diff --git a/README.md b/README.md
index aa337c7..d8c9cad 100644
--- a/README.md
+++ b/README.md
@@ -119,13 +119,13 @@ This is the core of SurfSense. Before we begin let's look at `.env` variables' t
 | EMBEDDING_MODEL| Name of the embedding model to use for vector embeddings. Currently works with Sentence Transformers only. Expect other embeddings soon. Eg. `mixedbread-ai/mxbai-embed-large-v1`|
 | RERANKERS_MODEL_NAME| Name of the reranker model for search result reranking. Eg. `ms-marco-MiniLM-L-12-v2`|
 | RERANKERS_MODEL_TYPE| Type of reranker model being used. Eg. `flashrank`|
-| FAST_LLM| LiteLLM routed Smaller, faster LLM for quick responses. Eg. `litellm:openai/gpt-4o`|
-| STRATEGIC_LLM| LiteLLM routed  Advanced LLM for complex reasoning tasks. Eg. `litellm:openai/gpt-4o`|
-| LONG_CONTEXT_LLM| LiteLLM routed  LLM capable of handling longer context windows. Eg. `litellm:gemini/gemini-2.0-flash`|
+| FAST_LLM| LiteLLM routed Smaller, faster LLM for quick responses. Eg. `openai/gpt-4o-mini`, `ollama/deepseek-r1:8b`|
+| STRATEGIC_LLM| LiteLLM routed  Advanced LLM for complex reasoning tasks. Eg. `openai/gpt-4o`, `ollama/gemma3:12b`|
+| LONG_CONTEXT_LLM| LiteLLM routed  LLM capable of handling longer context windows. Eg. `gemini/gemini-2.0-flash`, `ollama/deepseek-r1:8b`|
 | UNSTRUCTURED_API_KEY| API key for Unstructured.io service for document parsing|
 | FIRECRAWL_API_KEY| API key for Firecrawl service for web crawling and data extraction|
 
-IMPORTANT: Since LLM calls are routed through LiteLLM make sure to include API keys of LLM models you are using. For example if you used `litellm:openai/gpt-4o` make sure to include OpenAI API Key `OPENAI_API_KEY` or if you use `litellm:gemini/gemini-2.0-flash` then you include `GEMINI_API_KEY`.
+IMPORTANT: Since LLM calls are routed through LiteLLM make sure to include API keys of LLM models you are using. For example if you used `openai/gpt-4o` make sure to include OpenAI API Key `OPENAI_API_KEY` or if you use `gemini/gemini-2.0-flash` then you include `GEMINI_API_KEY`.
 
 You can also integrate any LLM just follow this https://docs.litellm.ai/docs/providers
 
diff --git a/surfsense_backend/.env.example b/surfsense_backend/.env.example
index bc3f5ce..2eee50d 100644
--- a/surfsense_backend/.env.example
+++ b/surfsense_backend/.env.example
@@ -4,15 +4,18 @@ SECRET_KEY="SECRET"
 GOOGLE_OAUTH_CLIENT_ID="924507538m"
 GOOGLE_OAUTH_CLIENT_SECRET="GOCSV"
 NEXT_FRONTEND_URL="http://localhost:3000"
+
 EMBEDDING_MODEL="mixedbread-ai/mxbai-embed-large-v1"
 
 RERANKERS_MODEL_NAME="ms-marco-MiniLM-L-12-v2"
 RERANKERS_MODEL_TYPE="flashrank"
 
-FAST_LLM="litellm:openai/gpt-4o-mini"
-STRATEGIC_LLM="litellm:openai/gpt-4o"
-LONG_CONTEXT_LLM="litellm:gemini/gemini-2.0-flash"
+# https://docs.litellm.ai/docs/providers
+FAST_LLM="openai/gpt-4o-mini"
+STRATEGIC_LLM="openai/gpt-4o"
+LONG_CONTEXT_LLM="gemini/gemini-2.0-flash"
 
+# Chosen LiteLLM Providers Keys
 OPENAI_API_KEY="sk-proj-iA"
 GEMINI_API_KEY="AIzaSyB6-1641124124124124124124124124124"
 
diff --git a/surfsense_backend/app/agents/researcher/sub_section_writer/nodes.py b/surfsense_backend/app/agents/researcher/sub_section_writer/nodes.py
index 5b11141..0bec461 100644
--- a/surfsense_backend/app/agents/researcher/sub_section_writer/nodes.py
+++ b/surfsense_backend/app/agents/researcher/sub_section_writer/nodes.py
@@ -37,8 +37,9 @@ async def rerank_documents(state: State, config: RunnableConfig) -> Dict[str, An
     if reranker_service:
         try:
             # Use the sub-section questions for reranking context
-            rerank_query = "\n".join(sub_section_questions)
-            
+            # rerank_query = "\n".join(sub_section_questions)
+            rerank_query = configuration.user_query
+
             # Convert documents to format expected by reranker if needed
             reranker_input_docs = [
                 {
diff --git a/surfsense_backend/app/config/__init__.py b/surfsense_backend/app/config/__init__.py
index 82517a8..c7f842b 100644
--- a/surfsense_backend/app/config/__init__.py
+++ b/surfsense_backend/app/config/__init__.py
@@ -15,17 +15,6 @@ env_file = BASE_DIR / ".env"
 load_dotenv(env_file)
 
 
-def extract_model_name(llm_string: str) -> str:
-    """Extract the model name from an LLM string.
-    Example: "litellm:openai/gpt-4o-mini" -> "openai/gpt-4o-mini"
-    
-    Args:
-        llm_string: The LLM string with optional prefix
-        
-    Returns:
-        str: The extracted model name
-    """
-    return llm_string.split(":", 1)[1] if ":" in llm_string else llm_string
 
 class Config:
     # Database
@@ -38,13 +27,13 @@ class Config:
     
     # LONG-CONTEXT LLMS
     LONG_CONTEXT_LLM = os.getenv("LONG_CONTEXT_LLM")
-    long_context_llm_instance = ChatLiteLLM(model=extract_model_name(LONG_CONTEXT_LLM))
+    long_context_llm_instance = ChatLiteLLM(model=LONG_CONTEXT_LLM)
     
     # GPT Researcher
     FAST_LLM = os.getenv("FAST_LLM")
     STRATEGIC_LLM = os.getenv("STRATEGIC_LLM")
-    fast_llm_instance = ChatLiteLLM(model=extract_model_name(FAST_LLM))
-    strategic_llm_instance = ChatLiteLLM(model=extract_model_name(STRATEGIC_LLM))
+    fast_llm_instance = ChatLiteLLM(model=FAST_LLM)
+    strategic_llm_instance = ChatLiteLLM(model=STRATEGIC_LLM)
     
 
     # Chonkie Configuration | Edit this to your needs