optional[str] = None, project_name: Optional[str] = None
) -> Any:
comet_ml = import_comet_ml()
experiment = comet_ml.Experiment( # type: ignore
workspace=workspace,
project_name=project_name,
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-2 | "coleman_liau_index": textstat.coleman_liau_index(text),
"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": tex... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-3 | "gulpease_index": textstat.gulpease_index(text),
"osman": textstat.osman(text),
}
return text_complexity_metrics
def _summarize_metrics_for_generated_outputs(metrics: Sequence) -> dict:
pd = import_pandas()
metrics_df = pd.DataFrame(metrics)
metrics_summary = metrics_df.describe()
return... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-4 | complexity_metrics (bool): Whether to log complexity metrics
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... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-5 | stream_logs: bool = True,
) -> None:
"""Initialize callback handler."""
self.comet_ml = import_comet_ml()
super().__init__()
self.task_type = task_type
self.workspace = workspace
self.project_name = project_name
self.tags = tags
self.visualizations = v... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-6 | "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 |
5e069f085165-7 | """Run when LLM starts."""
self.step += 1
self.llm_starts += 1
self.starts += 1
metadata = self._init_resp()
metadata.update({"action": "on_llm_start"})
metadata.update(flatten_dict(serialized))
metadata.update(self.get_custom_callback_meta())
for prompt i... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-8 | 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 |
5e069f085165-9 | for gen_idx, generation in enumerate(generations):
text = generation.text
generation_resp = deepcopy(metadata)
generation_resp.update(flatten_dict(generation.dict()))
complexity_metrics = self._get_complexity_metrics(text)
if complexity_met... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-10 | [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 |
5e069f085165-11 | input_resp = deepcopy(resp)
if self.stream_logs:
self._log_stream(chain_input_val, resp, self.step)
input_resp.update({chain_input_key: chain_input_val})
self.action_records.append(input_resp)
else:
self.comet_ml.LOGGER.warn... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-12 | 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 |
5e069f085165-13 | ) -> 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"})
resp.update(flatten_dict(serialized))
resp.update(self.get_custom_callback_meta())
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-14 | 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 |
5e069f085165-15 | if self.stream_logs:
self._log_stream(text, resp, self.step)
resp.update({"text": text})
self.action_records.append(resp)
[docs] def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
"""Run when agent ends running."""
self.step += 1
self.agent_ends... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-16 | self.action_records.append(resp)
[docs] def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
"""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.lo... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-17 | Parameters:
text (str): The text to analyze.
Returns:
(dict): A dictionary containing the complexity metrics.
"""
resp = {}
if self.complexity_metrics:
text_complexity_metrics = _fetch_text_complexity_metrics(text)
resp.update(text_complexi... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-18 | if self.custom_metrics:
custom_metrics = self.custom_metrics(generation, prompt_idx, gen_idx)
resp.update(custom_metrics)
return resp
[docs] def flush_tracker(
self,
langchain_asset: Any = None,
task_type: Optional[str] = "inference",
workspace: Optiona... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-19 | Args:
name: Name of the preformed session so far so it is identifyable
langchain_asset: The langchain asset to save.
finish: Whether to finish the run.
Returns:
None
"""
self._log_session(langchain_asset)
if langchain_asset:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-20 | custom_metrics,
)
def _log_stream(self, prompt: str, metadata: dict, step: int) -> None:
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.experi... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-21 | langchain_asset.save_agent(langchain_asset_path)
self.experiment.log_model(model_name, str(langchain_asset_path))
else:
self.comet_ml.LOGGER.error(
f"{e}"
" Could not save Langchain Asset "
f"for {langchain_asset.__c... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-22 | )
try:
metadata = {"langchain_version": str(langchain.__version__)}
# 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
)
exce... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-23 | if not metrics:
return
metrics_summary = _summarize_metrics_for_generated_outputs(metrics)
for key, value in metrics_summary.items():
self.experiment.log_metrics(value, prefix=key, step=step)
def _log_visualizations(self, session_df: Any) -> None:
if not (self.visuali... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-24 | options={"compact": True},
jupyter=False,
page=True,
)
self.experiment.log_asset_data(
html,
name=f"langchain-viz-{visualization}-{idx}.html",
metadata={"prompt... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-25 | complexity_metrics: bool = False,
custom_metrics: Optional[Callable] = None,
) -> None:
_task_type = task_type if task_type else self.task_type
_workspace = workspace if workspace else self.workspace
_project_name = project_name if project_name else self.project_name
_tags = ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-26 | visualizations=_visualizations,
complexity_metrics=_complexity_metrics,
custom_metrics=_custom_metrics,
)
self.reset_callback_meta()
self.temp_dir = tempfile.TemporaryDirectory()
def _create_session_analysis_dataframe(self, langchain_asset: Any = None) -> dict:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-27 | llm_session_df = pd.merge(
llm_start_records_df,
llm_end_records_df,
left_index=True,
right_index=True,
suffixes=["_llm_start", "_llm_end"],
)
return llm_session_df
def _get_llm_parameters(self, langchain_asset: Any = None) -> dict:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
5e069f085165-28 | except Exception:
return {}
return llm_parameters | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
89fa09f1071d-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 |
89fa09f1071d-1 | ) -> BaseCallbackHandler:
"""Construct a new StreamlitCallbackHandler. This CallbackHandler is geared towards
use with a LangChain Agent; it displays the Agent's LLM and tool-usage "thoughts"
inside a series of Streamlit expanders.
Parameters
----------
parent_container
The `st.container... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit.html |
89fa09f1071d-2 | collapse_completed_thoughts
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
---... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit.html |
89fa09f1071d-3 | from streamlit.external.langchain import (
StreamlitCallbackHandler as OfficialStreamlitCallbackHandler, # type: ignore # noqa: 501
)
return OfficialStreamlitCallbackHandler(
parent_container,
max_thought_containers=max_thought_containers,
expand_new_thou... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit.html |
dba41798e94d-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, Union
from langchain.callbacks.base import BaseCallbackHandler
from ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-1 | HISTORY_EMOJI = ":books:"
EXCEPTION_EMOJI = "⚠️"
class LLMThoughtState(Enum):
# The LLM is thinking about what to do next. We don't know which tool we'll run.
THINKING = "THINKING"
# The LLM has decided to run a tool. We don't have results from the tool yet.
RUNNING_TOOL = "RUNNING_TOOL"
# We have r... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-2 | """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:
"""Return the label for an LLMThought that has an associated
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-3 | emoji = EXCEPTION_EMOJI
name = "Parsing error"
idx = min([60, len(input)])
input = input[0:idx]
if len(tool.input_str) > idx:
input = input + "..."
input = input.replace("\n", " ")
label = f"{emoji} **{name}:** {input}"
return label
[docs] def g... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-4 | a tool.
"""
return f"{CHECKMARK_EMOJI} **Complete!**"
class LLMThought:
def __init__(
self,
parent_container: DeltaGenerator,
labeler: LLMThoughtLabeler,
expanded: bool,
collapse_on_complete: bool,
):
self._container = MutableExpander(
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-5 | """The container we're writing into."""
return self._container
@property
def last_tool(self) -> Optional[ToolRecord]:
"""The last tool executed by this thought"""
return self._last_tool
def _reset_llm_token_stream(self) -> None:
self._llm_token_stream = ""
self._llm_t... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-6 | self._llm_token_stream, index=self._llm_token_writer_idx
)
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
# `response` is the concatenation of all the tokens received by the LLM.
# If we're receiving streaming tokens from `on_llm_new_token`, this response
# data is... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-7 | ) -> None:
# Called with the name of the tool we're about to run (in `serialized[name]`),
# and its input. We change our container's label to be the tool name.
self._state = LLMThoughtState.RUNNING_TOOL
tool_name = serialized["name"]
self._last_tool = ToolRecord(name=tool_name, i... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-8 | def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
self._container.markdown("**Tool encountered an error...**")
self._container.exception(error)
def on_agent_action(
self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-9 | """Finish the thought."""
if final_label is None and self._state == LLMThoughtState.RUNNING_TOOL:
assert (
self._last_tool is not None
), "_last_tool should never be null when _state == RUNNING_TOOL"
final_label = self._labeler.get_tool_label(
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-10 | parent_container: DeltaGenerator,
*,
max_thought_containers: int = 4,
expand_new_thoughts: bool = True,
collapse_completed_thoughts: bool = True,
thought_labeler: Optional[LLMThoughtLabeler] = None,
):
"""Create a StreamlitCallbackHandler instance.
Parameters
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-11 | that expander is expanded by default. Defaults to True.
collapse_completed_thoughts
If True, LLM thought expanders will be collapsed when completed.
Defaults to True.
thought_labeler
An optional custom LLMThoughtLabeler instance. If unspecified, the handler
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-12 | self._thought_labeler = thought_labeler or LLMThoughtLabeler()
def _require_current_thought(self) -> LLMThought:
"""Return our current LLMThought. Raise an error if we have no current
thought.
"""
if self._current_thought is None:
raise RuntimeError("Current LLMThought is... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-13 | """The number of 'thought containers' we're currently showing: the
number of completed thought containers, the history container (if it exists),
and the current thought container (if it exists).
"""
count = len(self._completed_thoughts)
if self._history_container is not None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-14 | self._current_thought = None
def _prune_old_thought_containers(self) -> None:
"""If we have too many thoughts onscreen, move older thoughts to the
'history container.'
"""
while (
self._num_thought_containers > self._max_thought_containers
and len(self._comple... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-15 | expanded=False,
)
oldest_thought = self._completed_thoughts.pop(0)
if self._history_container is not None:
self._history_container.markdown(oldest_thought.container.label)
self._history_container.append_copy(oldest_thought.container)
ol... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-16 | # We don't prune_old_thought_containers here, because our container won't
# be visible until it has a child.
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
self._require_current_thought().on_llm_new_token(token, **kwargs)
self._prune_old_thought_containers()
def on_llm_en... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-17 | def on_tool_start(
self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
) -> None:
self._require_current_thought().on_tool_start(serialized, input_str, **kwargs)
self._prune_old_thought_containers()
def on_tool_end(
self,
output: str,
color: Optional[st... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-18 | ) -> None:
self._require_current_thought().on_tool_error(error, **kwargs)
self._prune_old_thought_containers()
def on_text(
self,
text: str,
color: Optional[str] = None,
end: str = "",
**kwargs: Any,
) -> None:
pass
def on_chain_start(
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
dba41798e94d-19 | pass
def on_agent_action(
self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
) -> Any:
self._require_current_thought().on_agent_action(action, color, **kwargs)
self._prune_old_thought_containers()
def on_agent_finish(
self, finish: AgentFinish, color: Optio... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
26728bf71168-0 | Source code for langchain.retrievers.zep
from __future__ import annotations
from typing import TYPE_CHECKING, Dict, List, Optional
from langchain.schema import BaseRetriever, Document
if TYPE_CHECKING:
from zep_python import MemorySearchResult
[docs]class ZepRetriever(BaseRetriever):
"""A Retriever implementati... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/zep.html |
26728bf71168-1 | For server installation instructions, see:
https://getzep.github.io/deployment/quickstart/
"""
def __init__(
self,
session_id: str,
url: str,
top_k: Optional[int] = None,
):
try:
from zep_python import ZepClient
except ImportError:
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/zep.html |
26728bf71168-2 | page_content=r.message.pop("content"),
metadata={"score": r.dist, **r.message},
)
for r in results
if r.message
]
[docs] def get_relevant_documents(
self, query: str, metadata: Optional[Dict] = None
) -> List[Document]:
from zep_python i... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/zep.html |
26728bf71168-3 | from zep_python import MemorySearchPayload
payload: MemorySearchPayload = MemorySearchPayload(
text=query, metadata=metadata
)
results: List[MemorySearchResult] = await self.zep_client.asearch_memory(
self.session_id, payload, limit=self.top_k
)
return sel... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/zep.html |
808bbf9ed015-0 | Source code for langchain.retrievers.chatgpt_plugin_retriever
from __future__ import annotations
from typing import List, Optional
import aiohttp
import requests
from pydantic import BaseModel
from langchain.schema import BaseRetriever, Document
[docs]class ChatGPTPluginRetriever(BaseRetriever, BaseModel):
url: str... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/chatgpt_plugin_retriever.html |
808bbf9ed015-1 | results = response.json()["results"][0]["results"]
docs = []
for d in results:
content = d.pop("text")
metadata = d.pop("metadata", d)
if metadata.get("source_id"):
metadata["source"] = metadata.pop("source_id")
docs.append(Document(page_co... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/chatgpt_plugin_retriever.html |
808bbf9ed015-2 | ) as response:
res = await response.json()
results = res["results"][0]["results"]
docs = []
for d in results:
content = d.pop("text")
metadata = d.pop("metadata", d)
if metadata.get("source_id"):
metadata["source"] = metadata.po... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/chatgpt_plugin_retriever.html |
808bbf9ed015-3 | "Content-Type": "application/json",
"Authorization": f"Bearer {self.bearer_token}",
}
return url, json, headers | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/chatgpt_plugin_retriever.html |
1145c28da0ec-0 | Source code for langchain.retrievers.databerry
from typing import List, Optional
import aiohttp
import requests
from langchain.schema import BaseRetriever, Document
[docs]class DataberryRetriever(BaseRetriever):
"""Retriever that uses the Databerry API."""
datastore_url: str
top_k: Optional[int]
api_key... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/databerry.html |
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