forked from Archives/langchain
Separate Runner Functions from Client (#5079)
Extract the methods specific to running an LLM or Chain on a dataset to separate utility functions. This simplifies the client a bit and lets us separate concerns of LCP details from running examples (e.g., for evals)searx_updates
Zander Chase
1 year ago
committed by
GitHub
parent
443ebe22f4
commit
ef7d015be5
@ -0,0 +1,375 @@
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"""Utilities for running LLMs/Chains over datasets."""
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from __future__ import annotations
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import asyncio
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import functools
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import logging
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from typing import Any, Callable, Coroutine, Dict, Iterator, List, Optional, Union
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from langchain.base_language import BaseLanguageModel
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from langchain.callbacks.base import BaseCallbackHandler
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from langchain.callbacks.manager import Callbacks
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from langchain.callbacks.tracers.langchain import LangChainTracer
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from langchain.chains.base import Chain
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from langchain.chat_models.base import BaseChatModel
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from langchain.client.models import Example
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from langchain.llms.base import BaseLLM
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from langchain.schema import (
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BaseMessage,
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ChatResult,
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HumanMessage,
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LLMResult,
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get_buffer_string,
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messages_from_dict,
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)
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logger = logging.getLogger(__name__)
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MODEL_OR_CHAIN_FACTORY = Union[Callable[[], Chain], BaseLanguageModel]
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class InputFormatError(Exception):
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"""Raised when input format is invalid."""
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def _get_prompts(inputs: Dict[str, Any]) -> List[str]:
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"""Get prompts from inputs."""
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if not inputs:
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raise InputFormatError("Inputs should not be empty.")
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prompts = []
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if "prompt" in inputs:
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if not isinstance(inputs["prompt"], str):
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raise InputFormatError(
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"Expected string for 'prompt', got"
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f" {type(inputs['prompt']).__name__}"
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)
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prompts = [inputs["prompt"]]
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elif "prompts" in inputs:
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if not isinstance(inputs["prompts"], list) or not all(
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isinstance(i, str) for i in inputs["prompts"]
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):
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raise InputFormatError(
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"Expected list of strings for 'prompts',"
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f" got {type(inputs['prompts']).__name__}"
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)
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prompts = inputs["prompts"]
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elif len(inputs) == 1:
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prompt_ = next(iter(inputs.values()))
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if isinstance(prompt_, str):
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prompts = [prompt_]
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elif isinstance(prompt_, list) and all(isinstance(i, str) for i in prompt_):
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prompts = prompt_
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else:
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raise InputFormatError(f"LLM Run expects string prompt input. Got {inputs}")
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else:
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raise InputFormatError(
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f"LLM Run expects 'prompt' or 'prompts' in inputs. Got {inputs}"
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)
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return prompts
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def _get_messages(inputs: Dict[str, Any]) -> List[List[BaseMessage]]:
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"""Get Chat Messages from inputs."""
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if not inputs:
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raise InputFormatError("Inputs should not be empty.")
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if "messages" in inputs:
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single_input = inputs["messages"]
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elif len(inputs) == 1:
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single_input = next(iter(inputs.values()))
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else:
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raise InputFormatError(f"Chat Run expects 'messages' in inputs. Got {inputs}")
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if isinstance(single_input, list) and all(
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isinstance(i, dict) for i in single_input
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):
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raw_messages = [single_input]
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elif isinstance(single_input, list) and all(
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isinstance(i, list) for i in single_input
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):
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raw_messages = single_input
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else:
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raise InputFormatError(
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f"Chat Run expects List[dict] or List[List[dict]] 'messages'"
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f" input. Got {inputs}"
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)
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return [messages_from_dict(batch) for batch in raw_messages]
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async def _arun_llm(
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llm: BaseLanguageModel,
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inputs: Dict[str, Any],
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langchain_tracer: Optional[LangChainTracer],
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) -> Union[LLMResult, ChatResult]:
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callbacks: Optional[List[BaseCallbackHandler]] = (
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[langchain_tracer] if langchain_tracer else None
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)
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if isinstance(llm, BaseLLM):
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try:
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llm_prompts = _get_prompts(inputs)
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llm_output = await llm.agenerate(llm_prompts, callbacks=callbacks)
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except InputFormatError:
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llm_messages = _get_messages(inputs)
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buffer_strings = [get_buffer_string(messages) for messages in llm_messages]
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llm_output = await llm.agenerate(buffer_strings, callbacks=callbacks)
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elif isinstance(llm, BaseChatModel):
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try:
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messages = _get_messages(inputs)
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llm_output = await llm.agenerate(messages, callbacks=callbacks)
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except InputFormatError:
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prompts = _get_prompts(inputs)
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converted_messages: List[List[BaseMessage]] = [
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[HumanMessage(content=prompt)] for prompt in prompts
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]
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llm_output = await llm.agenerate(converted_messages, callbacks=callbacks)
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else:
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raise ValueError(f"Unsupported LLM type {type(llm)}")
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return llm_output
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async def _arun_llm_or_chain(
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example: Example,
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llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
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n_repetitions: int,
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langchain_tracer: Optional[LangChainTracer],
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) -> Union[List[dict], List[str], List[LLMResult], List[ChatResult]]:
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"""Run the chain asynchronously."""
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if langchain_tracer is not None:
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previous_example_id = langchain_tracer.example_id
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langchain_tracer.example_id = example.id
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callbacks: Optional[List[BaseCallbackHandler]] = [langchain_tracer]
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else:
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previous_example_id = None
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callbacks = None
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outputs = []
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for _ in range(n_repetitions):
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try:
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if isinstance(llm_or_chain_factory, BaseLanguageModel):
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output: Any = await _arun_llm(
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llm_or_chain_factory, example.inputs, langchain_tracer
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)
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else:
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chain = llm_or_chain_factory()
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output = await chain.arun(example.inputs, callbacks=callbacks)
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outputs.append(output)
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except Exception as e:
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logger.warning(f"Chain failed for example {example.id}. Error: {e}")
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outputs.append({"Error": str(e)})
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if langchain_tracer is not None:
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langchain_tracer.example_id = previous_example_id
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return outputs
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async def _gather_with_concurrency(
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n: int,
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initializer: Callable[[], Coroutine[Any, Any, Optional[LangChainTracer]]],
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*async_funcs: Callable[[Optional[LangChainTracer], Dict], Coroutine[Any, Any, Any]],
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) -> List[Any]:
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"""
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Run coroutines with a concurrency limit.
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Args:
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n: The maximum number of concurrent tasks.
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initializer: A coroutine that initializes shared resources for the tasks.
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async_funcs: The async_funcs to be run concurrently.
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Returns:
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A list of results from the coroutines.
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"""
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semaphore = asyncio.Semaphore(n)
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job_state = {"num_processed": 0}
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tracer_queue: asyncio.Queue[Optional[LangChainTracer]] = asyncio.Queue()
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for _ in range(n):
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tracer_queue.put_nowait(await initializer())
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async def run_coroutine_with_semaphore(
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async_func: Callable[
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[Optional[LangChainTracer], Dict], Coroutine[Any, Any, Any]
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]
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) -> Any:
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async with semaphore:
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tracer = await tracer_queue.get()
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try:
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result = await async_func(tracer, job_state)
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finally:
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tracer_queue.put_nowait(tracer)
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return result
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return await asyncio.gather(
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*(run_coroutine_with_semaphore(function) for function in async_funcs)
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)
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async def _tracer_initializer(session_name: Optional[str]) -> Optional[LangChainTracer]:
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"""
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Initialize a tracer to share across tasks.
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Args:
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session_name: The session name for the tracer.
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Returns:
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A LangChainTracer instance with an active session.
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"""
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if session_name:
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tracer = LangChainTracer(session_name=session_name)
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tracer.ensure_session()
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return tracer
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else:
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return None
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async def arun_on_examples(
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examples: Iterator[Example],
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llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
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*,
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concurrency_level: int = 5,
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num_repetitions: int = 1,
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session_name: Optional[str] = None,
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verbose: bool = False,
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) -> Dict[str, Any]:
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"""
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Run the chain on examples and store traces to the specified session name.
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Args:
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examples: Examples to run the model or chain over
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llm_or_chain_factory: Language model or Chain constructor to run
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over the dataset. The Chain constructor is used to permit
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independent calls on each example without carrying over state.
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concurrency_level: The number of async tasks to run concurrently.
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num_repetitions: Number of times to run the model on each example.
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This is useful when testing success rates or generating confidence
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intervals.
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session_name: Session name to use when tracing runs.
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verbose: Whether to print progress.
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Returns:
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A dictionary mapping example ids to the model outputs.
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"""
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results: Dict[str, List[Any]] = {}
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async def process_example(
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example: Example, tracer: LangChainTracer, job_state: dict
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) -> None:
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"""Process a single example."""
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result = await _arun_llm_or_chain(
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example,
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llm_or_chain_factory,
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num_repetitions,
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tracer,
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)
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results[str(example.id)] = result
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job_state["num_processed"] += 1
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if verbose:
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print(
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f"Processed examples: {job_state['num_processed']}",
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end="\r",
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flush=True,
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)
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await _gather_with_concurrency(
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concurrency_level,
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functools.partial(_tracer_initializer, session_name),
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*(functools.partial(process_example, e) for e in examples),
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)
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return results
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def run_llm(
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llm: BaseLanguageModel,
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inputs: Dict[str, Any],
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callbacks: Callbacks,
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) -> Union[LLMResult, ChatResult]:
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"""Run the language model on the example."""
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if isinstance(llm, BaseLLM):
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try:
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llm_prompts = _get_prompts(inputs)
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llm_output = llm.generate(llm_prompts, callbacks=callbacks)
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except InputFormatError:
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llm_messages = _get_messages(inputs)
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buffer_strings = [get_buffer_string(messages) for messages in llm_messages]
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llm_output = llm.generate(buffer_strings, callbacks=callbacks)
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elif isinstance(llm, BaseChatModel):
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try:
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messages = _get_messages(inputs)
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llm_output = llm.generate(messages, callbacks=callbacks)
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except InputFormatError:
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prompts = _get_prompts(inputs)
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converted_messages: List[List[BaseMessage]] = [
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[HumanMessage(content=prompt)] for prompt in prompts
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]
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llm_output = llm.generate(converted_messages, callbacks=callbacks)
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else:
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raise ValueError(f"Unsupported LLM type {type(llm)}")
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return llm_output
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def run_llm_or_chain(
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example: Example,
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llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
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n_repetitions: int,
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langchain_tracer: Optional[LangChainTracer] = None,
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) -> Union[List[dict], List[str], List[LLMResult], List[ChatResult]]:
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"""Run the chain synchronously."""
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if langchain_tracer is not None:
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previous_example_id = langchain_tracer.example_id
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langchain_tracer.example_id = example.id
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callbacks: Optional[List[BaseCallbackHandler]] = [langchain_tracer]
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else:
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previous_example_id = None
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callbacks = None
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outputs = []
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for _ in range(n_repetitions):
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try:
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if isinstance(llm_or_chain_factory, BaseLanguageModel):
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output: Any = run_llm(llm_or_chain_factory, example.inputs, callbacks)
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else:
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chain = llm_or_chain_factory()
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output = chain.run(example.inputs, callbacks=callbacks)
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outputs.append(output)
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except Exception as e:
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logger.warning(f"Chain failed for example {example.id}. Error: {e}")
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outputs.append({"Error": str(e)})
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if langchain_tracer is not None:
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langchain_tracer.example_id = previous_example_id
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return outputs
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def run_on_examples(
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examples: Iterator[Example],
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llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
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*,
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num_repetitions: int = 1,
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session_name: Optional[str] = None,
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verbose: bool = False,
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) -> Dict[str, Any]:
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"""Run the chain on examples and store traces to the specified session name.
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Args:
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examples: Examples to run model or chain over.
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llm_or_chain_factory: Language model or Chain constructor to run
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over the dataset. The Chain constructor is used to permit
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independent calls on each example without carrying over state.
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concurrency_level: Number of async workers to run in parallel.
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num_repetitions: Number of times to run the model on each example.
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This is useful when testing success rates or generating confidence
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intervals.
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session_name: Session name to use when tracing runs.
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verbose: Whether to print progress.
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Returns:
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A dictionary mapping example ids to the model outputs.
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"""
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results: Dict[str, Any] = {}
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tracer = LangChainTracer(session_name=session_name) if session_name else None
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for i, example in enumerate(examples):
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result = run_llm_or_chain(
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example,
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llm_or_chain_factory,
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num_repetitions,
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langchain_tracer=tracer,
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)
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if verbose:
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print(f"{i+1} processed", flush=True, end="\r")
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results[str(example.id)] = result
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return results
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@ -0,0 +1,95 @@
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"""Test the LangChain+ client."""
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from typing import Any, Dict
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from unittest import mock
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import pytest
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from langchain.client.runner_utils import (
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InputFormatError,
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_get_messages,
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_get_prompts,
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run_llm,
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)
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from tests.unit_tests.llms.fake_chat_model import FakeChatModel
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from tests.unit_tests.llms.fake_llm import FakeLLM
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_EXAMPLE_MESSAGE = {
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"data": {"content": "Foo", "example": False, "additional_kwargs": {}},
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"type": "human",
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}
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_VALID_MESSAGES = [
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{"messages": [_EXAMPLE_MESSAGE], "other_key": "value"},
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{"messages": [], "other_key": "value"},
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{
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"messages": [[_EXAMPLE_MESSAGE, _EXAMPLE_MESSAGE], [_EXAMPLE_MESSAGE]],
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"other_key": "value",
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},
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{"any_key": [_EXAMPLE_MESSAGE]},
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{"any_key": [[_EXAMPLE_MESSAGE, _EXAMPLE_MESSAGE], [_EXAMPLE_MESSAGE]]},
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]
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_VALID_PROMPTS = [
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{"prompts": ["foo", "bar", "baz"], "other_key": "value"},
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{"prompt": "foo", "other_key": ["bar", "baz"]},
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{"some_key": "foo"},
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{"some_key": ["foo", "bar"]},
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]
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@pytest.mark.parametrize(
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"inputs",
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_VALID_MESSAGES,
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)
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def test__get_messages_valid(inputs: Dict[str, Any]) -> None:
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{"messages": []}
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_get_messages(inputs)
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@pytest.mark.parametrize(
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"inputs",
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_VALID_PROMPTS,
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)
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def test__get_prompts_valid(inputs: Dict[str, Any]) -> None:
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_get_prompts(inputs)
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@pytest.mark.parametrize(
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"inputs",
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[
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{"prompts": "foo"},
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{"prompt": ["foo"]},
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{"some_key": 3},
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{"some_key": "foo", "other_key": "bar"},
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],
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)
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def test__get_prompts_invalid(inputs: Dict[str, Any]) -> None:
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with pytest.raises(InputFormatError):
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_get_prompts(inputs)
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@pytest.mark.parametrize(
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"inputs",
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[
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{"one_key": [_EXAMPLE_MESSAGE], "other_key": "value"},
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{
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"messages": [[_EXAMPLE_MESSAGE, _EXAMPLE_MESSAGE], _EXAMPLE_MESSAGE],
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"other_key": "value",
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},
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{"prompts": "foo"},
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{},
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],
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)
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def test__get_messages_invalid(inputs: Dict[str, Any]) -> None:
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with pytest.raises(InputFormatError):
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_get_messages(inputs)
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@pytest.mark.parametrize("inputs", _VALID_PROMPTS + _VALID_MESSAGES)
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def test_run_llm_all_formats(inputs: Dict[str, Any]) -> None:
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llm = FakeLLM()
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run_llm(llm, inputs, mock.MagicMock())
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@pytest.mark.parametrize("inputs", _VALID_MESSAGES + _VALID_PROMPTS)
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def test_run_chat_model_all_formats(inputs: Dict[str, Any]) -> None:
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llm = FakeChatModel()
|
||||
run_llm(llm, inputs, mock.MagicMock())
|
||||
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Reference in New Issue