Source code for langchain_community.callbacks.arize_callback

from datetime import datetime
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 import_pandas


[docs]class ArizeCallbackHandler(BaseCallbackHandler): """Callback Handler that logs to Arize."""
[docs] def __init__( self, model_id: Optional[str] = None, model_version: Optional[str] = None, SPACE_KEY: Optional[str] = None, API_KEY: Optional[str] = None, ) -> None: """Initialize callback handler.""" super().__init__() self.model_id = model_id self.model_version = model_version self.space_key = SPACE_KEY self.api_key = API_KEY self.prompt_records: List[str] = [] self.response_records: List[str] = [] self.prediction_ids: List[str] = [] self.pred_timestamps: List[int] = [] self.response_embeddings: List[float] = [] self.prompt_embeddings: List[float] = [] self.prompt_tokens = 0 self.completion_tokens = 0 self.total_tokens = 0 self.step = 0 from arize.pandas.embeddings import EmbeddingGenerator, UseCases from arize.pandas.logger import Client self.generator = EmbeddingGenerator.from_use_case( use_case=UseCases.NLP.SEQUENCE_CLASSIFICATION, model_name="distilbert-base-uncased", tokenizer_max_length=512, batch_size=256, ) 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("βœ… Arize client setup done! Now you can start using Arize!") # noqa: T201
[docs] def on_llm_start( self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any ) -> None: for prompt in prompts: self.prompt_records.append(prompt.replace("\n", ""))
[docs] def on_llm_new_token(self, token: str, **kwargs: Any) -> None: """Do nothing.""" pass
[docs] def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: pd = import_pandas() from arize.utils.types import ( EmbeddingColumnNames, Environments, ModelTypes, Schema, ) # Safe check if 'llm_output' and 'token_usage' exist if response.llm_output and "token_usage" in response.llm_output: self.prompt_tokens = response.llm_output["token_usage"].get( "prompt_tokens", 0 ) self.total_tokens = response.llm_output["token_usage"].get( "total_tokens", 0 ) self.completion_tokens = response.llm_output["token_usage"].get( "completion_tokens", 0 ) else: self.prompt_tokens = self.total_tokens = self.completion_tokens = ( 0 # assign default value ) 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.Series(prompt.replace("\n", " ")) ).reset_index(drop=True) ) # Assigning text to response_text instead of response response_text = generation.text.replace("\n", " ") response_embedding = pd.Series( self.generator.generate_embeddings( text_col=pd.Series(generation.text.replace("\n", " ")) ).reset_index(drop=True) ) pred_timestamp = datetime.now().timestamp() # Define the columns and data columns = [ "prediction_ts", "response", "prompt", "response_vector", "prompt_vector", "prompt_token", "completion_token", "total_token", ] data = [ [ pred_timestamp, response_text, prompt, response_embedding[0], prompt_embedding[0], self.prompt_tokens, self.total_tokens, self.completion_tokens, ] ] # Create the DataFrame df = pd.DataFrame(data, columns=columns) # Declare prompt and response columns prompt_columns = EmbeddingColumnNames( vector_column_name="prompt_vector", data_column_name="prompt" ) response_columns = EmbeddingColumnNames( vector_column_name="response_vector", data_column_name="response" ) schema = Schema( timestamp_column_name="prediction_ts", tag_column_names=[ "prompt_token", "completion_token", "total_token", ], prompt_column_names=prompt_columns, response_column_names=response_columns, ) response_from_arize = self.arize_client.log( dataframe=df, schema=schema, model_id=self.model_id, model_version=self.model_version, model_type=ModelTypes.GENERATIVE_LLM, environment=Environments.PRODUCTION, ) if response_from_arize.status_code == 200: print("βœ… Successfully logged data to Arize!") # noqa: T201 else: print(f'❌ Logging failed "{response_from_arize.text}"') # noqa: T201
[docs] def on_llm_error(self, error: BaseException, **kwargs: Any) -> None: """Do nothing.""" pass
[docs] def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: pass
[docs] def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: """Do nothing.""" pass
[docs] def on_chain_error(self, error: BaseException, **kwargs: Any) -> None: """Do nothing.""" pass
[docs] def on_tool_start( self, serialized: Dict[str, Any], input_str: str, **kwargs: Any, ) -> None: pass
[docs] def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any: """Do nothing.""" pass
[docs] def on_tool_end( self, output: Any, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any, ) -> None: pass
[docs] def on_tool_error(self, error: BaseException, **kwargs: Any) -> None: pass
[docs] def on_text(self, text: str, **kwargs: Any) -> None: pass
[docs] def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None: pass