refactor: moved modules from src/deepsearch to src/

inference.py

@@ -20,7 +20,7 @@ from src import (
     format_search_results,
     get_system_prompt,
 )
-from src.deepsearch.search_module import load_vectorstore, search
+from src.search_module import load_vectorstore, search
 
 
 def setup_model_and_tokenizer(model_path: str):

scripts/generate_data.py

@@ -29,7 +29,7 @@ from langchain_community.document_loaders import UnstructuredMarkdownLoader
 from langchain_community.vectorstores import FAISS
 
 from config import DATA_DIR, logger
-from src.deepsearch.embeddings import CustomHuggingFaceEmbeddings
+from src.embeddings import CustomHuggingFaceEmbeddings
 
 # Load your markdown file (adjust the path as needed)
 loader = UnstructuredMarkdownLoader("./data/mission_report.md")

scripts/simple_qa.py

@@ -16,7 +16,7 @@ sys.path.append(str(project_root))
 
 # Import our search module and config
 from config import DATA_DIR, logger
-from src.deepsearch.search_module import get_question_answer, get_question_count, search
+from src.search_module import get_question_answer, get_question_count, search
 
 # TODO: Import verify function and router from appropriate module
 # TODO: Consider moving verify function to search_module.py for better organization

src/__init__.py

@@ -5,17 +5,17 @@ Main package exports for RL helpers.
 from trl.trainer.grpo_trainer import apply_chat_template
 
 from config import logger
-from src.deepsearch.agent import Agent, extract_search_query
-from src.deepsearch.evaluation import check_student_answers, run_eval, verify
-from src.deepsearch.prompts import build_user_prompt, format_search_results, get_system_prompt
-from src.deepsearch.rewards import (
+from src.agent import Agent, extract_search_query
+from src.evaluation import check_student_answers, run_eval, verify
+from src.prompts import build_user_prompt, format_search_results, get_system_prompt
+from src.rewards import (
     build_reward_correctness_fn,
     reward_em_chunk,
     reward_format,
     reward_retry,
 )
-from src.deepsearch.search_module import get_qa_dataset, search
-from src.deepsearch.tokenizer_adapter import LlamaTokenizerAdapter, R1DistilTokenizerAdapter
+from src.search_module import get_qa_dataset, search
+from src.tokenizer_adapter import LlamaTokenizerAdapter, R1DistilTokenizerAdapter
 
 __all__ = [
     # Prompts

src/agent.py

@@ -10,9 +10,9 @@ import torch
 from trl.trainer.grpo_trainer import apply_chat_template
 
 from config import logger
-from src.deepsearch.prompts import build_user_prompt, get_system_prompt
-from src.deepsearch.search_module import search
-from src.deepsearch.tokenizer_adapter import TokenizerAdapter
+from src.prompts import build_user_prompt, get_system_prompt
+from src.search_module import search
+from src.tokenizer_adapter import TokenizerAdapter
 
 
 def extract_search_query(text: str) -> str | None:
@@ -36,9 +36,15 @@ class AgenticOutputs:
 class Agent:
     """Base agent class for handling tool-based conversations."""
 
-    def __init__(self, tokenizer_adapter: TokenizerAdapter):
-        """Initialize the agent with a tokenizer adapter."""
+    def __init__(self, tokenizer_adapter: TokenizerAdapter, search_fn=None):
+        """Initialize the agent with a tokenizer adapter and optional search function.
+
+        Args:
+            tokenizer_adapter: Tokenizer adapter for handling text
+            search_fn: Optional custom search function. If None, uses default search.
+        """
         self.tokenizer_adapter = tokenizer_adapter
+        self.search_fn = search_fn or search  # Use provided search function or default
 
     def get_initial_chat(self, question: str) -> dict:
         """Initialize a chat state with the question."""
@@ -113,11 +119,10 @@ class Agent:
                 search_query = extract_search_query(assistant_response)
                 if search_query:
                     logger.info(f"🔍 Search Query: {search_query}")
-                    results = search(search_query, return_type=str, results=2)
+                    results = self.search_fn(search_query, return_type=str, results=2)
                     formatted_results = f"<information>{results}</information>"
                     logger.info(f"ℹ️ Information: {formatted_results}")
 
-                    # chat_state["messages"].append({"role": "ipython", "content": formatted_results})
                     chat_state["messages"].append({"role": "user", "content": formatted_results})
                     logger.debug("Added search results to chat state")
             except Exception as e:

src/evaluation.py

@@ -5,10 +5,10 @@ Evaluation utilities for RL training.
 import inspect
 from datetime import datetime
 
-from src.deepsearch.agent import Agent
 from config import logger
-from src.deepsearch.search_module import get_qa_dataset
-from src.deepsearch.tokenizer_adapter import LlamaTokenizerAdapter, R1DistilTokenizerAdapter
+from src.agent import Agent
+from src.search_module import get_qa_dataset
+from src.tokenizer_adapter import LlamaTokenizerAdapter, R1DistilTokenizerAdapter
 
 
 async def verify(student_answer: str, question: str, answer: str) -> bool:

src/rewards.py

@@ -10,7 +10,7 @@ from difflib import SequenceMatcher
 import numpy as np
 
 from config import LOG_FOLDER, logger
-from src.deepsearch.evaluation import check_student_answers
+from src.evaluation import check_student_answers
 
 
 def build_reward_correctness_fn(

src/search_module.py

@@ -10,7 +10,7 @@ from datasets import Dataset
 from langchain_community.vectorstores import FAISS
 
 from config import DATA_DIR, logger
-from src.deepsearch.embeddings import CustomHuggingFaceEmbeddings
+from src.embeddings import CustomHuggingFaceEmbeddings
 
 
 # Load pre-saved vectorstore

tests/test_agent.py

@@ -2,8 +2,8 @@
 
 from transformers import LlamaTokenizerFast
 
-from src.deepsearch.agent import Agent
-from src.deepsearch.tokenizer_adapter import LlamaTokenizerAdapter, R1DistilTokenizerAdapter
+from src.agent import Agent
+from src.tokenizer_adapter import LlamaTokenizerAdapter, R1DistilTokenizerAdapter
 
 
 def mock_generate_fn(prompts):

tests/test_rewards.py

@@ -4,7 +4,7 @@ Test cases for reward functions in rewards.py
 
 import pytest
 
-from src.deepsearch.rewards import (
+from src.rewards import (
     build_reward_correctness_fn,
     reward_em_chunk,
     reward_format,

tests/test_tokenizer_adapters.py

@@ -6,7 +6,7 @@ import torch
 from transformers import AutoTokenizer, LlamaTokenizerFast
 
 from config import logger
-from src.deepsearch.tokenizer_adapter import LlamaTokenizerAdapter, QwenTokenizerAdapter, R1DistilTokenizerAdapter
+from src.tokenizer_adapter import LlamaTokenizerAdapter, QwenTokenizerAdapter, R1DistilTokenizerAdapter
 
 # Test conversation used across all tests
 TEST_CHAT = [

train_grpo.py

@@ -6,11 +6,11 @@ import os
 
 from unsloth import FastLanguageModel, is_bfloat16_supported
 
-import src.deepsearch.UnslothGRPOTrainerTemp as UnslothGRPOTrainerTemp
+import src.UnslothGRPOTrainerTemp as UnslothGRPOTrainerTemp
 
 # Import reward functions
 from src import build_reward_correctness_fn, get_qa_dataset, reward_em_chunk, reward_format, reward_retry
-from src.deepsearch.agent import Agent
+from src.agent import Agent
 from config import (
     MODEL_CONFIG,
     MODEL_NAME,
@@ -21,7 +21,7 @@ from config import (
     logger,
     update_log_path,
 )
-from src.deepsearch.rewards import (
+from src.rewards import (
     build_reward_correctness_fn,
     reward_em_chunk,
     reward_format,
@@ -29,8 +29,8 @@ from src.deepsearch.rewards import (
     reward_search_diversity,
     reward_search_strategy,
 )
-from src.deepsearch.search_module import get_qa_dataset
-from src.deepsearch.tokenizer_adapter import LlamaTokenizerAdapter, QwenTokenizerAdapter, R1DistilTokenizerAdapter
+from src.search_module import get_qa_dataset
+from src.tokenizer_adapter import LlamaTokenizerAdapter, QwenTokenizerAdapter, R1DistilTokenizerAdapter
 
 # Initialize training directories
 paths = init_training_dirs()