diff --git a/config.template b/config.template
index 524a377..efe9409 100644
--- a/config.template
+++ b/config.template
@@ -56,8 +56,8 @@ wikipedia = True
 
 # Enable ollama LLM see more at https://ollama.com
 ollama = False
-# Ollama model to use (defaults to gemma2:2b)
-# ollamaModel = llama3.1
+# Ollama model to use (defaults to gemma3:270m)
+# ollamaModel = gemma3:latest
 # server instance to use (defaults to local machine install)
 ollamaHostName = http://localhost:11434
 # Produce LLM replies to messages that aren't commands?
diff --git a/install.sh b/install.sh
index 4c44c2f..7c49cce 100755
--- a/install.sh
+++ b/install.sh
@@ -250,7 +250,7 @@ if [[ $(echo "${embedded}" | grep -i "^n") ]]; then
 
     printf "\nOptionally if you want to install the multi gig LLM Ollama compnents we will execute the following commands\n"
     printf "\ncurl -fsSL https://ollama.com/install.sh | sh\n"
-    printf "ollama pull gemma2:2b\n"
+    printf "ollama pull gemma3:latest\n"
     printf "Total download is multi GB, recomend pi5/8GB or better for this\n"
     # ask if the user wants to install the LLM Ollama components
     printf "\nDo you want to install the LLM Ollama components? (y/n)"
@@ -258,12 +258,12 @@ if [[ $(echo "${embedded}" | grep -i "^n") ]]; then
     if [[ $(echo "${ollama}" | grep -i "^y") ]]; then
         curl -fsSL https://ollama.com/install.sh | sh
 
-        # ask if want to install gemma2:2b
-        printf "\n Ollama install done now we can install the Gemma2:2b components\n"
-        echo "Do you want to install the Gemma2:2b components? (y/n)"
+        # ask if want to install gemma3:latest
+        printf "\n Ollama install done now we can install the gemma3:latest components\n"
+        echo "Do you want to install the gemma3:latest components? (y/n)"
         read gemma
         if [[ $(echo "${gemma}" | grep -i "^y") ]]; then
-            ollama pull gemma2:2b
+            ollama pull gemma3:latest
         fi
     fi
 
diff --git a/modules/llm.py b/modules/llm.py
index b0c36fb..60bb055 100644
--- a/modules/llm.py
+++ b/modules/llm.py
@@ -10,25 +10,16 @@ import requests
 import json
 from googlesearch import search # pip install googlesearch-python
 
-# This is my attempt at a simple RAG implementation it will require some setup
-# you will need to have the RAG data in a folder named rag in the data directory (../data/rag)
-# This is lighter weight and can be used in a standalone environment, needs chromadb
-# "chat with a file" is the use concept here, the file is the RAG data
-# is anyone using this please let me know if you are Dec62024 -kelly
-ragDEV = False
-
-if ragDEV:
-    import os
-    import ollama # pip install ollama
-    import chromadb # pip install chromadb
-    from ollama import Client as OllamaClient
-    ollamaClient = OllamaClient(host=ollamaHostName)
-
 # LLM System Variables
 ollamaAPI = ollamaHostName + "/api/generate"
+rawQuery = True # if True, the input is sent raw to the LLM, if False, it is processed by the meshBotAI template
+
 openaiAPI = "https://api.openai.com/v1/completions" # not used, if you do push a enhancement!
+
+# Used in the meshBotAI template
 llmEnableHistory = True # enable last message history for the LLM model
 llmContext_fromGoogle = True # enable context from google search results adds to compute time but really helps with responses accuracy
+
 googleSearchResults = 3 # number of google search results to include in the context more results = more compute time
 antiFloodLLM = []
 llmChat_history = {}
@@ -74,73 +65,6 @@ if llmEnableHistory:
 
     """
 
-def llm_readTextFiles():
-    # read .txt files in ../data/rag
-    try:
-        text = []
-        directory = "../data/rag"
-        for filename in os.listdir(directory):
-            if filename.endswith(".txt"):
-                filepath = os.path.join(directory, filename)
-                with open(filepath, 'r') as f:
-                    text.append(f.read())
-        return text
-    except Exception as e:
-        logger.debug(f"System: LLM readTextFiles: {e}")
-        return False
-
-def store_text_embedding(text):
-    try:
-        # store each document in a vector embedding database
-        for i, d in enumerate(text):
-            response = ollama.embeddings(model="mxbai-embed-large", prompt=d)
-            embedding = response["embedding"]
-            collection.add(
-                ids=[str(i)],
-                embeddings=[embedding],
-                documents=[d]
-            )
-
-    except Exception as e:
-        logger.debug(f"System: Embedding failed: {e}")
-        return False
-
-## INITALIZATION of RAG
-if ragDEV:
-    try:
-        chromaHostname = "localhost:8000"
-        # connect to the chromaDB
-        chromaHost = chromaHostname.split(":")[0]
-        chromaPort = chromaHostname.split(":")[1]
-        if chromaHost == "localhost" and chromaPort == "8000":
-            # create a client using local python Client
-            chromaClient = chromadb.Client()
-        else:
-            # create a client using the remote python Client
-            # this isnt tested yet please test and report back
-            chromaClient = chromadb.Client(host=chromaHost, port=chromaPort)
-
-        clearCollection = False
-        if "meshBotAI" in chromaClient.list_collections() and clearCollection:
-            logger.debug(f"System: LLM: Clearing RAG files from chromaDB")
-            chromaClient.delete_collection("meshBotAI")
-        
-        # create a new collection
-        collection = chromaClient.create_collection("meshBotAI")
-
-        logger.debug(f"System: LLM: Cataloging RAG data")
-        store_text_embedding(llm_readTextFiles())
-
-    except Exception as e:
-        logger.debug(f"System: LLM: RAG Initalization failed: {e}")
-
-def query_collection(prompt):
-    # generate an embedding for the prompt and retrieve the most relevant doc
-    response = ollama.embeddings(prompt=prompt, model="mxbai-embed-large")
-    results = collection.query(query_embeddings=[response["embedding"]], n_results=1)
-    data = results['documents'][0][0]
-    return data
-
 def llm_query(input, nodeID=0, location_name=None):
     global antiFloodLLM, llmChat_history
     googleResults = []
@@ -162,7 +86,7 @@ def llm_query(input, nodeID=0, location_name=None):
     else:
         antiFloodLLM.append(nodeID)
 
-    if llmContext_fromGoogle:
+    if llmContext_fromGoogle and not rawQuery:
         # grab some context from the internet using google search hits (if available)
         # localization details at https://pypi.org/project/googlesearch-python/
 
@@ -193,36 +117,27 @@ def llm_query(input, nodeID=0, location_name=None):
     location_name += f" at the current time of {datetime.now().strftime('%Y-%m-%d %H:%M:%S %Z')}"
 
     try:
-        # RAG context inclusion testing
-        ragContext = False
-        if ragDEV:
-            ragContext = query_collection(input)
-
-        if ragContext:
-            ragContextGooogle = ragContext + '\n'.join(googleResults)
-            # Build the query from the template
-            modelPrompt = meshBotAI.format(input=input, context=ragContext, location_name=location_name, llmModel=llmModel, history=history)
-            # Query the model with RAG context
-            result = ollamaClient.generate(model=llmModel, prompt=modelPrompt)
-            # Condense the result to just needed
-            if isinstance(result, dict):
-                result = result.get("response")
+        if rawQuery:
+            # sanitize the input to remove tool call syntax
+            input = input.replace('```', '').replace('```bash', '').replace('```python', '')
+            modelPrompt = input
         else:
             # Build the query from the template
             modelPrompt = meshBotAI.format(input=input, context='\n'.join(googleResults), location_name=location_name, llmModel=llmModel, history=history)
-            llmQuery = {"model": llmModel, "prompt": modelPrompt, "stream": False}
-            # Query the model via Ollama web API
-            result = requests.post(ollamaAPI, data=json.dumps(llmQuery))
-            # Condense the result to just needed
-            if result.status_code == 200:
-                result_json = result.json()
-                result = result_json.get("response", "")
+            
+        llmQuery = {"model": llmModel, "prompt": modelPrompt, "stream": False}
+        # Query the model via Ollama web API
+        result = requests.post(ollamaAPI, data=json.dumps(llmQuery))
+        # Condense the result to just needed
+        if result.status_code == 200:
+            result_json = result.json()
+            result = result_json.get("response", "")
 
-                # deepseek-r1 has added <think> </think> tags to the response
-                if "<think>" in result:
-                    result = result.split("</think>")[1]
-            else:
-                raise Exception(f"HTTP Error: {result.status_code}")
+            # deepseek-r1 has added <think> </think> tags to the response
+            if "<think>" in result:
+                result = result.split("</think>")[1]
+        else:
+            raise Exception(f"HTTP Error: {result.status_code}")
 
         #logger.debug(f"System: LLM Response: " + result.strip().replace('\n', ' '))
     except Exception as e:
diff --git a/modules/settings.py b/modules/settings.py
index 50e871d..74ae700 100644
--- a/modules/settings.py
+++ b/modules/settings.py
@@ -219,7 +219,7 @@ try:
     solar_conditions_enabled = config['general'].getboolean('spaceWeather', True)
     wikipedia_enabled = config['general'].getboolean('wikipedia', False)
     llm_enabled = config['general'].getboolean('ollama', False) # https://ollama.com
-    llmModel = config['general'].get('ollamaModel', 'gemma2:2b') # default gemma2:2b
+    llmModel = config['general'].get('ollamaModel', 'gemma3:270m') # default gemma3:270m
     ollamaHostName = config['general'].get('ollamaHostName', 'http://localhost:11434') # default localhost
     llmReplyToNonCommands = config['general'].getboolean('llmReplyToNonCommands', True)
     dont_retry_disconnect = config['general'].getboolean('dont_retry_disconnect', False) # default False, retry on disconnect