viruPVS/langchain-docs huggingface dataset:
Source code for langchain_community.callbacks.clearml_callback
from __future__ import annotations
import tempfile
from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, Mapping, Optional, Sequence
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
from langchain_community.callbacks.utils import (
    BaseMetadataCallbackHandler,
    flatten_dict,
    hash_string,
    import_pandas,
    import_spacy,
    import_textstat,
    load_json,
)
if TYPE_CHECKING:
    import pandas as pd
[docs]def import_clearml() -> Any:
    """Import the clearml python package and raise an error if it is not installed."""
    try:
        import clearml  # noqa: F401
    except ImportError:
        raise ImportError(
            "To use the clearml callback manager you need to have the `clearml` python "
            "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", "testing" or "qc"
        project_name (str): The clearml project name
        tags (list): Tags to add to the task
        task_name (str): Name of the clearml task
        visualize (bool): Whether to visualize the run.
        complexity_metrics (bool): Whether to log complexity metrics
        stream_logs (bool): Whether to stream callback actions to ClearML
    This handler will utilize the associated callback method and formats
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 then logs the response to the ClearML console.
    """
[docs]    def __init__(
        self,
        task_type: Optional[str] = "inference",
        project_name: Optional[str] = "langchain_callback_demo",
        tags: Optional[Sequence] = None,
        task_name: Optional[str] = None,
        visualize: bool = False,
        complexity_metrics: bool = False,
        stream_logs: bool = False,
    ) -> None:
        """Initialize callback handler."""
        clearml = import_clearml()
        spacy = import_spacy()
        super().__init__()
        self.task_type = task_type
        self.project_name = project_name
        self.tags = tags
        self.task_name = task_name
        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.current_task()
        else:
            self.task = clearml.Task.init(  # type: ignore
                task_type=self.task_type,
                project_name=self.project_name,
                tags=self.tags,
                task_name=self.task_name,
                output_uri=True,
            )
        self.logger = self.task.get_logger()
        warning = (
            "The clearml callback is currently in beta and is subject to change "
"The clearml callback is currently in beta and is subject to change "
            "based on updates to `langchain`. Please report any issues to "
            "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
        self.nlp = spacy.load("en_core_web_sm")
    def _init_resp(self) -> Dict:
        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()
        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_records.append(prompt_resp)
            self.action_records.append(prompt_resp)
            if self.stream_logs:
                self.logger.report_text(prompt_resp)
[docs]    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
        """Run when LLM generates a new token."""
        self.step += 1
        self.llm_streams += 1
        resp = self._init_resp()
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.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."""
        self.step += 1
        self.llm_ends += 1
        self.ends += 1
        resp = self._init_resp()
        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_resp.update(flatten_dict(generation.dict()))
                generation_resp.update(self.analyze_text(generation.text))
                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: BaseException, **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
    ) -> None:
        """Run when chain starts running."""
        self.step += 1
        self.chain_starts += 1
        self.starts += 1
self.chain_starts += 1
        self.starts += 1
        resp = self._init_resp()
        resp.update({"action": "on_chain_start"})
        resp.update(flatten_dict(serialized))
        resp.update(self.get_custom_callback_meta())
        chain_input = inputs.get("input", inputs.get("human_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.logger.report_text(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.append(input_resp)
                self.action_records.append(input_resp)
                if self.stream_logs:
                    self.logger.report_text(input_resp)
        else:
            raise ValueError("Unexpected data format provided!")
[docs]    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
        """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.get("output", outputs.get("text")),
            }
        )
        resp.update(self.get_custom_callback_meta())
        self.on_chain_end_records.append(resp)
        self.action_records.append(resp)
        if self.stream_logs:
            self.logger.report_text(resp)
[docs]    def on_chain_error(self, error: BaseException, **kwargs: Any) -> None:
        """Run when chain errors."""
"""Run when chain errors."""
        self.step += 1
        self.errors += 1
[docs]    def on_tool_start(
        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
    ) -> 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_callback_meta())
        self.on_tool_start_records.append(resp)
        self.action_records.append(resp)
        if self.stream_logs:
            self.logger.report_text(resp)
[docs]    def on_tool_end(self, output: str, **kwargs: Any) -> None:
        """Run when tool ends running."""
        self.step += 1
        self.tool_ends += 1
        self.ends += 1
        resp = self._init_resp()
        resp.update({"action": "on_tool_end", "output": output})
        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: BaseException, **kwargs: Any) -> None:
        """Run when tool errors."""
        self.step += 1
        self.errors += 1
[docs]    def on_text(self, text: str, **kwargs: Any) -> None:
        """
        Run when agent is ending.
        """
        self.step += 1
        self.text_ctr += 1
        resp = self._init_resp()
self.text_ctr += 1
        resp = self._init_resp()
        resp.update({"action": "on_text", "text": text})
        resp.update(self.get_custom_callback_meta())
        self.on_text_records.append(resp)
        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 = self._init_resp()
        resp.update(
            {
                "action": "on_agent_finish",
                "output": finish.return_values["output"],
                "log": finish.log,
            }
        )
        resp.update(self.get_custom_callback_meta())
        self.on_agent_finish_records.append(resp)
        self.action_records.append(resp)
        if self.stream_logs:
            self.logger.report_text(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
        resp = self._init_resp()
        resp.update(
            {
                "action": "on_agent_action",
                "tool": action.tool,
                "tool_input": action.tool_input,
                "log": action.log,
            }
        )
        resp.update(self.get_custom_callback_meta())
        self.on_agent_action_records.append(resp)
        self.action_records.append(resp)
        if self.stream_logs:
            self.logger.report_text(resp)
[docs]    def analyze_text(self, text: str) -> dict:
[docs]    def analyze_text(self, text: str) -> dict:
        """Analyze text using textstat and spacy.
        Parameters:
            text (str): The text to analyze.
        Returns:
            (dict): A dictionary containing the complexity metrics.
        """
        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),
                "smog_index": textstat.smog_index(text),
                "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": 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),
                "gutierrez_polini": textstat.gutierrez_polini(text),
                "crawford": textstat.crawford(text),
                "gulpease_index": textstat.gulpease_index(text),
                "osman": textstat.osman(text),
            }
"osman": textstat.osman(text),
            }
            resp.update(text_complexity_metrics)
        if self.visualize and self.nlp and self.temp_dir.name is not None:
            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", encoding="utf-8").write(dep_out)
            ent_out = spacy.displacy.render(  # type: ignore
                doc, style="ent", jupyter=False, page=True
            )
            ent_output_path = Path(
                self.temp_dir.name, hash_string(f"ent-{text}") + ".html"
            )
            ent_output_path.open("w", encoding="utf-8").write(ent_out)
            self.logger.report_media(
                "Dependencies Plot", text, local_path=dep_output_path
            )
            self.logger.report_media("Entities Plot", text, local_path=ent_output_path)
        return resp
    @staticmethod
    def _build_llm_df(
        base_df: pd.DataFrame, base_df_fields: Sequence, rename_map: Mapping
    ) -> pd.DataFrame:
        base_df_fields = [field for field in base_df_fields if field in base_df]
        rename_map = {
            map_entry_k: map_entry_v
            for map_entry_k, map_entry_v in rename_map.items()
            if map_entry_k in base_df_fields
        }
        llm_df = base_df[base_df_fields].dropna(axis=1)
        if rename_map:
if rename_map:
            llm_df = llm_df.rename(rename_map, axis=1)
        return llm_df
    def _create_session_analysis_df(self) -> Any:
        """Create a dataframe with all the information from the session."""
        pd = import_pandas()
        on_llm_end_records_df = pd.DataFrame(self.on_llm_end_records)
        llm_input_prompts_df = ClearMLCallbackHandler._build_llm_df(
            base_df=on_llm_end_records_df,
            base_df_fields=["step", "prompts"]
            + (["name"] if "name" in on_llm_end_records_df else ["id"]),
            rename_map={"step": "prompt_step"},
        )
        complexity_metrics_columns = []
        visualizations_columns: List = []
        if self.complexity_metrics:
            complexity_metrics_columns = [
                "flesch_reading_ease",
                "flesch_kincaid_grade",
                "smog_index",
                "coleman_liau_index",
                "automated_readability_index",
                "dale_chall_readability_score",
                "difficult_words",
                "linsear_write_formula",
                "gunning_fog",
                "text_standard",
                "fernandez_huerta",
                "szigriszt_pazos",
                "gutierrez_polini",
                "crawford",
                "gulpease_index",
                "osman",
            ]
        llm_outputs_df = ClearMLCallbackHandler._build_llm_df(
            on_llm_end_records_df,
            [
                "step",
                "text",
                "token_usage_total_tokens",
                "token_usage_prompt_tokens",
                "token_usage_completion_tokens",
"token_usage_prompt_tokens",
                "token_usage_completion_tokens",
            ]
            + complexity_metrics_columns
            + visualizations_columns,
            {"step": "output_step", "text": "output"},
        )
        session_analysis_df = pd.concat([llm_input_prompts_df, llm_outputs_df], axis=1)
        return session_analysis_df
[docs]    def flush_tracker(
        self,
        name: Optional[str] = None,
        langchain_asset: Any = None,
        finish: bool = False,
    ) -> None:
        """Flush the tracker and setup the session.
        Everything after this will be a new table.
        Args:
            name: Name of the performed session so far so it is identifiable
            langchain_asset: The langchain asset to save.
            finish: Whether to finish the run.
            Returns:
                None
        """
        pd = import_pandas()
        clearml = import_clearml()
        # Log the action records
        self.logger.report_table(
            "Action Records", name, table_plot=pd.DataFrame(self.action_records)
        )
        # Session analysis
        session_analysis_df = self._create_session_analysis_df()
        self.logger.report_table(
            "Session Analysis", name, table_plot=session_analysis_df
        )
        if self.stream_logs:
            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")
            try:
                langchain_asset.save(langchain_asset_path)
                # Create output model and connect it to the task
                output_model = clearml.OutputModel(
output_model = clearml.OutputModel(
                    task=self.task, config_text=load_json(langchain_asset_path)
                )
                output_model.update_weights(
                    weights_filename=str(langchain_asset_path),
                    auto_delete_file=False,
                    target_filename=name,
                )
            except ValueError:
                langchain_asset.save_agent(langchain_asset_path)
                output_model = clearml.OutputModel(
                    task=self.task, config_text=load_json(langchain_asset_path)
                )
                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.")
                print(repr(e))
                pass
        # Cleanup after adding everything to ClearML
        self.task.flush(wait_for_uploads=True)
        self.temp_dir.cleanup()
        self.temp_dir = tempfile.TemporaryDirectory()
        self.reset_callback_meta()
        if finish:
            self.task.close()
Source code for langchain_community.callbacks.sagemaker_callback
import json
import os
import shutil
import tempfile
from copy import deepcopy
from typing import Any, Dict, List, Optional
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
from langchain_community.callbacks.utils import (
    flatten_dict,
)
[docs]def save_json(data: dict, file_path: str) -> None:
    """Save dict to local file path.
    Parameters:
        data (dict): The dictionary to be saved.
        file_path (str): Local file path.
    """
    with open(file_path, "w") as outfile:
        json.dump(data, outfile)
[docs]class SageMakerCallbackHandler(BaseCallbackHandler):
    """Callback Handler that logs prompt artifacts and metrics to SageMaker Experiments.
    Parameters:
        run (sagemaker.experiments.run.Run): Run object where the experiment is logged.
    """
[docs]    def __init__(self, run: Any) -> None:
        """Initialize callback handler."""
        super().__init__()
        self.run = run
        self.metrics = {
            "step": 0,
            "starts": 0,
            "ends": 0,
            "errors": 0,
            "text_ctr": 0,
            "chain_starts": 0,
            "chain_ends": 0,
            "llm_starts": 0,
            "llm_ends": 0,
            "llm_streams": 0,
            "tool_starts": 0,
            "tool_ends": 0,
            "agent_ends": 0,
        }
        # Create a temporary directory
"agent_ends": 0,
        }
        # Create a temporary directory
        self.temp_dir = tempfile.mkdtemp()
    def _reset(self) -> None:
        for k, v in self.metrics.items():
            self.metrics[k] = 0
[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"] += 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))
        resp.update(self.metrics)
        for idx, prompt in enumerate(prompts):
            prompt_resp = deepcopy(resp)
            prompt_resp["prompt"] = prompt
            self.jsonf(
                prompt_resp,
                self.temp_dir,
                f"llm_start_{llm_starts}_prompt_{idx}",
            )
[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_new_token", "token": token})
        resp.update(self.metrics)
        self.jsonf(resp, self.temp_dir, 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
        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 {}))
        resp.update(self.metrics)
        for generations in response.generations:
            for idx, generation in enumerate(generations):
                generation_resp = deepcopy(resp)
                generation_resp.update(flatten_dict(generation.dict()))
                self.jsonf(
                    resp,
                    self.temp_dir,
                    f"llm_end_{llm_ends}_generation_{idx}",
                )
[docs]    def on_llm_error(self, error: BaseException, **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:
        """Run when chain starts running."""
        self.metrics["step"] += 1
        self.metrics["chain_starts"] += 1
        self.metrics["starts"] += 1
        chain_starts = self.metrics["chain_starts"]
        resp: Dict[str, Any] = {}
        resp.update({"action": "on_chain_start"})
        resp.update(flatten_dict(serialized))
        resp.update(self.metrics)