Source code for langchain_community.callbacks.infino_callback

import time
from typing import Any, Dict, List, Optional, cast

from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.messages import BaseMessage
from langchain_core.outputs import ChatGeneration, LLMResult
from langchain_core.utils import guard_import

[docs]def import_infino() -> Any: """Import the infino client.""" return guard_import("infinopy").InfinoClient()
[docs]def import_tiktoken() -> Any: """Import tiktoken for counting tokens for OpenAI models.""" return guard_import("tiktoken")
[docs]def get_num_tokens(string: str, openai_model_name: str) -> int: """Calculate num tokens for OpenAI with tiktoken package. Official documentation: /examples/How_to_count_tokens_with_tiktoken.ipynb """ tiktoken = import_tiktoken() encoding = tiktoken.encoding_for_model(openai_model_name) num_tokens = len(encoding.encode(string)) return num_tokens
[docs]class InfinoCallbackHandler(BaseCallbackHandler): """Callback Handler that logs to Infino."""
[docs] def __init__( self, model_id: Optional[str] = None, model_version: Optional[str] = None, verbose: bool = False, ) -> None: # Set Infino client self.client = import_infino() self.model_id = model_id self.model_version = model_version self.verbose = verbose self.is_chat_openai_model = False self.chat_openai_model_name = "gpt-3.5-turbo"
def _send_to_infino( self, key: str, value: Any, is_ts: bool = True, ) -> None: """Send the key-value to Infino. Parameters: key (str): the key to send to Infino. value (Any): the value to send to Infino. is_ts (bool): if True, the value is part of a time series, else it is sent as a log message. """ payload = { "date": int(time.time()), key: value, "labels": { "model_id": self.model_id, "model_version": self.model_version, }, } if self.verbose: print(f"Tracking {key} with Infino: {payload}") # noqa: T201 # Append to Infino time series only if is_ts is True, otherwise # append to Infino log. if is_ts: self.client.append_ts(payload) else: self.client.append_log(payload)
[docs] def on_llm_start( self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any, ) -> None: """Log the prompts to Infino, and set start time and error flag.""" for prompt in prompts: self._send_to_infino("prompt", prompt, is_ts=False) # Set the error flag to indicate no error (this will get overridden # in on_llm_error if an error occurs). self.error = 0 # Set the start time (so that we can calculate the request # duration in on_llm_end). self.start_time = time.time()
[docs] def on_llm_new_token(self, token: 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 the latency, error, token usage, and response to Infino.""" # Calculate and track the request latency. self.end_time = time.time() duration = self.end_time - self.start_time self._send_to_infino("latency", duration) # Track success or error flag. self._send_to_infino("error", self.error) # Track prompt response. for generations in response.generations: for generation in generations: self._send_to_infino("prompt_response", generation.text, is_ts=False) # Track token usage (for non-chat models). 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: prompt_tokens = token_usage["prompt_tokens"] total_tokens = token_usage["total_tokens"] completion_tokens = token_usage["completion_tokens"] self._send_to_infino("prompt_tokens", prompt_tokens) self._send_to_infino("total_tokens", total_tokens) self._send_to_infino("completion_tokens", completion_tokens) # Track completion token usage (for openai chat models). if self.is_chat_openai_model: messages = " ".join( cast(str, cast(ChatGeneration, generation).message.content) for generation in generations ) completion_tokens = get_num_tokens( messages, openai_model_name=self.chat_openai_model_name ) self._send_to_infino("completion_tokens", completion_tokens)
[docs] def on_llm_error(self, error: BaseException, **kwargs: Any) -> None: """Set the error flag.""" self.error = 1
[docs] def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: """Do nothing when LLM chain starts.""" pass
[docs] def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: """Do nothing when LLM chain ends.""" pass
[docs] def on_chain_error(self, error: BaseException, **kwargs: Any) -> None: """Need to log the 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, action: AgentAction, **kwargs: Any) -> Any: """Do nothing when agent takes a specific action.""" pass
[docs] def on_tool_end( self, output: str, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any, ) -> None: """Do nothing when tool ends.""" pass
[docs] def on_tool_error(self, error: BaseException, **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, finish: AgentFinish, **kwargs: Any) -> None: """Do nothing.""" pass
[docs] def on_chat_model_start( self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs: Any, ) -> None: """Run when LLM starts running.""" # Currently, for chat models, we only support input prompts for ChatOpenAI. # Check if this model is a ChatOpenAI model. values = serialized.get("id") if values: for value in values: if value == "ChatOpenAI": self.is_chat_openai_model = True break # Track prompt tokens for ChatOpenAI model. if self.is_chat_openai_model: invocation_params = kwargs.get("invocation_params") if invocation_params: model_name = invocation_params.get("model_name") if model_name: self.chat_openai_model_name = model_name prompt_tokens = 0 for message_list in messages: message_string = " ".join( cast(str, msg.content) for msg in message_list ) num_tokens = get_num_tokens( message_string, openai_model_name=self.chat_openai_model_name, ) prompt_tokens += num_tokens self._send_to_infino("prompt_tokens", prompt_tokens) if self.verbose: print( # noqa: T201 f"on_chat_model_start: is_chat_openai_model= \ {self.is_chat_openai_model}, \ chat_openai_model_name={self.chat_openai_model_name}" ) # Send the prompt to infino prompt = " ".join( cast(str, msg.content) for sublist in messages for msg in sublist ) self._send_to_infino("prompt", prompt, is_ts=False) # Set the error flag to indicate no error (this will get overridden # in on_llm_error if an error occurs). self.error = 0 # Set the start time (so that we can calculate the request # duration in on_llm_end). self.start_time = time.time()