diff --git a/src/khoj/processor/conversation/gpt.py b/src/khoj/processor/conversation/gpt.py
index 8051afdb..fbcd4da5 100644
--- a/src/khoj/processor/conversation/gpt.py
+++ b/src/khoj/processor/conversation/gpt.py
@@ -1,5 +1,4 @@
 # Standard Packages
-import json
 import logging
 from datetime import datetime
 from typing import Optional
@@ -17,28 +16,6 @@ from khoj.processor.conversation.utils import (
 logger = logging.getLogger(__name__)
 
 
-def answer(text, user_query, model, api_key=None, temperature=0.5, max_tokens=500):
-    """
-    Answer user query using provided text as reference with OpenAI's GPT
-    """
-    # Setup Prompt from arguments
-    prompt = prompts.answer.format(text=text, user_query=user_query)
-
-    # Get Response from GPT
-    logger.debug(f"Prompt for GPT: {prompt}")
-    response = completion_with_backoff(
-        prompt=prompt,
-        model_name=model,
-        temperature=temperature,
-        max_tokens=max_tokens,
-        model_kwargs={"stop": ['"""']},
-        openai_api_key=api_key,
-    )
-
-    # Extract, Clean Message from GPT's Response
-    return str(response).replace("\n\n", "")
-
-
 def summarize(text, summary_type, model, user_query=None, api_key=None, temperature=0.5, max_tokens=200):
     """
     Summarize user input using OpenAI's GPT
@@ -126,31 +103,6 @@ def extract_questions(
     return questions
 
 
-def extract_search_type(text, model, api_key=None, temperature=0.5, max_tokens=100, verbose=0):
-    """
-    Extract search type from user query using OpenAI's GPT
-    """
-    # Setup Prompt to extract search type
-    prompt = prompts.search_type + f"{text}\nA:"
-    if verbose > 1:
-        print(f"Message -> Prompt: {text} -> {prompt}")
-
-    # Get Response from GPT
-    logger.debug(f"Prompt for GPT: {prompt}")
-    response = completion_with_backoff(
-        prompt=prompt,
-        model_name=model,
-        temperature=temperature,
-        max_tokens=max_tokens,
-        frequency_penalty=0.2,
-        model_kwargs={"stop": ["\n"]},
-        openai_api_key=api_key,
-    )
-
-    # Extract, Clean Message from GPT's Response
-    return json.loads(response.strip(empty_escape_sequences))
-
-
 def converse(
     references,
     user_query,
diff --git a/src/khoj/routers/api_beta.py b/src/khoj/routers/api_beta.py
index 01b7a086..83cca612 100644
--- a/src/khoj/routers/api_beta.py
+++ b/src/khoj/routers/api_beta.py
@@ -1,64 +1,9 @@
 # Standard Packages
 import logging
-from typing import Optional
 
 # External Packages
 from fastapi import APIRouter
 
-# Internal Packages
-from khoj.routers.api import search
-from khoj.processor.conversation.gpt import (
-    answer,
-    extract_search_type,
-)
-from khoj.utils.state import SearchType
-from khoj.utils.helpers import get_from_dict
-from khoj.utils import state
-
-
 # Initialize Router
 api_beta = APIRouter()
 logger = logging.getLogger(__name__)
-
-
-# Create Routes
-@api_beta.get("/search")
-def search_beta(q: str, n: Optional[int] = 1):
-    # Initialize Variables
-    model = state.processor_config.conversation.model
-    api_key = state.processor_config.conversation.openai_api_key
-
-    # Extract Search Type using GPT
-    try:
-        metadata = extract_search_type(q, model=model, api_key=api_key, verbose=state.verbose)
-        search_type = get_from_dict(metadata, "search-type")
-    except Exception as e:
-        return {"status": "error", "result": [str(e)], "type": None}
-
-    # Search
-    search_results = search(q, n=n, t=SearchType(search_type))
-
-    # Return response
-    return {"status": "ok", "result": search_results, "type": search_type}
-
-
-@api_beta.get("/answer")
-def answer_beta(q: str):
-    # Initialize Variables
-    model = state.processor_config.conversation.model
-    api_key = state.processor_config.conversation.openai_api_key
-
-    # Collate context for GPT
-    result_list = search(q, n=2, r=True, score_threshold=0, dedupe=False)
-    collated_result = "\n\n".join([f"# {item.additional['compiled']}" for item in result_list])
-    logger.debug(f"Reference Context:\n{collated_result}")
-
-    # Make GPT respond to user query using provided context
-    try:
-        gpt_response = answer(collated_result, user_query=q, model=model, api_key=api_key)
-        status = "ok"
-    except Exception as e:
-        gpt_response = str(e)
-        status = "error"
-
-    return {"status": status, "response": gpt_response}