Merge branch dev accepting all incoming changes

Author: finitearth

.pre-commit-config.yaml

@@ -1,5 +1,9 @@
 fail_fast: true
+<<<<<<< HEAD
 exclude: '^(?!promptolution/).*$|^promptolution/templates.py'
+=======
+exclude: '^(?!promptolution/).*$'
+>>>>>>> main
 repos:
   - repo: https://github.com/gitleaks/gitleaks
     rev: v8.18.2

dist/promptolution-0.1.1-py3-none-any.whl

@@ -1,5 +1,6 @@
 # Release Notes
 
+<<<<<<< HEAD
 ## Release v0.2.0
 
 ### What's Changed
@@ -66,3 +67,6 @@
 * Added experiment results and evaluation notebooks
 
 **Full Changelog**: [here](https://github.com/finitearth/promptolution/commits/0.1.0)
+=======
+coming soon...
+>>>>>>> main

dist/promptolution-0.1.1.tar.gz

@@ -17,6 +17,7 @@ def on_step_end(self, optimizer):
         """
         pass
 
+<<<<<<< HEAD
     def on_epoch_end(self, optimizer):
         """Called at the end of each optimization epoch.
 
@@ -31,6 +32,18 @@ def on_train_end(self, optimizer):
         Args:
         optimizer: The optimizer object that called the callback.
         """
+=======
+    def on_epoch_end(self, epoch):
+        """Called at the end of each optimization epoch.
+
+        Args:
+        epoch: The current epoch number.
+        """
+        pass
+
+    def on_train_end(self):
+        """Called at the end of the entire optimization process."""
+>>>>>>> main
         pass
 
 
@@ -57,11 +70,27 @@ def on_step_end(self, optimizer):
             self.logger.critical(f"*** Prompt {i}: Score: {score}")
             self.logger.critical(f"{prompt}")
 
+<<<<<<< HEAD
     def on_train_end(self, optimizer, logs=None):
         """Log information at the end of training.
 
         Args:
         optimizer: The optimizer object that called the callback.
+=======
+    def on_epoch_end(self, epoch, logs=None):
+        """Log information about the current epoch.
+
+        Args:
+        epoch: The current epoch number.
+        logs: Additional information to log.
+        """
+        self.logger.critical(f"Epoch {epoch} - {logs}")
+
+    def on_train_end(self, logs=None):
+        """Log information at the end of training.
+
+        Args:
+>>>>>>> main
         logs: Additional information to log.
         """
         self.logger.critical(f"Training ended - {logs}")
@@ -105,12 +134,17 @@ def on_step_end(self, optimizer):
         )
         df.to_csv(self.path, mode="a", header=False, index=False)
 
+<<<<<<< HEAD
     def on_train_end(self, optimizer):
         """Called at the end of training.
 
         Args:
         optimizer: The optimizer object that called the callback.
         """
+=======
+    def on_train_end(self):
+        """Called at the end of training."""
+>>>>>>> main
         pass
 
 
@@ -173,10 +207,15 @@ def on_step_end(self, optimizer):
         """
         self.pbar.update(1)
 
+<<<<<<< HEAD
     def on_train_end(self, optimizer):
         """Close the progress bar at the end of training.
 
         Args:
         optimizer: The optimizer object that called the callback.
         """
+=======
+    def on_train_end(self):
+        """Close the progress bar at the end of training."""
+>>>>>>> main
         self.pbar.close()

docs/release-notes.md

@@ -1,5 +1,10 @@
 """Configuration class for the promptolution library."""
+<<<<<<< HEAD
 import configparser
+=======
+
+from configparser import ConfigParser
+>>>>>>> main
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Any, Dict, Literal, Optional
@@ -43,6 +48,7 @@ class Config:
         n_eval_samples (int): how many examples to show to evaluation llm for evaluation.
     """
 
+<<<<<<< HEAD
     task_name: str = None
     ds_path: Path = None
     optimizer: str = None
@@ -52,6 +58,18 @@ class Config:
     n_steps: int = None
     init_pop_size: int = None
     logging_dir: Path = Path("logs/run.csv")
+=======
+    task_name: str
+    ds_path: str
+    n_steps: int
+    optimizer: str
+    meta_prompt_path: str
+    meta_llms: str
+    downstream_llm: str
+    evaluation_llm: str
+    init_pop_size: int = 10
+    logging_dir: str = "logs/run.csv"
+>>>>>>> main
     experiment_name: str = "experiment"
     include_task_desc: bool = True
     donor_random: bool = False
@@ -67,6 +85,7 @@ class Config:
     n_ds_samples_to_meta: Optional[int] = 2
     n_eval_samples: Optional[int] = 20
 
+<<<<<<< HEAD
     def __post_init__(self):
         """Validate the configuration after initialization."""
         self._validate_config()
@@ -75,6 +94,33 @@ def __post_init__(self):
     def from_dict(cls, config_dict: Dict[str, Any]) -> "Config":
         """Create a Config instance from a dictionary."""
         return cls(**cls._process_config_dict(config_dict))
+=======
+    def __init__(self, config_path: str = None, **kwargs):
+        """Initialize the Config object."""
+        if config_path:
+            self.config_path = config_path
+            self.config = ConfigParser()
+            self.config.read(config_path)
+            self._parse_config()
+        else:
+            for key, value in kwargs.items():
+                setattr(self, key, value)
+
+    def _parse_config(self):
+        """Parse the configuration settings from the config file."""
+        self.task_name = self.config["task"]["task_name"]
+        self.ds_path = self.config["task"]["ds_path"]
+        self.n_steps = int(self.config["task"]["steps"])
+        self.random_seed = int(self.config["task"]["random_seed"])
+        self.optimizer = self.config["optimizer"]["name"]
+        self.meta_prompt_path = self.config["optimizer"]["meta_prompt_path"]
+        self.meta_llm = self.config["meta_llm"]["name"]
+        self.downstream_llm = self.config["downstream_llm"]["name"]
+        self.evaluation_llm = self.config["evaluator_llm"]["name"]
+        self.init_pop_size = int(self.config["optimizer"]["init_pop_size"])
+        self.logging_dir = self.config["logging"]["dir"]
+        self.experiment_name = self.config["experiment"]["name"]
+>>>>>>> main
 
     @classmethod
     def from_file(cls, config_path: Path) -> "Config":

poetry.lock

@@ -5,14 +5,22 @@
 from logging import INFO, Logger
 from typing import List
 
+<<<<<<< HEAD
 import nest_asyncio
+=======
+>>>>>>> main
 import openai
 import requests
 from langchain_anthropic import ChatAnthropic
 from langchain_community.chat_models.deepinfra import ChatDeepInfra, ChatDeepInfraException
 from langchain_core.messages import HumanMessage
 from langchain_openai import ChatOpenAI
 
+<<<<<<< HEAD
+=======
+from promptolution.llms.deepinfra import ChatDeepInfra
+
+>>>>>>> main
 logger = Logger(__name__)
 logger.setLevel(INFO)
 
@@ -61,7 +69,11 @@ class APILLM:
         get_response_async: Asynchronously get responses for a list of prompts.
     """
 
+<<<<<<< HEAD
     def __init__(self, model_id: str, token: str = None):
+=======
+    def __init__(self, model_id: str):
+>>>>>>> main
         """Initialize the APILLM with a specific model.
 
         Args:
@@ -118,7 +130,11 @@ def get_response(self, prompts: List[str]) -> List[str]:
         # If the loop exits, it means max retries were reached
         raise requests.exceptions.ConnectionError("Max retries exceeded. Connection could not be established.")
 
+<<<<<<< HEAD
     async def get_response_async(self, prompts: list[str], max_concurrent_calls=200) -> list[str]:
+=======
+    async def _get_response(self, prompts: list[str], max_concurrent_calls=200) -> list[str]:
+>>>>>>> main
         """Asynchronously get responses for a list of prompts.
 
         This method uses a semaphore to limit the number of concurrent API calls.