Kye
1 year ago
parent
4b95a5c8fb
commit
afb69288e2
@ -1,352 +0,0 @@
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from __future__ import annotations
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"""Links are like Chains from Langlink but more fluid and seamless"""
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"""Chain that just formats a prompt and calls an LLM."""
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import warnings
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from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
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from langlink.callbacks.manager import (
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AsyncCallbackManager,
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AsyncCallbackManagerForChainRun,
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CallbackManager,
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CallbackManagerForChainRun,
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Callbacks,
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)
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from langlink.links.base import Chain
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from langlink.load.dump import dumpd
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from langlink.prompts.prompt import PromptTemplate
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from langlink.pydantic_v1 import Extra, Field
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from langlink.schema import (
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BaseLLMOutputParser,
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BasePromptTemplate,
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LLMResult,
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PromptValue,
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StrOutputParser,
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)
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from langlink.schema.language_model import BaseLanguageModel
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from langlink.utils.input import get_colored_text
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class Link(Chain):
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"""Chain to run queries against LLMs.
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Example:
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.. code-block:: python
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from langlink.links import Link
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from langlink.llms import OpenAI
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from langlink.prompts import PromptTemplate
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prompt_template = "Tell me a {adjective} joke"
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prompt = PromptTemplate(
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input_variables=["adjective"], template=prompt_template
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)
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llm = Link(llm=OpenAI(), prompt=prompt)
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"""
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@classmethod
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def is_lc_serializable(self) -> bool:
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return True
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prompt: BasePromptTemplate
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"""Prompt object to use."""
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llm: BaseLanguageModel
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"""Language model to call."""
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output_key: str = "text" #: :meta private:
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output_parser: BaseLLMOutputParser = Field(default_factory=StrOutputParser)
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"""Output parser to use.
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Defaults to one that takes the most likely string but does not change it
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otherwise."""
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return_final_only: bool = True
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"""Whether to return only the final parsed result. Defaults to True.
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If false, will return a bunch of extra information about the generation."""
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llm_kwargs: dict = Field(default_factory=dict)
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@property
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def input_keys(self) -> List[str]:
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"""Will be whatever keys the prompt expects.
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:meta private:
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"""
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return self.prompt.input_variables
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@property
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def output_keys(self) -> List[str]:
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"""Will always return text key.
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:meta private:
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"""
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if self.return_final_only:
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return [self.output_key]
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else:
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return [self.output_key, "full_generation"]
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def _call(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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response = self.run([inputs], run_manager=run_manager)
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return self.create_outputs(response)[0]
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def run(
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self,
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input_list: List[Dict[str, Any]],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> LLMResult:
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"""Generate LLM result from inputs."""
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prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)
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return self.llm.run_prompt(
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prompts,
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stop,
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callbacks=run_manager.get_child() if run_manager else None,
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**self.llm_kwargs,
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)
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async def arun(
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self,
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input_list: List[Dict[str, Any]],
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
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) -> LLMResult:
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"""Generate LLM result from inputs."""
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prompts, stop = await self.aprep_prompts(input_list, run_manager=run_manager)
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return await self.llm.arun_prompt(
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prompts,
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stop,
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callbacks=run_manager.get_child() if run_manager else None,
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**self.llm_kwargs,
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)
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def prep_prompts(
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self,
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input_list: List[Dict[str, Any]],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Tuple[List[PromptValue], Optional[List[str]]]:
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"""Prepare prompts from inputs."""
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stop = None
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if len(input_list) == 0:
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return [], stop
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if "stop" in input_list[0]:
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stop = input_list[0]["stop"]
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prompts = []
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for inputs in input_list:
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selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
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prompt = self.prompt.format_prompt(**selected_inputs)
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_colored_text = get_colored_text(prompt.to_string(), "green")
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_text = "Prompt after formatting:\n" + _colored_text
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if run_manager:
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run_manager.on_text(_text, end="\n", verbose=self.verbose)
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if "stop" in inputs and inputs["stop"] != stop:
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raise ValueError(
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"If `stop` is present in any inputs, should be present in all."
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)
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prompts.append(prompt)
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return prompts, stop
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async def aprep_prompts(
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self,
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input_list: List[Dict[str, Any]],
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
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) -> Tuple[List[PromptValue], Optional[List[str]]]:
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"""Prepare prompts from inputs."""
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stop = None
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if len(input_list) == 0:
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return [], stop
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if "stop" in input_list[0]:
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stop = input_list[0]["stop"]
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prompts = []
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for inputs in input_list:
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selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
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prompt = self.prompt.format_prompt(**selected_inputs)
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_colored_text = get_colored_text(prompt.to_string(), "green")
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_text = "Prompt after formatting:\n" + _colored_text
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if run_manager:
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await run_manager.on_text(_text, end="\n", verbose=self.verbose)
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if "stop" in inputs and inputs["stop"] != stop:
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raise ValueError(
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"If `stop` is present in any inputs, should be present in all."
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)
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prompts.append(prompt)
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return prompts, stop
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def apply(
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self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
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) -> List[Dict[str, str]]:
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"""Utilize the LLM run method for speed gains."""
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callback_manager = CallbackManager.configure(
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callbacks, self.callbacks, self.verbose
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)
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run_manager = callback_manager.on_link_start(
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dumpd(self),
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{"input_list": input_list},
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)
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try:
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response = self.run(input_list, run_manager=run_manager)
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except BaseException as e:
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run_manager.on_link_error(e)
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raise e
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outputs = self.create_outputs(response)
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run_manager.on_link_end({"outputs": outputs})
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return outputs
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async def aapply(
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self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
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) -> List[Dict[str, str]]:
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"""Utilize the LLM run method for speed gains."""
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callback_manager = AsyncCallbackManager.configure(
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callbacks, self.callbacks, self.verbose
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)
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run_manager = await callback_manager.on_link_start(
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dumpd(self),
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{"input_list": input_list},
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)
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try:
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response = await self.arun(input_list, run_manager=run_manager)
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except BaseException as e:
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await run_manager.on_link_error(e)
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raise e
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outputs = self.create_outputs(response)
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await run_manager.on_link_end({"outputs": outputs})
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return outputs
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@property
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def _run_output_key(self) -> str:
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return self.output_key
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def create_outputs(self, llm_result: LLMResult) -> List[Dict[str, Any]]:
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"""Create outputs from response."""
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result = [
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# Get the text of the top rund string.
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{
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self.output_key: self.output_parser.parse_result(generation),
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"full_generation": generation,
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}
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for generation in llm_result.generations
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]
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if self.return_final_only:
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result = [{self.output_key: r[self.output_key]} for r in result]
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return result
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async def _acall(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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response = await self.arun([inputs], run_manager=run_manager)
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return self.create_outputs(response)[0]
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def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:
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"""Format prompt with kwargs and pass to LLM.
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Args:
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callbacks: Callbacks to pass to Link
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**kwargs: Keys to pass to prompt template.
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Returns:
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Completion from LLM.
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Example:
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.. code-block:: python
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completion = llm.predict(adjective="funny")
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"""
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return self(kwargs, callbacks=callbacks)[self.output_key]
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async def apredict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:
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"""Format prompt with kwargs and pass to LLM.
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Args:
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callbacks: Callbacks to pass to Link
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**kwargs: Keys to pass to prompt template.
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Returns:
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Completion from LLM.
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Example:
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.. code-block:: python
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completion = llm.predict(adjective="funny")
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"""
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return (await self.acall(kwargs, callbacks=callbacks))[self.output_key]
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def predict_and_parse(
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self, callbacks: Callbacks = None, **kwargs: Any
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) -> Union[str, List[str], Dict[str, Any]]:
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"""Call predict and then parse the results."""
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warnings.warn(
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"The predict_and_parse method is deprecated, "
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"instead pass an output parser directly to Link."
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)
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result = self.predict(callbacks=callbacks, **kwargs)
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if self.prompt.output_parser is not None:
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return self.prompt.output_parser.parse(result)
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else:
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return result
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async def apredict_and_parse(
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self, callbacks: Callbacks = None, **kwargs: Any
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) -> Union[str, List[str], Dict[str, str]]:
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"""Call apredict and then parse the results."""
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warnings.warn(
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"The apredict_and_parse method is deprecated, "
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"instead pass an output parser directly to Link."
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)
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result = await self.apredict(callbacks=callbacks, **kwargs)
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if self.prompt.output_parser is not None:
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return self.prompt.output_parser.parse(result)
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else:
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return result
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def apply_and_parse(
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self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
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) -> Sequence[Union[str, List[str], Dict[str, str]]]:
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"""Call apply and then parse the results."""
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warnings.warn(
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"The apply_and_parse method is deprecated, "
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"instead pass an output parser directly to Link."
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)
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result = self.apply(input_list, callbacks=callbacks)
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return self._parse_generation(result)
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def _parse_generation(
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self, generation: List[Dict[str, str]]
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) -> Sequence[Union[str, List[str], Dict[str, str]]]:
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if self.prompt.output_parser is not None:
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return [
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self.prompt.output_parser.parse(res[self.output_key])
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for res in generation
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]
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else:
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return generation
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async def aapply_and_parse(
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self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
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) -> Sequence[Union[str, List[str], Dict[str, str]]]:
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"""Call apply and then parse the results."""
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warnings.warn(
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"The aapply_and_parse method is deprecated, "
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"instead pass an output parser directly to Link."
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)
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result = await self.aapply(input_list, callbacks=callbacks)
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return self._parse_generation(result)
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@property
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def _link_type(self) -> str:
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return "llm_link"
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@classmethod
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def from_string(cls, llm: BaseLanguageModel, template: str) -> Link:
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"""Create Link from LLM and template."""
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prompt_template = PromptTemplate.from_template(template)
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return cls(llm=llm, prompt=prompt_template)
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@ -0,0 +1,41 @@
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import pytest
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from unittest.mock import patch, MagicMock
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from swarms.models.mistral import Mistral
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def test_mistral_initialization():
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mistral = Mistral(device="cpu")
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assert isinstance(mistral, Mistral)
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assert mistral.ai_name == "Node Model Agent"
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assert mistral.system_prompt == None
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assert mistral.model_name == "mistralai/Mistral-7B-v0.1"
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assert mistral.device == "cpu"
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assert mistral.use_flash_attention == False
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assert mistral.temperature == 1.0
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assert mistral.max_length == 100
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assert mistral.history == []
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@patch('your_module.AutoModelForCausalLM.from_pretrained')
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@patch('your_module.AutoTokenizer.from_pretrained')
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def test_mistral_load_model(mock_tokenizer, mock_model):
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mistral = Mistral(device="cpu")
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mistral.load_model()
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mock_model.assert_called_once()
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mock_tokenizer.assert_called_once()
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@patch('your_module.Mistral.load_model')
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def test_mistral_run(mock_load_model):
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mistral = Mistral(device="cpu")
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mistral.run("What's the weather in miami")
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mock_load_model.assert_called_once()
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@patch('your_module.Mistral.run')
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def test_mistral_chat(mock_run):
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mistral = Mistral(device="cpu")
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mistral.chat("What's the weather in miami")
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mock_run.assert_called_once()
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def test_mistral__stream_response():
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mistral = Mistral(device="cpu")
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response = "It's sunny in Miami."
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tokens = list(mistral._stream_response(response))
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assert tokens == ["It's", "sunny", "in", "Miami."]
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@ -0,0 +1,63 @@
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import pytest
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from unittest.mock import patch, MagicMock
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from swarms.structs.nonlinear_workflow import NonLinearWorkflow, Task
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class MockTask(Task):
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def can_execute(self):
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return True
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def execute(self):
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return "Task executed"
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def test_nonlinearworkflow_initialization():
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agents = MagicMock()
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iters_per_task = MagicMock()
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workflow = NonLinearWorkflow(agents, iters_per_task)
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assert isinstance(workflow, NonLinearWorkflow)
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assert workflow.agents == agents
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assert workflow.tasks == []
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def test_nonlinearworkflow_add():
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agents = MagicMock()
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iters_per_task = MagicMock()
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workflow = NonLinearWorkflow(agents, iters_per_task)
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task = MockTask("task1")
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workflow.add(task)
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assert workflow.tasks == [task]
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@patch('your_module.NonLinearWorkflow.is_finished')
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@patch('your_module.NonLinearWorkflow.output_tasks')
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def test_nonlinearworkflow_run(mock_output_tasks, mock_is_finished):
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agents = MagicMock()
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iters_per_task = MagicMock()
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workflow = NonLinearWorkflow(agents, iters_per_task)
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task = MockTask("task1")
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workflow.add(task)
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mock_is_finished.return_value = False
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mock_output_tasks.return_value = [task]
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workflow.run()
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assert mock_output_tasks.called
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def test_nonlinearworkflow_output_tasks():
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agents = MagicMock()
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iters_per_task = MagicMock()
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workflow = NonLinearWorkflow(agents, iters_per_task)
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task = MockTask("task1")
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workflow.add(task)
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assert workflow.output_tasks() == [task]
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def test_nonlinearworkflow_to_graph():
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agents = MagicMock()
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iters_per_task = MagicMock()
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workflow = NonLinearWorkflow(agents, iters_per_task)
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task = MockTask("task1")
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workflow.add(task)
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assert workflow.to_graph() == {"task1": set()}
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def test_nonlinearworkflow_order_tasks():
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agents = MagicMock()
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iters_per_task = MagicMock()
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workflow = NonLinearWorkflow(agents, iters_per_task)
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task = MockTask("task1")
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workflow.add(task)
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assert workflow.order_tasks() == [task]
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@ -0,0 +1,59 @@
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import pytest
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from unittest.mock import patch, MagicMock
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from swarms.structs.workflow import Workflow
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def test_workflow_initialization():
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agent = MagicMock()
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workflow = Workflow(agent)
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assert isinstance(workflow, Workflow)
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assert workflow.agent == agent
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assert workflow.tasks == []
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assert workflow.parallel == False
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def test_workflow_add():
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agent = MagicMock()
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workflow = Workflow(agent)
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task = workflow.add("What's the weather in miami")
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assert isinstance(task, Workflow.Task)
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assert task.task == "What's the weather in miami"
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assert task.parents == []
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assert task.children == []
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assert task.output == None
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assert task.structure == workflow
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||||
def test_workflow_first_task():
|
||||
agent = MagicMock()
|
||||
workflow = Workflow(agent)
|
||||
assert workflow.first_task() == None
|
||||
workflow.add("What's the weather in miami")
|
||||
assert workflow.first_task().task == "What's the weather in miami"
|
||||
|
||||
def test_workflow_last_task():
|
||||
agent = MagicMock()
|
||||
workflow = Workflow(agent)
|
||||
assert workflow.last_task() == None
|
||||
workflow.add("What's the weather in miami")
|
||||
assert workflow.last_task().task == "What's the weather in miami"
|
||||
|
||||
@patch('your_module.Workflow.__run_from_task')
|
||||
def test_workflow_run(mock_run_from_task):
|
||||
agent = MagicMock()
|
||||
workflow = Workflow(agent)
|
||||
workflow.add("What's the weather in miami")
|
||||
workflow.run()
|
||||
mock_run_from_task.assert_called_once()
|
||||
|
||||
def test_workflow_context():
|
||||
agent = MagicMock()
|
||||
workflow = Workflow(agent)
|
||||
task = workflow.add("What's the weather in miami")
|
||||
assert workflow.context(task) == {"parent_output": None, "parent": None, "child": None}
|
||||
|
||||
@patch('your_module.Workflow.Task.execute')
|
||||
def test_workflow___run_from_task(mock_execute):
|
||||
agent = MagicMock()
|
||||
workflow = Workflow(agent)
|
||||
task = workflow.add("What's the weather in miami")
|
||||
mock_execute.return_value = "Sunny"
|
||||
workflow.__run_from_task(task)
|
||||
mock_execute.assert_called_once()
|
||||
Loading…
Reference in new issue