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common/aagents/core/__init__.py

@@ -0,0 +1,4 @@
+
+from .model import get_model_client
+
+__all__ = ["get_model_client"]

common/aagents/core/model.py

@@ -0,0 +1,36 @@
+from common.utility.openai_model_factory  import OpenAIModelFactory
+
+def get_model_client(provider:str = "openai"):
+    if provider.lower() == "google":
+        return OpenAIModelFactory.get_model(
+            provider="google",
+            model_name="gemini-2.5-flash",
+            temperature=0
+    )
+    elif provider.lower() == "openai":
+        return OpenAIModelFactory.get_model(
+            provider="openai",
+            model_name="gpt-4.1-mini",
+            temperature=0
+    )
+    elif provider.lower() == "azure":
+        return OpenAIModelFactory.get_model(
+            provider="azure",
+            model_name="gpt-4o-mini",
+            temperature=0
+    )
+    elif provider.lower() == "groq":
+        return OpenAIModelFactory.get_model(
+            provider="groq",
+            model_name="gpt-4o-mini",
+            temperature=0
+    )
+    elif provider.lower() == "ollama":
+        return OpenAIModelFactory.get_model(
+            provider="ollama",
+            model_name="gpt-4o-mini",
+            temperature=0
+    )
+    else: 
+        raise  ValueError(f"Unsupported provider: {provider}")
+

common/aagents/google_agent.py

@@ -1,30 +1,13 @@
 """Google search agent module for web search and information retrieval."""
-import os
-from agents import Agent, OpenAIChatCompletionsModel
-from dotenv import load_dotenv
+from agents import Agent
 from common.mcp.tools.google_tools import google_search, google_search_recent
 from common.mcp.tools.search_tools import duckduckgo_search, fetch_page_content
 from common.mcp.tools.time_tools import current_datetime
-from openai import AsyncOpenAI
-
-# ---------------------------------------------------------
-# Load environment variables
-# ---------------------------------------------------------
-load_dotenv()
-
-GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
-google_api_key = os.getenv('GOOGLE_API_KEY')
-gemini_client = AsyncOpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)
-gemini_model = OpenAIChatCompletionsModel(model="gemini-2.0-flash-exp", openai_client=gemini_client) 
-
-GROQ_BASE_URL = "https://api.groq.com/openai/v1"
-groq_api_key = os.getenv('GROQ_API_KEY')
-groq_client = AsyncOpenAI(base_url=GROQ_BASE_URL, api_key=groq_api_key)
-groq_model = OpenAIChatCompletionsModel(model="groq/compound", openai_client=groq_client)
+from .core.model import get_model_client
 
 google_agent = Agent(
     name="GoogleSearchAgent",
-    model=gemini_model,
+    model=get_model_client(),
     tools=[current_datetime, google_search, google_search_recent, duckduckgo_search, fetch_page_content],
     instructions="""
         You are a GoogleSearchAgent specialized in finding and retrieving information from the web.

common/aagents/healthcare_agent.py

@@ -1,42 +1,17 @@
 """Healthcare RAG Agent - Combines RAG retrieval with web search for comprehensive medical information."""
-import os
-from agents import Agent, OpenAIChatCompletionsModel
-from dotenv import load_dotenv
-from openai import AsyncOpenAI
-from langsmith import wrappers
-
-
-# Import tools
+from agents import Agent
 from common.mcp.tools.rag_tool import rag_search, UserContext
 from common.mcp.tools.search_tools import duckduckgo_search, fetch_page_content
 from common.mcp.tools.time_tools import current_datetime
-
-
-# ---------------------------------------------------------
-# Load environment variables
-# ---------------------------------------------------------
-load_dotenv()
-
-# ---------------------------------------------------------
-# Model Configuration
-# ---------------------------------------------------------
-GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
-google_api_key = os.getenv('GOOGLE_API_KEY')
-gemini_client = AsyncOpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)
-gemini_client = wrappers.wrap_openai(gemini_client)
-gemini_model = OpenAIChatCompletionsModel(model="gemini-2.0-flash-exp", openai_client=gemini_client)
-
-GROQ_BASE_URL = "https://api.groq.com/openai/v1"
-groq_api_key = os.getenv('GROQ_API_KEY')
-groq_client = AsyncOpenAI(base_url=GROQ_BASE_URL, api_key=groq_api_key)
-groq_model = OpenAIChatCompletionsModel(model="groq/compound", openai_client=groq_client)
+from .core.model import get_model_client
+      
 
 # ---------------------------------------------------------
 # Healthcare RAG Agent
 # ---------------------------------------------------------
 healthcare_agent = Agent[UserContext](
     name="HealthcareRAGAgent",
-    model=gemini_model,
+    model=get_model_client(),
     tools=[rag_search, duckduckgo_search, fetch_page_content],
     instructions="""
         You are a healthcare information retrieval agent. You retrieve information from tools and synthesize it into well-formatted markdown responses.

common/aagents/news_agent.py

@@ -1,29 +1,12 @@
 """News agent module for fetching and analyzing news articles."""
-import os
-from agents import Agent, OpenAIChatCompletionsModel
-from dotenv import load_dotenv
+from agents import Agent
 from common.mcp.tools.news_tools import get_top_headlines, search_news, get_news_by_category
 from common.mcp.tools.time_tools import current_datetime
-from openai import AsyncOpenAI
-
-# ---------------------------------------------------------
-# Load environment variables
-# ---------------------------------------------------------
-load_dotenv()
-
-GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
-google_api_key = os.getenv('GOOGLE_API_KEY')
-gemini_client = AsyncOpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)
-gemini_model = OpenAIChatCompletionsModel(model="gemini-2.0-flash-exp", openai_client=gemini_client) 
-
-GROQ_BASE_URL = "https://api.groq.com/openai/v1"
-groq_api_key = os.getenv('GROQ_API_KEY')
-groq_client = AsyncOpenAI(base_url=GROQ_BASE_URL, api_key=groq_api_key)
-groq_model = OpenAIChatCompletionsModel(model="groq/compound", openai_client=groq_client)
+from .core.model import get_model_client
 
 news_agent = Agent(
     name="NewsAgent",
-    model=gemini_model,
+    model=get_model_client(),
     tools=[current_datetime, get_top_headlines, search_news, get_news_by_category],
     instructions="""
         You are a NewsAgent specialized in fetching and analyzing recent news articles and headlines.

common/aagents/search_agent.py

@@ -1,24 +1,12 @@
 """Search agent module for comprehensive web searches."""
-import os
-from agents import Agent, OpenAIChatCompletionsModel
-from openai import AsyncOpenAI
-from dotenv import load_dotenv
+from agents import Agent
 from common.mcp.tools.search_tools import duckduckgo_search, fetch_page_content
 from common.mcp.tools.time_tools import current_datetime
-
-# ---------------------------------------------------------
-# Load environment variables
-# ---------------------------------------------------------
-load_dotenv()
-
-GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
-google_api_key = os.getenv('GOOGLE_API_KEY')
-gemini_client = AsyncOpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)
-gemini_model = OpenAIChatCompletionsModel(model="gemini-2.0-flash-exp", openai_client=gemini_client)
+from .core.model import get_model_client
 
 search_agent = Agent(
     name="Web Search Agent",
-    model=gemini_model,
+    model=get_model_client(),    
     tools=[current_datetime, duckduckgo_search, fetch_page_content],
     instructions="""
         You are a highly efficient and specialized **Web Search Agent** 🌐. Your sole function is to retrieve and analyze information from the internet using the **duckduckgo_search** and **fetch_page_content** functions. You must act as a digital librarian and researcher, providing synthesized, cited, and up-to-date answers.

common/aagents/weather_agent.py

@@ -1,37 +1,15 @@
 """Web search agent module for internet queries."""
 import os
 from agents import Agent
-from dotenv import load_dotenv
 from pydantic import BaseModel, Field
 from common.mcp.tools.weather_tools import get_weather_forecast, search_weather_fallback_ddgs, search_weather_fallback_bs
 from common.mcp.tools.time_tools import current_datetime
-from agents import Agent, OpenAIChatCompletionsModel
-from openai import AsyncOpenAI
-
-# ---------------------------------------------------------
-# Load environment variables
-# ---------------------------------------------------------
-load_dotenv()
-
-################################
-# Learning: gemini models struggles to construct the output_type when it's a Pydantic model.
-# So we use list[dict] as output_type instead of list[searchResult].
-# Then in the calling code, we can convert dicts back to searchResult models if needed.
-################################
-
-GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
-google_api_key = os.getenv('GOOGLE_API_KEY')
-gemini_client = AsyncOpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)
-gemini_model = OpenAIChatCompletionsModel(model="gemini-flash-latest", openai_client=gemini_client) 
-
-GROQ_BASE_URL = "https://api.groq.com/openai/v1"
-groq_api_key = os.getenv('GROQ_API_KEY')
-groq_client = AsyncOpenAI(base_url=GROQ_BASE_URL, api_key=groq_api_key)
-groq_model = OpenAIChatCompletionsModel(model="groq/compound", openai_client=groq_client)
+from .core.model import get_model_client
+    
 
 weather_agent = Agent(
     name="WeatherAgent",
-    model=gemini_model, #"gpt-4o-mini",
+    model=get_model_client(),
     tools=[current_datetime, get_weather_forecast, search_weather_fallback_ddgs, search_weather_fallback_bs],
     instructions="""
         You are a Weather Forecast agent who forecasts weather information ONLY.

common/aagents/web_agent.py

@@ -1,37 +1,11 @@
 """Web search agent module for internet queries."""
-import os
-from agents import AgentOutputSchema, function_tool, Agent
-from dotenv import load_dotenv
-from pydantic import BaseModel, Field
+from agents import Agent
 from common.mcp.tools.search_tools import duckduckgo_search, searchQuery, searchResult
-from agents import Agent, OpenAIChatCompletionsModel
-from openai import AsyncOpenAI
-
-# ---------------------------------------------------------
-# Load environment variables
-# ---------------------------------------------------------
-load_dotenv()
-
-################################
-# Learning: gemini models struggles to construct the output_type when it's a Pydantic model.
-# So we use list[dict] as output_type instead of list[searchResult].
-# Then in the calling code, we can convert dicts back to searchResult models if needed.
-################################
-
-GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
-google_api_key = os.getenv('GOOGLE_API_KEY')
-gemini_client = AsyncOpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)
-gemini_model = OpenAIChatCompletionsModel(model="gemini-2.0-flash-exp", openai_client=gemini_client) 
-
-GROQ_BASE_URL = "https://api.groq.com/openai/v1"
-groq_api_key = os.getenv('GROQ_API_KEY')
-groq_client = AsyncOpenAI(base_url=GROQ_BASE_URL, api_key=groq_api_key)
-groq_model = OpenAIChatCompletionsModel(model="groq/compound", openai_client=groq_client)
-
+from .core.model import get_model_client
+    
 web_agent = Agent(
     name="WebAgent",
-    model="gpt-4o-mini",
-    # description="An agent that can perform web searches using DuckDuckGo.",
+    model=get_model_client(),
     tools=[duckduckgo_search],
     instructions="""
         You are a WebAgent that can perform web searches to find information on the internet. 

common/aagents/web_research_agent.py

@@ -1,35 +1,11 @@
 """Web search agent module for internet queries."""
-import os
 from agents import AgentOutputSchema, function_tool, Agent
-from dotenv import load_dotenv
 from pydantic import BaseModel, Field
 from common.mcp.tools.search_tools import duckduckgo_search, searchQuery, searchResult, fetch_page_content
-from agents import Agent, OpenAIChatCompletionsModel
-from openai import AsyncOpenAI
-
-# ---------------------------------------------------------
-# Load environment variables
-# ---------------------------------------------------------
-load_dotenv()
-
-################################
-# Learning: gemini models struggles to construct the output_type when it's a Pydantic model.
-# So we use list[dict] as output_type instead of list[searchResult].
-# Then in the calling code, we can convert dicts back to searchResult models if needed.
-################################
-
-GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
-google_api_key = os.getenv('GOOGLE_API_KEY')
-gemini_client = AsyncOpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)
-gemini_model = OpenAIChatCompletionsModel(model="gemini-2.0-flash-exp", openai_client=gemini_client) 
-
-GROQ_BASE_URL = "https://api.groq.com/openai/v1"
-groq_api_key = os.getenv('GROQ_API_KEY')
-groq_client = AsyncOpenAI(base_url=GROQ_BASE_URL, api_key=groq_api_key)
-groq_model = OpenAIChatCompletionsModel(model="groq/compound", openai_client=groq_client)
+from .core.model import get_model_client
 
 web_research_agent = Agent(
-    model="gpt-4o-mini",
+    model=get_model_client(),
     tools=[duckduckgo_search, fetch_page_content],
     instructions="""
 You are WebResearchAgent — an advanced internet research assistant with two core abilities:

common/aagents/yf_agent.py

@@ -1,29 +1,13 @@
 """Yahoo Finance agent module for financial analysis and market research."""
-import os
-from agents import Agent, OpenAIChatCompletionsModel
-from dotenv import load_dotenv
+from agents import Agent
 from common.mcp.tools.yf_tools import get_summary, get_market_sentiment, get_history, get_analyst_recommendations, get_earnings_calendar
 from common.mcp.tools.time_tools import current_datetime
-from openai import AsyncOpenAI
+from .core.model import get_model_client
 
-# ---------------------------------------------------------
-# Load environment variables
-# ---------------------------------------------------------
-load_dotenv()
 
-GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
-google_api_key = os.getenv('GOOGLE_API_KEY')
-gemini_client = AsyncOpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)
-gemini_model = OpenAIChatCompletionsModel(model="gemini-2.0-flash-exp", openai_client=gemini_client) 
-
-GROQ_BASE_URL = "https://api.groq.com/openai/v1"
-groq_api_key = os.getenv('GROQ_API_KEY')
-groq_client = AsyncOpenAI(base_url=GROQ_BASE_URL, api_key=groq_api_key)
-groq_model = OpenAIChatCompletionsModel(model="groq/compound", openai_client=groq_client)
-
-yf_agent = Agent(
+yf_agent = Agent( 
     name="YahooFinanceAgent",
-    model=gemini_model,
+    model=get_model_client(),
     tools=[current_datetime, get_summary, get_market_sentiment, get_history, get_analyst_recommendations, get_earnings_calendar],
     instructions="""
         You are a specialized **Financial Analysis Agent** 💰, expert in market research, financial data retrieval, and market analysis. 

common/mcp/tools/google_tools.py

@@ -1,13 +1,15 @@
 import os
 import requests
-from dotenv import load_dotenv
+
 from agents import function_tool
 from typing import Optional
 
+
+
 # ---------------------------------------------------------
 # Load environment variables
 # ---------------------------------------------------------
-load_dotenv()
+
 
 # ============================================================
 # 🔹 GOOGLE SEARCH TOOLSET (Serper.dev API)

common/mcp/tools/news_tools.py

@@ -1,14 +1,16 @@
 import os
 import requests
-from dotenv import load_dotenv
+
 from agents import function_tool
 from typing import Optional
 import datetime
 
+
+
 # ---------------------------------------------------------
 # Load environment variables
 # ---------------------------------------------------------
-load_dotenv()
+
 
 # ============================================================
 # 🔹 NEWS TOOLSET (NewsAPI.org)

common/mcp/tools/rag_tool.py

@@ -2,10 +2,9 @@
 import os
 from pathlib import Path
 from agents import function_tool, RunContextWrapper
-from dotenv import load_dotenv
-from rag.rag import Retriever
-from dataclasses import dataclass
 
+from common.rag.rag import Retriever
+from dataclasses import dataclass
 
 @dataclass
 class UserContext:
@@ -15,18 +14,6 @@ class UserContext:
     similarity_threshold: float = 0.4  # FAISS L2 distance threshold for RAG relevance
 
 
-# ---------------------------------------------------------
-# Load environment variables
-# ---------------------------------------------------------
-load_dotenv()
-
-# ---------------------------------------------------------
-# Initialize RAG Retriever
-# ---------------------------------------------------------
-# Get the healthcare-rag-chatbot directory path
-# healthcare_dir = str(Path(__file__).parent.parent.parent)
-# retriever = None
-
 # ---------------------------------------------------------
 # RAG Search Tool
 # ---------------------------------------------------------

common/mcp/tools/search_tools.py

@@ -1,15 +1,17 @@
 import requests
 from ddgs import DDGS
 from agents import function_tool
-from dotenv import load_dotenv
+
 from pydantic import BaseModel, Field
 from bs4 import BeautifulSoup
 from typing import Optional
 
+
+
 # ---------------------------------------------------------
 # Load environment variables
 # ---------------------------------------------------------
-load_dotenv()
+
 
 # ---------------------- MODELS ---------------------------
 class searchQuery(BaseModel):
@@ -118,8 +120,23 @@ def _duckduckgo_search(params: searchQuery) -> list[dict]:
     return results
 
 @function_tool
-def duckduckgo_search(params: searchQuery) -> list[dict]:
-    """Perform a DuckDuckGo search and return only snippets.  
-    No page content fetched here."""
+def duckduckgo_search(query: str, max_results: int = 5, search_type: str = "text", timelimit: str = "d", region: str = "us-en") -> list[dict]:
+    """
+    Perform a DuckDuckGo search and return only snippets.
+    
+    Args:
+        query: The search query string.
+        max_results: The maximum number of search results to return (default: 5).
+        search_type: Search type: 'text' (default) or 'news'. Use 'news' to get publication dates.
+        timelimit: Time limit for search results: 'd' (day), 'w' (week), 'm' (month), 'y' (year).
+        region: Region for search results (e.g., 'us-en').
+    """
+    params = searchQuery(
+        query=query, 
+        max_results=max_results, 
+        search_type=search_type, 
+        timelimit=timelimit, 
+        region=region
+    )
     return _duckduckgo_search(params)
 

common/mcp/tools/time_tools.py

@@ -2,6 +2,8 @@ from datetime import datetime
 from agents import function_tool
 # from ..common.utility.logger import log_call
 
+
+
 @function_tool
 # @log_call
 def current_datetime(format: str = "natural") -> str:

common/mcp/tools/weather_tools.py

@@ -2,16 +2,18 @@ import os
 import re
 import requests
 import datetime
-from dotenv import load_dotenv
+
 from typing import Optional
 
 from ddgs import DDGS
 from agents import function_tool
 
+
+
 # ---------------------------------------------------------
 # Load environment variables
 # ---------------------------------------------------------
-load_dotenv()
+
 
 @function_tool
 def get_weather_forecast(city: str, date: Optional[str] = None) -> str:

common/mcp/tools/yf_tools.py

@@ -1,12 +1,14 @@
 import os
 import requests
 import yfinance as yf
-from dotenv import load_dotenv
+
 from agents import function_tool
 from datetime import datetime, timedelta
 
+
+
 # Load environment variables
-load_dotenv()
+
 
 
 # ============================================================

common/rag/rag.py

@@ -17,7 +17,7 @@ class Retriever:
         self.embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
         self.text_splitter = RecursiveCharacterTextSplitter(
             chunk_size=1024,
-            chunk_overlap=200,
+            chunk_overlap=300,
             length_function=len,
             # separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""],
             is_separator_regex=False,

common/utility/autogen_model_factory.py

@@ -0,0 +1,88 @@
+import os
+from azure.identity import DefaultAzureCredential, get_bearer_token_provider
+
+class AutoGenModelFactory:
+    """
+    Factory for creating AutoGen compatible model instances.
+    """
+
+    @staticmethod
+    def get_model(provider: str = "azure", # azure, openai, google, groq, ollama
+                  model_name: str = "gpt-4o", 
+                  temperature: float = 0,
+                  model_info: dict = None
+                  ):
+        """
+        Returns an AutoGen OpenAIChatCompletionClient instance.
+        """
+        
+        # Lazy import to avoid dependency issues if autogen is not installed
+        try:
+            from autogen_ext.models.openai import OpenAIChatCompletionClient
+        except ImportError as e:
+            raise ImportError("AutoGen libraries (autogen-agentchat, autogen-ext[openai]) are not installed.") from e
+
+        # ----------------------------------------------------------------------
+        # AZURE
+        # ----------------------------------------------------------------------
+        if provider.lower() == "azure":
+            token_provider = get_bearer_token_provider(
+                DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
+            )
+            return OpenAIChatCompletionClient(
+                model=model_name,
+                azure_endpoint=os.environ["AZURE_OPENAI_API_URI"],
+                api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+                azure_ad_token_provider=token_provider,
+                temperature=temperature,
+            )
+
+        # ----------------------------------------------------------------------
+        # OPENAI
+        # ----------------------------------------------------------------------
+        elif provider.lower() == "openai":
+            return OpenAIChatCompletionClient(
+                model=model_name,
+                api_key=os.environ["OPENAI_API_KEY"],
+                temperature=temperature,
+            )
+        
+        # ----------------------------------------------------------------------
+        # GOOGLE (GEMINI) via OpenAI Compat
+        # ----------------------------------------------------------------------
+        elif provider.lower() == "google" or provider.lower() == "gemini":
+            return OpenAIChatCompletionClient(
+                model=model_name,
+                base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
+                api_key=os.environ["GOOGLE_API_KEY"],
+                model_info=model_info, # Pass full model_info for capabilities
+                temperature=temperature,
+            )
+
+        # ----------------------------------------------------------------------
+        # GROQ
+        # ----------------------------------------------------------------------
+        elif provider.lower() == "groq":
+            return OpenAIChatCompletionClient(
+                model=model_name,
+                base_url="https://api.groq.com/openai/v1",
+                api_key=os.environ["GROQ_API_KEY"],
+                temperature=temperature,
+            )
+        
+        # ----------------------------------------------------------------------
+        # OLLAMA
+        # ----------------------------------------------------------------------
+        elif provider.lower() == "ollama":
+            # Ensure model_info defaults to empty dict if None
+            info = model_info if model_info is not None else {}
+            return OpenAIChatCompletionClient(
+                model=model_name,
+                base_url="http://localhost:11434/v1",
+                api_key="ollama", # dummy key
+                model_info=info,
+                temperature=temperature,
+            )
+        
+        else:
+            raise ValueError(f"Unsupported AutoGen provider: {provider}")

common/utility/bkp/embedding_factory.py

@@ -0,0 +1,49 @@
+import os
+from typing import Union
+# from azure.identity import DefaultAzureCredential
+from langchain_openai import AzureOpenAIEmbeddings, OpenAIEmbeddings
+from langchain_ollama import OllamaEmbeddings
+from langchain_huggingface import HuggingFaceEmbeddings
+
+
+class EmbeddingFactory:
+    """
+    A static utility class to create and return LLM Embedding instances based on the input type.
+    """
+
+    @staticmethod
+    def get_llm(llm_type: str) -> Union[AzureOpenAIEmbeddings, OpenAIEmbeddings]:
+        """
+        Returns an LLM instance based on the specified type.
+
+        Parameters:
+            llm_type (str): The type of LLM to return. Valid values are 'azure' or 'openai'.
+
+        Returns:
+            Union[AzureOpenAIEmbeddings, OpenAIEmbeddings]: The LLM instance.
+        """
+        if llm_type.lower() == "azure":
+            # Get the Azure Credential
+            # credential = DefaultAzureCredential()
+            # token=credential.get_token("https://cognitiveservices.azure.com/.default").token
+
+            # if not token:
+            #     raise ValueError("Token is required for AzureOpenAIEmbeddings.")
+            # return AzureOpenAIEmbeddings(
+            #     azure_endpoint=os.environ["AZURE_OPENAI_API_URI"],
+            #     azure_deployment="text-embedding-3-small", #os.environ["AZURE_OPENAI_API_BASE_MODEL"],
+            #     api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+            #     api_key=token
+            # )
+            pass
+        elif llm_type.lower() == "openai":
+            return OpenAIEmbeddings(
+                api_key=os.environ["OPENAI_API_KEY"],
+                model="text-embedding-3-large"
+            )
+        elif llm_type.lower() == "ollama": # must have ollama running locally with the following model
+            return OllamaEmbeddings(model="gemma:2b") 
+        elif llm_type.lower() == "hf": # must have key update in env:HF_TOKEN
+            return HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+        else:
+            raise ValueError("Invalid llm_type. Use 'azure' or 'openai'.")

common/utility/bkp/llm_factory.py

@@ -0,0 +1,130 @@
+import os
+import tiktoken
+from typing import Any
+from langchain_openai.chat_models import ChatOpenAI, AzureChatOpenAI
+from langchain_openai.embeddings import AzureOpenAIEmbeddings, OpenAIEmbeddings
+# from azure.identity import DefaultAzureCredential
+from huggingface_hub import login
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEmbeddings
+from langchain_ollama import ChatOllama, OllamaEmbeddings
+from langchain_groq import ChatGroq
+# from langchain_openai import OpenAIEmbeddings
+
+class LLMFactory:
+    """
+    Factory class to provide LLM and embedding model instances for different providers.
+    """
+
+    @staticmethod
+    def get_llm(provider: str, **kwargs) -> Any:
+        """
+        Returns a chat/completion LLM instance based on the provider.
+        Supported providers: openai, azureopenai, huggingface, ollama, groq
+        """
+        if provider == "openai":
+            # OpenAI Chat Model
+            return ChatOpenAI(
+                openai_api_key=kwargs.get("api_key", os.environ.get("OPENAI_API_KEY")),
+                model_name=kwargs.get("model_name", "gpt-4")
+            )
+
+        # elif provider == "azureopenai":
+        #     # Azure OpenAI Chat Model using Azure Identity for token
+        #     credential = DefaultAzureCredential()
+        #     token = credential.get_token("https://cognitiveservices.azure.com/.default").token
+        #     if not token:
+        #         raise ValueError("Token is required for AzureChatOpenAI.")
+        #     return AzureChatOpenAI(
+        #         azure_endpoint=kwargs["endpoint"],
+        #         azure_deployment=kwargs.get("deployment_name", "gpt-4"),
+        #         api_version=kwargs["api_version"],
+        #         api_key=token
+        #     )
+
+        # pip install langchain langchain-huggingface huggingface_hub
+        elif provider == "huggingface":
+            # If using a private model or endpoint, authenticate
+            login(token=kwargs.get("api_key", os.environ.get("HF_TOKEN")))  
+
+            return ChatHuggingFace(
+                repo_id=kwargs.get("model_name", "mistralai/Mistral-Nemo-Instruct-2407"),  # Or any other chat-friendly model
+                task="text-generation",
+                model_kwargs={
+                    "temperature": 0.7,
+                    "max_new_tokens": 256
+                }
+            )
+
+        elif provider == "ollama":
+            # Ollama local model
+            return ChatOllama(
+                model=kwargs.get("model_name", "gemma:2b"),
+                temperature=0
+            )
+
+        elif provider == "groq":
+            # Groq LLM
+            return ChatGroq(
+                model=kwargs.get("model_name", "Gemma2-9b-It"),
+                max_tokens=512,
+                api_key=kwargs.get("api_key", os.environ.get("GROQ_API_KEY"))
+            )
+
+        else:
+            raise ValueError(f"Unsupported provider: {provider}")
+
+    @staticmethod
+    def get_embedding_model(provider: str, **kwargs) -> Any:
+        """
+        Returns an embedding model instance based on the provider.
+        Supported providers: openai, huggingface
+        """
+        if provider == "openai":
+            return OpenAIEmbeddings(
+                model=kwargs.get("model_name", "text-embedding-3-large"),
+                openai_api_key=kwargs.get("api_key", os.environ.get("OPENAI_API_KEY"))
+            )
+        # if provider == "azureopenai":
+        #     # Get the Azure Credential
+        #     credential = DefaultAzureCredential()
+        #     token=credential.get_token("https://cognitiveservices.azure.com/.default").token
+
+        #     if not token:
+        #         raise ValueError("Token is required for AzureOpenAIEmbeddings.")
+        #     return AzureOpenAIEmbeddings(
+        #         azure_endpoint=os.environ["AZURE_OPENAI_API_URI"],
+        #         azure_deployment=kwargs.get("azure_deployment", "text-embedding-3-large"), 
+        #         api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+        #         api_key=token
+        #     )
+        elif provider == "huggingface":
+            # If using a private model or endpoint, authenticate
+            login(token=kwargs.get("api_key", os.environ.get("HF_TOKEN")))  
+
+            return HuggingFaceEmbeddings(
+                model_name=kwargs.get("model_name", "all-MiniLM-L6-v2")
+            )
+        elif provider == "groq":
+            raise ValueError(f"No embedding support from the provider: {provider}")
+        elif provider == "ollama":
+            return OllamaEmbeddings(model=kwargs.get("model_name", "gemma:2b")) 
+        else:
+            raise ValueError(f"Unsupported embedding provider: {provider}")
+
+    @staticmethod
+    def num_tokens_from_messages(messages) -> int:
+        """
+        Return the number of tokens used by a list of messages.
+        Adapted from the OpenAI cookbook token counter.
+        """
+        encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")
+        tokens_per_message = 3  # <|start|>, role, <|end|>
+        num_tokens = 0
+
+        for message in messages:
+            num_tokens += tokens_per_message
+            for key, value in message.items():
+                num_tokens += len(encoding.encode(value))
+
+        num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
+        return num_tokens

common/utility/bkp/llm_factory2.py

@@ -0,0 +1,75 @@
+import os
+import tiktoken
+from typing import Union
+# from azure.identity import DefaultAzureCredential
+from langchain_openai.chat_models import AzureChatOpenAI, ChatOpenAI
+
+
+class LLMFactory:
+    """
+    A static utility class to create and return LLM instances based on the input type.
+    """
+
+    @staticmethod
+    def get_llm(llm_type: str) -> Union[AzureChatOpenAI, ChatOpenAI]:
+        """
+        Returns an LLM instance based on the specified type.
+
+        Parameters:
+            llm_type (str): The type of LLM to return. Valid values are 'azure' or 'openai'.
+
+        Returns:
+            Union[AzureChatOpenAI, ChatOpenAI]: The LLM instance.
+        """
+        if llm_type.lower() == "azure":
+            # # Get the Azure Credential
+            # credential = DefaultAzureCredential()
+            # token=credential.get_token("https://cognitiveservices.azure.com/.default").token
+
+            # if not token:
+            #     raise ValueError("Token is required for AzureChatOpenAI.")
+            # return AzureChatOpenAI(
+            #     azure_endpoint=os.environ["AZURE_OPENAI_API_URI"],
+            #     azure_deployment=os.environ["AZURE_OPENAI_API_BASE_MODEL"],
+            #     api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+            #     api_key=token
+            # )
+            pass
+        elif llm_type.lower() == "openai":
+            return ChatOpenAI(
+                api_key=os.environ["OPENAI_API_KEY"],
+                model_name="gpt-4"
+            )
+        elif llm_type.lower() == "openai_chat":
+            return ChatOpenAI(
+                api_key=os.environ["OPENAI_API_KEY"],
+                model_name="gpt-4"
+            )
+        else:
+            raise ValueError("Invalid llm_type. Use 'azure' or 'openai'.")
+        
+    @staticmethod
+    def num_tokens_from_messages(messages):
+
+        """
+        Return the number of tokens used by a list of messages.
+        Adapted from the Open AI cookbook token counter
+        """
+
+        encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")
+
+        # Each message is sandwiched with <|start|>role and <|end|>
+        # Hence, messages look like: <|start|>system or user or assistant{message}<|end|>
+
+        tokens_per_message = 3 # token1:<|start|>, token2:system(or user or assistant), token3:<|end|>
+
+        num_tokens = 0
+
+        for message in messages:
+            num_tokens += tokens_per_message
+            for key, value in message.items():
+                num_tokens += len(encoding.encode(value))
+
+        num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
+
+        return num_tokens

common/utility/langchain_model_factory.py

@@ -0,0 +1,70 @@
+import os
+from azure.identity import DefaultAzureCredential, get_bearer_token_provider
+from langchain_openai import AzureChatOpenAI, ChatOpenAI
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
+from langchain_ollama import ChatOllama
+from huggingface_hub import login
+
+class LangChainModelFactory:
+    """
+    Factory for creating LangChain compatible model instances.
+    """
+
+    @staticmethod
+    def get_model(provider: str = "openai", # openai, azure, huggingface, ollama
+                  model_name: str = "gpt-4o", 
+                  temperature: float = 0
+                  ):
+        """
+        Returns a LangChain LLM instance.
+        """
+        
+        # ----------------------------------------------------------------------
+        # AZURE
+        # ----------------------------------------------------------------------
+        if provider.lower() == "azure":
+            token_provider = get_bearer_token_provider(
+                DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
+            )
+            return AzureChatOpenAI(
+                azure_endpoint=os.environ["AZURE_OPENAI_API_URI"],
+                azure_deployment=os.environ["AZURE_OPENAI_API_BASE_MODEL"], # Or specific model_name if deployment matches
+                api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+                azure_ad_token_provider=token_provider,
+                model_name=model_name,
+                temperature=temperature,
+            )
+        
+        # ----------------------------------------------------------------------
+        # OPENAI
+        # ----------------------------------------------------------------------
+        elif provider.lower() == "openai":
+            return ChatOpenAI(
+                api_key=os.environ["OPENAI_API_KEY"],
+                model_name=model_name,
+                temperature=temperature,
+            )
+        
+        # ----------------------------------------------------------------------
+        # HUGGING FACE
+        # ----------------------------------------------------------------------
+        elif provider.lower() == "huggingface":
+            if os.environ.get("HF_TOKEN"):
+                login(token=os.environ.get("HF_TOKEN"))
+            llm = HuggingFaceEndpoint(
+                repo_id=model_name,
+                task="text-generation",
+                temperature=temperature,
+                max_new_tokens=512,
+                huggingfacehub_api_token=os.environ.get("HF_TOKEN")
+            )
+            return ChatHuggingFace(llm=llm)
+
+        # ----------------------------------------------------------------------
+        # OLLAMA
+        # ----------------------------------------------------------------------
+        elif provider.lower() == "ollama":
+            return ChatOllama(model=model_name, temperature=temperature)
+        
+        else:
+            raise ValueError(f"Unsupported LangChain provider: {provider}")

common/utility/logger.py

@@ -14,7 +14,7 @@ def log_call(func):
         print(f"[{timestamp}] 🚀 Calling: {func.__name__}({arg_list})")
         try:
             result = func(*args, **kwargs)
-            print(f"[{timestamp}] ✅ Finished: {func.__name__}")
+            # print(f"[{timestamp}] ✅ Finished: {func.__name__}")
             return result
         except Exception as e:
             print(f"[{timestamp}] ❌ Error in {func.__name__}: {e}")

common/utility/model_factory_notused.py

@@ -0,0 +1,302 @@
+import os
+import tiktoken
+from typing import Union
+from azure.identity import DefaultAzureCredential, get_bearer_token_provider
+from langchain_openai import AzureChatOpenAI, ChatOpenAI, AzureOpenAIEmbeddings, OpenAIEmbeddings
+from agents import OpenAIChatCompletionsModel
+from openai import AsyncOpenAI, AsyncAzureOpenAI
+from huggingface_hub import login
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
+from langchain_ollama import ChatOllama, OllamaEmbeddings
+
+
+class ModelFactory:
+    """
+    A static utility class to create and return LLM instances based on the input type.
+    """
+
+    @staticmethod
+    def get_model(framework: str = "openai-sdk-agent", # openai-sdk-agent, langchain, autogen
+                  provider: str = "openai", # openai, azure, google, groq, huggingface, ollama
+                  model_name: str = "gpt-4o-mini", # gpt-4o-mini, gemini-flash-1.5, groq/compound
+                  model_info: dict = None, # additional info (e.g. backend provider for autogen/langchain)
+                  temperature: float = 0
+                  ) -> Union[AzureChatOpenAI, ChatOpenAI, OpenAIChatCompletionsModel, ChatHuggingFace, ChatOllama]:
+        """
+        Returns an LLM instance based on the specified parameters.
+
+        Parameters:
+            framework (str): The framework to use ('langchain', 'openai-sdk-agent', 'autogen').
+            provider (str): The model provider ('openai', 'azure', 'google', 'groq', 'huggingface', 'ollama').
+            model_name (str): The specific model name.
+            model_info (dict): Additional model info.
+            temperature (float): The temperature for generation (default 0).
+
+        Returns:
+            Union[...]: The model instance.
+        """
+        
+        # ----------------------------------------------------------------------
+        # AUTOGEN SUPPORT
+        # ----------------------------------------------------------------------
+        if framework.lower() == "autogen":
+            # Lazy import to avoid dependency issues if autogen is not installed
+            try:
+                from autogen_ext.models.openai import OpenAIChatCompletionClient
+            except ImportError as e:
+                raise ImportError("AutoGen libraries (autogen-agentchat, autogen-ext[openai]) are not installed.") from e
+
+            # Azure Backend
+            if provider.lower() == "azure":
+                token_provider = get_bearer_token_provider(
+                    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
+                )
+                return OpenAIChatCompletionClient(
+                    model=model_name,
+                    azure_endpoint=os.environ["AZURE_OPENAI_API_URI"],
+                    api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+                    azure_ad_token_provider=token_provider,
+                    temperature=temperature,
+                )
+
+            # OpenAI Backend
+            elif provider.lower() == "openai":
+                return OpenAIChatCompletionClient(
+                    model=model_name,
+                    api_key=os.environ["OPENAI_API_KEY"],
+                    temperature=temperature,
+                )
+            
+            # Google Backend (Gemini via OpenAI compat)
+            elif provider.lower() == "google" or provider.lower() == "gemini":
+                return OpenAIChatCompletionClient(
+                    model=model_name,
+                    base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
+                    api_key=os.environ["GOOGLE_API_KEY"],
+                    model_info=model_info, # Pass full model_info for capabilities
+                    temperature=temperature,
+                )
+
+            # Groq Backend
+            elif provider.lower() == "groq":
+                return OpenAIChatCompletionClient(
+                    model=model_name,
+                    base_url="https://api.groq.com/openai/v1",
+                    api_key=os.environ["GROQ_API_KEY"],
+                    temperature=temperature,
+                )
+            
+            # Ollama Backend
+            elif provider.lower() == "ollama":
+                # Ensure model_info defaults to empty dict if None
+                info = model_info if model_info is not None else {}
+                return OpenAIChatCompletionClient(
+                    model=model_name,
+                    base_url="http://localhost:11434/v1",
+                    api_key="ollama", # dummy key
+                    model_info=info,
+                    temperature=temperature,
+                )
+            
+            else:
+                raise ValueError(f"Unsupported AutoGen provider: {provider}")
+
+        # ----------------------------------------------------------------------
+        # LANGCHAIN SUPPORT
+        # ----------------------------------------------------------------------
+        elif framework.lower() == "langchain":
+            
+            if provider.lower() == "azure":
+                token_provider = get_bearer_token_provider(
+                    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
+                )
+                return AzureChatOpenAI(
+                    azure_endpoint=os.environ["AZURE_OPENAI_API_URI"],
+                    azure_deployment=os.environ["AZURE_OPENAI_API_BASE_MODEL"],
+                    api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+                    azure_ad_token_provider=token_provider,
+                    model_name=model_name,
+                    temperature=temperature,
+                )
+            
+            elif provider.lower() == "openai":
+                return ChatOpenAI(
+                    api_key=os.environ["OPENAI_API_KEY"],
+                    model_name=model_name,
+                    temperature=temperature,
+                )
+            
+            elif provider.lower() == "huggingface":
+                if os.environ.get("HF_TOKEN"):
+                    login(token=os.environ.get("HF_TOKEN"))
+                llm = HuggingFaceEndpoint(
+                    repo_id=model_name,
+                    task="text-generation",
+                    temperature=temperature,
+                    max_new_tokens=512,
+                    huggingfacehub_api_token=os.environ.get("HF_TOKEN")
+                )
+                return ChatHuggingFace(llm=llm)
+
+            elif provider.lower() == "ollama":
+                return ChatOllama(model=model_name, temperature=temperature)
+            
+            else:
+                raise ValueError(f"Unsupported LangChain provider: {provider}")
+        
+        # ----------------------------------------------------------------------
+        # STANDARD LOGIC (Agents Lib / OpenAI SDK)
+        # ----------------------------------------------------------------------
+        elif framework.lower() == "openai-sdk-agent" or framework.lower() == "openai-sdk" or framework.lower() == "openai":
+        
+            if provider.lower() == "azure":
+                token_provider = get_bearer_token_provider(
+                    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
+                )
+                client = AsyncAzureOpenAI(
+                    azure_endpoint=os.environ["AZURE_OPENAI_API_URI"],
+                    api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+                    azure_ad_token_provider=token_provider,
+                )
+                return OpenAIChatCompletionsModel(model=model_name, openai_client=client)
+
+            elif provider.lower() == "openai":
+                client = AsyncOpenAI(api_key=os.environ["OPENAI_API_KEY"])
+                return OpenAIChatCompletionsModel(model=model_name, openai_client=client)
+                
+            elif provider.lower() == "google":
+                GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
+                client = AsyncOpenAI(
+                    base_url=GEMINI_BASE_URL,
+                    api_key=os.environ["GOOGLE_API_KEY"]
+                )
+                return OpenAIChatCompletionsModel(model=model_name, openai_client=client)
+
+            elif provider.lower() == "groq":
+                GROQ_BASE_URL = "https://api.groq.com/openai/v1"
+                client = AsyncOpenAI(
+                    base_url=GROQ_BASE_URL,
+                    api_key=os.environ["GROQ_API_KEY"]
+                )
+                return OpenAIChatCompletionsModel(model=model_name, openai_client=client)
+
+            elif provider.lower() == "ollama":
+                client = AsyncOpenAI(
+                    base_url="http://localhost:11434/v1",
+                    api_key="ollama"
+                )
+                return OpenAIChatCompletionsModel(model=model_name, openai_client=client)
+
+            elif provider.lower() == "huggingface":
+                 # Agents lib doesn't have native HF support in the same way
+                 raise ValueError("For Hugging Face, please use framework='langchain'")
+
+            else:
+                raise ValueError(f"Unsupported provider for openai-sdk-agent: {provider}")
+
+        else:
+             raise ValueError(f"Unsupported framework: {framework}")
+
+
+    @staticmethod
+    def num_tokens_from_messages(messages, model: str = "gpt-4o"):
+        """
+        Return the number of tokens used by a list of messages.
+        """
+        try:
+            encoding = tiktoken.encoding_for_model(model)
+        except KeyError:
+            encoding = tiktoken.get_encoding("cl100k_base")
+
+        tokens_per_message = 3
+        num_tokens = 0
+
+        for message in messages:
+            num_tokens += tokens_per_message
+            for key, value in message.items():
+                if key == "name":
+                    num_tokens += 1
+                
+                # Encode values if they are strings
+                if isinstance(value, str):
+                    num_tokens += len(encoding.encode(value))
+                elif isinstance(value, list) and key == "content":
+                    for part in value:
+                        if isinstance(part, dict) and part.get("type") == "text":
+                             num_tokens += len(encoding.encode(part.get("text", "")))
+                        elif isinstance(part, dict) and part.get("type") == "image_url":
+                             num_tokens += 85 
+
+        num_tokens += 3 
+        return num_tokens
+
+
+class EmbeddingFactory:
+    """
+    A static utility class to create and return Embedding Model instances.
+    """
+
+    @staticmethod
+    def get_embedding_model(provider: str = "openai",
+                            model_name: str = "text-embedding-3-small"
+                            ) -> Union[AzureOpenAIEmbeddings, OpenAIEmbeddings, OllamaEmbeddings, HuggingFaceEmbeddings]:
+        
+        if provider.lower() == "azure":
+            token_provider = get_bearer_token_provider(
+                DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
+            )
+            return AzureOpenAIEmbeddings(
+                azure_endpoint=os.environ["AZURE_OPENAI_API_URI"],
+                azure_deployment=os.environ.get("AZURE_OPENAI_EMBEDDING_DEPLOYMENT", model_name),
+                api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+                azure_ad_token_provider=token_provider,
+            )
+        elif provider.lower() == "openai":
+            return OpenAIEmbeddings(
+                api_key=os.environ["OPENAI_API_KEY"],
+                model=model_name
+            )
+        elif provider.lower() == "ollama":
+            return OllamaEmbeddings(model=model_name)
+        elif provider.lower() == "huggingface":
+            if os.environ.get("HF_TOKEN"):
+                login(token=os.environ.get("HF_TOKEN"))
+            return HuggingFaceEmbeddings(model_name=model_name)
+        else:
+            raise ValueError(f"Unsupported embedding provider: {provider}")
+
+# =================================================================================================
+# GLOBAL HELPER FUNCTIONS (for agents)
+# =================================================================================================
+
+# model used for orchestrator or executor
+# def get_model(provider:str = "google", framework:str = "openai-sdk", model_name:str = "gemini-2.5-flash"):
+def get_model(provider:str = "openai", framework:str = "openai", model_name:str = "gpt-4-turbo"):
+# def get_model(provider:str = "groq", framework:str = "openai-sdk", model_name:str = "openai/gpt-oss-120b"):
+    model_info = None
+    if provider in list["gemini", "google"]:
+        model_info = {
+            "family": "gemini",
+            "vision": True,
+            "function_calling": True,
+            "json_output": True,
+            "structured_output": True,
+        }
+    
+    return ModelFactory.get_model(  framework=framework,
+                                    provider=provider,
+                                    model_name=model_name,
+                                    model_info=model_info,
+                                    temperature=0)
+    # else:
+    #     return ModelFactory.get_model(  framework="openai-sdk", 
+    #                                 provider="openai",
+    #                                 model_name="gpt-4o-mini",
+    #                                 temperature=0)
+
+# Use this model where agent executing tool and returning JSON
+def get_model_json(model_name: str = "gpt-4.1-mini", provider: str = "openai"):
+    return ModelFactory.get_model(  framework="openai-sdk",
+                                    provider=provider,
+                                    model_name=model_name, 
+                                    temperature=0)

common/utility/openai_model_factory.py

@@ -0,0 +1,179 @@
+import os
+import tiktoken
+from typing import Union
+from azure.identity import DefaultAzureCredential, get_bearer_token_provider
+from agents import OpenAIChatCompletionsModel
+from openai import AsyncOpenAI, AsyncAzureOpenAI
+from langchain_openai import AzureOpenAIEmbeddings, OpenAIEmbeddings
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_ollama import OllamaEmbeddings
+from huggingface_hub import login
+
+class OpenAIModelFactory:
+    """
+    Factory for creating OpenAI-SDK compatible model instances (using the 'agents' library).
+    Supports multiple providers via the OpenAI-compatible API format.
+    """
+
+    @staticmethod
+    def get_model(provider: str = "openai", # openai, azure, google, groq, ollama
+                  model_name: str = "gpt-4o", 
+                  temperature: float = 0
+                  ) -> OpenAIChatCompletionsModel:
+        """
+        Returns an OpenAIChatCompletionsModel instance.
+        """
+        
+        # ----------------------------------------------------------------------
+        # AZURE OPENAI
+        # ----------------------------------------------------------------------
+        if provider.lower() == "azure":
+            token_provider = get_bearer_token_provider(
+                DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
+            )
+            client = AsyncAzureOpenAI(
+                azure_endpoint=os.environ["AZURE_OPENAI_API_URI"],
+                api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+                azure_ad_token_provider=token_provider,
+            )
+            return OpenAIChatCompletionsModel(model=model_name, openai_client=client)
+
+        # ----------------------------------------------------------------------
+        # STANDARD OPENAI
+        # ----------------------------------------------------------------------
+        elif provider.lower() == "openai":
+            client = AsyncOpenAI(api_key=os.environ["OPENAI_API_KEY"])
+            return OpenAIChatCompletionsModel(model=model_name, openai_client=client)
+            
+        # ----------------------------------------------------------------------
+        # GOOGLE (GEMINI) via OpenAI Compat
+        # ----------------------------------------------------------------------
+        elif provider.lower() == "google" or provider.lower() == "gemini":
+            GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
+            client = AsyncOpenAI(
+                base_url=GEMINI_BASE_URL,
+                api_key=os.environ["GOOGLE_API_KEY"]
+            )
+            return OpenAIChatCompletionsModel(model=model_name, openai_client=client)
+
+        # ----------------------------------------------------------------------
+        # GROQ via OpenAI Compat
+        # ----------------------------------------------------------------------
+        elif provider.lower() == "groq":
+            GROQ_BASE_URL = "https://api.groq.com/openai/v1"
+            client = AsyncOpenAI(
+                base_url=GROQ_BASE_URL,
+                api_key=os.environ["GROQ_API_KEY"]
+            )
+            return OpenAIChatCompletionsModel(model=model_name, openai_client=client)
+
+        # ----------------------------------------------------------------------
+        # OLLAMA via OpenAI Compat
+        # ----------------------------------------------------------------------
+        elif provider.lower() == "ollama":
+            client = AsyncOpenAI(
+                base_url="http://localhost:11434/v1",
+                api_key="ollama"
+            )
+            return OpenAIChatCompletionsModel(model=model_name, openai_client=client)
+
+        # ----------------------------------------------------------------------
+        # UNSUPPORTED
+        # ----------------------------------------------------------------------
+        else:
+            raise ValueError(f"Unsupported provider for OpenAIModelFactory: {provider}")
+
+
+    @staticmethod
+    def num_tokens_from_messages(messages, model: str = "gpt-4o"):
+        """
+        Return the number of tokens used by a list of messages.
+        """
+        try:
+            encoding = tiktoken.encoding_for_model(model)
+        except KeyError:
+            encoding = tiktoken.get_encoding("cl100k_base")
+
+        tokens_per_message = 3
+        num_tokens = 0
+
+        for message in messages:
+            num_tokens += tokens_per_message
+            for key, value in message.items():
+                if key == "name":
+                    num_tokens += 1
+                
+                # Encode values if they are strings
+                if isinstance(value, str):
+                    num_tokens += len(encoding.encode(value))
+                elif isinstance(value, list) and key == "content":
+                    for part in value:
+                        if isinstance(part, dict) and part.get("type") == "text":
+                                num_tokens += len(encoding.encode(part.get("text", "")))
+                        elif isinstance(part, dict) and part.get("type") == "image_url":
+                                num_tokens += 85 
+
+        num_tokens += 3 
+        return num_tokens
+
+
+class EmbeddingFactory:
+    """
+    A static utility class to create and return Embedding Model instances.
+    """
+
+    @staticmethod
+    def get_embedding_model(provider: str = "openai",
+                            model_name: str = "text-embedding-3-small"
+                            ) -> Union[AzureOpenAIEmbeddings, OpenAIEmbeddings, OllamaEmbeddings, HuggingFaceEmbeddings]:
+        
+        if provider.lower() == "azure":
+            token_provider = get_bearer_token_provider(
+                DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
+            )
+            return AzureOpenAIEmbeddings(
+                azure_endpoint=os.environ["AZURE_OPENAI_API_URI"],
+                azure_deployment=os.environ.get("AZURE_OPENAI_EMBEDDING_DEPLOYMENT", model_name),
+                api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+                azure_ad_token_provider=token_provider,
+            )
+        elif provider.lower() == "openai":
+            return OpenAIEmbeddings(
+                api_key=os.environ["OPENAI_API_KEY"],
+                model=model_name
+            )
+        elif provider.lower() == "ollama":
+            return OllamaEmbeddings(model=model_name)
+        elif provider.lower() == "huggingface":
+            if os.environ.get("HF_TOKEN"):
+                login(token=os.environ.get("HF_TOKEN"))
+            return HuggingFaceEmbeddings(model_name=model_name)
+        else:
+            raise ValueError(f"Unsupported embedding provider: {provider}")
+
+
+# =================================================================================================
+# GLOBAL HELPER FUNCTIONS
+# =================================================================================================
+
+def get_model(provider:str = "openai", model_name:str = "gpt-4o"):
+    """
+    Global helper to get an OpenAI-SDK compatible model.
+    Defaults to OpenAI provider and gpt-4o.
+    """
+    return OpenAIModelFactory.get_model(
+        provider=provider,
+        model_name=model_name,
+        temperature=0
+    )
+
+def get_model_json(model_name: str = "gpt-4o-2024-08-06", provider: str = "openai"):
+    """
+    Global helper to get a JSON-capable model (Structured Outputs).
+    Defaults to gpt-4o-2024-08-06 on OpenAI.
+    """
+    return OpenAIModelFactory.get_model(
+        provider=provider,
+        model_name=model_name,
+        temperature=0
+    )

pyproject.toml

@@ -31,7 +31,7 @@ dependencies = [
     "langchain-text-splitters>=1.0.0",
     "langchain-chroma>=1.0.0",
     "html2text>=2025.4.15",
-    "langfuse>=2.0.0",
+    "traceloop-sdk>=0.33.0",
 
     # =======================
     # VECTOR DB / INDEXING
@@ -117,12 +117,13 @@ dependencies = [
     # =======================
     "ddgs>=9.9.2",
     "duckduckgo_search",
+    "azure-identity>=1.25.1",
 
     # =======================
     # OBSERVABILITY
     # =======================
     "openinference-instrumentation-autogen>=0.1.0",
-    "openinference-instrumentation-openai>=0.1.0",
+    "openinference-instrumentation-openai>=0.1.15",
     "opentelemetry-sdk>=1.20.0",
     "opentelemetry-exporter-otlp>=1.20.0",
     "opentelemetry-api>=1.20.0",

run.py

@@ -18,7 +18,13 @@ import subprocess
 import argparse
 from pathlib import Path
 from typing import Dict, Optional
+from agents import Runner, SQLiteSession
+# from agents import set_trace_processors
+# from langsmith.wrappers import OpenAIAgentsTracingProcessor
 
+# Load environment variables explicitly
+from dotenv import load_dotenv
+load_dotenv(override=True)
 
 # App registry - maps app names to their paths and entry points
 APP_REGISTRY: Dict[str, Dict[str, str]] = {
@@ -168,7 +174,6 @@ def launch_app(app_name: str, port: Optional[int] = None):
         print(f"\n❌ Error launching app: {e}")
         sys.exit(1)
 
-
 def main():
     """Main entry point."""
     parser = argparse.ArgumentParser(
@@ -222,4 +227,5 @@ Available Apps:
 
 
 if __name__ == "__main__":
+    # set_trace_processors([OpenAIAgentsTracingProcessor()])
     main()

src/deep-research/app.py

@@ -5,28 +5,12 @@ import html
 from io import BytesIO
 import sys
 import os
-
-# LangSmith Configuration (Overwrites)
-os.environ["LANGCHAIN_TRACING_V2"] = "true"
-os.environ["LANGCHAIN_PROJECT"] = "deep-research"
-
 from pathlib import Path
-
-# Add project root
-# sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), ".")))
-
-from dotenv import load_dotenv
 from reportlab.platypus import SimpleDocTemplate, Paragraph
 from reportlab.lib.styles import getSampleStyleSheet
 from appagents.orchestrator import Orchestrator
 from agents import SQLiteSession
 
-# ------------------------------------------------------------------------------
-# OpenTelemetry Setup (Removed)
-# ------------------------------------------------------------------------------
-
-
-load_dotenv(override=True)
 
 # --------------------
 # Page config
@@ -82,7 +66,7 @@ st.markdown("""
         }
         
         .hero-container {
-            margin-top: 0;
+            margin-top: -3rem;
             margin-left: -5rem;
             margin-right: -5rem;
             padding: 2.5rem 1rem 2rem 1rem; /* Compact desktop padding */
@@ -98,7 +82,7 @@ st.markdown("""
         }
         
         .hero-container {
-            margin-top: 0;
+            margin-top: -2rem;
             margin-left: -1rem;
             margin-right: -1rem;
             padding: 2rem 1rem 1.5rem 1rem; /* Compact mobile padding */
@@ -172,8 +156,8 @@ st.markdown("""
         color: #333;
     }
     
-    /* Buttons */
-    .stButton button {
+    /* Buttons (including Download Button) */
+    .stButton button, .stDownloadButton button {
         border-radius: 20px; /* Matching healthcare */
         min-height: 48px;
         font-weight: 500;
@@ -245,7 +229,7 @@ def make_pdf_bytes(text: str) -> bytes:
 # --------------------
 # Logic
 # --------------------
-async def run_research(query: str):
+async def run_research(query: str, report_format: str = "Academic", research_depth: str = "Standard"):
     session_id = st.session_state.session_id
     session = SQLiteSession(f"session_{session_id}.db")
     orchestrator = Orchestrator(session=session)
@@ -254,7 +238,7 @@ async def run_research(query: str):
     status_container = st.status("🔍 Researching...", expanded=True)
     
     try:
-        async for chunk in orchestrator.run(query):
+        async for chunk in orchestrator.run(query, report_format=report_format, research_depth=research_depth):
             # Filtering heuristic: Orchestrator yields status messages then the final report.
             # Status messages are short and specific.
             if (chunk.startswith("View trace") or 
@@ -286,7 +270,7 @@ async def run_research(query: str):
 st.markdown("""
 <div class="hero-container">
     <div class="hero-title">🧠 Deep Research</div>
-    <div class="hero-subtitle">OpenAI Agentic Research Assistant</div>
+    <div class="hero-subtitle">OpenAI SDK Powered Agentic Research Assistant</div>
 </div>
 """, unsafe_allow_html=True)
 
@@ -294,7 +278,7 @@ st.markdown("""
 with st.sidebar:
     st.header("⚙️ Configuration")
     research_depth = st.select_slider("Research Depth", options=["Quick", "Standard", "Deep"], value="Standard")
-    report_format = st.selectbox("Report Format", ["Academic", "Business", "Creative"])
+    report_format = st.selectbox("Report Format", ["Academic", "Business", "Humorous"], index=2)
     st.caption("Settings affect the tone and depth of the final report.")
     
     st.divider()
@@ -314,7 +298,7 @@ if not st.session_state.final_report and not st.session_state.is_researching:
     
     col_c1, col_c2, col_c3 = st.columns([1, 2, 1])
     with col_c2:
-        query = st.text_area("Research Topic", height=60, placeholder="e.g. The future of quantum computing in drug discovery...", label_visibility="collapsed", value="The future of quantum computing in drug discovery")
+        query = st.text_area("Research Topic", height=60, placeholder="e.g. The future of quantum computing in drug discovery...", label_visibility="collapsed", value="Impact of Quantum Computing on Drug Research.")
         
         col_b1, col_b2, col_b3 = st.columns([1, 1, 1])
         with col_b2:
@@ -333,14 +317,14 @@ elif st.session_state.is_researching:
     """, unsafe_allow_html=True)
     
     # Trigger async run
-    asyncio.run(run_research(st.session_state.current_query))
+    asyncio.run(run_research(st.session_state.current_query, report_format=report_format, research_depth=research_depth))
 
 else:
     # Result View - Title removed to let Sticky Header be the main branding, 
     # and Report itself be the focus.
     
     # Action Toolbar
-    col_a1, col_a2, col_a3, col_a4 = st.columns([2, 1, 1, 2])
+    col_a1, col_a2, col_a3, col_a4 = st.columns([1, 2, 2, 1])
     with col_a2:
         pdf_bytes = make_pdf_bytes(st.session_state.final_report)
         st.download_button("📄 Download PDF", pdf_bytes, "report.pdf", mime="application/pdf", use_container_width=True)

src/deep-research/appagents/email_agent.py

@@ -1,11 +1,10 @@
 import os
 from typing import Dict
-
 import sendgrid
 from sendgrid.helpers.mail import Email, Mail, Content, To
 from agents import Agent, function_tool
-from core.logger import log_call
-
+from common.utility.logger import log_call
+from core.model import get_model_client
 
 @function_tool
 @log_call
@@ -28,5 +27,5 @@ email_agent = Agent(
     name="Email agent",
     instructions=INSTRUCTIONS,
     tools=[send_email],
-    model="gpt-4o-mini",
-)
+    model=get_model_client(),
+)

src/deep-research/appagents/guardrail_agent.py

@@ -6,8 +6,7 @@ from agents import (
     input_guardrail,
     GuardrailFunctionOutput,
 )
-from tools.time_tools import TimeTools
-from openai import AsyncOpenAI
+from core.model import get_model_client
 
 
 # ✅ Step 1: Define structured output schema
@@ -26,7 +25,7 @@ guardrail_agent = Agent(
         "Otherwise, set it to false."
     ),
     output_type=UnparliamentaryCheckOutput,
-    model="gpt-4o-mini",
+    model=get_model_client(),
 )
 
 
@@ -42,4 +41,4 @@ async def guardrail_against_unparliamentary(ctx, agent, message: str):
             "found_unparliamentary_word": result.final_output.model_dump()
         },
         tripwire_triggered=has_unparliamentary_language,
-    )
+    )

src/deep-research/appagents/orchestrator.py

@@ -4,7 +4,7 @@ from appagents.planner_agent import planner_agent, WebSearchItem, WebSearchPlan
 from appagents.writer_agent import writer_agent, ReportData
 from appagents.email_agent import email_agent
 from agents.exceptions import InputGuardrailTripwireTriggered
-from core.logger import log_call
+from common.utility.logger import log_call
 import asyncio
 from langsmith import traceable
 
@@ -16,14 +16,19 @@ class Orchestrator:
 
     @log_call
     @traceable(name="Deep Research Run")
-    async def run(self, query: str):
+    async def run(self, query: str, report_format: str = "Academic", research_depth: str = "Standard"):
         """ Run the deep research process, yielding the status updates and the final report"""
         trace_id = gen_trace_id()
         with trace("Deep Research Orchestrator", trace_id=trace_id):
             print(f"View trace: https://platform.openai.com/traces/trace?trace_id={trace_id}")
             yield f"View trace: https://platform.openai.com/traces/trace?trace_id={trace_id}"
             print("Starting research...")
-            search_plan = await self.plan_searches(query)
+
+            # Map depth to number of searches
+            depth_map = {"Quick": 5, "Standard": 10, "Deep": 15}
+            num_searches = depth_map.get(research_depth, 10)
+
+            search_plan = await self.plan_searches(query, num_searches)
 
             if not search_plan or not getattr(search_plan, "searches", []):
                 note = getattr(search_plan, "note", "")
@@ -34,24 +39,24 @@ class Orchestrator:
                     yield note or "No search results found, ending research."
                     return
             
-            yield "Searches planned, starting to search..."     
+            yield f"Planned {len(search_plan.searches)} searches (Depth: {research_depth}), starting to search..."     
             search_results = await self.perform_searches(search_plan)
             yield "Searches complete, writing report..."
-            report = await self.write_report(query, search_results)
+            report = await self.write_report(query, search_results, report_format, research_depth)
             yield "Report written, sending email..."
             # await self.send_email(report)
             # yield "Email sent, research complete"
             yield report.markdown_report
         
     @log_call
-    async def plan_searches(self, query: str) -> WebSearchPlan:
+    async def plan_searches(self, query: str, num_searches: int = 10) -> WebSearchPlan:
         """Plan the searches to perform for the query."""
-        print("Planning searches...")
+        print(f"Planning {num_searches} searches...")
 
         try:
             result = await Runner.run(
                 planner_agent,              # use self. unless global
-                f"Query: {query}",
+                f"Query: {query}\nGenerate exactly {num_searches} search terms.",
                 session=self.session,
             )
 
@@ -99,10 +104,15 @@ class Orchestrator:
             return None
 
     @log_call
-    async def write_report(self, query: str, search_results: list[str]) -> ReportData:
+    async def write_report(self, query: str, search_results: list[str], report_format: str, research_depth: str) -> ReportData:
         """ Write the report for the query """
         print("Thinking about report...")
-        input = f"Original query: {query}\nSummarized search results: {search_results}"
+        input = (
+            f"Original query: {query}\n"
+            f"Report Format: {report_format}\n"
+            f"Research Depth: {research_depth}\n"
+            f"Summarized search results: {search_results}"
+        )
         result = await Runner.run(
             writer_agent,
             input,

src/deep-research/appagents/planner_agent.py

@@ -1,14 +1,12 @@
 import os
 from pydantic import BaseModel, Field
-from agents import Agent, OpenAIChatCompletionsModel
-from openai import AsyncOpenAI
+from agents import Agent
 from tools.time_tools import TimeTools
 from appagents.guardrail_agent import guardrail_against_unparliamentary
+from core.model import get_model_client
 
-HOW_MANY_SEARCHES = 10
-
-INSTRUCTIONS = f"You are a helpful research assistant. Given a query, come up with a set of web searches \
-to perform to best answer the query. Output {HOW_MANY_SEARCHES} terms to query for.  \
+INSTRUCTIONS = "You are a helpful research assistant. Given a query, come up with a set of web searches \
+to perform to best answer the query. \
 Use the tool to find current date & time, and use it where relevant to inform your search and summary."
 
 
@@ -21,17 +19,7 @@ class WebSearchItem(BaseModel):
 class WebSearchPlan(BaseModel):
     searches: list[WebSearchItem] = Field(description="A list of web searches to perform to best answer the query.")
 
-GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
-google_api_key = os.getenv('GOOGLE_API_KEY')
-gemini_client = AsyncOpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)
-gemini_model = OpenAIChatCompletionsModel(model="gemini-2.0-flash", openai_client=gemini_client)
-
-GROQ_BASE_URL = "https://api.groq.com/openai/v1"
-groq_api_key = os.getenv('GROQ_API_KEY')
-groq_client = AsyncOpenAI(base_url=GROQ_BASE_URL, api_key=groq_api_key)
-groq_model = OpenAIChatCompletionsModel(model="groq/compound", openai_client=groq_client)
-
-openai_model = "gpt-4.1-mini"
+openai_model = get_model_client()
 
 # Note: Many models do not like tool call and json output_schema used together.
 

src/deep-research/appagents/search_agent.py

@@ -1,11 +1,9 @@
 import os
-from agents import Agent, OpenAIChatCompletionsModel, WebSearchTool
-from openai import AsyncOpenAI
-from langsmith import wrappers
-
-
-from agents.model_settings import ModelSettings
+from agents import Agent
 from tools.google_tools import GoogleTools
+from core.model import get_model_client
+from common.utility.logger import log_call
+from agents.model_settings import ModelSettings
 
 # INSTRUCTIONS = "You are a research assistant. Given a search term, you search the web for that term and \
 # produce a concise summary of the results. The summary must 2-3 paragraphs and less than 300 \
@@ -21,29 +19,16 @@ from tools.google_tools import GoogleTools
 # Format the entire output as a single, detailed block of text in markdown format, ensuring ALL source links are visible and preserved."
 
 INSTRUCTIONS = "You are a research assistant. Given a search term, you search the web for that term and \
-produce a concise summary of the results. The summary must 3-5 paragraphs and less than 500 \
+produce a concise summary of the results. The summary must 5-6 paragraphs and less than 500 \
 words. Capture the main points. Write succintly, no need to have complete sentences or good \
 grammar. This will be consumed by someone synthesizing a report, so it's vital you capture the \
 essence and ignore any fluff. Do not include any additional commentary other than the summary itself."
 
-GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
-google_api_key = os.getenv('GOOGLE_API_KEY')
-gemini_client = AsyncOpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)
-gemini_client = wrappers.wrap_openai(gemini_client)
-gemini_model = OpenAIChatCompletionsModel(model="gemini-2.0-flash", openai_client=gemini_client)
-
-# search_agent = Agent(
-#     name="Search agent",
-#     instructions=INSTRUCTIONS,
-#     tools=[WebSearchTool(search_context_size="low")],
-#     # tools=[GoogleTools.search],
-#     model="gpt-4o-mini",
-#     model_settings=ModelSettings(tool_choice="required"),
-# )
 
 # -----------------------------
 # CONNECT TO MCP SERVER
 # -----------------------------
+@log_call
 async def setup_mcp_tools():
     """
     Starts the MCP server via stdio and returns its list of tools
@@ -68,23 +53,12 @@ async def setup_mcp_tools():
         print(f"✅ Connected to MCP server with {len(mcp_tools)} tool(s).")
         return mcp_tools
 
-# # Note: Gemini does not like 
-# search_agent = Agent(
-#     name="Search agent",
-#     instructions=INSTRUCTIONS,
-#     # tools=[WebSearchTool(search_context_size="low")],
-#     tools=[GoogleTools.search],
-#     model=gemini_model,
-#     model_settings=ModelSettings(tool_choice="required"),
-# )
-
-
 search_agent = Agent(
     name="Search agent",
     instructions=INSTRUCTIONS,
     # tools=[WebSearchTool(search_context_size="low")],
     tools=[GoogleTools.search],
-    model=gemini_model,
+    model=get_model_client(),
     model_settings=ModelSettings(tool_choice="required"),
 )
 

src/deep-research/appagents/writer_agent.py

@@ -1,15 +1,17 @@
 import os
 from pydantic import BaseModel, Field
-from agents import Agent, OpenAIChatCompletionsModel, WebSearchTool
-from openai import AsyncOpenAI
+from agents import Agent
+from core.model import get_model_client
 
 INSTRUCTIONS = (
     "You are a senior researcher tasked with writing a cohesive report for a research query. "
-    "You will be provided with the original query, and some initial research done by a research assistant.\n"
+    "You will be provided with the original query, some initial research done by a research assistant, "
+    "and a requested Report Format and Research Depth.\n"
     "You should first come up with an outline for the report that describes the structure and "
     "flow of the report. Then, generate the report and return that as your final output.\n"
-    "The final output should be in markdown format, and it should be lengthy and detailed. Aim "
-    "for 5-10 pages of content, at least 1000 words."
+    "The final output should be in markdown format. "
+    "Adjust the tone, structure, and length based on the requested Report Format and Research Depth. "
+    "Make the output colorful and add minimal emojis to make the content appealing and aesthetic."
 )
 
 
@@ -20,22 +22,9 @@ class ReportData(BaseModel):
 
     follow_up_questions: list[str] = Field(description="Suggested topics to research further")
 
-GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
-google_api_key = os.getenv('GOOGLE_API_KEY')
-gemini_client = AsyncOpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)
-gemini_model = OpenAIChatCompletionsModel(model="gemini-2.0-flash", openai_client=gemini_client)
-
-
-# writer_agent = Agent(
-#     name="WriterAgent",
-#     instructions=INSTRUCTIONS,
-#     model="gpt-5-mini",
-#     output_type=ReportData,
-# )
-
 writer_agent = Agent(
     name="WriterAgent",
     instructions=INSTRUCTIONS,
-    model=gemini_model,
+    model=get_model_client(),
     output_type=ReportData,
 )

src/deep-research/core/__init__.py

@@ -0,0 +1,4 @@
+
+from .model import get_model_client
+
+__all__ = ["get_model_client"]

src/deep-research/core/model.py

@@ -0,0 +1,36 @@
+from common.utility.openai_model_factory  import OpenAIModelFactory
+
+def get_model_client(provider:str = "openai"):
+    if provider.lower() == "google":
+        return OpenAIModelFactory.get_model(
+            provider="google",
+            model_name="gemini-2.5-flash",
+            temperature=0
+    )
+    elif provider.lower() == "openai":
+        return OpenAIModelFactory.get_model(
+            provider="openai",
+            model_name="gpt-4o-mini",
+            temperature=0
+    )
+    elif provider.lower() == "azure":
+        return OpenAIModelFactory.get_model(
+            provider="azure",
+            model_name="gpt-4o-mini",
+            temperature=0
+    )
+    elif provider.lower() == "groq":
+        return OpenAIModelFactory.get_model(
+            provider="groq",
+            model_name="gpt-4o-mini",
+            temperature=0
+    )
+    elif provider.lower() == "ollama":
+        return OpenAIModelFactory.get_model(
+            provider="ollama",
+            model_name="gpt-4o-mini",
+            temperature=0
+    )
+    else: 
+        raise  ValueError(f"Unsupported provider: {provider}")
+

src/deep-research/tools/google_tools.py

@@ -1,11 +1,7 @@
 import os
 import requests
-from dotenv import load_dotenv
 from agents import function_tool
-from core.logger import log_call
-
-# Load environment variables once
-load_dotenv()
+from common.utility.logger import log_call
 
 
 # ============================================================

src/deep-research/tools/time_tools.py

@@ -1,6 +1,8 @@
 from datetime import datetime
 from agents import function_tool
-from core.logger import log_call
+from common.utility.logger import log_call
+
+
 
 class TimeTools:
     """Provides tools related to current date and time."""

uv.lock

@@ -13,6 +13,7 @@ dependencies = [
     { name = "asyncio" },
     { name = "autogen-agentchat" },
     { name = "autogen-ext", extra = ["grpc", "mcp", "ollama", "openai"] },
+    { name = "azure-identity" },
     { name = "beautifulsoup4" },
     { name = "chromadb" },
     { name = "datasets" },
@@ -35,7 +36,6 @@ dependencies = [
     { name = "langchain-ollama" },
     { name = "langchain-openai" },
     { name = "langchain-text-splitters" },
-    { name = "langfuse" },
     { name = "langgraph" },
     { name = "langgraph-checkpoint-sqlite" },
     { name = "langsmith" },
@@ -46,6 +46,11 @@ dependencies = [
     { name = "openai" },
     { name = "openai-agents" },
     { name = "openai-whisper" },
+    { name = "openinference-instrumentation-autogen" },
+    { name = "openinference-instrumentation-openai" },
+    { name = "opentelemetry-api" },
+    { name = "opentelemetry-exporter-otlp" },
+    { name = "opentelemetry-sdk" },
     { name = "playwright" },
     { name = "plotly" },
     { name = "polygon-api-client" },
@@ -63,6 +68,7 @@ dependencies = [
     { name = "speedtest-cli" },
     { name = "streamlit" },
     { name = "textblob" },
+    { name = "traceloop-sdk" },
     { name = "wikipedia" },
     { name = "yfinance" },
     { name = "yt-dlp" },
@@ -83,6 +89,7 @@ requires-dist = [
     { name = "asyncio" },
     { name = "autogen-agentchat", specifier = ">=0.7.5" },
     { name = "autogen-ext", extras = ["grpc", "mcp", "ollama", "openai"], specifier = ">=0.7.5" },
+    { name = "azure-identity", specifier = ">=1.25.1" },
     { name = "beautifulsoup4", specifier = ">=4.12.3" },
     { name = "chromadb", specifier = "==1.3.5" },
     { name = "datasets", specifier = ">=4.4.1" },
@@ -105,7 +112,6 @@ requires-dist = [
     { name = "langchain-ollama", specifier = ">=1.0.0" },
     { name = "langchain-openai", specifier = ">=1.0.3" },
     { name = "langchain-text-splitters", specifier = ">=1.0.0" },
-    { name = "langfuse", specifier = ">=2.0.0" },
     { name = "langgraph", specifier = ">=1.0.3" },
     { name = "langgraph-checkpoint-sqlite", specifier = ">=3.0.0" },
     { name = "langsmith", specifier = ">=0.4.43" },
@@ -116,6 +122,11 @@ requires-dist = [
     { name = "openai", specifier = ">=2.8.1" },
     { name = "openai-agents", specifier = ">=0.5.1" },
     { name = "openai-whisper", specifier = ">=1.0.0" },
+    { name = "openinference-instrumentation-autogen", specifier = ">=0.1.0" },
+    { name = "openinference-instrumentation-openai", specifier = ">=0.1.15" },
+    { name = "opentelemetry-api", specifier = ">=1.20.0" },
+    { name = "opentelemetry-exporter-otlp", specifier = ">=1.20.0" },
+    { name = "opentelemetry-sdk", specifier = ">=1.20.0" },
     { name = "playwright", specifier = ">=1.51.0" },
     { name = "plotly", specifier = ">=6.5.0" },
     { name = "polygon-api-client", specifier = ">=1.16.3" },
@@ -133,6 +144,7 @@ requires-dist = [
     { name = "speedtest-cli", specifier = ">=2.1.3" },
     { name = "streamlit", specifier = ">=1.51.0" },
     { name = "textblob", specifier = ">=0.17.1" },
+    { name = "traceloop-sdk", specifier = ">=0.33.0" },
     { name = "wikipedia", specifier = ">=1.4.0" },
     { name = "yfinance", specifier = ">=0.2.66" },
     { name = "yt-dlp", specifier = ">=2025.11.12" },
@@ -405,6 +417,35 @@ openai = [
     { name = "tiktoken" },
 ]
 
+[[package]]
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+]
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+version = "1.25.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "azure-core" },
+    { name = "cryptography" },
+    { name = "msal" },
+    { name = "msal-extensions" },
+    { name = "typing-extensions" },
+]
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+]
+
 [[package]]
 name = "backoff"
 version = "2.2.1"
@@ -713,6 +754,12 @@ wheels = [
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 ]
 
+[[package]]
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+version = "0.4"
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+
 [[package]]
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 version = "0.13.0"
@@ -810,6 +857,18 @@ wheels = [
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 ]
 
+[[package]]
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+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "wrapt" },
+]
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 name = "dill"
 version = "0.4.0"
@@ -1377,6 +1436,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "inflection"
+version = "0.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e1/7e/691d061b7329bc8d54edbf0ec22fbfb2afe61facb681f9aaa9bff7a27d04/inflection-0.5.1.tar.gz", hash = "sha256:1a29730d366e996aaacffb2f1f1cb9593dc38e2ddd30c91250c6dde09ea9b417", size = 15091, upload-time = "2020-08-22T08:16:29.139Z" }
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+]
+
 [[package]]
 name = "iniconfig"
 version = "2.3.0"
@@ -1791,27 +1859,6 @@ wheels = [
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-    { name = "openai" },
-    { name = "opentelemetry-api" },
-    { name = "opentelemetry-exporter-otlp-proto-http" },
-    { name = "opentelemetry-sdk" },
-    { name = "packaging" },
-    { name = "pydantic" },
-    { name = "requests" },
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 ]
 
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