llm strategy
2.1.1
Implementierung des Strategiemusters mit LLMs.
Weitere Informationen finden Sie unter https://blog.blackhc.net/2022/12/llm_software_engineering/, um eine größere Perspektive zu erhalten, warum dies in Zukunft wichtig sein könnte.
Dieses Paket fügt einen Dekorator llm_strategy hinzu, der mit einem LLM (z. B. GPT-3 von OpenAI) verbunden ist und das LLM verwendet, um abstrakte Methoden in Schnittstellenklassen zu "implementieren". Dies geschieht, indem Anforderungen an die LLM weitergeleitet und die Antworten mithilfe von Pythons @dataclasses in Python -Daten zurückgeführt werden.
Es verwendet die DOC -Zeichenfolgen, Typ -Anmerkungen und Methoden-/Funktionsnamen als Eingabeaufforderungen für das LLM und kann die Ergebnisse automatisch in Python -Typen umwandeln (derzeit unterstützt @dataclasses derzeit nur). Es kann auch ein Datenschema extrahieren, um die LLM zur Interpretation an die LLM zu senden. Während das llm-strategy Paket immer noch auf einem Python-Code beruht, kann es in Zukunft die Notwendigkeit dieses Codes reduzieren, indem zusätzliche, billigere LLMs zur Automatisierung der Parsen strukturierter Daten verwendet werden.
Die neueste Version enthält auch ein Paket für Hyperparameter -Tracking und Sammeln von Spuren von LLMs.
Dies ermöglicht beispielsweise die Meta -Optimierung. Beispiele/Forschung für eine einfache Implementierung unter Verwendung von Generika.
Sie können ein Beispiel für Wandb -Trace finden unter: https://wandb.ai/blackhc/blackboard-pagi/reports/Meta-Optimization-Example-Trace--Vmlldzo3MDMxODEz?accessToken=p9hubfskmq1z5yj1uz7wx1idh304diiernp7pjlrjrybpaozlwv3dnitjt7vni1j
Die Eingabeaufforderungen, die das Muster mit Generika zeigen, sind unkompliziert:
T_TaskParameters = TypeVar ( "T_TaskParameters" )
T_TaskResults = TypeVar ( "T_TaskResults" )
T_Hyperparameters = TypeVar ( "T_Hyperparameters" )
class TaskRun ( GenericModel , Generic [ T_TaskParameters , T_TaskResults , T_Hyperparameters ]):
"""
The task run. This is the 'data' we use to optimize the hyperparameters.
"""
task_parameters : T_TaskParameters = Field (..., description = "The task parameters." )
hyperparameters : T_Hyperparameters = Field (
...,
description = "The hyperparameters used for the task. We optimize these." ,
)
all_chat_chains : dict = Field (..., description = "The chat chains from the task execution." )
return_value : T_TaskResults | None = Field (
..., description = "The results of the task. (None for exceptions/failure.)"
)
exception : list [ str ] | str | None = Field (..., description = "Exception that occurred during the task execution." )
class TaskReflection ( BaseModel ):
"""
The reflections on the task.
This contains the lessons we learn from each task run to come up with better
hyperparameters to try.
"""
feedback : str = Field (
...,
description = (
"Only look at the final results field. Does its content satisfy the "
"task description and task parameters? Does it contain all the relevant "
"information from the all_chains and all_prompts fields? What could be improved "
"in the results?"
),
)
evaluation : str = Field (
...,
description = (
"The evaluation of the outputs given the task. Is the output satisfying? What is wrong? What is missing?"
),
)
hyperparameter_suggestion : str = Field (
...,
description = "How we want to change the hyperparameters to improve the results. What could we try to change?" ,
)
hyperparameter_missing : str = Field (
...,
description = (
"What hyperparameters are missing to improve the results? What could "
"be changed that is not exposed via hyperparameters?"
),
)
class TaskInfo ( GenericModel , Generic [ T_TaskParameters , T_TaskResults , T_Hyperparameters ]):
"""
The task run and the reflection on the experiment.
"""
task_parameters : T_TaskParameters = Field (..., description = "The task parameters." )
hyperparameters : T_Hyperparameters = Field (
...,
description = "The hyperparameters used for the task. We optimize these." ,
)
reflection : TaskReflection = Field (..., description = "The reflection on the task." )
class OptimizationInfo ( GenericModel , Generic [ T_TaskParameters , T_TaskResults , T_Hyperparameters ]):
"""
The optimization information. This is the data we use to optimize the
hyperparameters.
"""
older_task_summary : str | None = Field (
None ,
description = (
"A summary of previous experiments and the proposed changes with "
"the goal of avoiding trying the same changes repeatedly."
),
)
task_infos : list [ TaskInfo [ T_TaskParameters , T_TaskResults , T_Hyperparameters ]] = Field (
..., description = "The most recent tasks we have run and our reflections on them."
)
best_hyperparameters : T_Hyperparameters = Field (..., description = "The best hyperparameters we have found so far." )
class OptimizationStep ( GenericModel , Generic [ T_TaskParameters , T_TaskResults , T_Hyperparameters ]):
"""
The next optimization steps. New hyperparameters we want to try experiments and new
task parameters we want to evaluate on given the previous experiments.
"""
best_hyperparameters : T_Hyperparameters = Field (
...,
description = "The best hyperparameters we have found so far given task_infos and history." ,
)
suggestion : str = Field (
...,
description = (
"The suggestions for the next experiments. What could we try to "
"change? We will try several tasks next and several sets of hyperparameters. "
"Let's think step by step."
),
)
task_parameters_suggestions : list [ T_TaskParameters ] = Field (
...,
description = "The task parameters we want to try next." ,
hint_min_items = 1 ,
hint_max_items = 4 ,
)
hyperparameter_suggestions : list [ T_Hyperparameters ] = Field (
...,
description = "The hyperparameters we want to try next." ,
hint_min_items = 1 ,
hint_max_items = 2 ,
)
class ImprovementProbability ( BaseModel ):
considerations : list [ str ] = Field (..., description = "The considerations for potential improvements." )
probability : float = Field (..., description = "The probability of improvement." )
class LLMOptimizer :
@ llm_explicit_function
@ staticmethod
def reflect_on_task_run (
language_model ,
task_run : TaskRun [ T_TaskParameters , T_TaskResults , T_Hyperparameters ],
) -> TaskReflection :
"""
Reflect on the results given the task parameters and hyperparameters.
This contains the lessons we learn from each task run to come up with better
hyperparameters to try.
"""
raise NotImplementedError ()
@ llm_explicit_function
@ staticmethod
def summarize_optimization_info (
language_model ,
optimization_info : OptimizationInfo [ T_TaskParameters , T_TaskResults , T_Hyperparameters ],
) -> str :
"""
Summarize the optimization info. We want to preserve all relevant knowledge for
improving the hyperparameters in the future. All information from previous
experiments will be forgotten except for what this summary.
"""
raise NotImplementedError ()
@ llm_explicit_function
@ staticmethod
def suggest_next_optimization_step (
language_model ,
optimization_info : OptimizationInfo [ T_TaskParameters , T_TaskResults , T_Hyperparameters ],
) -> OptimizationStep [ T_TaskParameters , T_TaskResults , T_Hyperparameters ]:
"""
Suggest the next optimization step.
"""
raise NotImplementedError ()
@ llm_explicit_function
@ staticmethod
def probability_for_improvement (
language_model ,
optimization_info : OptimizationInfo [ T_TaskParameters , T_TaskResults , T_Hyperparameters ],
) -> ImprovementProbability :
"""
Return the probability for improvement (between 0 and 1).
This is your confidence that your next optimization steps will improve the
hyperparameters given the information provided. If you think that the
information available is unlikely to lead to better hyperparameters, return 0.
If you think that the information available is very likely to lead to better
hyperparameters, return 1. Be concise.
"""
raise NotImplementedError () from dataclasses import dataclass
from llm_strategy import llm_strategy
from langchain . llms import OpenAI
@ llm_strategy ( OpenAI ( max_tokens = 256 ))
@ dataclass
class Customer :
key : str
first_name : str
last_name : str
birthdate : str
address : str
@ property
def age ( self ) -> int :
"""Return the current age of the customer.
This is a computed property based on `birthdate` and the current year (2022).
"""
raise NotImplementedError ()
@ dataclass
class CustomerDatabase :
customers : list [ Customer ]
def find_customer_key ( self , query : str ) -> list [ str ]:
"""Find the keys of the customers that match a natural language query best (sorted by closeness to the match).
We support semantic queries instead of SQL, so we can search for things like
"the customer that was born in 1990".
Args:
query: Natural language query
Returns:
The index of the best matching customer in the database.
"""
raise NotImplementedError ()
def load ( self ):
"""Load the customer database from a file."""
raise NotImplementedError ()
def store ( self ):
"""Store the customer database to a file."""
raise NotImplementedError ()
@ llm_strategy ( OpenAI ( max_tokens = 1024 ))
@ dataclass
class MockCustomerDatabase ( CustomerDatabase ):
def load ( self ):
self . customers = self . create_mock_customers ( 10 )
def store ( self ):
pass
@ staticmethod
def create_mock_customers ( num_customers : int = 1 ) -> list [ Customer ]:
"""
Create mock customers with believable data (our customers are world citizens).
"""
raise NotImplementedError ()Beispiele/Customer_Database_Search.py finden Sie ein vollständiges Beispiel.
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